[2024/07/27 11:19:01] ppocr INFO: Architecture : 
[2024/07/27 11:19:01] ppocr INFO:     Backbone : 
[2024/07/27 11:19:01] ppocr INFO:         model_name : large
[2024/07/27 11:19:01] ppocr INFO:         name : MobileNetV3
[2024/07/27 11:19:01] ppocr INFO:         scale : 0.5
[2024/07/27 11:19:01] ppocr INFO:     Head : 
[2024/07/27 11:19:01] ppocr INFO:         k : 50
[2024/07/27 11:19:01] ppocr INFO:         name : DBHead
[2024/07/27 11:19:01] ppocr INFO:     Neck : 
[2024/07/27 11:19:01] ppocr INFO:         name : DBFPN
[2024/07/27 11:19:01] ppocr INFO:         out_channels : 256
[2024/07/27 11:19:01] ppocr INFO:     Transform : None
[2024/07/27 11:19:01] ppocr INFO:     algorithm : DB
[2024/07/27 11:19:01] ppocr INFO:     model_type : det
[2024/07/27 11:19:01] ppocr INFO: Eval : 
[2024/07/27 11:19:01] ppocr INFO:     dataset : 
[2024/07/27 11:19:01] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/07/27 11:19:01] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
[2024/07/27 11:19:01] ppocr INFO:         name : SimpleDataSet
[2024/07/27 11:19:01] ppocr INFO:         transforms : 
[2024/07/27 11:19:01] ppocr INFO:             DecodeImage : 
[2024/07/27 11:19:01] ppocr INFO:                 channel_first : False
[2024/07/27 11:19:01] ppocr INFO:                 img_mode : BGR
[2024/07/27 11:19:01] ppocr INFO:             DetLabelEncode : None
[2024/07/27 11:19:01] ppocr INFO:             DetResizeForTest : 
[2024/07/27 11:19:01] ppocr INFO:                 image_shape : [736, 1280]
[2024/07/27 11:19:01] ppocr INFO:             NormalizeImage : 
[2024/07/27 11:19:01] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/07/27 11:19:01] ppocr INFO:                 order : hwc
[2024/07/27 11:19:01] ppocr INFO:                 scale : 1./255.
[2024/07/27 11:19:01] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/07/27 11:19:01] ppocr INFO:             ToCHWImage : None
[2024/07/27 11:19:01] ppocr INFO:             KeepKeys : 
[2024/07/27 11:19:01] ppocr INFO:                 keep_keys : ['image', 'shape', 'polys', 'ignore_tags']
[2024/07/27 11:19:01] ppocr INFO:     loader : 
[2024/07/27 11:19:01] ppocr INFO:         batch_size_per_card : 1
[2024/07/27 11:19:01] ppocr INFO:         drop_last : False
[2024/07/27 11:19:01] ppocr INFO:         num_workers : 0
[2024/07/27 11:19:01] ppocr INFO:         shuffle : False
[2024/07/27 11:19:01] ppocr INFO:         use_shared_memory : False
[2024/07/27 11:19:01] ppocr INFO: Global : 
[2024/07/27 11:19:01] ppocr INFO:     cal_metric_during_train : False
[2024/07/27 11:19:01] ppocr INFO:     checkpoints : None
[2024/07/27 11:19:01] ppocr INFO:     distributed : True
[2024/07/27 11:19:01] ppocr INFO:     epoch_num : 1500
[2024/07/27 11:19:01] ppocr INFO:     eval_batch_step : [0, 60]
[2024/07/27 11:19:01] ppocr INFO:     infer_img : doc/imgs_en/img_10.jpg
[2024/07/27 11:19:01] ppocr INFO:     log_smooth_window : 20
[2024/07/27 11:19:01] ppocr INFO:     pretrained_model : ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/07/27 11:19:01] ppocr INFO:     print_batch_step : 10
[2024/07/27 11:19:01] ppocr INFO:     save_epoch_step : 1200
[2024/07/27 11:19:01] ppocr INFO:     save_inference_dir : None
[2024/07/27 11:19:01] ppocr INFO:     save_model_dir : ./output/db_mv3/
[2024/07/27 11:19:01] ppocr INFO:     save_res_path : ./output/det_db/predicts_db.txt
[2024/07/27 11:19:01] ppocr INFO:     use_gpu : True
[2024/07/27 11:19:01] ppocr INFO:     use_visualdl : False
[2024/07/27 11:19:01] ppocr INFO:     use_xpu : False
[2024/07/27 11:19:01] ppocr INFO: Loss : 
[2024/07/27 11:19:01] ppocr INFO:     alpha : 5
[2024/07/27 11:19:01] ppocr INFO:     balance_loss : True
[2024/07/27 11:19:01] ppocr INFO:     beta : 10
[2024/07/27 11:19:01] ppocr INFO:     main_loss_type : DiceLoss
[2024/07/27 11:19:01] ppocr INFO:     name : DBLoss
[2024/07/27 11:19:01] ppocr INFO:     ohem_ratio : 3
[2024/07/27 11:19:01] ppocr INFO: Metric : 
[2024/07/27 11:19:01] ppocr INFO:     main_indicator : hmean
[2024/07/27 11:19:01] ppocr INFO:     name : DetMetric
[2024/07/27 11:19:01] ppocr INFO: Optimizer : 
[2024/07/27 11:19:01] ppocr INFO:     beta1 : 0.9
[2024/07/27 11:19:01] ppocr INFO:     beta2 : 0.999
[2024/07/27 11:19:01] ppocr INFO:     lr : 
[2024/07/27 11:19:01] ppocr INFO:         learning_rate : 0.001
[2024/07/27 11:19:01] ppocr INFO:     name : Adam
[2024/07/27 11:19:01] ppocr INFO:     regularizer : 
[2024/07/27 11:19:01] ppocr INFO:         factor : 0
[2024/07/27 11:19:01] ppocr INFO:         name : L2
[2024/07/27 11:19:01] ppocr INFO: PostProcess : 
[2024/07/27 11:19:01] ppocr INFO:     box_thresh : 0.6
[2024/07/27 11:19:01] ppocr INFO:     max_candidates : 1000
[2024/07/27 11:19:01] ppocr INFO:     name : DBPostProcess
[2024/07/27 11:19:01] ppocr INFO:     thresh : 0.3
[2024/07/27 11:19:01] ppocr INFO:     unclip_ratio : 1.5
[2024/07/27 11:19:01] ppocr INFO: Train : 
[2024/07/27 11:19:01] ppocr INFO:     dataset : 
[2024/07/27 11:19:01] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/07/27 11:19:01] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/07/27 11:19:01] ppocr INFO:         name : SimpleDataSet
[2024/07/27 11:19:01] ppocr INFO:         ratio_list : [1.0]
[2024/07/27 11:19:01] ppocr INFO:         transforms : 
[2024/07/27 11:19:01] ppocr INFO:             DecodeImage : 
[2024/07/27 11:19:01] ppocr INFO:                 channel_first : False
[2024/07/27 11:19:01] ppocr INFO:                 img_mode : BGR
[2024/07/27 11:19:01] ppocr INFO:             DetLabelEncode : None
[2024/07/27 11:19:01] ppocr INFO:             IaaAugment : 
[2024/07/27 11:19:01] ppocr INFO:                 augmenter_args : 
[2024/07/27 11:19:01] ppocr INFO:                     args : 
[2024/07/27 11:19:01] ppocr INFO:                         p : 0.5
[2024/07/27 11:19:01] ppocr INFO:                     type : Fliplr
[2024/07/27 11:19:01] ppocr INFO:                     args : 
[2024/07/27 11:19:01] ppocr INFO:                         rotate : [-10, 10]
[2024/07/27 11:19:01] ppocr INFO:                     type : Affine
[2024/07/27 11:19:01] ppocr INFO:                     args : 
[2024/07/27 11:19:01] ppocr INFO:                         size : [0.5, 3]
[2024/07/27 11:19:01] ppocr INFO:                     type : Resize
[2024/07/27 11:19:01] ppocr INFO:             EastRandomCropData : 
[2024/07/27 11:19:01] ppocr INFO:                 keep_ratio : True
[2024/07/27 11:19:01] ppocr INFO:                 max_tries : 50
[2024/07/27 11:19:01] ppocr INFO:                 size : [640, 640]
[2024/07/27 11:19:01] ppocr INFO:             MakeBorderMap : 
[2024/07/27 11:19:01] ppocr INFO:                 shrink_ratio : 0.4
[2024/07/27 11:19:01] ppocr INFO:                 thresh_max : 0.7
[2024/07/27 11:19:01] ppocr INFO:                 thresh_min : 0.3
[2024/07/27 11:19:01] ppocr INFO:             MakeShrinkMap : 
[2024/07/27 11:19:01] ppocr INFO:                 min_text_size : 8
[2024/07/27 11:19:01] ppocr INFO:                 shrink_ratio : 0.4
[2024/07/27 11:19:01] ppocr INFO:             NormalizeImage : 
[2024/07/27 11:19:01] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/07/27 11:19:01] ppocr INFO:                 order : hwc
[2024/07/27 11:19:01] ppocr INFO:                 scale : 1./255.
[2024/07/27 11:19:01] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/07/27 11:19:01] ppocr INFO:             ToCHWImage : None
[2024/07/27 11:19:01] ppocr INFO:             KeepKeys : 
[2024/07/27 11:19:01] ppocr INFO:                 keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask']
[2024/07/27 11:19:01] ppocr INFO:     loader : 
[2024/07/27 11:19:01] ppocr INFO:         batch_size_per_card : 48
[2024/07/27 11:19:01] ppocr INFO:         drop_last : False
[2024/07/27 11:19:01] ppocr INFO:         num_workers : 8
[2024/07/27 11:19:01] ppocr INFO:         shuffle : True
[2024/07/27 11:19:01] ppocr INFO:         use_shared_memory : False
[2024/07/27 11:19:01] ppocr INFO: profiler_options : None
[2024/07/27 11:19:01] ppocr INFO: train with paddle 2.5.2 and device Place(gpu:0)
======================= Modified FLAGS detected =======================
FLAGS(name='FLAGS_selected_gpus', current_value='0', default_value='')
FLAGS(name='FLAGS_cudnn_batchnorm_spatial_persistent', current_value=True, default_value=False)
=======================================================================
I0727 11:19:01.153348  2855 tcp_utils.cc:181] The server starts to listen on IP_ANY:52742
I0727 11:19:01.153559  2855 tcp_utils.cc:130] Successfully connected to 127.0.0.1:52742
I0727 11:19:04.271463  2855 process_group_nccl.cc:120] ProcessGroupNCCL pg_timeout_ 1800000
[2024/07/27 11:19:04] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/07/27 11:19:04] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
W0727 11:19:04.289928  2855 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 90.2, Driver API Version: 50724.2, Runtime API Version: 50724.2
[2024/07/27 11:19:05] ppocr INFO: load pretrain successful from ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/07/27 11:19:05] ppocr INFO: train dataloader has 3 iters
[2024/07/27 11:19:05] ppocr INFO: valid dataloader has 500 iters
[2024/07/27 11:19:05] ppocr INFO: During the training process, after the 0th iteration, an evaluation is run every 60 iterations
[2024/07/27 11:20:25] ppocr INFO: epoch: [1/1500], global_step: 3, lr: 0.001000, loss: 9.480772, loss_shrink_maps: 4.930692, loss_threshold_maps: 3.563909, loss_binary_maps: 0.986170, avg_reader_cost: 13.32391 s, avg_batch_cost: 19.60355 s, avg_samples: 12.5, ips: 0.63764 samples/s, eta: 3 days, 9:37:37
[2024/07/27 11:20:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:20:37] ppocr INFO: epoch: [2/1500], global_step: 6, lr: 0.001000, loss: 8.544098, loss_shrink_maps: 4.935206, loss_threshold_maps: 2.603798, loss_binary_maps: 0.988400, avg_reader_cost: 2.95120 s, avg_batch_cost: 3.18849 s, avg_samples: 12.5, ips: 3.92035 samples/s, eta: 1 day, 23:25:12
[2024/07/27 11:20:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:20:49] ppocr INFO: epoch: [3/1500], global_step: 9, lr: 0.001000, loss: 7.708382, loss_shrink_maps: 4.930692, loss_threshold_maps: 1.779346, loss_binary_maps: 0.987835, avg_reader_cost: 2.90512 s, avg_batch_cost: 3.15215 s, avg_samples: 12.5, ips: 3.96555 samples/s, eta: 1 day, 11:57:41
[2024/07/27 11:20:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:20:59] ppocr INFO: epoch: [4/1500], global_step: 10, lr: 0.001000, loss: 7.494616, loss_shrink_maps: 4.931758, loss_threshold_maps: 1.572014, loss_binary_maps: 0.987142, avg_reader_cost: 0.78054 s, avg_batch_cost: 1.00046 s, avg_samples: 4.8, ips: 4.79781 samples/s, eta: 1 day, 9:36:21
[2024/07/27 11:21:01] ppocr INFO: epoch: [4/1500], global_step: 12, lr: 0.001000, loss: 7.228681, loss_shrink_maps: 4.931758, loss_threshold_maps: 1.315077, loss_binary_maps: 0.986440, avg_reader_cost: 2.09298 s, avg_batch_cost: 2.23981 s, avg_samples: 7.7, ips: 3.43778 samples/s, eta: 1 day, 6:19:09
[2024/07/27 11:21:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:21:13] ppocr INFO: epoch: [5/1500], global_step: 15, lr: 0.001000, loss: 7.112224, loss_shrink_maps: 4.930692, loss_threshold_maps: 1.184255, loss_binary_maps: 0.986170, avg_reader_cost: 2.83620 s, avg_batch_cost: 3.17688 s, avg_samples: 12.5, ips: 3.93468 samples/s, eta: 1 day, 2:52:40
[2024/07/27 11:21:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:21:25] ppocr INFO: epoch: [6/1500], global_step: 18, lr: 0.001000, loss: 7.093850, loss_shrink_maps: 4.930370, loss_threshold_maps: 1.176808, loss_binary_maps: 0.985607, avg_reader_cost: 2.90469 s, avg_batch_cost: 3.14202 s, avg_samples: 12.5, ips: 3.97833 samples/s, eta: 1 day, 0:33:23
[2024/07/27 11:21:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:21:36] ppocr INFO: epoch: [7/1500], global_step: 20, lr: 0.001000, loss: 7.082152, loss_shrink_maps: 4.929456, loss_threshold_maps: 1.166440, loss_binary_maps: 0.985553, avg_reader_cost: 1.78845 s, avg_batch_cost: 2.07187 s, avg_samples: 9.6, ips: 4.63350 samples/s, eta: 23:22:48
[2024/07/27 11:21:37] ppocr INFO: epoch: [7/1500], global_step: 21, lr: 0.001000, loss: 7.067038, loss_shrink_maps: 4.929456, loss_threshold_maps: 1.150968, loss_binary_maps: 0.985553, avg_reader_cost: 1.08215 s, avg_batch_cost: 1.13755 s, avg_samples: 2.9, ips: 2.54934 samples/s, eta: 22:56:08
[2024/07/27 11:21:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:21:48] ppocr INFO: epoch: [8/1500], global_step: 24, lr: 0.001000, loss: 7.042310, loss_shrink_maps: 4.925900, loss_threshold_maps: 1.135616, loss_binary_maps: 0.983872, avg_reader_cost: 2.74143 s, avg_batch_cost: 3.07715 s, avg_samples: 12.5, ips: 4.06220 samples/s, eta: 21:38:58
[2024/07/27 11:21:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:22:00] ppocr INFO: epoch: [9/1500], global_step: 27, lr: 0.001000, loss: 6.963614, loss_shrink_maps: 4.904892, loss_threshold_maps: 1.096257, loss_binary_maps: 0.979010, avg_reader_cost: 2.87324 s, avg_batch_cost: 3.24731 s, avg_samples: 12.5, ips: 3.84934 samples/s, eta: 20:43:31
[2024/07/27 11:22:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:22:12] ppocr INFO: epoch: [10/1500], global_step: 30, lr: 0.001000, loss: 6.922710, loss_shrink_maps: 4.890026, loss_threshold_maps: 1.045500, loss_binary_maps: 0.974394, avg_reader_cost: 2.89083 s, avg_batch_cost: 3.13008 s, avg_samples: 12.5, ips: 3.99350 samples/s, eta: 19:56:09
[2024/07/27 11:22:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:22:24] ppocr INFO: epoch: [11/1500], global_step: 33, lr: 0.001000, loss: 6.859891, loss_shrink_maps: 4.888492, loss_threshold_maps: 1.009600, loss_binary_maps: 0.973823, avg_reader_cost: 2.75596 s, avg_batch_cost: 3.10461 s, avg_samples: 12.5, ips: 4.02628 samples/s, eta: 19:16:43
[2024/07/27 11:22:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:22:36] ppocr INFO: epoch: [12/1500], global_step: 36, lr: 0.001000, loss: 6.831796, loss_shrink_maps: 4.880192, loss_threshold_maps: 0.993817, loss_binary_maps: 0.972217, avg_reader_cost: 2.90053 s, avg_batch_cost: 3.25971 s, avg_samples: 12.5, ips: 3.83470 samples/s, eta: 18:46:59
[2024/07/27 11:22:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:22:48] ppocr INFO: epoch: [13/1500], global_step: 39, lr: 0.001000, loss: 6.818047, loss_shrink_maps: 4.862352, loss_threshold_maps: 0.956775, loss_binary_maps: 0.968630, avg_reader_cost: 2.75273 s, avg_batch_cost: 3.04437 s, avg_samples: 12.5, ips: 4.10594 samples/s, eta: 18:17:38
[2024/07/27 11:22:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:22:59] ppocr INFO: epoch: [14/1500], global_step: 40, lr: 0.001000, loss: 6.809054, loss_shrink_maps: 4.859376, loss_threshold_maps: 0.955596, loss_binary_maps: 0.967310, avg_reader_cost: 0.91054 s, avg_batch_cost: 1.00106 s, avg_samples: 4.8, ips: 4.79493 samples/s, eta: 18:08:33
[2024/07/27 11:23:00] ppocr INFO: epoch: [14/1500], global_step: 42, lr: 0.001000, loss: 6.778828, loss_shrink_maps: 4.852133, loss_threshold_maps: 0.944726, loss_binary_maps: 0.956009, avg_reader_cost: 2.09526 s, avg_batch_cost: 2.24368 s, avg_samples: 7.7, ips: 3.43186 samples/s, eta: 17:55:56
[2024/07/27 11:23:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:23:12] ppocr INFO: epoch: [15/1500], global_step: 45, lr: 0.001000, loss: 6.698614, loss_shrink_maps: 4.836128, loss_threshold_maps: 0.934905, loss_binary_maps: 0.946085, avg_reader_cost: 2.79339 s, avg_batch_cost: 3.19919 s, avg_samples: 12.5, ips: 3.90724 samples/s, eta: 17:36:19
[2024/07/27 11:23:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:23:24] ppocr INFO: epoch: [16/1500], global_step: 48, lr: 0.001000, loss: 6.661482, loss_shrink_maps: 4.807730, loss_threshold_maps: 0.914088, loss_binary_maps: 0.942309, avg_reader_cost: 2.78737 s, avg_batch_cost: 3.15760 s, avg_samples: 12.5, ips: 3.95870 samples/s, eta: 17:18:27
[2024/07/27 11:23:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:23:36] ppocr INFO: epoch: [17/1500], global_step: 50, lr: 0.001000, loss: 6.626173, loss_shrink_maps: 4.804862, loss_threshold_maps: 0.898176, loss_binary_maps: 0.934271, avg_reader_cost: 1.86880 s, avg_batch_cost: 2.05809 s, avg_samples: 9.6, ips: 4.66451 samples/s, eta: 17:06:59
[2024/07/27 11:23:36] ppocr INFO: epoch: [17/1500], global_step: 51, lr: 0.001000, loss: 6.610918, loss_shrink_maps: 4.804862, loss_threshold_maps: 0.891841, loss_binary_maps: 0.923617, avg_reader_cost: 1.07517 s, avg_batch_cost: 1.13042 s, avg_samples: 2.9, ips: 2.56542 samples/s, eta: 17:03:04
[2024/07/27 11:23:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:23:48] ppocr INFO: epoch: [18/1500], global_step: 54, lr: 0.001000, loss: 6.571888, loss_shrink_maps: 4.799198, loss_threshold_maps: 0.888733, loss_binary_maps: 0.911923, avg_reader_cost: 2.97674 s, avg_batch_cost: 3.30318 s, avg_samples: 12.5, ips: 3.78423 samples/s, eta: 16:50:54
[2024/07/27 11:23:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:24:00] ppocr INFO: epoch: [19/1500], global_step: 57, lr: 0.001000, loss: 6.543092, loss_shrink_maps: 4.794786, loss_threshold_maps: 0.876342, loss_binary_maps: 0.891371, avg_reader_cost: 2.77563 s, avg_batch_cost: 3.10389 s, avg_samples: 12.5, ips: 4.02720 samples/s, eta: 16:37:22
[2024/07/27 11:24:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:24:12] ppocr INFO: epoch: [20/1500], global_step: 60, lr: 0.001000, loss: 6.482062, loss_shrink_maps: 4.793821, loss_threshold_maps: 0.876342, loss_binary_maps: 0.812436, avg_reader_cost: 3.00557 s, avg_batch_cost: 3.24359 s, avg_samples: 12.5, ips: 3.85375 samples/s, eta: 16:26:52

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:57,  8.61it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.54it/s]
eval model::   1%|▏         | 7/500 [00:00<00:23, 20.73it/s]
eval model::   2%|▏         | 11/500 [00:00<00:21, 23.27it/s]
eval model::   3%|▎         | 14/500 [00:00<00:19, 25.07it/s]
eval model::   4%|▎         | 18/500 [00:00<00:17, 27.35it/s]
eval model::   4%|▍         | 21/500 [00:00<00:20, 23.18it/s]
eval model::   5%|▍         | 24/500 [00:01<00:22, 21.41it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 20.87it/s]
eval model::   6%|▌         | 30/500 [00:01<00:24, 19.43it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.21it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.37it/s]
eval model::   7%|▋         | 36/500 [00:01<00:25, 17.86it/s]
eval model::   8%|▊         | 39/500 [00:01<00:26, 17.56it/s]
eval model::   8%|▊         | 42/500 [00:02<00:23, 19.09it/s]
eval model::   9%|▉         | 45/500 [00:02<00:21, 20.95it/s]
eval model::  10%|▉         | 48/500 [00:02<00:20, 22.10it/s]
eval model::  10%|█         | 51/500 [00:02<00:19, 22.95it/s]
eval model::  11%|█         | 54/500 [00:02<00:19, 23.23it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:19, 23.04it/s]
eval model::  12%|█▏        | 60/500 [00:02<00:19, 22.97it/s]
eval model::  13%|█▎        | 63/500 [00:02<00:19, 22.81it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:19, 22.62it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:19, 21.84it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 21.91it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:19, 22.20it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:19, 22.17it/s]
eval model::  16%|█▌        | 81/500 [00:03<00:19, 21.17it/s]
eval model::  17%|█▋        | 84/500 [00:03<00:18, 21.97it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:19, 21.19it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:19, 21.12it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:19, 21.40it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:21, 19.11it/s]
eval model::  20%|█▉        | 98/500 [00:04<00:21, 19.03it/s]
eval model::  20%|██        | 100/500 [00:04<00:21, 18.23it/s]
eval model::  21%|██        | 103/500 [00:04<00:20, 19.25it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 20.32it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:19, 20.04it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:19, 19.89it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:19, 20.15it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:18, 20.96it/s]
eval model::  24%|██▍       | 121/500 [00:05<00:18, 20.50it/s]
eval model::  25%|██▍       | 124/500 [00:05<00:18, 20.46it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:17, 20.85it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:18, 20.29it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:18, 20.31it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:18, 20.02it/s]
eval model::  28%|██▊       | 139/500 [00:06<00:17, 20.51it/s]
eval model::  28%|██▊       | 142/500 [00:06<00:16, 21.08it/s]
eval model::  29%|██▉       | 145/500 [00:06<00:17, 20.55it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:17, 20.16it/s]
eval model::  30%|███       | 151/500 [00:07<00:17, 20.41it/s]
eval model::  31%|███       | 154/500 [00:07<00:16, 21.06it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:15, 22.19it/s]
eval model::  32%|███▏      | 160/500 [00:07<00:16, 20.17it/s]
eval model::  33%|███▎      | 163/500 [00:07<00:15, 21.46it/s]
eval model::  33%|███▎      | 166/500 [00:07<00:15, 21.51it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:15, 20.92it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:15, 21.25it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:16, 20.26it/s]
eval model::  36%|███▌      | 178/500 [00:08<00:15, 21.07it/s]
eval model::  36%|███▌      | 181/500 [00:08<00:15, 20.25it/s]
eval model::  37%|███▋      | 184/500 [00:08<00:14, 21.10it/s]
eval model::  37%|███▋      | 187/500 [00:08<00:15, 20.70it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:14, 21.55it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:14, 21.89it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:13, 21.84it/s]
eval model::  40%|███▉      | 199/500 [00:09<00:13, 21.67it/s]
eval model::  40%|████      | 202/500 [00:09<00:13, 21.51it/s]
eval model::  41%|████      | 205/500 [00:09<00:17, 16.62it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:16, 17.89it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:14, 19.60it/s]
eval model::  43%|████▎     | 214/500 [00:10<00:13, 21.23it/s]
eval model::  43%|████▎     | 217/500 [00:10<00:14, 19.90it/s]
eval model::  44%|████▍     | 220/500 [00:10<00:14, 19.96it/s]
eval model::  45%|████▍     | 223/500 [00:10<00:13, 19.83it/s]
eval model::  45%|████▌     | 226/500 [00:10<00:14, 19.37it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:13, 20.05it/s]
eval model::  46%|████▋     | 232/500 [00:11<00:13, 19.72it/s]
eval model::  47%|████▋     | 234/500 [00:11<00:13, 19.08it/s]
eval model::  47%|████▋     | 237/500 [00:11<00:12, 20.27it/s]
eval model::  48%|████▊     | 240/500 [00:11<00:13, 19.87it/s]
eval model::  48%|████▊     | 242/500 [00:11<00:13, 19.78it/s]
eval model::  49%|████▉     | 245/500 [00:11<00:12, 20.92it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:12, 20.60it/s]
eval model::  50%|█████     | 251/500 [00:12<00:11, 20.76it/s]
eval model::  51%|█████     | 254/500 [00:12<00:11, 21.07it/s]
eval model::  51%|█████▏    | 257/500 [00:12<00:11, 21.05it/s]
eval model::  52%|█████▏    | 260/500 [00:12<00:11, 21.50it/s]
eval model::  53%|█████▎    | 263/500 [00:12<00:11, 21.17it/s]
eval model::  53%|█████▎    | 266/500 [00:12<00:10, 21.64it/s]
eval model::  54%|█████▍    | 269/500 [00:12<00:10, 21.28it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:10, 21.92it/s]
eval model::  55%|█████▌    | 275/500 [00:13<00:10, 21.44it/s]
eval model::  56%|█████▌    | 278/500 [00:13<00:10, 21.27it/s]
eval model::  56%|█████▌    | 281/500 [00:13<00:10, 21.60it/s]
eval model::  57%|█████▋    | 284/500 [00:13<00:10, 21.39it/s]
eval model::  57%|█████▋    | 287/500 [00:13<00:10, 19.53it/s]
eval model::  58%|█████▊    | 290/500 [00:13<00:10, 20.84it/s]
eval model::  59%|█████▊    | 293/500 [00:14<00:09, 21.02it/s]
eval model::  59%|█████▉    | 296/500 [00:14<00:09, 20.75it/s]
eval model::  60%|█████▉    | 299/500 [00:14<00:09, 21.06it/s]
eval model::  60%|██████    | 302/500 [00:14<00:09, 20.08it/s]
eval model::  61%|██████    | 305/500 [00:14<00:09, 20.05it/s]
eval model::  62%|██████▏   | 308/500 [00:14<00:09, 20.55it/s]
eval model::  62%|██████▏   | 311/500 [00:15<00:09, 20.40it/s]
eval model::  63%|██████▎   | 314/500 [00:15<00:09, 20.35it/s]
eval model::  63%|██████▎   | 317/500 [00:15<00:09, 20.27it/s]
eval model::  64%|██████▍   | 320/500 [00:15<00:08, 20.19it/s]
eval model::  65%|██████▍   | 323/500 [00:15<00:08, 20.66it/s]
eval model::  65%|██████▌   | 326/500 [00:15<00:08, 20.49it/s]
eval model::  66%|██████▌   | 329/500 [00:15<00:08, 20.58it/s]
eval model::  66%|██████▋   | 332/500 [00:16<00:08, 20.94it/s]
eval model::  67%|██████▋   | 335/500 [00:16<00:08, 20.57it/s]
eval model::  68%|██████▊   | 338/500 [00:16<00:08, 20.12it/s]
eval model::  68%|██████▊   | 341/500 [00:16<00:08, 19.78it/s]
eval model::  69%|██████▉   | 344/500 [00:16<00:07, 21.05it/s]
eval model::  69%|██████▉   | 347/500 [00:16<00:07, 20.87it/s]
eval model::  70%|███████   | 350/500 [00:16<00:07, 20.90it/s]
eval model::  71%|███████   | 353/500 [00:17<00:06, 21.77it/s]
eval model::  71%|███████   | 356/500 [00:17<00:07, 20.43it/s]
eval model::  72%|███████▏  | 359/500 [00:17<00:06, 21.09it/s]
eval model::  72%|███████▏  | 362/500 [00:17<00:06, 20.12it/s]
eval model::  73%|███████▎  | 365/500 [00:17<00:06, 20.62it/s]
eval model::  74%|███████▎  | 368/500 [00:17<00:06, 20.79it/s]
eval model::  74%|███████▍  | 371/500 [00:17<00:06, 19.69it/s]
eval model::  75%|███████▍  | 374/500 [00:18<00:06, 19.89it/s]
eval model::  75%|███████▌  | 377/500 [00:18<00:05, 20.98it/s]
eval model::  76%|███████▌  | 380/500 [00:18<00:05, 20.73it/s]
eval model::  77%|███████▋  | 383/500 [00:18<00:05, 22.31it/s]
eval model::  77%|███████▋  | 386/500 [00:18<00:05, 20.48it/s]
eval model::  78%|███████▊  | 389/500 [00:18<00:05, 21.17it/s]
eval model::  78%|███████▊  | 392/500 [00:18<00:05, 21.21it/s]
eval model::  79%|███████▉  | 395/500 [00:19<00:05, 20.74it/s]
eval model::  80%|███████▉  | 398/500 [00:19<00:04, 21.14it/s]
eval model::  80%|████████  | 401/500 [00:19<00:04, 20.39it/s]
eval model::  81%|████████  | 404/500 [00:19<00:04, 20.46it/s]
eval model::  81%|████████▏ | 407/500 [00:19<00:04, 20.62it/s]
eval model::  82%|████████▏ | 410/500 [00:19<00:04, 20.91it/s]
eval model::  83%|████████▎ | 413/500 [00:19<00:04, 20.35it/s]
eval model::  83%|████████▎ | 416/500 [00:20<00:04, 20.60it/s]
eval model::  84%|████████▍ | 419/500 [00:20<00:03, 20.38it/s]
eval model::  84%|████████▍ | 422/500 [00:20<00:03, 20.22it/s]
eval model::  85%|████████▌ | 425/500 [00:20<00:03, 19.69it/s]
eval model::  86%|████████▌ | 428/500 [00:20<00:03, 21.04it/s]
eval model::  86%|████████▌ | 431/500 [00:20<00:03, 22.18it/s]
eval model::  87%|████████▋ | 434/500 [00:20<00:03, 21.37it/s]
eval model::  87%|████████▋ | 437/500 [00:21<00:03, 19.90it/s]
eval model::  88%|████████▊ | 440/500 [00:21<00:02, 20.23it/s]
eval model::  89%|████████▊ | 443/500 [00:21<00:02, 20.73it/s]
eval model::  89%|████████▉ | 446/500 [00:21<00:02, 20.99it/s]
eval model::  90%|████████▉ | 449/500 [00:21<00:02, 20.66it/s]
eval model::  90%|█████████ | 452/500 [00:21<00:02, 21.13it/s]
eval model::  91%|█████████ | 455/500 [00:21<00:02, 20.89it/s]
eval model::  92%|█████████▏| 458/500 [00:22<00:02, 20.30it/s]
eval model::  92%|█████████▏| 461/500 [00:22<00:01, 19.97it/s]
eval model::  93%|█████████▎| 464/500 [00:22<00:01, 19.63it/s]
eval model::  93%|█████████▎| 467/500 [00:22<00:01, 20.42it/s]
eval model::  94%|█████████▍| 470/500 [00:22<00:01, 19.76it/s]
eval model::  95%|█████████▍| 473/500 [00:22<00:01, 19.48it/s]
eval model::  95%|█████████▌| 476/500 [00:23<00:01, 20.87it/s]
eval model::  96%|█████████▌| 479/500 [00:23<00:01, 20.34it/s]
eval model::  96%|█████████▋| 482/500 [00:23<00:00, 20.43it/s]
eval model::  97%|█████████▋| 485/500 [00:23<00:00, 20.18it/s]
eval model::  98%|█████████▊| 488/500 [00:23<00:00, 19.89it/s]
eval model::  98%|█████████▊| 491/500 [00:23<00:00, 20.65it/s]
eval model::  99%|█████████▉| 494/500 [00:23<00:00, 21.03it/s]
eval model::  99%|█████████▉| 497/500 [00:24<00:00, 21.68it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 21.00it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 20.67it/s]
[2024/07/27 11:24:36] ppocr INFO: cur metric, precision: 0.08085106382978724, recall: 0.0091478093403948, hmean: 0.01643598615916955, fps: 43.91955985780141
[2024/07/27 11:24:37] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 11:24:37] ppocr INFO: best metric, hmean: 0.01643598615916955, precision: 0.08085106382978724, recall: 0.0091478093403948, fps: 43.91955985780141, best_epoch: 20
[2024/07/27 11:24:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:24:46] ppocr INFO: epoch: [21/1500], global_step: 63, lr: 0.001000, loss: 6.339468, loss_shrink_maps: 4.782884, loss_threshold_maps: 0.868179, loss_binary_maps: 0.725933, avg_reader_cost: 2.38862 s, avg_batch_cost: 2.72327 s, avg_samples: 12.5, ips: 4.59007 samples/s, eta: 16:11:12
[2024/07/27 11:24:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:24:56] ppocr INFO: epoch: [22/1500], global_step: 66, lr: 0.001000, loss: 6.322258, loss_shrink_maps: 4.781232, loss_threshold_maps: 0.854461, loss_binary_maps: 0.665587, avg_reader_cost: 2.25163 s, avg_batch_cost: 2.48823 s, avg_samples: 12.5, ips: 5.02365 samples/s, eta: 15:54:17
[2024/07/27 11:24:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:25:05] ppocr INFO: epoch: [23/1500], global_step: 69, lr: 0.001000, loss: 6.103568, loss_shrink_maps: 4.774416, loss_threshold_maps: 0.854461, loss_binary_maps: 0.465616, avg_reader_cost: 2.15667 s, avg_batch_cost: 2.51885 s, avg_samples: 12.5, ips: 4.96259 samples/s, eta: 15:39:08
[2024/07/27 11:25:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:25:14] ppocr INFO: epoch: [24/1500], global_step: 70, lr: 0.001000, loss: 6.086606, loss_shrink_maps: 4.774416, loss_threshold_maps: 0.850591, loss_binary_maps: 0.449583, avg_reader_cost: 0.55745 s, avg_batch_cost: 0.75289 s, avg_samples: 4.8, ips: 6.37542 samples/s, eta: 15:33:27
[2024/07/27 11:25:15] ppocr INFO: epoch: [24/1500], global_step: 72, lr: 0.001000, loss: 6.018610, loss_shrink_maps: 4.764674, loss_threshold_maps: 0.848938, loss_binary_maps: 0.413616, avg_reader_cost: 1.59758 s, avg_batch_cost: 1.74478 s, avg_samples: 7.7, ips: 4.41316 samples/s, eta: 15:25:00
[2024/07/27 11:25:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:25:25] ppocr INFO: epoch: [25/1500], global_step: 75, lr: 0.001000, loss: 5.980997, loss_shrink_maps: 4.736993, loss_threshold_maps: 0.847426, loss_binary_maps: 0.408017, avg_reader_cost: 2.16603 s, avg_batch_cost: 2.54404 s, avg_samples: 12.5, ips: 4.91344 samples/s, eta: 15:12:25
[2024/07/27 11:25:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:25:34] ppocr INFO: epoch: [26/1500], global_step: 78, lr: 0.001000, loss: 5.957236, loss_shrink_maps: 4.717703, loss_threshold_maps: 0.835125, loss_binary_maps: 0.398625, avg_reader_cost: 2.33872 s, avg_batch_cost: 2.57712 s, avg_samples: 12.5, ips: 4.85037 samples/s, eta: 15:01:05
[2024/07/27 11:25:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:25:44] ppocr INFO: epoch: [27/1500], global_step: 80, lr: 0.001000, loss: 5.940472, loss_shrink_maps: 4.689912, loss_threshold_maps: 0.826278, loss_binary_maps: 0.393523, avg_reader_cost: 1.47824 s, avg_batch_cost: 1.66239 s, avg_samples: 9.6, ips: 5.77482 samples/s, eta: 14:53:28
[2024/07/27 11:25:44] ppocr INFO: epoch: [27/1500], global_step: 81, lr: 0.001000, loss: 5.924828, loss_shrink_maps: 4.678408, loss_threshold_maps: 0.815844, loss_binary_maps: 0.385656, avg_reader_cost: 0.87729 s, avg_batch_cost: 0.93274 s, avg_samples: 2.9, ips: 3.10911 samples/s, eta: 14:50:43
[2024/07/27 11:25:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:25:54] ppocr INFO: epoch: [28/1500], global_step: 84, lr: 0.001000, loss: 5.821468, loss_shrink_maps: 4.631274, loss_threshold_maps: 0.803404, loss_binary_maps: 0.380633, avg_reader_cost: 2.34277 s, avg_batch_cost: 2.58121 s, avg_samples: 12.5, ips: 4.84270 samples/s, eta: 14:40:56
[2024/07/27 11:25:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:26:04] ppocr INFO: epoch: [29/1500], global_step: 87, lr: 0.001000, loss: 5.716532, loss_shrink_maps: 4.535442, loss_threshold_maps: 0.803404, loss_binary_maps: 0.381055, avg_reader_cost: 2.31168 s, avg_batch_cost: 2.56036 s, avg_samples: 12.5, ips: 4.88212 samples/s, eta: 14:31:37
[2024/07/27 11:26:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:26:13] ppocr INFO: epoch: [30/1500], global_step: 90, lr: 0.001000, loss: 5.669962, loss_shrink_maps: 4.499154, loss_threshold_maps: 0.801467, loss_binary_maps: 0.381055, avg_reader_cost: 2.09350 s, avg_batch_cost: 2.40920 s, avg_samples: 12.5, ips: 5.18845 samples/s, eta: 14:21:40
[2024/07/27 11:26:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:26:23] ppocr INFO: epoch: [31/1500], global_step: 93, lr: 0.001000, loss: 5.602889, loss_shrink_maps: 4.437448, loss_threshold_maps: 0.805613, loss_binary_maps: 0.378343, avg_reader_cost: 2.28828 s, avg_batch_cost: 2.67552 s, avg_samples: 12.5, ips: 4.67199 samples/s, eta: 14:14:26
[2024/07/27 11:26:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:26:33] ppocr INFO: epoch: [32/1500], global_step: 96, lr: 0.001000, loss: 5.532728, loss_shrink_maps: 4.359826, loss_threshold_maps: 0.805277, loss_binary_maps: 0.377218, avg_reader_cost: 2.33420 s, avg_batch_cost: 2.57252 s, avg_samples: 12.5, ips: 4.85904 samples/s, eta: 14:06:51
[2024/07/27 11:26:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:26:42] ppocr INFO: epoch: [33/1500], global_step: 99, lr: 0.001000, loss: 5.462482, loss_shrink_maps: 4.285815, loss_threshold_maps: 0.810783, loss_binary_maps: 0.356820, avg_reader_cost: 2.27970 s, avg_batch_cost: 2.52743 s, avg_samples: 12.5, ips: 4.94573 samples/s, eta: 13:59:21
[2024/07/27 11:26:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:26:50] ppocr INFO: epoch: [34/1500], global_step: 100, lr: 0.001000, loss: 5.439956, loss_shrink_maps: 4.254110, loss_threshold_maps: 0.816136, loss_binary_maps: 0.355817, avg_reader_cost: 0.53884 s, avg_batch_cost: 0.76928 s, avg_samples: 4.8, ips: 6.23963 samples/s, eta: 13:56:24
[2024/07/27 11:26:52] ppocr INFO: epoch: [34/1500], global_step: 102, lr: 0.001000, loss: 5.418988, loss_shrink_maps: 4.193313, loss_threshold_maps: 0.818774, loss_binary_maps: 0.368025, avg_reader_cost: 1.63135 s, avg_batch_cost: 1.77902 s, avg_samples: 7.7, ips: 4.32822 samples/s, eta: 13:52:25
[2024/07/27 11:26:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:27:01] ppocr INFO: epoch: [35/1500], global_step: 105, lr: 0.001000, loss: 5.241027, loss_shrink_maps: 4.066562, loss_threshold_maps: 0.816136, loss_binary_maps: 0.368025, avg_reader_cost: 2.23341 s, avg_batch_cost: 2.56447 s, avg_samples: 12.5, ips: 4.87430 samples/s, eta: 13:45:58
[2024/07/27 11:27:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:27:11] ppocr INFO: epoch: [36/1500], global_step: 108, lr: 0.001000, loss: 5.210048, loss_shrink_maps: 4.046406, loss_threshold_maps: 0.811586, loss_binary_maps: 0.354948, avg_reader_cost: 2.22985 s, avg_batch_cost: 2.57974 s, avg_samples: 12.5, ips: 4.84545 samples/s, eta: 13:39:58
[2024/07/27 11:27:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:27:20] ppocr INFO: epoch: [37/1500], global_step: 110, lr: 0.001000, loss: 5.163123, loss_shrink_maps: 4.001716, loss_threshold_maps: 0.811586, loss_binary_maps: 0.368025, avg_reader_cost: 1.39949 s, avg_batch_cost: 1.58243 s, avg_samples: 9.6, ips: 6.06663 samples/s, eta: 13:35:13
[2024/07/27 11:27:21] ppocr INFO: epoch: [37/1500], global_step: 111, lr: 0.001000, loss: 5.110232, loss_shrink_maps: 3.939732, loss_threshold_maps: 0.807682, loss_binary_maps: 0.354948, avg_reader_cost: 0.83710 s, avg_batch_cost: 0.89318 s, avg_samples: 2.9, ips: 3.24682 samples/s, eta: 13:33:34
[2024/07/27 11:27:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:27:31] ppocr INFO: epoch: [38/1500], global_step: 114, lr: 0.001000, loss: 5.030018, loss_shrink_maps: 3.817909, loss_threshold_maps: 0.798440, loss_binary_maps: 0.355179, avg_reader_cost: 2.29891 s, avg_batch_cost: 2.53576 s, avg_samples: 12.5, ips: 4.92950 samples/s, eta: 13:27:53
[2024/07/27 11:27:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:27:41] ppocr INFO: epoch: [39/1500], global_step: 117, lr: 0.001000, loss: 4.816878, loss_shrink_maps: 3.684157, loss_threshold_maps: 0.780125, loss_binary_maps: 0.351575, avg_reader_cost: 2.44644 s, avg_batch_cost: 2.68603 s, avg_samples: 12.5, ips: 4.65371 samples/s, eta: 13:23:24
[2024/07/27 11:27:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:27:50] ppocr INFO: epoch: [40/1500], global_step: 120, lr: 0.001000, loss: 4.570972, loss_shrink_maps: 3.460672, loss_threshold_maps: 0.782258, loss_binary_maps: 0.350812, avg_reader_cost: 2.11952 s, avg_batch_cost: 2.45039 s, avg_samples: 12.5, ips: 5.10123 samples/s, eta: 13:17:41

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.38it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 17.00it/s]
eval model::   1%|▏         | 7/500 [00:00<00:25, 19.00it/s]
eval model::   2%|▏         | 10/500 [00:00<00:25, 19.03it/s]
eval model::   2%|▏         | 12/500 [00:00<00:26, 18.44it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 18.81it/s]
eval model::   3%|▎         | 17/500 [00:00<00:22, 21.11it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 18.98it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 19.94it/s]
eval model::   5%|▌         | 26/500 [00:01<00:25, 18.84it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 19.68it/s]
eval model::   6%|▋         | 32/500 [00:01<00:27, 17.18it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 18.97it/s]
eval model::   8%|▊         | 38/500 [00:02<00:23, 19.62it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.48it/s]
eval model::   9%|▉         | 44/500 [00:02<00:22, 20.38it/s]
eval model::   9%|▉         | 47/500 [00:02<00:22, 20.04it/s]
eval model::  10%|█         | 50/500 [00:02<00:21, 20.71it/s]
eval model::  11%|█         | 53/500 [00:02<00:20, 21.37it/s]
eval model::  11%|█         | 56/500 [00:02<00:22, 19.37it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:22, 19.82it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:24, 18.13it/s]
eval model::  13%|█▎        | 65/500 [00:03<00:21, 19.80it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:21, 19.80it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 20.76it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:20, 20.46it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:20, 20.83it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:19, 21.66it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:18, 22.87it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:19, 21.60it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:19, 21.30it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:18, 21.53it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:20, 20.12it/s]
eval model::  20%|█▉        | 98/500 [00:04<00:18, 21.80it/s]
eval model::  20%|██        | 101/500 [00:05<00:18, 21.54it/s]
eval model::  21%|██        | 104/500 [00:05<00:17, 22.65it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.50it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 20.94it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 20.78it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:18, 20.98it/s]
eval model::  24%|██▍       | 119/500 [00:05<00:18, 20.84it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:19, 19.74it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 19.45it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 19.30it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 21.38it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:18, 19.95it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:19, 18.35it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:18, 19.41it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.41it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:18, 19.00it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:18, 19.31it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:17, 20.61it/s]
eval model::  30%|███       | 152/500 [00:07<00:18, 19.27it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 21.31it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:15, 22.67it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:17, 19.42it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:16, 20.19it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:16, 20.64it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 20.01it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:15, 21.00it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:15, 20.96it/s]
eval model::  36%|███▌      | 179/500 [00:08<00:14, 21.77it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:14, 21.37it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:14, 21.98it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:14, 22.14it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:14, 21.35it/s]
eval model::  39%|███▉      | 194/500 [00:09<00:14, 21.34it/s]
eval model::  39%|███▉      | 197/500 [00:09<00:15, 19.85it/s]
eval model::  40%|████      | 200/500 [00:09<00:14, 20.54it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 20.64it/s]
eval model::  41%|████      | 206/500 [00:10<00:18, 15.82it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:16, 17.30it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:16, 17.50it/s]
eval model::  43%|████▎     | 214/500 [00:10<00:14, 19.79it/s]
eval model::  43%|████▎     | 217/500 [00:10<00:17, 15.98it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 18.41it/s]
eval model::  45%|████▍     | 223/500 [00:11<00:15, 18.43it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:13, 19.83it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:12, 21.69it/s]
eval model::  46%|████▋     | 232/500 [00:11<00:11, 22.81it/s]
eval model::  47%|████▋     | 235/500 [00:11<00:11, 22.55it/s]
eval model::  48%|████▊     | 238/500 [00:11<00:11, 23.47it/s]
eval model::  48%|████▊     | 241/500 [00:11<00:11, 21.83it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:12, 20.39it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:11, 22.06it/s]
eval model::  50%|█████     | 250/500 [00:12<00:11, 20.98it/s]
eval model::  51%|█████     | 253/500 [00:12<00:10, 22.70it/s]
eval model::  51%|█████     | 256/500 [00:12<00:11, 22.11it/s]
eval model::  52%|█████▏    | 259/500 [00:12<00:10, 22.94it/s]
eval model::  52%|█████▏    | 262/500 [00:12<00:11, 20.90it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:12, 19.14it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:12, 18.76it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:12, 18.80it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:11, 20.13it/s]
eval model::  55%|█████▌    | 275/500 [00:13<00:10, 20.68it/s]
eval model::  56%|█████▌    | 278/500 [00:13<00:10, 21.76it/s]
eval model::  56%|█████▌    | 281/500 [00:13<00:10, 21.26it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 21.31it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:12, 17.30it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:09, 21.07it/s]
eval model::  59%|█████▉    | 294/500 [00:14<00:10, 20.57it/s]
eval model::  59%|█████▉    | 297/500 [00:14<00:09, 21.43it/s]
eval model::  60%|██████    | 300/500 [00:14<00:10, 19.86it/s]
eval model::  61%|██████    | 303/500 [00:14<00:09, 21.15it/s]
eval model::  61%|██████    | 306/500 [00:15<00:09, 20.86it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:08, 21.27it/s]
eval model::  62%|██████▏   | 312/500 [00:15<00:09, 20.81it/s]
eval model::  63%|██████▎   | 315/500 [00:15<00:09, 19.97it/s]
eval model::  64%|██████▎   | 318/500 [00:15<00:08, 20.34it/s]
eval model::  64%|██████▍   | 321/500 [00:15<00:09, 19.76it/s]
eval model::  65%|██████▍   | 323/500 [00:15<00:09, 18.96it/s]
eval model::  65%|██████▌   | 326/500 [00:16<00:08, 19.44it/s]
eval model::  66%|██████▌   | 329/500 [00:16<00:08, 19.95it/s]
eval model::  66%|██████▋   | 332/500 [00:16<00:08, 20.43it/s]
eval model::  67%|██████▋   | 335/500 [00:16<00:07, 22.10it/s]
eval model::  68%|██████▊   | 338/500 [00:16<00:07, 21.34it/s]
eval model::  68%|██████▊   | 341/500 [00:16<00:08, 19.31it/s]
eval model::  69%|██████▉   | 344/500 [00:16<00:07, 20.49it/s]
eval model::  69%|██████▉   | 347/500 [00:17<00:07, 20.36it/s]
eval model::  70%|███████   | 350/500 [00:17<00:07, 19.76it/s]
eval model::  71%|███████   | 353/500 [00:17<00:06, 21.54it/s]
eval model::  71%|███████   | 356/500 [00:17<00:07, 19.63it/s]
eval model::  72%|███████▏  | 359/500 [00:17<00:06, 21.19it/s]
eval model::  72%|███████▏  | 362/500 [00:17<00:06, 21.51it/s]
eval model::  73%|███████▎  | 365/500 [00:18<00:06, 19.72it/s]
eval model::  74%|███████▎  | 368/500 [00:18<00:06, 19.49it/s]
eval model::  74%|███████▍  | 371/500 [00:18<00:06, 19.80it/s]
eval model::  75%|███████▍  | 374/500 [00:18<00:05, 21.17it/s]
eval model::  75%|███████▌  | 377/500 [00:18<00:05, 22.71it/s]
eval model::  76%|███████▌  | 380/500 [00:18<00:05, 23.63it/s]
eval model::  77%|███████▋  | 383/500 [00:18<00:05, 22.41it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:06, 18.13it/s]
eval model::  78%|███████▊  | 389/500 [00:19<00:05, 20.13it/s]
eval model::  78%|███████▊  | 392/500 [00:19<00:05, 21.06it/s]
eval model::  79%|███████▉  | 395/500 [00:19<00:04, 21.31it/s]
eval model::  80%|███████▉  | 398/500 [00:19<00:04, 21.85it/s]
eval model::  80%|████████  | 401/500 [00:19<00:04, 21.25it/s]
eval model::  81%|████████  | 404/500 [00:19<00:04, 22.95it/s]
eval model::  81%|████████▏ | 407/500 [00:19<00:04, 21.77it/s]
eval model::  82%|████████▏ | 410/500 [00:20<00:04, 20.80it/s]
eval model::  83%|████████▎ | 413/500 [00:20<00:03, 21.92it/s]
eval model::  83%|████████▎ | 416/500 [00:20<00:03, 21.53it/s]
eval model::  84%|████████▍ | 419/500 [00:20<00:03, 21.87it/s]
eval model::  84%|████████▍ | 422/500 [00:20<00:04, 19.03it/s]
eval model::  85%|████████▌ | 425/500 [00:20<00:03, 20.45it/s]
eval model::  86%|████████▌ | 428/500 [00:20<00:03, 21.91it/s]
eval model::  86%|████████▌ | 431/500 [00:21<00:03, 20.54it/s]
eval model::  87%|████████▋ | 434/500 [00:21<00:03, 19.49it/s]
eval model::  87%|████████▋ | 437/500 [00:21<00:03, 20.86it/s]
eval model::  88%|████████▊ | 440/500 [00:21<00:03, 19.93it/s]
eval model::  89%|████████▊ | 443/500 [00:21<00:02, 20.80it/s]
eval model::  89%|████████▉ | 446/500 [00:21<00:02, 20.81it/s]
eval model::  90%|████████▉ | 449/500 [00:22<00:02, 20.19it/s]
eval model::  90%|█████████ | 452/500 [00:22<00:02, 22.30it/s]
eval model::  91%|█████████ | 455/500 [00:22<00:02, 20.87it/s]
eval model::  92%|█████████▏| 458/500 [00:22<00:01, 21.80it/s]
eval model::  92%|█████████▏| 461/500 [00:22<00:01, 20.11it/s]
eval model::  93%|█████████▎| 464/500 [00:22<00:01, 19.12it/s]
eval model::  93%|█████████▎| 466/500 [00:22<00:01, 18.80it/s]
eval model::  94%|█████████▍| 469/500 [00:23<00:01, 19.85it/s]
eval model::  94%|█████████▍| 472/500 [00:23<00:01, 19.73it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 19.29it/s]
eval model::  95%|█████████▌| 477/500 [00:23<00:01, 20.06it/s]
eval model::  96%|█████████▌| 480/500 [00:23<00:00, 20.17it/s]
eval model::  97%|█████████▋| 483/500 [00:23<00:00, 19.02it/s]
eval model::  97%|█████████▋| 486/500 [00:23<00:00, 18.35it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 19.75it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 21.47it/s]
eval model::  99%|█████████▉| 495/500 [00:24<00:00, 22.76it/s]
eval model:: 100%|█████████▉| 498/500 [00:24<00:00, 23.96it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 20.43it/s]
[2024/07/27 11:28:14] ppocr INFO: cur metric, precision: 0.46496815286624205, recall: 0.21088107847857487, hmean: 0.29016230539913884, fps: 44.44989400729777
[2024/07/27 11:28:15] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 11:28:15] ppocr INFO: best metric, hmean: 0.29016230539913884, precision: 0.46496815286624205, recall: 0.21088107847857487, fps: 44.44989400729777, best_epoch: 40
[2024/07/27 11:28:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:28:24] ppocr INFO: epoch: [41/1500], global_step: 123, lr: 0.001000, loss: 4.373784, loss_shrink_maps: 3.245492, loss_threshold_maps: 0.785686, loss_binary_maps: 0.344912, avg_reader_cost: 2.14669 s, avg_batch_cost: 2.50259 s, avg_samples: 12.5, ips: 4.99482 samples/s, eta: 13:12:32
[2024/07/27 11:28:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:28:34] ppocr INFO: epoch: [42/1500], global_step: 126, lr: 0.001000, loss: 4.145794, loss_shrink_maps: 2.993010, loss_threshold_maps: 0.785686, loss_binary_maps: 0.339903, avg_reader_cost: 2.37613 s, avg_batch_cost: 2.61000 s, avg_samples: 12.5, ips: 4.78927 samples/s, eta: 13:08:14
[2024/07/27 11:28:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:28:43] ppocr INFO: epoch: [43/1500], global_step: 129, lr: 0.001000, loss: 4.005169, loss_shrink_maps: 2.880129, loss_threshold_maps: 0.785686, loss_binary_maps: 0.339903, avg_reader_cost: 2.15037 s, avg_batch_cost: 2.43076 s, avg_samples: 12.5, ips: 5.14243 samples/s, eta: 13:03:06
[2024/07/27 11:28:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:28:51] ppocr INFO: epoch: [44/1500], global_step: 130, lr: 0.001000, loss: 4.005169, loss_shrink_maps: 2.880129, loss_threshold_maps: 0.785686, loss_binary_maps: 0.339903, avg_reader_cost: 0.57228 s, avg_batch_cost: 0.76069 s, avg_samples: 4.8, ips: 6.31005 samples/s, eta: 13:01:10
[2024/07/27 11:28:53] ppocr INFO: epoch: [44/1500], global_step: 132, lr: 0.001000, loss: 3.938630, loss_shrink_maps: 2.824890, loss_threshold_maps: 0.785686, loss_binary_maps: 0.328356, avg_reader_cost: 1.61358 s, avg_batch_cost: 1.76068 s, avg_samples: 7.7, ips: 4.37332 samples/s, eta: 12:58:41
[2024/07/27 11:28:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:29:02] ppocr INFO: epoch: [45/1500], global_step: 135, lr: 0.001000, loss: 3.862320, loss_shrink_maps: 2.732754, loss_threshold_maps: 0.790430, loss_binary_maps: 0.326136, avg_reader_cost: 2.13241 s, avg_batch_cost: 2.48675 s, avg_samples: 12.5, ips: 5.02663 samples/s, eta: 12:54:16
[2024/07/27 11:29:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:29:12] ppocr INFO: epoch: [46/1500], global_step: 138, lr: 0.001000, loss: 3.747629, loss_shrink_maps: 2.577841, loss_threshold_maps: 0.790430, loss_binary_maps: 0.319411, avg_reader_cost: 2.35472 s, avg_batch_cost: 2.60381 s, avg_samples: 12.5, ips: 4.80065 samples/s, eta: 12:50:38
[2024/07/27 11:29:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:29:21] ppocr INFO: epoch: [47/1500], global_step: 140, lr: 0.001000, loss: 3.694742, loss_shrink_maps: 2.544235, loss_threshold_maps: 0.790343, loss_binary_maps: 0.319411, avg_reader_cost: 1.42309 s, avg_batch_cost: 1.60623 s, avg_samples: 9.6, ips: 5.97672 samples/s, eta: 12:47:36
[2024/07/27 11:29:22] ppocr INFO: epoch: [47/1500], global_step: 141, lr: 0.001000, loss: 3.616952, loss_shrink_maps: 2.502080, loss_threshold_maps: 0.785709, loss_binary_maps: 0.319411, avg_reader_cost: 0.84910 s, avg_batch_cost: 0.90438 s, avg_samples: 2.9, ips: 3.20663 samples/s, eta: 12:46:39
[2024/07/27 11:29:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:29:32] ppocr INFO: epoch: [48/1500], global_step: 144, lr: 0.001000, loss: 3.504123, loss_shrink_maps: 2.434544, loss_threshold_maps: 0.778076, loss_binary_maps: 0.319411, avg_reader_cost: 2.24588 s, avg_batch_cost: 2.63490 s, avg_samples: 12.5, ips: 4.74401 samples/s, eta: 12:43:27
[2024/07/27 11:29:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:29:42] ppocr INFO: epoch: [49/1500], global_step: 147, lr: 0.001000, loss: 3.423800, loss_shrink_maps: 2.282567, loss_threshold_maps: 0.779277, loss_binary_maps: 0.319411, avg_reader_cost: 2.39339 s, avg_batch_cost: 2.63586 s, avg_samples: 12.5, ips: 4.74228 samples/s, eta: 12:40:21
[2024/07/27 11:29:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:29:52] ppocr INFO: epoch: [50/1500], global_step: 150, lr: 0.001000, loss: 3.249620, loss_shrink_maps: 2.167734, loss_threshold_maps: 0.778043, loss_binary_maps: 0.313547, avg_reader_cost: 2.26716 s, avg_batch_cost: 2.66112 s, avg_samples: 12.5, ips: 4.69727 samples/s, eta: 12:37:30
[2024/07/27 11:29:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:30:01] ppocr INFO: epoch: [51/1500], global_step: 153, lr: 0.001000, loss: 3.203667, loss_shrink_maps: 2.106681, loss_threshold_maps: 0.775603, loss_binary_maps: 0.303846, avg_reader_cost: 2.18688 s, avg_batch_cost: 2.54622 s, avg_samples: 12.5, ips: 4.90924 samples/s, eta: 12:34:11
[2024/07/27 11:30:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:30:10] ppocr INFO: epoch: [52/1500], global_step: 156, lr: 0.001000, loss: 3.046677, loss_shrink_maps: 1.983725, loss_threshold_maps: 0.770660, loss_binary_maps: 0.297511, avg_reader_cost: 1.99763 s, avg_batch_cost: 2.29117 s, avg_samples: 12.5, ips: 5.45572 samples/s, eta: 12:29:49
[2024/07/27 11:30:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:30:19] ppocr INFO: epoch: [53/1500], global_step: 159, lr: 0.001000, loss: 2.931374, loss_shrink_maps: 1.877990, loss_threshold_maps: 0.766977, loss_binary_maps: 0.295662, avg_reader_cost: 2.02069 s, avg_batch_cost: 2.31616 s, avg_samples: 12.5, ips: 5.39685 samples/s, eta: 12:25:42
[2024/07/27 11:30:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:30:28] ppocr INFO: epoch: [54/1500], global_step: 160, lr: 0.001000, loss: 2.931374, loss_shrink_maps: 1.877990, loss_threshold_maps: 0.763115, loss_binary_maps: 0.295662, avg_reader_cost: 0.70733 s, avg_batch_cost: 0.79955 s, avg_samples: 4.8, ips: 6.00336 samples/s, eta: 12:24:29
[2024/07/27 11:30:29] ppocr INFO: epoch: [54/1500], global_step: 162, lr: 0.001000, loss: 2.897375, loss_shrink_maps: 1.845251, loss_threshold_maps: 0.763115, loss_binary_maps: 0.291552, avg_reader_cost: 1.69096 s, avg_batch_cost: 1.83745 s, avg_samples: 7.7, ips: 4.19060 samples/s, eta: 12:23:09
[2024/07/27 11:30:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:30:39] ppocr INFO: epoch: [55/1500], global_step: 165, lr: 0.001000, loss: 2.883230, loss_shrink_maps: 1.797249, loss_threshold_maps: 0.763115, loss_binary_maps: 0.291552, avg_reader_cost: 2.26102 s, avg_batch_cost: 2.54722 s, avg_samples: 12.5, ips: 4.90731 samples/s, eta: 12:20:17
[2024/07/27 11:30:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:30:48] ppocr INFO: epoch: [56/1500], global_step: 168, lr: 0.001000, loss: 2.858840, loss_shrink_maps: 1.785184, loss_threshold_maps: 0.764523, loss_binary_maps: 0.291552, avg_reader_cost: 2.16678 s, avg_batch_cost: 2.50773 s, avg_samples: 12.5, ips: 4.98460 samples/s, eta: 12:17:20
[2024/07/27 11:30:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:30:58] ppocr INFO: epoch: [57/1500], global_step: 170, lr: 0.001000, loss: 2.820230, loss_shrink_maps: 1.776477, loss_threshold_maps: 0.764523, loss_binary_maps: 0.292439, avg_reader_cost: 1.50813 s, avg_batch_cost: 1.71711 s, avg_samples: 9.6, ips: 5.59080 samples/s, eta: 12:15:37
[2024/07/27 11:30:58] ppocr INFO: epoch: [57/1500], global_step: 171, lr: 0.001000, loss: 2.858840, loss_shrink_maps: 1.785184, loss_threshold_maps: 0.777358, loss_binary_maps: 0.299942, avg_reader_cost: 0.90482 s, avg_batch_cost: 0.96024 s, avg_samples: 2.9, ips: 3.02007 samples/s, eta: 12:15:12
[2024/07/27 11:30:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:31:08] ppocr INFO: epoch: [58/1500], global_step: 174, lr: 0.001000, loss: 2.879161, loss_shrink_maps: 1.790436, loss_threshold_maps: 0.777358, loss_binary_maps: 0.306894, avg_reader_cost: 2.34937 s, avg_batch_cost: 2.61980 s, avg_samples: 12.5, ips: 4.77136 samples/s, eta: 12:12:53
[2024/07/27 11:31:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:31:18] ppocr INFO: epoch: [59/1500], global_step: 177, lr: 0.001000, loss: 2.856964, loss_shrink_maps: 1.772817, loss_threshold_maps: 0.788402, loss_binary_maps: 0.299698, avg_reader_cost: 2.32294 s, avg_batch_cost: 2.56429 s, avg_samples: 12.5, ips: 4.87464 samples/s, eta: 12:10:24
[2024/07/27 11:31:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:31:27] ppocr INFO: epoch: [60/1500], global_step: 180, lr: 0.001000, loss: 2.813589, loss_shrink_maps: 1.763257, loss_threshold_maps: 0.786041, loss_binary_maps: 0.299698, avg_reader_cost: 2.21339 s, avg_batch_cost: 2.58156 s, avg_samples: 12.5, ips: 4.84203 samples/s, eta: 12:08:03

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:52,  9.57it/s]
eval model::   1%|          | 3/500 [00:00<00:32, 15.44it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.94it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 18.16it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.36it/s]
eval model::   3%|▎         | 14/500 [00:00<00:23, 20.75it/s]
eval model::   3%|▎         | 17/500 [00:00<00:20, 23.14it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.62it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 22.68it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 21.17it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.26it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 19.70it/s]
eval model::   7%|▋         | 35/500 [00:01<00:23, 19.95it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.38it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 19.07it/s]
eval model::   8%|▊         | 42/500 [00:02<00:23, 19.09it/s]
eval model::   9%|▉         | 45/500 [00:02<00:21, 20.78it/s]
eval model::  10%|▉         | 48/500 [00:02<00:23, 19.52it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 19.39it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 19.30it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 20.01it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:21, 20.78it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:23, 18.80it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:23, 18.83it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:21, 20.53it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.87it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 22.25it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:19, 21.31it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:19, 21.56it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 21.44it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 19.97it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.19it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:21, 19.00it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:21, 19.02it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:21, 19.19it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:21, 19.13it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:21, 18.81it/s]
eval model::  20%|██        | 101/500 [00:05<00:22, 17.75it/s]
eval model::  21%|██        | 104/500 [00:05<00:20, 19.35it/s]
eval model::  21%|██        | 106/500 [00:05<00:20, 19.41it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:19, 20.32it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 20.82it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:18, 20.42it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:19, 19.71it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:21, 18.06it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 20.34it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 19.30it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 20.55it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 18.33it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 20.06it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:16, 21.75it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.66it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:18, 19.31it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 19.46it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.88it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 18.51it/s]
eval model::  31%|███       | 156/500 [00:07<00:17, 19.54it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 21.00it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:16, 20.04it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:16, 20.20it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:16, 20.70it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:16, 20.50it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:18, 17.35it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:16, 19.17it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:17, 18.16it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 19.75it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 20.18it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 20.97it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.68it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:14, 20.61it/s]
eval model::  40%|███▉      | 198/500 [00:09<00:14, 21.18it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.62it/s]
eval model::  41%|████      | 204/500 [00:10<00:14, 21.06it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:21, 13.36it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:20, 14.11it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:19, 14.79it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:18, 15.72it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.73it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.95it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.15it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:16, 16.39it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:14, 18.61it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:13, 20.38it/s]
eval model::  46%|████▌     | 231/500 [00:11<00:12, 22.30it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 20.77it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 22.75it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 23.05it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:11, 22.54it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:10, 24.02it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:11, 22.26it/s]
eval model::  50%|█████     | 252/500 [00:12<00:12, 19.43it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.21it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 21.89it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 20.67it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 18.97it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:12, 18.90it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:12, 19.07it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:11, 19.06it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.68it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 20.92it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:09, 22.08it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:09, 23.77it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:11, 17.89it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:09, 21.24it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:10, 19.11it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:10, 18.48it/s]
eval model::  60%|██████    | 300/500 [00:15<00:11, 17.64it/s]
eval model::  60%|██████    | 302/500 [00:15<00:11, 17.29it/s]
eval model::  61%|██████    | 305/500 [00:15<00:10, 18.20it/s]
eval model::  62%|██████▏   | 308/500 [00:15<00:09, 20.20it/s]
eval model::  62%|██████▏   | 311/500 [00:15<00:09, 20.27it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:08, 21.00it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:08, 21.17it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:08, 22.39it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:08, 21.69it/s]
eval model::  65%|██████▌   | 326/500 [00:16<00:08, 21.30it/s]
eval model::  66%|██████▌   | 329/500 [00:16<00:08, 20.94it/s]
eval model::  66%|██████▋   | 332/500 [00:16<00:08, 20.70it/s]
eval model::  67%|██████▋   | 335/500 [00:16<00:07, 22.80it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:07, 21.09it/s]
eval model::  68%|██████▊   | 341/500 [00:17<00:08, 18.80it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:06, 22.32it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:07, 21.33it/s]
eval model::  70%|███████   | 351/500 [00:17<00:07, 18.67it/s]
eval model::  71%|███████   | 354/500 [00:17<00:07, 20.84it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:07, 19.36it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:06, 20.23it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:06, 21.07it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:06, 19.67it/s]
eval model::  74%|███████▍  | 369/500 [00:18<00:06, 21.19it/s]
eval model::  74%|███████▍  | 372/500 [00:18<00:06, 20.40it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 19.54it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 20.65it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:05, 19.89it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:05, 21.29it/s]
eval model::  77%|███████▋  | 387/500 [00:19<00:07, 14.87it/s]
eval model::  78%|███████▊  | 390/500 [00:19<00:06, 16.19it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 17.67it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 18.63it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:05, 19.76it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 20.85it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 22.03it/s]
eval model::  82%|████████▏ | 408/500 [00:20<00:03, 23.14it/s]
eval model::  82%|████████▏ | 411/500 [00:20<00:03, 22.82it/s]
eval model::  83%|████████▎ | 414/500 [00:20<00:03, 22.21it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 19.28it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 17.30it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 17.47it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 18.01it/s]
eval model::  85%|████████▌ | 427/500 [00:21<00:03, 20.05it/s]
eval model::  86%|████████▌ | 430/500 [00:21<00:03, 21.41it/s]
eval model::  87%|████████▋ | 433/500 [00:21<00:02, 23.45it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 24.66it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 21.57it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 22.31it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 22.63it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 21.90it/s]
eval model::  90%|█████████ | 451/500 [00:22<00:02, 20.15it/s]
eval model::  91%|█████████ | 454/500 [00:22<00:02, 21.25it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:02, 21.06it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:02, 19.81it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:02, 18.41it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:02, 16.17it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 18.97it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 20.70it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 20.55it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 21.55it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 21.89it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 23.16it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 18.57it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 20.16it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 21.07it/s]
eval model::  99%|█████████▉| 495/500 [00:24<00:00, 21.45it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 21.65it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.85it/s]
[2024/07/27 11:31:53] ppocr INFO: cur metric, precision: 0.5553145336225597, recall: 0.3697640828117477, hmean: 0.4439306358381503, fps: 44.30344234556225
[2024/07/27 11:31:53] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 11:31:53] ppocr INFO: best metric, hmean: 0.4439306358381503, precision: 0.5553145336225597, recall: 0.3697640828117477, fps: 44.30344234556225, best_epoch: 60
[2024/07/27 11:31:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:32:02] ppocr INFO: epoch: [61/1500], global_step: 183, lr: 0.001000, loss: 2.833910, loss_shrink_maps: 1.772817, loss_threshold_maps: 0.786041, loss_binary_maps: 0.306651, avg_reader_cost: 2.22672 s, avg_batch_cost: 2.47342 s, avg_samples: 12.5, ips: 5.05374 samples/s, eta: 12:05:20
[2024/07/27 11:32:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:32:12] ppocr INFO: epoch: [62/1500], global_step: 186, lr: 0.001000, loss: 2.715508, loss_shrink_maps: 1.648546, loss_threshold_maps: 0.785004, loss_binary_maps: 0.290951, avg_reader_cost: 2.23746 s, avg_batch_cost: 2.63291 s, avg_samples: 12.5, ips: 4.74761 samples/s, eta: 12:03:19
[2024/07/27 11:32:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:32:22] ppocr INFO: epoch: [63/1500], global_step: 189, lr: 0.001000, loss: 2.715508, loss_shrink_maps: 1.648546, loss_threshold_maps: 0.785004, loss_binary_maps: 0.290951, avg_reader_cost: 2.16136 s, avg_batch_cost: 2.50038 s, avg_samples: 12.5, ips: 4.99925 samples/s, eta: 12:00:51
[2024/07/27 11:32:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:32:30] ppocr INFO: epoch: [64/1500], global_step: 190, lr: 0.001000, loss: 2.692643, loss_shrink_maps: 1.635072, loss_threshold_maps: 0.788232, loss_binary_maps: 0.288117, avg_reader_cost: 0.69200 s, avg_batch_cost: 0.78126 s, avg_samples: 4.8, ips: 6.14396 samples/s, eta: 11:59:51
[2024/07/27 11:32:31] ppocr INFO: epoch: [64/1500], global_step: 192, lr: 0.001000, loss: 2.647173, loss_shrink_maps: 1.571369, loss_threshold_maps: 0.785004, loss_binary_maps: 0.284067, avg_reader_cost: 1.65490 s, avg_batch_cost: 1.80220 s, avg_samples: 7.7, ips: 4.27256 samples/s, eta: 11:58:45
[2024/07/27 11:32:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:32:41] ppocr INFO: epoch: [65/1500], global_step: 195, lr: 0.001000, loss: 2.633209, loss_shrink_maps: 1.571186, loss_threshold_maps: 0.784080, loss_binary_maps: 0.284067, avg_reader_cost: 2.22637 s, avg_batch_cost: 2.54946 s, avg_samples: 12.5, ips: 4.90300 samples/s, eta: 11:56:35
[2024/07/27 11:32:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:32:51] ppocr INFO: epoch: [66/1500], global_step: 198, lr: 0.001000, loss: 2.633209, loss_shrink_maps: 1.571186, loss_threshold_maps: 0.778685, loss_binary_maps: 0.285134, avg_reader_cost: 2.23982 s, avg_batch_cost: 2.62840 s, avg_samples: 12.5, ips: 4.75575 samples/s, eta: 11:54:45
[2024/07/27 11:32:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:33:00] ppocr INFO: epoch: [67/1500], global_step: 200, lr: 0.001000, loss: 2.600416, loss_shrink_maps: 1.559273, loss_threshold_maps: 0.773131, loss_binary_maps: 0.284836, avg_reader_cost: 1.33639 s, avg_batch_cost: 1.63011 s, avg_samples: 9.6, ips: 5.88918 samples/s, eta: 11:53:07
[2024/07/27 11:33:00] ppocr INFO: epoch: [67/1500], global_step: 201, lr: 0.001000, loss: 2.641981, loss_shrink_maps: 1.568643, loss_threshold_maps: 0.773131, loss_binary_maps: 0.288039, avg_reader_cost: 0.86122 s, avg_batch_cost: 0.91682 s, avg_samples: 2.9, ips: 3.16311 samples/s, eta: 11:52:40
[2024/07/27 11:33:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:33:09] ppocr INFO: epoch: [68/1500], global_step: 204, lr: 0.001000, loss: 2.588184, loss_shrink_maps: 1.547040, loss_threshold_maps: 0.761373, loss_binary_maps: 0.284836, avg_reader_cost: 2.03232 s, avg_batch_cost: 2.32431 s, avg_samples: 12.5, ips: 5.37794 samples/s, eta: 11:49:52
[2024/07/27 11:33:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:33:19] ppocr INFO: epoch: [69/1500], global_step: 207, lr: 0.001000, loss: 2.574394, loss_shrink_maps: 1.540800, loss_threshold_maps: 0.757682, loss_binary_maps: 0.285174, avg_reader_cost: 2.25271 s, avg_batch_cost: 2.63818 s, avg_samples: 12.5, ips: 4.73812 samples/s, eta: 11:48:12
[2024/07/27 11:33:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:33:29] ppocr INFO: epoch: [70/1500], global_step: 210, lr: 0.001000, loss: 2.572800, loss_shrink_maps: 1.540800, loss_threshold_maps: 0.753149, loss_binary_maps: 0.285174, avg_reader_cost: 2.37517 s, avg_batch_cost: 2.61095 s, avg_samples: 12.5, ips: 4.78753 samples/s, eta: 11:46:29
[2024/07/27 11:33:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:33:39] ppocr INFO: epoch: [71/1500], global_step: 213, lr: 0.001000, loss: 2.598715, loss_shrink_maps: 1.541923, loss_threshold_maps: 0.760191, loss_binary_maps: 0.289470, avg_reader_cost: 2.15657 s, avg_batch_cost: 2.51307 s, avg_samples: 12.5, ips: 4.97400 samples/s, eta: 11:44:29
[2024/07/27 11:33:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:33:49] ppocr INFO: epoch: [72/1500], global_step: 216, lr: 0.001000, loss: 2.579174, loss_shrink_maps: 1.541480, loss_threshold_maps: 0.753149, loss_binary_maps: 0.287909, avg_reader_cost: 2.24288 s, avg_batch_cost: 2.62335 s, avg_samples: 12.5, ips: 4.76491 samples/s, eta: 11:42:53
[2024/07/27 11:33:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:33:58] ppocr INFO: epoch: [73/1500], global_step: 219, lr: 0.001000, loss: 2.558097, loss_shrink_maps: 1.540800, loss_threshold_maps: 0.750546, loss_binary_maps: 0.285174, avg_reader_cost: 2.33002 s, avg_batch_cost: 2.57026 s, avg_samples: 12.5, ips: 4.86332 samples/s, eta: 11:41:08
[2024/07/27 11:33:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:34:06] ppocr INFO: epoch: [74/1500], global_step: 220, lr: 0.001000, loss: 2.558097, loss_shrink_maps: 1.540800, loss_threshold_maps: 0.750546, loss_binary_maps: 0.283395, avg_reader_cost: 0.54912 s, avg_batch_cost: 0.73788 s, avg_samples: 4.8, ips: 6.50511 samples/s, eta: 11:40:11
[2024/07/27 11:34:08] ppocr INFO: epoch: [74/1500], global_step: 222, lr: 0.001000, loss: 2.543368, loss_shrink_maps: 1.516465, loss_threshold_maps: 0.753149, loss_binary_maps: 0.279745, avg_reader_cost: 1.56805 s, avg_batch_cost: 1.71520 s, avg_samples: 7.7, ips: 4.48928 samples/s, eta: 11:39:04
[2024/07/27 11:34:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:34:17] ppocr INFO: epoch: [75/1500], global_step: 225, lr: 0.001000, loss: 2.526839, loss_shrink_maps: 1.492198, loss_threshold_maps: 0.757052, loss_binary_maps: 0.279745, avg_reader_cost: 2.19500 s, avg_batch_cost: 2.54653 s, avg_samples: 12.5, ips: 4.90865 samples/s, eta: 11:37:19
[2024/07/27 11:34:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:34:27] ppocr INFO: epoch: [76/1500], global_step: 228, lr: 0.001000, loss: 2.515232, loss_shrink_maps: 1.486124, loss_threshold_maps: 0.755195, loss_binary_maps: 0.276147, avg_reader_cost: 2.26152 s, avg_batch_cost: 2.64088 s, avg_samples: 12.5, ips: 4.73327 samples/s, eta: 11:35:54
[2024/07/27 11:34:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:34:36] ppocr INFO: epoch: [77/1500], global_step: 230, lr: 0.001000, loss: 2.494783, loss_shrink_maps: 1.466914, loss_threshold_maps: 0.750100, loss_binary_maps: 0.278140, avg_reader_cost: 1.45854 s, avg_batch_cost: 1.64300 s, avg_samples: 9.6, ips: 5.84297 samples/s, eta: 11:34:37
[2024/07/27 11:34:37] ppocr INFO: epoch: [77/1500], global_step: 231, lr: 0.001000, loss: 2.468024, loss_shrink_maps: 1.448205, loss_threshold_maps: 0.747669, loss_binary_maps: 0.274770, avg_reader_cost: 0.86753 s, avg_batch_cost: 0.92394 s, avg_samples: 2.9, ips: 3.13873 samples/s, eta: 11:34:18
[2024/07/27 11:34:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:34:47] ppocr INFO: epoch: [78/1500], global_step: 234, lr: 0.001000, loss: 2.454058, loss_shrink_maps: 1.420336, loss_threshold_maps: 0.750100, loss_binary_maps: 0.264973, avg_reader_cost: 2.17704 s, avg_batch_cost: 2.54773 s, avg_samples: 12.5, ips: 4.90632 samples/s, eta: 11:32:39
[2024/07/27 11:34:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:34:56] ppocr INFO: epoch: [79/1500], global_step: 237, lr: 0.001000, loss: 2.405500, loss_shrink_maps: 1.377960, loss_threshold_maps: 0.750100, loss_binary_maps: 0.258588, avg_reader_cost: 2.32726 s, avg_batch_cost: 2.59591 s, avg_samples: 12.5, ips: 4.81528 samples/s, eta: 11:31:11
[2024/07/27 11:34:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:35:06] ppocr INFO: epoch: [80/1500], global_step: 240, lr: 0.001000, loss: 2.376889, loss_shrink_maps: 1.393070, loss_threshold_maps: 0.747669, loss_binary_maps: 0.263818, avg_reader_cost: 2.24747 s, avg_batch_cost: 2.52580 s, avg_samples: 12.5, ips: 4.94894 samples/s, eta: 11:29:32

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:54,  9.09it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.55it/s]
eval model::   1%|▏         | 7/500 [00:00<00:22, 21.86it/s]
eval model::   2%|▏         | 10/500 [00:00<00:22, 21.92it/s]
eval model::   3%|▎         | 13/500 [00:00<00:24, 19.55it/s]
eval model::   3%|▎         | 16/500 [00:00<00:22, 21.30it/s]
eval model::   4%|▍         | 19/500 [00:00<00:22, 20.97it/s]
eval model::   4%|▍         | 22/500 [00:01<00:22, 21.72it/s]
eval model::   5%|▌         | 25/500 [00:01<00:22, 20.67it/s]
eval model::   6%|▌         | 28/500 [00:01<00:23, 19.71it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.68it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.21it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 19.68it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.50it/s]
eval model::   8%|▊         | 42/500 [00:02<00:23, 19.09it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.60it/s]
eval model::  10%|▉         | 48/500 [00:02<00:22, 20.29it/s]
eval model::  10%|█         | 51/500 [00:02<00:22, 19.66it/s]
eval model::  11%|█         | 54/500 [00:02<00:22, 20.21it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:22, 19.48it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:24, 18.16it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:25, 17.36it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:25, 17.13it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 19.59it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 22.33it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 20.85it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:20, 21.00it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:21, 19.88it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 20.37it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.66it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 19.64it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:21, 19.27it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:19, 20.90it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:18, 22.05it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:18, 21.96it/s]
eval model::  20%|██        | 102/500 [00:05<00:18, 21.34it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.89it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:19, 20.36it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:20, 19.38it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:19, 20.23it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:19, 20.15it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:20, 18.82it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.04it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:17, 20.99it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 20.00it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 19.15it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:19, 18.95it/s]
eval model::  27%|██▋       | 137/500 [00:06<00:19, 18.97it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:19, 18.05it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:19, 18.20it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.22it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:17, 19.76it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.56it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 19.85it/s]
eval model::  31%|███       | 156/500 [00:07<00:17, 20.03it/s]
eval model::  32%|███▏      | 159/500 [00:07<00:16, 20.52it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:17, 19.83it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:17, 19.48it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 18.49it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 19.60it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:16, 19.50it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:19, 16.86it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:16, 19.10it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:17, 18.48it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 19.55it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 20.00it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.26it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:13, 22.11it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:14, 21.47it/s]
eval model::  40%|███▉      | 198/500 [00:09<00:13, 22.32it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 21.16it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 21.91it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:17, 16.65it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:17, 16.59it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:16, 17.07it/s]
eval model::  43%|████▎     | 214/500 [00:10<00:15, 18.87it/s]
eval model::  43%|████▎     | 216/500 [00:10<00:16, 17.58it/s]
eval model::  44%|████▎     | 218/500 [00:11<00:16, 17.11it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:14, 19.58it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:13, 21.17it/s]
eval model::  45%|████▌     | 227/500 [00:11<00:12, 21.57it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:11, 23.19it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:11, 24.13it/s]
eval model::  47%|████▋     | 236/500 [00:11<00:11, 22.15it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:12, 20.54it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:14, 17.85it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:13, 19.25it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:12, 20.53it/s]
eval model::  50%|█████     | 251/500 [00:12<00:12, 19.63it/s]
eval model::  51%|█████     | 254/500 [00:12<00:12, 19.45it/s]
eval model::  51%|█████     | 256/500 [00:12<00:13, 18.66it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:11, 20.49it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 20.07it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:13, 17.47it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:13, 17.76it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:11, 20.50it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:11, 19.44it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 17.38it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 18.67it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 19.58it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:13, 15.37it/s]
eval model::  58%|█████▊    | 288/500 [00:14<00:11, 18.00it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:10, 19.42it/s]
eval model::  59%|█████▉    | 294/500 [00:14<00:10, 19.27it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:11, 18.45it/s]
eval model::  60%|██████    | 300/500 [00:15<00:10, 18.79it/s]
eval model::  61%|██████    | 303/500 [00:15<00:09, 20.10it/s]
eval model::  61%|██████    | 306/500 [00:15<00:09, 21.18it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:08, 22.21it/s]
eval model::  62%|██████▏   | 312/500 [00:15<00:07, 23.50it/s]
eval model::  63%|██████▎   | 315/500 [00:15<00:08, 21.88it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 22.26it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 21.23it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 20.80it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:08, 21.15it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:09, 18.88it/s]
eval model::  66%|██████▋   | 332/500 [00:16<00:08, 19.08it/s]
eval model::  67%|██████▋   | 335/500 [00:16<00:08, 20.60it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:07, 21.41it/s]
eval model::  68%|██████▊   | 341/500 [00:17<00:08, 18.14it/s]
eval model::  69%|██████▉   | 344/500 [00:17<00:07, 20.48it/s]
eval model::  69%|██████▉   | 347/500 [00:17<00:07, 21.85it/s]
eval model::  70%|███████   | 351/500 [00:17<00:06, 24.15it/s]
eval model::  71%|███████   | 354/500 [00:17<00:06, 22.59it/s]
eval model::  71%|███████▏  | 357/500 [00:17<00:06, 21.95it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:06, 21.10it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:06, 19.71it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:07, 16.91it/s]
eval model::  74%|███████▍  | 369/500 [00:18<00:06, 18.80it/s]
eval model::  74%|███████▍  | 372/500 [00:18<00:06, 20.52it/s]
eval model::  75%|███████▌  | 375/500 [00:18<00:05, 21.98it/s]
eval model::  76%|███████▌  | 378/500 [00:18<00:05, 22.00it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:05, 20.83it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:05, 20.56it/s]
eval model::  77%|███████▋  | 387/500 [00:19<00:07, 15.34it/s]
eval model::  78%|███████▊  | 390/500 [00:19<00:06, 17.68it/s]
eval model::  79%|███████▊  | 393/500 [00:19<00:05, 19.16it/s]
eval model::  79%|███████▉  | 396/500 [00:19<00:05, 19.17it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 21.20it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 20.68it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 20.80it/s]
eval model::  82%|████████▏ | 408/500 [00:20<00:04, 21.24it/s]
eval model::  82%|████████▏ | 411/500 [00:20<00:04, 22.01it/s]
eval model::  83%|████████▎ | 414/500 [00:20<00:03, 22.27it/s]
eval model::  83%|████████▎ | 417/500 [00:20<00:04, 20.66it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 19.07it/s]
eval model::  85%|████████▍ | 423/500 [00:21<00:03, 20.20it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:03, 19.68it/s]
eval model::  86%|████████▌ | 429/500 [00:21<00:03, 21.31it/s]
eval model::  86%|████████▋ | 432/500 [00:21<00:03, 21.43it/s]
eval model::  87%|████████▋ | 435/500 [00:21<00:03, 21.63it/s]
eval model::  88%|████████▊ | 438/500 [00:21<00:02, 21.27it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 21.00it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 21.30it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 19.84it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 19.30it/s]
eval model::  91%|█████████ | 453/500 [00:22<00:02, 20.56it/s]
eval model::  91%|█████████ | 456/500 [00:22<00:02, 20.64it/s]
eval model::  92%|█████████▏| 459/500 [00:22<00:01, 21.26it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:02, 18.84it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 18.85it/s]
eval model::  93%|█████████▎| 466/500 [00:23<00:02, 16.62it/s]
eval model::  94%|█████████▍| 469/500 [00:23<00:01, 18.59it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 18.75it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 19.05it/s]
eval model::  95%|█████████▌| 477/500 [00:23<00:01, 21.60it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 22.66it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 22.65it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 20.37it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 20.22it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 20.70it/s]
eval model::  99%|█████████▉| 495/500 [00:24<00:00, 21.54it/s]
eval model:: 100%|█████████▉| 498/500 [00:24<00:00, 21.72it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.98it/s]
[2024/07/27 11:35:31] ppocr INFO: cur metric, precision: 0.6403374233128835, recall: 0.4020221473278767, hmean: 0.49393670511682924, fps: 44.83595825243171
[2024/07/27 11:35:31] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 11:35:31] ppocr INFO: best metric, hmean: 0.49393670511682924, precision: 0.6403374233128835, recall: 0.4020221473278767, fps: 44.83595825243171, best_epoch: 80
[2024/07/27 11:35:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:35:40] ppocr INFO: epoch: [81/1500], global_step: 243, lr: 0.001000, loss: 2.376889, loss_shrink_maps: 1.387132, loss_threshold_maps: 0.742527, loss_binary_maps: 0.262431, avg_reader_cost: 2.27215 s, avg_batch_cost: 2.58621 s, avg_samples: 12.5, ips: 4.83332 samples/s, eta: 11:28:06
[2024/07/27 11:35:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:35:50] ppocr INFO: epoch: [82/1500], global_step: 246, lr: 0.001000, loss: 2.417046, loss_shrink_maps: 1.393070, loss_threshold_maps: 0.747669, loss_binary_maps: 0.263818, avg_reader_cost: 2.26997 s, avg_batch_cost: 2.64045 s, avg_samples: 12.5, ips: 4.73404 samples/s, eta: 11:26:50
[2024/07/27 11:35:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:36:00] ppocr INFO: epoch: [83/1500], global_step: 249, lr: 0.001000, loss: 2.376889, loss_shrink_maps: 1.380166, loss_threshold_maps: 0.753894, loss_binary_maps: 0.262822, avg_reader_cost: 2.17113 s, avg_batch_cost: 2.53329 s, avg_samples: 12.5, ips: 4.93429 samples/s, eta: 11:25:17
[2024/07/27 11:36:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:36:09] ppocr INFO: epoch: [84/1500], global_step: 250, lr: 0.001000, loss: 2.376889, loss_shrink_maps: 1.380166, loss_threshold_maps: 0.756147, loss_binary_maps: 0.262822, avg_reader_cost: 0.69594 s, avg_batch_cost: 0.78497 s, avg_samples: 4.8, ips: 6.11487 samples/s, eta: 11:24:37
[2024/07/27 11:36:10] ppocr INFO: epoch: [84/1500], global_step: 252, lr: 0.001000, loss: 2.357498, loss_shrink_maps: 1.365048, loss_threshold_maps: 0.756147, loss_binary_maps: 0.257622, avg_reader_cost: 1.66218 s, avg_batch_cost: 1.80924 s, avg_samples: 7.7, ips: 4.25592 samples/s, eta: 11:23:57
[2024/07/27 11:36:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:36:20] ppocr INFO: epoch: [85/1500], global_step: 255, lr: 0.001000, loss: 2.389248, loss_shrink_maps: 1.380166, loss_threshold_maps: 0.754568, loss_binary_maps: 0.262822, avg_reader_cost: 2.34962 s, avg_batch_cost: 2.58734 s, avg_samples: 12.5, ips: 4.83121 samples/s, eta: 11:22:36
[2024/07/27 11:36:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:36:29] ppocr INFO: epoch: [86/1500], global_step: 258, lr: 0.001000, loss: 2.408732, loss_shrink_maps: 1.382893, loss_threshold_maps: 0.762000, loss_binary_maps: 0.264208, avg_reader_cost: 2.20188 s, avg_batch_cost: 2.52218 s, avg_samples: 12.5, ips: 4.95604 samples/s, eta: 11:21:06
[2024/07/27 11:36:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:36:39] ppocr INFO: epoch: [87/1500], global_step: 260, lr: 0.001000, loss: 2.408732, loss_shrink_maps: 1.378973, loss_threshold_maps: 0.762000, loss_binary_maps: 0.262476, avg_reader_cost: 1.42724 s, avg_batch_cost: 1.61028 s, avg_samples: 9.6, ips: 5.96170 samples/s, eta: 11:19:55
[2024/07/27 11:36:39] ppocr INFO: epoch: [87/1500], global_step: 261, lr: 0.001000, loss: 2.396126, loss_shrink_maps: 1.377553, loss_threshold_maps: 0.756130, loss_binary_maps: 0.261300, avg_reader_cost: 0.85177 s, avg_batch_cost: 0.90671 s, avg_samples: 2.9, ips: 3.19839 samples/s, eta: 11:19:36
[2024/07/27 11:36:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:36:49] ppocr INFO: epoch: [88/1500], global_step: 264, lr: 0.001000, loss: 2.376585, loss_shrink_maps: 1.374301, loss_threshold_maps: 0.756130, loss_binary_maps: 0.259235, avg_reader_cost: 2.36753 s, avg_batch_cost: 2.60539 s, avg_samples: 12.5, ips: 4.79775 samples/s, eta: 11:18:22
[2024/07/27 11:36:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:36:59] ppocr INFO: epoch: [89/1500], global_step: 267, lr: 0.001000, loss: 2.376084, loss_shrink_maps: 1.364754, loss_threshold_maps: 0.749601, loss_binary_maps: 0.258214, avg_reader_cost: 2.41103 s, avg_batch_cost: 2.65308 s, avg_samples: 12.5, ips: 4.71150 samples/s, eta: 11:17:17
[2024/07/27 11:37:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:37:09] ppocr INFO: epoch: [90/1500], global_step: 270, lr: 0.001000, loss: 2.333001, loss_shrink_maps: 1.342931, loss_threshold_maps: 0.741683, loss_binary_maps: 0.256286, avg_reader_cost: 2.32775 s, avg_batch_cost: 2.56837 s, avg_samples: 12.5, ips: 4.86689 samples/s, eta: 11:15:59
[2024/07/27 11:37:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:37:19] ppocr INFO: epoch: [91/1500], global_step: 273, lr: 0.001000, loss: 2.374354, loss_shrink_maps: 1.366639, loss_threshold_maps: 0.727506, loss_binary_maps: 0.260114, avg_reader_cost: 2.37896 s, avg_batch_cost: 2.63176 s, avg_samples: 12.5, ips: 4.74967 samples/s, eta: 11:14:53
[2024/07/27 11:37:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:37:28] ppocr INFO: epoch: [92/1500], global_step: 276, lr: 0.001000, loss: 2.331272, loss_shrink_maps: 1.342931, loss_threshold_maps: 0.723828, loss_binary_maps: 0.257165, avg_reader_cost: 2.26221 s, avg_batch_cost: 2.50520 s, avg_samples: 12.5, ips: 4.98963 samples/s, eta: 11:13:28
[2024/07/27 11:37:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:37:38] ppocr INFO: epoch: [93/1500], global_step: 279, lr: 0.001000, loss: 2.289879, loss_shrink_maps: 1.303485, loss_threshold_maps: 0.726482, loss_binary_maps: 0.249150, avg_reader_cost: 2.25816 s, avg_batch_cost: 2.64012 s, avg_samples: 12.5, ips: 4.73463 samples/s, eta: 11:12:24
[2024/07/27 11:37:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:37:47] ppocr INFO: epoch: [94/1500], global_step: 280, lr: 0.001000, loss: 2.355325, loss_shrink_maps: 1.352263, loss_threshold_maps: 0.728009, loss_binary_maps: 0.258209, avg_reader_cost: 0.58635 s, avg_batch_cost: 0.79262 s, avg_samples: 4.8, ips: 6.05588 samples/s, eta: 11:11:50
[2024/07/27 11:37:48] ppocr INFO: epoch: [94/1500], global_step: 282, lr: 0.001000, loss: 2.355325, loss_shrink_maps: 1.352263, loss_threshold_maps: 0.728009, loss_binary_maps: 0.258209, avg_reader_cost: 1.67723 s, avg_batch_cost: 1.82421 s, avg_samples: 7.7, ips: 4.22101 samples/s, eta: 11:11:18
[2024/07/27 11:37:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:37:58] ppocr INFO: epoch: [95/1500], global_step: 285, lr: 0.001000, loss: 2.355325, loss_shrink_maps: 1.353528, loss_threshold_maps: 0.728009, loss_binary_maps: 0.259363, avg_reader_cost: 2.31738 s, avg_batch_cost: 2.56822 s, avg_samples: 12.5, ips: 4.86718 samples/s, eta: 11:10:05
[2024/07/27 11:37:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:38:08] ppocr INFO: epoch: [96/1500], global_step: 288, lr: 0.001000, loss: 2.268087, loss_shrink_maps: 1.303484, loss_threshold_maps: 0.725396, loss_binary_maps: 0.249748, avg_reader_cost: 2.42311 s, avg_batch_cost: 2.65947 s, avg_samples: 12.5, ips: 4.70019 samples/s, eta: 11:09:07
[2024/07/27 11:38:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:38:17] ppocr INFO: epoch: [97/1500], global_step: 290, lr: 0.001000, loss: 2.292064, loss_shrink_maps: 1.303484, loss_threshold_maps: 0.728009, loss_binary_maps: 0.249748, avg_reader_cost: 1.28110 s, avg_batch_cost: 1.52477 s, avg_samples: 9.6, ips: 6.29604 samples/s, eta: 11:07:53
[2024/07/27 11:38:17] ppocr INFO: epoch: [97/1500], global_step: 291, lr: 0.001000, loss: 2.292064, loss_shrink_maps: 1.303484, loss_threshold_maps: 0.725396, loss_binary_maps: 0.249748, avg_reader_cost: 0.80860 s, avg_batch_cost: 0.86394 s, avg_samples: 2.9, ips: 3.35672 samples/s, eta: 11:07:31
[2024/07/27 11:38:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:38:27] ppocr INFO: epoch: [98/1500], global_step: 294, lr: 0.001000, loss: 2.229996, loss_shrink_maps: 1.251807, loss_threshold_maps: 0.729822, loss_binary_maps: 0.241512, avg_reader_cost: 2.42634 s, avg_batch_cost: 2.68641 s, avg_samples: 12.5, ips: 4.65305 samples/s, eta: 11:06:38
[2024/07/27 11:38:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:38:37] ppocr INFO: epoch: [99/1500], global_step: 297, lr: 0.001000, loss: 2.292064, loss_shrink_maps: 1.303484, loss_threshold_maps: 0.725546, loss_binary_maps: 0.249748, avg_reader_cost: 2.15274 s, avg_batch_cost: 2.51817 s, avg_samples: 12.5, ips: 4.96392 samples/s, eta: 11:05:22
[2024/07/27 11:38:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:38:47] ppocr INFO: epoch: [100/1500], global_step: 300, lr: 0.001000, loss: 2.229996, loss_shrink_maps: 1.251807, loss_threshold_maps: 0.723469, loss_binary_maps: 0.241512, avg_reader_cost: 2.21671 s, avg_batch_cost: 2.56604 s, avg_samples: 12.5, ips: 4.87132 samples/s, eta: 11:04:14

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:52,  9.49it/s]
eval model::   1%|          | 4/500 [00:00<00:25, 19.50it/s]
eval model::   1%|          | 6/500 [00:00<00:25, 19.12it/s]
eval model::   2%|▏         | 8/500 [00:00<00:26, 18.75it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.71it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 18.94it/s]
eval model::   3%|▎         | 16/500 [00:00<00:25, 19.08it/s]
eval model::   4%|▍         | 19/500 [00:01<00:24, 19.47it/s]
eval model::   4%|▍         | 22/500 [00:01<00:23, 20.72it/s]
eval model::   5%|▌         | 25/500 [00:01<00:22, 20.95it/s]
eval model::   6%|▌         | 28/500 [00:01<00:23, 20.48it/s]
eval model::   6%|▌         | 31/500 [00:01<00:26, 17.94it/s]
eval model::   7%|▋         | 33/500 [00:01<00:26, 17.78it/s]
eval model::   7%|▋         | 35/500 [00:01<00:26, 17.29it/s]
eval model::   8%|▊         | 38/500 [00:02<00:24, 18.89it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.74it/s]
eval model::   8%|▊         | 42/500 [00:02<00:24, 18.92it/s]
eval model::   9%|▉         | 45/500 [00:02<00:21, 21.06it/s]
eval model::  10%|▉         | 48/500 [00:02<00:22, 20.28it/s]
eval model::  10%|█         | 51/500 [00:02<00:20, 22.00it/s]
eval model::  11%|█         | 54/500 [00:02<00:21, 21.03it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:20, 21.86it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:21, 20.38it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.27it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.37it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.08it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.07it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 20.95it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:19, 21.16it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:20, 20.39it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 20.12it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 18.95it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.32it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.46it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:20, 20.12it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:19, 20.88it/s]
eval model::  20%|██        | 100/500 [00:05<00:20, 19.10it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 20.76it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 21.99it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:19, 20.14it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 20.84it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:20, 18.55it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:19, 19.30it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:21, 17.45it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 19.86it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 18.95it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.66it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 18.31it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.42it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 21.02it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.56it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.40it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:17, 20.40it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 20.83it/s]
eval model::  31%|███       | 153/500 [00:07<00:16, 20.99it/s]
eval model::  31%|███       | 156/500 [00:07<00:15, 22.26it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 20.91it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:18, 17.93it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:17, 19.14it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:16, 19.76it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:16, 19.74it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:19, 16.70it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:17, 18.07it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:18, 17.38it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 19.13it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 21.06it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.65it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.56it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:13, 22.64it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:13, 22.28it/s]
eval model::  40%|████      | 201/500 [00:10<00:12, 23.21it/s]
eval model::  41%|████      | 204/500 [00:10<00:12, 23.56it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:20, 14.15it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:20, 14.49it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:19, 14.89it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.88it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.74it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.87it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.25it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 15.34it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:15, 18.05it/s]
eval model::  45%|████▌     | 227/500 [00:11<00:14, 18.37it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:13, 19.73it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.24it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.24it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.65it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:11, 21.76it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:11, 21.93it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:10, 23.60it/s]
eval model::  50%|█████     | 251/500 [00:12<00:11, 21.97it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 20.96it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:12, 19.45it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 22.38it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:11, 20.49it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:11, 19.96it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:10, 21.83it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 20.12it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 17.92it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 18.60it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 20.48it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:13, 15.63it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:13, 15.95it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:11, 17.91it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:11, 18.29it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.09it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:14, 13.90it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:12, 16.65it/s]
eval model::  60%|██████    | 301/500 [00:15<00:12, 15.91it/s]
eval model::  61%|██████    | 304/500 [00:15<00:10, 18.14it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.70it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:10, 18.37it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 19.41it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 19.11it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 20.69it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:08, 21.71it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:07, 22.46it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:07, 22.15it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:07, 23.18it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 21.71it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:07, 21.84it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 16.28it/s]
eval model::  69%|██████▉   | 344/500 [00:17<00:07, 19.63it/s]
eval model::  69%|██████▉   | 347/500 [00:17<00:07, 20.10it/s]
eval model::  70%|███████   | 350/500 [00:18<00:06, 21.90it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 21.94it/s]
eval model::  71%|███████   | 356/500 [00:18<00:07, 19.55it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:06, 21.09it/s]
eval model::  72%|███████▏  | 362/500 [00:18<00:06, 21.46it/s]
eval model::  73%|███████▎  | 365/500 [00:18<00:06, 19.87it/s]
eval model::  74%|███████▎  | 368/500 [00:18<00:06, 19.44it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 20.16it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:05, 21.92it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 22.29it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:05, 21.12it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:05, 22.76it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:07, 14.73it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 16.88it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:05, 18.17it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 20.04it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:04, 21.50it/s]
eval model::  80%|████████  | 401/500 [00:20<00:04, 21.09it/s]
eval model::  81%|████████  | 404/500 [00:20<00:04, 21.94it/s]
eval model::  81%|████████▏ | 407/500 [00:20<00:04, 23.10it/s]
eval model::  82%|████████▏ | 410/500 [00:20<00:03, 23.83it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:03, 23.20it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:03, 22.40it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:03, 21.06it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 17.88it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 16.10it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:04, 16.20it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 17.99it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:04, 16.83it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 20.70it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 22.78it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 21.81it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 22.91it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 23.35it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 21.54it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 22.03it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:01, 22.09it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:01, 21.67it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:01, 19.67it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:02, 17.10it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 18.63it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.38it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.42it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 20.63it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 22.35it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 23.13it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 19.70it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 20.76it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 20.13it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 20.30it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 21.87it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.66it/s]
[2024/07/27 11:39:12] ppocr INFO: cur metric, precision: 0.6155291170945523, recall: 0.4732787674530573, hmean: 0.5351115949918346, fps: 44.41464951164158
[2024/07/27 11:39:12] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 11:39:12] ppocr INFO: best metric, hmean: 0.5351115949918346, precision: 0.6155291170945523, recall: 0.4732787674530573, fps: 44.41464951164158, best_epoch: 100
[2024/07/27 11:39:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:39:21] ppocr INFO: epoch: [101/1500], global_step: 303, lr: 0.001000, loss: 2.217708, loss_shrink_maps: 1.244360, loss_threshold_maps: 0.723340, loss_binary_maps: 0.240204, avg_reader_cost: 2.27050 s, avg_batch_cost: 2.52753 s, avg_samples: 12.5, ips: 4.94555 samples/s, eta: 11:03:01
[2024/07/27 11:39:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:39:31] ppocr INFO: epoch: [102/1500], global_step: 306, lr: 0.001000, loss: 2.223476, loss_shrink_maps: 1.251949, loss_threshold_maps: 0.721323, loss_binary_maps: 0.241512, avg_reader_cost: 2.21046 s, avg_batch_cost: 2.58221 s, avg_samples: 12.5, ips: 4.84081 samples/s, eta: 11:01:57
[2024/07/27 11:39:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:39:41] ppocr INFO: epoch: [103/1500], global_step: 309, lr: 0.001000, loss: 2.217374, loss_shrink_maps: 1.244360, loss_threshold_maps: 0.721323, loss_binary_maps: 0.239133, avg_reader_cost: 2.39361 s, avg_batch_cost: 2.63364 s, avg_samples: 12.5, ips: 4.74628 samples/s, eta: 11:01:00
[2024/07/27 11:39:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:39:49] ppocr INFO: epoch: [104/1500], global_step: 310, lr: 0.001000, loss: 2.217374, loss_shrink_maps: 1.244360, loss_threshold_maps: 0.721323, loss_binary_maps: 0.239133, avg_reader_cost: 0.57471 s, avg_batch_cost: 0.78608 s, avg_samples: 4.8, ips: 6.10626 samples/s, eta: 11:00:29
[2024/07/27 11:39:51] ppocr INFO: epoch: [104/1500], global_step: 312, lr: 0.001000, loss: 2.217374, loss_shrink_maps: 1.244360, loss_threshold_maps: 0.725928, loss_binary_maps: 0.239133, avg_reader_cost: 1.66392 s, avg_batch_cost: 1.80993 s, avg_samples: 7.7, ips: 4.25430 samples/s, eta: 10:59:59
[2024/07/27 11:39:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:40:01] ppocr INFO: epoch: [105/1500], global_step: 315, lr: 0.001000, loss: 2.201108, loss_shrink_maps: 1.244360, loss_threshold_maps: 0.715313, loss_binary_maps: 0.239133, avg_reader_cost: 2.26961 s, avg_batch_cost: 2.52244 s, avg_samples: 12.5, ips: 4.95552 samples/s, eta: 10:58:49
[2024/07/27 11:40:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:40:10] ppocr INFO: epoch: [106/1500], global_step: 318, lr: 0.001000, loss: 2.198608, loss_shrink_maps: 1.240934, loss_threshold_maps: 0.710656, loss_binary_maps: 0.237924, avg_reader_cost: 2.33506 s, avg_batch_cost: 2.57966 s, avg_samples: 12.5, ips: 4.84561 samples/s, eta: 10:57:48
[2024/07/27 11:40:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:40:20] ppocr INFO: epoch: [107/1500], global_step: 320, lr: 0.001000, loss: 2.198608, loss_shrink_maps: 1.240934, loss_threshold_maps: 0.708843, loss_binary_maps: 0.237604, avg_reader_cost: 1.49183 s, avg_batch_cost: 1.67554 s, avg_samples: 9.6, ips: 5.72949 samples/s, eta: 10:57:01
[2024/07/27 11:40:20] ppocr INFO: epoch: [107/1500], global_step: 321, lr: 0.001000, loss: 2.185762, loss_shrink_maps: 1.226035, loss_threshold_maps: 0.708041, loss_binary_maps: 0.235691, avg_reader_cost: 0.88439 s, avg_batch_cost: 0.93985 s, avg_samples: 2.9, ips: 3.08560 samples/s, eta: 10:56:51
[2024/07/27 11:40:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:40:30] ppocr INFO: epoch: [108/1500], global_step: 324, lr: 0.001000, loss: 2.193759, loss_shrink_maps: 1.226035, loss_threshold_maps: 0.708843, loss_binary_maps: 0.235691, avg_reader_cost: 2.32133 s, avg_batch_cost: 2.59616 s, avg_samples: 12.5, ips: 4.81480 samples/s, eta: 10:55:53
[2024/07/27 11:40:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:40:39] ppocr INFO: epoch: [109/1500], global_step: 327, lr: 0.001000, loss: 2.088571, loss_shrink_maps: 1.157036, loss_threshold_maps: 0.707324, loss_binary_maps: 0.223408, avg_reader_cost: 2.08741 s, avg_batch_cost: 2.42199 s, avg_samples: 12.5, ips: 5.16104 samples/s, eta: 10:54:33
[2024/07/27 11:40:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:40:49] ppocr INFO: epoch: [110/1500], global_step: 330, lr: 0.001000, loss: 2.088571, loss_shrink_maps: 1.157036, loss_threshold_maps: 0.707324, loss_binary_maps: 0.223408, avg_reader_cost: 2.26703 s, avg_batch_cost: 2.63106 s, avg_samples: 12.5, ips: 4.75094 samples/s, eta: 10:53:40
[2024/07/27 11:40:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:41:00] ppocr INFO: epoch: [111/1500], global_step: 333, lr: 0.001000, loss: 2.088571, loss_shrink_maps: 1.157036, loss_threshold_maps: 0.708041, loss_binary_maps: 0.223408, avg_reader_cost: 2.44824 s, avg_batch_cost: 2.69437 s, avg_samples: 12.5, ips: 4.63931 samples/s, eta: 10:52:56
[2024/07/27 11:41:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:41:09] ppocr INFO: epoch: [112/1500], global_step: 336, lr: 0.001000, loss: 2.085973, loss_shrink_maps: 1.157036, loss_threshold_maps: 0.707324, loss_binary_maps: 0.223408, avg_reader_cost: 2.38538 s, avg_batch_cost: 2.63016 s, avg_samples: 12.5, ips: 4.75255 samples/s, eta: 10:52:04
[2024/07/27 11:41:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:41:19] ppocr INFO: epoch: [113/1500], global_step: 339, lr: 0.001000, loss: 2.085973, loss_shrink_maps: 1.157036, loss_threshold_maps: 0.705566, loss_binary_maps: 0.223408, avg_reader_cost: 2.28785 s, avg_batch_cost: 2.56707 s, avg_samples: 12.5, ips: 4.86937 samples/s, eta: 10:51:05
[2024/07/27 11:41:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:41:28] ppocr INFO: epoch: [114/1500], global_step: 340, lr: 0.001000, loss: 2.085973, loss_shrink_maps: 1.160859, loss_threshold_maps: 0.705566, loss_binary_maps: 0.224732, avg_reader_cost: 0.70850 s, avg_batch_cost: 0.80161 s, avg_samples: 4.8, ips: 5.98797 samples/s, eta: 10:50:39
[2024/07/27 11:41:29] ppocr INFO: epoch: [114/1500], global_step: 342, lr: 0.001000, loss: 2.110958, loss_shrink_maps: 1.168868, loss_threshold_maps: 0.717597, loss_binary_maps: 0.226381, avg_reader_cost: 1.69597 s, avg_batch_cost: 1.84290 s, avg_samples: 7.7, ips: 4.17820 samples/s, eta: 10:50:16
[2024/07/27 11:41:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:41:39] ppocr INFO: epoch: [115/1500], global_step: 345, lr: 0.001000, loss: 2.120387, loss_shrink_maps: 1.180806, loss_threshold_maps: 0.706810, loss_binary_maps: 0.228061, avg_reader_cost: 2.26697 s, avg_batch_cost: 2.65485 s, avg_samples: 12.5, ips: 4.70836 samples/s, eta: 10:49:29
[2024/07/27 11:41:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:41:49] ppocr INFO: epoch: [116/1500], global_step: 348, lr: 0.001000, loss: 2.140878, loss_shrink_maps: 1.192774, loss_threshold_maps: 0.708891, loss_binary_maps: 0.229788, avg_reader_cost: 2.38624 s, avg_batch_cost: 2.62050 s, avg_samples: 12.5, ips: 4.77008 samples/s, eta: 10:48:38
[2024/07/27 11:41:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:41:59] ppocr INFO: epoch: [117/1500], global_step: 350, lr: 0.001000, loss: 2.116744, loss_shrink_maps: 1.172983, loss_threshold_maps: 0.706543, loss_binary_maps: 0.227598, avg_reader_cost: 1.50621 s, avg_batch_cost: 1.68730 s, avg_samples: 9.6, ips: 5.68955 samples/s, eta: 10:47:57
[2024/07/27 11:41:59] ppocr INFO: epoch: [117/1500], global_step: 351, lr: 0.001000, loss: 2.095402, loss_shrink_maps: 1.168737, loss_threshold_maps: 0.704929, loss_binary_maps: 0.226381, avg_reader_cost: 0.89007 s, avg_batch_cost: 0.94507 s, avg_samples: 2.9, ips: 3.06855 samples/s, eta: 10:47:48
[2024/07/27 11:42:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:42:09] ppocr INFO: epoch: [118/1500], global_step: 354, lr: 0.001000, loss: 2.116744, loss_shrink_maps: 1.180675, loss_threshold_maps: 0.704127, loss_binary_maps: 0.229324, avg_reader_cost: 2.38103 s, avg_batch_cost: 2.61877 s, avg_samples: 12.5, ips: 4.77323 samples/s, eta: 10:46:58
[2024/07/27 11:42:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:42:19] ppocr INFO: epoch: [119/1500], global_step: 357, lr: 0.001000, loss: 2.157428, loss_shrink_maps: 1.213190, loss_threshold_maps: 0.707010, loss_binary_maps: 0.235855, avg_reader_cost: 2.26153 s, avg_batch_cost: 2.50037 s, avg_samples: 12.5, ips: 4.99926 samples/s, eta: 10:45:54
[2024/07/27 11:42:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:42:28] ppocr INFO: epoch: [120/1500], global_step: 360, lr: 0.001000, loss: 2.137235, loss_shrink_maps: 1.185725, loss_threshold_maps: 0.708891, loss_binary_maps: 0.229610, avg_reader_cost: 2.33216 s, avg_batch_cost: 2.58414 s, avg_samples: 12.5, ips: 4.83719 samples/s, eta: 10:45:00

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:00,  8.31it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.48it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.19it/s]
eval model::   2%|▏         | 9/500 [00:00<00:24, 19.85it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.50it/s]
eval model::   3%|▎         | 13/500 [00:00<00:27, 18.01it/s]
eval model::   3%|▎         | 15/500 [00:00<00:26, 18.34it/s]
eval model::   4%|▎         | 18/500 [00:00<00:22, 20.98it/s]
eval model::   4%|▍         | 21/500 [00:01<00:26, 18.21it/s]
eval model::   5%|▌         | 25/500 [00:01<00:22, 20.88it/s]
eval model::   6%|▌         | 28/500 [00:01<00:21, 21.71it/s]
eval model::   6%|▌         | 31/500 [00:01<00:22, 21.10it/s]
eval model::   7%|▋         | 34/500 [00:01<00:22, 20.74it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 21.18it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.29it/s]
eval model::   9%|▊         | 43/500 [00:02<00:23, 19.20it/s]
eval model::   9%|▉         | 46/500 [00:02<00:22, 19.93it/s]
eval model::  10%|▉         | 49/500 [00:02<00:21, 21.26it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 20.74it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 21.10it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:21, 20.55it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:24, 18.18it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:25, 17.37it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:21, 19.76it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 21.43it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 21.95it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:19, 21.88it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:20, 20.88it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.61it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.14it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.40it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.36it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 17.81it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:20, 19.55it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:18, 21.73it/s]
eval model::  20%|██        | 100/500 [00:05<00:21, 18.68it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 20.22it/s]
eval model::  21%|██        | 106/500 [00:05<00:20, 19.42it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:20, 18.73it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:20, 18.89it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:19, 19.90it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:18, 20.30it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.39it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 19.85it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 18.92it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 18.64it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:20, 17.86it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:20, 18.34it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:17, 20.31it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.63it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 18.86it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.34it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.45it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.98it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 18.76it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 20.25it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:17, 19.63it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:17, 18.82it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:17, 19.45it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:16, 20.14it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 19.02it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:18, 18.04it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:16, 19.54it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:17, 17.79it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 19.12it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.29it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.24it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:15, 20.00it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:15, 19.67it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:15, 20.09it/s]
eval model::  40%|████      | 201/500 [00:10<00:15, 19.68it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 21.88it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:17, 16.91it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:17, 16.79it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:17, 16.79it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.49it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.23it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 14.93it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 17.03it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:17, 15.93it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:14, 18.55it/s]
eval model::  45%|████▌     | 227/500 [00:11<00:14, 18.66it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:13, 20.48it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 22.16it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.45it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.49it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 20.85it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:11, 22.59it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 22.46it/s]
eval model::  50%|█████     | 251/500 [00:12<00:11, 21.99it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.98it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.85it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 22.87it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 22.83it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:10, 21.35it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:11, 19.83it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:11, 19.49it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.05it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 20.50it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.66it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 21.52it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:14, 14.62it/s]
eval model::  58%|█████▊    | 290/500 [00:14<00:12, 16.76it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:11, 17.90it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:11, 17.46it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:10, 18.98it/s]
eval model::  60%|██████    | 302/500 [00:15<00:11, 17.61it/s]
eval model::  61%|██████    | 304/500 [00:15<00:10, 17.98it/s]
eval model::  62%|██████▏   | 308/500 [00:15<00:08, 21.65it/s]
eval model::  62%|██████▏   | 312/500 [00:15<00:08, 22.07it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 20.76it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 21.54it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:07, 22.93it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 21.95it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:07, 23.62it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:08, 21.07it/s]
eval model::  67%|██████▋   | 333/500 [00:16<00:07, 22.91it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 22.22it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 20.39it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:09, 16.22it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:08, 18.32it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:07, 20.47it/s]
eval model::  70%|███████   | 351/500 [00:17<00:07, 19.74it/s]
eval model::  71%|███████   | 354/500 [00:18<00:07, 20.01it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:07, 19.37it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:06, 20.18it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:07, 19.16it/s]
eval model::  73%|███████▎  | 365/500 [00:18<00:07, 18.32it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:07, 17.04it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:07, 17.91it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 19.42it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:06, 20.37it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 22.39it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 23.01it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:04, 23.14it/s]
eval model::  78%|███████▊  | 388/500 [00:19<00:07, 15.75it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:05, 18.78it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 18.91it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:04, 20.95it/s]
eval model::  80%|████████  | 401/500 [00:20<00:05, 19.39it/s]
eval model::  81%|████████  | 404/500 [00:20<00:04, 20.67it/s]
eval model::  81%|████████▏ | 407/500 [00:20<00:04, 22.22it/s]
eval model::  82%|████████▏ | 410/500 [00:20<00:03, 22.53it/s]
eval model::  83%|████████▎ | 413/500 [00:20<00:03, 21.77it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:04, 20.28it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 19.51it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 16.18it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 15.96it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:04, 16.51it/s]
eval model::  86%|████████▌ | 429/500 [00:21<00:03, 18.61it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 20.31it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 21.37it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 20.67it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:03, 18.95it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 20.82it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 20.65it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 19.38it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 19.79it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:02, 20.97it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:02, 19.60it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:02, 18.35it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 18.59it/s]
eval model::  93%|█████████▎| 466/500 [00:23<00:01, 17.18it/s]
eval model::  94%|█████████▍| 469/500 [00:23<00:01, 18.39it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 19.16it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 17.81it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 19.59it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 20.84it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 21.70it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 21.81it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 21.41it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 21.38it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 22.91it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 23.74it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.70it/s]
[2024/07/27 11:42:54] ppocr INFO: cur metric, precision: 0.6751085383502171, recall: 0.4492055849783341, hmean: 0.5394622723330443, fps: 44.37247188339091
[2024/07/27 11:42:54] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 11:42:54] ppocr INFO: best metric, hmean: 0.5394622723330443, precision: 0.6751085383502171, recall: 0.4492055849783341, fps: 44.37247188339091, best_epoch: 120
[2024/07/27 11:42:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:43:03] ppocr INFO: epoch: [121/1500], global_step: 363, lr: 0.001000, loss: 2.130540, loss_shrink_maps: 1.202247, loss_threshold_maps: 0.704594, loss_binary_maps: 0.234727, avg_reader_cost: 2.26825 s, avg_batch_cost: 2.51180 s, avg_samples: 12.5, ips: 4.97651 samples/s, eta: 10:43:59
[2024/07/27 11:43:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:43:13] ppocr INFO: epoch: [122/1500], global_step: 366, lr: 0.001000, loss: 2.145734, loss_shrink_maps: 1.203057, loss_threshold_maps: 0.707634, loss_binary_maps: 0.234727, avg_reader_cost: 2.20465 s, avg_batch_cost: 2.56974 s, avg_samples: 12.5, ips: 4.86431 samples/s, eta: 10:43:05
[2024/07/27 11:43:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:43:23] ppocr INFO: epoch: [123/1500], global_step: 369, lr: 0.001000, loss: 2.145734, loss_shrink_maps: 1.203057, loss_threshold_maps: 0.713919, loss_binary_maps: 0.234727, avg_reader_cost: 2.40866 s, avg_batch_cost: 2.65052 s, avg_samples: 12.5, ips: 4.71606 samples/s, eta: 10:42:20
[2024/07/27 11:43:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:43:31] ppocr INFO: epoch: [124/1500], global_step: 370, lr: 0.001000, loss: 2.172360, loss_shrink_maps: 1.214000, loss_threshold_maps: 0.706020, loss_binary_maps: 0.236212, avg_reader_cost: 0.52684 s, avg_batch_cost: 0.78548 s, avg_samples: 4.8, ips: 6.11095 samples/s, eta: 10:41:54
[2024/07/27 11:43:33] ppocr INFO: epoch: [124/1500], global_step: 372, lr: 0.001000, loss: 2.197069, loss_shrink_maps: 1.224396, loss_threshold_maps: 0.713919, loss_binary_maps: 0.238614, avg_reader_cost: 1.66299 s, avg_batch_cost: 1.81034 s, avg_samples: 7.7, ips: 4.25333 samples/s, eta: 10:41:29
[2024/07/27 11:43:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:43:43] ppocr INFO: epoch: [125/1500], global_step: 375, lr: 0.001000, loss: 2.130540, loss_shrink_maps: 1.197906, loss_threshold_maps: 0.697358, loss_binary_maps: 0.233468, avg_reader_cost: 2.35317 s, avg_batch_cost: 2.59584 s, avg_samples: 12.5, ips: 4.81541 samples/s, eta: 10:40:39
[2024/07/27 11:43:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:43:52] ppocr INFO: epoch: [126/1500], global_step: 378, lr: 0.001000, loss: 2.127622, loss_shrink_maps: 1.189903, loss_threshold_maps: 0.693253, loss_binary_maps: 0.231577, avg_reader_cost: 2.31441 s, avg_batch_cost: 2.55540 s, avg_samples: 12.5, ips: 4.89160 samples/s, eta: 10:39:45
[2024/07/27 11:43:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:44:02] ppocr INFO: epoch: [127/1500], global_step: 380, lr: 0.001000, loss: 2.130540, loss_shrink_maps: 1.203057, loss_threshold_maps: 0.693253, loss_binary_maps: 0.234727, avg_reader_cost: 1.47698 s, avg_batch_cost: 1.65483 s, avg_samples: 9.6, ips: 5.80122 samples/s, eta: 10:39:04
[2024/07/27 11:44:02] ppocr INFO: epoch: [127/1500], global_step: 381, lr: 0.001000, loss: 2.129863, loss_shrink_maps: 1.203052, loss_threshold_maps: 0.692247, loss_binary_maps: 0.235848, avg_reader_cost: 0.87366 s, avg_batch_cost: 0.92911 s, avg_samples: 2.9, ips: 3.12127 samples/s, eta: 10:38:54
[2024/07/27 11:44:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:44:12] ppocr INFO: epoch: [128/1500], global_step: 384, lr: 0.001000, loss: 2.158925, loss_shrink_maps: 1.214992, loss_threshold_maps: 0.696671, loss_binary_maps: 0.238250, avg_reader_cost: 2.35634 s, avg_batch_cost: 2.59616 s, avg_samples: 12.5, ips: 4.81480 samples/s, eta: 10:38:05
[2024/07/27 11:44:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:44:22] ppocr INFO: epoch: [129/1500], global_step: 387, lr: 0.001000, loss: 2.150966, loss_shrink_maps: 1.214992, loss_threshold_maps: 0.696671, loss_binary_maps: 0.238250, avg_reader_cost: 2.21807 s, avg_batch_cost: 2.62538 s, avg_samples: 12.5, ips: 4.76121 samples/s, eta: 10:37:20
[2024/07/27 11:44:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:44:32] ppocr INFO: epoch: [130/1500], global_step: 390, lr: 0.001000, loss: 2.150966, loss_shrink_maps: 1.218902, loss_threshold_maps: 0.696758, loss_binary_maps: 0.239124, avg_reader_cost: 2.22221 s, avg_batch_cost: 2.59607 s, avg_samples: 12.5, ips: 4.81498 samples/s, eta: 10:36:32
[2024/07/27 11:44:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:44:41] ppocr INFO: epoch: [131/1500], global_step: 393, lr: 0.001000, loss: 2.177174, loss_shrink_maps: 1.217144, loss_threshold_maps: 0.704681, loss_binary_maps: 0.237301, avg_reader_cost: 2.08378 s, avg_batch_cost: 2.41214 s, avg_samples: 12.5, ips: 5.18211 samples/s, eta: 10:35:25
[2024/07/27 11:44:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:44:51] ppocr INFO: epoch: [132/1500], global_step: 396, lr: 0.001000, loss: 2.177174, loss_shrink_maps: 1.228020, loss_threshold_maps: 0.710381, loss_binary_maps: 0.238316, avg_reader_cost: 2.22014 s, avg_batch_cost: 2.58805 s, avg_samples: 12.5, ips: 4.82989 samples/s, eta: 10:34:36
[2024/07/27 11:44:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:45:01] ppocr INFO: epoch: [133/1500], global_step: 399, lr: 0.001000, loss: 2.145588, loss_shrink_maps: 1.207350, loss_threshold_maps: 0.709004, loss_binary_maps: 0.235423, avg_reader_cost: 2.38131 s, avg_batch_cost: 2.62252 s, avg_samples: 12.5, ips: 4.76640 samples/s, eta: 10:33:52
[2024/07/27 11:45:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:45:09] ppocr INFO: epoch: [134/1500], global_step: 400, lr: 0.001000, loss: 2.115786, loss_shrink_maps: 1.183076, loss_threshold_maps: 0.702173, loss_binary_maps: 0.230192, avg_reader_cost: 0.54604 s, avg_batch_cost: 0.75935 s, avg_samples: 4.8, ips: 6.32123 samples/s, eta: 10:33:25
[2024/07/27 11:45:10] ppocr INFO: epoch: [134/1500], global_step: 402, lr: 0.001000, loss: 2.075028, loss_shrink_maps: 1.151134, loss_threshold_maps: 0.702173, loss_binary_maps: 0.224395, avg_reader_cost: 1.61197 s, avg_batch_cost: 1.75964 s, avg_samples: 7.7, ips: 4.37589 samples/s, eta: 10:32:57
[2024/07/27 11:45:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:45:20] ppocr INFO: epoch: [135/1500], global_step: 405, lr: 0.001000, loss: 2.075028, loss_shrink_maps: 1.149428, loss_threshold_maps: 0.702173, loss_binary_maps: 0.224395, avg_reader_cost: 2.27282 s, avg_batch_cost: 2.66630 s, avg_samples: 12.5, ips: 4.68814 samples/s, eta: 10:32:18
[2024/07/27 11:45:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:45:30] ppocr INFO: epoch: [136/1500], global_step: 408, lr: 0.001000, loss: 2.122453, loss_shrink_maps: 1.173679, loss_threshold_maps: 0.710620, loss_binary_maps: 0.229114, avg_reader_cost: 2.19758 s, avg_batch_cost: 2.57332 s, avg_samples: 12.5, ips: 4.85754 samples/s, eta: 10:31:30
[2024/07/27 11:45:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:45:40] ppocr INFO: epoch: [137/1500], global_step: 410, lr: 0.001000, loss: 2.010427, loss_shrink_maps: 1.128413, loss_threshold_maps: 0.696383, loss_binary_maps: 0.220201, avg_reader_cost: 1.35875 s, avg_batch_cost: 1.68760 s, avg_samples: 9.6, ips: 5.68854 samples/s, eta: 10:30:55
[2024/07/27 11:45:40] ppocr INFO: epoch: [137/1500], global_step: 411, lr: 0.001000, loss: 2.010427, loss_shrink_maps: 1.128413, loss_threshold_maps: 0.696383, loss_binary_maps: 0.220201, avg_reader_cost: 0.88968 s, avg_batch_cost: 0.94516 s, avg_samples: 2.9, ips: 3.06826 samples/s, eta: 10:30:47
[2024/07/27 11:45:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:45:50] ppocr INFO: epoch: [138/1500], global_step: 414, lr: 0.001000, loss: 2.010427, loss_shrink_maps: 1.128413, loss_threshold_maps: 0.694914, loss_binary_maps: 0.220201, avg_reader_cost: 2.20765 s, avg_batch_cost: 2.63968 s, avg_samples: 12.5, ips: 4.73542 samples/s, eta: 10:30:06
[2024/07/27 11:45:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:46:00] ppocr INFO: epoch: [139/1500], global_step: 417, lr: 0.001000, loss: 2.005837, loss_shrink_maps: 1.108655, loss_threshold_maps: 0.697440, loss_binary_maps: 0.216090, avg_reader_cost: 2.46799 s, avg_batch_cost: 2.70859 s, avg_samples: 12.5, ips: 4.61495 samples/s, eta: 10:29:32
[2024/07/27 11:46:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:46:10] ppocr INFO: epoch: [140/1500], global_step: 420, lr: 0.001000, loss: 2.051293, loss_shrink_maps: 1.145747, loss_threshold_maps: 0.697440, loss_binary_maps: 0.223170, avg_reader_cost: 2.27425 s, avg_batch_cost: 2.51713 s, avg_samples: 12.5, ips: 4.96597 samples/s, eta: 10:28:39

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:01,  8.14it/s]
eval model::   1%|          | 3/500 [00:00<00:34, 14.49it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.81it/s]
eval model::   2%|▏         | 9/500 [00:00<00:23, 21.09it/s]
eval model::   2%|▏         | 12/500 [00:00<00:28, 16.98it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.43it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.59it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.16it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 21.98it/s]
eval model::   5%|▌         | 26/500 [00:01<00:21, 21.77it/s]
eval model::   6%|▌         | 29/500 [00:01<00:20, 23.36it/s]
eval model::   6%|▋         | 32/500 [00:01<00:22, 20.96it/s]
eval model::   7%|▋         | 35/500 [00:01<00:23, 19.62it/s]
eval model::   8%|▊         | 38/500 [00:01<00:22, 20.43it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.94it/s]
eval model::   9%|▉         | 44/500 [00:02<00:23, 19.44it/s]
eval model::   9%|▉         | 47/500 [00:02<00:21, 21.44it/s]
eval model::  10%|█         | 50/500 [00:02<00:20, 22.38it/s]
eval model::  11%|█         | 53/500 [00:02<00:19, 22.58it/s]
eval model::  11%|█         | 56/500 [00:02<00:20, 21.73it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:21, 20.95it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:25, 17.05it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:25, 17.29it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 19.10it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:22, 19.54it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 21.52it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 22.28it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:18, 22.43it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.63it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.01it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 20.42it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:20, 19.53it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:19, 20.40it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:19, 20.52it/s]
eval model::  20%|██        | 100/500 [00:04<00:20, 19.96it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 21.27it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.45it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 21.66it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:17, 21.87it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:17, 22.27it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:19, 19.61it/s]
eval model::  24%|██▍       | 121/500 [00:05<00:18, 20.27it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 19.30it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 19.25it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.82it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 18.72it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:17, 20.74it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:18, 19.73it/s]
eval model::  28%|██▊       | 141/500 [00:06<00:18, 19.35it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.02it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.51it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 18.95it/s]
eval model::  30%|███       | 152/500 [00:07<00:19, 18.05it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 20.43it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:15, 21.41it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:18, 18.10it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:18, 18.42it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.30it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 20.20it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:15, 21.51it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:16, 19.88it/s]
eval model::  36%|███▌      | 178/500 [00:08<00:15, 20.22it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:16, 19.13it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:16, 19.68it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 19.31it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:15, 19.64it/s]
eval model::  39%|███▉      | 194/500 [00:09<00:14, 21.57it/s]
eval model::  39%|███▉      | 197/500 [00:09<00:14, 21.04it/s]
eval model::  40%|████      | 200/500 [00:09<00:13, 21.94it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 20.53it/s]
eval model::  41%|████      | 206/500 [00:10<00:22, 13.01it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:21, 13.58it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:20, 14.20it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:18, 15.83it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.91it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.57it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 16.88it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:17, 16.15it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:14, 18.36it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:13, 20.00it/s]
eval model::  46%|████▋     | 232/500 [00:11<00:11, 23.54it/s]
eval model::  47%|████▋     | 235/500 [00:11<00:11, 22.97it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:10, 23.96it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 23.37it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:11, 22.75it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:11, 21.87it/s]
eval model::  50%|█████     | 250/500 [00:12<00:12, 19.38it/s]
eval model::  51%|█████     | 253/500 [00:12<00:12, 20.42it/s]
eval model::  51%|█████     | 256/500 [00:12<00:11, 21.62it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:11, 21.38it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 20.77it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:12, 19.07it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:12, 18.82it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:12, 18.44it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:12, 18.19it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.12it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 20.55it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.40it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 19.76it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:15, 13.81it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:12, 17.17it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:11, 17.97it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:11, 18.05it/s]
eval model::  60%|██████    | 300/500 [00:15<00:11, 17.42it/s]
eval model::  60%|██████    | 302/500 [00:15<00:12, 16.22it/s]
eval model::  61%|██████    | 305/500 [00:15<00:10, 18.02it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:10, 18.44it/s]
eval model::  62%|██████▏   | 310/500 [00:15<00:09, 20.35it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 20.70it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 20.54it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 21.52it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:08, 20.93it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:07, 22.90it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:07, 23.92it/s]
eval model::  66%|██████▌   | 331/500 [00:16<00:07, 24.03it/s]
eval model::  67%|██████▋   | 334/500 [00:16<00:06, 24.34it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:07, 22.39it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:08, 18.44it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:07, 20.74it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:06, 22.53it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:06, 22.98it/s]
eval model::  70%|███████   | 352/500 [00:17<00:07, 21.01it/s]
eval model::  71%|███████   | 355/500 [00:17<00:06, 20.75it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 21.51it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:06, 20.57it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.25it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:07, 18.38it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:06, 19.75it/s]
eval model::  75%|███████▍  | 373/500 [00:18<00:06, 20.50it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:06, 20.48it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 21.03it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:06, 19.48it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 20.50it/s]
eval model::  78%|███████▊  | 388/500 [00:19<00:07, 14.75it/s]
eval model::  78%|███████▊  | 391/500 [00:19<00:06, 17.23it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:05, 18.20it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 19.58it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 19.58it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 20.64it/s]
eval model::  81%|████████  | 406/500 [00:20<00:04, 21.79it/s]
eval model::  82%|████████▏ | 409/500 [00:20<00:03, 23.51it/s]
eval model::  82%|████████▏ | 412/500 [00:20<00:03, 24.72it/s]
eval model::  83%|████████▎ | 415/500 [00:20<00:03, 22.42it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:03, 21.22it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:04, 17.72it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 18.07it/s]
eval model::  85%|████████▌ | 427/500 [00:21<00:03, 19.57it/s]
eval model::  86%|████████▌ | 430/500 [00:21<00:03, 21.33it/s]
eval model::  87%|████████▋ | 433/500 [00:21<00:02, 23.25it/s]
eval model::  87%|████████▋ | 437/500 [00:21<00:02, 25.72it/s]
eval model::  88%|████████▊ | 440/500 [00:22<00:02, 22.07it/s]
eval model::  89%|████████▊ | 443/500 [00:22<00:02, 22.65it/s]
eval model::  89%|████████▉ | 446/500 [00:22<00:02, 21.04it/s]
eval model::  90%|████████▉ | 449/500 [00:22<00:02, 20.34it/s]
eval model::  90%|█████████ | 452/500 [00:22<00:02, 21.07it/s]
eval model::  91%|█████████ | 455/500 [00:22<00:02, 19.95it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:02, 19.87it/s]
eval model::  92%|█████████▏| 461/500 [00:23<00:01, 20.59it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 20.91it/s]
eval model::  93%|█████████▎| 467/500 [00:23<00:01, 19.76it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 22.43it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 21.19it/s]
eval model::  95%|█████████▌| 477/500 [00:23<00:01, 21.62it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 23.42it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 23.21it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 22.16it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 23.45it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 21.52it/s]
eval model::  99%|█████████▉| 495/500 [00:24<00:00, 21.99it/s]
eval model:: 100%|█████████▉| 498/500 [00:24<00:00, 23.29it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 20.03it/s]
[2024/07/27 11:46:35] ppocr INFO: cur metric, precision: 0.7161667885881492, recall: 0.47135291285507946, hmean: 0.5685249709639955, fps: 44.87849093026925
[2024/07/27 11:46:35] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 11:46:35] ppocr INFO: best metric, hmean: 0.5685249709639955, precision: 0.7161667885881492, recall: 0.47135291285507946, fps: 44.87849093026925, best_epoch: 140
[2024/07/27 11:46:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:46:45] ppocr INFO: epoch: [141/1500], global_step: 423, lr: 0.001000, loss: 2.051293, loss_shrink_maps: 1.134870, loss_threshold_maps: 0.694914, loss_binary_maps: 0.221254, avg_reader_cost: 2.28277 s, avg_batch_cost: 2.69336 s, avg_samples: 12.5, ips: 4.64105 samples/s, eta: 10:28:03
[2024/07/27 11:46:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:46:55] ppocr INFO: epoch: [142/1500], global_step: 426, lr: 0.001000, loss: 1.980067, loss_shrink_maps: 1.096803, loss_threshold_maps: 0.686623, loss_binary_maps: 0.213801, avg_reader_cost: 2.18554 s, avg_batch_cost: 2.56574 s, avg_samples: 12.5, ips: 4.87188 samples/s, eta: 10:27:16
[2024/07/27 11:46:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:47:05] ppocr INFO: epoch: [143/1500], global_step: 429, lr: 0.001000, loss: 2.067499, loss_shrink_maps: 1.140371, loss_threshold_maps: 0.694914, loss_binary_maps: 0.222271, avg_reader_cost: 2.43633 s, avg_batch_cost: 2.68955 s, avg_samples: 12.5, ips: 4.64761 samples/s, eta: 10:26:40
[2024/07/27 11:47:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:47:13] ppocr INFO: epoch: [144/1500], global_step: 430, lr: 0.001000, loss: 2.092960, loss_shrink_maps: 1.161974, loss_threshold_maps: 0.695169, loss_binary_maps: 0.227078, avg_reader_cost: 0.66593 s, avg_batch_cost: 0.78640 s, avg_samples: 4.8, ips: 6.10378 samples/s, eta: 10:26:18
[2024/07/27 11:47:15] ppocr INFO: epoch: [144/1500], global_step: 432, lr: 0.001000, loss: 2.067499, loss_shrink_maps: 1.140371, loss_threshold_maps: 0.690313, loss_binary_maps: 0.222271, avg_reader_cost: 1.66673 s, avg_batch_cost: 1.81557 s, avg_samples: 7.7, ips: 4.24110 samples/s, eta: 10:25:57
[2024/07/27 11:47:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:47:25] ppocr INFO: epoch: [145/1500], global_step: 435, lr: 0.001000, loss: 2.067499, loss_shrink_maps: 1.140371, loss_threshold_maps: 0.684644, loss_binary_maps: 0.222271, avg_reader_cost: 2.31707 s, avg_batch_cost: 2.55363 s, avg_samples: 12.5, ips: 4.89499 samples/s, eta: 10:25:09
[2024/07/27 11:47:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:47:34] ppocr INFO: epoch: [146/1500], global_step: 438, lr: 0.001000, loss: 2.067499, loss_shrink_maps: 1.165013, loss_threshold_maps: 0.671613, loss_binary_maps: 0.228832, avg_reader_cost: 2.39155 s, avg_batch_cost: 2.62884 s, avg_samples: 12.5, ips: 4.75494 samples/s, eta: 10:24:28
[2024/07/27 11:47:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:47:44] ppocr INFO: epoch: [147/1500], global_step: 440, lr: 0.001000, loss: 2.067560, loss_shrink_maps: 1.167685, loss_threshold_maps: 0.667546, loss_binary_maps: 0.228832, avg_reader_cost: 1.37016 s, avg_batch_cost: 1.63946 s, avg_samples: 9.6, ips: 5.85559 samples/s, eta: 10:23:51
[2024/07/27 11:47:44] ppocr INFO: epoch: [147/1500], global_step: 441, lr: 0.001000, loss: 2.067560, loss_shrink_maps: 1.167685, loss_threshold_maps: 0.675718, loss_binary_maps: 0.228832, avg_reader_cost: 0.86585 s, avg_batch_cost: 0.92084 s, avg_samples: 2.9, ips: 3.14929 samples/s, eta: 10:23:42
[2024/07/27 11:47:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:47:54] ppocr INFO: epoch: [148/1500], global_step: 444, lr: 0.001000, loss: 2.120530, loss_shrink_maps: 1.193967, loss_threshold_maps: 0.701160, loss_binary_maps: 0.232803, avg_reader_cost: 2.36084 s, avg_batch_cost: 2.61844 s, avg_samples: 12.5, ips: 4.77383 samples/s, eta: 10:23:00
[2024/07/27 11:47:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:48:04] ppocr INFO: epoch: [149/1500], global_step: 447, lr: 0.001000, loss: 2.120530, loss_shrink_maps: 1.193967, loss_threshold_maps: 0.707010, loss_binary_maps: 0.232803, avg_reader_cost: 2.21868 s, avg_batch_cost: 2.56995 s, avg_samples: 12.5, ips: 4.86390 samples/s, eta: 10:22:15
[2024/07/27 11:48:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:48:14] ppocr INFO: epoch: [150/1500], global_step: 450, lr: 0.001000, loss: 2.030647, loss_shrink_maps: 1.136124, loss_threshold_maps: 0.694943, loss_binary_maps: 0.220390, avg_reader_cost: 2.33601 s, avg_batch_cost: 2.57956 s, avg_samples: 12.5, ips: 4.84578 samples/s, eta: 10:21:31
[2024/07/27 11:48:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:48:23] ppocr INFO: epoch: [151/1500], global_step: 453, lr: 0.001000, loss: 2.042392, loss_shrink_maps: 1.160906, loss_threshold_maps: 0.699070, loss_binary_maps: 0.226589, avg_reader_cost: 2.33637 s, avg_batch_cost: 2.59203 s, avg_samples: 12.5, ips: 4.82247 samples/s, eta: 10:20:48
[2024/07/27 11:48:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:48:33] ppocr INFO: epoch: [152/1500], global_step: 456, lr: 0.001000, loss: 2.030647, loss_shrink_maps: 1.135024, loss_threshold_maps: 0.698437, loss_binary_maps: 0.219890, avg_reader_cost: 2.32402 s, avg_batch_cost: 2.55449 s, avg_samples: 12.5, ips: 4.89334 samples/s, eta: 10:20:02
[2024/07/27 11:48:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:48:42] ppocr INFO: epoch: [153/1500], global_step: 459, lr: 0.001000, loss: 2.058670, loss_shrink_maps: 1.138599, loss_threshold_maps: 0.703284, loss_binary_maps: 0.221568, avg_reader_cost: 2.16863 s, avg_batch_cost: 2.40950 s, avg_samples: 12.5, ips: 5.18779 samples/s, eta: 10:19:04
[2024/07/27 11:48:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:48:51] ppocr INFO: epoch: [154/1500], global_step: 460, lr: 0.001000, loss: 2.019472, loss_shrink_maps: 1.108149, loss_threshold_maps: 0.699070, loss_binary_maps: 0.216027, avg_reader_cost: 0.69460 s, avg_batch_cost: 0.78609 s, avg_samples: 4.8, ips: 6.10617 samples/s, eta: 10:18:43
[2024/07/27 11:48:52] ppocr INFO: epoch: [154/1500], global_step: 462, lr: 0.001000, loss: 2.009475, loss_shrink_maps: 1.108149, loss_threshold_maps: 0.699033, loss_binary_maps: 0.215924, avg_reader_cost: 1.66496 s, avg_batch_cost: 1.81167 s, avg_samples: 7.7, ips: 4.25022 samples/s, eta: 10:18:22
[2024/07/27 11:48:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:49:02] ppocr INFO: epoch: [155/1500], global_step: 465, lr: 0.001000, loss: 2.009475, loss_shrink_maps: 1.108149, loss_threshold_maps: 0.691731, loss_binary_maps: 0.215924, avg_reader_cost: 2.15230 s, avg_batch_cost: 2.50630 s, avg_samples: 12.5, ips: 4.98744 samples/s, eta: 10:17:33
[2024/07/27 11:49:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:49:12] ppocr INFO: epoch: [156/1500], global_step: 468, lr: 0.001000, loss: 2.013740, loss_shrink_maps: 1.112015, loss_threshold_maps: 0.691519, loss_binary_maps: 0.216772, avg_reader_cost: 2.23555 s, avg_batch_cost: 2.59581 s, avg_samples: 12.5, ips: 4.81546 samples/s, eta: 10:16:52
[2024/07/27 11:49:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:49:21] ppocr INFO: epoch: [157/1500], global_step: 470, lr: 0.001000, loss: 2.051852, loss_shrink_maps: 1.137897, loss_threshold_maps: 0.691731, loss_binary_maps: 0.222314, avg_reader_cost: 1.37417 s, avg_batch_cost: 1.62886 s, avg_samples: 9.6, ips: 5.89369 samples/s, eta: 10:16:15
[2024/07/27 11:49:21] ppocr INFO: epoch: [157/1500], global_step: 471, lr: 0.001000, loss: 2.043262, loss_shrink_maps: 1.121563, loss_threshold_maps: 0.691731, loss_binary_maps: 0.220783, avg_reader_cost: 0.86057 s, avg_batch_cost: 0.91603 s, avg_samples: 2.9, ips: 3.16582 samples/s, eta: 10:16:06
[2024/07/27 11:49:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:49:31] ppocr INFO: epoch: [158/1500], global_step: 474, lr: 0.001000, loss: 2.043262, loss_shrink_maps: 1.121563, loss_threshold_maps: 0.695591, loss_binary_maps: 0.220783, avg_reader_cost: 2.35805 s, avg_batch_cost: 2.62641 s, avg_samples: 12.5, ips: 4.75936 samples/s, eta: 10:15:28
[2024/07/27 11:49:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:49:41] ppocr INFO: epoch: [159/1500], global_step: 477, lr: 0.001000, loss: 2.005841, loss_shrink_maps: 1.098203, loss_threshold_maps: 0.691731, loss_binary_maps: 0.215328, avg_reader_cost: 2.27285 s, avg_batch_cost: 2.53907 s, avg_samples: 12.5, ips: 4.92307 samples/s, eta: 10:14:42
[2024/07/27 11:49:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:49:51] ppocr INFO: epoch: [160/1500], global_step: 480, lr: 0.001000, loss: 2.005841, loss_shrink_maps: 1.090640, loss_threshold_maps: 0.693919, loss_binary_maps: 0.212677, avg_reader_cost: 2.35091 s, avg_batch_cost: 2.59552 s, avg_samples: 12.5, ips: 4.81600 samples/s, eta: 10:14:02

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.29it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.86it/s]
eval model::   1%|          | 6/500 [00:00<00:29, 16.57it/s]
eval model::   2%|▏         | 9/500 [00:00<00:25, 19.48it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.17it/s]
eval model::   3%|▎         | 13/500 [00:00<00:26, 18.12it/s]
eval model::   3%|▎         | 16/500 [00:00<00:24, 20.06it/s]
eval model::   4%|▍         | 19/500 [00:01<00:25, 19.21it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 19.12it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.62it/s]
eval model::   5%|▌         | 27/500 [00:01<00:23, 19.96it/s]
eval model::   6%|▌         | 30/500 [00:01<00:23, 20.02it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 20.19it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 19.21it/s]
eval model::   8%|▊         | 38/500 [00:01<00:24, 18.95it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.06it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.03it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.51it/s]
eval model::   9%|▉         | 47/500 [00:02<00:23, 18.91it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 19.11it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 19.22it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 19.37it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:24, 18.30it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:22, 19.66it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:29, 14.90it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:29, 14.68it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:27, 15.68it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:26, 16.52it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:23, 18.06it/s]
eval model::  15%|█▍        | 74/500 [00:04<00:21, 20.00it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:23, 17.64it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:23, 17.96it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:23, 17.78it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:23, 17.85it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.42it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:24, 16.99it/s]
eval model::  18%|█▊        | 91/500 [00:05<00:25, 16.36it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:24, 16.74it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:21, 18.79it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 21.07it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.47it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.21it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:19, 20.57it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 19.66it/s]
eval model::  23%|██▎       | 114/500 [00:06<00:19, 19.40it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:19, 19.45it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:19, 19.41it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:20, 18.34it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 19.60it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 19.58it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 21.23it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:18, 19.43it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:17, 21.20it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:15, 23.14it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.63it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.54it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.42it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 19.38it/s]
eval model::  31%|███       | 153/500 [00:08<00:19, 18.21it/s]
eval model::  31%|███       | 156/500 [00:08<00:17, 20.09it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:18, 18.80it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:20, 16.59it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.21it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 19.45it/s]
eval model::  34%|███▍      | 169/500 [00:09<00:17, 19.10it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:18, 18.05it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 16.44it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:17, 18.10it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.42it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.33it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:16, 18.68it/s]
eval model::  37%|███▋      | 187/500 [00:10<00:17, 17.82it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:16, 18.93it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:14, 20.88it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:13, 21.73it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:12, 23.53it/s]
eval model::  40%|████      | 202/500 [00:10<00:12, 24.73it/s]
eval model::  41%|████      | 205/500 [00:11<00:21, 13.59it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:19, 15.24it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 15.06it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.67it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 14.00it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.21it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:19, 14.33it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:19, 14.14it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:16, 16.75it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.78it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:13, 19.40it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 21.28it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 20.73it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 22.62it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:11, 22.62it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:12, 20.15it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:12, 21.05it/s]
eval model::  50%|█████     | 250/500 [00:13<00:12, 20.00it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 22.21it/s]
eval model::  51%|█████     | 256/500 [00:13<00:10, 22.68it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 22.31it/s]
eval model::  52%|█████▏    | 262/500 [00:14<00:11, 20.99it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:12, 18.09it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:13, 17.61it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 19.48it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:10, 21.00it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:11, 19.56it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:10, 20.48it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:09, 22.22it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:14, 14.62it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:12, 17.22it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:10, 20.28it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:12, 16.68it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:10, 18.82it/s]
eval model::  60%|██████    | 301/500 [00:16<00:11, 17.78it/s]
eval model::  61%|██████    | 304/500 [00:16<00:10, 19.46it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:08, 21.46it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:08, 22.84it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 22.55it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 21.58it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 21.92it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 21.68it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:07, 23.29it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 21.03it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 19.18it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:08, 19.28it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:08, 19.11it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:08, 19.30it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 14.58it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.18it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 18.46it/s]
eval model::  70%|███████   | 350/500 [00:18<00:06, 21.98it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 22.44it/s]
eval model::  71%|███████   | 356/500 [00:18<00:06, 20.99it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:06, 22.04it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:06, 21.13it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:06, 20.53it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:06, 19.90it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 20.16it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 20.93it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 20.53it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:06, 18.49it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 20.08it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:09, 12.46it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:07, 14.80it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 16.48it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:05, 18.41it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:04, 20.42it/s]
eval model::  80%|████████  | 401/500 [00:21<00:04, 19.89it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 20.13it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 20.94it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 21.73it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:04, 21.52it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 19.27it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 19.06it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:04, 16.09it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 17.25it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 17.43it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 18.67it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 20.08it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:02, 21.73it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 22.51it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 19.73it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 20.09it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 21.57it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 19.61it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 21.78it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:01, 23.45it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 22.37it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 18.66it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 17.48it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.71it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 19.82it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 17.87it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 19.50it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:01, 19.03it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 20.37it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 20.63it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 21.07it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 21.18it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 20.91it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 24.96it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.15it/s]
[2024/07/27 11:50:17] ppocr INFO: cur metric, precision: 0.6613520408163265, recall: 0.4992778045257583, hmean: 0.5689986282578874, fps: 43.625946665913894
[2024/07/27 11:50:17] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 11:50:17] ppocr INFO: best metric, hmean: 0.5689986282578874, precision: 0.6613520408163265, recall: 0.4992778045257583, fps: 43.625946665913894, best_epoch: 160
[2024/07/27 11:50:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:50:26] ppocr INFO: epoch: [161/1500], global_step: 483, lr: 0.001000, loss: 2.005841, loss_shrink_maps: 1.092295, loss_threshold_maps: 0.691731, loss_binary_maps: 0.213922, avg_reader_cost: 2.13497 s, avg_batch_cost: 2.37207 s, avg_samples: 12.5, ips: 5.26966 samples/s, eta: 10:13:03
[2024/07/27 11:50:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:50:35] ppocr INFO: epoch: [162/1500], global_step: 486, lr: 0.001000, loss: 1.999200, loss_shrink_maps: 1.081984, loss_threshold_maps: 0.682721, loss_binary_maps: 0.212677, avg_reader_cost: 2.35335 s, avg_batch_cost: 2.59265 s, avg_samples: 12.5, ips: 4.82133 samples/s, eta: 10:12:23
[2024/07/27 11:50:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:50:45] ppocr INFO: epoch: [163/1500], global_step: 489, lr: 0.001000, loss: 1.968724, loss_shrink_maps: 1.079454, loss_threshold_maps: 0.680592, loss_binary_maps: 0.211640, avg_reader_cost: 2.21793 s, avg_batch_cost: 2.56966 s, avg_samples: 12.5, ips: 4.86445 samples/s, eta: 10:11:41
[2024/07/27 11:50:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:50:53] ppocr INFO: epoch: [164/1500], global_step: 490, lr: 0.001000, loss: 1.989066, loss_shrink_maps: 1.081984, loss_threshold_maps: 0.685592, loss_binary_maps: 0.212677, avg_reader_cost: 0.68521 s, avg_batch_cost: 0.77793 s, avg_samples: 4.8, ips: 6.17022 samples/s, eta: 10:11:21
[2024/07/27 11:50:55] ppocr INFO: epoch: [164/1500], global_step: 492, lr: 0.001000, loss: 1.968724, loss_shrink_maps: 1.079454, loss_threshold_maps: 0.684200, loss_binary_maps: 0.211640, avg_reader_cost: 1.64773 s, avg_batch_cost: 1.79479 s, avg_samples: 7.7, ips: 4.29019 samples/s, eta: 10:10:59
[2024/07/27 11:50:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:51:05] ppocr INFO: epoch: [165/1500], global_step: 495, lr: 0.001000, loss: 1.957905, loss_shrink_maps: 1.069208, loss_threshold_maps: 0.680592, loss_binary_maps: 0.209621, avg_reader_cost: 2.36873 s, avg_batch_cost: 2.60409 s, avg_samples: 12.5, ips: 4.80015 samples/s, eta: 10:10:21
[2024/07/27 11:51:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:51:14] ppocr INFO: epoch: [166/1500], global_step: 498, lr: 0.001000, loss: 1.957905, loss_shrink_maps: 1.058032, loss_threshold_maps: 0.680592, loss_binary_maps: 0.208104, avg_reader_cost: 2.20529 s, avg_batch_cost: 2.55775 s, avg_samples: 12.5, ips: 4.88711 samples/s, eta: 10:09:38
[2024/07/27 11:51:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:51:24] ppocr INFO: epoch: [167/1500], global_step: 500, lr: 0.001000, loss: 1.984157, loss_shrink_maps: 1.078904, loss_threshold_maps: 0.684200, loss_binary_maps: 0.210895, avg_reader_cost: 1.44830 s, avg_batch_cost: 1.63078 s, avg_samples: 9.6, ips: 5.88676 samples/s, eta: 10:09:04
[2024/07/27 11:51:24] ppocr INFO: epoch: [167/1500], global_step: 501, lr: 0.001000, loss: 1.995076, loss_shrink_maps: 1.078904, loss_threshold_maps: 0.687688, loss_binary_maps: 0.210895, avg_reader_cost: 0.86184 s, avg_batch_cost: 0.91685 s, avg_samples: 2.9, ips: 3.16299 samples/s, eta: 10:08:55
[2024/07/27 11:51:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:51:34] ppocr INFO: epoch: [168/1500], global_step: 504, lr: 0.001000, loss: 1.984157, loss_shrink_maps: 1.069208, loss_threshold_maps: 0.687688, loss_binary_maps: 0.209621, avg_reader_cost: 2.33094 s, avg_batch_cost: 2.57111 s, avg_samples: 12.5, ips: 4.86172 samples/s, eta: 10:08:15
[2024/07/27 11:51:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:51:43] ppocr INFO: epoch: [169/1500], global_step: 507, lr: 0.001000, loss: 1.995076, loss_shrink_maps: 1.078904, loss_threshold_maps: 0.687688, loss_binary_maps: 0.210895, avg_reader_cost: 2.00215 s, avg_batch_cost: 2.27782 s, avg_samples: 12.5, ips: 5.48771 samples/s, eta: 10:07:11
[2024/07/27 11:51:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:51:52] ppocr INFO: epoch: [170/1500], global_step: 510, lr: 0.001000, loss: 1.995076, loss_shrink_maps: 1.067728, loss_threshold_maps: 0.692206, loss_binary_maps: 0.208753, avg_reader_cost: 2.19046 s, avg_batch_cost: 2.59566 s, avg_samples: 12.5, ips: 4.81572 samples/s, eta: 10:06:32
[2024/07/27 11:51:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:52:02] ppocr INFO: epoch: [171/1500], global_step: 513, lr: 0.001000, loss: 1.995076, loss_shrink_maps: 1.067728, loss_threshold_maps: 0.694174, loss_binary_maps: 0.208753, avg_reader_cost: 2.24456 s, avg_batch_cost: 2.49561 s, avg_samples: 12.5, ips: 5.00879 samples/s, eta: 10:05:46
[2024/07/27 11:52:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:52:12] ppocr INFO: epoch: [172/1500], global_step: 516, lr: 0.001000, loss: 1.958274, loss_shrink_maps: 1.053050, loss_threshold_maps: 0.692206, loss_binary_maps: 0.207149, avg_reader_cost: 2.34877 s, avg_batch_cost: 2.58664 s, avg_samples: 12.5, ips: 4.83253 samples/s, eta: 10:05:07
[2024/07/27 11:52:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:52:22] ppocr INFO: epoch: [173/1500], global_step: 519, lr: 0.001000, loss: 1.920439, loss_shrink_maps: 1.047769, loss_threshold_maps: 0.692206, loss_binary_maps: 0.206283, avg_reader_cost: 2.33910 s, avg_batch_cost: 2.57802 s, avg_samples: 12.5, ips: 4.84868 samples/s, eta: 10:04:28
[2024/07/27 11:52:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:52:30] ppocr INFO: epoch: [174/1500], global_step: 520, lr: 0.001000, loss: 1.920439, loss_shrink_maps: 1.047769, loss_threshold_maps: 0.688171, loss_binary_maps: 0.206283, avg_reader_cost: 0.56882 s, avg_batch_cost: 0.81195 s, avg_samples: 4.8, ips: 5.91172 samples/s, eta: 10:04:11
[2024/07/27 11:52:32] ppocr INFO: epoch: [174/1500], global_step: 522, lr: 0.001000, loss: 1.902882, loss_shrink_maps: 1.041378, loss_threshold_maps: 0.680896, loss_binary_maps: 0.204326, avg_reader_cost: 1.71730 s, avg_batch_cost: 1.86569 s, avg_samples: 7.7, ips: 4.12715 samples/s, eta: 10:03:57
[2024/07/27 11:52:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:52:41] ppocr INFO: epoch: [175/1500], global_step: 525, lr: 0.001000, loss: 1.920439, loss_shrink_maps: 1.041378, loss_threshold_maps: 0.685409, loss_binary_maps: 0.204326, avg_reader_cost: 2.18589 s, avg_batch_cost: 2.60624 s, avg_samples: 12.5, ips: 4.79619 samples/s, eta: 10:03:20
[2024/07/27 11:52:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:52:51] ppocr INFO: epoch: [176/1500], global_step: 528, lr: 0.001000, loss: 1.935327, loss_shrink_maps: 1.049419, loss_threshold_maps: 0.680896, loss_binary_maps: 0.206283, avg_reader_cost: 2.37013 s, avg_batch_cost: 2.60920 s, avg_samples: 12.5, ips: 4.79073 samples/s, eta: 10:02:43
[2024/07/27 11:52:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:53:01] ppocr INFO: epoch: [177/1500], global_step: 530, lr: 0.001000, loss: 1.920439, loss_shrink_maps: 1.041378, loss_threshold_maps: 0.674052, loss_binary_maps: 0.204326, avg_reader_cost: 1.48988 s, avg_batch_cost: 1.66715 s, avg_samples: 9.6, ips: 5.75831 samples/s, eta: 10:02:13
[2024/07/27 11:53:01] ppocr INFO: epoch: [177/1500], global_step: 531, lr: 0.001000, loss: 1.935327, loss_shrink_maps: 1.049419, loss_threshold_maps: 0.675748, loss_binary_maps: 0.206283, avg_reader_cost: 0.87977 s, avg_batch_cost: 0.93548 s, avg_samples: 2.9, ips: 3.10002 samples/s, eta: 10:02:06
[2024/07/27 11:53:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:53:11] ppocr INFO: epoch: [178/1500], global_step: 534, lr: 0.001000, loss: 1.964069, loss_shrink_maps: 1.100806, loss_threshold_maps: 0.674052, loss_binary_maps: 0.216608, avg_reader_cost: 2.29526 s, avg_batch_cost: 2.61057 s, avg_samples: 12.5, ips: 4.78822 samples/s, eta: 10:01:30
[2024/07/27 11:53:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:53:21] ppocr INFO: epoch: [179/1500], global_step: 537, lr: 0.001000, loss: 2.005196, loss_shrink_maps: 1.115405, loss_threshold_maps: 0.671709, loss_binary_maps: 0.219785, avg_reader_cost: 2.25048 s, avg_batch_cost: 2.66621 s, avg_samples: 12.5, ips: 4.68830 samples/s, eta: 10:00:58
[2024/07/27 11:53:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:53:31] ppocr INFO: epoch: [180/1500], global_step: 540, lr: 0.001000, loss: 1.986127, loss_shrink_maps: 1.110781, loss_threshold_maps: 0.663206, loss_binary_maps: 0.218908, avg_reader_cost: 2.16324 s, avg_batch_cost: 2.53435 s, avg_samples: 12.5, ips: 4.93223 samples/s, eta: 10:00:16

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:54,  9.10it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.55it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.64it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.97it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.48it/s]
eval model::   3%|▎         | 13/500 [00:00<00:26, 18.10it/s]
eval model::   3%|▎         | 16/500 [00:00<00:24, 20.01it/s]
eval model::   4%|▍         | 19/500 [00:01<00:24, 19.38it/s]
eval model::   4%|▍         | 22/500 [00:01<00:22, 20.90it/s]
eval model::   5%|▌         | 25/500 [00:01<00:22, 20.67it/s]
eval model::   6%|▌         | 28/500 [00:01<00:21, 21.57it/s]
eval model::   6%|▌         | 31/500 [00:01<00:23, 19.69it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 18.84it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.13it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.52it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 18.14it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.09it/s]
eval model::  10%|▉         | 48/500 [00:02<00:22, 19.80it/s]
eval model::  10%|█         | 51/500 [00:02<00:22, 19.85it/s]
eval model::  11%|█         | 54/500 [00:02<00:21, 20.73it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:20, 21.70it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:22, 19.93it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.83it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 18.01it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 19.89it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 20.97it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 20.90it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:21, 20.06it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 21.06it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:22, 18.82it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.13it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.15it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 19.06it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.46it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 21.01it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.68it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 20.71it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:20, 19.34it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:21, 18.43it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:19, 20.12it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:18, 20.53it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 21.02it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:16, 22.87it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:17, 21.38it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 20.67it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 18.82it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.81it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:19, 18.82it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:19, 18.10it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:19, 18.24it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:19, 18.39it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:19, 18.51it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:20, 16.82it/s]
eval model::  30%|███       | 151/500 [00:07<00:21, 16.54it/s]
eval model::  31%|███       | 154/500 [00:07<00:17, 19.30it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:16, 21.08it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:22, 15.19it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:22, 15.34it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:20, 16.07it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:19, 17.35it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:17, 19.02it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:16, 20.05it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:17, 18.29it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:17, 18.52it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:18, 17.10it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 18.87it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 19.68it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:16, 19.36it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 20.64it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 21.03it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.87it/s]
eval model::  40%|████      | 201/500 [00:10<00:15, 19.76it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 21.97it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:18, 16.27it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:17, 16.29it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:18, 16.05it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.59it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.57it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.79it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 17.21it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 15.19it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:15, 18.11it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:14, 18.35it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 20.72it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:13, 20.01it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 21.92it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 23.27it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 21.23it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:11, 22.87it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:12, 19.70it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 21.35it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.13it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 20.21it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 20.21it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 19.35it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:11, 19.84it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 19.33it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 19.31it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.45it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 20.19it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 21.05it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 20.78it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:17, 12.42it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.16it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 17.10it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:12, 16.67it/s]
eval model::  60%|██████    | 300/500 [00:15<00:12, 16.13it/s]
eval model::  61%|██████    | 303/500 [00:16<00:10, 18.02it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 18.43it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 19.23it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 19.46it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 20.04it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 21.04it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 22.34it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 21.99it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 23.50it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 20.58it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:08, 19.98it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:08, 20.14it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:08, 18.54it/s]
eval model::  68%|██████▊   | 341/500 [00:17<00:10, 15.66it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:08, 17.99it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 19.02it/s]
eval model::  70%|███████   | 350/500 [00:18<00:07, 20.94it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 22.06it/s]
eval model::  71%|███████   | 356/500 [00:18<00:06, 20.92it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:06, 21.85it/s]
eval model::  72%|███████▏  | 362/500 [00:18<00:06, 21.34it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 18.92it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 17.63it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.08it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 19.24it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:06, 20.22it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 21.60it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 20.97it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:05, 21.48it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:08, 13.98it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:06, 15.93it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:06, 17.53it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 19.07it/s]
eval model::  80%|████████  | 400/500 [00:20<00:04, 20.01it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 22.64it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 22.21it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 21.93it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:04, 20.81it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:03, 21.29it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:03, 21.73it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 18.87it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 18.66it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:03, 19.54it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 20.51it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 19.10it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 19.12it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 21.61it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 21.42it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 22.57it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 21.41it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 20.37it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 22.19it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 22.69it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 21.45it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 18.83it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:01, 17.77it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 19.29it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 19.82it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 18.25it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 21.13it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 21.63it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 21.95it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 19.40it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 20.58it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 20.38it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 21.23it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 23.65it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.44it/s]
[2024/07/27 11:53:56] ppocr INFO: cur metric, precision: 0.7022251308900523, recall: 0.516610495907559, hmean: 0.5952843273231623, fps: 44.151565781685974
[2024/07/27 11:53:57] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 11:53:57] ppocr INFO: best metric, hmean: 0.5952843273231623, precision: 0.7022251308900523, recall: 0.516610495907559, fps: 44.151565781685974, best_epoch: 180
[2024/07/27 11:53:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:54:06] ppocr INFO: epoch: [181/1500], global_step: 543, lr: 0.001000, loss: 1.948149, loss_shrink_maps: 1.086574, loss_threshold_maps: 0.661343, loss_binary_maps: 0.213366, avg_reader_cost: 2.33282 s, avg_batch_cost: 2.59843 s, avg_samples: 12.5, ips: 4.81060 samples/s, eta: 9:59:40
[2024/07/27 11:54:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:54:16] ppocr INFO: epoch: [182/1500], global_step: 546, lr: 0.001000, loss: 1.948149, loss_shrink_maps: 1.086574, loss_threshold_maps: 0.655733, loss_binary_maps: 0.213366, avg_reader_cost: 2.27413 s, avg_batch_cost: 2.51779 s, avg_samples: 12.5, ips: 4.96467 samples/s, eta: 9:58:57
[2024/07/27 11:54:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:54:25] ppocr INFO: epoch: [183/1500], global_step: 549, lr: 0.001000, loss: 1.983855, loss_shrink_maps: 1.105288, loss_threshold_maps: 0.663206, loss_binary_maps: 0.217702, avg_reader_cost: 2.28336 s, avg_batch_cost: 2.53067 s, avg_samples: 12.5, ips: 4.93940 samples/s, eta: 9:58:16
[2024/07/27 11:54:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:54:34] ppocr INFO: epoch: [184/1500], global_step: 550, lr: 0.001000, loss: 2.010080, loss_shrink_maps: 1.115799, loss_threshold_maps: 0.666506, loss_binary_maps: 0.219873, avg_reader_cost: 0.59741 s, avg_batch_cost: 0.79668 s, avg_samples: 4.8, ips: 6.02499 samples/s, eta: 9:57:59
[2024/07/27 11:54:35] ppocr INFO: epoch: [184/1500], global_step: 552, lr: 0.001000, loss: 1.965744, loss_shrink_maps: 1.071850, loss_threshold_maps: 0.666506, loss_binary_maps: 0.210271, avg_reader_cost: 1.68595 s, avg_batch_cost: 1.83344 s, avg_samples: 7.7, ips: 4.19975 samples/s, eta: 9:57:42
[2024/07/27 11:54:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:54:45] ppocr INFO: epoch: [185/1500], global_step: 555, lr: 0.001000, loss: 1.935037, loss_shrink_maps: 1.065441, loss_threshold_maps: 0.663206, loss_binary_maps: 0.209320, avg_reader_cost: 2.22372 s, avg_batch_cost: 2.58179 s, avg_samples: 12.5, ips: 4.84161 samples/s, eta: 9:57:04
[2024/07/27 11:54:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:54:55] ppocr INFO: epoch: [186/1500], global_step: 558, lr: 0.001000, loss: 1.891047, loss_shrink_maps: 1.035544, loss_threshold_maps: 0.655750, loss_binary_maps: 0.204160, avg_reader_cost: 2.29362 s, avg_batch_cost: 2.62605 s, avg_samples: 12.5, ips: 4.76000 samples/s, eta: 9:56:30
[2024/07/27 11:54:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:55:04] ppocr INFO: epoch: [187/1500], global_step: 560, lr: 0.001000, loss: 1.889768, loss_shrink_maps: 1.048107, loss_threshold_maps: 0.649360, loss_binary_maps: 0.206208, avg_reader_cost: 1.48658 s, avg_batch_cost: 1.68291 s, avg_samples: 9.6, ips: 5.70441 samples/s, eta: 9:56:03
[2024/07/27 11:55:05] ppocr INFO: epoch: [187/1500], global_step: 561, lr: 0.001000, loss: 1.889768, loss_shrink_maps: 1.048107, loss_threshold_maps: 0.649360, loss_binary_maps: 0.206208, avg_reader_cost: 0.88760 s, avg_batch_cost: 0.94267 s, avg_samples: 2.9, ips: 3.07637 samples/s, eta: 9:55:56
[2024/07/27 11:55:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:55:15] ppocr INFO: epoch: [188/1500], global_step: 564, lr: 0.001000, loss: 1.905583, loss_shrink_maps: 1.047480, loss_threshold_maps: 0.658905, loss_binary_maps: 0.206208, avg_reader_cost: 2.35677 s, avg_batch_cost: 2.59796 s, avg_samples: 12.5, ips: 4.81147 samples/s, eta: 9:55:20
[2024/07/27 11:55:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:55:24] ppocr INFO: epoch: [189/1500], global_step: 567, lr: 0.001000, loss: 1.878333, loss_shrink_maps: 1.026002, loss_threshold_maps: 0.657198, loss_binary_maps: 0.201429, avg_reader_cost: 2.33487 s, avg_batch_cost: 2.56767 s, avg_samples: 12.5, ips: 4.86822 samples/s, eta: 9:54:42
[2024/07/27 11:55:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:55:34] ppocr INFO: epoch: [190/1500], global_step: 570, lr: 0.001000, loss: 1.899988, loss_shrink_maps: 1.038398, loss_threshold_maps: 0.649360, loss_binary_maps: 0.204826, avg_reader_cost: 2.26081 s, avg_batch_cost: 2.64085 s, avg_samples: 12.5, ips: 4.73333 samples/s, eta: 9:54:09
[2024/07/27 11:55:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:55:44] ppocr INFO: epoch: [191/1500], global_step: 573, lr: 0.001000, loss: 1.911422, loss_shrink_maps: 1.049876, loss_threshold_maps: 0.660532, loss_binary_maps: 0.207172, avg_reader_cost: 2.33772 s, avg_batch_cost: 2.59456 s, avg_samples: 12.5, ips: 4.81778 samples/s, eta: 9:53:33
[2024/07/27 11:55:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:55:54] ppocr INFO: epoch: [192/1500], global_step: 576, lr: 0.001000, loss: 1.917041, loss_shrink_maps: 1.049876, loss_threshold_maps: 0.664063, loss_binary_maps: 0.207286, avg_reader_cost: 2.32000 s, avg_batch_cost: 2.56097 s, avg_samples: 12.5, ips: 4.88097 samples/s, eta: 9:52:55
[2024/07/27 11:55:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:56:04] ppocr INFO: epoch: [193/1500], global_step: 579, lr: 0.001000, loss: 1.901996, loss_shrink_maps: 1.047112, loss_threshold_maps: 0.665716, loss_binary_maps: 0.206462, avg_reader_cost: 2.35915 s, avg_batch_cost: 2.59621 s, avg_samples: 12.5, ips: 4.81471 samples/s, eta: 9:52:20
[2024/07/27 11:56:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:56:12] ppocr INFO: epoch: [194/1500], global_step: 580, lr: 0.001000, loss: 1.901996, loss_shrink_maps: 1.047112, loss_threshold_maps: 0.665716, loss_binary_maps: 0.206462, avg_reader_cost: 0.54961 s, avg_batch_cost: 0.74410 s, avg_samples: 4.8, ips: 6.45072 samples/s, eta: 9:52:00
[2024/07/27 11:56:13] ppocr INFO: epoch: [194/1500], global_step: 582, lr: 0.001000, loss: 1.919880, loss_shrink_maps: 1.056194, loss_threshold_maps: 0.667289, loss_binary_maps: 0.207958, avg_reader_cost: 1.58023 s, avg_batch_cost: 1.72764 s, avg_samples: 7.7, ips: 4.45695 samples/s, eta: 9:51:36
[2024/07/27 11:56:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:56:23] ppocr INFO: epoch: [195/1500], global_step: 585, lr: 0.001000, loss: 1.901996, loss_shrink_maps: 1.025515, loss_threshold_maps: 0.665716, loss_binary_maps: 0.202629, avg_reader_cost: 2.26402 s, avg_batch_cost: 2.49980 s, avg_samples: 12.5, ips: 5.00039 samples/s, eta: 9:50:54
[2024/07/27 11:56:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:56:33] ppocr INFO: epoch: [196/1500], global_step: 588, lr: 0.001000, loss: 1.901996, loss_shrink_maps: 1.025515, loss_threshold_maps: 0.668108, loss_binary_maps: 0.202629, avg_reader_cost: 2.14446 s, avg_batch_cost: 2.54888 s, avg_samples: 12.5, ips: 4.90411 samples/s, eta: 9:50:16
[2024/07/27 11:56:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:56:42] ppocr INFO: epoch: [197/1500], global_step: 590, lr: 0.001000, loss: 1.904835, loss_shrink_maps: 1.025515, loss_threshold_maps: 0.669261, loss_binary_maps: 0.202629, avg_reader_cost: 1.35566 s, avg_batch_cost: 1.67744 s, avg_samples: 9.6, ips: 5.72300 samples/s, eta: 9:49:49
[2024/07/27 11:56:43] ppocr INFO: epoch: [197/1500], global_step: 591, lr: 0.001000, loss: 1.912294, loss_shrink_maps: 1.045025, loss_threshold_maps: 0.670419, loss_binary_maps: 0.206150, avg_reader_cost: 0.88486 s, avg_batch_cost: 0.94040 s, avg_samples: 2.9, ips: 3.08379 samples/s, eta: 9:49:42
[2024/07/27 11:56:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:56:51] ppocr INFO: epoch: [198/1500], global_step: 594, lr: 0.001000, loss: 1.904835, loss_shrink_maps: 1.027566, loss_threshold_maps: 0.666589, loss_binary_maps: 0.203396, avg_reader_cost: 2.07514 s, avg_batch_cost: 2.35073 s, avg_samples: 12.5, ips: 5.31751 samples/s, eta: 9:48:51
[2024/07/27 11:56:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:57:01] ppocr INFO: epoch: [199/1500], global_step: 597, lr: 0.001000, loss: 1.904835, loss_shrink_maps: 1.019337, loss_threshold_maps: 0.661857, loss_binary_maps: 0.201465, avg_reader_cost: 2.13710 s, avg_batch_cost: 2.49780 s, avg_samples: 12.5, ips: 5.00440 samples/s, eta: 9:48:10
[2024/07/27 11:57:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:57:11] ppocr INFO: epoch: [200/1500], global_step: 600, lr: 0.001000, loss: 1.916384, loss_shrink_maps: 1.028391, loss_threshold_maps: 0.670139, loss_binary_maps: 0.203396, avg_reader_cost: 2.18637 s, avg_batch_cost: 2.54160 s, avg_samples: 12.5, ips: 4.91816 samples/s, eta: 9:47:31

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:07,  7.43it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.26it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.17it/s]
eval model::   2%|▏         | 9/500 [00:00<00:23, 20.57it/s]
eval model::   2%|▏         | 12/500 [00:00<00:25, 19.40it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 19.43it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.78it/s]
eval model::   4%|▍         | 19/500 [00:01<00:25, 18.55it/s]
eval model::   4%|▍         | 22/500 [00:01<00:23, 20.29it/s]
eval model::   5%|▌         | 25/500 [00:01<00:24, 19.77it/s]
eval model::   6%|▌         | 28/500 [00:01<00:22, 20.79it/s]
eval model::   6%|▌         | 31/500 [00:01<00:23, 19.92it/s]
eval model::   7%|▋         | 34/500 [00:01<00:23, 19.51it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.95it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.76it/s]
eval model::   8%|▊         | 42/500 [00:02<00:24, 18.38it/s]
eval model::   9%|▉         | 46/500 [00:02<00:23, 19.37it/s]
eval model::  10%|█         | 50/500 [00:02<00:21, 20.99it/s]
eval model::  11%|█         | 53/500 [00:02<00:19, 22.38it/s]
eval model::  11%|█         | 56/500 [00:02<00:22, 19.71it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:23, 18.58it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:25, 16.92it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.63it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.79it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.48it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 20.92it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 20.40it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:21, 19.28it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:20, 20.57it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:21, 19.16it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:21, 18.86it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:22, 18.57it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.79it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 19.11it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.28it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.67it/s]
eval model::  20%|██        | 101/500 [00:05<00:20, 19.32it/s]
eval model::  21%|██        | 104/500 [00:05<00:19, 20.16it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.28it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 20.68it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:17, 21.66it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:18, 21.23it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 22.10it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:17, 21.59it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:18, 19.82it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 20.56it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:18, 19.77it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:17, 20.78it/s]
eval model::  27%|██▋       | 137/500 [00:06<00:18, 19.68it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 19.92it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:16, 21.31it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 20.34it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:19, 18.47it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 17.58it/s]
eval model::  31%|███       | 154/500 [00:07<00:17, 19.37it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:16, 20.88it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:20, 16.55it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:18, 17.83it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.13it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:15, 20.72it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:16, 20.11it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:16, 19.50it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:15, 20.88it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:16, 19.67it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:14, 21.10it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:14, 21.52it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:13, 22.25it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:13, 22.53it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:14, 21.18it/s]
eval model::  40%|███▉      | 199/500 [00:09<00:13, 22.25it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 22.63it/s]
eval model::  41%|████      | 205/500 [00:10<00:21, 13.49it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:19, 15.17it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:18, 15.56it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:17, 16.17it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.64it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.20it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 18.16it/s]
eval model::  45%|████▍     | 223/500 [00:11<00:15, 17.97it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:14, 19.17it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:12, 22.16it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:11, 22.78it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:11, 22.59it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:10, 23.93it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 21.06it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:11, 21.92it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 21.90it/s]
eval model::  50%|█████     | 251/500 [00:12<00:11, 21.66it/s]
eval model::  51%|█████     | 254/500 [00:12<00:10, 23.30it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 23.51it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 23.86it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 21.96it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:11, 20.14it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:10, 21.58it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:10, 21.12it/s]
eval model::  55%|█████▌    | 275/500 [00:13<00:12, 18.40it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.83it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.69it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:09, 21.91it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:14, 14.60it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:11, 18.51it/s]
eval model::  59%|█████▉    | 294/500 [00:14<00:10, 19.54it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:11, 17.30it/s]
eval model::  60%|██████    | 300/500 [00:15<00:10, 18.90it/s]
eval model::  61%|██████    | 303/500 [00:15<00:09, 20.66it/s]
eval model::  61%|██████    | 306/500 [00:15<00:09, 20.52it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:08, 21.87it/s]
eval model::  62%|██████▏   | 312/500 [00:15<00:08, 21.89it/s]
eval model::  63%|██████▎   | 315/500 [00:15<00:08, 22.36it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:07, 24.00it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:07, 23.47it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:07, 22.09it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:07, 23.37it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:08, 20.53it/s]
eval model::  67%|██████▋   | 333/500 [00:16<00:07, 22.37it/s]
eval model::  67%|██████▋   | 336/500 [00:16<00:08, 20.00it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:08, 20.12it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:08, 18.38it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:07, 20.41it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:07, 21.48it/s]
eval model::  70%|███████   | 351/500 [00:17<00:07, 21.26it/s]
eval model::  71%|███████   | 354/500 [00:17<00:06, 22.48it/s]
eval model::  71%|███████▏  | 357/500 [00:17<00:07, 20.31it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:06, 21.23it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:06, 21.08it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:06, 19.20it/s]
eval model::  74%|███████▎  | 368/500 [00:18<00:06, 19.28it/s]
eval model::  74%|███████▍  | 371/500 [00:18<00:06, 20.34it/s]
eval model::  75%|███████▍  | 374/500 [00:18<00:05, 21.50it/s]
eval model::  75%|███████▌  | 377/500 [00:18<00:05, 21.83it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:06, 18.99it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:05, 20.44it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:08, 13.43it/s]
eval model::  78%|███████▊  | 389/500 [00:19<00:07, 15.72it/s]
eval model::  78%|███████▊  | 392/500 [00:19<00:06, 17.15it/s]
eval model::  79%|███████▉  | 395/500 [00:19<00:05, 18.60it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 21.39it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 21.81it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 22.17it/s]
eval model::  82%|████████▏ | 408/500 [00:20<00:03, 23.04it/s]
eval model::  82%|████████▏ | 411/500 [00:20<00:03, 22.87it/s]
eval model::  83%|████████▎ | 414/500 [00:20<00:04, 20.43it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 17.89it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 18.20it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:04, 16.21it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 17.31it/s]
eval model::  85%|████████▌ | 427/500 [00:21<00:03, 20.00it/s]
eval model::  86%|████████▌ | 431/500 [00:21<00:03, 21.68it/s]
eval model::  87%|████████▋ | 435/500 [00:21<00:02, 24.84it/s]
eval model::  88%|████████▊ | 438/500 [00:21<00:02, 25.09it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 23.22it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 22.94it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 21.81it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 20.33it/s]
eval model::  91%|█████████ | 453/500 [00:22<00:02, 21.22it/s]
eval model::  91%|█████████ | 456/500 [00:22<00:02, 21.51it/s]
eval model::  92%|█████████▏| 459/500 [00:22<00:01, 21.60it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:02, 18.92it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:01, 18.99it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 19.86it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 20.80it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 18.20it/s]
eval model::  95%|█████████▌| 477/500 [00:23<00:01, 20.06it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 20.79it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 20.47it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 18.92it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 21.14it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 19.70it/s]
eval model::  99%|█████████▉| 495/500 [00:24<00:00, 20.34it/s]
eval model:: 100%|█████████▉| 498/500 [00:24<00:00, 20.25it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.97it/s]
[2024/07/27 11:57:36] ppocr INFO: cur metric, precision: 0.7149825783972126, recall: 0.4939817043813192, hmean: 0.5842824601366743, fps: 45.705798172114164
[2024/07/27 11:57:36] ppocr INFO: best metric, hmean: 0.5952843273231623, precision: 0.7022251308900523, recall: 0.516610495907559, fps: 44.151565781685974, best_epoch: 180
[2024/07/27 11:57:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:57:45] ppocr INFO: epoch: [201/1500], global_step: 603, lr: 0.001000, loss: 1.916384, loss_shrink_maps: 1.028391, loss_threshold_maps: 0.667546, loss_binary_maps: 0.203396, avg_reader_cost: 2.30425 s, avg_batch_cost: 2.72515 s, avg_samples: 12.5, ips: 4.58690 samples/s, eta: 9:47:05
[2024/07/27 11:57:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:57:56] ppocr INFO: epoch: [202/1500], global_step: 606, lr: 0.001000, loss: 1.943778, loss_shrink_maps: 1.057755, loss_threshold_maps: 0.661857, loss_binary_maps: 0.206831, avg_reader_cost: 2.23685 s, avg_batch_cost: 2.66917 s, avg_samples: 12.5, ips: 4.68311 samples/s, eta: 9:46:35
[2024/07/27 11:57:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:58:06] ppocr INFO: epoch: [203/1500], global_step: 609, lr: 0.001000, loss: 1.907046, loss_shrink_maps: 1.027557, loss_threshold_maps: 0.652997, loss_binary_maps: 0.202090, avg_reader_cost: 2.27344 s, avg_batch_cost: 2.65736 s, avg_samples: 12.5, ips: 4.70392 samples/s, eta: 9:46:05
[2024/07/27 11:58:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:58:14] ppocr INFO: epoch: [204/1500], global_step: 610, lr: 0.001000, loss: 1.907046, loss_shrink_maps: 1.027557, loss_threshold_maps: 0.654647, loss_binary_maps: 0.202090, avg_reader_cost: 0.68576 s, avg_batch_cost: 0.77856 s, avg_samples: 4.8, ips: 6.16524 samples/s, eta: 9:45:48
[2024/07/27 11:58:15] ppocr INFO: epoch: [204/1500], global_step: 612, lr: 0.001000, loss: 1.885425, loss_shrink_maps: 1.019337, loss_threshold_maps: 0.654647, loss_binary_maps: 0.201465, avg_reader_cost: 1.64881 s, avg_batch_cost: 1.79543 s, avg_samples: 7.7, ips: 4.28866 samples/s, eta: 9:45:29
[2024/07/27 11:58:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:58:25] ppocr INFO: epoch: [205/1500], global_step: 615, lr: 0.001000, loss: 1.885425, loss_shrink_maps: 1.017890, loss_threshold_maps: 0.658857, loss_binary_maps: 0.200657, avg_reader_cost: 2.20172 s, avg_batch_cost: 2.56082 s, avg_samples: 12.5, ips: 4.88124 samples/s, eta: 9:44:52
[2024/07/27 11:58:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:58:35] ppocr INFO: epoch: [206/1500], global_step: 618, lr: 0.001000, loss: 1.861118, loss_shrink_maps: 1.010138, loss_threshold_maps: 0.654647, loss_binary_maps: 0.199198, avg_reader_cost: 2.30299 s, avg_batch_cost: 2.54322 s, avg_samples: 12.5, ips: 4.91503 samples/s, eta: 9:44:15
[2024/07/27 11:58:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:58:44] ppocr INFO: epoch: [207/1500], global_step: 620, lr: 0.001000, loss: 1.861118, loss_shrink_maps: 1.010138, loss_threshold_maps: 0.654647, loss_binary_maps: 0.199198, avg_reader_cost: 1.43868 s, avg_batch_cost: 1.62108 s, avg_samples: 9.6, ips: 5.92199 samples/s, eta: 9:43:45
[2024/07/27 11:58:44] ppocr INFO: epoch: [207/1500], global_step: 621, lr: 0.001000, loss: 1.872712, loss_shrink_maps: 1.022141, loss_threshold_maps: 0.654647, loss_binary_maps: 0.201283, avg_reader_cost: 0.85657 s, avg_batch_cost: 0.91216 s, avg_samples: 2.9, ips: 3.17926 samples/s, eta: 9:43:37
[2024/07/27 11:58:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:58:54] ppocr INFO: epoch: [208/1500], global_step: 624, lr: 0.001000, loss: 1.857388, loss_shrink_maps: 1.010138, loss_threshold_maps: 0.654647, loss_binary_maps: 0.199198, avg_reader_cost: 2.39850 s, avg_batch_cost: 2.65130 s, avg_samples: 12.5, ips: 4.71466 samples/s, eta: 9:43:06
[2024/07/27 11:58:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:59:04] ppocr INFO: epoch: [209/1500], global_step: 627, lr: 0.001000, loss: 1.864810, loss_shrink_maps: 1.006270, loss_threshold_maps: 0.660900, loss_binary_maps: 0.197802, avg_reader_cost: 2.32080 s, avg_batch_cost: 2.55695 s, avg_samples: 12.5, ips: 4.88864 samples/s, eta: 9:42:30
[2024/07/27 11:59:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:59:14] ppocr INFO: epoch: [210/1500], global_step: 630, lr: 0.001000, loss: 1.848346, loss_shrink_maps: 0.996547, loss_threshold_maps: 0.655221, loss_binary_maps: 0.195381, avg_reader_cost: 2.19094 s, avg_batch_cost: 2.55789 s, avg_samples: 12.5, ips: 4.88683 samples/s, eta: 9:41:53
[2024/07/27 11:59:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:59:23] ppocr INFO: epoch: [211/1500], global_step: 633, lr: 0.001000, loss: 1.825054, loss_shrink_maps: 0.977095, loss_threshold_maps: 0.639175, loss_binary_maps: 0.192650, avg_reader_cost: 2.34831 s, avg_batch_cost: 2.59870 s, avg_samples: 12.5, ips: 4.81009 samples/s, eta: 9:41:20
[2024/07/27 11:59:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:59:33] ppocr INFO: epoch: [212/1500], global_step: 636, lr: 0.001000, loss: 1.843089, loss_shrink_maps: 0.989084, loss_threshold_maps: 0.642220, loss_binary_maps: 0.194542, avg_reader_cost: 2.25026 s, avg_batch_cost: 2.49065 s, avg_samples: 12.5, ips: 5.01878 samples/s, eta: 9:40:40
[2024/07/27 11:59:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:59:43] ppocr INFO: epoch: [213/1500], global_step: 639, lr: 0.001000, loss: 1.821631, loss_shrink_maps: 0.973642, loss_threshold_maps: 0.633607, loss_binary_maps: 0.191060, avg_reader_cost: 2.33269 s, avg_batch_cost: 2.58409 s, avg_samples: 12.5, ips: 4.83729 samples/s, eta: 9:40:05
[2024/07/27 11:59:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 11:59:51] ppocr INFO: epoch: [214/1500], global_step: 640, lr: 0.001000, loss: 1.833708, loss_shrink_maps: 0.985632, loss_threshold_maps: 0.633607, loss_binary_maps: 0.193275, avg_reader_cost: 0.67934 s, avg_batch_cost: 0.79126 s, avg_samples: 4.8, ips: 6.06629 samples/s, eta: 9:39:50
[2024/07/27 11:59:53] ppocr INFO: epoch: [214/1500], global_step: 642, lr: 0.001000, loss: 1.826481, loss_shrink_maps: 0.976424, loss_threshold_maps: 0.640763, loss_binary_maps: 0.192253, avg_reader_cost: 1.67536 s, avg_batch_cost: 1.82309 s, avg_samples: 7.7, ips: 4.22361 samples/s, eta: 9:39:33
[2024/07/27 11:59:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:00:02] ppocr INFO: epoch: [215/1500], global_step: 645, lr: 0.001000, loss: 1.851744, loss_shrink_maps: 1.008244, loss_threshold_maps: 0.655192, loss_binary_maps: 0.198080, avg_reader_cost: 2.24440 s, avg_batch_cost: 2.62805 s, avg_samples: 12.5, ips: 4.75638 samples/s, eta: 9:39:01
[2024/07/27 12:00:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:00:12] ppocr INFO: epoch: [216/1500], global_step: 648, lr: 0.001000, loss: 1.835624, loss_shrink_maps: 0.999037, loss_threshold_maps: 0.655192, loss_binary_maps: 0.197058, avg_reader_cost: 2.15426 s, avg_batch_cost: 2.50121 s, avg_samples: 12.5, ips: 4.99758 samples/s, eta: 9:38:22
[2024/07/27 12:00:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:00:21] ppocr INFO: epoch: [217/1500], global_step: 650, lr: 0.001000, loss: 1.823546, loss_shrink_maps: 0.976424, loss_threshold_maps: 0.660011, loss_binary_maps: 0.191930, avg_reader_cost: 1.50659 s, avg_batch_cost: 1.70916 s, avg_samples: 9.6, ips: 5.61678 samples/s, eta: 9:37:59
[2024/07/27 12:00:22] ppocr INFO: epoch: [217/1500], global_step: 651, lr: 0.001000, loss: 1.823546, loss_shrink_maps: 0.976424, loss_threshold_maps: 0.656915, loss_binary_maps: 0.191930, avg_reader_cost: 0.90072 s, avg_batch_cost: 0.95646 s, avg_samples: 2.9, ips: 3.03200 samples/s, eta: 9:37:53
[2024/07/27 12:00:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:00:32] ppocr INFO: epoch: [218/1500], global_step: 654, lr: 0.001000, loss: 1.848876, loss_shrink_maps: 1.007937, loss_threshold_maps: 0.662795, loss_binary_maps: 0.198626, avg_reader_cost: 2.24150 s, avg_batch_cost: 2.62797 s, avg_samples: 12.5, ips: 4.75652 samples/s, eta: 9:37:21
[2024/07/27 12:00:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:00:42] ppocr INFO: epoch: [219/1500], global_step: 657, lr: 0.001000, loss: 1.855414, loss_shrink_maps: 1.018047, loss_threshold_maps: 0.660011, loss_binary_maps: 0.200293, avg_reader_cost: 2.34880 s, avg_batch_cost: 2.59701 s, avg_samples: 12.5, ips: 4.81323 samples/s, eta: 9:36:48
[2024/07/27 12:00:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:00:51] ppocr INFO: epoch: [220/1500], global_step: 660, lr: 0.001000, loss: 1.922415, loss_shrink_maps: 1.036285, loss_threshold_maps: 0.668731, loss_binary_maps: 0.204062, avg_reader_cost: 2.23117 s, avg_batch_cost: 2.55338 s, avg_samples: 12.5, ips: 4.89547 samples/s, eta: 9:36:13

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:56,  8.88it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 16.68it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.09it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.74it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.77it/s]
eval model::   3%|▎         | 14/500 [00:00<00:24, 19.66it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.13it/s]
eval model::   4%|▍         | 19/500 [00:01<00:26, 18.27it/s]
eval model::   4%|▍         | 22/500 [00:01<00:23, 20.42it/s]
eval model::   5%|▌         | 25/500 [00:01<00:24, 19.46it/s]
eval model::   6%|▌         | 28/500 [00:01<00:23, 20.32it/s]
eval model::   6%|▌         | 31/500 [00:01<00:24, 19.54it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 19.32it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.76it/s]
eval model::   8%|▊         | 40/500 [00:02<00:27, 16.82it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.71it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 18.15it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 18.04it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 19.40it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 20.45it/s]
eval model::  11%|█         | 56/500 [00:02<00:22, 20.06it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:21, 20.48it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:25, 17.26it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:25, 17.13it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 19.49it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 20.88it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 19.88it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.72it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 22.07it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.59it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 21.65it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:19, 20.82it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:21, 19.03it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.44it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:22, 18.41it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:21, 18.56it/s]
eval model::  20%|██        | 100/500 [00:05<00:21, 18.80it/s]
eval model::  21%|██        | 103/500 [00:05<00:20, 19.73it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.08it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.53it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:17, 22.58it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:16, 23.36it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:15, 24.67it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 20.88it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 20.77it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:17, 20.84it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 18.99it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:18, 20.38it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:17, 20.68it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.75it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.29it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.19it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 19.03it/s]
eval model::  30%|███       | 151/500 [00:07<00:18, 18.94it/s]
eval model::  31%|███       | 154/500 [00:07<00:17, 20.32it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:17, 19.82it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:20, 16.34it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.63it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.10it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 19.76it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 18.82it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:18, 17.40it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:16, 19.06it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:16, 19.95it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:14, 21.20it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 22.35it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.47it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 20.74it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:13, 22.49it/s]
eval model::  40%|███▉      | 199/500 [00:09<00:12, 23.85it/s]
eval model::  40%|████      | 202/500 [00:10<00:12, 23.99it/s]
eval model::  41%|████      | 205/500 [00:10<00:17, 17.16it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:17, 17.18it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:17, 16.25it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:16, 17.58it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:17, 16.22it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:17, 16.22it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:14, 18.76it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:15, 17.42it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:13, 19.61it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:11, 23.01it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:11, 23.45it/s]
eval model::  47%|████▋     | 236/500 [00:11<00:12, 21.87it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:12, 20.91it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 20.13it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:12, 21.12it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 21.96it/s]
eval model::  50%|█████     | 251/500 [00:12<00:12, 20.66it/s]
eval model::  51%|█████     | 254/500 [00:12<00:11, 22.36it/s]
eval model::  51%|█████▏    | 257/500 [00:12<00:11, 21.87it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:11, 21.71it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 21.67it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:11, 21.23it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:11, 19.66it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:10, 21.34it/s]
eval model::  55%|█████▌    | 275/500 [00:13<00:11, 20.09it/s]
eval model::  56%|█████▌    | 278/500 [00:13<00:10, 21.55it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:09, 22.15it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 21.43it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:14, 14.50it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:11, 17.44it/s]
eval model::  59%|█████▉    | 294/500 [00:14<00:11, 18.33it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:11, 17.21it/s]
eval model::  60%|██████    | 300/500 [00:15<00:11, 17.50it/s]
eval model::  60%|██████    | 302/500 [00:15<00:11, 17.89it/s]
eval model::  61%|██████    | 305/500 [00:15<00:09, 19.63it/s]
eval model::  62%|██████▏   | 308/500 [00:15<00:09, 21.12it/s]
eval model::  62%|██████▏   | 311/500 [00:15<00:08, 21.26it/s]
eval model::  63%|██████▎   | 314/500 [00:15<00:08, 21.78it/s]
eval model::  63%|██████▎   | 317/500 [00:15<00:08, 22.87it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:07, 23.36it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:08, 20.80it/s]
eval model::  65%|██████▌   | 326/500 [00:16<00:08, 19.98it/s]
eval model::  66%|██████▌   | 329/500 [00:16<00:08, 19.04it/s]
eval model::  66%|██████▋   | 332/500 [00:16<00:08, 20.00it/s]
eval model::  67%|██████▋   | 335/500 [00:16<00:07, 21.70it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:07, 21.51it/s]
eval model::  68%|██████▊   | 341/500 [00:17<00:09, 16.53it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:09, 16.87it/s]
eval model::  69%|██████▉   | 347/500 [00:17<00:07, 19.93it/s]
eval model::  70%|███████   | 350/500 [00:17<00:07, 21.18it/s]
eval model::  71%|███████   | 353/500 [00:17<00:06, 22.12it/s]
eval model::  71%|███████   | 356/500 [00:17<00:06, 20.88it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:06, 21.19it/s]
eval model::  72%|███████▏  | 362/500 [00:18<00:06, 20.46it/s]
eval model::  73%|███████▎  | 365/500 [00:18<00:06, 19.62it/s]
eval model::  74%|███████▎  | 368/500 [00:18<00:06, 20.86it/s]
eval model::  74%|███████▍  | 371/500 [00:18<00:06, 20.41it/s]
eval model::  75%|███████▍  | 374/500 [00:18<00:05, 21.25it/s]
eval model::  75%|███████▌  | 377/500 [00:18<00:05, 21.93it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:05, 20.38it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:05, 21.79it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:08, 13.53it/s]
eval model::  78%|███████▊  | 390/500 [00:19<00:06, 16.74it/s]
eval model::  79%|███████▊  | 393/500 [00:19<00:05, 18.46it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 19.71it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 21.05it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 20.55it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 21.76it/s]
eval model::  82%|████████▏ | 408/500 [00:20<00:04, 21.30it/s]
eval model::  82%|████████▏ | 411/500 [00:20<00:04, 21.04it/s]
eval model::  83%|████████▎ | 414/500 [00:20<00:03, 21.66it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 20.05it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:05, 15.83it/s]
eval model::  85%|████████▍ | 423/500 [00:21<00:04, 17.39it/s]
eval model::  85%|████████▌ | 425/500 [00:21<00:04, 17.56it/s]
eval model::  86%|████████▌ | 428/500 [00:21<00:03, 19.48it/s]
eval model::  86%|████████▌ | 431/500 [00:21<00:03, 20.36it/s]
eval model::  87%|████████▋ | 434/500 [00:21<00:02, 22.55it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 25.74it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 25.01it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 24.24it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 22.52it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 20.70it/s]
eval model::  91%|█████████ | 453/500 [00:22<00:02, 22.20it/s]
eval model::  91%|█████████ | 456/500 [00:22<00:02, 21.80it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:01, 21.01it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:01, 20.75it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:01, 18.35it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 19.71it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 20.85it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 20.65it/s]
eval model::  95%|█████████▌| 477/500 [00:23<00:01, 21.65it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 21.70it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 20.95it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 19.91it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 19.58it/s]
eval model::  98%|█████████▊| 491/500 [00:24<00:00, 19.04it/s]
eval model::  99%|█████████▉| 494/500 [00:24<00:00, 20.11it/s]
eval model::  99%|█████████▉| 497/500 [00:24<00:00, 21.41it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 22.92it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 20.00it/s]
[2024/07/27 12:01:16] ppocr INFO: cur metric, precision: 0.773972602739726, recall: 0.5440539239287434, hmean: 0.6389595702572801, fps: 43.996993903511786
[2024/07/27 12:01:16] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 12:01:16] ppocr INFO: best metric, hmean: 0.6389595702572801, precision: 0.773972602739726, recall: 0.5440539239287434, fps: 43.996993903511786, best_epoch: 220
[2024/07/27 12:01:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:01:26] ppocr INFO: epoch: [221/1500], global_step: 663, lr: 0.001000, loss: 1.943085, loss_shrink_maps: 1.048398, loss_threshold_maps: 0.676565, loss_binary_maps: 0.205983, avg_reader_cost: 2.32059 s, avg_batch_cost: 2.66357 s, avg_samples: 12.5, ips: 4.69295 samples/s, eta: 9:35:43
[2024/07/27 12:01:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:01:36] ppocr INFO: epoch: [222/1500], global_step: 666, lr: 0.001000, loss: 1.961616, loss_shrink_maps: 1.060085, loss_threshold_maps: 0.667851, loss_binary_maps: 0.206872, avg_reader_cost: 2.21574 s, avg_batch_cost: 2.55257 s, avg_samples: 12.5, ips: 4.89703 samples/s, eta: 9:35:08
[2024/07/27 12:01:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:01:45] ppocr INFO: epoch: [223/1500], global_step: 669, lr: 0.001000, loss: 1.961616, loss_shrink_maps: 1.060085, loss_threshold_maps: 0.667851, loss_binary_maps: 0.207116, avg_reader_cost: 2.33302 s, avg_batch_cost: 2.58174 s, avg_samples: 12.5, ips: 4.84169 samples/s, eta: 9:34:34
[2024/07/27 12:01:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:01:54] ppocr INFO: epoch: [224/1500], global_step: 670, lr: 0.001000, loss: 1.961616, loss_shrink_maps: 1.060085, loss_threshold_maps: 0.667851, loss_binary_maps: 0.207116, avg_reader_cost: 0.67238 s, avg_batch_cost: 0.78505 s, avg_samples: 4.8, ips: 6.11423 samples/s, eta: 9:34:19
[2024/07/27 12:01:55] ppocr INFO: epoch: [224/1500], global_step: 672, lr: 0.001000, loss: 1.943085, loss_shrink_maps: 1.060085, loss_threshold_maps: 0.661134, loss_binary_maps: 0.207116, avg_reader_cost: 1.66181 s, avg_batch_cost: 1.80873 s, avg_samples: 7.7, ips: 4.25714 samples/s, eta: 9:34:01
[2024/07/27 12:01:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:02:05] ppocr INFO: epoch: [225/1500], global_step: 675, lr: 0.001000, loss: 1.941370, loss_shrink_maps: 1.060085, loss_threshold_maps: 0.662683, loss_binary_maps: 0.207116, avg_reader_cost: 2.35634 s, avg_batch_cost: 2.59214 s, avg_samples: 12.5, ips: 4.82227 samples/s, eta: 9:33:28
[2024/07/27 12:02:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:02:15] ppocr INFO: epoch: [226/1500], global_step: 678, lr: 0.001000, loss: 1.941370, loss_shrink_maps: 1.060085, loss_threshold_maps: 0.670456, loss_binary_maps: 0.207116, avg_reader_cost: 2.17683 s, avg_batch_cost: 2.55100 s, avg_samples: 12.5, ips: 4.90005 samples/s, eta: 9:32:53
[2024/07/27 12:02:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:02:23] ppocr INFO: epoch: [227/1500], global_step: 680, lr: 0.001000, loss: 1.961616, loss_shrink_maps: 1.086130, loss_threshold_maps: 0.667237, loss_binary_maps: 0.214765, avg_reader_cost: 1.32345 s, avg_batch_cost: 1.57193 s, avg_samples: 9.6, ips: 6.10714 samples/s, eta: 9:32:22
[2024/07/27 12:02:24] ppocr INFO: epoch: [227/1500], global_step: 681, lr: 0.001000, loss: 1.959901, loss_shrink_maps: 1.086130, loss_threshold_maps: 0.664678, loss_binary_maps: 0.214765, avg_reader_cost: 0.83215 s, avg_batch_cost: 0.88760 s, avg_samples: 2.9, ips: 3.26725 samples/s, eta: 9:32:12
[2024/07/27 12:02:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:02:34] ppocr INFO: epoch: [228/1500], global_step: 684, lr: 0.001000, loss: 1.959901, loss_shrink_maps: 1.086130, loss_threshold_maps: 0.667237, loss_binary_maps: 0.214765, avg_reader_cost: 2.36116 s, avg_batch_cost: 2.59986 s, avg_samples: 12.5, ips: 4.80796 samples/s, eta: 9:31:40
[2024/07/27 12:02:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:02:44] ppocr INFO: epoch: [229/1500], global_step: 687, lr: 0.001000, loss: 1.926779, loss_shrink_maps: 1.059666, loss_threshold_maps: 0.667237, loss_binary_maps: 0.209152, avg_reader_cost: 2.20691 s, avg_batch_cost: 2.57623 s, avg_samples: 12.5, ips: 4.85205 samples/s, eta: 9:31:06
[2024/07/27 12:02:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:02:54] ppocr INFO: epoch: [230/1500], global_step: 690, lr: 0.001000, loss: 1.944013, loss_shrink_maps: 1.041716, loss_threshold_maps: 0.669544, loss_binary_maps: 0.205767, avg_reader_cost: 2.27510 s, avg_batch_cost: 2.66283 s, avg_samples: 12.5, ips: 4.69425 samples/s, eta: 9:30:38
[2024/07/27 12:02:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:03:03] ppocr INFO: epoch: [231/1500], global_step: 693, lr: 0.001000, loss: 1.921157, loss_shrink_maps: 1.041788, loss_threshold_maps: 0.679168, loss_binary_maps: 0.206593, avg_reader_cost: 2.25644 s, avg_batch_cost: 2.64057 s, avg_samples: 12.5, ips: 4.73382 samples/s, eta: 9:30:08
[2024/07/27 12:03:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:03:13] ppocr INFO: epoch: [232/1500], global_step: 696, lr: 0.001000, loss: 1.921157, loss_shrink_maps: 1.041788, loss_threshold_maps: 0.679168, loss_binary_maps: 0.206593, avg_reader_cost: 2.31393 s, avg_batch_cost: 2.63276 s, avg_samples: 12.5, ips: 4.74787 samples/s, eta: 9:29:37
[2024/07/27 12:03:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:03:23] ppocr INFO: epoch: [233/1500], global_step: 699, lr: 0.001000, loss: 1.882493, loss_shrink_maps: 1.011518, loss_threshold_maps: 0.662172, loss_binary_maps: 0.199459, avg_reader_cost: 2.20288 s, avg_batch_cost: 2.51884 s, avg_samples: 12.5, ips: 4.96259 samples/s, eta: 9:29:01
[2024/07/27 12:03:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:03:32] ppocr INFO: epoch: [234/1500], global_step: 700, lr: 0.001000, loss: 1.882493, loss_shrink_maps: 1.011518, loss_threshold_maps: 0.660060, loss_binary_maps: 0.199459, avg_reader_cost: 0.70687 s, avg_batch_cost: 0.79980 s, avg_samples: 4.8, ips: 6.00149 samples/s, eta: 9:28:46
[2024/07/27 12:03:33] ppocr INFO: epoch: [234/1500], global_step: 702, lr: 0.001000, loss: 1.884361, loss_shrink_maps: 1.015422, loss_threshold_maps: 0.659994, loss_binary_maps: 0.199582, avg_reader_cost: 1.69119 s, avg_batch_cost: 1.83829 s, avg_samples: 7.7, ips: 4.18868 samples/s, eta: 9:28:31
[2024/07/27 12:03:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:03:43] ppocr INFO: epoch: [235/1500], global_step: 705, lr: 0.001000, loss: 1.849108, loss_shrink_maps: 0.997606, loss_threshold_maps: 0.649739, loss_binary_maps: 0.196628, avg_reader_cost: 2.26110 s, avg_batch_cost: 2.62233 s, avg_samples: 12.5, ips: 4.76675 samples/s, eta: 9:28:00
[2024/07/27 12:03:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:03:53] ppocr INFO: epoch: [236/1500], global_step: 708, lr: 0.001000, loss: 1.880954, loss_shrink_maps: 1.015422, loss_threshold_maps: 0.666453, loss_binary_maps: 0.199582, avg_reader_cost: 2.25461 s, avg_batch_cost: 2.62530 s, avg_samples: 12.5, ips: 4.76136 samples/s, eta: 9:27:29
[2024/07/27 12:03:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:04:02] ppocr INFO: epoch: [237/1500], global_step: 710, lr: 0.001000, loss: 1.866230, loss_shrink_maps: 1.000652, loss_threshold_maps: 0.656466, loss_binary_maps: 0.196742, avg_reader_cost: 1.37272 s, avg_batch_cost: 1.72080 s, avg_samples: 9.6, ips: 5.57881 samples/s, eta: 9:27:07
[2024/07/27 12:04:03] ppocr INFO: epoch: [237/1500], global_step: 711, lr: 0.001000, loss: 1.868099, loss_shrink_maps: 1.000652, loss_threshold_maps: 0.662910, loss_binary_maps: 0.196742, avg_reader_cost: 0.90650 s, avg_batch_cost: 0.96204 s, avg_samples: 2.9, ips: 3.01444 samples/s, eta: 9:27:02
[2024/07/27 12:04:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:04:13] ppocr INFO: epoch: [238/1500], global_step: 714, lr: 0.001000, loss: 1.820244, loss_shrink_maps: 0.982837, loss_threshold_maps: 0.656466, loss_binary_maps: 0.194052, avg_reader_cost: 2.20654 s, avg_batch_cost: 2.57215 s, avg_samples: 12.5, ips: 4.85975 samples/s, eta: 9:26:28
[2024/07/27 12:04:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:04:22] ppocr INFO: epoch: [239/1500], global_step: 717, lr: 0.001000, loss: 1.837367, loss_shrink_maps: 0.987077, loss_threshold_maps: 0.662910, loss_binary_maps: 0.194460, avg_reader_cost: 2.22230 s, avg_batch_cost: 2.59524 s, avg_samples: 12.5, ips: 4.81651 samples/s, eta: 9:25:56
[2024/07/27 12:04:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:04:32] ppocr INFO: epoch: [240/1500], global_step: 720, lr: 0.001000, loss: 1.818947, loss_shrink_maps: 0.987077, loss_threshold_maps: 0.641976, loss_binary_maps: 0.194460, avg_reader_cost: 2.24003 s, avg_batch_cost: 2.61803 s, avg_samples: 12.5, ips: 4.77458 samples/s, eta: 9:25:25

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:33, 14.92it/s]
eval model::   1%|          | 5/500 [00:00<00:23, 21.25it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.41it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.85it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 19.40it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.29it/s]
eval model::   4%|▍         | 20/500 [00:01<00:27, 17.25it/s]
eval model::   5%|▍         | 23/500 [00:01<00:25, 18.98it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.11it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.05it/s]
eval model::   6%|▋         | 32/500 [00:01<00:22, 20.76it/s]
eval model::   7%|▋         | 35/500 [00:01<00:22, 20.50it/s]
eval model::   8%|▊         | 38/500 [00:01<00:22, 20.61it/s]
eval model::   8%|▊         | 41/500 [00:02<00:24, 18.58it/s]
eval model::   9%|▊         | 43/500 [00:02<00:26, 17.31it/s]
eval model::   9%|▉         | 46/500 [00:02<00:26, 17.45it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 19.14it/s]
eval model::  10%|█         | 51/500 [00:02<00:23, 19.05it/s]
eval model::  11%|█         | 54/500 [00:02<00:21, 20.75it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:22, 19.74it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:23, 18.35it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:31, 14.09it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:29, 14.80it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:24, 17.37it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:22, 19.07it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 19.85it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:19, 21.68it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:20, 20.49it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.25it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.54it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.25it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:24, 16.73it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:23, 17.28it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:22, 17.79it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:20, 19.32it/s]
eval model::  20%|██        | 101/500 [00:05<00:19, 20.71it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 21.88it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 21.96it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 21.87it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:17, 22.03it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:19, 19.34it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:19, 19.71it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 19.90it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:19, 18.93it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:19, 19.20it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:20, 18.12it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 18.32it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:17, 20.95it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.73it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.68it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.88it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:17, 19.73it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 20.71it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 20.17it/s]
eval model::  31%|███       | 156/500 [00:08<00:16, 21.37it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 21.21it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:17, 19.54it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:16, 20.11it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:16, 19.79it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:16, 20.38it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:17, 18.25it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:14, 21.47it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:15, 20.28it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:15, 20.47it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:14, 21.46it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:14, 21.56it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:13, 23.27it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:12, 23.44it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:12, 24.64it/s]
eval model::  40%|████      | 202/500 [00:10<00:12, 24.58it/s]
eval model::  41%|████      | 205/500 [00:10<00:18, 15.55it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:18, 16.21it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:17, 16.25it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.82it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 14.10it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.30it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 17.25it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:16, 16.95it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:14, 19.51it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:13, 20.25it/s]
eval model::  46%|████▌     | 231/500 [00:11<00:12, 22.11it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 21.27it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 22.71it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:10, 24.15it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:11, 22.85it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:10, 23.13it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:11, 21.82it/s]
eval model::  50%|█████     | 252/500 [00:12<00:10, 23.49it/s]
eval model::  51%|█████     | 255/500 [00:12<00:11, 22.04it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:10, 22.78it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 21.84it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:13, 17.56it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:12, 18.50it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:12, 18.25it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:11, 19.82it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.70it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 21.04it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:09, 22.93it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:14, 14.37it/s]
eval model::  58%|█████▊    | 288/500 [00:14<00:12, 16.41it/s]
eval model::  58%|█████▊    | 292/500 [00:14<00:10, 19.66it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:12, 15.97it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:10, 19.12it/s]
eval model::  60%|██████    | 302/500 [00:15<00:11, 18.00it/s]
eval model::  61%|██████    | 305/500 [00:15<00:10, 19.47it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:08, 22.43it/s]
eval model::  62%|██████▏   | 312/500 [00:15<00:08, 22.33it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 21.60it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 22.00it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 20.97it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 20.55it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:08, 20.53it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:08, 20.67it/s]
eval model::  67%|██████▋   | 333/500 [00:16<00:07, 21.21it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 20.64it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 20.18it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:11, 14.20it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:09, 16.07it/s]
eval model::  69%|██████▉   | 347/500 [00:17<00:09, 16.51it/s]
eval model::  70%|███████   | 350/500 [00:17<00:07, 19.05it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 21.48it/s]
eval model::  71%|███████   | 356/500 [00:18<00:07, 19.47it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:07, 19.49it/s]
eval model::  72%|███████▏  | 362/500 [00:18<00:07, 19.19it/s]
eval model::  73%|███████▎  | 365/500 [00:18<00:06, 19.35it/s]
eval model::  74%|███████▎  | 368/500 [00:18<00:06, 20.00it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 21.09it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:05, 22.12it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 22.38it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:05, 20.98it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:05, 21.13it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:08, 13.09it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:07, 15.23it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:07, 15.48it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:05, 17.81it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 19.88it/s]
eval model::  80%|████████  | 400/500 [00:20<00:04, 21.29it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 21.21it/s]
eval model::  81%|████████  | 406/500 [00:20<00:04, 21.07it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 21.32it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:03, 22.78it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:03, 21.41it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:03, 20.57it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:04, 16.33it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 17.08it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:04, 17.09it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 19.31it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 19.56it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 20.66it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 21.15it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:03, 18.75it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 20.12it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 21.19it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 20.22it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 22.32it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:02, 22.00it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:01, 21.81it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:02, 17.02it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:01, 17.86it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 19.53it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 21.81it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.60it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 21.47it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 21.52it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 20.82it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 20.47it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 21.90it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 20.70it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 21.97it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 23.06it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.59it/s]
[2024/07/27 12:04:58] ppocr INFO: cur metric, precision: 0.7413680781758958, recall: 0.5479056331246991, hmean: 0.6301218161683277, fps: 43.91681079425031
[2024/07/27 12:04:58] ppocr INFO: best metric, hmean: 0.6389595702572801, precision: 0.773972602739726, recall: 0.5440539239287434, fps: 43.996993903511786, best_epoch: 220
[2024/07/27 12:04:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:05:07] ppocr INFO: epoch: [241/1500], global_step: 723, lr: 0.001000, loss: 1.837367, loss_shrink_maps: 0.987077, loss_threshold_maps: 0.634158, loss_binary_maps: 0.194460, avg_reader_cost: 2.24073 s, avg_batch_cost: 2.61084 s, avg_samples: 12.5, ips: 4.78772 samples/s, eta: 9:24:54
[2024/07/27 12:05:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:05:17] ppocr INFO: epoch: [242/1500], global_step: 726, lr: 0.001000, loss: 1.837367, loss_shrink_maps: 0.987077, loss_threshold_maps: 0.641976, loss_binary_maps: 0.194460, avg_reader_cost: 2.35172 s, avg_batch_cost: 2.58795 s, avg_samples: 12.5, ips: 4.83008 samples/s, eta: 9:24:22
[2024/07/27 12:05:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:05:27] ppocr INFO: epoch: [243/1500], global_step: 729, lr: 0.001000, loss: 1.818947, loss_shrink_maps: 0.986541, loss_threshold_maps: 0.634158, loss_binary_maps: 0.193698, avg_reader_cost: 2.27674 s, avg_batch_cost: 2.67090 s, avg_samples: 12.5, ips: 4.68008 samples/s, eta: 9:23:54
[2024/07/27 12:05:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:05:36] ppocr INFO: epoch: [244/1500], global_step: 730, lr: 0.001000, loss: 1.818947, loss_shrink_maps: 0.986541, loss_threshold_maps: 0.631281, loss_binary_maps: 0.193698, avg_reader_cost: 0.68309 s, avg_batch_cost: 0.77421 s, avg_samples: 4.8, ips: 6.19990 samples/s, eta: 9:23:38
[2024/07/27 12:05:37] ppocr INFO: epoch: [244/1500], global_step: 732, lr: 0.001000, loss: 1.790500, loss_shrink_maps: 0.969882, loss_threshold_maps: 0.626932, loss_binary_maps: 0.191418, avg_reader_cost: 1.64047 s, avg_batch_cost: 1.78766 s, avg_samples: 7.7, ips: 4.30731 samples/s, eta: 9:23:20
[2024/07/27 12:05:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:05:47] ppocr INFO: epoch: [245/1500], global_step: 735, lr: 0.001000, loss: 1.787203, loss_shrink_maps: 0.965328, loss_threshold_maps: 0.626932, loss_binary_maps: 0.190341, avg_reader_cost: 2.18232 s, avg_batch_cost: 2.55018 s, avg_samples: 12.5, ips: 4.90162 samples/s, eta: 9:22:46
[2024/07/27 12:05:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:05:57] ppocr INFO: epoch: [246/1500], global_step: 738, lr: 0.001000, loss: 1.824008, loss_shrink_maps: 0.972182, loss_threshold_maps: 0.641343, loss_binary_maps: 0.191418, avg_reader_cost: 2.30971 s, avg_batch_cost: 2.54836 s, avg_samples: 12.5, ips: 4.90511 samples/s, eta: 9:22:12
[2024/07/27 12:05:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:06:06] ppocr INFO: epoch: [247/1500], global_step: 740, lr: 0.001000, loss: 1.846309, loss_shrink_maps: 0.979950, loss_threshold_maps: 0.649575, loss_binary_maps: 0.192556, avg_reader_cost: 1.35614 s, avg_batch_cost: 1.68379 s, avg_samples: 9.6, ips: 5.70143 samples/s, eta: 9:21:48
[2024/07/27 12:06:06] ppocr INFO: epoch: [247/1500], global_step: 741, lr: 0.001000, loss: 1.846309, loss_shrink_maps: 0.979950, loss_threshold_maps: 0.652529, loss_binary_maps: 0.192556, avg_reader_cost: 0.88780 s, avg_batch_cost: 0.94329 s, avg_samples: 2.9, ips: 3.07436 samples/s, eta: 9:21:42
[2024/07/27 12:06:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:06:16] ppocr INFO: epoch: [248/1500], global_step: 744, lr: 0.001000, loss: 1.855557, loss_shrink_maps: 1.012670, loss_threshold_maps: 0.655117, loss_binary_maps: 0.200038, avg_reader_cost: 2.25413 s, avg_batch_cost: 2.62843 s, avg_samples: 12.5, ips: 4.75569 samples/s, eta: 9:21:12
[2024/07/27 12:06:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:06:26] ppocr INFO: epoch: [249/1500], global_step: 747, lr: 0.001000, loss: 1.855557, loss_shrink_maps: 1.012670, loss_threshold_maps: 0.652529, loss_binary_maps: 0.200038, avg_reader_cost: 2.33006 s, avg_batch_cost: 2.56320 s, avg_samples: 12.5, ips: 4.87671 samples/s, eta: 9:20:39
[2024/07/27 12:06:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:06:36] ppocr INFO: epoch: [250/1500], global_step: 750, lr: 0.001000, loss: 1.847323, loss_shrink_maps: 0.996109, loss_threshold_maps: 0.652529, loss_binary_maps: 0.197436, avg_reader_cost: 2.33726 s, avg_batch_cost: 2.58372 s, avg_samples: 12.5, ips: 4.83799 samples/s, eta: 9:20:06
[2024/07/27 12:06:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:06:46] ppocr INFO: epoch: [251/1500], global_step: 753, lr: 0.001000, loss: 1.842210, loss_shrink_maps: 1.008389, loss_threshold_maps: 0.649575, loss_binary_maps: 0.199158, avg_reader_cost: 2.42851 s, avg_batch_cost: 2.66907 s, avg_samples: 12.5, ips: 4.68328 samples/s, eta: 9:19:39
[2024/07/27 12:06:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:06:55] ppocr INFO: epoch: [252/1500], global_step: 756, lr: 0.001000, loss: 1.828677, loss_shrink_maps: 0.997099, loss_threshold_maps: 0.640707, loss_binary_maps: 0.197436, avg_reader_cost: 2.11432 s, avg_batch_cost: 2.44995 s, avg_samples: 12.5, ips: 5.10214 samples/s, eta: 9:19:00
[2024/07/27 12:06:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:07:05] ppocr INFO: epoch: [253/1500], global_step: 759, lr: 0.001000, loss: 1.828677, loss_shrink_maps: 0.987664, loss_threshold_maps: 0.643807, loss_binary_maps: 0.195198, avg_reader_cost: 2.36099 s, avg_batch_cost: 2.61054 s, avg_samples: 12.5, ips: 4.78828 samples/s, eta: 9:18:29
[2024/07/27 12:07:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:07:13] ppocr INFO: epoch: [254/1500], global_step: 760, lr: 0.001000, loss: 1.822904, loss_shrink_maps: 0.985432, loss_threshold_maps: 0.643807, loss_binary_maps: 0.194680, avg_reader_cost: 0.56243 s, avg_batch_cost: 0.76686 s, avg_samples: 4.8, ips: 6.25929 samples/s, eta: 9:18:14
[2024/07/27 12:07:15] ppocr INFO: epoch: [254/1500], global_step: 762, lr: 0.001000, loss: 1.822904, loss_shrink_maps: 0.985432, loss_threshold_maps: 0.643807, loss_binary_maps: 0.194680, avg_reader_cost: 1.62643 s, avg_batch_cost: 1.77401 s, avg_samples: 7.7, ips: 4.34045 samples/s, eta: 9:17:55
[2024/07/27 12:07:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:07:25] ppocr INFO: epoch: [255/1500], global_step: 765, lr: 0.001000, loss: 1.822904, loss_shrink_maps: 0.985432, loss_threshold_maps: 0.643807, loss_binary_maps: 0.194680, avg_reader_cost: 2.16585 s, avg_batch_cost: 2.54468 s, avg_samples: 12.5, ips: 4.91222 samples/s, eta: 9:17:21
[2024/07/27 12:07:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:07:35] ppocr INFO: epoch: [256/1500], global_step: 768, lr: 0.001000, loss: 1.828677, loss_shrink_maps: 0.987664, loss_threshold_maps: 0.647595, loss_binary_maps: 0.195198, avg_reader_cost: 2.42281 s, avg_batch_cost: 2.65974 s, avg_samples: 12.5, ips: 4.69970 samples/s, eta: 9:16:53
[2024/07/27 12:07:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:07:44] ppocr INFO: epoch: [257/1500], global_step: 770, lr: 0.001000, loss: 1.828677, loss_shrink_maps: 0.986422, loss_threshold_maps: 0.647595, loss_binary_maps: 0.194680, avg_reader_cost: 1.31307 s, avg_batch_cost: 1.66461 s, avg_samples: 9.6, ips: 5.76712 samples/s, eta: 9:16:29
[2024/07/27 12:07:44] ppocr INFO: epoch: [257/1500], global_step: 771, lr: 0.001000, loss: 1.828677, loss_shrink_maps: 0.986422, loss_threshold_maps: 0.647595, loss_binary_maps: 0.194680, avg_reader_cost: 0.87896 s, avg_batch_cost: 0.93457 s, avg_samples: 2.9, ips: 3.10304 samples/s, eta: 9:16:22
[2024/07/27 12:07:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:07:54] ppocr INFO: epoch: [258/1500], global_step: 774, lr: 0.001000, loss: 1.859625, loss_shrink_maps: 0.989751, loss_threshold_maps: 0.655056, loss_binary_maps: 0.195198, avg_reader_cost: 2.17629 s, avg_batch_cost: 2.54977 s, avg_samples: 12.5, ips: 4.90240 samples/s, eta: 9:15:49
[2024/07/27 12:07:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:08:04] ppocr INFO: epoch: [259/1500], global_step: 777, lr: 0.001000, loss: 1.859625, loss_shrink_maps: 0.987520, loss_threshold_maps: 0.655056, loss_binary_maps: 0.194694, avg_reader_cost: 2.33622 s, avg_batch_cost: 2.59811 s, avg_samples: 12.5, ips: 4.81119 samples/s, eta: 9:15:18
[2024/07/27 12:08:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:08:14] ppocr INFO: epoch: [260/1500], global_step: 780, lr: 0.001000, loss: 1.862653, loss_shrink_maps: 0.994471, loss_threshold_maps: 0.647595, loss_binary_maps: 0.196191, avg_reader_cost: 2.42391 s, avg_batch_cost: 2.67976 s, avg_samples: 12.5, ips: 4.66460 samples/s, eta: 9:14:51

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:39, 12.71it/s]
eval model::   1%|          | 5/500 [00:00<00:27, 17.89it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 18.11it/s]
eval model::   2%|▏         | 11/500 [00:00<00:29, 16.39it/s]
eval model::   3%|▎         | 14/500 [00:00<00:28, 17.26it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 20.03it/s]
eval model::   4%|▍         | 20/500 [00:01<00:22, 20.88it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 22.18it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 21.40it/s]
eval model::   6%|▌         | 29/500 [00:01<00:20, 22.58it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 20.29it/s]
eval model::   7%|▋         | 35/500 [00:01<00:23, 19.79it/s]
eval model::   8%|▊         | 38/500 [00:01<00:22, 20.66it/s]
eval model::   8%|▊         | 41/500 [00:02<00:21, 20.88it/s]
eval model::   9%|▉         | 44/500 [00:02<00:23, 19.72it/s]
eval model::   9%|▉         | 47/500 [00:02<00:27, 16.43it/s]
eval model::  10%|█         | 50/500 [00:02<00:25, 17.94it/s]
eval model::  11%|█         | 53/500 [00:02<00:23, 19.43it/s]
eval model::  11%|█         | 56/500 [00:02<00:23, 18.90it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:22, 19.36it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:26, 16.38it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:26, 16.59it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:20, 20.90it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:19, 22.31it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:19, 21.37it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:20, 20.35it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:19, 22.00it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:17, 23.62it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:18, 22.55it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.38it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:21, 19.40it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:20, 19.75it/s]
eval model::  20%|█▉        | 98/500 [00:04<00:19, 20.22it/s]
eval model::  20%|██        | 101/500 [00:05<00:20, 19.08it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 21.02it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 22.25it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:19, 20.24it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:20, 18.77it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:19, 20.04it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:18, 20.65it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:20, 18.66it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 17.91it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 17.85it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 19.20it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 18.50it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:19, 18.41it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:19, 18.79it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:20, 18.01it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:19, 17.99it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:18, 19.45it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:18, 19.31it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 18.07it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 17.52it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 20.95it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 21.63it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:20, 16.38it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:20, 16.66it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 18.89it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 18.94it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:18, 17.36it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 15.55it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:18, 17.44it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 16.91it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.95it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:19, 16.56it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:19, 15.98it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:21, 14.62it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:20, 15.35it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:17, 17.38it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:16, 18.64it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.20it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 20.07it/s]
eval model::  41%|████      | 203/500 [00:10<00:13, 22.27it/s]
eval model::  41%|████      | 206/500 [00:11<00:20, 14.29it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:20, 14.17it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 14.43it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.94it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.69it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 14.12it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.45it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:17, 16.03it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:14, 18.52it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:13, 19.52it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 21.49it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 21.69it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 21.88it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 23.06it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:13, 19.53it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:12, 20.19it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:12, 19.99it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 19.95it/s]
eval model::  51%|█████     | 255/500 [00:13<00:12, 20.33it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 21.18it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 22.18it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:12, 18.91it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:12, 18.67it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.90it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.52it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.11it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 20.00it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:09, 22.12it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:09, 23.04it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:15, 13.65it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:12, 16.22it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:11, 18.00it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:12, 15.77it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:11, 17.82it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 16.67it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.85it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.66it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 19.82it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 20.86it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 20.72it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:09, 19.87it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 19.15it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:09, 18.94it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 20.11it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 19.06it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:08, 19.32it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 20.72it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:07, 20.43it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:09, 16.27it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 18.65it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:07, 19.95it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 20.46it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 21.10it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:07, 20.26it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:06, 20.96it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:06, 20.90it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 18.00it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 19.54it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:07, 18.26it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 19.84it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 20.95it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:05, 20.43it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:04, 23.63it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:07, 14.45it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:07, 14.99it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 15.48it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 16.34it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 18.04it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 18.29it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 17.34it/s]
eval model::  81%|████████  | 404/500 [00:21<00:05, 19.09it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 20.98it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 22.27it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:04, 21.58it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 20.11it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 19.11it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:04, 17.29it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 17.49it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 17.70it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 19.50it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 18.50it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 20.94it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:03, 18.90it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:03, 17.80it/s]
eval model::  89%|████████▊ | 443/500 [00:23<00:02, 20.17it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 21.33it/s]
eval model::  90%|████████▉ | 449/500 [00:23<00:02, 20.78it/s]
eval model::  90%|█████████ | 452/500 [00:23<00:02, 20.56it/s]
eval model::  91%|█████████ | 455/500 [00:24<00:02, 20.65it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:01, 21.40it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 22.12it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:01, 19.75it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 18.26it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 20.38it/s]
eval model::  95%|█████████▍| 473/500 [00:24<00:01, 21.47it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 20.71it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:00, 21.68it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 22.23it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 24.07it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 19.43it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 19.27it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 20.51it/s]
eval model::  99%|█████████▉| 497/500 [00:26<00:00, 22.29it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.09it/s]
[2024/07/27 12:08:40] ppocr INFO: cur metric, precision: 0.5780141843971631, recall: 0.5493500240731825, hmean: 0.563317699333498, fps: 44.6490035775801
[2024/07/27 12:08:40] ppocr INFO: best metric, hmean: 0.6389595702572801, precision: 0.773972602739726, recall: 0.5440539239287434, fps: 43.996993903511786, best_epoch: 220
[2024/07/27 12:08:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:08:49] ppocr INFO: epoch: [261/1500], global_step: 783, lr: 0.001000, loss: 1.829927, loss_shrink_maps: 0.994471, loss_threshold_maps: 0.618463, loss_binary_maps: 0.196191, avg_reader_cost: 2.12975 s, avg_batch_cost: 2.36818 s, avg_samples: 12.5, ips: 5.27833 samples/s, eta: 9:14:09
[2024/07/27 12:08:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:08:59] ppocr INFO: epoch: [262/1500], global_step: 786, lr: 0.001000, loss: 1.668969, loss_shrink_maps: 0.908879, loss_threshold_maps: 0.604319, loss_binary_maps: 0.178661, avg_reader_cost: 2.22133 s, avg_batch_cost: 2.57411 s, avg_samples: 12.5, ips: 4.85604 samples/s, eta: 9:13:37
[2024/07/27 12:08:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:09:08] ppocr INFO: epoch: [263/1500], global_step: 789, lr: 0.001000, loss: 1.670591, loss_shrink_maps: 0.908879, loss_threshold_maps: 0.604319, loss_binary_maps: 0.178661, avg_reader_cost: 2.31287 s, avg_batch_cost: 2.60078 s, avg_samples: 12.5, ips: 4.80625 samples/s, eta: 9:13:06
[2024/07/27 12:09:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:09:17] ppocr INFO: epoch: [264/1500], global_step: 790, lr: 0.001000, loss: 1.670591, loss_shrink_maps: 0.908879, loss_threshold_maps: 0.604319, loss_binary_maps: 0.178661, avg_reader_cost: 0.56944 s, avg_batch_cost: 0.77836 s, avg_samples: 4.8, ips: 6.16680 samples/s, eta: 9:12:52
[2024/07/27 12:09:18] ppocr INFO: epoch: [264/1500], global_step: 792, lr: 0.001000, loss: 1.754801, loss_shrink_maps: 0.946499, loss_threshold_maps: 0.612585, loss_binary_maps: 0.187003, avg_reader_cost: 1.64831 s, avg_batch_cost: 1.79550 s, avg_samples: 7.7, ips: 4.28850 samples/s, eta: 9:12:34
[2024/07/27 12:09:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:09:28] ppocr INFO: epoch: [265/1500], global_step: 795, lr: 0.001000, loss: 1.748010, loss_shrink_maps: 0.922417, loss_threshold_maps: 0.612585, loss_binary_maps: 0.182465, avg_reader_cost: 2.20357 s, avg_batch_cost: 2.57067 s, avg_samples: 12.5, ips: 4.86254 samples/s, eta: 9:12:02
[2024/07/27 12:09:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:09:38] ppocr INFO: epoch: [266/1500], global_step: 798, lr: 0.001000, loss: 1.695194, loss_shrink_maps: 0.909895, loss_threshold_maps: 0.612585, loss_binary_maps: 0.179420, avg_reader_cost: 2.23218 s, avg_batch_cost: 2.60816 s, avg_samples: 12.5, ips: 4.79266 samples/s, eta: 9:11:32
[2024/07/27 12:09:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:09:47] ppocr INFO: epoch: [267/1500], global_step: 800, lr: 0.001000, loss: 1.695194, loss_shrink_maps: 0.904606, loss_threshold_maps: 0.612585, loss_binary_maps: 0.179149, avg_reader_cost: 1.34442 s, avg_batch_cost: 1.63424 s, avg_samples: 9.6, ips: 5.87429 samples/s, eta: 9:11:07
[2024/07/27 12:09:48] ppocr INFO: epoch: [267/1500], global_step: 801, lr: 0.001000, loss: 1.695194, loss_shrink_maps: 0.904606, loss_threshold_maps: 0.612585, loss_binary_maps: 0.179149, avg_reader_cost: 0.86357 s, avg_batch_cost: 0.91878 s, avg_samples: 2.9, ips: 3.15636 samples/s, eta: 9:10:59
[2024/07/27 12:09:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:09:58] ppocr INFO: epoch: [268/1500], global_step: 804, lr: 0.001000, loss: 1.675009, loss_shrink_maps: 0.885282, loss_threshold_maps: 0.620346, loss_binary_maps: 0.175322, avg_reader_cost: 2.40037 s, avg_batch_cost: 2.63855 s, avg_samples: 12.5, ips: 4.73745 samples/s, eta: 9:10:30
[2024/07/27 12:09:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:10:08] ppocr INFO: epoch: [269/1500], global_step: 807, lr: 0.001000, loss: 1.708247, loss_shrink_maps: 0.899755, loss_threshold_maps: 0.625133, loss_binary_maps: 0.177653, avg_reader_cost: 2.37092 s, avg_batch_cost: 2.62099 s, avg_samples: 12.5, ips: 4.76920 samples/s, eta: 9:10:01
[2024/07/27 12:10:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:10:17] ppocr INFO: epoch: [270/1500], global_step: 810, lr: 0.001000, loss: 1.713915, loss_shrink_maps: 0.901702, loss_threshold_maps: 0.626439, loss_binary_maps: 0.178835, avg_reader_cost: 2.16707 s, avg_batch_cost: 2.51129 s, avg_samples: 12.5, ips: 4.97751 samples/s, eta: 9:09:26
[2024/07/27 12:10:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:10:27] ppocr INFO: epoch: [271/1500], global_step: 813, lr: 0.001000, loss: 1.713915, loss_shrink_maps: 0.909133, loss_threshold_maps: 0.626439, loss_binary_maps: 0.179548, avg_reader_cost: 2.48103 s, avg_batch_cost: 2.73245 s, avg_samples: 12.5, ips: 4.57465 samples/s, eta: 9:09:02
[2024/07/27 12:10:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:10:37] ppocr INFO: epoch: [272/1500], global_step: 816, lr: 0.001000, loss: 1.713915, loss_shrink_maps: 0.909133, loss_threshold_maps: 0.625133, loss_binary_maps: 0.179548, avg_reader_cost: 2.34064 s, avg_batch_cost: 2.57761 s, avg_samples: 12.5, ips: 4.84945 samples/s, eta: 9:08:30
[2024/07/27 12:10:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:10:47] ppocr INFO: epoch: [273/1500], global_step: 819, lr: 0.001000, loss: 1.730526, loss_shrink_maps: 0.914910, loss_threshold_maps: 0.625133, loss_binary_maps: 0.181202, avg_reader_cost: 2.32703 s, avg_batch_cost: 2.58031 s, avg_samples: 12.5, ips: 4.84438 samples/s, eta: 9:07:59
[2024/07/27 12:10:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:10:55] ppocr INFO: epoch: [274/1500], global_step: 820, lr: 0.001000, loss: 1.732335, loss_shrink_maps: 0.917430, loss_threshold_maps: 0.625133, loss_binary_maps: 0.181786, avg_reader_cost: 0.56631 s, avg_batch_cost: 0.72382 s, avg_samples: 4.8, ips: 6.63150 samples/s, eta: 9:07:43
[2024/07/27 12:10:56] ppocr INFO: epoch: [274/1500], global_step: 822, lr: 0.001000, loss: 1.760055, loss_shrink_maps: 0.921710, loss_threshold_maps: 0.626439, loss_binary_maps: 0.182987, avg_reader_cost: 1.54001 s, avg_batch_cost: 1.68784 s, avg_samples: 7.7, ips: 4.56203 samples/s, eta: 9:07:20
[2024/07/27 12:10:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:11:06] ppocr INFO: epoch: [275/1500], global_step: 825, lr: 0.001000, loss: 1.793020, loss_shrink_maps: 0.955269, loss_threshold_maps: 0.637476, loss_binary_maps: 0.188451, avg_reader_cost: 2.33985 s, avg_batch_cost: 2.59423 s, avg_samples: 12.5, ips: 4.81839 samples/s, eta: 9:06:50
[2024/07/27 12:11:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:11:16] ppocr INFO: epoch: [276/1500], global_step: 828, lr: 0.001000, loss: 1.762410, loss_shrink_maps: 0.928557, loss_threshold_maps: 0.628832, loss_binary_maps: 0.184089, avg_reader_cost: 2.20706 s, avg_batch_cost: 2.56152 s, avg_samples: 12.5, ips: 4.87992 samples/s, eta: 9:06:18
[2024/07/27 12:11:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:11:25] ppocr INFO: epoch: [277/1500], global_step: 830, lr: 0.001000, loss: 1.762410, loss_shrink_maps: 0.928557, loss_threshold_maps: 0.637476, loss_binary_maps: 0.184089, avg_reader_cost: 1.45880 s, avg_batch_cost: 1.64862 s, avg_samples: 9.6, ips: 5.82304 samples/s, eta: 9:05:54
[2024/07/27 12:11:26] ppocr INFO: epoch: [277/1500], global_step: 831, lr: 0.001000, loss: 1.762410, loss_shrink_maps: 0.928059, loss_threshold_maps: 0.637476, loss_binary_maps: 0.184089, avg_reader_cost: 0.87060 s, avg_batch_cost: 0.92576 s, avg_samples: 2.9, ips: 3.13257 samples/s, eta: 9:05:46
[2024/07/27 12:11:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:11:36] ppocr INFO: epoch: [278/1500], global_step: 834, lr: 0.001000, loss: 1.717741, loss_shrink_maps: 0.906606, loss_threshold_maps: 0.617359, loss_binary_maps: 0.179959, avg_reader_cost: 2.27315 s, avg_batch_cost: 2.64267 s, avg_samples: 12.5, ips: 4.73007 samples/s, eta: 9:05:18
[2024/07/27 12:11:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:11:45] ppocr INFO: epoch: [279/1500], global_step: 837, lr: 0.001000, loss: 1.717741, loss_shrink_maps: 0.906606, loss_threshold_maps: 0.620130, loss_binary_maps: 0.179959, avg_reader_cost: 2.31596 s, avg_batch_cost: 2.56535 s, avg_samples: 12.5, ips: 4.87264 samples/s, eta: 9:04:46
[2024/07/27 12:11:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:11:55] ppocr INFO: epoch: [280/1500], global_step: 840, lr: 0.001000, loss: 1.717741, loss_shrink_maps: 0.913453, loss_threshold_maps: 0.609931, loss_binary_maps: 0.180693, avg_reader_cost: 2.39986 s, avg_batch_cost: 2.63724 s, avg_samples: 12.5, ips: 4.73980 samples/s, eta: 9:04:18

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:59,  8.39it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 16.90it/s]
eval model::   1%|▏         | 7/500 [00:00<00:25, 19.10it/s]
eval model::   2%|▏         | 9/500 [00:00<00:30, 16.34it/s]
eval model::   2%|▏         | 11/500 [00:00<00:33, 14.64it/s]
eval model::   3%|▎         | 13/500 [00:00<00:31, 15.37it/s]
eval model::   3%|▎         | 16/500 [00:00<00:28, 16.97it/s]
eval model::   4%|▍         | 19/500 [00:01<00:25, 18.65it/s]
eval model::   4%|▍         | 21/500 [00:01<00:26, 18.10it/s]
eval model::   5%|▍         | 24/500 [00:01<00:24, 19.74it/s]
eval model::   5%|▌         | 26/500 [00:01<00:25, 18.73it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 20.48it/s]
eval model::   6%|▋         | 32/500 [00:01<00:26, 17.70it/s]
eval model::   7%|▋         | 34/500 [00:01<00:26, 17.35it/s]
eval model::   7%|▋         | 37/500 [00:02<00:23, 20.04it/s]
eval model::   8%|▊         | 40/500 [00:02<00:27, 16.81it/s]
eval model::   8%|▊         | 42/500 [00:02<00:28, 16.02it/s]
eval model::   9%|▉         | 44/500 [00:02<00:28, 16.21it/s]
eval model::   9%|▉         | 46/500 [00:02<00:29, 15.63it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.54it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 19.65it/s]
eval model::  11%|█         | 55/500 [00:03<00:21, 20.75it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:21, 20.25it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:25, 17.24it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.39it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.46it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.80it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.36it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:19, 21.68it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.84it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 23.19it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.75it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.37it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:24, 16.91it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:22, 17.81it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:22, 18.15it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:23, 17.51it/s]
eval model::  20%|██        | 100/500 [00:05<00:22, 17.97it/s]
eval model::  20%|██        | 102/500 [00:05<00:21, 18.24it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 20.52it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 20.83it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 21.41it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:17, 22.25it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:15, 24.45it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:17, 21.90it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:19, 19.41it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:19, 19.53it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 18.49it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:20, 18.23it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:19, 18.42it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:19, 18.85it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.00it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.47it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.02it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 19.24it/s]
eval model::  30%|███       | 152/500 [00:08<00:19, 18.14it/s]
eval model::  31%|███       | 156/500 [00:08<00:15, 21.85it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:15, 21.90it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:17, 19.28it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:16, 19.84it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:16, 19.63it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 19.23it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 17.00it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.72it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.15it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:21, 14.64it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:20, 15.32it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:19, 16.42it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:18, 16.68it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:18, 16.90it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:14, 21.82it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:15, 19.93it/s]
eval model::  40%|████      | 200/500 [00:10<00:13, 21.49it/s]
eval model::  41%|████      | 203/500 [00:10<00:13, 22.27it/s]
eval model::  41%|████      | 206/500 [00:11<00:20, 14.14it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:20, 14.34it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 14.24it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.93it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.45it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 14.03it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:18, 15.18it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:18, 14.90it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:16, 17.14it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:14, 19.26it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:11, 22.89it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 21.66it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 22.29it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:12, 21.10it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:15, 16.82it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:14, 18.03it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:14, 16.84it/s]
eval model::  50%|█████     | 250/500 [00:13<00:15, 15.69it/s]
eval model::  51%|█████     | 253/500 [00:13<00:14, 16.78it/s]
eval model::  51%|█████     | 256/500 [00:13<00:13, 17.98it/s]
eval model::  52%|█████▏    | 259/500 [00:14<00:12, 19.74it/s]
eval model::  52%|█████▏    | 262/500 [00:14<00:12, 19.42it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:13, 17.36it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:13, 17.66it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.15it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 20.60it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.29it/s]
eval model::  56%|█████▌    | 278/500 [00:15<00:10, 20.30it/s]
eval model::  56%|█████▌    | 281/500 [00:15<00:10, 21.43it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:09, 22.40it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:16, 12.89it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:13, 15.18it/s]
eval model::  59%|█████▊    | 293/500 [00:16<00:12, 16.83it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:16, 12.60it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:13, 14.41it/s]
eval model::  60%|██████    | 301/500 [00:16<00:14, 13.49it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 15.32it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:11, 17.11it/s]
eval model::  62%|██████▏   | 310/500 [00:17<00:10, 18.14it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:10, 18.46it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:09, 19.51it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:08, 21.39it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 21.41it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 21.21it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 23.10it/s]
eval model::  66%|██████▌   | 330/500 [00:18<00:08, 19.68it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:08, 20.65it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:07, 22.05it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:07, 20.58it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 14.43it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:10, 15.37it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:09, 16.06it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:08, 17.87it/s]
eval model::  70%|███████   | 351/500 [00:19<00:08, 17.25it/s]
eval model::  71%|███████   | 354/500 [00:19<00:07, 19.61it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 18.86it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:08, 17.52it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 17.74it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 17.86it/s]
eval model::  73%|███████▎  | 365/500 [00:20<00:08, 16.49it/s]
eval model::  73%|███████▎  | 367/500 [00:20<00:08, 14.98it/s]
eval model::  74%|███████▍  | 370/500 [00:20<00:07, 16.98it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 16.87it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 18.03it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 20.72it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:06, 19.64it/s]
eval model::  77%|███████▋  | 385/500 [00:21<00:05, 22.88it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:08, 12.79it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:07, 14.37it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 15.72it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 18.20it/s]
eval model::  80%|████████  | 400/500 [00:22<00:05, 16.80it/s]
eval model::  80%|████████  | 402/500 [00:22<00:05, 16.90it/s]
eval model::  81%|████████  | 404/500 [00:22<00:05, 17.44it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:05, 18.57it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 19.64it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:04, 19.67it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 19.36it/s]
eval model::  84%|████████▎ | 418/500 [00:23<00:04, 19.18it/s]
eval model::  84%|████████▍ | 420/500 [00:23<00:04, 16.07it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:04, 16.11it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:04, 16.47it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:04, 17.11it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:03, 20.02it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 18.82it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 20.64it/s]
eval model::  88%|████████▊ | 438/500 [00:24<00:02, 22.42it/s]
eval model::  88%|████████▊ | 441/500 [00:24<00:02, 20.37it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 22.01it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 20.29it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 18.20it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 19.55it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:02, 19.09it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:01, 20.72it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 18.10it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:01, 17.78it/s]
eval model::  93%|█████████▎| 467/500 [00:25<00:01, 17.76it/s]
eval model::  94%|█████████▍| 470/500 [00:25<00:01, 19.38it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 19.32it/s]
eval model::  95%|█████████▌| 475/500 [00:26<00:01, 17.38it/s]
eval model::  96%|█████████▌| 478/500 [00:26<00:01, 18.60it/s]
eval model::  96%|█████████▌| 481/500 [00:26<00:00, 20.80it/s]
eval model::  97%|█████████▋| 484/500 [00:26<00:00, 22.40it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 22.06it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 22.11it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 19.89it/s]
eval model::  99%|█████████▉| 496/500 [00:27<00:00, 21.09it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 25.14it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.40it/s]
[2024/07/27 12:12:22] ppocr INFO: cur metric, precision: 0.6631165919282511, recall: 0.5695714973519499, hmean: 0.6127946127946128, fps: 42.61087426686213
[2024/07/27 12:12:22] ppocr INFO: best metric, hmean: 0.6389595702572801, precision: 0.773972602739726, recall: 0.5440539239287434, fps: 43.996993903511786, best_epoch: 220
[2024/07/27 12:12:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:12:31] ppocr INFO: epoch: [281/1500], global_step: 843, lr: 0.001000, loss: 1.796375, loss_shrink_maps: 0.977703, loss_threshold_maps: 0.620130, loss_binary_maps: 0.193575, avg_reader_cost: 2.03107 s, avg_batch_cost: 2.30864 s, avg_samples: 12.5, ips: 5.41444 samples/s, eta: 9:03:35
[2024/07/27 12:12:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:12:40] ppocr INFO: epoch: [282/1500], global_step: 846, lr: 0.001000, loss: 1.764000, loss_shrink_maps: 0.956250, loss_threshold_maps: 0.613103, loss_binary_maps: 0.189446, avg_reader_cost: 2.30342 s, avg_batch_cost: 2.54345 s, avg_samples: 12.5, ips: 4.91458 samples/s, eta: 9:03:03
[2024/07/27 12:12:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:12:50] ppocr INFO: epoch: [283/1500], global_step: 849, lr: 0.001000, loss: 1.801460, loss_shrink_maps: 0.976074, loss_threshold_maps: 0.617863, loss_binary_maps: 0.193561, avg_reader_cost: 2.21300 s, avg_batch_cost: 2.56203 s, avg_samples: 12.5, ips: 4.87895 samples/s, eta: 9:02:31
[2024/07/27 12:12:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:12:58] ppocr INFO: epoch: [284/1500], global_step: 850, lr: 0.001000, loss: 1.724984, loss_shrink_maps: 0.917714, loss_threshold_maps: 0.610836, loss_binary_maps: 0.182268, avg_reader_cost: 0.65347 s, avg_batch_cost: 0.74594 s, avg_samples: 4.8, ips: 6.43485 samples/s, eta: 9:02:16
[2024/07/27 12:13:00] ppocr INFO: epoch: [284/1500], global_step: 852, lr: 0.001000, loss: 1.724984, loss_shrink_maps: 0.917714, loss_threshold_maps: 0.610836, loss_binary_maps: 0.182268, avg_reader_cost: 1.58382 s, avg_batch_cost: 1.73090 s, avg_samples: 7.7, ips: 4.44855 samples/s, eta: 9:01:56
[2024/07/27 12:13:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:13:09] ppocr INFO: epoch: [285/1500], global_step: 855, lr: 0.001000, loss: 1.756800, loss_shrink_maps: 0.933284, loss_threshold_maps: 0.613874, loss_binary_maps: 0.184886, avg_reader_cost: 2.30560 s, avg_batch_cost: 2.54490 s, avg_samples: 12.5, ips: 4.91179 samples/s, eta: 9:01:24
[2024/07/27 12:13:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:13:19] ppocr INFO: epoch: [286/1500], global_step: 858, lr: 0.001000, loss: 1.768348, loss_shrink_maps: 0.952688, loss_threshold_maps: 0.630513, loss_binary_maps: 0.189053, avg_reader_cost: 2.37069 s, avg_batch_cost: 2.61940 s, avg_samples: 12.5, ips: 4.77208 samples/s, eta: 9:00:55
[2024/07/27 12:13:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:13:28] ppocr INFO: epoch: [287/1500], global_step: 860, lr: 0.001000, loss: 1.756800, loss_shrink_maps: 0.933284, loss_threshold_maps: 0.630748, loss_binary_maps: 0.184886, avg_reader_cost: 1.48888 s, avg_batch_cost: 1.67195 s, avg_samples: 9.6, ips: 5.74180 samples/s, eta: 9:00:32
[2024/07/27 12:13:29] ppocr INFO: epoch: [287/1500], global_step: 861, lr: 0.001000, loss: 1.756800, loss_shrink_maps: 0.933284, loss_threshold_maps: 0.630748, loss_binary_maps: 0.184886, avg_reader_cost: 0.88208 s, avg_batch_cost: 0.93718 s, avg_samples: 2.9, ips: 3.09439 samples/s, eta: 9:00:25
[2024/07/27 12:13:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:13:39] ppocr INFO: epoch: [288/1500], global_step: 864, lr: 0.001000, loss: 1.721497, loss_shrink_maps: 0.914389, loss_threshold_maps: 0.627400, loss_binary_maps: 0.181212, avg_reader_cost: 2.21131 s, avg_batch_cost: 2.57519 s, avg_samples: 12.5, ips: 4.85400 samples/s, eta: 8:59:54
[2024/07/27 12:13:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:13:48] ppocr INFO: epoch: [289/1500], global_step: 867, lr: 0.001000, loss: 1.721497, loss_shrink_maps: 0.914389, loss_threshold_maps: 0.627400, loss_binary_maps: 0.181212, avg_reader_cost: 2.26214 s, avg_batch_cost: 2.50826 s, avg_samples: 12.5, ips: 4.98354 samples/s, eta: 8:59:21
[2024/07/27 12:13:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:13:58] ppocr INFO: epoch: [290/1500], global_step: 870, lr: 0.001000, loss: 1.764862, loss_shrink_maps: 0.949363, loss_threshold_maps: 0.636692, loss_binary_maps: 0.187997, avg_reader_cost: 2.28537 s, avg_batch_cost: 2.54475 s, avg_samples: 12.5, ips: 4.91208 samples/s, eta: 8:58:49
[2024/07/27 12:13:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:14:08] ppocr INFO: epoch: [291/1500], global_step: 873, lr: 0.001000, loss: 1.770162, loss_shrink_maps: 0.967576, loss_threshold_maps: 0.638321, loss_binary_maps: 0.191480, avg_reader_cost: 2.21866 s, avg_batch_cost: 2.67106 s, avg_samples: 12.5, ips: 4.67980 samples/s, eta: 8:58:22
[2024/07/27 12:14:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:14:17] ppocr INFO: epoch: [292/1500], global_step: 876, lr: 0.001000, loss: 1.740368, loss_shrink_maps: 0.958484, loss_threshold_maps: 0.638321, loss_binary_maps: 0.189636, avg_reader_cost: 2.26468 s, avg_batch_cost: 2.51249 s, avg_samples: 12.5, ips: 4.97515 samples/s, eta: 8:57:49
[2024/07/27 12:14:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:14:27] ppocr INFO: epoch: [293/1500], global_step: 879, lr: 0.001000, loss: 1.719001, loss_shrink_maps: 0.943727, loss_threshold_maps: 0.606457, loss_binary_maps: 0.186980, avg_reader_cost: 2.28936 s, avg_batch_cost: 2.65773 s, avg_samples: 12.5, ips: 4.70327 samples/s, eta: 8:57:21
[2024/07/27 12:14:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:14:36] ppocr INFO: epoch: [294/1500], global_step: 880, lr: 0.001000, loss: 1.719001, loss_shrink_maps: 0.943727, loss_threshold_maps: 0.604997, loss_binary_maps: 0.186980, avg_reader_cost: 0.71426 s, avg_batch_cost: 0.80716 s, avg_samples: 4.8, ips: 5.94678 samples/s, eta: 8:57:09
[2024/07/27 12:14:37] ppocr INFO: epoch: [294/1500], global_step: 882, lr: 0.001000, loss: 1.739159, loss_shrink_maps: 0.958484, loss_threshold_maps: 0.605273, loss_binary_maps: 0.189371, avg_reader_cost: 1.70694 s, avg_batch_cost: 1.85341 s, avg_samples: 7.7, ips: 4.15451 samples/s, eta: 8:56:54
[2024/07/27 12:14:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:14:47] ppocr INFO: epoch: [295/1500], global_step: 885, lr: 0.001000, loss: 1.719001, loss_shrink_maps: 0.943727, loss_threshold_maps: 0.605849, loss_binary_maps: 0.186980, avg_reader_cost: 2.41467 s, avg_batch_cost: 2.66229 s, avg_samples: 12.5, ips: 4.69520 samples/s, eta: 8:56:27
[2024/07/27 12:14:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:14:57] ppocr INFO: epoch: [296/1500], global_step: 888, lr: 0.001000, loss: 1.719001, loss_shrink_maps: 0.934930, loss_threshold_maps: 0.605849, loss_binary_maps: 0.185231, avg_reader_cost: 2.36782 s, avg_batch_cost: 2.60340 s, avg_samples: 12.5, ips: 4.80141 samples/s, eta: 8:55:58
[2024/07/27 12:14:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:15:07] ppocr INFO: epoch: [297/1500], global_step: 890, lr: 0.001000, loss: 1.719001, loss_shrink_maps: 0.934930, loss_threshold_maps: 0.605849, loss_binary_maps: 0.185231, avg_reader_cost: 1.50994 s, avg_batch_cost: 1.70272 s, avg_samples: 9.6, ips: 5.63805 samples/s, eta: 8:55:37
[2024/07/27 12:15:07] ppocr INFO: epoch: [297/1500], global_step: 891, lr: 0.001000, loss: 1.715063, loss_shrink_maps: 0.930822, loss_threshold_maps: 0.602713, loss_binary_maps: 0.184782, avg_reader_cost: 0.89735 s, avg_batch_cost: 0.95276 s, avg_samples: 2.9, ips: 3.04379 samples/s, eta: 8:55:30
[2024/07/27 12:15:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:15:17] ppocr INFO: epoch: [298/1500], global_step: 894, lr: 0.001000, loss: 1.715063, loss_shrink_maps: 0.930822, loss_threshold_maps: 0.607560, loss_binary_maps: 0.184782, avg_reader_cost: 2.20293 s, avg_batch_cost: 2.57632 s, avg_samples: 12.5, ips: 4.85187 samples/s, eta: 8:55:00
[2024/07/27 12:15:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:15:27] ppocr INFO: epoch: [299/1500], global_step: 897, lr: 0.001000, loss: 1.720989, loss_shrink_maps: 0.930822, loss_threshold_maps: 0.612727, loss_binary_maps: 0.184782, avg_reader_cost: 2.33153 s, avg_batch_cost: 2.66524 s, avg_samples: 12.5, ips: 4.69001 samples/s, eta: 8:54:33
[2024/07/27 12:15:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:15:37] ppocr INFO: epoch: [300/1500], global_step: 900, lr: 0.001000, loss: 1.742830, loss_shrink_maps: 0.935354, loss_threshold_maps: 0.627909, loss_binary_maps: 0.185707, avg_reader_cost: 2.21800 s, avg_batch_cost: 2.45859 s, avg_samples: 12.5, ips: 5.08421 samples/s, eta: 8:53:58

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.39it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.33it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.06it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.87it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.17it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 19.23it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.91it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.75it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 22.21it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 20.87it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 21.48it/s]
eval model::   6%|▋         | 32/500 [00:01<00:22, 21.04it/s]
eval model::   7%|▋         | 35/500 [00:01<00:22, 20.47it/s]
eval model::   8%|▊         | 38/500 [00:01<00:21, 21.29it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.88it/s]
eval model::   9%|▉         | 44/500 [00:02<00:22, 19.93it/s]
eval model::   9%|▉         | 47/500 [00:02<00:22, 20.36it/s]
eval model::  10%|█         | 50/500 [00:02<00:22, 19.77it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 20.65it/s]
eval model::  11%|█         | 56/500 [00:02<00:22, 19.45it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:22, 19.93it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:24, 17.87it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:25, 16.91it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 19.40it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 21.66it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 22.35it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:18, 22.90it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:19, 21.26it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 19.92it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.40it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 19.94it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.13it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.24it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:20, 19.91it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:18, 21.21it/s]
eval model::  20%|██        | 102/500 [00:05<00:18, 21.99it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.57it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:20, 18.73it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:21, 18.08it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:19, 19.47it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:20, 18.91it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:19, 19.36it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.23it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 19.95it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:21, 17.72it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:19, 18.76it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:21, 17.29it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:18, 19.81it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:17, 20.60it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:17, 20.56it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.81it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 19.78it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 17.90it/s]
eval model::  30%|███       | 151/500 [00:07<00:18, 19.22it/s]
eval model::  31%|███       | 154/500 [00:07<00:16, 20.37it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:16, 21.39it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:19, 17.16it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:18, 18.01it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 19.78it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 19.86it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 19.29it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:18, 17.45it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:16, 19.23it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:17, 18.09it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:15, 19.88it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 19.53it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:15, 19.94it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:14, 21.64it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:15, 20.15it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 21.18it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 21.18it/s]
eval model::  41%|████      | 205/500 [00:10<00:20, 14.35it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:19, 15.33it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:19, 15.22it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.38it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.59it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 14.10it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.44it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:17, 15.48it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:14, 18.52it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:13, 20.46it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:11, 22.52it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 21.41it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:12, 21.22it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 22.03it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:12, 20.53it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:11, 21.39it/s]
eval model::  50%|█████     | 250/500 [00:12<00:12, 20.37it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 22.41it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 21.46it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:11, 21.74it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 20.03it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:13, 17.49it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:12, 19.01it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:11, 19.78it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:11, 20.42it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 18.62it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:12, 17.81it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 19.10it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:17, 12.37it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:15, 13.33it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.98it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.34it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:14, 14.56it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:12, 16.70it/s]
eval model::  60%|██████    | 301/500 [00:15<00:12, 15.83it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 17.76it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:09, 19.52it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:08, 21.45it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 22.76it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 22.46it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:07, 23.35it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:08, 21.54it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:07, 22.54it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 21.33it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:07, 21.23it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 21.73it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:09, 17.40it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:09, 17.30it/s]
eval model::  68%|██████▊   | 341/500 [00:17<00:11, 13.72it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 16.03it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 18.29it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 20.43it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.84it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 22.33it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:06, 20.92it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:06, 19.67it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:06, 20.70it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.88it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 20.03it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 20.83it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 21.80it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 21.64it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 21.78it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:08, 12.90it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:07, 14.70it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:06, 16.62it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 18.62it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 19.21it/s]
eval model::  81%|████████  | 403/500 [00:21<00:04, 20.31it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 21.63it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:03, 22.90it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:03, 22.28it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:04, 20.43it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 18.69it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.70it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 13.48it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 14.46it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.41it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 18.58it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 18.85it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 20.73it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 22.83it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 21.45it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 21.70it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 21.65it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 20.34it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 22.20it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:02, 20.72it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:01, 20.50it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 16.98it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.95it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 16.97it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 19.50it/s]
eval model::  95%|█████████▍| 473/500 [00:24<00:01, 19.84it/s]
eval model::  95%|█████████▌| 476/500 [00:24<00:01, 20.29it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:01, 20.16it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 18.49it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 20.49it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 21.50it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 20.27it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 21.03it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 22.94it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.26it/s]
[2024/07/27 12:16:02] ppocr INFO: cur metric, precision: 0.6918671248568156, recall: 0.5816080885893115, hmean: 0.6319644258435784, fps: 45.484915826905066
[2024/07/27 12:16:02] ppocr INFO: best metric, hmean: 0.6389595702572801, precision: 0.773972602739726, recall: 0.5440539239287434, fps: 43.996993903511786, best_epoch: 220
[2024/07/27 12:16:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:16:12] ppocr INFO: epoch: [301/1500], global_step: 903, lr: 0.001000, loss: 1.742830, loss_shrink_maps: 0.935354, loss_threshold_maps: 0.621018, loss_binary_maps: 0.185707, avg_reader_cost: 2.06392 s, avg_batch_cost: 2.46932 s, avg_samples: 12.5, ips: 5.06213 samples/s, eta: 8:53:23
[2024/07/27 12:16:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:16:21] ppocr INFO: epoch: [302/1500], global_step: 906, lr: 0.001000, loss: 1.775544, loss_shrink_maps: 0.970886, loss_threshold_maps: 0.627909, loss_binary_maps: 0.192592, avg_reader_cost: 2.34966 s, avg_batch_cost: 2.58840 s, avg_samples: 12.5, ips: 4.82923 samples/s, eta: 8:52:53
[2024/07/27 12:16:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:16:31] ppocr INFO: epoch: [303/1500], global_step: 909, lr: 0.001000, loss: 1.766799, loss_shrink_maps: 0.956956, loss_threshold_maps: 0.627909, loss_binary_maps: 0.189021, avg_reader_cost: 2.28132 s, avg_batch_cost: 2.51866 s, avg_samples: 12.5, ips: 4.96295 samples/s, eta: 8:52:20
[2024/07/27 12:16:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:16:40] ppocr INFO: epoch: [304/1500], global_step: 910, lr: 0.001000, loss: 1.760615, loss_shrink_maps: 0.937556, loss_threshold_maps: 0.614421, loss_binary_maps: 0.185753, avg_reader_cost: 0.65233 s, avg_batch_cost: 0.79525 s, avg_samples: 4.8, ips: 6.03587 samples/s, eta: 8:52:08
[2024/07/27 12:16:41] ppocr INFO: epoch: [304/1500], global_step: 912, lr: 0.001000, loss: 1.760615, loss_shrink_maps: 0.937556, loss_threshold_maps: 0.615041, loss_binary_maps: 0.185753, avg_reader_cost: 1.68267 s, avg_batch_cost: 1.82928 s, avg_samples: 7.7, ips: 4.20930 samples/s, eta: 8:51:52
[2024/07/27 12:16:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:16:50] ppocr INFO: epoch: [305/1500], global_step: 915, lr: 0.001000, loss: 1.781728, loss_shrink_maps: 0.990123, loss_threshold_maps: 0.630681, loss_binary_maps: 0.195179, avg_reader_cost: 2.11439 s, avg_batch_cost: 2.42906 s, avg_samples: 12.5, ips: 5.14603 samples/s, eta: 8:51:16
[2024/07/27 12:16:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:17:00] ppocr INFO: epoch: [306/1500], global_step: 918, lr: 0.001000, loss: 1.781728, loss_shrink_maps: 0.964565, loss_threshold_maps: 0.615041, loss_binary_maps: 0.189171, avg_reader_cost: 2.29524 s, avg_batch_cost: 2.58432 s, avg_samples: 12.5, ips: 4.83687 samples/s, eta: 8:50:46
[2024/07/27 12:17:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:17:09] ppocr INFO: epoch: [307/1500], global_step: 920, lr: 0.001000, loss: 1.797531, loss_shrink_maps: 0.981234, loss_threshold_maps: 0.634993, loss_binary_maps: 0.193456, avg_reader_cost: 1.33214 s, avg_batch_cost: 1.62688 s, avg_samples: 9.6, ips: 5.90088 samples/s, eta: 8:50:22
[2024/07/27 12:17:10] ppocr INFO: epoch: [307/1500], global_step: 921, lr: 0.001000, loss: 1.808585, loss_shrink_maps: 0.981234, loss_threshold_maps: 0.634993, loss_binary_maps: 0.193456, avg_reader_cost: 0.85966 s, avg_batch_cost: 0.91483 s, avg_samples: 2.9, ips: 3.16999 samples/s, eta: 8:50:14
[2024/07/27 12:17:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:17:20] ppocr INFO: epoch: [308/1500], global_step: 924, lr: 0.001000, loss: 1.785156, loss_shrink_maps: 0.964565, loss_threshold_maps: 0.633606, loss_binary_maps: 0.189171, avg_reader_cost: 2.32555 s, avg_batch_cost: 2.56552 s, avg_samples: 12.5, ips: 4.87230 samples/s, eta: 8:49:44
[2024/07/27 12:17:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:17:29] ppocr INFO: epoch: [309/1500], global_step: 927, lr: 0.001000, loss: 1.748738, loss_shrink_maps: 0.942489, loss_threshold_maps: 0.632793, loss_binary_maps: 0.185906, avg_reader_cost: 2.21119 s, avg_batch_cost: 2.59395 s, avg_samples: 12.5, ips: 4.81891 samples/s, eta: 8:49:14
[2024/07/27 12:17:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:17:39] ppocr INFO: epoch: [310/1500], global_step: 930, lr: 0.001000, loss: 1.733932, loss_shrink_maps: 0.940688, loss_threshold_maps: 0.630353, loss_binary_maps: 0.186184, avg_reader_cost: 2.19270 s, avg_batch_cost: 2.50032 s, avg_samples: 12.5, ips: 4.99937 samples/s, eta: 8:48:41
[2024/07/27 12:17:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:17:49] ppocr INFO: epoch: [311/1500], global_step: 933, lr: 0.001000, loss: 1.713462, loss_shrink_maps: 0.931023, loss_threshold_maps: 0.622187, loss_binary_maps: 0.184349, avg_reader_cost: 2.35236 s, avg_batch_cost: 2.59357 s, avg_samples: 12.5, ips: 4.81962 samples/s, eta: 8:48:12
[2024/07/27 12:17:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:17:59] ppocr INFO: epoch: [312/1500], global_step: 936, lr: 0.001000, loss: 1.690365, loss_shrink_maps: 0.900221, loss_threshold_maps: 0.608183, loss_binary_maps: 0.177512, avg_reader_cost: 2.29340 s, avg_batch_cost: 2.66901 s, avg_samples: 12.5, ips: 4.68339 samples/s, eta: 8:47:45
[2024/07/27 12:17:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:18:08] ppocr INFO: epoch: [313/1500], global_step: 939, lr: 0.001000, loss: 1.675180, loss_shrink_maps: 0.895244, loss_threshold_maps: 0.596020, loss_binary_maps: 0.176605, avg_reader_cost: 2.31921 s, avg_batch_cost: 2.57932 s, avg_samples: 12.5, ips: 4.84624 samples/s, eta: 8:47:16
[2024/07/27 12:18:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:18:17] ppocr INFO: epoch: [314/1500], global_step: 940, lr: 0.001000, loss: 1.675180, loss_shrink_maps: 0.895244, loss_threshold_maps: 0.596020, loss_binary_maps: 0.176605, avg_reader_cost: 0.69182 s, avg_batch_cost: 0.77719 s, avg_samples: 4.8, ips: 6.17610 samples/s, eta: 8:47:02
[2024/07/27 12:18:18] ppocr INFO: epoch: [314/1500], global_step: 942, lr: 0.001000, loss: 1.675180, loss_shrink_maps: 0.895244, loss_threshold_maps: 0.596020, loss_binary_maps: 0.176605, avg_reader_cost: 1.64675 s, avg_batch_cost: 1.79418 s, avg_samples: 7.7, ips: 4.29165 samples/s, eta: 8:46:45
[2024/07/27 12:18:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:18:28] ppocr INFO: epoch: [315/1500], global_step: 945, lr: 0.001000, loss: 1.676326, loss_shrink_maps: 0.903594, loss_threshold_maps: 0.596020, loss_binary_maps: 0.178208, avg_reader_cost: 2.29559 s, avg_batch_cost: 2.68859 s, avg_samples: 12.5, ips: 4.64928 samples/s, eta: 8:46:20
[2024/07/27 12:18:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:18:38] ppocr INFO: epoch: [316/1500], global_step: 948, lr: 0.001000, loss: 1.676326, loss_shrink_maps: 0.895244, loss_threshold_maps: 0.596998, loss_binary_maps: 0.176605, avg_reader_cost: 2.43988 s, avg_batch_cost: 2.69627 s, avg_samples: 12.5, ips: 4.63603 samples/s, eta: 8:45:54
[2024/07/27 12:18:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:18:48] ppocr INFO: epoch: [317/1500], global_step: 950, lr: 0.001000, loss: 1.676326, loss_shrink_maps: 0.903594, loss_threshold_maps: 0.593229, loss_binary_maps: 0.178208, avg_reader_cost: 1.37970 s, avg_batch_cost: 1.68992 s, avg_samples: 9.6, ips: 5.68074 samples/s, eta: 8:45:33
[2024/07/27 12:18:48] ppocr INFO: epoch: [317/1500], global_step: 951, lr: 0.001000, loss: 1.676326, loss_shrink_maps: 0.903594, loss_threshold_maps: 0.592332, loss_binary_maps: 0.178208, avg_reader_cost: 0.89136 s, avg_batch_cost: 0.94739 s, avg_samples: 2.9, ips: 3.06105 samples/s, eta: 8:45:26
[2024/07/27 12:18:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:18:58] ppocr INFO: epoch: [318/1500], global_step: 954, lr: 0.001000, loss: 1.675328, loss_shrink_maps: 0.905309, loss_threshold_maps: 0.593229, loss_binary_maps: 0.179030, avg_reader_cost: 2.16814 s, avg_batch_cost: 2.52504 s, avg_samples: 12.5, ips: 4.95042 samples/s, eta: 8:44:55
[2024/07/27 12:18:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:19:08] ppocr INFO: epoch: [319/1500], global_step: 957, lr: 0.001000, loss: 1.683143, loss_shrink_maps: 0.910743, loss_threshold_maps: 0.611379, loss_binary_maps: 0.179887, avg_reader_cost: 2.23308 s, avg_batch_cost: 2.60223 s, avg_samples: 12.5, ips: 4.80357 samples/s, eta: 8:44:26
[2024/07/27 12:19:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:19:18] ppocr INFO: epoch: [320/1500], global_step: 960, lr: 0.001000, loss: 1.683143, loss_shrink_maps: 0.905309, loss_threshold_maps: 0.614008, loss_binary_maps: 0.179030, avg_reader_cost: 2.25638 s, avg_batch_cost: 2.51696 s, avg_samples: 12.5, ips: 4.96632 samples/s, eta: 8:43:54

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:55,  8.93it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.59it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.45it/s]
eval model::   2%|▏         | 8/500 [00:00<00:26, 18.81it/s]
eval model::   2%|▏         | 11/500 [00:00<00:25, 18.88it/s]
eval model::   3%|▎         | 14/500 [00:00<00:24, 20.11it/s]
eval model::   4%|▎         | 18/500 [00:00<00:20, 23.33it/s]
eval model::   4%|▍         | 21/500 [00:01<00:23, 20.60it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.57it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 20.70it/s]
eval model::   6%|▌         | 30/500 [00:01<00:20, 22.48it/s]
eval model::   7%|▋         | 33/500 [00:01<00:21, 22.04it/s]
eval model::   7%|▋         | 36/500 [00:01<00:22, 21.05it/s]
eval model::   8%|▊         | 39/500 [00:01<00:23, 19.50it/s]
eval model::   8%|▊         | 42/500 [00:02<00:24, 18.82it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 19.96it/s]
eval model::  10%|▉         | 48/500 [00:02<00:24, 18.72it/s]
eval model::  10%|█         | 51/500 [00:02<00:22, 19.64it/s]
eval model::  11%|█         | 54/500 [00:02<00:20, 21.56it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:19, 22.56it/s]
eval model::  12%|█▏        | 60/500 [00:02<00:22, 19.88it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 14.84it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:23, 18.72it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 20.69it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:20, 20.32it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:21, 20.09it/s]
eval model::  16%|█▌        | 80/500 [00:03<00:19, 22.10it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:18, 22.93it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:19, 21.08it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:21, 19.14it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:20, 19.57it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:20, 19.64it/s]
eval model::  20%|█▉        | 98/500 [00:04<00:18, 21.59it/s]
eval model::  20%|██        | 101/500 [00:05<00:18, 21.03it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 21.12it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.02it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 21.02it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 21.39it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:18, 20.72it/s]
eval model::  24%|██▍       | 119/500 [00:05<00:18, 20.97it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:19, 19.23it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.50it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 19.96it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:18, 19.72it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 18.14it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 20.09it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:17, 20.25it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.15it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.26it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 19.34it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 20.83it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 19.28it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:15, 22.76it/s]
eval model::  32%|███▏      | 160/500 [00:07<00:18, 18.59it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:17, 19.16it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.19it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:18, 18.22it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:19, 17.00it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:21, 15.49it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:19, 16.33it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 17.99it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.43it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 18.41it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.61it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:16, 18.54it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:16, 18.89it/s]
eval model::  39%|███▉      | 194/500 [00:09<00:14, 20.66it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.41it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 20.56it/s]
eval model::  41%|████      | 203/500 [00:10<00:13, 21.89it/s]
eval model::  41%|████      | 206/500 [00:10<00:21, 13.64it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:21, 13.89it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:20, 14.00it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.42it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.67it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.43it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 15.98it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 14.88it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:15, 17.80it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:13, 20.36it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 22.20it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:11, 22.54it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 23.83it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:10, 24.34it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 21.01it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:11, 22.84it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:11, 20.96it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 20.35it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 20.90it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 21.06it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 19.94it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 19.56it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:11, 20.39it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:11, 20.38it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 19.51it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.16it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.64it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.50it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 20.40it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:16, 13.18it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:13, 15.53it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:11, 17.26it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:14, 13.76it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:12, 16.42it/s]
eval model::  60%|██████    | 302/500 [00:15<00:12, 15.64it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 16.18it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 17.85it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:10, 17.67it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 19.41it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 19.91it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 20.91it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 21.49it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 20.47it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 21.51it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 19.75it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:08, 19.55it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 21.25it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 20.93it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:10, 15.75it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:09, 17.09it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 17.53it/s]
eval model::  70%|███████   | 350/500 [00:18<00:07, 19.82it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 21.53it/s]
eval model::  71%|███████   | 356/500 [00:18<00:06, 22.10it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:06, 21.50it/s]
eval model::  72%|███████▏  | 362/500 [00:18<00:06, 21.36it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 18.87it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 17.40it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 18.72it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 20.75it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 22.15it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:05, 23.36it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:04, 24.64it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 15.14it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 17.52it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 18.41it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 19.55it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 21.44it/s]
eval model::  80%|████████  | 402/500 [00:20<00:05, 19.02it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 19.25it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 20.88it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 21.24it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 22.14it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 21.03it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 17.57it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 16.53it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.25it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.78it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 18.81it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 17.49it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 19.48it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 21.84it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 19.84it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 22.09it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 22.69it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 19.80it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 21.11it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:02, 21.09it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:01, 21.97it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:01, 19.84it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:01, 18.75it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 19.95it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 21.58it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.27it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 21.91it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 23.12it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 23.91it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 23.29it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 23.13it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 22.59it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 21.85it/s]
eval model:: 100%|█████████▉| 499/500 [00:25<00:00, 21.98it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.45it/s]
[2024/07/27 12:19:43] ppocr INFO: cur metric, precision: 0.6529284164859002, recall: 0.5796822339913337, hmean: 0.6141290487120632, fps: 45.44940131851358
[2024/07/27 12:19:43] ppocr INFO: best metric, hmean: 0.6389595702572801, precision: 0.773972602739726, recall: 0.5440539239287434, fps: 43.996993903511786, best_epoch: 220
[2024/07/27 12:19:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:19:52] ppocr INFO: epoch: [321/1500], global_step: 963, lr: 0.001000, loss: 1.683143, loss_shrink_maps: 0.905309, loss_threshold_maps: 0.614008, loss_binary_maps: 0.179030, avg_reader_cost: 2.15365 s, avg_batch_cost: 2.51023 s, avg_samples: 12.5, ips: 4.97962 samples/s, eta: 8:43:21
[2024/07/27 12:19:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:20:02] ppocr INFO: epoch: [322/1500], global_step: 966, lr: 0.001000, loss: 1.682145, loss_shrink_maps: 0.906196, loss_threshold_maps: 0.610335, loss_binary_maps: 0.179246, avg_reader_cost: 2.25392 s, avg_batch_cost: 2.62094 s, avg_samples: 12.5, ips: 4.76928 samples/s, eta: 8:42:53
[2024/07/27 12:20:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:20:12] ppocr INFO: epoch: [323/1500], global_step: 969, lr: 0.001000, loss: 1.724637, loss_shrink_maps: 0.921360, loss_threshold_maps: 0.631828, loss_binary_maps: 0.181999, avg_reader_cost: 2.18885 s, avg_batch_cost: 2.58431 s, avg_samples: 12.5, ips: 4.83688 samples/s, eta: 8:42:24
[2024/07/27 12:20:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:20:20] ppocr INFO: epoch: [324/1500], global_step: 970, lr: 0.001000, loss: 1.724637, loss_shrink_maps: 0.914472, loss_threshold_maps: 0.631828, loss_binary_maps: 0.180971, avg_reader_cost: 0.69640 s, avg_batch_cost: 0.78903 s, avg_samples: 4.8, ips: 6.08338 samples/s, eta: 8:42:11
[2024/07/27 12:20:22] ppocr INFO: epoch: [324/1500], global_step: 972, lr: 0.001000, loss: 1.731234, loss_shrink_maps: 0.916989, loss_threshold_maps: 0.638951, loss_binary_maps: 0.181424, avg_reader_cost: 1.67081 s, avg_batch_cost: 1.81830 s, avg_samples: 7.7, ips: 4.23472 samples/s, eta: 8:41:55
[2024/07/27 12:20:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:20:32] ppocr INFO: epoch: [325/1500], global_step: 975, lr: 0.001000, loss: 1.723701, loss_shrink_maps: 0.897391, loss_threshold_maps: 0.630230, loss_binary_maps: 0.177677, avg_reader_cost: 2.19727 s, avg_batch_cost: 2.56722 s, avg_samples: 12.5, ips: 4.86907 samples/s, eta: 8:41:25
[2024/07/27 12:20:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:20:42] ppocr INFO: epoch: [326/1500], global_step: 978, lr: 0.001000, loss: 1.722454, loss_shrink_maps: 0.916989, loss_threshold_maps: 0.622297, loss_binary_maps: 0.181424, avg_reader_cost: 2.24976 s, avg_batch_cost: 2.60502 s, avg_samples: 12.5, ips: 4.79844 samples/s, eta: 8:40:56
[2024/07/27 12:20:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:20:51] ppocr INFO: epoch: [327/1500], global_step: 980, lr: 0.001000, loss: 1.737364, loss_shrink_maps: 0.929029, loss_threshold_maps: 0.629260, loss_binary_maps: 0.183629, avg_reader_cost: 1.50305 s, avg_batch_cost: 1.70926 s, avg_samples: 9.6, ips: 5.61645 samples/s, eta: 8:40:36
[2024/07/27 12:20:52] ppocr INFO: epoch: [327/1500], global_step: 981, lr: 0.001000, loss: 1.737364, loss_shrink_maps: 0.929029, loss_threshold_maps: 0.629260, loss_binary_maps: 0.183629, avg_reader_cost: 0.90109 s, avg_batch_cost: 0.95632 s, avg_samples: 2.9, ips: 3.03246 samples/s, eta: 8:40:30
[2024/07/27 12:20:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:21:02] ppocr INFO: epoch: [328/1500], global_step: 984, lr: 0.001000, loss: 1.643622, loss_shrink_maps: 0.873408, loss_threshold_maps: 0.607167, loss_binary_maps: 0.173130, avg_reader_cost: 2.23894 s, avg_batch_cost: 2.63039 s, avg_samples: 12.5, ips: 4.75214 samples/s, eta: 8:40:02
[2024/07/27 12:21:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:21:11] ppocr INFO: epoch: [329/1500], global_step: 987, lr: 0.001000, loss: 1.623988, loss_shrink_maps: 0.867907, loss_threshold_maps: 0.587261, loss_binary_maps: 0.171727, avg_reader_cost: 2.18255 s, avg_batch_cost: 2.54663 s, avg_samples: 12.5, ips: 4.90845 samples/s, eta: 8:39:31
[2024/07/27 12:21:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:21:21] ppocr INFO: epoch: [330/1500], global_step: 990, lr: 0.001000, loss: 1.605482, loss_shrink_maps: 0.867147, loss_threshold_maps: 0.589623, loss_binary_maps: 0.171454, avg_reader_cost: 2.23193 s, avg_batch_cost: 2.59840 s, avg_samples: 12.5, ips: 4.81066 samples/s, eta: 8:39:02
[2024/07/27 12:21:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:21:31] ppocr INFO: epoch: [331/1500], global_step: 993, lr: 0.001000, loss: 1.647953, loss_shrink_maps: 0.888259, loss_threshold_maps: 0.589623, loss_binary_maps: 0.175924, avg_reader_cost: 2.14988 s, avg_batch_cost: 2.49753 s, avg_samples: 12.5, ips: 5.00494 samples/s, eta: 8:38:30
[2024/07/27 12:21:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:21:40] ppocr INFO: epoch: [332/1500], global_step: 996, lr: 0.001000, loss: 1.647953, loss_shrink_maps: 0.888259, loss_threshold_maps: 0.598223, loss_binary_maps: 0.175924, avg_reader_cost: 2.33611 s, avg_batch_cost: 2.57490 s, avg_samples: 12.5, ips: 4.85455 samples/s, eta: 8:38:00
[2024/07/27 12:21:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:21:50] ppocr INFO: epoch: [333/1500], global_step: 999, lr: 0.001000, loss: 1.565772, loss_shrink_maps: 0.813649, loss_threshold_maps: 0.592691, loss_binary_maps: 0.161470, avg_reader_cost: 2.29144 s, avg_batch_cost: 2.54441 s, avg_samples: 12.5, ips: 4.91273 samples/s, eta: 8:37:29
[2024/07/27 12:21:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:21:58] ppocr INFO: epoch: [334/1500], global_step: 1000, lr: 0.001000, loss: 1.565772, loss_shrink_maps: 0.813649, loss_threshold_maps: 0.592691, loss_binary_maps: 0.161470, avg_reader_cost: 0.68411 s, avg_batch_cost: 0.77596 s, avg_samples: 4.8, ips: 6.18586 samples/s, eta: 8:37:17
[2024/07/27 12:22:00] ppocr INFO: epoch: [334/1500], global_step: 1002, lr: 0.001000, loss: 1.630585, loss_shrink_maps: 0.859619, loss_threshold_maps: 0.599652, loss_binary_maps: 0.170580, avg_reader_cost: 1.64470 s, avg_batch_cost: 1.79301 s, avg_samples: 7.7, ips: 4.29446 samples/s, eta: 8:37:00
[2024/07/27 12:22:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:22:10] ppocr INFO: epoch: [335/1500], global_step: 1005, lr: 0.001000, loss: 1.719001, loss_shrink_maps: 0.927460, loss_threshold_maps: 0.608854, loss_binary_maps: 0.184001, avg_reader_cost: 2.35418 s, avg_batch_cost: 2.61029 s, avg_samples: 12.5, ips: 4.78875 samples/s, eta: 8:36:31
[2024/07/27 12:22:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:22:20] ppocr INFO: epoch: [336/1500], global_step: 1008, lr: 0.001000, loss: 1.736602, loss_shrink_maps: 0.933544, loss_threshold_maps: 0.612797, loss_binary_maps: 0.185048, avg_reader_cost: 2.21016 s, avg_batch_cost: 2.57945 s, avg_samples: 12.5, ips: 4.84599 samples/s, eta: 8:36:02
[2024/07/27 12:22:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:22:29] ppocr INFO: epoch: [337/1500], global_step: 1010, lr: 0.001000, loss: 1.718738, loss_shrink_maps: 0.927460, loss_threshold_maps: 0.612797, loss_binary_maps: 0.184001, avg_reader_cost: 1.47154 s, avg_batch_cost: 1.66735 s, avg_samples: 9.6, ips: 5.75765 samples/s, eta: 8:35:40
[2024/07/27 12:22:29] ppocr INFO: epoch: [337/1500], global_step: 1011, lr: 0.001000, loss: 1.718738, loss_shrink_maps: 0.927460, loss_threshold_maps: 0.612797, loss_binary_maps: 0.184001, avg_reader_cost: 0.87982 s, avg_batch_cost: 0.93497 s, avg_samples: 2.9, ips: 3.10172 samples/s, eta: 8:35:33
[2024/07/27 12:22:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:22:39] ppocr INFO: epoch: [338/1500], global_step: 1014, lr: 0.001000, loss: 1.688377, loss_shrink_maps: 0.894452, loss_threshold_maps: 0.610152, loss_binary_maps: 0.177183, avg_reader_cost: 2.29693 s, avg_batch_cost: 2.67632 s, avg_samples: 12.5, ips: 4.67059 samples/s, eta: 8:35:07
[2024/07/27 12:22:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:22:49] ppocr INFO: epoch: [339/1500], global_step: 1017, lr: 0.001000, loss: 1.732676, loss_shrink_maps: 0.927916, loss_threshold_maps: 0.615392, loss_binary_maps: 0.184265, avg_reader_cost: 2.23272 s, avg_batch_cost: 2.57819 s, avg_samples: 12.5, ips: 4.84836 samples/s, eta: 8:34:38
[2024/07/27 12:22:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:22:59] ppocr INFO: epoch: [340/1500], global_step: 1020, lr: 0.001000, loss: 1.735215, loss_shrink_maps: 0.933544, loss_threshold_maps: 0.617018, loss_binary_maps: 0.185048, avg_reader_cost: 2.39332 s, avg_batch_cost: 2.63502 s, avg_samples: 12.5, ips: 4.74380 samples/s, eta: 8:34:10

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.76it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.53it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.71it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 18.20it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.55it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 18.89it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.36it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 19.12it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 21.17it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 20.04it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 20.53it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 19.66it/s]
eval model::   7%|▋         | 35/500 [00:01<00:23, 19.50it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 20.07it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.52it/s]
eval model::   9%|▊         | 43/500 [00:02<00:25, 17.67it/s]
eval model::   9%|▉         | 46/500 [00:02<00:25, 17.59it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 19.59it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 21.08it/s]
eval model::  11%|█         | 55/500 [00:02<00:19, 22.36it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:21, 20.86it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.48it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.58it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:21, 19.91it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 20.94it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 19.82it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:20, 21.05it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:18, 22.43it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:16, 24.96it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:18, 22.83it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:21, 19.44it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:21, 18.72it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:21, 18.59it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:20, 19.99it/s]
eval model::  20%|██        | 100/500 [00:05<00:20, 19.52it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 21.39it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.58it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.35it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 20.74it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:18, 20.61it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:17, 22.40it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:19, 19.55it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 19.25it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.38it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 19.37it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 19.29it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 20.26it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:17, 20.19it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.25it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:18, 18.98it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:16, 21.03it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:18, 19.39it/s]
eval model::  30%|███       | 152/500 [00:07<00:18, 18.47it/s]
eval model::  31%|███       | 155/500 [00:07<00:17, 20.03it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:16, 20.76it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:18, 18.26it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:18, 18.53it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:15, 21.13it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:14, 22.26it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:16, 19.60it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:18, 17.14it/s]
eval model::  36%|███▌      | 178/500 [00:08<00:16, 19.07it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:16, 19.66it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:13, 22.66it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:14, 21.42it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:13, 22.27it/s]
eval model::  39%|███▉      | 194/500 [00:09<00:13, 22.36it/s]
eval model::  39%|███▉      | 197/500 [00:09<00:14, 20.55it/s]
eval model::  40%|████      | 200/500 [00:09<00:14, 21.22it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 19.83it/s]
eval model::  41%|████      | 206/500 [00:10<00:19, 15.01it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:18, 15.48it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:19, 15.18it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:17, 16.43it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.64it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 14.99it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 18.02it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:15, 17.42it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:13, 20.94it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:11, 23.10it/s]
eval model::  46%|████▋     | 232/500 [00:11<00:10, 24.77it/s]
eval model::  47%|████▋     | 235/500 [00:11<00:10, 24.42it/s]
eval model::  48%|████▊     | 238/500 [00:11<00:10, 25.37it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:09, 26.23it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:11, 22.77it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:11, 22.02it/s]
eval model::  50%|█████     | 250/500 [00:12<00:12, 20.56it/s]
eval model::  51%|█████     | 253/500 [00:12<00:11, 21.79it/s]
eval model::  51%|█████     | 256/500 [00:12<00:11, 21.84it/s]
eval model::  52%|█████▏    | 259/500 [00:12<00:10, 22.75it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 21.18it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:11, 19.65it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:12, 19.31it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:11, 19.29it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:11, 19.92it/s]
eval model::  55%|█████▌    | 276/500 [00:13<00:12, 18.27it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 19.20it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 21.06it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:15, 13.97it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:14, 14.51it/s]
eval model::  58%|█████▊    | 290/500 [00:14<00:12, 17.32it/s]
eval model::  59%|█████▊    | 293/500 [00:14<00:10, 19.24it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:12, 15.76it/s]
eval model::  60%|██████    | 300/500 [00:15<00:11, 18.00it/s]
eval model::  61%|██████    | 303/500 [00:15<00:10, 19.26it/s]
eval model::  61%|██████    | 306/500 [00:15<00:09, 20.51it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:08, 21.62it/s]
eval model::  62%|██████▏   | 312/500 [00:15<00:08, 21.85it/s]
eval model::  63%|██████▎   | 315/500 [00:15<00:08, 20.82it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 21.63it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 20.70it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 20.79it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:07, 21.81it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:07, 21.40it/s]
eval model::  67%|██████▋   | 334/500 [00:16<00:06, 24.67it/s]
eval model::  67%|██████▋   | 337/500 [00:16<00:07, 22.36it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:10, 15.59it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:09, 17.16it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:08, 17.95it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:07, 19.51it/s]
eval model::  70%|███████   | 352/500 [00:17<00:07, 20.37it/s]
eval model::  71%|███████   | 355/500 [00:17<00:06, 21.09it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:07, 20.02it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:06, 19.95it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.46it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:06, 19.22it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:06, 20.83it/s]
eval model::  75%|███████▍  | 373/500 [00:18<00:06, 19.82it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 20.90it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 21.58it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 20.69it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 21.93it/s]
eval model::  78%|███████▊  | 388/500 [00:19<00:07, 14.69it/s]
eval model::  78%|███████▊  | 391/500 [00:19<00:06, 16.72it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:06, 17.53it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 19.94it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 19.64it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 19.44it/s]
eval model::  81%|████████  | 406/500 [00:20<00:04, 21.58it/s]
eval model::  82%|████████▏ | 410/500 [00:20<00:03, 23.60it/s]
eval model::  83%|████████▎ | 413/500 [00:20<00:04, 21.53it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:04, 20.34it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 20.01it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 15.77it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 15.61it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:04, 15.47it/s]
eval model::  86%|████████▌ | 429/500 [00:21<00:03, 17.85it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:04, 16.89it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 20.22it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 20.69it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 20.22it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 21.53it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 22.36it/s]
eval model::  90%|█████████ | 451/500 [00:22<00:02, 21.71it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 22.31it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 22.61it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 20.83it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 19.32it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:01, 19.30it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 20.02it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 19.68it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.55it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 20.84it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 21.02it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 21.63it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 18.87it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 20.23it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 19.54it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 20.96it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 22.37it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.77it/s]
[2024/07/27 12:23:24] ppocr INFO: cur metric, precision: 0.7545857052498419, recall: 0.5743861338468945, hmean: 0.6522689994532532, fps: 44.608739620472015
[2024/07/27 12:23:25] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 12:23:25] ppocr INFO: best metric, hmean: 0.6522689994532532, precision: 0.7545857052498419, recall: 0.5743861338468945, fps: 44.608739620472015, best_epoch: 340
[2024/07/27 12:23:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:23:34] ppocr INFO: epoch: [341/1500], global_step: 1023, lr: 0.001000, loss: 1.728294, loss_shrink_maps: 0.922572, loss_threshold_maps: 0.609758, loss_binary_maps: 0.183101, avg_reader_cost: 2.34382 s, avg_batch_cost: 2.62152 s, avg_samples: 12.5, ips: 4.76822 samples/s, eta: 8:33:43
[2024/07/27 12:23:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:23:44] ppocr INFO: epoch: [342/1500], global_step: 1026, lr: 0.001000, loss: 1.657974, loss_shrink_maps: 0.869796, loss_threshold_maps: 0.605337, loss_binary_maps: 0.172275, avg_reader_cost: 2.14580 s, avg_batch_cost: 2.49849 s, avg_samples: 12.5, ips: 5.00302 samples/s, eta: 8:33:10
[2024/07/27 12:23:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:23:54] ppocr INFO: epoch: [343/1500], global_step: 1029, lr: 0.001000, loss: 1.630020, loss_shrink_maps: 0.869796, loss_threshold_maps: 0.594932, loss_binary_maps: 0.172275, avg_reader_cost: 2.27951 s, avg_batch_cost: 2.64670 s, avg_samples: 12.5, ips: 4.72285 samples/s, eta: 8:32:43
[2024/07/27 12:23:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:24:02] ppocr INFO: epoch: [344/1500], global_step: 1030, lr: 0.001000, loss: 1.630020, loss_shrink_maps: 0.869796, loss_threshold_maps: 0.594932, loss_binary_maps: 0.172275, avg_reader_cost: 0.71970 s, avg_batch_cost: 0.81555 s, avg_samples: 4.8, ips: 5.88563 samples/s, eta: 8:32:32
[2024/07/27 12:24:04] ppocr INFO: epoch: [344/1500], global_step: 1032, lr: 0.001000, loss: 1.618798, loss_shrink_maps: 0.861421, loss_threshold_maps: 0.589819, loss_binary_maps: 0.170711, avg_reader_cost: 1.72340 s, avg_batch_cost: 1.87128 s, avg_samples: 7.7, ips: 4.11482 samples/s, eta: 8:32:18
[2024/07/27 12:24:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:24:14] ppocr INFO: epoch: [345/1500], global_step: 1035, lr: 0.001000, loss: 1.618890, loss_shrink_maps: 0.869044, loss_threshold_maps: 0.590391, loss_binary_maps: 0.172027, avg_reader_cost: 2.36466 s, avg_batch_cost: 2.60600 s, avg_samples: 12.5, ips: 4.79662 samples/s, eta: 8:31:49
[2024/07/27 12:24:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:24:23] ppocr INFO: epoch: [346/1500], global_step: 1038, lr: 0.001000, loss: 1.618890, loss_shrink_maps: 0.869044, loss_threshold_maps: 0.590391, loss_binary_maps: 0.172027, avg_reader_cost: 2.09731 s, avg_batch_cost: 2.41226 s, avg_samples: 12.5, ips: 5.18187 samples/s, eta: 8:31:15
[2024/07/27 12:24:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:24:32] ppocr INFO: epoch: [347/1500], global_step: 1040, lr: 0.001000, loss: 1.618890, loss_shrink_maps: 0.869044, loss_threshold_maps: 0.590391, loss_binary_maps: 0.172027, avg_reader_cost: 1.43156 s, avg_batch_cost: 1.64865 s, avg_samples: 9.6, ips: 5.82295 samples/s, eta: 8:30:53
[2024/07/27 12:24:33] ppocr INFO: epoch: [347/1500], global_step: 1041, lr: 0.001000, loss: 1.628310, loss_shrink_maps: 0.869564, loss_threshold_maps: 0.590391, loss_binary_maps: 0.172755, avg_reader_cost: 0.87079 s, avg_batch_cost: 0.92575 s, avg_samples: 2.9, ips: 3.13260 samples/s, eta: 8:30:45
[2024/07/27 12:24:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:24:43] ppocr INFO: epoch: [348/1500], global_step: 1044, lr: 0.001000, loss: 1.631128, loss_shrink_maps: 0.864365, loss_threshold_maps: 0.601950, loss_binary_maps: 0.171697, avg_reader_cost: 2.20862 s, avg_batch_cost: 2.53534 s, avg_samples: 12.5, ips: 4.93030 samples/s, eta: 8:30:15
[2024/07/27 12:24:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:24:52] ppocr INFO: epoch: [349/1500], global_step: 1047, lr: 0.001000, loss: 1.670306, loss_shrink_maps: 0.887214, loss_threshold_maps: 0.604722, loss_binary_maps: 0.176279, avg_reader_cost: 2.24591 s, avg_batch_cost: 2.47927 s, avg_samples: 12.5, ips: 5.04180 samples/s, eta: 8:29:42
[2024/07/27 12:24:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:25:02] ppocr INFO: epoch: [350/1500], global_step: 1050, lr: 0.001000, loss: 1.671012, loss_shrink_maps: 0.883489, loss_threshold_maps: 0.608379, loss_binary_maps: 0.175662, avg_reader_cost: 2.33151 s, avg_batch_cost: 2.57543 s, avg_samples: 12.5, ips: 4.85356 samples/s, eta: 8:29:13
[2024/07/27 12:25:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:25:12] ppocr INFO: epoch: [351/1500], global_step: 1053, lr: 0.001000, loss: 1.685991, loss_shrink_maps: 0.896762, loss_threshold_maps: 0.615299, loss_binary_maps: 0.177859, avg_reader_cost: 2.28325 s, avg_batch_cost: 2.52500 s, avg_samples: 12.5, ips: 4.95049 samples/s, eta: 8:28:42
[2024/07/27 12:25:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:25:21] ppocr INFO: epoch: [352/1500], global_step: 1056, lr: 0.001000, loss: 1.677038, loss_shrink_maps: 0.883489, loss_threshold_maps: 0.612287, loss_binary_maps: 0.175662, avg_reader_cost: 2.23202 s, avg_batch_cost: 2.57493 s, avg_samples: 12.5, ips: 4.85450 samples/s, eta: 8:28:13
[2024/07/27 12:25:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:25:31] ppocr INFO: epoch: [353/1500], global_step: 1059, lr: 0.001000, loss: 1.677038, loss_shrink_maps: 0.883489, loss_threshold_maps: 0.612287, loss_binary_maps: 0.175662, avg_reader_cost: 2.27362 s, avg_batch_cost: 2.51149 s, avg_samples: 12.5, ips: 4.97712 samples/s, eta: 8:27:42
[2024/07/27 12:25:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:25:39] ppocr INFO: epoch: [354/1500], global_step: 1060, lr: 0.001000, loss: 1.683215, loss_shrink_maps: 0.883489, loss_threshold_maps: 0.615299, loss_binary_maps: 0.175662, avg_reader_cost: 0.67754 s, avg_batch_cost: 0.76911 s, avg_samples: 4.8, ips: 6.24098 samples/s, eta: 8:27:29
[2024/07/27 12:25:40] ppocr INFO: epoch: [354/1500], global_step: 1062, lr: 0.001000, loss: 1.683215, loss_shrink_maps: 0.883489, loss_threshold_maps: 0.609564, loss_binary_maps: 0.175662, avg_reader_cost: 1.63007 s, avg_batch_cost: 1.77706 s, avg_samples: 7.7, ips: 4.33299 samples/s, eta: 8:27:11
[2024/07/27 12:25:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:25:50] ppocr INFO: epoch: [355/1500], global_step: 1065, lr: 0.001000, loss: 1.683215, loss_shrink_maps: 0.883489, loss_threshold_maps: 0.609564, loss_binary_maps: 0.175662, avg_reader_cost: 2.42777 s, avg_batch_cost: 2.67942 s, avg_samples: 12.5, ips: 4.66520 samples/s, eta: 8:26:46
[2024/07/27 12:25:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:26:00] ppocr INFO: epoch: [356/1500], global_step: 1068, lr: 0.001000, loss: 1.646456, loss_shrink_maps: 0.872939, loss_threshold_maps: 0.596045, loss_binary_maps: 0.173083, avg_reader_cost: 2.21861 s, avg_batch_cost: 2.52637 s, avg_samples: 12.5, ips: 4.94781 samples/s, eta: 8:26:15
[2024/07/27 12:26:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:26:09] ppocr INFO: epoch: [357/1500], global_step: 1070, lr: 0.001000, loss: 1.636692, loss_shrink_maps: 0.869790, loss_threshold_maps: 0.596045, loss_binary_maps: 0.172319, avg_reader_cost: 1.43354 s, avg_batch_cost: 1.61840 s, avg_samples: 9.6, ips: 5.93179 samples/s, eta: 8:25:52
[2024/07/27 12:26:09] ppocr INFO: epoch: [357/1500], global_step: 1071, lr: 0.001000, loss: 1.636692, loss_shrink_maps: 0.869790, loss_threshold_maps: 0.596045, loss_binary_maps: 0.172319, avg_reader_cost: 0.85571 s, avg_batch_cost: 0.91152 s, avg_samples: 2.9, ips: 3.18149 samples/s, eta: 8:25:44
[2024/07/27 12:26:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:26:19] ppocr INFO: epoch: [358/1500], global_step: 1074, lr: 0.001000, loss: 1.610624, loss_shrink_maps: 0.851299, loss_threshold_maps: 0.593802, loss_binary_maps: 0.168560, avg_reader_cost: 2.30096 s, avg_batch_cost: 2.53990 s, avg_samples: 12.5, ips: 4.92146 samples/s, eta: 8:25:14
[2024/07/27 12:26:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:26:29] ppocr INFO: epoch: [359/1500], global_step: 1077, lr: 0.001000, loss: 1.610624, loss_shrink_maps: 0.851299, loss_threshold_maps: 0.593308, loss_binary_maps: 0.168560, avg_reader_cost: 2.30421 s, avg_batch_cost: 2.54474 s, avg_samples: 12.5, ips: 4.91209 samples/s, eta: 8:24:44
[2024/07/27 12:26:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:26:38] ppocr INFO: epoch: [360/1500], global_step: 1080, lr: 0.001000, loss: 1.589877, loss_shrink_maps: 0.824195, loss_threshold_maps: 0.590613, loss_binary_maps: 0.162611, avg_reader_cost: 2.32417 s, avg_batch_cost: 2.58264 s, avg_samples: 12.5, ips: 4.84000 samples/s, eta: 8:24:15

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.27it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 16.60it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.24it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.37it/s]
eval model::   2%|▏         | 11/500 [00:00<00:32, 14.86it/s]
eval model::   3%|▎         | 14/500 [00:00<00:30, 15.98it/s]
eval model::   3%|▎         | 17/500 [00:01<00:26, 18.17it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 19.03it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 20.81it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 21.40it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 22.22it/s]
eval model::   6%|▋         | 32/500 [00:01<00:21, 21.30it/s]
eval model::   7%|▋         | 35/500 [00:01<00:21, 21.21it/s]
eval model::   8%|▊         | 38/500 [00:01<00:21, 21.36it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.42it/s]
eval model::   9%|▊         | 43/500 [00:02<00:26, 17.01it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 17.53it/s]
eval model::   9%|▉         | 47/500 [00:02<00:28, 15.99it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.18it/s]
eval model::  11%|█         | 53/500 [00:02<00:22, 19.63it/s]
eval model::  11%|█         | 56/500 [00:02<00:20, 21.50it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:22, 19.63it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:25, 16.94it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:25, 16.86it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:20, 20.85it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 20.98it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:20, 20.77it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:20, 20.53it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:19, 21.94it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:18, 22.66it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:19, 21.61it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.08it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:24, 16.99it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:24, 16.62it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:21, 18.55it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.90it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.74it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 22.95it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.67it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:17, 21.64it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:17, 22.56it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:17, 21.36it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 20.55it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 20.79it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.37it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:21, 17.40it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:22, 16.72it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 16.74it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:21, 17.37it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:20, 17.62it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:20, 17.69it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:20, 17.55it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:19, 18.26it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.43it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.82it/s]
eval model::  30%|███       | 152/500 [00:07<00:19, 17.70it/s]
eval model::  31%|███       | 155/500 [00:08<00:17, 19.81it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:16, 20.34it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:20, 16.43it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.07it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:18, 17.69it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 17.64it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:18, 18.07it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:19, 17.04it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:21, 14.94it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 15.68it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.45it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.20it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:18, 17.02it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:18, 17.37it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:18, 17.21it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:16, 19.03it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:14, 21.60it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:14, 21.01it/s]
eval model::  40%|████      | 200/500 [00:10<00:13, 22.89it/s]
eval model::  41%|████      | 203/500 [00:10<00:12, 24.20it/s]
eval model::  41%|████      | 206/500 [00:11<00:22, 12.88it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:22, 12.95it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:21, 13.43it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.91it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.42it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.39it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:18, 15.48it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.84it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 16.36it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:16, 16.36it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:14, 18.96it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:13, 19.89it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:13, 20.18it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.06it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 19.19it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.91it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 22.28it/s]
eval model::  50%|█████     | 251/500 [00:13<00:10, 22.89it/s]
eval model::  51%|█████     | 254/500 [00:13<00:10, 24.24it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 24.14it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 22.75it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:11, 20.15it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:12, 18.02it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.43it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:13, 17.18it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.33it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:10, 20.33it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 21.66it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:18, 11.90it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:16, 12.66it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:13, 15.02it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:13, 15.33it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:12, 15.89it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:14, 13.66it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 16.07it/s]
eval model::  60%|██████    | 301/500 [00:16<00:13, 15.21it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.75it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.85it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.08it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:08, 20.87it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:08, 20.66it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 22.01it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.75it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 21.12it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:07, 22.76it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:07, 23.03it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:07, 22.65it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 18.74it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 14.77it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.71it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 19.07it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.54it/s]
eval model::  70%|███████   | 352/500 [00:19<00:06, 21.37it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 21.56it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 22.23it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 17.92it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.54it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.53it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 19.96it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:06, 20.50it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:05, 21.49it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:04, 24.66it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 22.68it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:04, 23.93it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:07, 15.27it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:06, 16.83it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:05, 18.52it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 20.36it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 19.54it/s]
eval model::  81%|████████  | 403/500 [00:21<00:05, 19.32it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 19.73it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 19.98it/s]
eval model::  82%|████████▏ | 412/500 [00:22<00:04, 21.84it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:03, 21.52it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 17.69it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.38it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 14.22it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 13.69it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.55it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 18.80it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 18.51it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 19.46it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 21.59it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 20.79it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 21.01it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 19.93it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 19.18it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 20.51it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:02, 20.41it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:02, 20.06it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 16.63it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 16.16it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 15.58it/s]
eval model::  94%|█████████▍| 469/500 [00:25<00:01, 17.35it/s]
eval model::  94%|█████████▍| 472/500 [00:25<00:01, 18.54it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 17.92it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 19.82it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 20.13it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 21.47it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 18.76it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 20.59it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 19.89it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 21.66it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 23.67it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.68it/s]
[2024/07/27 12:27:05] ppocr INFO: cur metric, precision: 0.6566523605150214, recall: 0.5893115069812229, hmean: 0.6211621415884293, fps: 45.232768599544
[2024/07/27 12:27:05] ppocr INFO: best metric, hmean: 0.6522689994532532, precision: 0.7545857052498419, recall: 0.5743861338468945, fps: 44.608739620472015, best_epoch: 340
[2024/07/27 12:27:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:27:14] ppocr INFO: epoch: [361/1500], global_step: 1083, lr: 0.001000, loss: 1.627146, loss_shrink_maps: 0.870661, loss_threshold_maps: 0.591430, loss_binary_maps: 0.172955, avg_reader_cost: 2.04921 s, avg_batch_cost: 2.35534 s, avg_samples: 12.5, ips: 5.30709 samples/s, eta: 8:23:39
[2024/07/27 12:27:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:27:23] ppocr INFO: epoch: [362/1500], global_step: 1086, lr: 0.001000, loss: 1.639126, loss_shrink_maps: 0.870661, loss_threshold_maps: 0.593308, loss_binary_maps: 0.172955, avg_reader_cost: 2.19651 s, avg_batch_cost: 2.56076 s, avg_samples: 12.5, ips: 4.88137 samples/s, eta: 8:23:10
[2024/07/27 12:27:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:27:34] ppocr INFO: epoch: [363/1500], global_step: 1089, lr: 0.001000, loss: 1.627146, loss_shrink_maps: 0.858169, loss_threshold_maps: 0.593532, loss_binary_maps: 0.170930, avg_reader_cost: 2.42886 s, avg_batch_cost: 2.66820 s, avg_samples: 12.5, ips: 4.68481 samples/s, eta: 8:22:44
[2024/07/27 12:27:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:27:42] ppocr INFO: epoch: [364/1500], global_step: 1090, lr: 0.001000, loss: 1.610336, loss_shrink_maps: 0.832298, loss_threshold_maps: 0.592124, loss_binary_maps: 0.165384, avg_reader_cost: 0.69288 s, avg_batch_cost: 0.78654 s, avg_samples: 4.8, ips: 6.10268 samples/s, eta: 8:22:32
[2024/07/27 12:27:43] ppocr INFO: epoch: [364/1500], global_step: 1092, lr: 0.001000, loss: 1.577479, loss_shrink_maps: 0.808791, loss_threshold_maps: 0.588450, loss_binary_maps: 0.160515, avg_reader_cost: 1.66604 s, avg_batch_cost: 1.81389 s, avg_samples: 7.7, ips: 4.24502 samples/s, eta: 8:22:16
[2024/07/27 12:27:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:27:53] ppocr INFO: epoch: [365/1500], global_step: 1095, lr: 0.001000, loss: 1.542491, loss_shrink_maps: 0.808791, loss_threshold_maps: 0.577127, loss_binary_maps: 0.160515, avg_reader_cost: 2.22672 s, avg_batch_cost: 2.58095 s, avg_samples: 12.5, ips: 4.84318 samples/s, eta: 8:21:47
[2024/07/27 12:27:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:28:03] ppocr INFO: epoch: [366/1500], global_step: 1098, lr: 0.001000, loss: 1.555213, loss_shrink_maps: 0.814050, loss_threshold_maps: 0.582250, loss_binary_maps: 0.161545, avg_reader_cost: 2.27913 s, avg_batch_cost: 2.63226 s, avg_samples: 12.5, ips: 4.74878 samples/s, eta: 8:21:20
[2024/07/27 12:28:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:28:13] ppocr INFO: epoch: [367/1500], global_step: 1100, lr: 0.001000, loss: 1.566954, loss_shrink_maps: 0.825996, loss_threshold_maps: 0.584319, loss_binary_maps: 0.163663, avg_reader_cost: 1.42491 s, avg_batch_cost: 1.74463 s, avg_samples: 9.6, ips: 5.50260 samples/s, eta: 8:21:01
[2024/07/27 12:28:13] ppocr INFO: epoch: [367/1500], global_step: 1101, lr: 0.001000, loss: 1.555213, loss_shrink_maps: 0.814050, loss_threshold_maps: 0.584319, loss_binary_maps: 0.161545, avg_reader_cost: 0.91841 s, avg_batch_cost: 0.97370 s, avg_samples: 2.9, ips: 2.97833 samples/s, eta: 8:20:55
[2024/07/27 12:28:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:28:23] ppocr INFO: epoch: [368/1500], global_step: 1104, lr: 0.001000, loss: 1.555213, loss_shrink_maps: 0.814050, loss_threshold_maps: 0.584319, loss_binary_maps: 0.161545, avg_reader_cost: 2.33381 s, avg_batch_cost: 2.57130 s, avg_samples: 12.5, ips: 4.86135 samples/s, eta: 8:20:26
[2024/07/27 12:28:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:28:33] ppocr INFO: epoch: [369/1500], global_step: 1107, lr: 0.001000, loss: 1.542491, loss_shrink_maps: 0.814050, loss_threshold_maps: 0.583304, loss_binary_maps: 0.161512, avg_reader_cost: 2.36138 s, avg_batch_cost: 2.62078 s, avg_samples: 12.5, ips: 4.76958 samples/s, eta: 8:19:59
[2024/07/27 12:28:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:28:43] ppocr INFO: epoch: [370/1500], global_step: 1110, lr: 0.001000, loss: 1.592217, loss_shrink_maps: 0.834901, loss_threshold_maps: 0.598775, loss_binary_maps: 0.164971, avg_reader_cost: 2.39581 s, avg_batch_cost: 2.63176 s, avg_samples: 12.5, ips: 4.74967 samples/s, eta: 8:19:32
[2024/07/27 12:28:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:28:52] ppocr INFO: epoch: [371/1500], global_step: 1113, lr: 0.001000, loss: 1.550358, loss_shrink_maps: 0.809861, loss_threshold_maps: 0.604167, loss_binary_maps: 0.160477, avg_reader_cost: 2.11614 s, avg_batch_cost: 2.44385 s, avg_samples: 12.5, ips: 5.11489 samples/s, eta: 8:18:59
[2024/07/27 12:28:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:29:02] ppocr INFO: epoch: [372/1500], global_step: 1116, lr: 0.001000, loss: 1.524041, loss_shrink_maps: 0.779264, loss_threshold_maps: 0.600784, loss_binary_maps: 0.154351, avg_reader_cost: 2.24749 s, avg_batch_cost: 2.61412 s, avg_samples: 12.5, ips: 4.78172 samples/s, eta: 8:18:31
[2024/07/27 12:29:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:29:11] ppocr INFO: epoch: [373/1500], global_step: 1119, lr: 0.001000, loss: 1.569808, loss_shrink_maps: 0.811535, loss_threshold_maps: 0.604560, loss_binary_maps: 0.160662, avg_reader_cost: 2.06699 s, avg_batch_cost: 2.31759 s, avg_samples: 12.5, ips: 5.39353 samples/s, eta: 8:17:54
[2024/07/27 12:29:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:29:19] ppocr INFO: epoch: [374/1500], global_step: 1120, lr: 0.001000, loss: 1.569808, loss_shrink_maps: 0.811535, loss_threshold_maps: 0.600784, loss_binary_maps: 0.160662, avg_reader_cost: 0.54848 s, avg_batch_cost: 0.78420 s, avg_samples: 4.8, ips: 6.12089 samples/s, eta: 8:17:43
[2024/07/27 12:29:20] ppocr INFO: epoch: [374/1500], global_step: 1122, lr: 0.001000, loss: 1.529591, loss_shrink_maps: 0.794969, loss_threshold_maps: 0.597782, loss_binary_maps: 0.157741, avg_reader_cost: 1.66097 s, avg_batch_cost: 1.80722 s, avg_samples: 7.7, ips: 4.26068 samples/s, eta: 8:17:26
[2024/07/27 12:29:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:29:30] ppocr INFO: epoch: [375/1500], global_step: 1125, lr: 0.001000, loss: 1.529591, loss_shrink_maps: 0.787738, loss_threshold_maps: 0.582820, loss_binary_maps: 0.155925, avg_reader_cost: 2.28082 s, avg_batch_cost: 2.52337 s, avg_samples: 12.5, ips: 4.95370 samples/s, eta: 8:16:56
[2024/07/27 12:29:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:29:40] ppocr INFO: epoch: [376/1500], global_step: 1128, lr: 0.001000, loss: 1.529591, loss_shrink_maps: 0.787738, loss_threshold_maps: 0.564117, loss_binary_maps: 0.155925, avg_reader_cost: 2.36121 s, avg_batch_cost: 2.61231 s, avg_samples: 12.5, ips: 4.78504 samples/s, eta: 8:16:28
[2024/07/27 12:29:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:29:49] ppocr INFO: epoch: [377/1500], global_step: 1130, lr: 0.001000, loss: 1.536876, loss_shrink_maps: 0.805329, loss_threshold_maps: 0.571490, loss_binary_maps: 0.159504, avg_reader_cost: 1.38755 s, avg_batch_cost: 1.69750 s, avg_samples: 9.6, ips: 5.65539 samples/s, eta: 8:16:08
[2024/07/27 12:29:50] ppocr INFO: epoch: [377/1500], global_step: 1131, lr: 0.001000, loss: 1.545118, loss_shrink_maps: 0.807510, loss_threshold_maps: 0.571490, loss_binary_maps: 0.160246, avg_reader_cost: 0.89514 s, avg_batch_cost: 0.95027 s, avg_samples: 2.9, ips: 3.05177 samples/s, eta: 8:16:02
[2024/07/27 12:29:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:30:00] ppocr INFO: epoch: [378/1500], global_step: 1134, lr: 0.001000, loss: 1.554641, loss_shrink_maps: 0.822002, loss_threshold_maps: 0.577362, loss_binary_maps: 0.163203, avg_reader_cost: 2.25878 s, avg_batch_cost: 2.64372 s, avg_samples: 12.5, ips: 4.72819 samples/s, eta: 8:15:35
[2024/07/27 12:30:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:30:10] ppocr INFO: epoch: [379/1500], global_step: 1137, lr: 0.001000, loss: 1.554641, loss_shrink_maps: 0.822002, loss_threshold_maps: 0.588564, loss_binary_maps: 0.163203, avg_reader_cost: 2.34817 s, avg_batch_cost: 2.59773 s, avg_samples: 12.5, ips: 4.81189 samples/s, eta: 8:15:07
[2024/07/27 12:30:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:30:20] ppocr INFO: epoch: [380/1500], global_step: 1140, lr: 0.001000, loss: 1.546909, loss_shrink_maps: 0.807510, loss_threshold_maps: 0.580543, loss_binary_maps: 0.160246, avg_reader_cost: 2.26841 s, avg_batch_cost: 2.64482 s, avg_samples: 12.5, ips: 4.72622 samples/s, eta: 8:14:40

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:56,  8.76it/s]
eval model::   1%|          | 4/500 [00:00<00:32, 15.48it/s]
eval model::   1%|          | 6/500 [00:00<00:32, 15.30it/s]
eval model::   2%|▏         | 8/500 [00:00<00:30, 16.10it/s]
eval model::   2%|▏         | 10/500 [00:00<00:28, 17.03it/s]
eval model::   2%|▏         | 12/500 [00:00<00:33, 14.52it/s]
eval model::   3%|▎         | 14/500 [00:00<00:31, 15.57it/s]
eval model::   3%|▎         | 17/500 [00:01<00:25, 18.95it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 18.56it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.62it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 20.68it/s]
eval model::   6%|▌         | 30/500 [00:01<00:20, 22.78it/s]
eval model::   7%|▋         | 33/500 [00:01<00:21, 21.77it/s]
eval model::   7%|▋         | 36/500 [00:01<00:23, 19.95it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 18.26it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 17.84it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.34it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 17.64it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.50it/s]
eval model::  10%|█         | 52/500 [00:02<00:24, 18.58it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 20.31it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 17.90it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.22it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:30, 14.54it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:23, 18.42it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 20.56it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 21.38it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:18, 22.77it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.17it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.62it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.00it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:19, 20.98it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:25, 15.89it/s]
eval model::  19%|█▉        | 94/500 [00:05<00:23, 17.54it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:21, 18.82it/s]
eval model::  20%|██        | 100/500 [00:05<00:21, 18.18it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 19.87it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 19.79it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:19, 20.35it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 21.08it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:17, 21.60it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:19, 19.19it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:20, 18.50it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:19, 19.16it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:23, 16.30it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:20, 18.57it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:21, 17.49it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:21, 16.81it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:20, 18.19it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:20, 17.73it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:20, 17.32it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.07it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 21.11it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:17, 20.07it/s]
eval model::  30%|███       | 151/500 [00:08<00:19, 17.60it/s]
eval model::  31%|███       | 154/500 [00:08<00:18, 19.13it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:16, 20.32it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:22, 15.34it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:20, 16.54it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:18, 18.16it/s]
eval model::  34%|███▎      | 168/500 [00:09<00:18, 17.88it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:18, 18.21it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:18, 18.10it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:21, 15.31it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:18, 17.89it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:19, 16.66it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 19.26it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 20.87it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:15, 20.53it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:15, 20.29it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 20.64it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:15, 19.91it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.59it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.29it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:22, 12.92it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:21, 13.44it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:21, 13.51it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:20, 14.31it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.30it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:21, 13.22it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:17, 15.65it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:18, 15.04it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:15, 18.03it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:13, 19.70it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:11, 23.14it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:11, 22.46it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 23.71it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:11, 23.05it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:13, 19.45it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:11, 21.42it/s]
eval model::  50%|█████     | 250/500 [00:13<00:12, 20.14it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 22.09it/s]
eval model::  51%|█████     | 256/500 [00:13<00:10, 22.81it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 23.79it/s]
eval model::  52%|█████▏    | 262/500 [00:14<00:11, 20.32it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:12, 18.38it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:12, 18.42it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.37it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 20.25it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 20.01it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 21.73it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:09, 22.41it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:09, 22.61it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:15, 13.35it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.79it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:11, 17.61it/s]
eval model::  59%|█████▉    | 297/500 [00:16<00:13, 15.29it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 16.08it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.56it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 17.86it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 19.44it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:08, 21.24it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:09, 20.64it/s]
eval model::  63%|██████▎   | 317/500 [00:17<00:08, 20.73it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:08, 21.50it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:08, 20.18it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:08, 20.66it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 20.87it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:07, 21.09it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 22.85it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 19.74it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:10, 15.89it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:08, 17.84it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 18.41it/s]
eval model::  70%|███████   | 350/500 [00:18<00:07, 20.48it/s]
eval model::  71%|███████   | 353/500 [00:18<00:07, 20.91it/s]
eval model::  71%|███████   | 356/500 [00:19<00:07, 19.35it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:06, 20.15it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:07, 19.62it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 18.57it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 18.67it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:05, 21.65it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:05, 23.08it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 24.48it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 23.40it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:04, 24.67it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:07, 15.25it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:05, 18.49it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 19.97it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:04, 21.46it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:04, 22.62it/s]
eval model::  80%|████████  | 402/500 [00:21<00:04, 20.96it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 20.10it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.43it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 23.41it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 23.12it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 22.13it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 15.18it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 15.54it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 16.02it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.32it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 18.29it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 19.05it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 20.47it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 23.25it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 21.77it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 22.54it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 22.65it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.87it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 22.78it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:02, 20.57it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:02, 18.97it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 15.50it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 15.51it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:02, 16.16it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 18.19it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 18.21it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.03it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:01, 21.61it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 22.65it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 21.77it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 20.32it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 18.52it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 15.81it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 16.58it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 18.77it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.99it/s]
[2024/07/27 12:30:46] ppocr INFO: cur metric, precision: 0.7187857961053837, recall: 0.6042368801155513, hmean: 0.6565524457232539, fps: 45.296458194658534
[2024/07/27 12:30:46] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 12:30:46] ppocr INFO: best metric, hmean: 0.6565524457232539, precision: 0.7187857961053837, recall: 0.6042368801155513, fps: 45.296458194658534, best_epoch: 380
[2024/07/27 12:30:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:30:55] ppocr INFO: epoch: [381/1500], global_step: 1143, lr: 0.001000, loss: 1.592096, loss_shrink_maps: 0.855868, loss_threshold_maps: 0.585414, loss_binary_maps: 0.168762, avg_reader_cost: 2.11430 s, avg_batch_cost: 2.50845 s, avg_samples: 12.5, ips: 4.98316 samples/s, eta: 8:14:10
[2024/07/27 12:30:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:31:05] ppocr INFO: epoch: [382/1500], global_step: 1146, lr: 0.001000, loss: 1.674035, loss_shrink_maps: 0.881887, loss_threshold_maps: 0.597282, loss_binary_maps: 0.175039, avg_reader_cost: 2.16976 s, avg_batch_cost: 2.51161 s, avg_samples: 12.5, ips: 4.97690 samples/s, eta: 8:13:39
[2024/07/27 12:31:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:31:15] ppocr INFO: epoch: [383/1500], global_step: 1149, lr: 0.001000, loss: 1.626851, loss_shrink_maps: 0.869450, loss_threshold_maps: 0.593112, loss_binary_maps: 0.171953, avg_reader_cost: 2.33104 s, avg_batch_cost: 2.60550 s, avg_samples: 12.5, ips: 4.79754 samples/s, eta: 8:13:11
[2024/07/27 12:31:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:31:23] ppocr INFO: epoch: [384/1500], global_step: 1150, lr: 0.001000, loss: 1.674035, loss_shrink_maps: 0.881887, loss_threshold_maps: 0.597282, loss_binary_maps: 0.175039, avg_reader_cost: 0.73096 s, avg_batch_cost: 0.81790 s, avg_samples: 4.8, ips: 5.86871 samples/s, eta: 8:13:01
[2024/07/27 12:31:25] ppocr INFO: epoch: [384/1500], global_step: 1152, lr: 0.001000, loss: 1.668110, loss_shrink_maps: 0.890897, loss_threshold_maps: 0.594563, loss_binary_maps: 0.176684, avg_reader_cost: 1.72817 s, avg_batch_cost: 1.87579 s, avg_samples: 7.7, ips: 4.10494 samples/s, eta: 8:12:46
[2024/07/27 12:31:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:31:35] ppocr INFO: epoch: [385/1500], global_step: 1155, lr: 0.001000, loss: 1.676580, loss_shrink_maps: 0.890897, loss_threshold_maps: 0.595018, loss_binary_maps: 0.176684, avg_reader_cost: 2.32088 s, avg_batch_cost: 2.55769 s, avg_samples: 12.5, ips: 4.88722 samples/s, eta: 8:12:17
[2024/07/27 12:31:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:31:45] ppocr INFO: epoch: [386/1500], global_step: 1158, lr: 0.001000, loss: 1.662027, loss_shrink_maps: 0.879427, loss_threshold_maps: 0.591414, loss_binary_maps: 0.174724, avg_reader_cost: 2.41780 s, avg_batch_cost: 2.65870 s, avg_samples: 12.5, ips: 4.70154 samples/s, eta: 8:11:51
[2024/07/27 12:31:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:31:54] ppocr INFO: epoch: [387/1500], global_step: 1160, lr: 0.001000, loss: 1.662027, loss_shrink_maps: 0.879427, loss_threshold_maps: 0.595018, loss_binary_maps: 0.174724, avg_reader_cost: 1.44517 s, avg_batch_cost: 1.62307 s, avg_samples: 9.6, ips: 5.91470 samples/s, eta: 8:11:29
[2024/07/27 12:31:54] ppocr INFO: epoch: [387/1500], global_step: 1161, lr: 0.001000, loss: 1.662027, loss_shrink_maps: 0.879427, loss_threshold_maps: 0.595018, loss_binary_maps: 0.174724, avg_reader_cost: 0.85760 s, avg_batch_cost: 0.91315 s, avg_samples: 2.9, ips: 3.17580 samples/s, eta: 8:11:21
[2024/07/27 12:31:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:32:04] ppocr INFO: epoch: [388/1500], global_step: 1164, lr: 0.001000, loss: 1.662027, loss_shrink_maps: 0.879427, loss_threshold_maps: 0.597736, loss_binary_maps: 0.174724, avg_reader_cost: 2.26605 s, avg_batch_cost: 2.62957 s, avg_samples: 12.5, ips: 4.75362 samples/s, eta: 8:10:54
[2024/07/27 12:32:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:32:14] ppocr INFO: epoch: [389/1500], global_step: 1167, lr: 0.001000, loss: 1.591082, loss_shrink_maps: 0.830461, loss_threshold_maps: 0.595018, loss_binary_maps: 0.164571, avg_reader_cost: 2.33310 s, avg_batch_cost: 2.57371 s, avg_samples: 12.5, ips: 4.85681 samples/s, eta: 8:10:25
[2024/07/27 12:32:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:32:24] ppocr INFO: epoch: [390/1500], global_step: 1170, lr: 0.001000, loss: 1.591082, loss_shrink_maps: 0.830461, loss_threshold_maps: 0.595018, loss_binary_maps: 0.164571, avg_reader_cost: 2.39806 s, avg_batch_cost: 2.63556 s, avg_samples: 12.5, ips: 4.74282 samples/s, eta: 8:09:58
[2024/07/27 12:32:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:32:34] ppocr INFO: epoch: [391/1500], global_step: 1173, lr: 0.001000, loss: 1.591082, loss_shrink_maps: 0.830461, loss_threshold_maps: 0.603691, loss_binary_maps: 0.164571, avg_reader_cost: 2.30377 s, avg_batch_cost: 2.59459 s, avg_samples: 12.5, ips: 4.81771 samples/s, eta: 8:09:30
[2024/07/27 12:32:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:32:44] ppocr INFO: epoch: [392/1500], global_step: 1176, lr: 0.001000, loss: 1.591082, loss_shrink_maps: 0.830461, loss_threshold_maps: 0.600280, loss_binary_maps: 0.164571, avg_reader_cost: 2.27145 s, avg_batch_cost: 2.71360 s, avg_samples: 12.5, ips: 4.60642 samples/s, eta: 8:09:06
[2024/07/27 12:32:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:32:54] ppocr INFO: epoch: [393/1500], global_step: 1179, lr: 0.001000, loss: 1.591082, loss_shrink_maps: 0.830461, loss_threshold_maps: 0.592374, loss_binary_maps: 0.164571, avg_reader_cost: 2.21797 s, avg_batch_cost: 2.59694 s, avg_samples: 12.5, ips: 4.81335 samples/s, eta: 8:08:38
[2024/07/27 12:32:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:33:02] ppocr INFO: epoch: [394/1500], global_step: 1180, lr: 0.001000, loss: 1.615077, loss_shrink_maps: 0.853106, loss_threshold_maps: 0.592374, loss_binary_maps: 0.168995, avg_reader_cost: 0.56925 s, avg_batch_cost: 0.76110 s, avg_samples: 4.8, ips: 6.30669 samples/s, eta: 8:08:26
[2024/07/27 12:33:03] ppocr INFO: epoch: [394/1500], global_step: 1182, lr: 0.001000, loss: 1.634840, loss_shrink_maps: 0.870392, loss_threshold_maps: 0.601841, loss_binary_maps: 0.172074, avg_reader_cost: 1.61484 s, avg_batch_cost: 1.76243 s, avg_samples: 7.7, ips: 4.36896 samples/s, eta: 8:08:08
[2024/07/27 12:33:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:33:13] ppocr INFO: epoch: [395/1500], global_step: 1185, lr: 0.001000, loss: 1.615077, loss_shrink_maps: 0.855516, loss_threshold_maps: 0.592374, loss_binary_maps: 0.169868, avg_reader_cost: 2.39495 s, avg_batch_cost: 2.64425 s, avg_samples: 12.5, ips: 4.72724 samples/s, eta: 8:07:41
[2024/07/27 12:33:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:33:23] ppocr INFO: epoch: [396/1500], global_step: 1188, lr: 0.001000, loss: 1.617032, loss_shrink_maps: 0.855516, loss_threshold_maps: 0.585168, loss_binary_maps: 0.169868, avg_reader_cost: 2.14127 s, avg_batch_cost: 2.56111 s, avg_samples: 12.5, ips: 4.88070 samples/s, eta: 8:07:12
[2024/07/27 12:33:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:33:32] ppocr INFO: epoch: [397/1500], global_step: 1190, lr: 0.001000, loss: 1.617032, loss_shrink_maps: 0.858837, loss_threshold_maps: 0.585168, loss_binary_maps: 0.169901, avg_reader_cost: 1.31527 s, avg_batch_cost: 1.65691 s, avg_samples: 9.6, ips: 5.79392 samples/s, eta: 8:06:52
[2024/07/27 12:33:32] ppocr INFO: epoch: [397/1500], global_step: 1191, lr: 0.001000, loss: 1.617032, loss_shrink_maps: 0.858837, loss_threshold_maps: 0.585168, loss_binary_maps: 0.169901, avg_reader_cost: 0.87512 s, avg_batch_cost: 0.93010 s, avg_samples: 2.9, ips: 3.11794 samples/s, eta: 8:06:44
[2024/07/27 12:33:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:33:43] ppocr INFO: epoch: [398/1500], global_step: 1194, lr: 0.001000, loss: 1.617032, loss_shrink_maps: 0.858837, loss_threshold_maps: 0.585168, loss_binary_maps: 0.169901, avg_reader_cost: 2.27532 s, avg_batch_cost: 2.66473 s, avg_samples: 12.5, ips: 4.69090 samples/s, eta: 8:06:18
[2024/07/27 12:33:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:33:52] ppocr INFO: epoch: [399/1500], global_step: 1197, lr: 0.001000, loss: 1.622668, loss_shrink_maps: 0.873018, loss_threshold_maps: 0.585168, loss_binary_maps: 0.172601, avg_reader_cost: 2.10748 s, avg_batch_cost: 2.46514 s, avg_samples: 12.5, ips: 5.07070 samples/s, eta: 8:05:47
[2024/07/27 12:33:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:34:02] ppocr INFO: epoch: [400/1500], global_step: 1200, lr: 0.001000, loss: 1.641140, loss_shrink_maps: 0.898587, loss_threshold_maps: 0.586921, loss_binary_maps: 0.178214, avg_reader_cost: 2.26257 s, avg_batch_cost: 2.50273 s, avg_samples: 12.5, ips: 4.99455 samples/s, eta: 8:05:16

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:57,  8.72it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.17it/s]
eval model::   1%|▏         | 7/500 [00:00<00:23, 20.98it/s]
eval model::   2%|▏         | 10/500 [00:00<00:26, 18.77it/s]
eval model::   2%|▏         | 12/500 [00:00<00:32, 15.24it/s]
eval model::   3%|▎         | 14/500 [00:00<00:30, 15.82it/s]
eval model::   4%|▎         | 18/500 [00:00<00:23, 20.95it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.93it/s]
eval model::   5%|▍         | 24/500 [00:01<00:22, 21.08it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 20.90it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 21.81it/s]
eval model::   7%|▋         | 33/500 [00:01<00:22, 20.43it/s]
eval model::   7%|▋         | 36/500 [00:01<00:23, 19.98it/s]
eval model::   8%|▊         | 39/500 [00:02<00:24, 18.65it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 17.77it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 18.98it/s]
eval model::   9%|▉         | 47/500 [00:02<00:29, 15.18it/s]
eval model::  10%|█         | 50/500 [00:02<00:27, 16.62it/s]
eval model::  10%|█         | 52/500 [00:02<00:25, 17.25it/s]
eval model::  11%|█         | 55/500 [00:02<00:23, 18.60it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:24, 18.15it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:26, 16.73it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.18it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.35it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.60it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 21.22it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:18, 23.17it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:19, 22.07it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.81it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:17, 23.42it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.14it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.03it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.23it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:23, 17.73it/s]
eval model::  19%|█▉        | 94/500 [00:05<00:22, 17.88it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:20, 19.51it/s]
eval model::  20%|██        | 100/500 [00:05<00:20, 19.61it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 21.44it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.60it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:16, 23.68it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:16, 23.92it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:17, 22.15it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:17, 21.68it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:20, 18.29it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 19.02it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 19.18it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.90it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 18.54it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.81it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.26it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:19, 18.07it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:18, 19.21it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:18, 19.07it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 18.04it/s]
eval model::  30%|███       | 151/500 [00:07<00:20, 16.86it/s]
eval model::  31%|███       | 155/500 [00:08<00:16, 20.63it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.03it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:19, 17.39it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.57it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 19.31it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 19.59it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:16, 19.75it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:18, 17.29it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:16, 19.00it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:17, 18.21it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:15, 20.31it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 20.03it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:15, 20.10it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:14, 21.50it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:13, 21.87it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 21.39it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 21.18it/s]
eval model::  41%|████      | 205/500 [00:10<00:23, 12.30it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:22, 13.30it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:20, 13.91it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 14.12it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.57it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:22, 12.61it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.77it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.46it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:17, 15.48it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:14, 18.48it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:14, 18.39it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.15it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.13it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:13, 19.84it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:12, 20.87it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:14, 17.90it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 19.94it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 20.70it/s]
eval model::  50%|█████     | 251/500 [00:13<00:12, 19.87it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.10it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.85it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:11, 20.90it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:11, 21.10it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:12, 18.58it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 19.21it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 19.88it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.76it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.46it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.52it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:09, 21.70it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:16, 12.84it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.14it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.40it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:14, 14.06it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 15.84it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.07it/s]
eval model::  61%|██████    | 305/500 [00:16<00:11, 17.05it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:10, 18.62it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 20.64it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:09, 20.59it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:08, 20.94it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:08, 21.43it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:08, 20.21it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:08, 20.32it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 19.87it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:08, 20.82it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 21.92it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 19.14it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:09, 16.44it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:08, 18.23it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:07, 19.66it/s]
eval model::  70%|███████   | 350/500 [00:18<00:06, 21.64it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 21.77it/s]
eval model::  71%|███████   | 356/500 [00:18<00:07, 19.98it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:06, 20.71it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:07, 19.30it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 18.22it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:06, 19.24it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 19.66it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 20.77it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 21.50it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 21.17it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:04, 23.70it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 14.98it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 17.23it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 17.45it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 18.58it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 19.40it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 18.46it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 19.81it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.24it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 21.92it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 22.39it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 20.65it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 15.69it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:04, 17.29it/s]
eval model::  85%|████████▌ | 425/500 [00:22<00:04, 16.91it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 20.58it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 21.02it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:02, 22.35it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 23.95it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 23.02it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 23.59it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 21.71it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 20.99it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 22.83it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:01, 22.78it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 22.73it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:01, 18.47it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 18.19it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.26it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.06it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 18.42it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.32it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:01, 19.68it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 20.35it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 20.43it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 22.13it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 20.99it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 20.08it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 20.19it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.15it/s]
[2024/07/27 12:34:28] ppocr INFO: cur metric, precision: 0.6933701657458563, recall: 0.6042368801155513, hmean: 0.6457422176485721, fps: 43.84275627479317
[2024/07/27 12:34:28] ppocr INFO: best metric, hmean: 0.6565524457232539, precision: 0.7187857961053837, recall: 0.6042368801155513, fps: 45.296458194658534, best_epoch: 380
[2024/07/27 12:34:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:34:38] ppocr INFO: epoch: [401/1500], global_step: 1203, lr: 0.001000, loss: 1.641140, loss_shrink_maps: 0.898587, loss_threshold_maps: 0.591718, loss_binary_maps: 0.178214, avg_reader_cost: 2.41312 s, avg_batch_cost: 2.73851 s, avg_samples: 12.5, ips: 4.56453 samples/s, eta: 8:04:52
[2024/07/27 12:34:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:34:48] ppocr INFO: epoch: [402/1500], global_step: 1206, lr: 0.001000, loss: 1.641140, loss_shrink_maps: 0.898587, loss_threshold_maps: 0.597815, loss_binary_maps: 0.178214, avg_reader_cost: 2.26844 s, avg_batch_cost: 2.66696 s, avg_samples: 12.5, ips: 4.68698 samples/s, eta: 8:04:26
[2024/07/27 12:34:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:34:58] ppocr INFO: epoch: [403/1500], global_step: 1209, lr: 0.001000, loss: 1.678286, loss_shrink_maps: 0.900732, loss_threshold_maps: 0.599061, loss_binary_maps: 0.178577, avg_reader_cost: 2.21465 s, avg_batch_cost: 2.57555 s, avg_samples: 12.5, ips: 4.85333 samples/s, eta: 8:03:58
[2024/07/27 12:34:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:35:05] ppocr INFO: epoch: [404/1500], global_step: 1210, lr: 0.001000, loss: 1.641140, loss_shrink_maps: 0.898587, loss_threshold_maps: 0.596144, loss_binary_maps: 0.178214, avg_reader_cost: 0.54731 s, avg_batch_cost: 0.72036 s, avg_samples: 4.8, ips: 6.66330 samples/s, eta: 8:03:45
[2024/07/27 12:35:07] ppocr INFO: epoch: [404/1500], global_step: 1212, lr: 0.001000, loss: 1.611546, loss_shrink_maps: 0.870738, loss_threshold_maps: 0.596144, loss_binary_maps: 0.173034, avg_reader_cost: 1.53277 s, avg_batch_cost: 1.67968 s, avg_samples: 7.7, ips: 4.58422 samples/s, eta: 8:03:25
[2024/07/27 12:35:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:35:16] ppocr INFO: epoch: [405/1500], global_step: 1215, lr: 0.001000, loss: 1.627452, loss_shrink_maps: 0.870738, loss_threshold_maps: 0.596144, loss_binary_maps: 0.173034, avg_reader_cost: 2.28793 s, avg_batch_cost: 2.52826 s, avg_samples: 12.5, ips: 4.94410 samples/s, eta: 8:02:55
[2024/07/27 12:35:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:35:26] ppocr INFO: epoch: [406/1500], global_step: 1218, lr: 0.001000, loss: 1.671737, loss_shrink_maps: 0.917296, loss_threshold_maps: 0.596144, loss_binary_maps: 0.181329, avg_reader_cost: 2.32618 s, avg_batch_cost: 2.57825 s, avg_samples: 12.5, ips: 4.84825 samples/s, eta: 8:02:27
[2024/07/27 12:35:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:35:36] ppocr INFO: epoch: [407/1500], global_step: 1220, lr: 0.001000, loss: 1.586820, loss_shrink_maps: 0.841276, loss_threshold_maps: 0.583286, loss_binary_maps: 0.166733, avg_reader_cost: 1.51201 s, avg_batch_cost: 1.69836 s, avg_samples: 9.6, ips: 5.65251 samples/s, eta: 8:02:07
[2024/07/27 12:35:36] ppocr INFO: epoch: [407/1500], global_step: 1221, lr: 0.001000, loss: 1.567882, loss_shrink_maps: 0.824456, loss_threshold_maps: 0.569807, loss_binary_maps: 0.162808, avg_reader_cost: 0.89609 s, avg_batch_cost: 0.95092 s, avg_samples: 2.9, ips: 3.04967 samples/s, eta: 8:02:00
[2024/07/27 12:35:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:35:46] ppocr INFO: epoch: [408/1500], global_step: 1224, lr: 0.001000, loss: 1.601179, loss_shrink_maps: 0.840415, loss_threshold_maps: 0.583854, loss_binary_maps: 0.166012, avg_reader_cost: 2.39513 s, avg_batch_cost: 2.63308 s, avg_samples: 12.5, ips: 4.74729 samples/s, eta: 8:01:34
[2024/07/27 12:35:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:35:56] ppocr INFO: epoch: [409/1500], global_step: 1227, lr: 0.001000, loss: 1.582242, loss_shrink_maps: 0.824121, loss_threshold_maps: 0.583854, loss_binary_maps: 0.163081, avg_reader_cost: 2.30298 s, avg_batch_cost: 2.66904 s, avg_samples: 12.5, ips: 4.68334 samples/s, eta: 8:01:08
[2024/07/27 12:35:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:36:06] ppocr INFO: epoch: [410/1500], global_step: 1230, lr: 0.001000, loss: 1.604825, loss_shrink_maps: 0.824121, loss_threshold_maps: 0.590801, loss_binary_maps: 0.163081, avg_reader_cost: 2.32339 s, avg_batch_cost: 2.57243 s, avg_samples: 12.5, ips: 4.85921 samples/s, eta: 8:00:39
[2024/07/27 12:36:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:36:15] ppocr INFO: epoch: [411/1500], global_step: 1233, lr: 0.001000, loss: 1.604825, loss_shrink_maps: 0.824121, loss_threshold_maps: 0.590801, loss_binary_maps: 0.163081, avg_reader_cost: 2.31095 s, avg_batch_cost: 2.55288 s, avg_samples: 12.5, ips: 4.89643 samples/s, eta: 8:00:10
[2024/07/27 12:36:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:36:25] ppocr INFO: epoch: [412/1500], global_step: 1236, lr: 0.001000, loss: 1.579718, loss_shrink_maps: 0.809413, loss_threshold_maps: 0.592933, loss_binary_maps: 0.160510, avg_reader_cost: 2.32819 s, avg_batch_cost: 2.57127 s, avg_samples: 12.5, ips: 4.86141 samples/s, eta: 7:59:42
[2024/07/27 12:36:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:36:35] ppocr INFO: epoch: [413/1500], global_step: 1239, lr: 0.001000, loss: 1.559668, loss_shrink_maps: 0.809413, loss_threshold_maps: 0.587700, loss_binary_maps: 0.160510, avg_reader_cost: 2.29551 s, avg_batch_cost: 2.69176 s, avg_samples: 12.5, ips: 4.64380 samples/s, eta: 7:59:17
[2024/07/27 12:36:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:36:43] ppocr INFO: epoch: [414/1500], global_step: 1240, lr: 0.001000, loss: 1.542926, loss_shrink_maps: 0.806733, loss_threshold_maps: 0.581229, loss_binary_maps: 0.160154, avg_reader_cost: 0.45747 s, avg_batch_cost: 0.77126 s, avg_samples: 4.8, ips: 6.22355 samples/s, eta: 7:59:05
[2024/07/27 12:36:45] ppocr INFO: epoch: [414/1500], global_step: 1242, lr: 0.001000, loss: 1.572853, loss_shrink_maps: 0.824053, loss_threshold_maps: 0.587700, loss_binary_maps: 0.163528, avg_reader_cost: 1.63509 s, avg_batch_cost: 1.78304 s, avg_samples: 7.7, ips: 4.31848 samples/s, eta: 7:58:48
[2024/07/27 12:36:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:36:55] ppocr INFO: epoch: [415/1500], global_step: 1245, lr: 0.001000, loss: 1.572853, loss_shrink_maps: 0.819639, loss_threshold_maps: 0.587700, loss_binary_maps: 0.162343, avg_reader_cost: 2.19878 s, avg_batch_cost: 2.55254 s, avg_samples: 12.5, ips: 4.89709 samples/s, eta: 7:58:19
[2024/07/27 12:36:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:37:05] ppocr INFO: epoch: [416/1500], global_step: 1248, lr: 0.001000, loss: 1.602302, loss_shrink_maps: 0.837178, loss_threshold_maps: 0.576955, loss_binary_maps: 0.165915, avg_reader_cost: 2.43473 s, avg_batch_cost: 2.67516 s, avg_samples: 12.5, ips: 4.67263 samples/s, eta: 7:57:53
[2024/07/27 12:37:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:37:14] ppocr INFO: epoch: [417/1500], global_step: 1250, lr: 0.001000, loss: 1.581780, loss_shrink_maps: 0.837178, loss_threshold_maps: 0.579086, loss_binary_maps: 0.165915, avg_reader_cost: 1.44613 s, avg_batch_cost: 1.62452 s, avg_samples: 9.6, ips: 5.90942 samples/s, eta: 7:57:32
[2024/07/27 12:37:14] ppocr INFO: epoch: [417/1500], global_step: 1251, lr: 0.001000, loss: 1.581780, loss_shrink_maps: 0.836803, loss_threshold_maps: 0.579086, loss_binary_maps: 0.165693, avg_reader_cost: 0.85863 s, avg_batch_cost: 0.91386 s, avg_samples: 2.9, ips: 3.17335 samples/s, eta: 7:57:24
[2024/07/27 12:37:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:37:24] ppocr INFO: epoch: [418/1500], global_step: 1254, lr: 0.001000, loss: 1.591474, loss_shrink_maps: 0.837022, loss_threshold_maps: 0.591340, loss_binary_maps: 0.165693, avg_reader_cost: 2.26443 s, avg_batch_cost: 2.64460 s, avg_samples: 12.5, ips: 4.72661 samples/s, eta: 7:56:58
[2024/07/27 12:37:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:37:34] ppocr INFO: epoch: [419/1500], global_step: 1257, lr: 0.001000, loss: 1.569614, loss_shrink_maps: 0.821557, loss_threshold_maps: 0.577117, loss_binary_maps: 0.162877, avg_reader_cost: 2.34239 s, avg_batch_cost: 2.58679 s, avg_samples: 12.5, ips: 4.83225 samples/s, eta: 7:56:30
[2024/07/27 12:37:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:37:43] ppocr INFO: epoch: [420/1500], global_step: 1260, lr: 0.001000, loss: 1.611645, loss_shrink_maps: 0.839465, loss_threshold_maps: 0.592966, loss_binary_maps: 0.166044, avg_reader_cost: 2.23238 s, avg_batch_cost: 2.47159 s, avg_samples: 12.5, ips: 5.05748 samples/s, eta: 7:55:59

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:01,  8.10it/s]
eval model::   1%|          | 4/500 [00:00<00:31, 15.85it/s]
eval model::   1%|▏         | 7/500 [00:00<00:26, 18.30it/s]
eval model::   2%|▏         | 9/500 [00:00<00:27, 18.14it/s]
eval model::   2%|▏         | 11/500 [00:00<00:31, 15.70it/s]
eval model::   3%|▎         | 14/500 [00:00<00:29, 16.69it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.79it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.61it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 21.42it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 20.45it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 21.16it/s]
eval model::   6%|▋         | 32/500 [00:01<00:22, 20.96it/s]
eval model::   7%|▋         | 35/500 [00:01<00:23, 20.06it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.98it/s]
eval model::   8%|▊         | 41/500 [00:02<00:25, 18.07it/s]
eval model::   9%|▊         | 43/500 [00:02<00:28, 15.96it/s]
eval model::   9%|▉         | 46/500 [00:02<00:28, 16.16it/s]
eval model::  10%|▉         | 49/500 [00:02<00:26, 17.27it/s]
eval model::  10%|█         | 51/500 [00:02<00:25, 17.80it/s]
eval model::  11%|█         | 54/500 [00:02<00:23, 19.03it/s]
eval model::  11%|█         | 56/500 [00:03<00:23, 19.20it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 18.54it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:29, 15.01it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 14.63it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.73it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.52it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 22.25it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:19, 21.74it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.12it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.75it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.68it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 20.04it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.12it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.11it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.43it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.60it/s]
eval model::  20%|██        | 102/500 [00:05<00:18, 21.32it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 20.89it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.67it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 20.50it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 20.54it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:18, 20.26it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 20.01it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.25it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 18.31it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 19.14it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:22, 16.77it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:23, 15.90it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:22, 16.26it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:21, 16.62it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:21, 17.05it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:19, 18.71it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.25it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.76it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 21.20it/s]
eval model::  31%|███       | 153/500 [00:08<00:16, 20.96it/s]
eval model::  31%|███       | 156/500 [00:08<00:15, 22.86it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:15, 22.26it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:20, 16.79it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:20, 16.71it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:19, 17.15it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:18, 18.27it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 15.88it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:18, 17.68it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:19, 16.61it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:18, 17.23it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 20.37it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.34it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:14, 21.69it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 20.90it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:13, 21.93it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 22.69it/s]
eval model::  41%|████      | 205/500 [00:10<00:20, 14.13it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:19, 15.08it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 15.37it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.33it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.81it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.96it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.04it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:18, 15.37it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 16.87it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:16, 16.75it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:14, 19.23it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:13, 20.16it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:13, 20.13it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:12, 21.05it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 18.49it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.92it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.80it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 21.12it/s]
eval model::  51%|█████     | 254/500 [00:13<00:10, 22.94it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:09, 24.58it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 21.89it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:11, 21.20it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:12, 18.29it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:13, 17.71it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.99it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.37it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.89it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.27it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:10, 19.97it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:16, 12.83it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:13, 15.45it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:12, 16.77it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:14, 14.50it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 16.00it/s]
eval model::  60%|██████    | 301/500 [00:16<00:13, 14.93it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 16.24it/s]
eval model::  61%|██████    | 306/500 [00:16<00:11, 16.92it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:10, 18.59it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:10, 18.66it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:10, 18.14it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:09, 19.44it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:09, 19.10it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:10, 17.22it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:09, 18.64it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 19.86it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:07, 21.14it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:07, 23.04it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:07, 21.21it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:09, 17.54it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 18.77it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 18.25it/s]
eval model::  70%|███████   | 350/500 [00:18<00:07, 19.35it/s]
eval model::  71%|███████   | 353/500 [00:18<00:07, 19.81it/s]
eval model::  71%|███████   | 356/500 [00:19<00:07, 18.44it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:06, 20.16it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:07, 18.44it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:08, 16.31it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:08, 15.91it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 17.74it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 17.12it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 19.14it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 20.37it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:06, 18.93it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 21.02it/s]
eval model::  77%|███████▋  | 387/500 [00:21<00:08, 13.48it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:07, 14.26it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:07, 14.40it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 16.40it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 20.04it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 17.76it/s]
eval model::  81%|████████  | 403/500 [00:21<00:05, 17.50it/s]
eval model::  81%|████████  | 405/500 [00:21<00:05, 17.81it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:04, 19.11it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:04, 20.72it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:04, 20.05it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 18.28it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 17.85it/s]
eval model::  84%|████████▍ | 421/500 [00:23<00:06, 11.44it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 13.34it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.11it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 15.79it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 15.24it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 16.93it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:03, 17.48it/s]
eval model::  88%|████████▊ | 439/500 [00:24<00:03, 18.48it/s]
eval model::  88%|████████▊ | 442/500 [00:24<00:02, 20.50it/s]
eval model::  89%|████████▉ | 446/500 [00:24<00:02, 23.70it/s]
eval model::  90%|████████▉ | 449/500 [00:24<00:02, 22.11it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:01, 23.59it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 23.80it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 22.92it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 17.52it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 17.84it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:01, 17.57it/s]
eval model::  94%|█████████▍| 469/500 [00:25<00:01, 19.68it/s]
eval model::  94%|█████████▍| 472/500 [00:25<00:01, 21.63it/s]
eval model::  95%|█████████▌| 475/500 [00:25<00:01, 20.63it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:00, 22.46it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 24.13it/s]
eval model::  97%|█████████▋| 484/500 [00:26<00:00, 25.65it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 23.72it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 24.38it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 22.08it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 21.47it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 24.44it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.69it/s]
[2024/07/27 12:38:10] ppocr INFO: cur metric, precision: 0.730185497470489, recall: 0.6254212806933076, hmean: 0.6737551867219916, fps: 43.42892415983237
[2024/07/27 12:38:10] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 12:38:10] ppocr INFO: best metric, hmean: 0.6737551867219916, precision: 0.730185497470489, recall: 0.6254212806933076, fps: 43.42892415983237, best_epoch: 420
[2024/07/27 12:38:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:38:19] ppocr INFO: epoch: [421/1500], global_step: 1263, lr: 0.001000, loss: 1.569614, loss_shrink_maps: 0.817048, loss_threshold_maps: 0.567414, loss_binary_maps: 0.161843, avg_reader_cost: 2.00087 s, avg_batch_cost: 2.32028 s, avg_samples: 12.5, ips: 5.38729 samples/s, eta: 7:55:24
[2024/07/27 12:38:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:38:28] ppocr INFO: epoch: [422/1500], global_step: 1266, lr: 0.001000, loss: 1.589784, loss_shrink_maps: 0.817048, loss_threshold_maps: 0.576190, loss_binary_maps: 0.161843, avg_reader_cost: 2.31348 s, avg_batch_cost: 2.55419 s, avg_samples: 12.5, ips: 4.89391 samples/s, eta: 7:54:55
[2024/07/27 12:38:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:38:38] ppocr INFO: epoch: [423/1500], global_step: 1269, lr: 0.001000, loss: 1.589784, loss_shrink_maps: 0.817048, loss_threshold_maps: 0.601029, loss_binary_maps: 0.161843, avg_reader_cost: 2.25126 s, avg_batch_cost: 2.63735 s, avg_samples: 12.5, ips: 4.73961 samples/s, eta: 7:54:29
[2024/07/27 12:38:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:38:46] ppocr INFO: epoch: [424/1500], global_step: 1270, lr: 0.001000, loss: 1.611645, loss_shrink_maps: 0.850140, loss_threshold_maps: 0.593405, loss_binary_maps: 0.168686, avg_reader_cost: 0.69076 s, avg_batch_cost: 0.78026 s, avg_samples: 4.8, ips: 6.15182 samples/s, eta: 7:54:17
[2024/07/27 12:38:48] ppocr INFO: epoch: [424/1500], global_step: 1272, lr: 0.001000, loss: 1.568171, loss_shrink_maps: 0.829532, loss_threshold_maps: 0.586332, loss_binary_maps: 0.164053, avg_reader_cost: 1.65239 s, avg_batch_cost: 1.79845 s, avg_samples: 7.7, ips: 4.28147 samples/s, eta: 7:54:01
[2024/07/27 12:38:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:38:57] ppocr INFO: epoch: [425/1500], global_step: 1275, lr: 0.001000, loss: 1.597786, loss_shrink_maps: 0.838932, loss_threshold_maps: 0.587703, loss_binary_maps: 0.165190, avg_reader_cost: 2.23498 s, avg_batch_cost: 2.56255 s, avg_samples: 12.5, ips: 4.87795 samples/s, eta: 7:53:32
[2024/07/27 12:38:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:39:08] ppocr INFO: epoch: [426/1500], global_step: 1278, lr: 0.001000, loss: 1.604107, loss_shrink_maps: 0.847658, loss_threshold_maps: 0.591930, loss_binary_maps: 0.167673, avg_reader_cost: 2.25758 s, avg_batch_cost: 2.63521 s, avg_samples: 12.5, ips: 4.74345 samples/s, eta: 7:53:06
[2024/07/27 12:39:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:39:17] ppocr INFO: epoch: [427/1500], global_step: 1280, lr: 0.001000, loss: 1.590894, loss_shrink_maps: 0.838974, loss_threshold_maps: 0.591930, loss_binary_maps: 0.165458, avg_reader_cost: 1.32081 s, avg_batch_cost: 1.62851 s, avg_samples: 9.6, ips: 5.89497 samples/s, eta: 7:52:45
[2024/07/27 12:39:17] ppocr INFO: epoch: [427/1500], global_step: 1281, lr: 0.001000, loss: 1.590894, loss_shrink_maps: 0.838974, loss_threshold_maps: 0.591930, loss_binary_maps: 0.165458, avg_reader_cost: 0.86021 s, avg_batch_cost: 0.91598 s, avg_samples: 2.9, ips: 3.16601 samples/s, eta: 7:52:37
[2024/07/27 12:39:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:39:27] ppocr INFO: epoch: [428/1500], global_step: 1284, lr: 0.001000, loss: 1.606357, loss_shrink_maps: 0.847658, loss_threshold_maps: 0.598621, loss_binary_maps: 0.167673, avg_reader_cost: 2.14779 s, avg_batch_cost: 2.51462 s, avg_samples: 12.5, ips: 4.97094 samples/s, eta: 7:52:07
[2024/07/27 12:39:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:39:36] ppocr INFO: epoch: [429/1500], global_step: 1287, lr: 0.001000, loss: 1.590894, loss_shrink_maps: 0.838974, loss_threshold_maps: 0.598159, loss_binary_maps: 0.165458, avg_reader_cost: 2.27441 s, avg_batch_cost: 2.51275 s, avg_samples: 12.5, ips: 4.97464 samples/s, eta: 7:51:37
[2024/07/27 12:39:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:39:46] ppocr INFO: epoch: [430/1500], global_step: 1290, lr: 0.001000, loss: 1.590894, loss_shrink_maps: 0.838974, loss_threshold_maps: 0.592791, loss_binary_maps: 0.165458, avg_reader_cost: 2.29252 s, avg_batch_cost: 2.52544 s, avg_samples: 12.5, ips: 4.94963 samples/s, eta: 7:51:08
[2024/07/27 12:39:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:39:56] ppocr INFO: epoch: [431/1500], global_step: 1293, lr: 0.001000, loss: 1.606357, loss_shrink_maps: 0.847658, loss_threshold_maps: 0.601747, loss_binary_maps: 0.167672, avg_reader_cost: 2.29232 s, avg_batch_cost: 2.69010 s, avg_samples: 12.5, ips: 4.64667 samples/s, eta: 7:50:43
[2024/07/27 12:39:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:40:06] ppocr INFO: epoch: [432/1500], global_step: 1296, lr: 0.001000, loss: 1.574859, loss_shrink_maps: 0.827072, loss_threshold_maps: 0.595726, loss_binary_maps: 0.163808, avg_reader_cost: 2.37005 s, avg_batch_cost: 2.60981 s, avg_samples: 12.5, ips: 4.78961 samples/s, eta: 7:50:16
[2024/07/27 12:40:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:40:16] ppocr INFO: epoch: [433/1500], global_step: 1299, lr: 0.001000, loss: 1.562148, loss_shrink_maps: 0.821343, loss_threshold_maps: 0.596172, loss_binary_maps: 0.162063, avg_reader_cost: 2.21670 s, avg_batch_cost: 2.58495 s, avg_samples: 12.5, ips: 4.83569 samples/s, eta: 7:49:48
[2024/07/27 12:40:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:40:24] ppocr INFO: epoch: [434/1500], global_step: 1300, lr: 0.001000, loss: 1.548687, loss_shrink_maps: 0.809905, loss_threshold_maps: 0.590805, loss_binary_maps: 0.159993, avg_reader_cost: 0.70324 s, avg_batch_cost: 0.81226 s, avg_samples: 4.8, ips: 5.90941 samples/s, eta: 7:49:37
[2024/07/27 12:40:26] ppocr INFO: epoch: [434/1500], global_step: 1302, lr: 0.001000, loss: 1.538627, loss_shrink_maps: 0.796229, loss_threshold_maps: 0.589803, loss_binary_maps: 0.157371, avg_reader_cost: 1.71673 s, avg_batch_cost: 1.86390 s, avg_samples: 7.7, ips: 4.13112 samples/s, eta: 7:49:22
[2024/07/27 12:40:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:40:36] ppocr INFO: epoch: [435/1500], global_step: 1305, lr: 0.001000, loss: 1.535734, loss_shrink_maps: 0.795294, loss_threshold_maps: 0.582806, loss_binary_maps: 0.157088, avg_reader_cost: 2.25837 s, avg_batch_cost: 2.64358 s, avg_samples: 12.5, ips: 4.72844 samples/s, eta: 7:48:56
[2024/07/27 12:40:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:40:46] ppocr INFO: epoch: [436/1500], global_step: 1308, lr: 0.001000, loss: 1.527606, loss_shrink_maps: 0.789917, loss_threshold_maps: 0.572915, loss_binary_maps: 0.156180, avg_reader_cost: 2.29815 s, avg_batch_cost: 2.67203 s, avg_samples: 12.5, ips: 4.67808 samples/s, eta: 7:48:30
[2024/07/27 12:40:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:40:55] ppocr INFO: epoch: [437/1500], global_step: 1310, lr: 0.001000, loss: 1.518457, loss_shrink_maps: 0.786223, loss_threshold_maps: 0.565922, loss_binary_maps: 0.155441, avg_reader_cost: 1.32561 s, avg_batch_cost: 1.60887 s, avg_samples: 9.6, ips: 5.96693 samples/s, eta: 7:48:09
[2024/07/27 12:40:55] ppocr INFO: epoch: [437/1500], global_step: 1311, lr: 0.001000, loss: 1.527606, loss_shrink_maps: 0.789917, loss_threshold_maps: 0.571540, loss_binary_maps: 0.156180, avg_reader_cost: 0.85027 s, avg_batch_cost: 0.90592 s, avg_samples: 2.9, ips: 3.20118 samples/s, eta: 7:48:01
[2024/07/27 12:40:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:41:05] ppocr INFO: epoch: [438/1500], global_step: 1314, lr: 0.001000, loss: 1.518457, loss_shrink_maps: 0.786223, loss_threshold_maps: 0.565922, loss_binary_maps: 0.155441, avg_reader_cost: 2.30244 s, avg_batch_cost: 2.60110 s, avg_samples: 12.5, ips: 4.80566 samples/s, eta: 7:47:33
[2024/07/27 12:41:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:41:14] ppocr INFO: epoch: [439/1500], global_step: 1317, lr: 0.001000, loss: 1.537310, loss_shrink_maps: 0.795294, loss_threshold_maps: 0.571540, loss_binary_maps: 0.157088, avg_reader_cost: 2.15069 s, avg_batch_cost: 2.51548 s, avg_samples: 12.5, ips: 4.96923 samples/s, eta: 7:47:04
[2024/07/27 12:41:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:41:25] ppocr INFO: epoch: [440/1500], global_step: 1320, lr: 0.001000, loss: 1.544006, loss_shrink_maps: 0.814079, loss_threshold_maps: 0.571540, loss_binary_maps: 0.161029, avg_reader_cost: 2.40378 s, avg_batch_cost: 2.64960 s, avg_samples: 12.5, ips: 4.71770 samples/s, eta: 7:46:38

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:31, 15.85it/s]
eval model::   1%|          | 5/500 [00:00<00:25, 19.14it/s]
eval model::   1%|▏         | 7/500 [00:00<00:26, 18.31it/s]
eval model::   2%|▏         | 9/500 [00:00<00:27, 18.15it/s]
eval model::   2%|▏         | 11/500 [00:00<00:30, 15.89it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.74it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.40it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.95it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 21.01it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.58it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 19.99it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 20.26it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 18.87it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.95it/s]
eval model::   8%|▊         | 41/500 [00:02<00:24, 18.80it/s]
eval model::   9%|▊         | 43/500 [00:02<00:25, 17.81it/s]
eval model::   9%|▉         | 46/500 [00:02<00:29, 15.42it/s]
eval model::  10%|▉         | 49/500 [00:02<00:27, 16.52it/s]
eval model::  10%|█         | 52/500 [00:02<00:24, 18.44it/s]
eval model::  11%|█         | 54/500 [00:02<00:23, 18.62it/s]
eval model::  11%|█         | 56/500 [00:03<00:24, 18.50it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:25, 17.10it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.50it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 15.01it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 19.18it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 20.62it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 21.41it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:18, 22.43it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:20, 20.83it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 20.03it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:21, 19.19it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.12it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.23it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.10it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.53it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:21, 18.70it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:20, 20.03it/s]
eval model::  20%|██        | 101/500 [00:05<00:20, 19.69it/s]
eval model::  21%|██        | 104/500 [00:05<00:19, 20.54it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.75it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 21.29it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 21.47it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:19, 19.51it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:18, 20.30it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:20, 18.84it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:21, 17.31it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:22, 16.92it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.61it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:21, 17.01it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:19, 18.31it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:20, 18.08it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:19, 18.16it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:19, 17.99it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:19, 17.81it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:19, 17.85it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:20, 16.94it/s]
eval model::  30%|███       | 151/500 [00:08<00:21, 16.43it/s]
eval model::  31%|███       | 155/500 [00:08<00:16, 20.56it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.54it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:20, 16.33it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:22, 15.31it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:21, 15.82it/s]
eval model::  33%|███▎      | 167/500 [00:09<00:20, 16.46it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:17, 18.52it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:19, 17.23it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:21, 14.85it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:21, 15.22it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:19, 16.19it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:21, 15.01it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:19, 16.03it/s]
eval model::  37%|███▋      | 186/500 [00:10<00:16, 19.10it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:16, 18.61it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:16, 18.66it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:14, 20.66it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:16, 18.91it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 20.36it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 21.53it/s]
eval model::  41%|████      | 205/500 [00:11<00:23, 12.31it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:22, 13.11it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:21, 13.48it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:21, 13.47it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:20, 14.26it/s]
eval model::  43%|████▎     | 215/500 [00:12<00:22, 12.94it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:25, 11.19it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:23, 12.10it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:22, 12.58it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:18, 14.90it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:17, 15.23it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:15, 17.97it/s]
eval model::  46%|████▋     | 232/500 [00:13<00:13, 19.53it/s]
eval model::  47%|████▋     | 235/500 [00:13<00:14, 17.82it/s]
eval model::  48%|████▊     | 238/500 [00:13<00:13, 19.24it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:13, 19.13it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:16, 15.58it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:13, 18.49it/s]
eval model::  50%|████▉     | 249/500 [00:14<00:13, 18.22it/s]
eval model::  50%|█████     | 252/500 [00:14<00:12, 19.29it/s]
eval model::  51%|█████     | 255/500 [00:14<00:11, 21.49it/s]
eval model::  52%|█████▏    | 259/500 [00:14<00:09, 24.96it/s]
eval model::  52%|█████▏    | 262/500 [00:14<00:09, 26.20it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:10, 21.90it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:10, 21.38it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:10, 21.65it/s]
eval model::  55%|█████▍    | 274/500 [00:15<00:10, 22.13it/s]
eval model::  55%|█████▌    | 277/500 [00:15<00:10, 21.24it/s]
eval model::  56%|█████▌    | 280/500 [00:15<00:09, 22.35it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:09, 23.20it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:16, 12.73it/s]
eval model::  58%|█████▊    | 289/500 [00:16<00:14, 14.48it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:13, 15.73it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:13, 15.16it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:16, 12.33it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:15, 13.41it/s]
eval model::  60%|██████    | 300/500 [00:16<00:15, 13.09it/s]
eval model::  60%|██████    | 302/500 [00:17<00:14, 13.31it/s]
eval model::  61%|██████    | 304/500 [00:17<00:14, 13.90it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:11, 16.61it/s]
eval model::  62%|██████▏   | 309/500 [00:17<00:11, 16.65it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:10, 17.45it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:09, 18.87it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:09, 19.93it/s]
eval model::  64%|██████▍   | 321/500 [00:18<00:09, 19.59it/s]
eval model::  65%|██████▍   | 323/500 [00:18<00:09, 17.95it/s]
eval model::  65%|██████▌   | 326/500 [00:18<00:09, 19.31it/s]
eval model::  66%|██████▌   | 328/500 [00:18<00:09, 18.25it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:09, 18.70it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:08, 20.02it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 18.48it/s]
eval model::  68%|██████▊   | 339/500 [00:19<00:08, 18.30it/s]
eval model::  68%|██████▊   | 341/500 [00:19<00:10, 14.49it/s]
eval model::  69%|██████▊   | 343/500 [00:19<00:10, 14.73it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:09, 16.55it/s]
eval model::  70%|██████▉   | 348/500 [00:19<00:08, 17.16it/s]
eval model::  70%|███████   | 350/500 [00:19<00:08, 17.60it/s]
eval model::  70%|███████   | 352/500 [00:19<00:08, 18.14it/s]
eval model::  71%|███████   | 355/500 [00:20<00:07, 18.92it/s]
eval model::  71%|███████▏  | 357/500 [00:20<00:07, 18.66it/s]
eval model::  72%|███████▏  | 359/500 [00:20<00:07, 17.87it/s]
eval model::  72%|███████▏  | 361/500 [00:20<00:08, 16.02it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:08, 16.26it/s]
eval model::  73%|███████▎  | 367/500 [00:20<00:07, 18.22it/s]
eval model::  74%|███████▍  | 370/500 [00:20<00:06, 19.56it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:06, 20.63it/s]
eval model::  75%|███████▌  | 376/500 [00:21<00:05, 20.72it/s]
eval model::  76%|███████▌  | 379/500 [00:21<00:05, 21.85it/s]
eval model::  76%|███████▋  | 382/500 [00:21<00:05, 20.30it/s]
eval model::  77%|███████▋  | 385/500 [00:21<00:05, 22.01it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:07, 14.25it/s]
eval model::  78%|███████▊  | 391/500 [00:22<00:06, 15.78it/s]
eval model::  79%|███████▊  | 393/500 [00:22<00:06, 16.52it/s]
eval model::  79%|███████▉  | 395/500 [00:22<00:06, 17.04it/s]
eval model::  80%|███████▉  | 399/500 [00:22<00:04, 20.37it/s]
eval model::  80%|████████  | 402/500 [00:22<00:05, 19.33it/s]
eval model::  81%|████████  | 405/500 [00:22<00:04, 20.10it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:04, 19.88it/s]
eval model::  82%|████████▏ | 411/500 [00:23<00:04, 19.77it/s]
eval model::  83%|████████▎ | 414/500 [00:23<00:04, 20.44it/s]
eval model::  83%|████████▎ | 417/500 [00:23<00:04, 17.67it/s]
eval model::  84%|████████▍ | 419/500 [00:23<00:04, 16.68it/s]
eval model::  84%|████████▍ | 421/500 [00:23<00:06, 11.33it/s]
eval model::  85%|████████▍ | 424/500 [00:24<00:06, 12.58it/s]
eval model::  85%|████████▌ | 426/500 [00:24<00:05, 13.20it/s]
eval model::  86%|████████▌ | 429/500 [00:24<00:04, 16.20it/s]
eval model::  86%|████████▌ | 431/500 [00:24<00:04, 14.65it/s]
eval model::  87%|████████▋ | 434/500 [00:24<00:03, 16.95it/s]
eval model::  87%|████████▋ | 437/500 [00:24<00:03, 19.17it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:03, 18.58it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 22.52it/s]
eval model::  89%|████████▉ | 447/500 [00:25<00:02, 22.23it/s]
eval model::  90%|█████████ | 450/500 [00:25<00:02, 17.27it/s]
eval model::  91%|█████████ | 453/500 [00:25<00:02, 19.17it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:02, 19.92it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:02, 19.86it/s]
eval model::  92%|█████████▏| 462/500 [00:26<00:02, 17.21it/s]
eval model::  93%|█████████▎| 464/500 [00:26<00:02, 17.31it/s]
eval model::  93%|█████████▎| 466/500 [00:26<00:02, 15.95it/s]
eval model::  94%|█████████▎| 468/500 [00:26<00:01, 16.36it/s]
eval model::  94%|█████████▍| 471/500 [00:26<00:01, 19.16it/s]
eval model::  95%|█████████▍| 474/500 [00:26<00:01, 18.20it/s]
eval model::  95%|█████████▌| 477/500 [00:26<00:01, 20.21it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:00, 21.12it/s]
eval model::  97%|█████████▋| 483/500 [00:27<00:00, 22.79it/s]
eval model::  97%|█████████▋| 486/500 [00:27<00:00, 22.89it/s]
eval model::  98%|█████████▊| 489/500 [00:27<00:00, 24.34it/s]
eval model::  98%|█████████▊| 492/500 [00:27<00:00, 22.47it/s]
eval model::  99%|█████████▉| 495/500 [00:27<00:00, 24.23it/s]
eval model:: 100%|█████████▉| 499/500 [00:27<00:00, 27.17it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.06it/s]
[2024/07/27 12:41:52] ppocr INFO: cur metric, precision: 0.689058524173028, recall: 0.6519017814155031, hmean: 0.6699653636813458, fps: 43.26346339996799
[2024/07/27 12:41:52] ppocr INFO: best metric, hmean: 0.6737551867219916, precision: 0.730185497470489, recall: 0.6254212806933076, fps: 43.42892415983237, best_epoch: 420
[2024/07/27 12:41:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:42:00] ppocr INFO: epoch: [441/1500], global_step: 1323, lr: 0.001000, loss: 1.528162, loss_shrink_maps: 0.810593, loss_threshold_maps: 0.568908, loss_binary_maps: 0.160340, avg_reader_cost: 1.91593 s, avg_batch_cost: 2.17844 s, avg_samples: 12.5, ips: 5.73805 samples/s, eta: 7:46:00
[2024/07/27 12:42:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:42:10] ppocr INFO: epoch: [442/1500], global_step: 1326, lr: 0.001000, loss: 1.564077, loss_shrink_maps: 0.815076, loss_threshold_maps: 0.572097, loss_binary_maps: 0.161449, avg_reader_cost: 2.27556 s, avg_batch_cost: 2.52050 s, avg_samples: 12.5, ips: 4.95933 samples/s, eta: 7:45:31
[2024/07/27 12:42:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:42:20] ppocr INFO: epoch: [443/1500], global_step: 1329, lr: 0.001000, loss: 1.564077, loss_shrink_maps: 0.815076, loss_threshold_maps: 0.576110, loss_binary_maps: 0.161449, avg_reader_cost: 2.30334 s, avg_batch_cost: 2.55261 s, avg_samples: 12.5, ips: 4.89695 samples/s, eta: 7:45:02
[2024/07/27 12:42:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:42:28] ppocr INFO: epoch: [444/1500], global_step: 1330, lr: 0.001000, loss: 1.537274, loss_shrink_maps: 0.794104, loss_threshold_maps: 0.572097, loss_binary_maps: 0.158032, avg_reader_cost: 0.59395 s, avg_batch_cost: 0.82093 s, avg_samples: 4.8, ips: 5.84701 samples/s, eta: 7:44:52
[2024/07/27 12:42:30] ppocr INFO: epoch: [444/1500], global_step: 1332, lr: 0.001000, loss: 1.545995, loss_shrink_maps: 0.794104, loss_threshold_maps: 0.572866, loss_binary_maps: 0.158032, avg_reader_cost: 1.73412 s, avg_batch_cost: 1.88141 s, avg_samples: 7.7, ips: 4.09267 samples/s, eta: 7:44:37
[2024/07/27 12:42:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:42:39] ppocr INFO: epoch: [445/1500], global_step: 1335, lr: 0.001000, loss: 1.545995, loss_shrink_maps: 0.794104, loss_threshold_maps: 0.572866, loss_binary_maps: 0.158032, avg_reader_cost: 2.25099 s, avg_batch_cost: 2.49883 s, avg_samples: 12.5, ips: 5.00234 samples/s, eta: 7:44:08
[2024/07/27 12:42:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:42:49] ppocr INFO: epoch: [446/1500], global_step: 1338, lr: 0.001000, loss: 1.472114, loss_shrink_maps: 0.761715, loss_threshold_maps: 0.569631, loss_binary_maps: 0.150443, avg_reader_cost: 2.34817 s, avg_batch_cost: 2.58758 s, avg_samples: 12.5, ips: 4.83077 samples/s, eta: 7:43:40
[2024/07/27 12:42:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:42:58] ppocr INFO: epoch: [447/1500], global_step: 1340, lr: 0.001000, loss: 1.444325, loss_shrink_maps: 0.733880, loss_threshold_maps: 0.557376, loss_binary_maps: 0.145375, avg_reader_cost: 1.31267 s, avg_batch_cost: 1.63552 s, avg_samples: 9.6, ips: 5.86969 samples/s, eta: 7:43:19
[2024/07/27 12:42:59] ppocr INFO: epoch: [447/1500], global_step: 1341, lr: 0.001000, loss: 1.444325, loss_shrink_maps: 0.733880, loss_threshold_maps: 0.557376, loss_binary_maps: 0.145375, avg_reader_cost: 0.86374 s, avg_batch_cost: 0.91929 s, avg_samples: 2.9, ips: 3.15462 samples/s, eta: 7:43:12
[2024/07/27 12:43:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:43:09] ppocr INFO: epoch: [448/1500], global_step: 1344, lr: 0.001000, loss: 1.444339, loss_shrink_maps: 0.733880, loss_threshold_maps: 0.561275, loss_binary_maps: 0.145385, avg_reader_cost: 2.18877 s, avg_batch_cost: 2.54451 s, avg_samples: 12.5, ips: 4.91254 samples/s, eta: 7:42:43
[2024/07/27 12:43:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:43:18] ppocr INFO: epoch: [449/1500], global_step: 1347, lr: 0.001000, loss: 1.486248, loss_shrink_maps: 0.760240, loss_threshold_maps: 0.561275, loss_binary_maps: 0.151081, avg_reader_cost: 2.23204 s, avg_batch_cost: 2.59305 s, avg_samples: 12.5, ips: 4.82058 samples/s, eta: 7:42:16
[2024/07/27 12:43:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:43:28] ppocr INFO: epoch: [450/1500], global_step: 1350, lr: 0.001000, loss: 1.483073, loss_shrink_maps: 0.776700, loss_threshold_maps: 0.561275, loss_binary_maps: 0.154520, avg_reader_cost: 2.09195 s, avg_batch_cost: 2.49307 s, avg_samples: 12.5, ips: 5.01389 samples/s, eta: 7:41:46
[2024/07/27 12:43:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:43:38] ppocr INFO: epoch: [451/1500], global_step: 1353, lr: 0.001000, loss: 1.503118, loss_shrink_maps: 0.781058, loss_threshold_maps: 0.554999, loss_binary_maps: 0.155178, avg_reader_cost: 2.18204 s, avg_batch_cost: 2.54703 s, avg_samples: 12.5, ips: 4.90767 samples/s, eta: 7:41:17
[2024/07/27 12:43:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:43:47] ppocr INFO: epoch: [452/1500], global_step: 1356, lr: 0.001000, loss: 1.503118, loss_shrink_maps: 0.781058, loss_threshold_maps: 0.561481, loss_binary_maps: 0.155178, avg_reader_cost: 2.33672 s, avg_batch_cost: 2.57824 s, avg_samples: 12.5, ips: 4.84826 samples/s, eta: 7:40:49
[2024/07/27 12:43:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:43:57] ppocr INFO: epoch: [453/1500], global_step: 1359, lr: 0.001000, loss: 1.515872, loss_shrink_maps: 0.791162, loss_threshold_maps: 0.570046, loss_binary_maps: 0.157594, avg_reader_cost: 2.38108 s, avg_batch_cost: 2.63741 s, avg_samples: 12.5, ips: 4.73950 samples/s, eta: 7:40:23
[2024/07/27 12:43:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:44:06] ppocr INFO: epoch: [454/1500], global_step: 1360, lr: 0.001000, loss: 1.515872, loss_shrink_maps: 0.791162, loss_threshold_maps: 0.572086, loss_binary_maps: 0.157594, avg_reader_cost: 0.56875 s, avg_batch_cost: 0.78262 s, avg_samples: 4.8, ips: 6.13324 samples/s, eta: 7:40:12
[2024/07/27 12:44:07] ppocr INFO: epoch: [454/1500], global_step: 1362, lr: 0.001000, loss: 1.512648, loss_shrink_maps: 0.791162, loss_threshold_maps: 0.569392, loss_binary_maps: 0.157594, avg_reader_cost: 1.65876 s, avg_batch_cost: 1.80396 s, avg_samples: 7.7, ips: 4.26840 samples/s, eta: 7:39:55
[2024/07/27 12:44:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:44:17] ppocr INFO: epoch: [455/1500], global_step: 1365, lr: 0.001000, loss: 1.502616, loss_shrink_maps: 0.781144, loss_threshold_maps: 0.569392, loss_binary_maps: 0.155176, avg_reader_cost: 2.33698 s, avg_batch_cost: 2.58560 s, avg_samples: 12.5, ips: 4.83448 samples/s, eta: 7:39:28
[2024/07/27 12:44:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:44:27] ppocr INFO: epoch: [456/1500], global_step: 1368, lr: 0.001000, loss: 1.496083, loss_shrink_maps: 0.781144, loss_threshold_maps: 0.563060, loss_binary_maps: 0.155168, avg_reader_cost: 2.30074 s, avg_batch_cost: 2.54023 s, avg_samples: 12.5, ips: 4.92081 samples/s, eta: 7:38:59
[2024/07/27 12:44:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:44:36] ppocr INFO: epoch: [457/1500], global_step: 1370, lr: 0.001000, loss: 1.496083, loss_shrink_maps: 0.776759, loss_threshold_maps: 0.563060, loss_binary_maps: 0.153701, avg_reader_cost: 1.35683 s, avg_batch_cost: 1.63880 s, avg_samples: 9.6, ips: 5.85794 samples/s, eta: 7:38:39
[2024/07/27 12:44:36] ppocr INFO: epoch: [457/1500], global_step: 1371, lr: 0.001000, loss: 1.496083, loss_shrink_maps: 0.776759, loss_threshold_maps: 0.563060, loss_binary_maps: 0.153701, avg_reader_cost: 0.86607 s, avg_batch_cost: 0.92137 s, avg_samples: 2.9, ips: 3.14750 samples/s, eta: 7:38:31
[2024/07/27 12:44:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:44:46] ppocr INFO: epoch: [458/1500], global_step: 1374, lr: 0.001000, loss: 1.503782, loss_shrink_maps: 0.776759, loss_threshold_maps: 0.569040, loss_binary_maps: 0.153615, avg_reader_cost: 2.37472 s, avg_batch_cost: 2.60864 s, avg_samples: 12.5, ips: 4.79176 samples/s, eta: 7:38:04
[2024/07/27 12:44:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:44:56] ppocr INFO: epoch: [459/1500], global_step: 1377, lr: 0.001000, loss: 1.489758, loss_shrink_maps: 0.771064, loss_threshold_maps: 0.563060, loss_binary_maps: 0.152344, avg_reader_cost: 2.30613 s, avg_batch_cost: 2.64476 s, avg_samples: 12.5, ips: 4.72632 samples/s, eta: 7:37:38
[2024/07/27 12:44:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:45:05] ppocr INFO: epoch: [460/1500], global_step: 1380, lr: 0.001000, loss: 1.484211, loss_shrink_maps: 0.771064, loss_threshold_maps: 0.551365, loss_binary_maps: 0.152344, avg_reader_cost: 2.09896 s, avg_batch_cost: 2.39042 s, avg_samples: 12.5, ips: 5.22920 samples/s, eta: 7:37:06

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:39, 12.52it/s]
eval model::   1%|          | 5/500 [00:00<00:27, 17.95it/s]
eval model::   1%|▏         | 7/500 [00:00<00:28, 17.26it/s]
eval model::   2%|▏         | 9/500 [00:00<00:27, 17.76it/s]
eval model::   2%|▏         | 11/500 [00:00<00:33, 14.76it/s]
eval model::   3%|▎         | 13/500 [00:00<00:32, 14.90it/s]
eval model::   3%|▎         | 15/500 [00:00<00:30, 15.76it/s]
eval model::   4%|▎         | 18/500 [00:01<00:26, 18.40it/s]
eval model::   4%|▍         | 20/500 [00:01<00:27, 17.37it/s]
eval model::   5%|▍         | 23/500 [00:01<00:24, 19.11it/s]
eval model::   5%|▌         | 25/500 [00:01<00:26, 18.19it/s]
eval model::   5%|▌         | 27/500 [00:01<00:25, 18.24it/s]
eval model::   6%|▌         | 30/500 [00:01<00:23, 20.25it/s]
eval model::   7%|▋         | 33/500 [00:01<00:24, 19.16it/s]
eval model::   7%|▋         | 35/500 [00:02<00:26, 17.41it/s]
eval model::   8%|▊         | 38/500 [00:02<00:23, 19.26it/s]
eval model::   8%|▊         | 40/500 [00:02<00:27, 16.47it/s]
eval model::   8%|▊         | 42/500 [00:02<00:30, 15.08it/s]
eval model::   9%|▉         | 45/500 [00:02<00:26, 17.44it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 16.78it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 19.19it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 19.33it/s]
eval model::  11%|█         | 55/500 [00:03<00:21, 20.88it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:22, 19.88it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.45it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.60it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.34it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.68it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 21.77it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:20, 21.23it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.71it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 23.08it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.94it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:18, 21.95it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:21, 18.97it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:23, 17.49it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:23, 17.53it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:21, 18.80it/s]
eval model::  20%|██        | 100/500 [00:05<00:21, 18.57it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 20.40it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.15it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.44it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 21.47it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:17, 22.06it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:16, 23.30it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 20.11it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 19.93it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 19.86it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:20, 17.76it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.72it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:19, 19.10it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:19, 19.07it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:19, 18.89it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 20.18it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:16, 21.76it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:15, 22.10it/s]
eval model::  30%|███       | 152/500 [00:07<00:17, 20.06it/s]
eval model::  31%|███       | 155/500 [00:08<00:16, 21.33it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.65it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:18, 17.88it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:18, 18.18it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:18, 18.55it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 18.16it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 20.41it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:17, 19.05it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:21, 14.84it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:19, 16.86it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:20, 15.34it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:17, 17.82it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 19.90it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.36it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:15, 19.34it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:18, 16.69it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:16, 18.24it/s]
eval model::  40%|████      | 201/500 [00:10<00:15, 19.76it/s]
eval model::  41%|████      | 205/500 [00:10<00:19, 14.82it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:18, 15.56it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 15.18it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.71it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.26it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.32it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:19, 14.18it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:19, 14.20it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:16, 16.37it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.55it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:14, 19.13it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 21.46it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 20.52it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:12, 21.42it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 21.80it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:14, 17.56it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:13, 18.63it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:14, 17.87it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 19.15it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.01it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 21.88it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 22.22it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:11, 19.87it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 19.82it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 20.46it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 19.78it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:11, 19.10it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 19.60it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 20.66it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:16, 12.82it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:15, 13.64it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:12, 16.25it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:12, 17.20it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:15, 12.96it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:12, 15.71it/s]
eval model::  60%|██████    | 300/500 [00:16<00:13, 15.29it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.24it/s]
eval model::  61%|██████    | 305/500 [00:16<00:11, 16.70it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:11, 17.28it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:10, 17.80it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:09, 19.23it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:09, 19.47it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:08, 20.60it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 20.79it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:09, 19.43it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 20.74it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 19.65it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:08, 19.40it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:07, 20.58it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:08, 18.46it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:10, 14.98it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 17.11it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 18.99it/s]
eval model::  70%|███████   | 350/500 [00:18<00:07, 20.63it/s]
eval model::  71%|███████   | 353/500 [00:19<00:06, 21.88it/s]
eval model::  71%|███████   | 356/500 [00:19<00:06, 20.74it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:06, 21.33it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:06, 20.01it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 19.09it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:08, 16.47it/s]
eval model::  74%|███████▍  | 370/500 [00:20<00:07, 18.25it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 18.16it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 20.38it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:04, 24.42it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 22.06it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:04, 23.74it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:07, 15.18it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:06, 16.85it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:05, 18.60it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 19.92it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 18.30it/s]
eval model::  81%|████████  | 403/500 [00:21<00:05, 18.48it/s]
eval model::  81%|████████  | 405/500 [00:21<00:05, 18.57it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 19.91it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:04, 21.61it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:04, 21.16it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 20.19it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 15.16it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 15.81it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.30it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:04, 15.60it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 17.22it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 19.14it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 20.53it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 21.70it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 20.07it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 21.69it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 21.90it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 18.95it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 20.76it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 22.67it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 22.56it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 18.36it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 17.42it/s]
eval model::  93%|█████████▎| 467/500 [00:25<00:01, 17.64it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 21.12it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.93it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 19.65it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:01, 19.98it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 19.41it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 19.56it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 21.83it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 20.28it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 21.60it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 21.83it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.75it/s]
[2024/07/27 12:45:32] ppocr INFO: cur metric, precision: 0.6865121180189674, recall: 0.6273471352912855, hmean: 0.6555974842767296, fps: 44.366496892317244
[2024/07/27 12:45:32] ppocr INFO: best metric, hmean: 0.6737551867219916, precision: 0.730185497470489, recall: 0.6254212806933076, fps: 43.42892415983237, best_epoch: 420
[2024/07/27 12:45:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:45:41] ppocr INFO: epoch: [461/1500], global_step: 1383, lr: 0.001000, loss: 1.503782, loss_shrink_maps: 0.776759, loss_threshold_maps: 0.555536, loss_binary_maps: 0.153615, avg_reader_cost: 2.25704 s, avg_batch_cost: 2.49584 s, avg_samples: 12.5, ips: 5.00834 samples/s, eta: 7:36:36
[2024/07/27 12:45:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:45:51] ppocr INFO: epoch: [462/1500], global_step: 1386, lr: 0.001000, loss: 1.503786, loss_shrink_maps: 0.778486, loss_threshold_maps: 0.555536, loss_binary_maps: 0.153855, avg_reader_cost: 2.35760 s, avg_batch_cost: 2.59628 s, avg_samples: 12.5, ips: 4.81458 samples/s, eta: 7:36:09
[2024/07/27 12:45:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:46:01] ppocr INFO: epoch: [463/1500], global_step: 1389, lr: 0.001000, loss: 1.542582, loss_shrink_maps: 0.791448, loss_threshold_maps: 0.564428, loss_binary_maps: 0.156938, avg_reader_cost: 2.11554 s, avg_batch_cost: 2.46604 s, avg_samples: 12.5, ips: 5.06886 samples/s, eta: 7:35:39
[2024/07/27 12:46:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:46:09] ppocr INFO: epoch: [464/1500], global_step: 1390, lr: 0.001000, loss: 1.542582, loss_shrink_maps: 0.793781, loss_threshold_maps: 0.569767, loss_binary_maps: 0.157024, avg_reader_cost: 0.57096 s, avg_batch_cost: 0.79350 s, avg_samples: 4.8, ips: 6.04918 samples/s, eta: 7:35:28
[2024/07/27 12:46:11] ppocr INFO: epoch: [464/1500], global_step: 1392, lr: 0.001000, loss: 1.546976, loss_shrink_maps: 0.809021, loss_threshold_maps: 0.564428, loss_binary_maps: 0.160525, avg_reader_cost: 1.67886 s, avg_batch_cost: 1.82564 s, avg_samples: 7.7, ips: 4.21771 samples/s, eta: 7:35:12
[2024/07/27 12:46:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:46:20] ppocr INFO: epoch: [465/1500], global_step: 1395, lr: 0.001000, loss: 1.497557, loss_shrink_maps: 0.774640, loss_threshold_maps: 0.556111, loss_binary_maps: 0.153390, avg_reader_cost: 2.25638 s, avg_batch_cost: 2.49557 s, avg_samples: 12.5, ips: 5.00888 samples/s, eta: 7:34:43
[2024/07/27 12:46:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:46:30] ppocr INFO: epoch: [466/1500], global_step: 1398, lr: 0.001000, loss: 1.534269, loss_shrink_maps: 0.793781, loss_threshold_maps: 0.561439, loss_binary_maps: 0.157024, avg_reader_cost: 2.37187 s, avg_batch_cost: 2.61422 s, avg_samples: 12.5, ips: 4.78154 samples/s, eta: 7:34:16
[2024/07/27 12:46:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:46:39] ppocr INFO: epoch: [467/1500], global_step: 1400, lr: 0.001000, loss: 1.515092, loss_shrink_maps: 0.792435, loss_threshold_maps: 0.561439, loss_binary_maps: 0.156746, avg_reader_cost: 1.37312 s, avg_batch_cost: 1.67484 s, avg_samples: 9.6, ips: 5.73189 samples/s, eta: 7:33:56
[2024/07/27 12:46:40] ppocr INFO: epoch: [467/1500], global_step: 1401, lr: 0.001000, loss: 1.470594, loss_shrink_maps: 0.772372, loss_threshold_maps: 0.552495, loss_binary_maps: 0.153263, avg_reader_cost: 0.88391 s, avg_batch_cost: 0.93923 s, avg_samples: 2.9, ips: 3.08763 samples/s, eta: 7:33:49
[2024/07/27 12:46:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:46:49] ppocr INFO: epoch: [468/1500], global_step: 1404, lr: 0.001000, loss: 1.489565, loss_shrink_maps: 0.775135, loss_threshold_maps: 0.553091, loss_binary_maps: 0.153263, avg_reader_cost: 2.10634 s, avg_batch_cost: 2.41899 s, avg_samples: 12.5, ips: 5.16744 samples/s, eta: 7:33:18
[2024/07/27 12:46:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:46:59] ppocr INFO: epoch: [469/1500], global_step: 1407, lr: 0.001000, loss: 1.515092, loss_shrink_maps: 0.792435, loss_threshold_maps: 0.553091, loss_binary_maps: 0.156746, avg_reader_cost: 2.36420 s, avg_batch_cost: 2.62956 s, avg_samples: 12.5, ips: 4.75366 samples/s, eta: 7:32:51
[2024/07/27 12:47:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:47:09] ppocr INFO: epoch: [470/1500], global_step: 1410, lr: 0.001000, loss: 1.524834, loss_shrink_maps: 0.811455, loss_threshold_maps: 0.553091, loss_binary_maps: 0.160884, avg_reader_cost: 2.15238 s, avg_batch_cost: 2.48121 s, avg_samples: 12.5, ips: 5.03786 samples/s, eta: 7:32:22
[2024/07/27 12:47:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:47:18] ppocr INFO: epoch: [471/1500], global_step: 1413, lr: 0.001000, loss: 1.496320, loss_shrink_maps: 0.788907, loss_threshold_maps: 0.559487, loss_binary_maps: 0.157049, avg_reader_cost: 2.19622 s, avg_batch_cost: 2.46196 s, avg_samples: 12.5, ips: 5.07725 samples/s, eta: 7:31:51
[2024/07/27 12:47:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:47:28] ppocr INFO: epoch: [472/1500], global_step: 1416, lr: 0.001000, loss: 1.550617, loss_shrink_maps: 0.805152, loss_threshold_maps: 0.570568, loss_binary_maps: 0.159518, avg_reader_cost: 2.24716 s, avg_batch_cost: 2.48495 s, avg_samples: 12.5, ips: 5.03028 samples/s, eta: 7:31:22
[2024/07/27 12:47:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:47:38] ppocr INFO: epoch: [473/1500], global_step: 1419, lr: 0.001000, loss: 1.577475, loss_shrink_maps: 0.818738, loss_threshold_maps: 0.573817, loss_binary_maps: 0.162209, avg_reader_cost: 2.20433 s, avg_batch_cost: 2.50535 s, avg_samples: 12.5, ips: 4.98933 samples/s, eta: 7:30:53
[2024/07/27 12:47:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:47:46] ppocr INFO: epoch: [474/1500], global_step: 1420, lr: 0.001000, loss: 1.577475, loss_shrink_maps: 0.818738, loss_threshold_maps: 0.573817, loss_binary_maps: 0.162209, avg_reader_cost: 0.67368 s, avg_batch_cost: 0.78117 s, avg_samples: 4.8, ips: 6.14465 samples/s, eta: 7:30:42
[2024/07/27 12:47:47] ppocr INFO: epoch: [474/1500], global_step: 1422, lr: 0.001000, loss: 1.577475, loss_shrink_maps: 0.818738, loss_threshold_maps: 0.576614, loss_binary_maps: 0.162209, avg_reader_cost: 1.65442 s, avg_batch_cost: 1.80215 s, avg_samples: 7.7, ips: 4.27268 samples/s, eta: 7:30:25
[2024/07/27 12:47:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:47:57] ppocr INFO: epoch: [475/1500], global_step: 1425, lr: 0.001000, loss: 1.541735, loss_shrink_maps: 0.802324, loss_threshold_maps: 0.573817, loss_binary_maps: 0.158867, avg_reader_cost: 2.19212 s, avg_batch_cost: 2.55547 s, avg_samples: 12.5, ips: 4.89147 samples/s, eta: 7:29:57
[2024/07/27 12:47:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:48:06] ppocr INFO: epoch: [476/1500], global_step: 1428, lr: 0.001000, loss: 1.507667, loss_shrink_maps: 0.786809, loss_threshold_maps: 0.570568, loss_binary_maps: 0.155919, avg_reader_cost: 1.96355 s, avg_batch_cost: 2.24718 s, avg_samples: 12.5, ips: 5.56252 samples/s, eta: 7:29:22
[2024/07/27 12:48:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:48:15] ppocr INFO: epoch: [477/1500], global_step: 1430, lr: 0.001000, loss: 1.507667, loss_shrink_maps: 0.786809, loss_threshold_maps: 0.573817, loss_binary_maps: 0.155919, avg_reader_cost: 1.47643 s, avg_batch_cost: 1.66061 s, avg_samples: 9.6, ips: 5.78101 samples/s, eta: 7:29:03
[2024/07/27 12:48:16] ppocr INFO: epoch: [477/1500], global_step: 1431, lr: 0.001000, loss: 1.507667, loss_shrink_maps: 0.786809, loss_threshold_maps: 0.573817, loss_binary_maps: 0.155641, avg_reader_cost: 0.87640 s, avg_batch_cost: 0.93179 s, avg_samples: 2.9, ips: 3.11228 samples/s, eta: 7:28:55
[2024/07/27 12:48:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:48:26] ppocr INFO: epoch: [478/1500], global_step: 1434, lr: 0.001000, loss: 1.487547, loss_shrink_maps: 0.779164, loss_threshold_maps: 0.550533, loss_binary_maps: 0.154695, avg_reader_cost: 2.24370 s, avg_batch_cost: 2.62901 s, avg_samples: 12.5, ips: 4.75464 samples/s, eta: 7:28:29
[2024/07/27 12:48:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:48:35] ppocr INFO: epoch: [479/1500], global_step: 1437, lr: 0.001000, loss: 1.451421, loss_shrink_maps: 0.752466, loss_threshold_maps: 0.544329, loss_binary_maps: 0.149056, avg_reader_cost: 2.19117 s, avg_batch_cost: 2.53629 s, avg_samples: 12.5, ips: 4.92847 samples/s, eta: 7:28:00
[2024/07/27 12:48:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:48:45] ppocr INFO: epoch: [480/1500], global_step: 1440, lr: 0.001000, loss: 1.401566, loss_shrink_maps: 0.731362, loss_threshold_maps: 0.542727, loss_binary_maps: 0.145341, avg_reader_cost: 2.31389 s, avg_batch_cost: 2.58507 s, avg_samples: 12.5, ips: 4.83545 samples/s, eta: 7:27:33

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:05,  7.60it/s]
eval model::   1%|          | 3/500 [00:00<00:36, 13.51it/s]
eval model::   1%|          | 6/500 [00:00<00:29, 16.67it/s]
eval model::   2%|▏         | 8/500 [00:00<00:30, 15.93it/s]
eval model::   2%|▏         | 11/500 [00:00<00:32, 14.94it/s]
eval model::   3%|▎         | 13/500 [00:00<00:31, 15.67it/s]
eval model::   3%|▎         | 15/500 [00:00<00:29, 16.63it/s]
eval model::   4%|▍         | 19/500 [00:01<00:23, 20.63it/s]
eval model::   4%|▍         | 22/500 [00:01<00:23, 20.36it/s]
eval model::   5%|▌         | 25/500 [00:01<00:24, 19.24it/s]
eval model::   6%|▌         | 28/500 [00:01<00:23, 19.97it/s]
eval model::   6%|▌         | 31/500 [00:01<00:24, 19.30it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.61it/s]
eval model::   7%|▋         | 37/500 [00:02<00:23, 19.44it/s]
eval model::   8%|▊         | 39/500 [00:02<00:26, 17.45it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.82it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 17.94it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 17.65it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 19.36it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 19.38it/s]
eval model::  11%|█         | 55/500 [00:03<00:21, 20.36it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:21, 20.12it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.66it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.28it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 19.13it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 22.07it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 22.33it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:19, 22.29it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:18, 22.71it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.31it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.68it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 18.93it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:24, 16.90it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:23, 17.09it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:23, 17.42it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:19, 20.18it/s]
eval model::  20%|██        | 101/500 [00:05<00:19, 20.41it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 21.64it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 22.85it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 20.99it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 21.08it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:19, 19.84it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 21.35it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.64it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:21, 17.67it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:19, 19.03it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:21, 17.11it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:21, 16.88it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:19, 18.39it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:20, 18.09it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:19, 18.32it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 20.28it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:18, 19.38it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 18.08it/s]
eval model::  30%|███       | 151/500 [00:08<00:20, 16.84it/s]
eval model::  31%|███       | 155/500 [00:08<00:16, 20.33it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 21.19it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:19, 16.92it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:20, 16.63it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 17.58it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:17, 19.27it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:17, 18.33it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:21, 15.44it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:18, 17.08it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:20, 15.56it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:19, 16.40it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.28it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:16, 19.27it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:15, 20.07it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:14, 21.82it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:14, 20.44it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 22.51it/s]
eval model::  41%|████      | 205/500 [00:11<00:20, 14.29it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:19, 15.02it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:19, 15.21it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 14.33it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.81it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.40it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:24, 11.69it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:19, 14.21it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:19, 14.41it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 16.66it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:14, 19.24it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:11, 22.61it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 21.47it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 22.28it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:10, 23.75it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:12, 20.57it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:11, 21.80it/s]
eval model::  50%|█████     | 250/500 [00:13<00:11, 21.32it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 21.65it/s]
eval model::  51%|█████     | 256/500 [00:13<00:12, 20.28it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:11, 21.13it/s]
eval model::  52%|█████▏    | 262/500 [00:14<00:11, 20.84it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:13, 16.80it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:12, 18.32it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:12, 18.04it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.40it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.57it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 17.90it/s]
eval model::  56%|█████▌    | 280/500 [00:15<00:11, 19.02it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:11, 19.47it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.05it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 15.01it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 17.40it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:11, 17.88it/s]
eval model::  59%|█████▉    | 297/500 [00:16<00:13, 15.26it/s]
eval model::  60%|██████    | 300/500 [00:16<00:13, 14.99it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 16.80it/s]
eval model::  61%|██████    | 305/500 [00:16<00:11, 16.71it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:10, 18.73it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 20.35it/s]
eval model::  63%|██████▎   | 314/500 [00:17<00:09, 20.08it/s]
eval model::  63%|██████▎   | 317/500 [00:17<00:09, 19.31it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:09, 19.73it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:09, 17.93it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:09, 19.27it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 19.63it/s]
eval model::  66%|██████▋   | 332/500 [00:18<00:08, 20.45it/s]
eval model::  67%|██████▋   | 335/500 [00:18<00:08, 20.58it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 18.54it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 14.77it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.77it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 18.24it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 19.90it/s]
eval model::  70%|███████   | 352/500 [00:19<00:07, 20.65it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 19.94it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 19.96it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.25it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.03it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 18.86it/s]
eval model::  74%|███████▍  | 370/500 [00:20<00:06, 20.77it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:05, 21.21it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 22.63it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 22.78it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:04, 24.39it/s]
eval model::  77%|███████▋  | 386/500 [00:21<00:07, 14.52it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:06, 16.18it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:06, 16.89it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:05, 18.35it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 19.92it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 18.71it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 20.94it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 22.07it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:03, 24.53it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 23.42it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:03, 21.85it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:04, 16.12it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 16.54it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 17.12it/s]
eval model::  85%|████████▌ | 427/500 [00:23<00:03, 18.64it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 19.91it/s]
eval model::  87%|████████▋ | 433/500 [00:23<00:03, 19.77it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:03, 20.79it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 21.94it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 22.04it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 22.54it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 20.78it/s]
eval model::  90%|█████████ | 451/500 [00:24<00:02, 20.49it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 21.20it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:02, 20.30it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:02, 19.83it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 14.95it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 14.17it/s]
eval model::  93%|█████████▎| 467/500 [00:25<00:02, 14.67it/s]
eval model::  94%|█████████▍| 470/500 [00:25<00:01, 17.06it/s]
eval model::  94%|█████████▍| 472/500 [00:25<00:01, 17.46it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 17.43it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.17it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 21.76it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 22.97it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 22.06it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 23.03it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 21.34it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 22.93it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 24.60it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.80it/s]
[2024/07/27 12:49:12] ppocr INFO: cur metric, precision: 0.7108369098712446, recall: 0.6379393355801637, hmean: 0.6724181679776706, fps: 45.567165250916844
[2024/07/27 12:49:12] ppocr INFO: best metric, hmean: 0.6737551867219916, precision: 0.730185497470489, recall: 0.6254212806933076, fps: 43.42892415983237, best_epoch: 420
[2024/07/27 12:49:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:49:21] ppocr INFO: epoch: [481/1500], global_step: 1443, lr: 0.001000, loss: 1.401566, loss_shrink_maps: 0.731362, loss_threshold_maps: 0.542630, loss_binary_maps: 0.145341, avg_reader_cost: 2.20205 s, avg_batch_cost: 2.49481 s, avg_samples: 12.5, ips: 5.01040 samples/s, eta: 7:27:04
[2024/07/27 12:49:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:49:30] ppocr INFO: epoch: [482/1500], global_step: 1446, lr: 0.001000, loss: 1.438306, loss_shrink_maps: 0.752466, loss_threshold_maps: 0.545931, loss_binary_maps: 0.149056, avg_reader_cost: 2.20681 s, avg_batch_cost: 2.53814 s, avg_samples: 12.5, ips: 4.92487 samples/s, eta: 7:26:36
[2024/07/27 12:49:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:49:40] ppocr INFO: epoch: [483/1500], global_step: 1449, lr: 0.001000, loss: 1.427514, loss_shrink_maps: 0.721652, loss_threshold_maps: 0.544656, loss_binary_maps: 0.143186, avg_reader_cost: 2.16941 s, avg_batch_cost: 2.49614 s, avg_samples: 12.5, ips: 5.00773 samples/s, eta: 7:26:06
[2024/07/27 12:49:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:49:48] ppocr INFO: epoch: [484/1500], global_step: 1450, lr: 0.001000, loss: 1.427514, loss_shrink_maps: 0.721652, loss_threshold_maps: 0.545833, loss_binary_maps: 0.143186, avg_reader_cost: 0.66857 s, avg_batch_cost: 0.76339 s, avg_samples: 4.8, ips: 6.28771 samples/s, eta: 7:25:55
[2024/07/27 12:49:49] ppocr INFO: epoch: [484/1500], global_step: 1452, lr: 0.001000, loss: 1.438306, loss_shrink_maps: 0.741000, loss_threshold_maps: 0.564573, loss_binary_maps: 0.146978, avg_reader_cost: 1.61909 s, avg_batch_cost: 1.76647 s, avg_samples: 7.7, ips: 4.35897 samples/s, eta: 7:25:38
[2024/07/27 12:49:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:49:59] ppocr INFO: epoch: [485/1500], global_step: 1455, lr: 0.001000, loss: 1.438306, loss_shrink_maps: 0.732544, loss_threshold_maps: 0.556540, loss_binary_maps: 0.145179, avg_reader_cost: 2.19441 s, avg_batch_cost: 2.55001 s, avg_samples: 12.5, ips: 4.90195 samples/s, eta: 7:25:10
[2024/07/27 12:50:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:50:09] ppocr INFO: epoch: [486/1500], global_step: 1458, lr: 0.001000, loss: 1.438306, loss_shrink_maps: 0.732544, loss_threshold_maps: 0.556540, loss_binary_maps: 0.145179, avg_reader_cost: 2.30433 s, avg_batch_cost: 2.58647 s, avg_samples: 12.5, ips: 4.83285 samples/s, eta: 7:24:43
[2024/07/27 12:50:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:50:18] ppocr INFO: epoch: [487/1500], global_step: 1460, lr: 0.001000, loss: 1.438306, loss_shrink_maps: 0.732544, loss_threshold_maps: 0.556540, loss_binary_maps: 0.145179, avg_reader_cost: 1.51827 s, avg_batch_cost: 1.69990 s, avg_samples: 9.6, ips: 5.64738 samples/s, eta: 7:24:24
[2024/07/27 12:50:19] ppocr INFO: epoch: [487/1500], global_step: 1461, lr: 0.001000, loss: 1.444771, loss_shrink_maps: 0.732544, loss_threshold_maps: 0.564573, loss_binary_maps: 0.145179, avg_reader_cost: 0.89603 s, avg_batch_cost: 0.95166 s, avg_samples: 2.9, ips: 3.04730 samples/s, eta: 7:24:17
[2024/07/27 12:50:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:50:29] ppocr INFO: epoch: [488/1500], global_step: 1464, lr: 0.001000, loss: 1.478381, loss_shrink_maps: 0.760547, loss_threshold_maps: 0.564573, loss_binary_maps: 0.150475, avg_reader_cost: 2.40539 s, avg_batch_cost: 2.64494 s, avg_samples: 12.5, ips: 4.72601 samples/s, eta: 7:23:51
[2024/07/27 12:50:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:50:39] ppocr INFO: epoch: [489/1500], global_step: 1467, lr: 0.001000, loss: 1.478381, loss_shrink_maps: 0.760547, loss_threshold_maps: 0.556923, loss_binary_maps: 0.150475, avg_reader_cost: 2.27319 s, avg_batch_cost: 2.66740 s, avg_samples: 12.5, ips: 4.68621 samples/s, eta: 7:23:25
[2024/07/27 12:50:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:50:48] ppocr INFO: epoch: [490/1500], global_step: 1470, lr: 0.001000, loss: 1.445618, loss_shrink_maps: 0.734106, loss_threshold_maps: 0.553966, loss_binary_maps: 0.145644, avg_reader_cost: 2.18820 s, avg_batch_cost: 2.53373 s, avg_samples: 12.5, ips: 4.93343 samples/s, eta: 7:22:57
[2024/07/27 12:50:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:50:58] ppocr INFO: epoch: [491/1500], global_step: 1473, lr: 0.001000, loss: 1.465705, loss_shrink_maps: 0.757064, loss_threshold_maps: 0.553966, loss_binary_maps: 0.150025, avg_reader_cost: 2.36440 s, avg_batch_cost: 2.61665 s, avg_samples: 12.5, ips: 4.77710 samples/s, eta: 7:22:30
[2024/07/27 12:50:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:51:08] ppocr INFO: epoch: [492/1500], global_step: 1476, lr: 0.001000, loss: 1.504862, loss_shrink_maps: 0.794522, loss_threshold_maps: 0.554348, loss_binary_maps: 0.157207, avg_reader_cost: 2.16289 s, avg_batch_cost: 2.52197 s, avg_samples: 12.5, ips: 4.95644 samples/s, eta: 7:22:02
[2024/07/27 12:51:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:51:17] ppocr INFO: epoch: [493/1500], global_step: 1479, lr: 0.001000, loss: 1.428700, loss_shrink_maps: 0.733640, loss_threshold_maps: 0.546869, loss_binary_maps: 0.145413, avg_reader_cost: 2.09430 s, avg_batch_cost: 2.41907 s, avg_samples: 12.5, ips: 5.16727 samples/s, eta: 7:21:31
[2024/07/27 12:51:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:51:26] ppocr INFO: epoch: [494/1500], global_step: 1480, lr: 0.001000, loss: 1.461458, loss_shrink_maps: 0.760081, loss_threshold_maps: 0.553465, loss_binary_maps: 0.150244, avg_reader_cost: 0.58033 s, avg_batch_cost: 0.76701 s, avg_samples: 4.8, ips: 6.25806 samples/s, eta: 7:21:20
[2024/07/27 12:51:27] ppocr INFO: epoch: [494/1500], global_step: 1482, lr: 0.001000, loss: 1.483674, loss_shrink_maps: 0.790458, loss_threshold_maps: 0.553465, loss_binary_maps: 0.156789, avg_reader_cost: 1.62720 s, avg_batch_cost: 1.77471 s, avg_samples: 7.7, ips: 4.33874 samples/s, eta: 7:21:03
[2024/07/27 12:51:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:51:37] ppocr INFO: epoch: [495/1500], global_step: 1485, lr: 0.001000, loss: 1.475090, loss_shrink_maps: 0.748819, loss_threshold_maps: 0.549197, loss_binary_maps: 0.148955, avg_reader_cost: 2.18074 s, avg_batch_cost: 2.54441 s, avg_samples: 12.5, ips: 4.91274 samples/s, eta: 7:20:35
[2024/07/27 12:51:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:51:47] ppocr INFO: epoch: [496/1500], global_step: 1488, lr: 0.001000, loss: 1.477000, loss_shrink_maps: 0.767412, loss_threshold_maps: 0.561329, loss_binary_maps: 0.152526, avg_reader_cost: 2.27235 s, avg_batch_cost: 2.64051 s, avg_samples: 12.5, ips: 4.73394 samples/s, eta: 7:20:09
[2024/07/27 12:51:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:51:56] ppocr INFO: epoch: [497/1500], global_step: 1490, lr: 0.001000, loss: 1.477956, loss_shrink_maps: 0.783290, loss_threshold_maps: 0.561329, loss_binary_maps: 0.155526, avg_reader_cost: 1.29555 s, avg_batch_cost: 1.60715 s, avg_samples: 9.6, ips: 5.97332 samples/s, eta: 7:19:48
[2024/07/27 12:51:56] ppocr INFO: epoch: [497/1500], global_step: 1491, lr: 0.001000, loss: 1.477956, loss_shrink_maps: 0.783290, loss_threshold_maps: 0.552084, loss_binary_maps: 0.155526, avg_reader_cost: 0.84951 s, avg_batch_cost: 0.90528 s, avg_samples: 2.9, ips: 3.20341 samples/s, eta: 7:19:40
[2024/07/27 12:51:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:52:06] ppocr INFO: epoch: [498/1500], global_step: 1494, lr: 0.001000, loss: 1.477956, loss_shrink_maps: 0.783290, loss_threshold_maps: 0.549219, loss_binary_maps: 0.155526, avg_reader_cost: 2.39316 s, avg_batch_cost: 2.63162 s, avg_samples: 12.5, ips: 4.74993 samples/s, eta: 7:19:14
[2024/07/27 12:52:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:52:16] ppocr INFO: epoch: [499/1500], global_step: 1497, lr: 0.001000, loss: 1.529144, loss_shrink_maps: 0.804843, loss_threshold_maps: 0.570338, loss_binary_maps: 0.159908, avg_reader_cost: 2.30824 s, avg_batch_cost: 2.68638 s, avg_samples: 12.5, ips: 4.65310 samples/s, eta: 7:18:49
[2024/07/27 12:52:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:52:25] ppocr INFO: epoch: [500/1500], global_step: 1500, lr: 0.001000, loss: 1.536467, loss_shrink_maps: 0.807353, loss_threshold_maps: 0.570338, loss_binary_maps: 0.159908, avg_reader_cost: 2.07486 s, avg_batch_cost: 2.34844 s, avg_samples: 12.5, ips: 5.32269 samples/s, eta: 7:18:17

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.31it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 17.88it/s]
eval model::   1%|▏         | 7/500 [00:00<00:24, 20.33it/s]
eval model::   2%|▏         | 10/500 [00:00<00:23, 21.21it/s]
eval model::   3%|▎         | 13/500 [00:00<00:26, 18.67it/s]
eval model::   3%|▎         | 16/500 [00:00<00:24, 20.01it/s]
eval model::   4%|▍         | 19/500 [00:00<00:24, 19.81it/s]
eval model::   4%|▍         | 22/500 [00:01<00:23, 20.22it/s]
eval model::   5%|▌         | 25/500 [00:01<00:24, 19.47it/s]
eval model::   6%|▌         | 28/500 [00:01<00:23, 20.05it/s]
eval model::   6%|▌         | 31/500 [00:01<00:24, 18.85it/s]
eval model::   7%|▋         | 34/500 [00:01<00:23, 19.94it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 21.25it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 19.16it/s]
eval model::   8%|▊         | 42/500 [00:02<00:24, 18.73it/s]
eval model::   9%|▉         | 44/500 [00:02<00:24, 18.47it/s]
eval model::   9%|▉         | 46/500 [00:02<00:26, 17.05it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 19.52it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 21.01it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.57it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:22, 19.96it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:24, 17.77it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:25, 17.32it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 19.65it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:19, 21.66it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 22.14it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:19, 21.56it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:19, 21.48it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:17, 23.41it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.88it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 20.33it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.79it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.08it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.35it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:18, 21.89it/s]
eval model::  20%|██        | 102/500 [00:05<00:18, 21.87it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 23.18it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:16, 23.69it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:16, 23.64it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:16, 23.89it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:17, 21.91it/s]
eval model::  24%|██▍       | 120/500 [00:05<00:19, 19.47it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 20.96it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 18.89it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 21.11it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:18, 20.03it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:16, 21.96it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:16, 21.68it/s]
eval model::  28%|██▊       | 141/500 [00:06<00:17, 20.82it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.91it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.98it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 19.31it/s]
eval model::  30%|███       | 152/500 [00:07<00:18, 18.66it/s]
eval model::  31%|███       | 155/500 [00:07<00:18, 19.05it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:17, 19.51it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:23, 14.65it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:20, 16.14it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:18, 18.14it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:18, 17.78it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:18, 17.33it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:20, 16.08it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:19, 16.57it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.32it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:18, 17.27it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:14, 21.68it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:14, 21.11it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:14, 21.44it/s]
eval model::  39%|███▉      | 194/500 [00:09<00:13, 23.25it/s]
eval model::  39%|███▉      | 197/500 [00:09<00:14, 20.62it/s]
eval model::  40%|████      | 200/500 [00:10<00:13, 21.54it/s]
eval model::  41%|████      | 203/500 [00:10<00:13, 22.02it/s]
eval model::  41%|████      | 206/500 [00:10<00:24, 11.95it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:23, 12.47it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:22, 12.95it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.75it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.68it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 14.02it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 16.95it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 15.37it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:14, 18.54it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:13, 20.14it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:10, 24.46it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:11, 23.30it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:10, 24.72it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:10, 23.70it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:12, 20.51it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:12, 19.95it/s]
eval model::  50%|█████     | 250/500 [00:12<00:12, 19.41it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 21.62it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 21.30it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 22.42it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:10, 22.42it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:11, 20.28it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:11, 20.32it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:11, 20.32it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:10, 21.80it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 20.18it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 19.09it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:11, 19.66it/s]
eval model::  57%|█████▋    | 286/500 [00:14<00:16, 12.62it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:14, 15.00it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 17.01it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:14, 14.53it/s]
eval model::  60%|█████▉    | 298/500 [00:15<00:11, 17.12it/s]
eval model::  60%|██████    | 301/500 [00:15<00:11, 16.97it/s]
eval model::  61%|██████    | 304/500 [00:15<00:10, 18.77it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:09, 20.63it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.00it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 21.23it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 21.80it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 21.86it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:08, 20.83it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:07, 22.27it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:07, 21.83it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:07, 21.20it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 22.41it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:07, 20.57it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 16.21it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:08, 18.13it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 20.14it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:06, 22.14it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 23.14it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 22.12it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 21.10it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 19.76it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 17.94it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:07, 17.73it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 19.15it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 19.02it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 18.90it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 20.96it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:06, 18.92it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:05, 20.52it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:08, 12.93it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:07, 15.07it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 15.67it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 17.72it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 20.52it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 19.91it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 21.61it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.56it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 22.74it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 22.92it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 21.36it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:05, 15.46it/s]
eval model::  85%|████████▍ | 423/500 [00:21<00:04, 17.21it/s]
eval model::  85%|████████▌ | 425/500 [00:22<00:04, 16.11it/s]
eval model::  86%|████████▌ | 428/500 [00:22<00:03, 18.00it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 18.94it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:02, 22.23it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 24.40it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 23.85it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 23.27it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 22.42it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 20.93it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 20.87it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 22.11it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 20.26it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:02, 18.11it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 17.40it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 19.69it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 21.40it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 20.63it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 22.62it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 22.58it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 22.36it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 21.31it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 20.86it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 18.48it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 18.91it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 19.66it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.47it/s]
[2024/07/27 12:52:51] ppocr INFO: cur metric, precision: 0.7079953650057937, recall: 0.588348579682234, hmean: 0.64265053904812, fps: 45.22652360392477
[2024/07/27 12:52:51] ppocr INFO: best metric, hmean: 0.6737551867219916, precision: 0.730185497470489, recall: 0.6254212806933076, fps: 43.42892415983237, best_epoch: 420
[2024/07/27 12:52:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:53:01] ppocr INFO: epoch: [501/1500], global_step: 1503, lr: 0.001000, loss: 1.536467, loss_shrink_maps: 0.807353, loss_threshold_maps: 0.570338, loss_binary_maps: 0.159908, avg_reader_cost: 2.21045 s, avg_batch_cost: 2.65404 s, avg_samples: 12.5, ips: 4.70980 samples/s, eta: 7:17:51
[2024/07/27 12:53:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:53:11] ppocr INFO: epoch: [502/1500], global_step: 1506, lr: 0.001000, loss: 1.556230, loss_shrink_maps: 0.810570, loss_threshold_maps: 0.571051, loss_binary_maps: 0.160799, avg_reader_cost: 2.38743 s, avg_batch_cost: 2.63002 s, avg_samples: 12.5, ips: 4.75282 samples/s, eta: 7:17:25
[2024/07/27 12:53:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:53:20] ppocr INFO: epoch: [503/1500], global_step: 1509, lr: 0.001000, loss: 1.572172, loss_shrink_maps: 0.826539, loss_threshold_maps: 0.563837, loss_binary_maps: 0.164170, avg_reader_cost: 2.28556 s, avg_batch_cost: 2.54719 s, avg_samples: 12.5, ips: 4.90737 samples/s, eta: 7:16:57
[2024/07/27 12:53:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:53:29] ppocr INFO: epoch: [504/1500], global_step: 1510, lr: 0.001000, loss: 1.572172, loss_shrink_maps: 0.826539, loss_threshold_maps: 0.563837, loss_binary_maps: 0.164170, avg_reader_cost: 0.67070 s, avg_batch_cost: 0.76678 s, avg_samples: 4.8, ips: 6.25997 samples/s, eta: 7:16:46
[2024/07/27 12:53:30] ppocr INFO: epoch: [504/1500], global_step: 1512, lr: 0.001000, loss: 1.572172, loss_shrink_maps: 0.826539, loss_threshold_maps: 0.563837, loss_binary_maps: 0.164170, avg_reader_cost: 1.62621 s, avg_batch_cost: 1.77432 s, avg_samples: 7.7, ips: 4.33970 samples/s, eta: 7:16:29
[2024/07/27 12:53:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:53:39] ppocr INFO: epoch: [505/1500], global_step: 1515, lr: 0.001000, loss: 1.550279, loss_shrink_maps: 0.825484, loss_threshold_maps: 0.567934, loss_binary_maps: 0.163489, avg_reader_cost: 2.03518 s, avg_batch_cost: 2.30407 s, avg_samples: 12.5, ips: 5.42517 samples/s, eta: 7:15:56
[2024/07/27 12:53:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:53:48] ppocr INFO: epoch: [506/1500], global_step: 1518, lr: 0.001000, loss: 1.567124, loss_shrink_maps: 0.838984, loss_threshold_maps: 0.568447, loss_binary_maps: 0.166383, avg_reader_cost: 2.11056 s, avg_batch_cost: 2.43972 s, avg_samples: 12.5, ips: 5.12355 samples/s, eta: 7:15:26
[2024/07/27 12:53:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:53:57] ppocr INFO: epoch: [507/1500], global_step: 1520, lr: 0.001000, loss: 1.567124, loss_shrink_maps: 0.838984, loss_threshold_maps: 0.568447, loss_binary_maps: 0.166383, avg_reader_cost: 1.28937 s, avg_batch_cost: 1.55624 s, avg_samples: 9.6, ips: 6.16873 samples/s, eta: 7:15:04
[2024/07/27 12:53:58] ppocr INFO: epoch: [507/1500], global_step: 1521, lr: 0.001000, loss: 1.585711, loss_shrink_maps: 0.847526, loss_threshold_maps: 0.571614, loss_binary_maps: 0.167828, avg_reader_cost: 0.82437 s, avg_batch_cost: 0.88003 s, avg_samples: 2.9, ips: 3.29534 samples/s, eta: 7:14:56
[2024/07/27 12:53:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:54:07] ppocr INFO: epoch: [508/1500], global_step: 1524, lr: 0.001000, loss: 1.550279, loss_shrink_maps: 0.833117, loss_threshold_maps: 0.568447, loss_binary_maps: 0.164873, avg_reader_cost: 2.26525 s, avg_batch_cost: 2.63946 s, avg_samples: 12.5, ips: 4.73582 samples/s, eta: 7:14:30
[2024/07/27 12:54:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:54:17] ppocr INFO: epoch: [509/1500], global_step: 1527, lr: 0.001000, loss: 1.550279, loss_shrink_maps: 0.833117, loss_threshold_maps: 0.567033, loss_binary_maps: 0.164873, avg_reader_cost: 2.27792 s, avg_batch_cost: 2.58294 s, avg_samples: 12.5, ips: 4.83945 samples/s, eta: 7:14:03
[2024/07/27 12:54:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:54:26] ppocr INFO: epoch: [510/1500], global_step: 1530, lr: 0.001000, loss: 1.545284, loss_shrink_maps: 0.817250, loss_threshold_maps: 0.567033, loss_binary_maps: 0.161928, avg_reader_cost: 2.09225 s, avg_batch_cost: 2.43443 s, avg_samples: 12.5, ips: 5.13467 samples/s, eta: 7:13:33
[2024/07/27 12:54:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:54:37] ppocr INFO: epoch: [511/1500], global_step: 1533, lr: 0.001000, loss: 1.540698, loss_shrink_maps: 0.821951, loss_threshold_maps: 0.565431, loss_binary_maps: 0.162711, avg_reader_cost: 2.27035 s, avg_batch_cost: 2.65729 s, avg_samples: 12.5, ips: 4.70405 samples/s, eta: 7:13:07
[2024/07/27 12:54:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:54:46] ppocr INFO: epoch: [512/1500], global_step: 1536, lr: 0.001000, loss: 1.491914, loss_shrink_maps: 0.790039, loss_threshold_maps: 0.558882, loss_binary_maps: 0.156317, avg_reader_cost: 2.16632 s, avg_batch_cost: 2.53858 s, avg_samples: 12.5, ips: 4.92402 samples/s, eta: 7:12:39
[2024/07/27 12:54:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:54:56] ppocr INFO: epoch: [513/1500], global_step: 1539, lr: 0.001000, loss: 1.467707, loss_shrink_maps: 0.755934, loss_threshold_maps: 0.556587, loss_binary_maps: 0.150015, avg_reader_cost: 2.38017 s, avg_batch_cost: 2.61419 s, avg_samples: 12.5, ips: 4.78160 samples/s, eta: 7:12:12
[2024/07/27 12:54:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:55:05] ppocr INFO: epoch: [514/1500], global_step: 1540, lr: 0.001000, loss: 1.467707, loss_shrink_maps: 0.755934, loss_threshold_maps: 0.557724, loss_binary_maps: 0.150015, avg_reader_cost: 0.68541 s, avg_batch_cost: 0.78272 s, avg_samples: 4.8, ips: 6.13243 samples/s, eta: 7:12:02
[2024/07/27 12:55:06] ppocr INFO: epoch: [514/1500], global_step: 1542, lr: 0.001000, loss: 1.448835, loss_shrink_maps: 0.748693, loss_threshold_maps: 0.556587, loss_binary_maps: 0.148245, avg_reader_cost: 1.65761 s, avg_batch_cost: 1.80460 s, avg_samples: 7.7, ips: 4.26688 samples/s, eta: 7:11:45
[2024/07/27 12:55:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:55:16] ppocr INFO: epoch: [515/1500], global_step: 1545, lr: 0.001000, loss: 1.441646, loss_shrink_maps: 0.739361, loss_threshold_maps: 0.554897, loss_binary_maps: 0.146251, avg_reader_cost: 2.25630 s, avg_batch_cost: 2.62423 s, avg_samples: 12.5, ips: 4.76330 samples/s, eta: 7:11:19
[2024/07/27 12:55:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:55:26] ppocr INFO: epoch: [516/1500], global_step: 1548, lr: 0.001000, loss: 1.448835, loss_shrink_maps: 0.750840, loss_threshold_maps: 0.554897, loss_binary_maps: 0.148722, avg_reader_cost: 2.23294 s, avg_batch_cost: 2.60146 s, avg_samples: 12.5, ips: 4.80499 samples/s, eta: 7:10:52
[2024/07/27 12:55:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:55:35] ppocr INFO: epoch: [517/1500], global_step: 1550, lr: 0.001000, loss: 1.462454, loss_shrink_maps: 0.772061, loss_threshold_maps: 0.556587, loss_binary_maps: 0.153473, avg_reader_cost: 1.44267 s, avg_batch_cost: 1.67077 s, avg_samples: 9.6, ips: 5.74585 samples/s, eta: 7:10:33
[2024/07/27 12:55:36] ppocr INFO: epoch: [517/1500], global_step: 1551, lr: 0.001000, loss: 1.472510, loss_shrink_maps: 0.790671, loss_threshold_maps: 0.562344, loss_binary_maps: 0.157136, avg_reader_cost: 0.88161 s, avg_batch_cost: 0.93718 s, avg_samples: 2.9, ips: 3.09438 samples/s, eta: 7:10:26
[2024/07/27 12:55:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:55:45] ppocr INFO: epoch: [518/1500], global_step: 1554, lr: 0.001000, loss: 1.456824, loss_shrink_maps: 0.758107, loss_threshold_maps: 0.562344, loss_binary_maps: 0.150417, avg_reader_cost: 2.24633 s, avg_batch_cost: 2.61788 s, avg_samples: 12.5, ips: 4.77486 samples/s, eta: 7:09:59
[2024/07/27 12:55:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:55:55] ppocr INFO: epoch: [519/1500], global_step: 1557, lr: 0.001000, loss: 1.444570, loss_shrink_maps: 0.744048, loss_threshold_maps: 0.557615, loss_binary_maps: 0.147460, avg_reader_cost: 2.22542 s, avg_batch_cost: 2.55669 s, avg_samples: 12.5, ips: 4.88913 samples/s, eta: 7:09:31
[2024/07/27 12:55:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:56:04] ppocr INFO: epoch: [520/1500], global_step: 1560, lr: 0.001000, loss: 1.459498, loss_shrink_maps: 0.767240, loss_threshold_maps: 0.557615, loss_binary_maps: 0.152313, avg_reader_cost: 2.15185 s, avg_batch_cost: 2.39404 s, avg_samples: 12.5, ips: 5.22131 samples/s, eta: 7:09:01

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:56,  8.83it/s]
eval model::   1%|          | 3/500 [00:00<00:33, 14.92it/s]
eval model::   1%|          | 6/500 [00:00<00:29, 16.65it/s]
eval model::   2%|▏         | 9/500 [00:00<00:25, 18.98it/s]
eval model::   2%|▏         | 11/500 [00:00<00:29, 16.48it/s]
eval model::   3%|▎         | 14/500 [00:00<00:28, 17.34it/s]
eval model::   3%|▎         | 17/500 [00:00<00:25, 19.22it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.20it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 20.88it/s]
eval model::   5%|▌         | 26/500 [00:01<00:25, 18.56it/s]
eval model::   6%|▌         | 29/500 [00:01<00:24, 19.59it/s]
eval model::   6%|▋         | 32/500 [00:01<00:25, 18.11it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.23it/s]
eval model::   7%|▋         | 36/500 [00:01<00:25, 18.53it/s]
eval model::   8%|▊         | 38/500 [00:02<00:25, 18.17it/s]
eval model::   8%|▊         | 40/500 [00:02<00:28, 16.37it/s]
eval model::   8%|▊         | 42/500 [00:02<00:29, 15.43it/s]
eval model::   9%|▉         | 44/500 [00:02<00:27, 16.31it/s]
eval model::   9%|▉         | 46/500 [00:02<00:30, 14.75it/s]
eval model::  10%|▉         | 49/500 [00:02<00:26, 16.79it/s]
eval model::  10%|█         | 51/500 [00:02<00:25, 17.27it/s]
eval model::  11%|█         | 53/500 [00:03<00:25, 17.70it/s]
eval model::  11%|█         | 56/500 [00:03<00:25, 17.22it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 17.83it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 15.90it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 14.99it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.39it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 19.74it/s]
eval model::  14%|█▍        | 72/500 [00:04<00:19, 22.03it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:19, 21.41it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:18, 22.79it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:17, 24.05it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 20.12it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.20it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:24, 16.59it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:23, 17.11it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:22, 18.30it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.64it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.37it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 19.98it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:20, 19.04it/s]
eval model::  22%|██▏       | 110/500 [00:06<00:21, 18.39it/s]
eval model::  22%|██▏       | 112/500 [00:06<00:21, 18.35it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:20, 18.55it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:18, 20.50it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:22, 16.75it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:22, 16.99it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:20, 18.57it/s]
eval model::  26%|██▌       | 129/500 [00:07<00:20, 18.30it/s]
eval model::  26%|██▌       | 131/500 [00:07<00:21, 17.02it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:22, 16.12it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:19, 18.55it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:19, 18.27it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:21, 16.85it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:19, 18.46it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:19, 18.56it/s]
eval model::  29%|██▉       | 147/500 [00:08<00:20, 17.16it/s]
eval model::  30%|███       | 150/500 [00:08<00:17, 20.16it/s]
eval model::  31%|███       | 153/500 [00:08<00:18, 19.15it/s]
eval model::  31%|███       | 156/500 [00:08<00:15, 21.71it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 21.07it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:21, 16.00it/s]
eval model::  33%|███▎      | 164/500 [00:09<00:20, 16.28it/s]
eval model::  33%|███▎      | 167/500 [00:09<00:20, 16.63it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:17, 18.46it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:18, 17.39it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:21, 14.86it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:21, 15.24it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.65it/s]
eval model::  36%|███▌      | 181/500 [00:10<00:20, 15.28it/s]
eval model::  37%|███▋      | 184/500 [00:10<00:18, 17.47it/s]
eval model::  37%|███▋      | 187/500 [00:10<00:16, 18.69it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:17, 17.30it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:17, 17.71it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:15, 19.17it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:17, 17.69it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:15, 20.04it/s]
eval model::  40%|████      | 202/500 [00:11<00:13, 21.75it/s]
eval model::  41%|████      | 205/500 [00:11<00:22, 13.40it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:20, 14.52it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 14.35it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.69it/s]
eval model::  43%|████▎     | 215/500 [00:12<00:21, 13.57it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:22, 12.41it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:20, 13.56it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:21, 12.94it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:17, 15.45it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:17, 15.78it/s]
eval model::  46%|████▌     | 229/500 [00:13<00:14, 18.40it/s]
eval model::  46%|████▋     | 232/500 [00:13<00:13, 19.38it/s]
eval model::  47%|████▋     | 235/500 [00:13<00:14, 17.80it/s]
eval model::  48%|████▊     | 238/500 [00:13<00:13, 19.08it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 18.88it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.56it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.40it/s]
eval model::  50%|█████     | 251/500 [00:14<00:12, 20.48it/s]
eval model::  51%|█████     | 254/500 [00:14<00:12, 20.09it/s]
eval model::  51%|█████▏    | 257/500 [00:14<00:11, 20.56it/s]
eval model::  52%|█████▏    | 260/500 [00:14<00:10, 22.01it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:11, 20.49it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:12, 18.39it/s]
eval model::  54%|█████▍    | 269/500 [00:15<00:12, 18.42it/s]
eval model::  54%|█████▍    | 272/500 [00:15<00:11, 19.47it/s]
eval model::  55%|█████▌    | 275/500 [00:15<00:12, 18.69it/s]
eval model::  55%|█████▌    | 277/500 [00:15<00:11, 18.87it/s]
eval model::  56%|█████▌    | 280/500 [00:15<00:10, 20.14it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:10, 20.35it/s]
eval model::  57%|█████▋    | 286/500 [00:16<00:17, 12.10it/s]
eval model::  58%|█████▊    | 289/500 [00:16<00:14, 14.14it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:12, 16.12it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:14, 14.43it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:17, 11.73it/s]
eval model::  60%|█████▉    | 299/500 [00:17<00:13, 14.51it/s]
eval model::  60%|██████    | 301/500 [00:17<00:15, 13.19it/s]
eval model::  61%|██████    | 304/500 [00:17<00:13, 14.97it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:11, 16.89it/s]
eval model::  62%|██████▏   | 309/500 [00:17<00:11, 17.00it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:10, 17.47it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:10, 18.48it/s]
eval model::  64%|██████▎   | 318/500 [00:18<00:08, 20.51it/s]
eval model::  64%|██████▍   | 321/500 [00:18<00:08, 21.12it/s]
eval model::  65%|██████▍   | 324/500 [00:18<00:09, 19.38it/s]
eval model::  65%|██████▌   | 327/500 [00:18<00:07, 21.66it/s]
eval model::  66%|██████▌   | 330/500 [00:18<00:07, 21.84it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:07, 21.49it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 23.08it/s]
eval model::  68%|██████▊   | 340/500 [00:19<00:10, 15.28it/s]
eval model::  69%|██████▊   | 343/500 [00:19<00:09, 16.54it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:08, 18.36it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:07, 19.59it/s]
eval model::  70%|███████   | 352/500 [00:19<00:07, 21.13it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 19.93it/s]
eval model::  72%|███████▏  | 358/500 [00:20<00:06, 21.33it/s]
eval model::  72%|███████▏  | 361/500 [00:20<00:07, 19.04it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:07, 18.47it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:07, 18.41it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:06, 20.60it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:06, 19.57it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 20.44it/s]
eval model::  76%|███████▌  | 379/500 [00:21<00:05, 22.98it/s]
eval model::  76%|███████▋  | 382/500 [00:21<00:05, 21.44it/s]
eval model::  77%|███████▋  | 385/500 [00:21<00:04, 23.35it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:08, 13.69it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:07, 14.59it/s]
eval model::  78%|███████▊  | 392/500 [00:22<00:07, 14.65it/s]
eval model::  79%|███████▉  | 395/500 [00:22<00:06, 16.81it/s]
eval model::  80%|███████▉  | 399/500 [00:22<00:05, 19.49it/s]
eval model::  80%|████████  | 402/500 [00:22<00:05, 18.33it/s]
eval model::  81%|████████  | 405/500 [00:22<00:04, 19.73it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:04, 20.51it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:04, 21.51it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 22.49it/s]
eval model::  83%|████████▎ | 417/500 [00:23<00:03, 21.48it/s]
eval model::  84%|████████▍ | 420/500 [00:23<00:05, 14.70it/s]
eval model::  85%|████████▍ | 423/500 [00:23<00:04, 16.19it/s]
eval model::  85%|████████▌ | 425/500 [00:23<00:05, 14.34it/s]
eval model::  86%|████████▌ | 428/500 [00:23<00:04, 17.11it/s]
eval model::  86%|████████▌ | 431/500 [00:24<00:03, 17.30it/s]
eval model::  87%|████████▋ | 435/500 [00:24<00:03, 20.63it/s]
eval model::  88%|████████▊ | 438/500 [00:24<00:02, 21.23it/s]
eval model::  88%|████████▊ | 441/500 [00:24<00:02, 20.10it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 21.47it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 22.33it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 19.27it/s]
eval model::  91%|█████████ | 453/500 [00:25<00:02, 20.06it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:02, 21.29it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:02, 20.10it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 17.32it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 17.80it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 16.60it/s]
eval model::  94%|█████████▎| 468/500 [00:26<00:01, 16.46it/s]
eval model::  94%|█████████▍| 471/500 [00:26<00:01, 18.35it/s]
eval model::  95%|█████████▍| 474/500 [00:26<00:01, 17.89it/s]
eval model::  95%|█████████▌| 477/500 [00:26<00:01, 19.22it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:00, 20.65it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 21.78it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 21.62it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 22.67it/s]
eval model::  98%|█████████▊| 492/500 [00:27<00:00, 21.44it/s]
eval model::  99%|█████████▉| 495/500 [00:27<00:00, 22.63it/s]
eval model:: 100%|█████████▉| 499/500 [00:27<00:00, 26.21it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.26it/s]
[2024/07/27 12:56:32] ppocr INFO: cur metric, precision: 0.6823122529644269, recall: 0.6649012999518537, hmean: 0.6734942696903194, fps: 44.911414105672
[2024/07/27 12:56:32] ppocr INFO: best metric, hmean: 0.6737551867219916, precision: 0.730185497470489, recall: 0.6254212806933076, fps: 43.42892415983237, best_epoch: 420
[2024/07/27 12:56:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:56:40] ppocr INFO: epoch: [521/1500], global_step: 1563, lr: 0.001000, loss: 1.486342, loss_shrink_maps: 0.782774, loss_threshold_maps: 0.571567, loss_binary_maps: 0.155834, avg_reader_cost: 2.03437 s, avg_batch_cost: 2.27316 s, avg_samples: 12.5, ips: 5.49895 samples/s, eta: 7:08:28
[2024/07/27 12:56:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:56:50] ppocr INFO: epoch: [522/1500], global_step: 1566, lr: 0.001000, loss: 1.461908, loss_shrink_maps: 0.767240, loss_threshold_maps: 0.557615, loss_binary_maps: 0.152313, avg_reader_cost: 2.27870 s, avg_batch_cost: 2.54975 s, avg_samples: 12.5, ips: 4.90244 samples/s, eta: 7:08:00
[2024/07/27 12:56:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:57:00] ppocr INFO: epoch: [523/1500], global_step: 1569, lr: 0.001000, loss: 1.444570, loss_shrink_maps: 0.741400, loss_threshold_maps: 0.542769, loss_binary_maps: 0.147081, avg_reader_cost: 2.37551 s, avg_batch_cost: 2.61495 s, avg_samples: 12.5, ips: 4.78020 samples/s, eta: 7:07:34
[2024/07/27 12:57:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:57:08] ppocr INFO: epoch: [524/1500], global_step: 1570, lr: 0.001000, loss: 1.442619, loss_shrink_maps: 0.732769, loss_threshold_maps: 0.542095, loss_binary_maps: 0.145428, avg_reader_cost: 0.55670 s, avg_batch_cost: 0.77178 s, avg_samples: 4.8, ips: 6.21937 samples/s, eta: 7:07:23
[2024/07/27 12:57:09] ppocr INFO: epoch: [524/1500], global_step: 1572, lr: 0.001000, loss: 1.448897, loss_shrink_maps: 0.755805, loss_threshold_maps: 0.546648, loss_binary_maps: 0.150197, avg_reader_cost: 1.63576 s, avg_batch_cost: 1.78340 s, avg_samples: 7.7, ips: 4.31760 samples/s, eta: 7:07:06
[2024/07/27 12:57:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:57:19] ppocr INFO: epoch: [525/1500], global_step: 1575, lr: 0.001000, loss: 1.515327, loss_shrink_maps: 0.782774, loss_threshold_maps: 0.558297, loss_binary_maps: 0.155834, avg_reader_cost: 2.20827 s, avg_batch_cost: 2.58046 s, avg_samples: 12.5, ips: 4.84411 samples/s, eta: 7:06:39
[2024/07/27 12:57:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:57:29] ppocr INFO: epoch: [526/1500], global_step: 1578, lr: 0.001000, loss: 1.531930, loss_shrink_maps: 0.796522, loss_threshold_maps: 0.564106, loss_binary_maps: 0.157696, avg_reader_cost: 2.40304 s, avg_batch_cost: 2.64485 s, avg_samples: 12.5, ips: 4.72617 samples/s, eta: 7:06:13
[2024/07/27 12:57:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:57:38] ppocr INFO: epoch: [527/1500], global_step: 1580, lr: 0.001000, loss: 1.505363, loss_shrink_maps: 0.776600, loss_threshold_maps: 0.558297, loss_binary_maps: 0.154812, avg_reader_cost: 1.33986 s, avg_batch_cost: 1.70364 s, avg_samples: 9.6, ips: 5.63501 samples/s, eta: 7:05:55
[2024/07/27 12:57:39] ppocr INFO: epoch: [527/1500], global_step: 1581, lr: 0.001000, loss: 1.531930, loss_shrink_maps: 0.789607, loss_threshold_maps: 0.561338, loss_binary_maps: 0.157170, avg_reader_cost: 0.89796 s, avg_batch_cost: 0.95421 s, avg_samples: 2.9, ips: 3.03917 samples/s, eta: 7:05:48
[2024/07/27 12:57:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:57:49] ppocr INFO: epoch: [528/1500], global_step: 1584, lr: 0.001000, loss: 1.444272, loss_shrink_maps: 0.749462, loss_threshold_maps: 0.551847, loss_binary_maps: 0.148801, avg_reader_cost: 2.30259 s, avg_batch_cost: 2.54286 s, avg_samples: 12.5, ips: 4.91573 samples/s, eta: 7:05:20
[2024/07/27 12:57:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:57:59] ppocr INFO: epoch: [529/1500], global_step: 1587, lr: 0.001000, loss: 1.426334, loss_shrink_maps: 0.749462, loss_threshold_maps: 0.540496, loss_binary_maps: 0.148801, avg_reader_cost: 2.26496 s, avg_batch_cost: 2.64689 s, avg_samples: 12.5, ips: 4.72253 samples/s, eta: 7:04:54
[2024/07/27 12:57:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:58:08] ppocr INFO: epoch: [530/1500], global_step: 1590, lr: 0.001000, loss: 1.491797, loss_shrink_maps: 0.778699, loss_threshold_maps: 0.557189, loss_binary_maps: 0.154607, avg_reader_cost: 2.16575 s, avg_batch_cost: 2.57437 s, avg_samples: 12.5, ips: 4.85556 samples/s, eta: 7:04:27
[2024/07/27 12:58:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:58:18] ppocr INFO: epoch: [531/1500], global_step: 1593, lr: 0.001000, loss: 1.462812, loss_shrink_maps: 0.767716, loss_threshold_maps: 0.525737, loss_binary_maps: 0.152636, avg_reader_cost: 2.23645 s, avg_batch_cost: 2.60289 s, avg_samples: 12.5, ips: 4.80235 samples/s, eta: 7:04:00
[2024/07/27 12:58:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:58:28] ppocr INFO: epoch: [532/1500], global_step: 1596, lr: 0.001000, loss: 1.462812, loss_shrink_maps: 0.763360, loss_threshold_maps: 0.528078, loss_binary_maps: 0.152292, avg_reader_cost: 2.27411 s, avg_batch_cost: 2.65006 s, avg_samples: 12.5, ips: 4.71688 samples/s, eta: 7:03:34
[2024/07/27 12:58:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:58:38] ppocr INFO: epoch: [533/1500], global_step: 1599, lr: 0.001000, loss: 1.450152, loss_shrink_maps: 0.754404, loss_threshold_maps: 0.537857, loss_binary_maps: 0.149895, avg_reader_cost: 2.35762 s, avg_batch_cost: 2.58848 s, avg_samples: 12.5, ips: 4.82909 samples/s, eta: 7:03:07
[2024/07/27 12:58:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:58:46] ppocr INFO: epoch: [534/1500], global_step: 1600, lr: 0.001000, loss: 1.435720, loss_shrink_maps: 0.745054, loss_threshold_maps: 0.535112, loss_binary_maps: 0.147769, avg_reader_cost: 0.67647 s, avg_batch_cost: 0.76868 s, avg_samples: 4.8, ips: 6.24443 samples/s, eta: 7:02:57
[2024/07/27 12:58:48] ppocr INFO: epoch: [534/1500], global_step: 1602, lr: 0.001000, loss: 1.417957, loss_shrink_maps: 0.729451, loss_threshold_maps: 0.535112, loss_binary_maps: 0.144542, avg_reader_cost: 1.62962 s, avg_batch_cost: 1.77731 s, avg_samples: 7.7, ips: 4.33239 samples/s, eta: 7:02:40
[2024/07/27 12:58:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:58:57] ppocr INFO: epoch: [535/1500], global_step: 1605, lr: 0.001000, loss: 1.450152, loss_shrink_maps: 0.753691, loss_threshold_maps: 0.546710, loss_binary_maps: 0.150166, avg_reader_cost: 2.16066 s, avg_batch_cost: 2.49813 s, avg_samples: 12.5, ips: 5.00374 samples/s, eta: 7:02:11
[2024/07/27 12:58:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:59:07] ppocr INFO: epoch: [536/1500], global_step: 1608, lr: 0.001000, loss: 1.435720, loss_shrink_maps: 0.742083, loss_threshold_maps: 0.546710, loss_binary_maps: 0.146869, avg_reader_cost: 2.17548 s, avg_batch_cost: 2.53879 s, avg_samples: 12.5, ips: 4.92360 samples/s, eta: 7:01:43
[2024/07/27 12:59:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:59:16] ppocr INFO: epoch: [537/1500], global_step: 1610, lr: 0.001000, loss: 1.417957, loss_shrink_maps: 0.729451, loss_threshold_maps: 0.543028, loss_binary_maps: 0.144542, avg_reader_cost: 1.33210 s, avg_batch_cost: 1.68342 s, avg_samples: 9.6, ips: 5.70269 samples/s, eta: 7:01:25
[2024/07/27 12:59:17] ppocr INFO: epoch: [537/1500], global_step: 1611, lr: 0.001000, loss: 1.401452, loss_shrink_maps: 0.718029, loss_threshold_maps: 0.543028, loss_binary_maps: 0.143100, avg_reader_cost: 0.88776 s, avg_batch_cost: 0.94328 s, avg_samples: 2.9, ips: 3.07437 samples/s, eta: 7:01:17
[2024/07/27 12:59:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:59:26] ppocr INFO: epoch: [538/1500], global_step: 1614, lr: 0.001000, loss: 1.435720, loss_shrink_maps: 0.742083, loss_threshold_maps: 0.551614, loss_binary_maps: 0.146869, avg_reader_cost: 2.16662 s, avg_batch_cost: 2.48872 s, avg_samples: 12.5, ips: 5.02267 samples/s, eta: 7:00:48
[2024/07/27 12:59:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:59:36] ppocr INFO: epoch: [539/1500], global_step: 1617, lr: 0.001000, loss: 1.417957, loss_shrink_maps: 0.730736, loss_threshold_maps: 0.550178, loss_binary_maps: 0.144762, avg_reader_cost: 2.26834 s, avg_batch_cost: 2.50148 s, avg_samples: 12.5, ips: 4.99704 samples/s, eta: 7:00:20
[2024/07/27 12:59:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 12:59:45] ppocr INFO: epoch: [540/1500], global_step: 1620, lr: 0.001000, loss: 1.463501, loss_shrink_maps: 0.747738, loss_threshold_maps: 0.550178, loss_binary_maps: 0.148676, avg_reader_cost: 2.30442 s, avg_batch_cost: 2.54263 s, avg_samples: 12.5, ips: 4.91617 samples/s, eta: 6:59:52

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:54,  9.22it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.07it/s]
eval model::   1%|          | 6/500 [00:00<00:29, 17.02it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.82it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.04it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.58it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.37it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.71it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 20.92it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 20.49it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.21it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.40it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.62it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.35it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 17.75it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.82it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.36it/s]
eval model::   9%|▉         | 47/500 [00:02<00:27, 16.64it/s]
eval model::  10%|▉         | 49/500 [00:02<00:25, 17.35it/s]
eval model::  10%|█         | 52/500 [00:02<00:24, 18.55it/s]
eval model::  11%|█         | 55/500 [00:02<00:23, 18.95it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:23, 18.90it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:25, 17.43it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:30, 14.58it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:31, 14.06it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:25, 17.22it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 21.17it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 22.18it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:18, 23.18it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:18, 23.25it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.10it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.73it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:23, 17.77it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:26, 15.69it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:24, 16.92it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:21, 18.68it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.68it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.33it/s]
eval model::  21%|██        | 106/500 [00:05<00:16, 23.29it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:16, 24.18it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:17, 21.93it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:18, 20.45it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:18, 21.15it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:19, 19.66it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.49it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 18.20it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 19.49it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:20, 17.87it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:21, 17.37it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:20, 17.74it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:20, 18.12it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:19, 18.11it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 19.92it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 20.93it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:17, 19.87it/s]
eval model::  30%|███       | 151/500 [00:07<00:18, 19.09it/s]
eval model::  31%|███       | 155/500 [00:08<00:15, 22.12it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:14, 23.32it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:22, 15.21it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:21, 15.59it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:18, 17.68it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 18.92it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:17, 18.57it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:19, 16.29it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:18, 17.48it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:21, 15.19it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:18, 17.09it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.25it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:15, 20.57it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:14, 21.26it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.66it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:15, 19.51it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.07it/s]
eval model::  41%|████      | 205/500 [00:10<00:18, 15.64it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:17, 16.68it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 15.51it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.71it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.60it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 12.87it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:18, 14.83it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:21, 12.72it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:18, 15.23it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:17, 15.59it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:14, 18.71it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 20.78it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:14, 18.61it/s]
eval model::  48%|████▊     | 239/500 [00:13<00:12, 20.22it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 18.53it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 19.88it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.00it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 20.84it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.93it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.33it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 22.09it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:10, 21.82it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:11, 20.42it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 20.50it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 19.43it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.27it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 20.03it/s]
eval model::  56%|█████▌    | 281/500 [00:15<00:11, 19.70it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:10, 21.21it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:16, 12.68it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:13, 15.29it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:12, 16.61it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:15, 13.48it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 16.07it/s]
eval model::  60%|██████    | 302/500 [00:16<00:13, 15.01it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 15.42it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 17.94it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 19.66it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:09, 19.61it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 19.59it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 20.39it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 18.50it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 20.37it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 21.20it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:07, 21.32it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:07, 22.18it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 22.87it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:09, 16.70it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:08, 17.83it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 20.05it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:06, 21.63it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 22.03it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 21.34it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 21.19it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 19.11it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.88it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 18.30it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 19.88it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:06, 20.09it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:06, 19.95it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 20.93it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 20.24it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:05, 21.98it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:07, 14.48it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:06, 16.66it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:05, 18.88it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:04, 22.22it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 19.69it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 20.40it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 21.80it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:03, 22.79it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:04, 21.56it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 19.89it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 19.13it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:05, 14.12it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.33it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:04, 17.29it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 18.88it/s]
eval model::  87%|████████▋ | 433/500 [00:23<00:03, 18.54it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:03, 19.64it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 21.21it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 21.16it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 21.72it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 21.81it/s]
eval model::  90%|█████████ | 451/500 [00:24<00:02, 21.01it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 22.58it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 23.63it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 24.11it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 18.73it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 18.11it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 19.19it/s]
eval model::  94%|█████████▍| 472/500 [00:25<00:01, 20.40it/s]
eval model::  95%|█████████▌| 475/500 [00:25<00:01, 19.50it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:01, 19.94it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 20.25it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 21.26it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 22.26it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 22.02it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 19.29it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 19.05it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 22.42it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.93it/s]
[2024/07/27 13:00:12] ppocr INFO: cur metric, precision: 0.7268266085059978, recall: 0.6417910447761194, hmean: 0.6816670928151367, fps: 44.83859351120756
[2024/07/27 13:00:12] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 13:00:12] ppocr INFO: best metric, hmean: 0.6816670928151367, precision: 0.7268266085059978, recall: 0.6417910447761194, fps: 44.83859351120756, best_epoch: 540
[2024/07/27 13:00:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:00:21] ppocr INFO: epoch: [541/1500], global_step: 1623, lr: 0.001000, loss: 1.476462, loss_shrink_maps: 0.761303, loss_threshold_maps: 0.554721, loss_binary_maps: 0.151194, avg_reader_cost: 2.13111 s, avg_batch_cost: 2.47455 s, avg_samples: 12.5, ips: 5.05143 samples/s, eta: 6:59:23
[2024/07/27 13:00:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:00:31] ppocr INFO: epoch: [542/1500], global_step: 1626, lr: 0.001000, loss: 1.472922, loss_shrink_maps: 0.747826, loss_threshold_maps: 0.554721, loss_binary_maps: 0.148676, avg_reader_cost: 2.22615 s, avg_batch_cost: 2.58073 s, avg_samples: 12.5, ips: 4.84358 samples/s, eta: 6:58:56
[2024/07/27 13:00:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:00:41] ppocr INFO: epoch: [543/1500], global_step: 1629, lr: 0.001000, loss: 1.490400, loss_shrink_maps: 0.783288, loss_threshold_maps: 0.554721, loss_binary_maps: 0.156092, avg_reader_cost: 2.26804 s, avg_batch_cost: 2.65711 s, avg_samples: 12.5, ips: 4.70435 samples/s, eta: 6:58:31
[2024/07/27 13:00:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:00:49] ppocr INFO: epoch: [544/1500], global_step: 1630, lr: 0.001000, loss: 1.490400, loss_shrink_maps: 0.783288, loss_threshold_maps: 0.554721, loss_binary_maps: 0.156092, avg_reader_cost: 0.69624 s, avg_batch_cost: 0.80698 s, avg_samples: 4.8, ips: 5.94813 samples/s, eta: 6:58:21
[2024/07/27 13:00:51] ppocr INFO: epoch: [544/1500], global_step: 1632, lr: 0.001000, loss: 1.482812, loss_shrink_maps: 0.783288, loss_threshold_maps: 0.547701, loss_binary_maps: 0.156092, avg_reader_cost: 1.70581 s, avg_batch_cost: 1.85365 s, avg_samples: 7.7, ips: 4.15397 samples/s, eta: 6:58:05
[2024/07/27 13:00:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:01:01] ppocr INFO: epoch: [545/1500], global_step: 1635, lr: 0.001000, loss: 1.474979, loss_shrink_maps: 0.761015, loss_threshold_maps: 0.542542, loss_binary_maps: 0.151733, avg_reader_cost: 2.21012 s, avg_batch_cost: 2.61404 s, avg_samples: 12.5, ips: 4.78187 samples/s, eta: 6:57:39
[2024/07/27 13:01:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:01:10] ppocr INFO: epoch: [546/1500], global_step: 1638, lr: 0.001000, loss: 1.500864, loss_shrink_maps: 0.804123, loss_threshold_maps: 0.561789, loss_binary_maps: 0.160130, avg_reader_cost: 2.37331 s, avg_batch_cost: 2.61231 s, avg_samples: 12.5, ips: 4.78504 samples/s, eta: 6:57:12
[2024/07/27 13:01:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:01:20] ppocr INFO: epoch: [547/1500], global_step: 1640, lr: 0.001000, loss: 1.509818, loss_shrink_maps: 0.806588, loss_threshold_maps: 0.561789, loss_binary_maps: 0.160821, avg_reader_cost: 1.49623 s, avg_batch_cost: 1.68627 s, avg_samples: 9.6, ips: 5.69305 samples/s, eta: 6:56:54
[2024/07/27 13:01:20] ppocr INFO: epoch: [547/1500], global_step: 1641, lr: 0.001000, loss: 1.525020, loss_shrink_maps: 0.821781, loss_threshold_maps: 0.562815, loss_binary_maps: 0.163534, avg_reader_cost: 0.88919 s, avg_batch_cost: 0.94455 s, avg_samples: 2.9, ips: 3.07025 samples/s, eta: 6:56:46
[2024/07/27 13:01:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:01:31] ppocr INFO: epoch: [548/1500], global_step: 1644, lr: 0.001000, loss: 1.509818, loss_shrink_maps: 0.806588, loss_threshold_maps: 0.546128, loss_binary_maps: 0.160821, avg_reader_cost: 2.39571 s, avg_batch_cost: 2.64118 s, avg_samples: 12.5, ips: 4.73274 samples/s, eta: 6:56:20
[2024/07/27 13:01:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:01:40] ppocr INFO: epoch: [549/1500], global_step: 1647, lr: 0.001000, loss: 1.470290, loss_shrink_maps: 0.770895, loss_threshold_maps: 0.546128, loss_binary_maps: 0.153267, avg_reader_cost: 2.08223 s, avg_batch_cost: 2.35755 s, avg_samples: 12.5, ips: 5.30211 samples/s, eta: 6:55:49
[2024/07/27 13:01:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:01:49] ppocr INFO: epoch: [550/1500], global_step: 1650, lr: 0.001000, loss: 1.430115, loss_shrink_maps: 0.737003, loss_threshold_maps: 0.547300, loss_binary_maps: 0.146088, avg_reader_cost: 2.28227 s, avg_batch_cost: 2.52741 s, avg_samples: 12.5, ips: 4.94578 samples/s, eta: 6:55:21
[2024/07/27 13:01:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:01:59] ppocr INFO: epoch: [551/1500], global_step: 1653, lr: 0.001000, loss: 1.530949, loss_shrink_maps: 0.803503, loss_threshold_maps: 0.562815, loss_binary_maps: 0.159582, avg_reader_cost: 2.40477 s, avg_batch_cost: 2.64313 s, avg_samples: 12.5, ips: 4.72925 samples/s, eta: 6:54:55
[2024/07/27 13:02:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:02:08] ppocr INFO: epoch: [552/1500], global_step: 1656, lr: 0.001000, loss: 1.450402, loss_shrink_maps: 0.752312, loss_threshold_maps: 0.548962, loss_binary_maps: 0.149426, avg_reader_cost: 2.01929 s, avg_batch_cost: 2.28115 s, avg_samples: 12.5, ips: 5.47969 samples/s, eta: 6:54:23
[2024/07/27 13:02:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:02:18] ppocr INFO: epoch: [553/1500], global_step: 1659, lr: 0.001000, loss: 1.470910, loss_shrink_maps: 0.761690, loss_threshold_maps: 0.544544, loss_binary_maps: 0.151485, avg_reader_cost: 2.13623 s, avg_batch_cost: 2.49777 s, avg_samples: 12.5, ips: 5.00447 samples/s, eta: 6:53:55
[2024/07/27 13:02:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:02:26] ppocr INFO: epoch: [554/1500], global_step: 1660, lr: 0.001000, loss: 1.470910, loss_shrink_maps: 0.761690, loss_threshold_maps: 0.544544, loss_binary_maps: 0.151485, avg_reader_cost: 0.67900 s, avg_batch_cost: 0.77294 s, avg_samples: 4.8, ips: 6.21002 samples/s, eta: 6:53:45
[2024/07/27 13:02:27] ppocr INFO: epoch: [554/1500], global_step: 1662, lr: 0.001000, loss: 1.495374, loss_shrink_maps: 0.784561, loss_threshold_maps: 0.546863, loss_binary_maps: 0.156052, avg_reader_cost: 1.63824 s, avg_batch_cost: 1.78548 s, avg_samples: 7.7, ips: 4.31257 samples/s, eta: 6:53:28
[2024/07/27 13:02:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:02:37] ppocr INFO: epoch: [555/1500], global_step: 1665, lr: 0.001000, loss: 1.539991, loss_shrink_maps: 0.811635, loss_threshold_maps: 0.555729, loss_binary_maps: 0.161438, avg_reader_cost: 2.32172 s, avg_batch_cost: 2.57808 s, avg_samples: 12.5, ips: 4.84857 samples/s, eta: 6:53:01
[2024/07/27 13:02:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:02:47] ppocr INFO: epoch: [556/1500], global_step: 1668, lr: 0.001000, loss: 1.539991, loss_shrink_maps: 0.811635, loss_threshold_maps: 0.555729, loss_binary_maps: 0.161438, avg_reader_cost: 2.23444 s, avg_batch_cost: 2.60432 s, avg_samples: 12.5, ips: 4.79972 samples/s, eta: 6:52:34
[2024/07/27 13:02:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:02:56] ppocr INFO: epoch: [557/1500], global_step: 1670, lr: 0.001000, loss: 1.552270, loss_shrink_maps: 0.816448, loss_threshold_maps: 0.558992, loss_binary_maps: 0.162670, avg_reader_cost: 1.46898 s, avg_batch_cost: 1.65482 s, avg_samples: 9.6, ips: 5.80124 samples/s, eta: 6:52:15
[2024/07/27 13:02:57] ppocr INFO: epoch: [557/1500], global_step: 1671, lr: 0.001000, loss: 1.552270, loss_shrink_maps: 0.816448, loss_threshold_maps: 0.558992, loss_binary_maps: 0.162670, avg_reader_cost: 0.87345 s, avg_batch_cost: 0.92904 s, avg_samples: 2.9, ips: 3.12150 samples/s, eta: 6:52:07
[2024/07/27 13:02:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:03:07] ppocr INFO: epoch: [558/1500], global_step: 1674, lr: 0.001000, loss: 1.562842, loss_shrink_maps: 0.826067, loss_threshold_maps: 0.563000, loss_binary_maps: 0.163879, avg_reader_cost: 2.37118 s, avg_batch_cost: 2.61212 s, avg_samples: 12.5, ips: 4.78539 samples/s, eta: 6:51:41
[2024/07/27 13:03:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:03:17] ppocr INFO: epoch: [559/1500], global_step: 1677, lr: 0.001000, loss: 1.601385, loss_shrink_maps: 0.851912, loss_threshold_maps: 0.574832, loss_binary_maps: 0.168831, avg_reader_cost: 2.36414 s, avg_batch_cost: 2.60654 s, avg_samples: 12.5, ips: 4.79563 samples/s, eta: 6:51:14
[2024/07/27 13:03:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:03:27] ppocr INFO: epoch: [560/1500], global_step: 1680, lr: 0.001000, loss: 1.613096, loss_shrink_maps: 0.873236, loss_threshold_maps: 0.575182, loss_binary_maps: 0.173326, avg_reader_cost: 2.40303 s, avg_batch_cost: 2.64174 s, avg_samples: 12.5, ips: 4.73173 samples/s, eta: 6:50:49

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:56,  8.78it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 16.80it/s]
eval model::   1%|▏         | 7/500 [00:00<00:25, 19.38it/s]
eval model::   2%|▏         | 9/500 [00:00<00:26, 18.43it/s]
eval model::   2%|▏         | 11/500 [00:00<00:29, 16.37it/s]
eval model::   3%|▎         | 14/500 [00:00<00:28, 17.34it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.26it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.34it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.70it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 20.39it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.32it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 19.56it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.45it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 18.65it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 18.04it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 18.08it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.65it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 17.69it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.37it/s]
eval model::  11%|█         | 53/500 [00:02<00:22, 20.12it/s]
eval model::  11%|█         | 56/500 [00:02<00:22, 19.87it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:23, 18.47it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 15.68it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.76it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:21, 20.06it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 20.68it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 20.12it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:20, 20.93it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.69it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.72it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.88it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:22, 18.45it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:28, 14.52it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:27, 14.62it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:26, 15.37it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:22, 17.90it/s]
eval model::  20%|██        | 100/500 [00:05<00:23, 17.39it/s]
eval model::  21%|██        | 103/500 [00:05<00:20, 19.08it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 22.13it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 20.83it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 21.13it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:19, 19.73it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 21.41it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.23it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:19, 18.78it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 20.06it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:20, 17.83it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:20, 17.95it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:18, 19.85it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:17, 20.67it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:16, 21.75it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:15, 22.37it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:16, 20.73it/s]
eval model::  30%|███       | 151/500 [00:07<00:18, 18.58it/s]
eval model::  31%|███       | 155/500 [00:08<00:15, 21.79it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.26it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:20, 16.83it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.22it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 19.14it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 19.59it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:16, 19.35it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:18, 17.28it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:18, 17.75it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 17.88it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.41it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 18.05it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:17, 18.38it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:16, 18.77it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:16, 19.16it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:14, 21.35it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:15, 19.33it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 21.33it/s]
eval model::  41%|████      | 203/500 [00:10<00:13, 21.99it/s]
eval model::  41%|████      | 206/500 [00:10<00:20, 14.33it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:19, 14.79it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 14.39it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.80it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 14.18it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 12.94it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:19, 14.28it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:20, 13.59it/s]
eval model::  45%|████▍     | 223/500 [00:12<00:18, 14.81it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.31it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.11it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 22.31it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:11, 22.01it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.72it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:13, 19.03it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.39it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 22.25it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 21.57it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.80it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 20.78it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:11, 20.85it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 21.58it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:12, 19.23it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 19.29it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 20.36it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.71it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 20.16it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 21.64it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:09, 21.73it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:16, 13.00it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.94it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.52it/s]
eval model::  59%|█████▉    | 297/500 [00:16<00:15, 13.10it/s]
eval model::  60%|██████    | 300/500 [00:16<00:14, 13.57it/s]
eval model::  60%|██████    | 302/500 [00:16<00:14, 13.85it/s]
eval model::  61%|██████    | 304/500 [00:16<00:13, 14.59it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:11, 17.07it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:10, 18.37it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 19.82it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 20.08it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 21.62it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 21.42it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:07, 22.39it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 20.89it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 19.79it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 21.23it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 21.30it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:09, 16.15it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:08, 17.81it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 20.16it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:06, 21.88it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 23.42it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 22.88it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 22.61it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:06, 20.82it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:06, 20.22it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:06, 20.34it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.41it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 20.71it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 21.61it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 21.61it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 23.30it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:07, 14.28it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 16.23it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 17.37it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:05, 18.49it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 19.37it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 17.66it/s]
eval model::  81%|████████  | 404/500 [00:21<00:05, 18.65it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 20.57it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 22.29it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:04, 20.86it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 20.29it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 18.73it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:05, 14.20it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.37it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.17it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:04, 17.18it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 17.15it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 19.95it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:03, 20.76it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:02, 20.78it/s]
eval model::  89%|████████▊ | 443/500 [00:23<00:02, 22.79it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 22.80it/s]
eval model::  90%|████████▉ | 449/500 [00:23<00:02, 21.06it/s]
eval model::  90%|█████████ | 452/500 [00:23<00:02, 22.23it/s]
eval model::  91%|█████████ | 455/500 [00:24<00:02, 21.79it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:01, 22.10it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 22.72it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:01, 20.53it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 19.11it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 21.18it/s]
eval model::  95%|█████████▍| 473/500 [00:24<00:01, 20.80it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 21.10it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:00, 21.95it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 22.41it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 23.39it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 21.42it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 20.73it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 21.14it/s]
eval model::  99%|█████████▉| 497/500 [00:26<00:00, 22.74it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.14it/s]
[2024/07/27 13:03:53] ppocr INFO: cur metric, precision: 0.7518539646320593, recall: 0.6345690900337024, hmean: 0.6882506527415143, fps: 43.15300095228087
[2024/07/27 13:03:53] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 13:03:53] ppocr INFO: best metric, hmean: 0.6882506527415143, precision: 0.7518539646320593, recall: 0.6345690900337024, fps: 43.15300095228087, best_epoch: 560
[2024/07/27 13:03:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:04:02] ppocr INFO: epoch: [561/1500], global_step: 1683, lr: 0.001000, loss: 1.612444, loss_shrink_maps: 0.859930, loss_threshold_maps: 0.571502, loss_binary_maps: 0.170339, avg_reader_cost: 2.17119 s, avg_batch_cost: 2.40959 s, avg_samples: 12.5, ips: 5.18760 samples/s, eta: 6:50:19
[2024/07/27 13:04:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:04:12] ppocr INFO: epoch: [562/1500], global_step: 1686, lr: 0.001000, loss: 1.591481, loss_shrink_maps: 0.830983, loss_threshold_maps: 0.571502, loss_binary_maps: 0.164242, avg_reader_cost: 2.35258 s, avg_batch_cost: 2.60217 s, avg_samples: 12.5, ips: 4.80369 samples/s, eta: 6:49:52
[2024/07/27 13:04:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:04:22] ppocr INFO: epoch: [563/1500], global_step: 1689, lr: 0.001000, loss: 1.570407, loss_shrink_maps: 0.830983, loss_threshold_maps: 0.565437, loss_binary_maps: 0.164242, avg_reader_cost: 2.29906 s, avg_batch_cost: 2.69205 s, avg_samples: 12.5, ips: 4.64329 samples/s, eta: 6:49:27
[2024/07/27 13:04:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:04:30] ppocr INFO: epoch: [564/1500], global_step: 1690, lr: 0.001000, loss: 1.570407, loss_shrink_maps: 0.830983, loss_threshold_maps: 0.565437, loss_binary_maps: 0.164242, avg_reader_cost: 0.69067 s, avg_batch_cost: 0.79770 s, avg_samples: 4.8, ips: 6.01733 samples/s, eta: 6:49:17
[2024/07/27 13:04:32] ppocr INFO: epoch: [564/1500], global_step: 1692, lr: 0.001000, loss: 1.536950, loss_shrink_maps: 0.811144, loss_threshold_maps: 0.559358, loss_binary_maps: 0.161109, avg_reader_cost: 1.68757 s, avg_batch_cost: 1.83407 s, avg_samples: 7.7, ips: 4.19830 samples/s, eta: 6:49:01
[2024/07/27 13:04:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:04:42] ppocr INFO: epoch: [565/1500], global_step: 1695, lr: 0.001000, loss: 1.497245, loss_shrink_maps: 0.796851, loss_threshold_maps: 0.544197, loss_binary_maps: 0.158071, avg_reader_cost: 2.25013 s, avg_batch_cost: 2.65124 s, avg_samples: 12.5, ips: 4.71477 samples/s, eta: 6:48:35
[2024/07/27 13:04:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:04:51] ppocr INFO: epoch: [566/1500], global_step: 1698, lr: 0.001000, loss: 1.497245, loss_shrink_maps: 0.793811, loss_threshold_maps: 0.541731, loss_binary_maps: 0.157680, avg_reader_cost: 2.21902 s, avg_batch_cost: 2.56581 s, avg_samples: 12.5, ips: 4.87175 samples/s, eta: 6:48:08
[2024/07/27 13:04:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:05:01] ppocr INFO: epoch: [567/1500], global_step: 1700, lr: 0.001000, loss: 1.497245, loss_shrink_maps: 0.793811, loss_threshold_maps: 0.550290, loss_binary_maps: 0.157680, avg_reader_cost: 1.47055 s, avg_batch_cost: 1.65304 s, avg_samples: 9.6, ips: 5.80748 samples/s, eta: 6:47:49
[2024/07/27 13:05:01] ppocr INFO: epoch: [567/1500], global_step: 1701, lr: 0.001000, loss: 1.514227, loss_shrink_maps: 0.796851, loss_threshold_maps: 0.556494, loss_binary_maps: 0.157680, avg_reader_cost: 0.87251 s, avg_batch_cost: 0.92833 s, avg_samples: 2.9, ips: 3.12388 samples/s, eta: 6:47:41
[2024/07/27 13:05:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:05:11] ppocr INFO: epoch: [568/1500], global_step: 1704, lr: 0.001000, loss: 1.490224, loss_shrink_maps: 0.790185, loss_threshold_maps: 0.550290, loss_binary_maps: 0.156839, avg_reader_cost: 2.44631 s, avg_batch_cost: 2.70472 s, avg_samples: 12.5, ips: 4.62156 samples/s, eta: 6:47:16
[2024/07/27 13:05:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:05:21] ppocr INFO: epoch: [569/1500], global_step: 1707, lr: 0.001000, loss: 1.479343, loss_shrink_maps: 0.767636, loss_threshold_maps: 0.547066, loss_binary_maps: 0.152135, avg_reader_cost: 2.27027 s, avg_batch_cost: 2.50393 s, avg_samples: 12.5, ips: 4.99215 samples/s, eta: 6:46:48
[2024/07/27 13:05:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:05:30] ppocr INFO: epoch: [570/1500], global_step: 1710, lr: 0.001000, loss: 1.440588, loss_shrink_maps: 0.751882, loss_threshold_maps: 0.536310, loss_binary_maps: 0.149079, avg_reader_cost: 2.15453 s, avg_batch_cost: 2.46958 s, avg_samples: 12.5, ips: 5.06159 samples/s, eta: 6:46:20
[2024/07/27 13:05:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:05:39] ppocr INFO: epoch: [571/1500], global_step: 1713, lr: 0.001000, loss: 1.463827, loss_shrink_maps: 0.755625, loss_threshold_maps: 0.542165, loss_binary_maps: 0.149632, avg_reader_cost: 2.02058 s, avg_batch_cost: 2.27111 s, avg_samples: 12.5, ips: 5.50392 samples/s, eta: 6:45:48
[2024/07/27 13:05:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:05:49] ppocr INFO: epoch: [572/1500], global_step: 1716, lr: 0.001000, loss: 1.479343, loss_shrink_maps: 0.767636, loss_threshold_maps: 0.547066, loss_binary_maps: 0.152135, avg_reader_cost: 2.25577 s, avg_batch_cost: 2.49757 s, avg_samples: 12.5, ips: 5.00486 samples/s, eta: 6:45:19
[2024/07/27 13:05:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:05:59] ppocr INFO: epoch: [573/1500], global_step: 1719, lr: 0.001000, loss: 1.424040, loss_shrink_maps: 0.749160, loss_threshold_maps: 0.532437, loss_binary_maps: 0.149021, avg_reader_cost: 2.30703 s, avg_batch_cost: 2.55132 s, avg_samples: 12.5, ips: 4.89943 samples/s, eta: 6:44:52
[2024/07/27 13:05:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:06:07] ppocr INFO: epoch: [574/1500], global_step: 1720, lr: 0.001000, loss: 1.424040, loss_shrink_maps: 0.749160, loss_threshold_maps: 0.532437, loss_binary_maps: 0.149021, avg_reader_cost: 0.56461 s, avg_batch_cost: 0.83413 s, avg_samples: 4.8, ips: 5.75452 samples/s, eta: 6:44:43
[2024/07/27 13:06:09] ppocr INFO: epoch: [574/1500], global_step: 1722, lr: 0.001000, loss: 1.424040, loss_shrink_maps: 0.749160, loss_threshold_maps: 0.532437, loss_binary_maps: 0.149021, avg_reader_cost: 1.76043 s, avg_batch_cost: 1.90799 s, avg_samples: 7.7, ips: 4.03567 samples/s, eta: 6:44:28
[2024/07/27 13:06:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:06:18] ppocr INFO: epoch: [575/1500], global_step: 1725, lr: 0.001000, loss: 1.388718, loss_shrink_maps: 0.725460, loss_threshold_maps: 0.532437, loss_binary_maps: 0.144174, avg_reader_cost: 2.18579 s, avg_batch_cost: 2.51790 s, avg_samples: 12.5, ips: 4.96445 samples/s, eta: 6:44:00
[2024/07/27 13:06:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:06:28] ppocr INFO: epoch: [576/1500], global_step: 1728, lr: 0.001000, loss: 1.377297, loss_shrink_maps: 0.709069, loss_threshold_maps: 0.529236, loss_binary_maps: 0.140527, avg_reader_cost: 2.25466 s, avg_batch_cost: 2.48894 s, avg_samples: 12.5, ips: 5.02221 samples/s, eta: 6:43:32
[2024/07/27 13:06:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:06:37] ppocr INFO: epoch: [577/1500], global_step: 1730, lr: 0.001000, loss: 1.404280, loss_shrink_maps: 0.728842, loss_threshold_maps: 0.534116, loss_binary_maps: 0.144428, avg_reader_cost: 1.49449 s, avg_batch_cost: 1.69113 s, avg_samples: 9.6, ips: 5.67669 samples/s, eta: 6:43:13
[2024/07/27 13:06:38] ppocr INFO: epoch: [577/1500], global_step: 1731, lr: 0.001000, loss: 1.377297, loss_shrink_maps: 0.709069, loss_threshold_maps: 0.527940, loss_binary_maps: 0.140527, avg_reader_cost: 0.89211 s, avg_batch_cost: 0.94719 s, avg_samples: 2.9, ips: 3.06169 samples/s, eta: 6:43:06
[2024/07/27 13:06:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:06:48] ppocr INFO: epoch: [578/1500], global_step: 1734, lr: 0.001000, loss: 1.418510, loss_shrink_maps: 0.730555, loss_threshold_maps: 0.537727, loss_binary_maps: 0.144842, avg_reader_cost: 2.35411 s, avg_batch_cost: 2.61493 s, avg_samples: 12.5, ips: 4.78024 samples/s, eta: 6:42:40
[2024/07/27 13:06:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:06:58] ppocr INFO: epoch: [579/1500], global_step: 1737, lr: 0.001000, loss: 1.404280, loss_shrink_maps: 0.717441, loss_threshold_maps: 0.534116, loss_binary_maps: 0.142135, avg_reader_cost: 2.30791 s, avg_batch_cost: 2.72408 s, avg_samples: 12.5, ips: 4.58870 samples/s, eta: 6:42:15
[2024/07/27 13:06:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:07:07] ppocr INFO: epoch: [580/1500], global_step: 1740, lr: 0.001000, loss: 1.347242, loss_shrink_maps: 0.675901, loss_threshold_maps: 0.527940, loss_binary_maps: 0.134000, avg_reader_cost: 2.18603 s, avg_batch_cost: 2.55106 s, avg_samples: 12.5, ips: 4.89992 samples/s, eta: 6:41:48

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:50,  9.84it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 16.64it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.32it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 18.14it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.32it/s]
eval model::   3%|▎         | 13/500 [00:00<00:27, 17.72it/s]
eval model::   3%|▎         | 15/500 [00:00<00:27, 17.50it/s]
eval model::   4%|▍         | 19/500 [00:01<00:24, 19.75it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.36it/s]
eval model::   5%|▍         | 24/500 [00:01<00:22, 21.14it/s]
eval model::   5%|▌         | 27/500 [00:01<00:21, 21.57it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 21.47it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 19.61it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 18.81it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.79it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.58it/s]
eval model::   8%|▊         | 42/500 [00:02<00:24, 18.32it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.02it/s]
eval model::  10%|▉         | 48/500 [00:02<00:27, 16.65it/s]
eval model::  10%|█         | 51/500 [00:02<00:25, 17.36it/s]
eval model::  11%|█         | 54/500 [00:02<00:24, 18.19it/s]
eval model::  11%|█         | 56/500 [00:03<00:24, 17.89it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:25, 17.22it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:24, 17.64it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:30, 14.16it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:28, 15.19it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:21, 20.06it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 21.23it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:21, 19.97it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:21, 20.09it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:18, 22.20it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:17, 23.51it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:18, 22.14it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:21, 19.15it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:20, 19.52it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:18, 22.15it/s]
eval model::  20%|██        | 100/500 [00:05<00:18, 21.69it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 20.97it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.79it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.22it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 21.31it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:17, 21.96it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:16, 23.00it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 20.57it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:17, 20.97it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:17, 21.74it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 19.39it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:18, 19.75it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:17, 21.06it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 20.03it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:16, 21.32it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 20.97it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 19.19it/s]
eval model::  30%|███       | 151/500 [00:07<00:21, 16.58it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 20.48it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:15, 21.83it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:21, 16.07it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:20, 16.33it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:19, 17.02it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:18, 18.39it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:18, 17.82it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 16.12it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.58it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.41it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.40it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:18, 16.94it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.88it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:17, 17.91it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:17, 18.14it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:14, 20.57it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:16, 18.96it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 20.23it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 21.82it/s]
eval model::  41%|████      | 205/500 [00:10<00:20, 14.13it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:20, 14.59it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:20, 14.45it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 14.05it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.61it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:22, 12.75it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.45it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.00it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:18, 14.77it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.61it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:13, 19.64it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 22.48it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.33it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:10, 23.92it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 20.29it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:11, 21.24it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:12, 20.80it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 22.20it/s]
eval model::  51%|█████     | 255/500 [00:13<00:10, 23.11it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:10, 22.51it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:09, 24.03it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:10, 22.80it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:10, 21.45it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 19.83it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:10, 20.68it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:11, 18.83it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 19.06it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 19.43it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.24it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.75it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 17.08it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.45it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:14, 14.47it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:11, 16.75it/s]
eval model::  60%|██████    | 301/500 [00:16<00:13, 15.09it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 16.06it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.66it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.76it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 22.28it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 22.64it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 21.41it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 19.64it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 19.50it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:09, 18.60it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 19.12it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 20.80it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:08, 19.32it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 15.37it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 17.07it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 18.67it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.14it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 21.47it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.31it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 20.57it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 17.87it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.60it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 18.42it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 19.40it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:07, 17.19it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 18.48it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:06, 20.28it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:06, 18.76it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 19.86it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:08, 13.22it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:07, 15.39it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:06, 16.13it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 16.86it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 19.24it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 19.74it/s]
eval model::  81%|████████  | 403/500 [00:21<00:04, 20.65it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 21.33it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 22.40it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:03, 24.07it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:03, 22.86it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:03, 20.66it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:04, 16.19it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.78it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.19it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 17.84it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:03, 18.09it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 21.12it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 23.69it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 23.40it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 23.29it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 21.54it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 20.58it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 21.67it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 22.51it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 22.26it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 17.53it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:01, 17.76it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 19.86it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 21.00it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 20.21it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.59it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 21.66it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 22.14it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 21.30it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 22.15it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 19.98it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 21.35it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 22.83it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.09it/s]
[2024/07/27 13:07:33] ppocr INFO: cur metric, precision: 0.7427792915531335, recall: 0.6562349542609534, hmean: 0.6968302658486708, fps: 44.61269678926526
[2024/07/27 13:07:33] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 13:07:33] ppocr INFO: best metric, hmean: 0.6968302658486708, precision: 0.7427792915531335, recall: 0.6562349542609534, fps: 44.61269678926526, best_epoch: 580
[2024/07/27 13:07:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:07:43] ppocr INFO: epoch: [581/1500], global_step: 1743, lr: 0.001000, loss: 1.326809, loss_shrink_maps: 0.660677, loss_threshold_maps: 0.522900, loss_binary_maps: 0.131300, avg_reader_cost: 2.33847 s, avg_batch_cost: 2.61705 s, avg_samples: 12.5, ips: 4.77638 samples/s, eta: 6:41:21
[2024/07/27 13:07:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:07:53] ppocr INFO: epoch: [582/1500], global_step: 1746, lr: 0.001000, loss: 1.327924, loss_shrink_maps: 0.685105, loss_threshold_maps: 0.519720, loss_binary_maps: 0.135724, avg_reader_cost: 2.18888 s, avg_batch_cost: 2.54672 s, avg_samples: 12.5, ips: 4.90828 samples/s, eta: 6:40:54
[2024/07/27 13:07:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:08:03] ppocr INFO: epoch: [583/1500], global_step: 1749, lr: 0.001000, loss: 1.327924, loss_shrink_maps: 0.685105, loss_threshold_maps: 0.519720, loss_binary_maps: 0.135724, avg_reader_cost: 2.45767 s, avg_batch_cost: 2.72721 s, avg_samples: 12.5, ips: 4.58343 samples/s, eta: 6:40:30
[2024/07/27 13:08:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:08:12] ppocr INFO: epoch: [584/1500], global_step: 1750, lr: 0.001000, loss: 1.379115, loss_shrink_maps: 0.712935, loss_threshold_maps: 0.522900, loss_binary_maps: 0.141432, avg_reader_cost: 0.69669 s, avg_batch_cost: 0.81037 s, avg_samples: 4.8, ips: 5.92325 samples/s, eta: 6:40:20
[2024/07/27 13:08:13] ppocr INFO: epoch: [584/1500], global_step: 1752, lr: 0.001000, loss: 1.363547, loss_shrink_maps: 0.712935, loss_threshold_maps: 0.522900, loss_binary_maps: 0.141432, avg_reader_cost: 1.71329 s, avg_batch_cost: 1.86066 s, avg_samples: 7.7, ips: 4.13831 samples/s, eta: 6:40:04
[2024/07/27 13:08:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:08:23] ppocr INFO: epoch: [585/1500], global_step: 1755, lr: 0.001000, loss: 1.363547, loss_shrink_maps: 0.713862, loss_threshold_maps: 0.522900, loss_binary_maps: 0.141953, avg_reader_cost: 2.23822 s, avg_batch_cost: 2.63711 s, avg_samples: 12.5, ips: 4.74004 samples/s, eta: 6:39:38
[2024/07/27 13:08:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:08:33] ppocr INFO: epoch: [586/1500], global_step: 1758, lr: 0.001000, loss: 1.397437, loss_shrink_maps: 0.736987, loss_threshold_maps: 0.524929, loss_binary_maps: 0.146009, avg_reader_cost: 2.18790 s, avg_batch_cost: 2.51296 s, avg_samples: 12.5, ips: 4.97421 samples/s, eta: 6:39:10
[2024/07/27 13:08:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:08:42] ppocr INFO: epoch: [587/1500], global_step: 1760, lr: 0.001000, loss: 1.440918, loss_shrink_maps: 0.756047, loss_threshold_maps: 0.530698, loss_binary_maps: 0.149763, avg_reader_cost: 1.37322 s, avg_batch_cost: 1.71398 s, avg_samples: 9.6, ips: 5.60101 samples/s, eta: 6:38:52
[2024/07/27 13:08:43] ppocr INFO: epoch: [587/1500], global_step: 1761, lr: 0.001000, loss: 1.440918, loss_shrink_maps: 0.756047, loss_threshold_maps: 0.530698, loss_binary_maps: 0.149763, avg_reader_cost: 0.90335 s, avg_batch_cost: 0.95846 s, avg_samples: 2.9, ips: 3.02570 samples/s, eta: 6:38:45
[2024/07/27 13:08:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:08:53] ppocr INFO: epoch: [588/1500], global_step: 1764, lr: 0.001000, loss: 1.431991, loss_shrink_maps: 0.749636, loss_threshold_maps: 0.530698, loss_binary_maps: 0.148609, avg_reader_cost: 2.42041 s, avg_batch_cost: 2.67389 s, avg_samples: 12.5, ips: 4.67484 samples/s, eta: 6:38:19
[2024/07/27 13:08:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:09:03] ppocr INFO: epoch: [589/1500], global_step: 1767, lr: 0.001000, loss: 1.440918, loss_shrink_maps: 0.751332, loss_threshold_maps: 0.532041, loss_binary_maps: 0.149182, avg_reader_cost: 2.21028 s, avg_batch_cost: 2.57059 s, avg_samples: 12.5, ips: 4.86269 samples/s, eta: 6:37:53
[2024/07/27 13:09:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:09:12] ppocr INFO: epoch: [590/1500], global_step: 1770, lr: 0.001000, loss: 1.410450, loss_shrink_maps: 0.728241, loss_threshold_maps: 0.529051, loss_binary_maps: 0.144316, avg_reader_cost: 2.09878 s, avg_batch_cost: 2.40937 s, avg_samples: 12.5, ips: 5.18807 samples/s, eta: 6:37:23
[2024/07/27 13:09:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:09:22] ppocr INFO: epoch: [591/1500], global_step: 1773, lr: 0.001000, loss: 1.437944, loss_shrink_maps: 0.742586, loss_threshold_maps: 0.532041, loss_binary_maps: 0.147489, avg_reader_cost: 2.25165 s, avg_batch_cost: 2.61324 s, avg_samples: 12.5, ips: 4.78333 samples/s, eta: 6:36:57
[2024/07/27 13:09:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:09:32] ppocr INFO: epoch: [592/1500], global_step: 1776, lr: 0.001000, loss: 1.428588, loss_shrink_maps: 0.731674, loss_threshold_maps: 0.532041, loss_binary_maps: 0.145356, avg_reader_cost: 2.36235 s, avg_batch_cost: 2.60027 s, avg_samples: 12.5, ips: 4.80720 samples/s, eta: 6:36:30
[2024/07/27 13:09:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:09:41] ppocr INFO: epoch: [593/1500], global_step: 1779, lr: 0.001000, loss: 1.393822, loss_shrink_maps: 0.699207, loss_threshold_maps: 0.530395, loss_binary_maps: 0.139020, avg_reader_cost: 2.15576 s, avg_batch_cost: 2.48163 s, avg_samples: 12.5, ips: 5.03702 samples/s, eta: 6:36:02
[2024/07/27 13:09:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:09:49] ppocr INFO: epoch: [594/1500], global_step: 1780, lr: 0.001000, loss: 1.393822, loss_shrink_maps: 0.699207, loss_threshold_maps: 0.537447, loss_binary_maps: 0.139020, avg_reader_cost: 0.66666 s, avg_batch_cost: 0.75864 s, avg_samples: 4.8, ips: 6.32710 samples/s, eta: 6:35:51
[2024/07/27 13:09:51] ppocr INFO: epoch: [594/1500], global_step: 1782, lr: 0.001000, loss: 1.393822, loss_shrink_maps: 0.722590, loss_threshold_maps: 0.537447, loss_binary_maps: 0.142905, avg_reader_cost: 1.60952 s, avg_batch_cost: 1.75637 s, avg_samples: 7.7, ips: 4.38404 samples/s, eta: 6:35:34
[2024/07/27 13:09:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:10:01] ppocr INFO: epoch: [595/1500], global_step: 1785, lr: 0.001000, loss: 1.393822, loss_shrink_maps: 0.727918, loss_threshold_maps: 0.537447, loss_binary_maps: 0.144089, avg_reader_cost: 2.40136 s, avg_batch_cost: 2.63872 s, avg_samples: 12.5, ips: 4.73714 samples/s, eta: 6:35:08
[2024/07/27 13:10:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:10:10] ppocr INFO: epoch: [596/1500], global_step: 1788, lr: 0.001000, loss: 1.421108, loss_shrink_maps: 0.731836, loss_threshold_maps: 0.538692, loss_binary_maps: 0.145171, avg_reader_cost: 2.26871 s, avg_batch_cost: 2.62866 s, avg_samples: 12.5, ips: 4.75528 samples/s, eta: 6:34:42
[2024/07/27 13:10:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:10:20] ppocr INFO: epoch: [597/1500], global_step: 1790, lr: 0.001000, loss: 1.424402, loss_shrink_maps: 0.734455, loss_threshold_maps: 0.544080, loss_binary_maps: 0.145867, avg_reader_cost: 1.34853 s, avg_batch_cost: 1.66128 s, avg_samples: 9.6, ips: 5.77867 samples/s, eta: 6:34:23
[2024/07/27 13:10:20] ppocr INFO: epoch: [597/1500], global_step: 1791, lr: 0.001000, loss: 1.429998, loss_shrink_maps: 0.738912, loss_threshold_maps: 0.544080, loss_binary_maps: 0.146355, avg_reader_cost: 0.87677 s, avg_batch_cost: 0.93249 s, avg_samples: 2.9, ips: 3.10994 samples/s, eta: 6:34:16
[2024/07/27 13:10:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:10:29] ppocr INFO: epoch: [598/1500], global_step: 1794, lr: 0.001000, loss: 1.426240, loss_shrink_maps: 0.731405, loss_threshold_maps: 0.535687, loss_binary_maps: 0.145205, avg_reader_cost: 2.21032 s, avg_batch_cost: 2.44971 s, avg_samples: 12.5, ips: 5.10264 samples/s, eta: 6:33:47
[2024/07/27 13:10:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:10:39] ppocr INFO: epoch: [599/1500], global_step: 1797, lr: 0.001000, loss: 1.433617, loss_shrink_maps: 0.738277, loss_threshold_maps: 0.551411, loss_binary_maps: 0.146237, avg_reader_cost: 2.25779 s, avg_batch_cost: 2.62779 s, avg_samples: 12.5, ips: 4.75685 samples/s, eta: 6:33:21
[2024/07/27 13:10:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:10:49] ppocr INFO: epoch: [600/1500], global_step: 1800, lr: 0.001000, loss: 1.433617, loss_shrink_maps: 0.737884, loss_threshold_maps: 0.535687, loss_binary_maps: 0.146237, avg_reader_cost: 2.16061 s, avg_batch_cost: 2.47498 s, avg_samples: 12.5, ips: 5.05055 samples/s, eta: 6:32:52

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:58,  8.50it/s]
eval model::   1%|          | 3/500 [00:00<00:34, 14.22it/s]
eval model::   1%|          | 6/500 [00:00<00:25, 19.17it/s]
eval model::   2%|▏         | 9/500 [00:00<00:24, 20.44it/s]
eval model::   2%|▏         | 12/500 [00:00<00:24, 20.09it/s]
eval model::   3%|▎         | 15/500 [00:00<00:24, 19.65it/s]
eval model::   4%|▎         | 18/500 [00:00<00:22, 21.38it/s]
eval model::   4%|▍         | 21/500 [00:01<00:23, 20.56it/s]
eval model::   5%|▍         | 24/500 [00:01<00:21, 22.41it/s]
eval model::   5%|▌         | 27/500 [00:01<00:23, 20.31it/s]
eval model::   6%|▌         | 30/500 [00:01<00:23, 20.21it/s]
eval model::   7%|▋         | 33/500 [00:01<00:24, 19.43it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 19.16it/s]
eval model::   8%|▊         | 38/500 [00:01<00:22, 20.46it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.83it/s]
eval model::   9%|▉         | 44/500 [00:02<00:24, 18.60it/s]
eval model::   9%|▉         | 46/500 [00:02<00:29, 15.14it/s]
eval model::  10%|▉         | 49/500 [00:02<00:26, 17.31it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 19.32it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 20.00it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:25, 17.42it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:24, 17.73it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:28, 15.44it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:26, 16.34it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 18.95it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:21, 20.27it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 20.72it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.97it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.64it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.31it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 21.94it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.55it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.04it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.22it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.73it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.79it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.63it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.89it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:20, 19.38it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 19.65it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 20.56it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:19, 20.06it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.57it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.09it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.05it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 18.87it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:22, 16.50it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:19, 18.61it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.34it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.72it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 19.84it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.44it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.37it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 19.17it/s]
eval model::  31%|███       | 156/500 [00:08<00:16, 20.97it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 21.23it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:20, 16.54it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:19, 17.55it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:19, 17.44it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 19.50it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:17, 18.93it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:20, 16.22it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:17, 18.32it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:20, 15.34it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:17, 17.90it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.53it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:16, 18.58it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:16, 18.58it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:14, 21.18it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:15, 19.24it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 20.72it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 19.99it/s]
eval model::  41%|████      | 206/500 [00:11<00:23, 12.46it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:22, 13.11it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:22, 12.72it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:20, 13.81it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.43it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.30it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 15.79it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:19, 14.09it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 16.19it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:15, 17.94it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:13, 20.33it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:13, 19.75it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 21.09it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:12, 21.53it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:13, 18.77it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:12, 21.16it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:12, 20.10it/s]
eval model::  51%|█████     | 253/500 [00:13<00:10, 23.05it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 21.84it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:11, 21.05it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 20.38it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:12, 18.41it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:11, 19.67it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:11, 19.38it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:11, 19.94it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 18.24it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 19.75it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:10, 20.40it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:17, 12.36it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:14, 14.73it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 16.58it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:14, 14.15it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:11, 17.82it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 17.84it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 18.76it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 19.93it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 20.58it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:09, 19.88it/s]
eval model::  63%|██████▎   | 317/500 [00:17<00:08, 20.57it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:08, 20.84it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:09, 18.76it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:08, 19.66it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 19.10it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:09, 18.74it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:08, 20.28it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 19.51it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:08, 19.39it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:10, 15.38it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:08, 17.81it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 18.86it/s]
eval model::  70%|███████   | 350/500 [00:18<00:07, 20.20it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 22.21it/s]
eval model::  71%|███████   | 356/500 [00:19<00:06, 21.57it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:06, 23.39it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:06, 20.88it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:06, 19.60it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:06, 20.50it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 21.09it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 20.64it/s]
eval model::  75%|███████▌  | 377/500 [00:20<00:06, 20.23it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:06, 19.17it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 22.43it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:08, 13.99it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:06, 16.99it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:05, 18.27it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 20.51it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 19.54it/s]
eval model::  81%|████████  | 403/500 [00:21<00:04, 19.99it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 19.76it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 21.15it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:04, 21.20it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:04, 19.96it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 19.95it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:05, 14.16it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 16.19it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.32it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 19.99it/s]
eval model::  87%|████████▋ | 433/500 [00:23<00:03, 20.38it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:03, 21.31it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 22.69it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 23.57it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 23.24it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 22.21it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.69it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:01, 23.35it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 22.37it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 20.30it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 16.86it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.08it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 16.81it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.29it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.96it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 19.54it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 20.34it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 20.73it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 19.15it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 19.87it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 19.57it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 20.31it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 22.14it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.98it/s]
[2024/07/27 13:11:15] ppocr INFO: cur metric, precision: 0.7212220403709766, recall: 0.6364949446316803, hmean: 0.6762148337595908, fps: 44.76344214159595
[2024/07/27 13:11:15] ppocr INFO: best metric, hmean: 0.6968302658486708, precision: 0.7427792915531335, recall: 0.6562349542609534, fps: 44.61269678926526, best_epoch: 580
[2024/07/27 13:11:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:11:25] ppocr INFO: epoch: [601/1500], global_step: 1803, lr: 0.001000, loss: 1.426240, loss_shrink_maps: 0.737884, loss_threshold_maps: 0.535687, loss_binary_maps: 0.146237, avg_reader_cost: 2.38584 s, avg_batch_cost: 2.67071 s, avg_samples: 12.5, ips: 4.68041 samples/s, eta: 6:32:27
[2024/07/27 13:11:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:11:35] ppocr INFO: epoch: [602/1500], global_step: 1806, lr: 0.001000, loss: 1.394702, loss_shrink_maps: 0.723318, loss_threshold_maps: 0.527407, loss_binary_maps: 0.143910, avg_reader_cost: 2.22800 s, avg_batch_cost: 2.61162 s, avg_samples: 12.5, ips: 4.78631 samples/s, eta: 6:32:01
[2024/07/27 13:11:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:11:45] ppocr INFO: epoch: [603/1500], global_step: 1809, lr: 0.001000, loss: 1.420798, loss_shrink_maps: 0.728066, loss_threshold_maps: 0.544744, loss_binary_maps: 0.145020, avg_reader_cost: 2.29982 s, avg_batch_cost: 2.71194 s, avg_samples: 12.5, ips: 4.60924 samples/s, eta: 6:31:36
[2024/07/27 13:11:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:11:53] ppocr INFO: epoch: [604/1500], global_step: 1810, lr: 0.001000, loss: 1.398098, loss_shrink_maps: 0.725625, loss_threshold_maps: 0.536464, loss_binary_maps: 0.143767, avg_reader_cost: 0.65408 s, avg_batch_cost: 0.76752 s, avg_samples: 4.8, ips: 6.25387 samples/s, eta: 6:31:26
[2024/07/27 13:11:55] ppocr INFO: epoch: [604/1500], global_step: 1812, lr: 0.001000, loss: 1.398098, loss_shrink_maps: 0.725625, loss_threshold_maps: 0.548421, loss_binary_maps: 0.143767, avg_reader_cost: 1.62726 s, avg_batch_cost: 1.77490 s, avg_samples: 7.7, ips: 4.33828 samples/s, eta: 6:31:08
[2024/07/27 13:11:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:12:05] ppocr INFO: epoch: [605/1500], global_step: 1815, lr: 0.001000, loss: 1.365296, loss_shrink_maps: 0.699338, loss_threshold_maps: 0.530216, loss_binary_maps: 0.139095, avg_reader_cost: 2.27962 s, avg_batch_cost: 2.66391 s, avg_samples: 12.5, ips: 4.69235 samples/s, eta: 6:30:43
[2024/07/27 13:12:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:12:15] ppocr INFO: epoch: [606/1500], global_step: 1818, lr: 0.001000, loss: 1.365296, loss_shrink_maps: 0.699338, loss_threshold_maps: 0.538375, loss_binary_maps: 0.139095, avg_reader_cost: 2.23890 s, avg_batch_cost: 2.62893 s, avg_samples: 12.5, ips: 4.75479 samples/s, eta: 6:30:17
[2024/07/27 13:12:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:12:24] ppocr INFO: epoch: [607/1500], global_step: 1820, lr: 0.001000, loss: 1.376554, loss_shrink_maps: 0.710763, loss_threshold_maps: 0.538375, loss_binary_maps: 0.141653, avg_reader_cost: 1.38100 s, avg_batch_cost: 1.71928 s, avg_samples: 9.6, ips: 5.58373 samples/s, eta: 6:29:59
[2024/07/27 13:12:24] ppocr INFO: epoch: [607/1500], global_step: 1821, lr: 0.001000, loss: 1.376554, loss_shrink_maps: 0.710763, loss_threshold_maps: 0.538375, loss_binary_maps: 0.141653, avg_reader_cost: 0.90560 s, avg_batch_cost: 0.96164 s, avg_samples: 2.9, ips: 3.01568 samples/s, eta: 6:29:52
[2024/07/27 13:12:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:12:34] ppocr INFO: epoch: [608/1500], global_step: 1824, lr: 0.001000, loss: 1.385684, loss_shrink_maps: 0.718488, loss_threshold_maps: 0.542167, loss_binary_maps: 0.142415, avg_reader_cost: 2.36739 s, avg_batch_cost: 2.61648 s, avg_samples: 12.5, ips: 4.77741 samples/s, eta: 6:29:25
[2024/07/27 13:12:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:12:44] ppocr INFO: epoch: [609/1500], global_step: 1827, lr: 0.001000, loss: 1.407227, loss_shrink_maps: 0.723759, loss_threshold_maps: 0.546996, loss_binary_maps: 0.143301, avg_reader_cost: 2.33466 s, avg_batch_cost: 2.57818 s, avg_samples: 12.5, ips: 4.84838 samples/s, eta: 6:28:59
[2024/07/27 13:12:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:12:53] ppocr INFO: epoch: [610/1500], global_step: 1830, lr: 0.001000, loss: 1.450992, loss_shrink_maps: 0.721734, loss_threshold_maps: 0.550359, loss_binary_maps: 0.143459, avg_reader_cost: 2.33090 s, avg_batch_cost: 2.57255 s, avg_samples: 12.5, ips: 4.85900 samples/s, eta: 6:28:32
[2024/07/27 13:12:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:13:03] ppocr INFO: epoch: [611/1500], global_step: 1833, lr: 0.001000, loss: 1.450992, loss_shrink_maps: 0.721734, loss_threshold_maps: 0.550359, loss_binary_maps: 0.143459, avg_reader_cost: 2.12913 s, avg_batch_cost: 2.39853 s, avg_samples: 12.5, ips: 5.21153 samples/s, eta: 6:28:02
[2024/07/27 13:13:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:13:12] ppocr INFO: epoch: [612/1500], global_step: 1836, lr: 0.001000, loss: 1.459077, loss_shrink_maps: 0.733456, loss_threshold_maps: 0.550359, loss_binary_maps: 0.145407, avg_reader_cost: 2.33507 s, avg_batch_cost: 2.56939 s, avg_samples: 12.5, ips: 4.86496 samples/s, eta: 6:27:35
[2024/07/27 13:13:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:13:22] ppocr INFO: epoch: [613/1500], global_step: 1839, lr: 0.001000, loss: 1.450992, loss_shrink_maps: 0.734416, loss_threshold_maps: 0.538064, loss_binary_maps: 0.146168, avg_reader_cost: 2.19438 s, avg_batch_cost: 2.57007 s, avg_samples: 12.5, ips: 4.86368 samples/s, eta: 6:27:08
[2024/07/27 13:13:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:13:30] ppocr INFO: epoch: [614/1500], global_step: 1840, lr: 0.001000, loss: 1.459077, loss_shrink_maps: 0.745096, loss_threshold_maps: 0.550359, loss_binary_maps: 0.147825, avg_reader_cost: 0.57170 s, avg_batch_cost: 0.74166 s, avg_samples: 4.8, ips: 6.47196 samples/s, eta: 6:26:58
[2024/07/27 13:13:32] ppocr INFO: epoch: [614/1500], global_step: 1842, lr: 0.001000, loss: 1.451200, loss_shrink_maps: 0.769741, loss_threshold_maps: 0.529269, loss_binary_maps: 0.152301, avg_reader_cost: 1.57509 s, avg_batch_cost: 1.72191 s, avg_samples: 7.7, ips: 4.47177 samples/s, eta: 6:26:40
[2024/07/27 13:13:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:13:42] ppocr INFO: epoch: [615/1500], global_step: 1845, lr: 0.001000, loss: 1.446757, loss_shrink_maps: 0.740479, loss_threshold_maps: 0.527399, loss_binary_maps: 0.147462, avg_reader_cost: 2.45635 s, avg_batch_cost: 2.70602 s, avg_samples: 12.5, ips: 4.61934 samples/s, eta: 6:26:15
[2024/07/27 13:13:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:13:51] ppocr INFO: epoch: [616/1500], global_step: 1848, lr: 0.001000, loss: 1.446757, loss_shrink_maps: 0.759061, loss_threshold_maps: 0.527399, loss_binary_maps: 0.150644, avg_reader_cost: 2.15780 s, avg_batch_cost: 2.44664 s, avg_samples: 12.5, ips: 5.10906 samples/s, eta: 6:25:46
[2024/07/27 13:13:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:14:01] ppocr INFO: epoch: [617/1500], global_step: 1850, lr: 0.001000, loss: 1.403153, loss_shrink_maps: 0.728839, loss_threshold_maps: 0.526822, loss_binary_maps: 0.145045, avg_reader_cost: 1.45340 s, avg_batch_cost: 1.65404 s, avg_samples: 9.6, ips: 5.80396 samples/s, eta: 6:25:28
[2024/07/27 13:14:01] ppocr INFO: epoch: [617/1500], global_step: 1851, lr: 0.001000, loss: 1.403153, loss_shrink_maps: 0.728839, loss_threshold_maps: 0.526822, loss_binary_maps: 0.145045, avg_reader_cost: 0.87349 s, avg_batch_cost: 0.92958 s, avg_samples: 2.9, ips: 3.11968 samples/s, eta: 6:25:20
[2024/07/27 13:14:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:14:11] ppocr INFO: epoch: [618/1500], global_step: 1854, lr: 0.001000, loss: 1.426568, loss_shrink_maps: 0.742414, loss_threshold_maps: 0.529269, loss_binary_maps: 0.148264, avg_reader_cost: 2.42620 s, avg_batch_cost: 2.68164 s, avg_samples: 12.5, ips: 4.66133 samples/s, eta: 6:24:55
[2024/07/27 13:14:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:14:21] ppocr INFO: epoch: [619/1500], global_step: 1857, lr: 0.001000, loss: 1.424347, loss_shrink_maps: 0.740196, loss_threshold_maps: 0.528692, loss_binary_maps: 0.147236, avg_reader_cost: 2.14947 s, avg_batch_cost: 2.48887 s, avg_samples: 12.5, ips: 5.02236 samples/s, eta: 6:24:26
[2024/07/27 13:14:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:14:31] ppocr INFO: epoch: [620/1500], global_step: 1860, lr: 0.001000, loss: 1.444744, loss_shrink_maps: 0.752811, loss_threshold_maps: 0.533575, loss_binary_maps: 0.149694, avg_reader_cost: 2.36603 s, avg_batch_cost: 2.68851 s, avg_samples: 12.5, ips: 4.64942 samples/s, eta: 6:24:01

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:50,  9.90it/s]
eval model::   1%|          | 4/500 [00:00<00:30, 16.20it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.49it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.31it/s]
eval model::   2%|▏         | 11/500 [00:00<00:29, 16.86it/s]
eval model::   3%|▎         | 13/500 [00:00<00:27, 17.66it/s]
eval model::   3%|▎         | 15/500 [00:00<00:26, 18.09it/s]
eval model::   4%|▎         | 18/500 [00:00<00:23, 20.93it/s]
eval model::   4%|▍         | 21/500 [00:01<00:23, 19.97it/s]
eval model::   5%|▌         | 25/500 [00:01<00:22, 21.39it/s]
eval model::   6%|▌         | 28/500 [00:01<00:21, 21.60it/s]
eval model::   6%|▌         | 31/500 [00:01<00:21, 21.67it/s]
eval model::   7%|▋         | 34/500 [00:01<00:23, 20.16it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 21.71it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.35it/s]
eval model::   9%|▊         | 43/500 [00:02<00:24, 18.89it/s]
eval model::   9%|▉         | 46/500 [00:02<00:25, 17.77it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 19.94it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 21.17it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 22.04it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:21, 20.99it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.39it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.40it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 19.30it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 21.39it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 20.82it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.81it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.90it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.23it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 21.57it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 18.91it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 17.86it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 19.24it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.55it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.66it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.16it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.83it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.27it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 19.99it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:19, 20.03it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:18, 20.98it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.24it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 19.97it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 18.32it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.69it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.61it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.33it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.38it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.33it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.88it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.72it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.09it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 19.23it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:16, 21.37it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:19, 17.46it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:18, 18.29it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 19.39it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:18, 18.38it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 18.53it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:20, 16.13it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.40it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.44it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.75it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:16, 19.32it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 20.53it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:16, 18.66it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:14, 20.65it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:15, 19.27it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 20.57it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 21.21it/s]
eval model::  41%|████      | 205/500 [00:10<00:20, 14.60it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:19, 15.13it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 15.07it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.58it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:22, 12.82it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 12.96it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.31it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:19, 14.02it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 16.82it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:15, 17.27it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 20.27it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:13, 19.85it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:14, 18.55it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:12, 20.52it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:15, 17.14it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:13, 18.73it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:13, 18.63it/s]
eval model::  50%|█████     | 250/500 [00:13<00:13, 18.17it/s]
eval model::  51%|█████     | 253/500 [00:13<00:13, 18.95it/s]
eval model::  51%|█████     | 256/500 [00:13<00:12, 19.02it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:12, 19.02it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 20.71it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:12, 19.63it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 20.32it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 20.35it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 20.55it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 18.53it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 19.08it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 19.07it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.44it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.67it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 17.04it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:11, 17.63it/s]
eval model::  59%|█████▉    | 297/500 [00:16<00:12, 16.00it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 16.21it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 16.65it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.56it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 19.08it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.57it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 21.50it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 20.75it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 21.84it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.18it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 21.27it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 21.07it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 20.35it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 21.17it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:08, 18.59it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:08, 18.25it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:11, 14.29it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 17.08it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 18.94it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 21.21it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 22.13it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 20.23it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:06, 21.33it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 18.83it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 17.66it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 17.26it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 19.17it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:07, 17.06it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 18.34it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 20.34it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:06, 19.52it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 21.50it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:08, 13.55it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 15.81it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 17.72it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 17.89it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 19.71it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 17.84it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 19.06it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 20.44it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 21.67it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:04, 20.48it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 18.68it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 18.02it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:05, 13.20it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 14.21it/s]
eval model::  85%|████████▌ | 427/500 [00:23<00:04, 16.14it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 17.57it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:04, 16.91it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 18.17it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:03, 20.11it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:03, 19.25it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 21.15it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 22.14it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 21.53it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:01, 24.10it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 23.33it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 21.95it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 16.59it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.19it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 17.65it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.70it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.98it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.53it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 20.37it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 21.00it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 21.06it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 22.72it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 20.00it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 20.24it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 23.93it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.88it/s]
[2024/07/27 13:14:57] ppocr INFO: cur metric, precision: 0.7152524726704841, recall: 0.6615310544053924, hmean: 0.687343671835918, fps: 45.94529313415217
[2024/07/27 13:14:57] ppocr INFO: best metric, hmean: 0.6968302658486708, precision: 0.7427792915531335, recall: 0.6562349542609534, fps: 44.61269678926526, best_epoch: 580
[2024/07/27 13:14:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:15:07] ppocr INFO: epoch: [621/1500], global_step: 1863, lr: 0.001000, loss: 1.506233, loss_shrink_maps: 0.799052, loss_threshold_maps: 0.541797, loss_binary_maps: 0.158114, avg_reader_cost: 2.31387 s, avg_batch_cost: 2.57455 s, avg_samples: 12.5, ips: 4.85522 samples/s, eta: 6:23:34
[2024/07/27 13:15:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:15:17] ppocr INFO: epoch: [622/1500], global_step: 1866, lr: 0.001000, loss: 1.506233, loss_shrink_maps: 0.799052, loss_threshold_maps: 0.543641, loss_binary_maps: 0.158114, avg_reader_cost: 2.20011 s, avg_batch_cost: 2.55750 s, avg_samples: 12.5, ips: 4.88759 samples/s, eta: 6:23:07
[2024/07/27 13:15:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:15:26] ppocr INFO: epoch: [623/1500], global_step: 1869, lr: 0.001000, loss: 1.476543, loss_shrink_maps: 0.766233, loss_threshold_maps: 0.540295, loss_binary_maps: 0.153070, avg_reader_cost: 2.24532 s, avg_batch_cost: 2.66187 s, avg_samples: 12.5, ips: 4.69595 samples/s, eta: 6:22:42
[2024/07/27 13:15:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:15:35] ppocr INFO: epoch: [624/1500], global_step: 1870, lr: 0.001000, loss: 1.476543, loss_shrink_maps: 0.766233, loss_threshold_maps: 0.540295, loss_binary_maps: 0.153070, avg_reader_cost: 0.52938 s, avg_batch_cost: 0.78181 s, avg_samples: 4.8, ips: 6.13956 samples/s, eta: 6:22:32
[2024/07/27 13:15:36] ppocr INFO: epoch: [624/1500], global_step: 1872, lr: 0.001000, loss: 1.476543, loss_shrink_maps: 0.766233, loss_threshold_maps: 0.541882, loss_binary_maps: 0.153070, avg_reader_cost: 1.65564 s, avg_batch_cost: 1.80270 s, avg_samples: 7.7, ips: 4.27137 samples/s, eta: 6:22:15
[2024/07/27 13:15:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:15:46] ppocr INFO: epoch: [625/1500], global_step: 1875, lr: 0.001000, loss: 1.450146, loss_shrink_maps: 0.752164, loss_threshold_maps: 0.525396, loss_binary_maps: 0.150248, avg_reader_cost: 2.18286 s, avg_batch_cost: 2.54334 s, avg_samples: 12.5, ips: 4.91480 samples/s, eta: 6:21:48
[2024/07/27 13:15:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:15:56] ppocr INFO: epoch: [626/1500], global_step: 1878, lr: 0.001000, loss: 1.384349, loss_shrink_maps: 0.713366, loss_threshold_maps: 0.512942, loss_binary_maps: 0.142088, avg_reader_cost: 2.23789 s, avg_batch_cost: 2.60899 s, avg_samples: 12.5, ips: 4.79112 samples/s, eta: 6:21:22
[2024/07/27 13:15:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:16:05] ppocr INFO: epoch: [627/1500], global_step: 1880, lr: 0.001000, loss: 1.384349, loss_shrink_maps: 0.713366, loss_threshold_maps: 0.524990, loss_binary_maps: 0.142088, avg_reader_cost: 1.54767 s, avg_batch_cost: 1.72950 s, avg_samples: 9.6, ips: 5.55074 samples/s, eta: 6:21:04
[2024/07/27 13:16:06] ppocr INFO: epoch: [627/1500], global_step: 1881, lr: 0.001000, loss: 1.384349, loss_shrink_maps: 0.713366, loss_threshold_maps: 0.512942, loss_binary_maps: 0.142088, avg_reader_cost: 0.91087 s, avg_batch_cost: 0.96644 s, avg_samples: 2.9, ips: 3.00070 samples/s, eta: 6:20:57
[2024/07/27 13:16:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:16:15] ppocr INFO: epoch: [628/1500], global_step: 1884, lr: 0.001000, loss: 1.352347, loss_shrink_maps: 0.699930, loss_threshold_maps: 0.515044, loss_binary_maps: 0.139332, avg_reader_cost: 2.07969 s, avg_batch_cost: 2.39474 s, avg_samples: 12.5, ips: 5.21978 samples/s, eta: 6:20:27
[2024/07/27 13:16:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:16:25] ppocr INFO: epoch: [629/1500], global_step: 1887, lr: 0.001000, loss: 1.352347, loss_shrink_maps: 0.698048, loss_threshold_maps: 0.519733, loss_binary_maps: 0.139255, avg_reader_cost: 2.24743 s, avg_batch_cost: 2.62052 s, avg_samples: 12.5, ips: 4.77004 samples/s, eta: 6:20:01
[2024/07/27 13:16:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:16:35] ppocr INFO: epoch: [630/1500], global_step: 1890, lr: 0.001000, loss: 1.390712, loss_shrink_maps: 0.710940, loss_threshold_maps: 0.534718, loss_binary_maps: 0.141114, avg_reader_cost: 2.25592 s, avg_batch_cost: 2.62275 s, avg_samples: 12.5, ips: 4.76599 samples/s, eta: 6:19:35
[2024/07/27 13:16:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:16:45] ppocr INFO: epoch: [631/1500], global_step: 1893, lr: 0.001000, loss: 1.343571, loss_shrink_maps: 0.690230, loss_threshold_maps: 0.523975, loss_binary_maps: 0.136673, avg_reader_cost: 2.26662 s, avg_batch_cost: 2.51617 s, avg_samples: 12.5, ips: 4.96787 samples/s, eta: 6:19:07
[2024/07/27 13:16:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:16:54] ppocr INFO: epoch: [632/1500], global_step: 1896, lr: 0.001000, loss: 1.386787, loss_shrink_maps: 0.716364, loss_threshold_maps: 0.529695, loss_binary_maps: 0.142196, avg_reader_cost: 2.16071 s, avg_batch_cost: 2.52209 s, avg_samples: 12.5, ips: 4.95620 samples/s, eta: 6:18:40
[2024/07/27 13:16:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:17:04] ppocr INFO: epoch: [633/1500], global_step: 1899, lr: 0.001000, loss: 1.381226, loss_shrink_maps: 0.723137, loss_threshold_maps: 0.523975, loss_binary_maps: 0.143786, avg_reader_cost: 2.26615 s, avg_batch_cost: 2.52333 s, avg_samples: 12.5, ips: 4.95376 samples/s, eta: 6:18:13
[2024/07/27 13:17:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:17:12] ppocr INFO: epoch: [634/1500], global_step: 1900, lr: 0.001000, loss: 1.381226, loss_shrink_maps: 0.723137, loss_threshold_maps: 0.523975, loss_binary_maps: 0.143786, avg_reader_cost: 0.56123 s, avg_batch_cost: 0.77806 s, avg_samples: 4.8, ips: 6.16923 samples/s, eta: 6:18:02
[2024/07/27 13:17:13] ppocr INFO: epoch: [634/1500], global_step: 1902, lr: 0.001000, loss: 1.360161, loss_shrink_maps: 0.697260, loss_threshold_maps: 0.528947, loss_binary_maps: 0.138281, avg_reader_cost: 1.64886 s, avg_batch_cost: 1.79642 s, avg_samples: 7.7, ips: 4.28630 samples/s, eta: 6:17:46
[2024/07/27 13:17:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:17:23] ppocr INFO: epoch: [635/1500], global_step: 1905, lr: 0.001000, loss: 1.375054, loss_shrink_maps: 0.703373, loss_threshold_maps: 0.531320, loss_binary_maps: 0.139997, avg_reader_cost: 2.38823 s, avg_batch_cost: 2.64161 s, avg_samples: 12.5, ips: 4.73196 samples/s, eta: 6:17:20
[2024/07/27 13:17:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:17:33] ppocr INFO: epoch: [636/1500], global_step: 1908, lr: 0.001000, loss: 1.381866, loss_shrink_maps: 0.723857, loss_threshold_maps: 0.532069, loss_binary_maps: 0.144454, avg_reader_cost: 2.21268 s, avg_batch_cost: 2.61771 s, avg_samples: 12.5, ips: 4.77517 samples/s, eta: 6:16:54
[2024/07/27 13:17:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:17:42] ppocr INFO: epoch: [637/1500], global_step: 1910, lr: 0.001000, loss: 1.381866, loss_shrink_maps: 0.723857, loss_threshold_maps: 0.534239, loss_binary_maps: 0.144454, avg_reader_cost: 1.43063 s, avg_batch_cost: 1.61010 s, avg_samples: 9.6, ips: 5.96237 samples/s, eta: 6:16:34
[2024/07/27 13:17:43] ppocr INFO: epoch: [637/1500], global_step: 1911, lr: 0.001000, loss: 1.381866, loss_shrink_maps: 0.723857, loss_threshold_maps: 0.531320, loss_binary_maps: 0.144454, avg_reader_cost: 0.85112 s, avg_batch_cost: 0.90723 s, avg_samples: 2.9, ips: 3.19653 samples/s, eta: 6:16:26
[2024/07/27 13:17:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:17:53] ppocr INFO: epoch: [638/1500], global_step: 1914, lr: 0.001000, loss: 1.393293, loss_shrink_maps: 0.735295, loss_threshold_maps: 0.534239, loss_binary_maps: 0.146557, avg_reader_cost: 2.21559 s, avg_batch_cost: 2.57612 s, avg_samples: 12.5, ips: 4.85226 samples/s, eta: 6:15:59
[2024/07/27 13:17:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:18:02] ppocr INFO: epoch: [639/1500], global_step: 1917, lr: 0.001000, loss: 1.387933, loss_shrink_maps: 0.710875, loss_threshold_maps: 0.534239, loss_binary_maps: 0.141294, avg_reader_cost: 2.22371 s, avg_batch_cost: 2.59830 s, avg_samples: 12.5, ips: 4.81084 samples/s, eta: 6:15:33
[2024/07/27 13:18:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:18:12] ppocr INFO: epoch: [640/1500], global_step: 1920, lr: 0.001000, loss: 1.391231, loss_shrink_maps: 0.710875, loss_threshold_maps: 0.536555, loss_binary_maps: 0.141294, avg_reader_cost: 2.36369 s, avg_batch_cost: 2.60621 s, avg_samples: 12.5, ips: 4.79624 samples/s, eta: 6:15:07

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:02,  7.98it/s]
eval model::   1%|          | 3/500 [00:00<00:36, 13.81it/s]
eval model::   1%|          | 6/500 [00:00<00:30, 16.18it/s]
eval model::   2%|▏         | 8/500 [00:00<00:29, 16.95it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.68it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 19.05it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.85it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.87it/s]
eval model::   5%|▍         | 24/500 [00:01<00:22, 21.38it/s]
eval model::   5%|▌         | 27/500 [00:01<00:21, 21.71it/s]
eval model::   6%|▌         | 31/500 [00:01<00:21, 22.00it/s]
eval model::   7%|▋         | 34/500 [00:01<00:20, 22.38it/s]
eval model::   7%|▋         | 37/500 [00:01<00:20, 22.64it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.34it/s]
eval model::   9%|▊         | 43/500 [00:02<00:26, 17.35it/s]
eval model::   9%|▉         | 46/500 [00:02<00:29, 15.48it/s]
eval model::  10%|▉         | 49/500 [00:02<00:25, 17.76it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 19.05it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 19.60it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 18.59it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:23, 18.52it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:29, 14.78it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:30, 14.39it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:28, 15.32it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:24, 17.56it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:22, 19.11it/s]
eval model::  15%|█▍        | 74/500 [00:04<00:22, 18.60it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:21, 19.76it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:20, 20.86it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:19, 21.06it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:22, 18.76it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:22, 18.31it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.15it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:22, 18.27it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.91it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.97it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.21it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.22it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:19, 19.86it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 19.47it/s]
eval model::  23%|██▎       | 114/500 [00:06<00:19, 19.32it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:21, 18.12it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:19, 19.84it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 19.90it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:20, 17.90it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 20.05it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:18, 19.66it/s]
eval model::  27%|██▋       | 134/500 [00:07<00:18, 20.28it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:17, 20.40it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:17, 20.43it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:16, 22.31it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:15, 22.17it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:16, 20.84it/s]
eval model::  30%|███       | 152/500 [00:07<00:18, 19.29it/s]
eval model::  31%|███       | 154/500 [00:08<00:18, 18.81it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:17, 19.92it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:24, 13.94it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:23, 14.56it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:21, 15.44it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:20, 16.27it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:17, 18.80it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:17, 18.50it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:20, 16.24it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:19, 16.52it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.18it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.01it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:19, 16.61it/s]
eval model::  37%|███▋      | 186/500 [00:10<00:17, 17.71it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:16, 18.99it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:16, 19.02it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:14, 21.06it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.86it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 21.42it/s]
eval model::  41%|████      | 205/500 [00:11<00:19, 15.21it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:18, 15.88it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 15.25it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.62it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.40it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.78it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:18, 14.82it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:19, 13.96it/s]
eval model::  45%|████▍     | 223/500 [00:12<00:18, 15.04it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.81it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 20.58it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.53it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.17it/s]
eval model::  48%|████▊     | 239/500 [00:13<00:12, 21.69it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:14, 17.79it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 19.71it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 20.40it/s]
eval model::  50%|█████     | 251/500 [00:13<00:13, 18.83it/s]
eval model::  51%|█████     | 254/500 [00:13<00:12, 20.09it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.03it/s]
eval model::  52%|█████▏    | 260/500 [00:14<00:10, 22.60it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:09, 24.00it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:09, 23.80it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 19.60it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:10, 20.77it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.62it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:10, 22.01it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:09, 23.21it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:16, 12.71it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:13, 15.16it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:11, 17.81it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:14, 14.59it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:11, 16.84it/s]
eval model::  60%|██████    | 301/500 [00:16<00:12, 15.41it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.55it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.19it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:10, 18.89it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:09, 19.48it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 19.73it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 21.66it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.79it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:07, 22.27it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 21.36it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:07, 22.20it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:06, 23.89it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 22.29it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 15.84it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:08, 17.59it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 20.05it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:06, 21.97it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 22.22it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 21.34it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 21.40it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 19.82it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.87it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.95it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 18.70it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 18.71it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:07, 17.71it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:06, 19.56it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 20.91it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 22.63it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:08, 13.73it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 16.31it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:06, 17.01it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:05, 18.36it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 20.13it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 19.05it/s]
eval model::  81%|████████  | 404/500 [00:21<00:05, 19.04it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 20.17it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 22.11it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:03, 22.22it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:03, 21.06it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 19.87it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 16.36it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 16.63it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:04, 18.03it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 19.99it/s]
eval model::  87%|████████▋ | 433/500 [00:23<00:03, 20.38it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:03, 20.82it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 20.86it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 21.06it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 21.71it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 22.37it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 22.07it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 22.80it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:01, 24.85it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 23.73it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:01, 21.50it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 18.31it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 20.43it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 19.93it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 20.01it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:01, 20.58it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 20.32it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 21.93it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 21.64it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 18.91it/s]
eval model::  99%|█████████▉| 494/500 [00:26<00:00, 19.41it/s]
eval model::  99%|█████████▉| 497/500 [00:26<00:00, 20.38it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 22.18it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.01it/s]
[2024/07/27 13:18:39] ppocr INFO: cur metric, precision: 0.7743055555555556, recall: 0.6441983630235917, hmean: 0.7032851511169513, fps: 43.729055311097895
[2024/07/27 13:18:39] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 13:18:39] ppocr INFO: best metric, hmean: 0.7032851511169513, precision: 0.7743055555555556, recall: 0.6441983630235917, fps: 43.729055311097895, best_epoch: 640
[2024/07/27 13:18:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:18:47] ppocr INFO: epoch: [641/1500], global_step: 1923, lr: 0.001000, loss: 1.396592, loss_shrink_maps: 0.720808, loss_threshold_maps: 0.538031, loss_binary_maps: 0.143396, avg_reader_cost: 2.13800 s, avg_batch_cost: 2.37584 s, avg_samples: 12.5, ips: 5.26131 samples/s, eta: 6:14:37
[2024/07/27 13:18:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:18:57] ppocr INFO: epoch: [642/1500], global_step: 1926, lr: 0.001000, loss: 1.396592, loss_shrink_maps: 0.720808, loss_threshold_maps: 0.536555, loss_binary_maps: 0.143396, avg_reader_cost: 2.38017 s, avg_batch_cost: 2.61841 s, avg_samples: 12.5, ips: 4.77388 samples/s, eta: 6:14:11
[2024/07/27 13:18:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:19:07] ppocr INFO: epoch: [643/1500], global_step: 1929, lr: 0.001000, loss: 1.391231, loss_shrink_maps: 0.710875, loss_threshold_maps: 0.534816, loss_binary_maps: 0.141156, avg_reader_cost: 2.33451 s, avg_batch_cost: 2.57448 s, avg_samples: 12.5, ips: 4.85536 samples/s, eta: 6:13:44
[2024/07/27 13:19:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:19:16] ppocr INFO: epoch: [644/1500], global_step: 1930, lr: 0.001000, loss: 1.396592, loss_shrink_maps: 0.720808, loss_threshold_maps: 0.534322, loss_binary_maps: 0.143396, avg_reader_cost: 0.69581 s, avg_batch_cost: 0.79271 s, avg_samples: 4.8, ips: 6.05519 samples/s, eta: 6:13:35
[2024/07/27 13:19:17] ppocr INFO: epoch: [644/1500], global_step: 1932, lr: 0.001000, loss: 1.377522, loss_shrink_maps: 0.701274, loss_threshold_maps: 0.534322, loss_binary_maps: 0.139432, avg_reader_cost: 1.67769 s, avg_batch_cost: 1.82532 s, avg_samples: 7.7, ips: 4.21845 samples/s, eta: 6:13:18
[2024/07/27 13:19:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:19:27] ppocr INFO: epoch: [645/1500], global_step: 1935, lr: 0.001000, loss: 1.380925, loss_shrink_maps: 0.705047, loss_threshold_maps: 0.535842, loss_binary_maps: 0.140199, avg_reader_cost: 2.29684 s, avg_batch_cost: 2.54410 s, avg_samples: 12.5, ips: 4.91332 samples/s, eta: 6:12:51
[2024/07/27 13:19:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:19:37] ppocr INFO: epoch: [646/1500], global_step: 1938, lr: 0.001000, loss: 1.437252, loss_shrink_maps: 0.746548, loss_threshold_maps: 0.539214, loss_binary_maps: 0.147522, avg_reader_cost: 2.23782 s, avg_batch_cost: 2.59887 s, avg_samples: 12.5, ips: 4.80978 samples/s, eta: 6:12:25
[2024/07/27 13:19:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:19:46] ppocr INFO: epoch: [647/1500], global_step: 1940, lr: 0.001000, loss: 1.448045, loss_shrink_maps: 0.758821, loss_threshold_maps: 0.546513, loss_binary_maps: 0.150477, avg_reader_cost: 1.36310 s, avg_batch_cost: 1.67803 s, avg_samples: 9.6, ips: 5.72098 samples/s, eta: 6:12:06
[2024/07/27 13:19:47] ppocr INFO: epoch: [647/1500], global_step: 1941, lr: 0.001000, loss: 1.483537, loss_shrink_maps: 0.775841, loss_threshold_maps: 0.551715, loss_binary_maps: 0.153797, avg_reader_cost: 0.88465 s, avg_batch_cost: 0.94047 s, avg_samples: 2.9, ips: 3.08357 samples/s, eta: 6:11:59
[2024/07/27 13:19:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:19:56] ppocr INFO: epoch: [648/1500], global_step: 1944, lr: 0.001000, loss: 1.445180, loss_shrink_maps: 0.758821, loss_threshold_maps: 0.539214, loss_binary_maps: 0.150477, avg_reader_cost: 2.17589 s, avg_batch_cost: 2.52978 s, avg_samples: 12.5, ips: 4.94113 samples/s, eta: 6:11:31
[2024/07/27 13:19:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:20:06] ppocr INFO: epoch: [649/1500], global_step: 1947, lr: 0.001000, loss: 1.445180, loss_shrink_maps: 0.758821, loss_threshold_maps: 0.539214, loss_binary_maps: 0.150477, avg_reader_cost: 2.25898 s, avg_batch_cost: 2.62547 s, avg_samples: 12.5, ips: 4.76105 samples/s, eta: 6:11:05
[2024/07/27 13:20:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:20:16] ppocr INFO: epoch: [650/1500], global_step: 1950, lr: 0.001000, loss: 1.408027, loss_shrink_maps: 0.729562, loss_threshold_maps: 0.539214, loss_binary_maps: 0.145065, avg_reader_cost: 2.22735 s, avg_batch_cost: 2.59073 s, avg_samples: 12.5, ips: 4.82490 samples/s, eta: 6:10:39
[2024/07/27 13:20:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:20:25] ppocr INFO: epoch: [651/1500], global_step: 1953, lr: 0.001000, loss: 1.459089, loss_shrink_maps: 0.758424, loss_threshold_maps: 0.550204, loss_binary_maps: 0.150461, avg_reader_cost: 2.38329 s, avg_batch_cost: 2.62353 s, avg_samples: 12.5, ips: 4.76457 samples/s, eta: 6:10:13
[2024/07/27 13:20:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:20:35] ppocr INFO: epoch: [652/1500], global_step: 1956, lr: 0.001000, loss: 1.411142, loss_shrink_maps: 0.723831, loss_threshold_maps: 0.548837, loss_binary_maps: 0.143165, avg_reader_cost: 2.32408 s, avg_batch_cost: 2.55651 s, avg_samples: 12.5, ips: 4.88948 samples/s, eta: 6:09:46
[2024/07/27 13:20:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:20:45] ppocr INFO: epoch: [653/1500], global_step: 1959, lr: 0.001000, loss: 1.330190, loss_shrink_maps: 0.689116, loss_threshold_maps: 0.528702, loss_binary_maps: 0.136795, avg_reader_cost: 2.20812 s, avg_batch_cost: 2.58101 s, avg_samples: 12.5, ips: 4.84307 samples/s, eta: 6:09:19
[2024/07/27 13:20:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:20:53] ppocr INFO: epoch: [654/1500], global_step: 1960, lr: 0.001000, loss: 1.348426, loss_shrink_maps: 0.694969, loss_threshold_maps: 0.532033, loss_binary_maps: 0.137573, avg_reader_cost: 0.70422 s, avg_batch_cost: 0.79969 s, avg_samples: 4.8, ips: 6.00231 samples/s, eta: 6:09:09
[2024/07/27 13:20:55] ppocr INFO: epoch: [654/1500], global_step: 1962, lr: 0.001000, loss: 1.330190, loss_shrink_maps: 0.689116, loss_threshold_maps: 0.528702, loss_binary_maps: 0.136795, avg_reader_cost: 1.69134 s, avg_batch_cost: 1.83887 s, avg_samples: 7.7, ips: 4.18735 samples/s, eta: 6:08:53
[2024/07/27 13:20:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:21:04] ppocr INFO: epoch: [655/1500], global_step: 1965, lr: 0.001000, loss: 1.381233, loss_shrink_maps: 0.716966, loss_threshold_maps: 0.532033, loss_binary_maps: 0.142051, avg_reader_cost: 2.17892 s, avg_batch_cost: 2.53361 s, avg_samples: 12.5, ips: 4.93368 samples/s, eta: 6:08:26
[2024/07/27 13:21:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:21:14] ppocr INFO: epoch: [656/1500], global_step: 1968, lr: 0.001000, loss: 1.348426, loss_shrink_maps: 0.694969, loss_threshold_maps: 0.520199, loss_binary_maps: 0.137573, avg_reader_cost: 2.31428 s, avg_batch_cost: 2.70432 s, avg_samples: 12.5, ips: 4.62224 samples/s, eta: 6:08:01
[2024/07/27 13:21:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:21:24] ppocr INFO: epoch: [657/1500], global_step: 1970, lr: 0.001000, loss: 1.397353, loss_shrink_maps: 0.734018, loss_threshold_maps: 0.523510, loss_binary_maps: 0.145821, avg_reader_cost: 1.36948 s, avg_batch_cost: 1.70534 s, avg_samples: 9.6, ips: 5.62937 samples/s, eta: 6:07:43
[2024/07/27 13:21:24] ppocr INFO: epoch: [657/1500], global_step: 1971, lr: 0.001000, loss: 1.397353, loss_shrink_maps: 0.734018, loss_threshold_maps: 0.523443, loss_binary_maps: 0.145821, avg_reader_cost: 0.89874 s, avg_batch_cost: 0.95389 s, avg_samples: 2.9, ips: 3.04017 samples/s, eta: 6:07:35
[2024/07/27 13:21:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:21:34] ppocr INFO: epoch: [658/1500], global_step: 1974, lr: 0.001000, loss: 1.397353, loss_shrink_maps: 0.732257, loss_threshold_maps: 0.523443, loss_binary_maps: 0.145193, avg_reader_cost: 2.21356 s, avg_batch_cost: 2.60040 s, avg_samples: 12.5, ips: 4.80695 samples/s, eta: 6:07:09
[2024/07/27 13:21:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:21:45] ppocr INFO: epoch: [659/1500], global_step: 1977, lr: 0.001000, loss: 1.382502, loss_shrink_maps: 0.715264, loss_threshold_maps: 0.517513, loss_binary_maps: 0.141542, avg_reader_cost: 2.28878 s, avg_batch_cost: 2.67011 s, avg_samples: 12.5, ips: 4.68146 samples/s, eta: 6:06:44
[2024/07/27 13:21:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:21:54] ppocr INFO: epoch: [660/1500], global_step: 1980, lr: 0.001000, loss: 1.382571, loss_shrink_maps: 0.716760, loss_threshold_maps: 0.517513, loss_binary_maps: 0.142745, avg_reader_cost: 2.29388 s, avg_batch_cost: 2.53333 s, avg_samples: 12.5, ips: 4.93422 samples/s, eta: 6:06:16

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:35, 14.01it/s]
eval model::   1%|          | 5/500 [00:00<00:23, 20.96it/s]
eval model::   2%|▏         | 8/500 [00:00<00:23, 20.58it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.92it/s]
eval model::   3%|▎         | 13/500 [00:00<00:27, 17.41it/s]
eval model::   3%|▎         | 15/500 [00:00<00:27, 17.53it/s]
eval model::   4%|▎         | 18/500 [00:00<00:23, 20.15it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.56it/s]
eval model::   5%|▍         | 24/500 [00:01<00:24, 19.05it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 20.68it/s]
eval model::   6%|▌         | 31/500 [00:01<00:21, 21.96it/s]
eval model::   7%|▋         | 34/500 [00:01<00:21, 22.17it/s]
eval model::   7%|▋         | 37/500 [00:01<00:20, 22.29it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.69it/s]
eval model::   9%|▊         | 43/500 [00:02<00:24, 18.66it/s]
eval model::   9%|▉         | 46/500 [00:02<00:24, 18.26it/s]
eval model::  10%|▉         | 49/500 [00:02<00:21, 20.58it/s]
eval model::  10%|█         | 52/500 [00:02<00:20, 21.74it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.81it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:20, 21.06it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:25, 17.07it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.48it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 19.16it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 22.41it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:18, 23.34it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:17, 24.32it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:17, 23.82it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:17, 24.47it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:19, 21.77it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.32it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:23, 17.53it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:23, 17.15it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:19, 20.59it/s]
eval model::  20%|██        | 100/500 [00:04<00:19, 20.14it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 21.70it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 20.49it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:19, 20.47it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:22, 17.44it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:21, 17.59it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:19, 19.63it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:21, 17.47it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:22, 16.94it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:20, 18.46it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:21, 17.20it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 17.30it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.58it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:19, 18.18it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.31it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.71it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.96it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 20.67it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 19.48it/s]
eval model::  31%|███       | 156/500 [00:07<00:15, 21.52it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 20.68it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:17, 19.09it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:16, 19.91it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:16, 20.75it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:16, 20.17it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:18, 17.30it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.10it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:18, 17.65it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:20, 15.53it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:17, 18.20it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.41it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.37it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.30it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:14, 21.25it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.18it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.14it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 21.65it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:25, 11.46it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:23, 12.28it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:22, 12.92it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:20, 13.86it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:22, 12.43it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.41it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:19, 14.74it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:19, 14.56it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 18.19it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.42it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.93it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.99it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.92it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:13, 19.79it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:12, 21.19it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.43it/s]
eval model::  50%|█████     | 251/500 [00:13<00:13, 18.93it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 20.77it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.56it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 23.48it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:09, 24.47it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:11, 19.68it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.93it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 20.38it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 20.38it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:09, 22.21it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:09, 22.51it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 21.20it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:18, 11.75it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:14, 14.90it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:13, 15.59it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:14, 14.20it/s]
eval model::  60%|██████    | 300/500 [00:15<00:12, 16.23it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.85it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 18.24it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 20.24it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:08, 21.99it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:08, 22.16it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:07, 23.59it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:07, 24.57it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:08, 21.12it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:07, 22.65it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:07, 22.15it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:07, 21.79it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 22.65it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:08, 19.58it/s]
eval model::  68%|██████▊   | 341/500 [00:17<00:09, 15.96it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.67it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 19.32it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.72it/s]
eval model::  70%|███████   | 352/500 [00:18<00:07, 20.36it/s]
eval model::  71%|███████   | 355/500 [00:18<00:07, 19.93it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 20.29it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 18.75it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.28it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 18.72it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 19.44it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 19.58it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 21.28it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 22.93it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 21.45it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:08, 13.58it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:07, 15.78it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 17.42it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 19.06it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 21.94it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 18.89it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 19.98it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 20.45it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 20.49it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:04, 21.25it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 18.22it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 16.75it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:06, 11.99it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 14.13it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:04, 16.35it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 18.86it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 20.15it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 22.14it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 23.63it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 23.78it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 25.18it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 24.42it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:01, 25.47it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:01, 23.06it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 22.54it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 21.52it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 17.66it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 17.06it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 19.29it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.91it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.38it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 19.10it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 20.76it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 20.79it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 18.78it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 20.10it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 18.19it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 18.56it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 19.93it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.20it/s]
[2024/07/27 13:22:20] ppocr INFO: cur metric, precision: 0.7255520504731862, recall: 0.6644198363023591, hmean: 0.6936416184971098, fps: 46.657166232662625
[2024/07/27 13:22:20] ppocr INFO: best metric, hmean: 0.7032851511169513, precision: 0.7743055555555556, recall: 0.6441983630235917, fps: 43.729055311097895, best_epoch: 640
[2024/07/27 13:22:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:22:30] ppocr INFO: epoch: [661/1500], global_step: 1983, lr: 0.001000, loss: 1.382571, loss_shrink_maps: 0.716760, loss_threshold_maps: 0.519541, loss_binary_maps: 0.142745, avg_reader_cost: 2.34071 s, avg_batch_cost: 2.61835 s, avg_samples: 12.5, ips: 4.77400 samples/s, eta: 6:05:50
[2024/07/27 13:22:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:22:39] ppocr INFO: epoch: [662/1500], global_step: 1986, lr: 0.001000, loss: 1.372753, loss_shrink_maps: 0.701704, loss_threshold_maps: 0.523581, loss_binary_maps: 0.139443, avg_reader_cost: 2.31339 s, avg_batch_cost: 2.55788 s, avg_samples: 12.5, ips: 4.88686 samples/s, eta: 6:05:23
[2024/07/27 13:22:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:22:49] ppocr INFO: epoch: [663/1500], global_step: 1989, lr: 0.001000, loss: 1.377507, loss_shrink_maps: 0.705232, loss_threshold_maps: 0.527444, loss_binary_maps: 0.140614, avg_reader_cost: 2.27290 s, avg_batch_cost: 2.68100 s, avg_samples: 12.5, ips: 4.66244 samples/s, eta: 6:04:58
[2024/07/27 13:22:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:22:58] ppocr INFO: epoch: [664/1500], global_step: 1990, lr: 0.001000, loss: 1.377507, loss_shrink_maps: 0.705232, loss_threshold_maps: 0.528012, loss_binary_maps: 0.140614, avg_reader_cost: 0.73758 s, avg_batch_cost: 0.82947 s, avg_samples: 4.8, ips: 5.78680 samples/s, eta: 6:04:49
[2024/07/27 13:23:00] ppocr INFO: epoch: [664/1500], global_step: 1992, lr: 0.001000, loss: 1.377507, loss_shrink_maps: 0.701704, loss_threshold_maps: 0.528012, loss_binary_maps: 0.139443, avg_reader_cost: 1.75124 s, avg_batch_cost: 1.89859 s, avg_samples: 7.7, ips: 4.05563 samples/s, eta: 6:04:33
[2024/07/27 13:23:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:23:09] ppocr INFO: epoch: [665/1500], global_step: 1995, lr: 0.001000, loss: 1.395266, loss_shrink_maps: 0.716760, loss_threshold_maps: 0.530938, loss_binary_maps: 0.142745, avg_reader_cost: 2.35474 s, avg_batch_cost: 2.59745 s, avg_samples: 12.5, ips: 4.81241 samples/s, eta: 6:04:07
[2024/07/27 13:23:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:23:19] ppocr INFO: epoch: [666/1500], global_step: 1998, lr: 0.001000, loss: 1.455084, loss_shrink_maps: 0.753396, loss_threshold_maps: 0.531045, loss_binary_maps: 0.149503, avg_reader_cost: 2.18440 s, avg_batch_cost: 2.55101 s, avg_samples: 12.5, ips: 4.90003 samples/s, eta: 6:03:40
[2024/07/27 13:23:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:23:28] ppocr INFO: epoch: [667/1500], global_step: 2000, lr: 0.001000, loss: 1.462554, loss_shrink_maps: 0.761945, loss_threshold_maps: 0.533770, loss_binary_maps: 0.151647, avg_reader_cost: 1.31147 s, avg_batch_cost: 1.63112 s, avg_samples: 9.6, ips: 5.88552 samples/s, eta: 6:03:21
[2024/07/27 13:23:29] ppocr INFO: epoch: [667/1500], global_step: 2001, lr: 0.001000, loss: 1.462554, loss_shrink_maps: 0.755972, loss_threshold_maps: 0.537155, loss_binary_maps: 0.150917, avg_reader_cost: 0.86159 s, avg_batch_cost: 0.91688 s, avg_samples: 2.9, ips: 3.16291 samples/s, eta: 6:03:13
[2024/07/27 13:23:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:23:39] ppocr INFO: epoch: [668/1500], global_step: 2004, lr: 0.001000, loss: 1.462971, loss_shrink_maps: 0.761945, loss_threshold_maps: 0.537155, loss_binary_maps: 0.151647, avg_reader_cost: 2.42354 s, avg_batch_cost: 2.74227 s, avg_samples: 12.5, ips: 4.55827 samples/s, eta: 6:02:48
[2024/07/27 13:23:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:23:49] ppocr INFO: epoch: [669/1500], global_step: 2007, lr: 0.001000, loss: 1.462554, loss_shrink_maps: 0.761945, loss_threshold_maps: 0.533770, loss_binary_maps: 0.151647, avg_reader_cost: 2.25772 s, avg_batch_cost: 2.49659 s, avg_samples: 12.5, ips: 5.00684 samples/s, eta: 6:02:21
[2024/07/27 13:23:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:23:59] ppocr INFO: epoch: [670/1500], global_step: 2010, lr: 0.001000, loss: 1.431854, loss_shrink_maps: 0.739124, loss_threshold_maps: 0.531045, loss_binary_maps: 0.147390, avg_reader_cost: 2.35282 s, avg_batch_cost: 2.60436 s, avg_samples: 12.5, ips: 4.79965 samples/s, eta: 6:01:54
[2024/07/27 13:23:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:24:09] ppocr INFO: epoch: [671/1500], global_step: 2013, lr: 0.001000, loss: 1.416751, loss_shrink_maps: 0.723686, loss_threshold_maps: 0.533763, loss_binary_maps: 0.144335, avg_reader_cost: 2.24844 s, avg_batch_cost: 2.63935 s, avg_samples: 12.5, ips: 4.73601 samples/s, eta: 6:01:28
[2024/07/27 13:24:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:24:19] ppocr INFO: epoch: [672/1500], global_step: 2016, lr: 0.001000, loss: 1.397024, loss_shrink_maps: 0.720054, loss_threshold_maps: 0.536189, loss_binary_maps: 0.143508, avg_reader_cost: 2.32204 s, avg_batch_cost: 2.69631 s, avg_samples: 12.5, ips: 4.63596 samples/s, eta: 6:01:03
[2024/07/27 13:24:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:24:29] ppocr INFO: epoch: [673/1500], global_step: 2019, lr: 0.001000, loss: 1.378199, loss_shrink_maps: 0.716684, loss_threshold_maps: 0.530706, loss_binary_maps: 0.142398, avg_reader_cost: 2.31608 s, avg_batch_cost: 2.56139 s, avg_samples: 12.5, ips: 4.88017 samples/s, eta: 6:00:36
[2024/07/27 13:24:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:24:37] ppocr INFO: epoch: [674/1500], global_step: 2020, lr: 0.001000, loss: 1.378199, loss_shrink_maps: 0.709173, loss_threshold_maps: 0.530706, loss_binary_maps: 0.141057, avg_reader_cost: 0.56453 s, avg_batch_cost: 0.78077 s, avg_samples: 4.8, ips: 6.14779 samples/s, eta: 6:00:27
[2024/07/27 13:24:38] ppocr INFO: epoch: [674/1500], global_step: 2022, lr: 0.001000, loss: 1.378199, loss_shrink_maps: 0.709173, loss_threshold_maps: 0.538161, loss_binary_maps: 0.141057, avg_reader_cost: 1.65324 s, avg_batch_cost: 1.80067 s, avg_samples: 7.7, ips: 4.27619 samples/s, eta: 6:00:10
[2024/07/27 13:24:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:24:48] ppocr INFO: epoch: [675/1500], global_step: 2025, lr: 0.001000, loss: 1.387238, loss_shrink_maps: 0.711182, loss_threshold_maps: 0.543584, loss_binary_maps: 0.141411, avg_reader_cost: 2.26054 s, avg_batch_cost: 2.62348 s, avg_samples: 12.5, ips: 4.76467 samples/s, eta: 5:59:44
[2024/07/27 13:24:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:24:58] ppocr INFO: epoch: [676/1500], global_step: 2028, lr: 0.001000, loss: 1.383086, loss_shrink_maps: 0.701283, loss_threshold_maps: 0.546695, loss_binary_maps: 0.139718, avg_reader_cost: 2.25932 s, avg_batch_cost: 2.61694 s, avg_samples: 12.5, ips: 4.77657 samples/s, eta: 5:59:18
[2024/07/27 13:24:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:25:07] ppocr INFO: epoch: [677/1500], global_step: 2030, lr: 0.001000, loss: 1.387982, loss_shrink_maps: 0.701283, loss_threshold_maps: 0.550718, loss_binary_maps: 0.139718, avg_reader_cost: 1.46641 s, avg_batch_cost: 1.65101 s, avg_samples: 9.6, ips: 5.81461 samples/s, eta: 5:58:59
[2024/07/27 13:25:08] ppocr INFO: epoch: [677/1500], global_step: 2031, lr: 0.001000, loss: 1.387982, loss_shrink_maps: 0.701283, loss_threshold_maps: 0.546695, loss_binary_maps: 0.139718, avg_reader_cost: 0.87192 s, avg_batch_cost: 0.92713 s, avg_samples: 2.9, ips: 3.12792 samples/s, eta: 5:58:51
[2024/07/27 13:25:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:25:18] ppocr INFO: epoch: [678/1500], global_step: 2034, lr: 0.001000, loss: 1.387982, loss_shrink_maps: 0.701283, loss_threshold_maps: 0.546695, loss_binary_maps: 0.139718, avg_reader_cost: 2.36577 s, avg_batch_cost: 2.65534 s, avg_samples: 12.5, ips: 4.70750 samples/s, eta: 5:58:25
[2024/07/27 13:25:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:25:28] ppocr INFO: epoch: [679/1500], global_step: 2037, lr: 0.001000, loss: 1.372066, loss_shrink_maps: 0.697220, loss_threshold_maps: 0.540817, loss_binary_maps: 0.138413, avg_reader_cost: 2.27911 s, avg_batch_cost: 2.62663 s, avg_samples: 12.5, ips: 4.75894 samples/s, eta: 5:57:59
[2024/07/27 13:25:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:25:37] ppocr INFO: epoch: [680/1500], global_step: 2040, lr: 0.001000, loss: 1.367347, loss_shrink_maps: 0.694543, loss_threshold_maps: 0.541276, loss_binary_maps: 0.137778, avg_reader_cost: 2.16287 s, avg_batch_cost: 2.50192 s, avg_samples: 12.5, ips: 4.99615 samples/s, eta: 5:57:32

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:57,  8.68it/s]
eval model::   1%|          | 4/500 [00:00<00:30, 16.17it/s]
eval model::   1%|          | 6/500 [00:00<00:32, 14.97it/s]
eval model::   2%|▏         | 8/500 [00:00<00:31, 15.39it/s]
eval model::   2%|▏         | 10/500 [00:00<00:30, 15.99it/s]
eval model::   2%|▏         | 12/500 [00:00<00:32, 14.79it/s]
eval model::   3%|▎         | 14/500 [00:00<00:31, 15.67it/s]
eval model::   3%|▎         | 17/500 [00:01<00:27, 17.59it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 18.81it/s]
eval model::   5%|▍         | 24/500 [00:01<00:21, 22.52it/s]
eval model::   5%|▌         | 27/500 [00:01<00:21, 22.43it/s]
eval model::   6%|▌         | 30/500 [00:01<00:20, 23.41it/s]
eval model::   7%|▋         | 33/500 [00:01<00:21, 21.47it/s]
eval model::   7%|▋         | 36/500 [00:01<00:22, 20.77it/s]
eval model::   8%|▊         | 39/500 [00:02<00:22, 20.23it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 18.27it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 19.85it/s]
eval model::  10%|▉         | 48/500 [00:02<00:25, 17.77it/s]
eval model::  10%|█         | 51/500 [00:02<00:23, 19.17it/s]
eval model::  11%|█         | 54/500 [00:02<00:22, 19.52it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:21, 20.69it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:22, 19.29it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:27, 16.00it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:27, 15.70it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:23, 18.61it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 20.94it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 21.20it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.66it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.53it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.81it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 21.99it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 19.70it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.49it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:20, 19.79it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:20, 19.28it/s]
eval model::  20%|██        | 100/500 [00:05<00:21, 18.77it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 19.86it/s]
eval model::  21%|██        | 106/500 [00:05<00:20, 19.68it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:19, 20.28it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:20, 19.20it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:20, 19.03it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:22, 17.42it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:20, 18.94it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:20, 18.86it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 19.18it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 19.20it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 21.02it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.77it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:18, 19.77it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 20.10it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.50it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.68it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.70it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.38it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 20.26it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:14, 23.50it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:19, 17.89it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:18, 18.42it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 19.40it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 19.53it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 19.25it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 16.58it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.24it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.31it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.63it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 18.38it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 20.11it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:14, 21.29it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:13, 22.66it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:14, 21.47it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:13, 22.32it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 21.47it/s]
eval model::  41%|████      | 205/500 [00:10<00:25, 11.36it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:22, 12.98it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:21, 13.41it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.77it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.58it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.41it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:19, 14.56it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:18, 14.74it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:16, 17.25it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.87it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 22.02it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 22.97it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:11, 22.36it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.74it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:14, 18.01it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:13, 18.91it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 20.01it/s]
eval model::  50%|█████     | 251/500 [00:13<00:12, 20.05it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.79it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 22.69it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 22.28it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:12, 19.57it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:12, 18.88it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.58it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 20.57it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.69it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 20.38it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 21.47it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 21.50it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:19, 11.15it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:14, 14.88it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:13, 15.48it/s]
eval model::  59%|█████▉    | 297/500 [00:16<00:14, 13.94it/s]
eval model::  60%|██████    | 300/500 [00:16<00:13, 14.78it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 16.58it/s]
eval model::  61%|██████    | 305/500 [00:16<00:11, 17.17it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:10, 18.43it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 19.39it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:09, 19.82it/s]
eval model::  63%|██████▎   | 317/500 [00:17<00:08, 20.36it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:08, 21.15it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:08, 20.64it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:07, 22.14it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:07, 21.42it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:07, 22.09it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 23.42it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:07, 20.49it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:09, 17.44it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:08, 19.02it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:07, 19.94it/s]
eval model::  70%|███████   | 350/500 [00:18<00:06, 21.71it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 22.32it/s]
eval model::  71%|███████   | 356/500 [00:18<00:07, 20.50it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:06, 21.63it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:07, 19.02it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 18.06it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 17.43it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 19.45it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 18.70it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:06, 20.55it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 22.63it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 21.20it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:05, 22.29it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:08, 13.96it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:06, 15.69it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 17.37it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 19.78it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 19.05it/s]
eval model::  81%|████████  | 403/500 [00:21<00:04, 19.98it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 20.95it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 22.54it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:03, 23.73it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:03, 21.50it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 19.53it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:05, 13.64it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 14.55it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:04, 16.76it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 18.34it/s]
eval model::  87%|████████▋ | 433/500 [00:23<00:03, 19.15it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:03, 19.25it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 20.70it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 20.98it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 22.07it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 20.51it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 20.78it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 22.07it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 22.79it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 23.20it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 20.78it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 20.63it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 20.28it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 21.36it/s]
eval model::  95%|█████████▌| 475/500 [00:25<00:01, 20.95it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:01, 20.94it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 22.37it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 22.33it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 20.56it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 21.75it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 18.59it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 18.40it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 21.82it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.09it/s]
[2024/07/27 13:26:04] ppocr INFO: cur metric, precision: 0.7449481157837248, recall: 0.6567164179104478, hmean: 0.6980552712384851, fps: 45.44814353672277
[2024/07/27 13:26:04] ppocr INFO: best metric, hmean: 0.7032851511169513, precision: 0.7743055555555556, recall: 0.6441983630235917, fps: 43.729055311097895, best_epoch: 640
[2024/07/27 13:26:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:26:13] ppocr INFO: epoch: [681/1500], global_step: 2043, lr: 0.001000, loss: 1.370521, loss_shrink_maps: 0.695754, loss_threshold_maps: 0.541276, loss_binary_maps: 0.138344, avg_reader_cost: 2.18508 s, avg_batch_cost: 2.56347 s, avg_samples: 12.5, ips: 4.87619 samples/s, eta: 5:57:05
[2024/07/27 13:26:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:26:22] ppocr INFO: epoch: [682/1500], global_step: 2046, lr: 0.001000, loss: 1.354039, loss_shrink_maps: 0.694251, loss_threshold_maps: 0.523273, loss_binary_maps: 0.137778, avg_reader_cost: 2.11769 s, avg_batch_cost: 2.45097 s, avg_samples: 12.5, ips: 5.10003 samples/s, eta: 5:56:37
[2024/07/27 13:26:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:26:32] ppocr INFO: epoch: [683/1500], global_step: 2049, lr: 0.001000, loss: 1.347845, loss_shrink_maps: 0.688795, loss_threshold_maps: 0.523273, loss_binary_maps: 0.137220, avg_reader_cost: 2.28219 s, avg_batch_cost: 2.54821 s, avg_samples: 12.5, ips: 4.90541 samples/s, eta: 5:56:10
[2024/07/27 13:26:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:26:40] ppocr INFO: epoch: [684/1500], global_step: 2050, lr: 0.001000, loss: 1.370778, loss_shrink_maps: 0.695754, loss_threshold_maps: 0.525212, loss_binary_maps: 0.138883, avg_reader_cost: 0.66598 s, avg_batch_cost: 0.75767 s, avg_samples: 4.8, ips: 6.33521 samples/s, eta: 5:56:00
[2024/07/27 13:26:42] ppocr INFO: epoch: [684/1500], global_step: 2052, lr: 0.001000, loss: 1.403164, loss_shrink_maps: 0.710916, loss_threshold_maps: 0.531769, loss_binary_maps: 0.141339, avg_reader_cost: 1.60768 s, avg_batch_cost: 1.75519 s, avg_samples: 7.7, ips: 4.38700 samples/s, eta: 5:55:42
[2024/07/27 13:26:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:26:52] ppocr INFO: epoch: [685/1500], global_step: 2055, lr: 0.001000, loss: 1.440399, loss_shrink_maps: 0.745571, loss_threshold_maps: 0.541874, loss_binary_maps: 0.148516, avg_reader_cost: 2.42849 s, avg_batch_cost: 2.67075 s, avg_samples: 12.5, ips: 4.68034 samples/s, eta: 5:55:17
[2024/07/27 13:26:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:27:02] ppocr INFO: epoch: [686/1500], global_step: 2058, lr: 0.001000, loss: 1.459922, loss_shrink_maps: 0.751143, loss_threshold_maps: 0.548161, loss_binary_maps: 0.149512, avg_reader_cost: 2.34230 s, avg_batch_cost: 2.58280 s, avg_samples: 12.5, ips: 4.83971 samples/s, eta: 5:54:50
[2024/07/27 13:27:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:27:11] ppocr INFO: epoch: [687/1500], global_step: 2060, lr: 0.001000, loss: 1.459922, loss_shrink_maps: 0.751143, loss_threshold_maps: 0.548161, loss_binary_maps: 0.149512, avg_reader_cost: 1.33909 s, avg_batch_cost: 1.63654 s, avg_samples: 9.6, ips: 5.86605 samples/s, eta: 5:54:32
[2024/07/27 13:27:11] ppocr INFO: epoch: [687/1500], global_step: 2061, lr: 0.001000, loss: 1.440399, loss_shrink_maps: 0.745571, loss_threshold_maps: 0.538790, loss_binary_maps: 0.148516, avg_reader_cost: 0.86457 s, avg_batch_cost: 0.91993 s, avg_samples: 2.9, ips: 3.15241 samples/s, eta: 5:54:23
[2024/07/27 13:27:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:27:21] ppocr INFO: epoch: [688/1500], global_step: 2064, lr: 0.001000, loss: 1.430732, loss_shrink_maps: 0.745571, loss_threshold_maps: 0.531400, loss_binary_maps: 0.148516, avg_reader_cost: 2.21862 s, avg_batch_cost: 2.56954 s, avg_samples: 12.5, ips: 4.86469 samples/s, eta: 5:53:57
[2024/07/27 13:27:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:27:31] ppocr INFO: epoch: [689/1500], global_step: 2067, lr: 0.001000, loss: 1.392482, loss_shrink_maps: 0.721674, loss_threshold_maps: 0.523768, loss_binary_maps: 0.143453, avg_reader_cost: 2.37389 s, avg_batch_cost: 2.61249 s, avg_samples: 12.5, ips: 4.78471 samples/s, eta: 5:53:31
[2024/07/27 13:27:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:27:41] ppocr INFO: epoch: [690/1500], global_step: 2070, lr: 0.001000, loss: 1.353974, loss_shrink_maps: 0.702736, loss_threshold_maps: 0.519514, loss_binary_maps: 0.140103, avg_reader_cost: 2.30523 s, avg_batch_cost: 2.54254 s, avg_samples: 12.5, ips: 4.91635 samples/s, eta: 5:53:04
[2024/07/27 13:27:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:27:50] ppocr INFO: epoch: [691/1500], global_step: 2073, lr: 0.001000, loss: 1.342308, loss_shrink_maps: 0.687191, loss_threshold_maps: 0.519514, loss_binary_maps: 0.136824, avg_reader_cost: 2.20998 s, avg_batch_cost: 2.55233 s, avg_samples: 12.5, ips: 4.89749 samples/s, eta: 5:52:37
[2024/07/27 13:27:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:28:00] ppocr INFO: epoch: [692/1500], global_step: 2076, lr: 0.001000, loss: 1.333815, loss_shrink_maps: 0.687191, loss_threshold_maps: 0.520880, loss_binary_maps: 0.136824, avg_reader_cost: 2.32026 s, avg_batch_cost: 2.55351 s, avg_samples: 12.5, ips: 4.89521 samples/s, eta: 5:52:10
[2024/07/27 13:28:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:28:10] ppocr INFO: epoch: [693/1500], global_step: 2079, lr: 0.001000, loss: 1.333815, loss_shrink_maps: 0.687191, loss_threshold_maps: 0.520880, loss_binary_maps: 0.136824, avg_reader_cost: 2.19457 s, avg_batch_cost: 2.53892 s, avg_samples: 12.5, ips: 4.92336 samples/s, eta: 5:51:43
[2024/07/27 13:28:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:28:18] ppocr INFO: epoch: [694/1500], global_step: 2080, lr: 0.001000, loss: 1.316568, loss_shrink_maps: 0.677197, loss_threshold_maps: 0.520880, loss_binary_maps: 0.134540, avg_reader_cost: 0.56786 s, avg_batch_cost: 0.79980 s, avg_samples: 4.8, ips: 6.00151 samples/s, eta: 5:51:33
[2024/07/27 13:28:20] ppocr INFO: epoch: [694/1500], global_step: 2082, lr: 0.001000, loss: 1.354608, loss_shrink_maps: 0.699911, loss_threshold_maps: 0.521801, loss_binary_maps: 0.139403, avg_reader_cost: 1.69194 s, avg_batch_cost: 1.83969 s, avg_samples: 7.7, ips: 4.18548 samples/s, eta: 5:51:17
[2024/07/27 13:28:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:28:30] ppocr INFO: epoch: [695/1500], global_step: 2085, lr: 0.001000, loss: 1.359453, loss_shrink_maps: 0.699911, loss_threshold_maps: 0.521801, loss_binary_maps: 0.139403, avg_reader_cost: 2.27850 s, avg_batch_cost: 2.62985 s, avg_samples: 12.5, ips: 4.75312 samples/s, eta: 5:50:51
[2024/07/27 13:28:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:28:39] ppocr INFO: epoch: [696/1500], global_step: 2088, lr: 0.001000, loss: 1.359453, loss_shrink_maps: 0.689918, loss_threshold_maps: 0.522634, loss_binary_maps: 0.137221, avg_reader_cost: 2.24536 s, avg_batch_cost: 2.60315 s, avg_samples: 12.5, ips: 4.80188 samples/s, eta: 5:50:25
[2024/07/27 13:28:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:28:49] ppocr INFO: epoch: [697/1500], global_step: 2090, lr: 0.001000, loss: 1.369631, loss_shrink_maps: 0.715955, loss_threshold_maps: 0.522634, loss_binary_maps: 0.142664, avg_reader_cost: 1.52392 s, avg_batch_cost: 1.70454 s, avg_samples: 9.6, ips: 5.63201 samples/s, eta: 5:50:07
[2024/07/27 13:28:49] ppocr INFO: epoch: [697/1500], global_step: 2091, lr: 0.001000, loss: 1.359453, loss_shrink_maps: 0.693959, loss_threshold_maps: 0.522634, loss_binary_maps: 0.138103, avg_reader_cost: 0.89864 s, avg_batch_cost: 0.95389 s, avg_samples: 2.9, ips: 3.04019 samples/s, eta: 5:49:59
[2024/07/27 13:28:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:28:59] ppocr INFO: epoch: [698/1500], global_step: 2094, lr: 0.001000, loss: 1.373971, loss_shrink_maps: 0.729468, loss_threshold_maps: 0.515913, loss_binary_maps: 0.145284, avg_reader_cost: 2.34221 s, avg_batch_cost: 2.58216 s, avg_samples: 12.5, ips: 4.84091 samples/s, eta: 5:49:32
[2024/07/27 13:29:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:29:09] ppocr INFO: epoch: [699/1500], global_step: 2097, lr: 0.001000, loss: 1.359453, loss_shrink_maps: 0.697497, loss_threshold_maps: 0.525134, loss_binary_maps: 0.139118, avg_reader_cost: 2.14851 s, avg_batch_cost: 2.47995 s, avg_samples: 12.5, ips: 5.04041 samples/s, eta: 5:49:05
[2024/07/27 13:29:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:29:19] ppocr INFO: epoch: [700/1500], global_step: 2100, lr: 0.001000, loss: 1.348890, loss_shrink_maps: 0.671811, loss_threshold_maps: 0.509365, loss_binary_maps: 0.134216, avg_reader_cost: 2.19174 s, avg_batch_cost: 2.64763 s, avg_samples: 12.5, ips: 4.72120 samples/s, eta: 5:48:39

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.40it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 17.99it/s]
eval model::   1%|          | 6/500 [00:00<00:30, 16.12it/s]
eval model::   2%|▏         | 8/500 [00:00<00:30, 16.16it/s]
eval model::   2%|▏         | 10/500 [00:00<00:28, 17.32it/s]
eval model::   2%|▏         | 12/500 [00:00<00:29, 16.70it/s]
eval model::   3%|▎         | 14/500 [00:00<00:30, 15.87it/s]
eval model::   3%|▎         | 17/500 [00:00<00:26, 18.19it/s]
eval model::   4%|▍         | 19/500 [00:01<00:26, 17.96it/s]
eval model::   4%|▍         | 21/500 [00:01<00:29, 16.50it/s]
eval model::   5%|▍         | 24/500 [00:01<00:25, 18.40it/s]
eval model::   5%|▌         | 26/500 [00:01<00:25, 18.57it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.26it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.05it/s]
eval model::   7%|▋         | 34/500 [00:01<00:26, 17.79it/s]
eval model::   7%|▋         | 37/500 [00:02<00:23, 19.45it/s]
eval model::   8%|▊         | 39/500 [00:02<00:27, 16.70it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.42it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.47it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 16.81it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.02it/s]
eval model::  11%|█         | 53/500 [00:02<00:22, 19.85it/s]
eval model::  11%|█         | 56/500 [00:03<00:20, 21.16it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:20, 21.08it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:25, 16.98it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:24, 17.51it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 19.39it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 21.56it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 21.56it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:17, 23.61it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:18, 22.83it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 20.56it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.25it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:22, 18.25it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.32it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:22, 18.09it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.71it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.10it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.17it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 20.32it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:19, 20.51it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 20.55it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:18, 21.09it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:16, 22.76it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 20.37it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 20.59it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.63it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 18.60it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.66it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:18, 19.74it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.20it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:19, 18.47it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:20, 17.09it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:20, 17.03it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:20, 17.21it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.23it/s]
eval model::  31%|███       | 153/500 [00:08<00:17, 20.12it/s]
eval model::  31%|███       | 156/500 [00:08<00:15, 21.54it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 20.90it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:20, 16.16it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:19, 16.90it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 18.18it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:15, 20.80it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:15, 21.21it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:17, 18.22it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.68it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:17, 18.24it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 21.09it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 19.74it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:16, 19.07it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.18it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.20it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 21.13it/s]
eval model::  41%|████      | 204/500 [00:10<00:14, 20.61it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:21, 13.90it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:20, 14.43it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 14.07it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.47it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.07it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 12.95it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.33it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:19, 14.23it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.31it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.12it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.82it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 20.89it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:12, 21.37it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:13, 19.55it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.80it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.24it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 21.65it/s]
eval model::  51%|█████     | 255/500 [00:13<00:10, 24.49it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:10, 24.03it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 23.20it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 19.62it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 19.89it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:12, 17.97it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.21it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.39it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:13, 17.05it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:12, 17.62it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 19.18it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:11, 19.13it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:21, 10.14it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:15, 13.31it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 16.33it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:14, 13.73it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:11, 17.61it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 17.75it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 18.56it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 19.50it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 20.98it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:09, 20.19it/s]
eval model::  63%|██████▎   | 317/500 [00:17<00:08, 20.33it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:08, 21.56it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:08, 19.78it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:08, 20.34it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:09, 17.57it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:09, 17.36it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:09, 18.02it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 18.60it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 14.94it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:08, 17.45it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 19.21it/s]
eval model::  70%|███████   | 350/500 [00:18<00:06, 22.06it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 22.45it/s]
eval model::  71%|███████   | 356/500 [00:19<00:07, 20.57it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:06, 20.35it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:07, 18.33it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.33it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 18.48it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.56it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 21.10it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 21.66it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 23.56it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 22.43it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:04, 23.53it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:08, 13.00it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:07, 15.20it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 16.50it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 18.59it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 17.67it/s]
eval model::  81%|████████  | 403/500 [00:21<00:05, 18.11it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 18.80it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 20.43it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:03, 22.07it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:04, 19.36it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:05, 16.26it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:06, 12.68it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 13.74it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 14.84it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:04, 15.42it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:03, 17.83it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 16.82it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 19.81it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:03, 20.81it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:02, 21.24it/s]
eval model::  89%|████████▊ | 443/500 [00:23<00:02, 21.71it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 20.62it/s]
eval model::  90%|████████▉ | 449/500 [00:24<00:02, 19.46it/s]
eval model::  90%|█████████ | 452/500 [00:24<00:02, 21.63it/s]
eval model::  91%|█████████ | 455/500 [00:24<00:02, 21.22it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:01, 22.38it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 20.66it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 17.05it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 16.10it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 16.79it/s]
eval model::  94%|█████████▍| 472/500 [00:25<00:01, 19.42it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.86it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:01, 21.45it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 19.52it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 20.11it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 17.71it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 17.69it/s]
eval model::  98%|█████████▊| 491/500 [00:26<00:00, 17.12it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 17.07it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 18.76it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 23.11it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.70it/s]
[2024/07/27 13:29:46] ppocr INFO: cur metric, precision: 0.7651083238312428, recall: 0.6461242176215696, hmean: 0.700600365439833, fps: 44.512996991016394
[2024/07/27 13:29:46] ppocr INFO: best metric, hmean: 0.7032851511169513, precision: 0.7743055555555556, recall: 0.6441983630235917, fps: 43.729055311097895, best_epoch: 640
[2024/07/27 13:29:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:29:55] ppocr INFO: epoch: [701/1500], global_step: 2103, lr: 0.001000, loss: 1.330779, loss_shrink_maps: 0.666978, loss_threshold_maps: 0.515645, loss_binary_maps: 0.132875, avg_reader_cost: 2.13095 s, avg_batch_cost: 2.46654 s, avg_samples: 12.5, ips: 5.06782 samples/s, eta: 5:48:11
[2024/07/27 13:29:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:30:05] ppocr INFO: epoch: [702/1500], global_step: 2106, lr: 0.001000, loss: 1.330779, loss_shrink_maps: 0.671811, loss_threshold_maps: 0.515645, loss_binary_maps: 0.134216, avg_reader_cost: 2.23476 s, avg_batch_cost: 2.58589 s, avg_samples: 12.5, ips: 4.83393 samples/s, eta: 5:47:45
[2024/07/27 13:30:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:30:14] ppocr INFO: epoch: [703/1500], global_step: 2109, lr: 0.001000, loss: 1.365547, loss_shrink_maps: 0.696005, loss_threshold_maps: 0.524340, loss_binary_maps: 0.138514, avg_reader_cost: 2.20249 s, avg_batch_cost: 2.56007 s, avg_samples: 12.5, ips: 4.88269 samples/s, eta: 5:47:18
[2024/07/27 13:30:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:30:22] ppocr INFO: epoch: [704/1500], global_step: 2110, lr: 0.001000, loss: 1.365547, loss_shrink_maps: 0.696005, loss_threshold_maps: 0.524340, loss_binary_maps: 0.138514, avg_reader_cost: 0.67331 s, avg_batch_cost: 0.76359 s, avg_samples: 4.8, ips: 6.28607 samples/s, eta: 5:47:08
[2024/07/27 13:30:24] ppocr INFO: epoch: [704/1500], global_step: 2112, lr: 0.001000, loss: 1.365547, loss_shrink_maps: 0.696005, loss_threshold_maps: 0.528805, loss_binary_maps: 0.138514, avg_reader_cost: 1.61966 s, avg_batch_cost: 1.76674 s, avg_samples: 7.7, ips: 4.35832 samples/s, eta: 5:46:51
[2024/07/27 13:30:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:30:33] ppocr INFO: epoch: [705/1500], global_step: 2115, lr: 0.001000, loss: 1.399205, loss_shrink_maps: 0.722781, loss_threshold_maps: 0.528805, loss_binary_maps: 0.143241, avg_reader_cost: 2.15391 s, avg_batch_cost: 2.49059 s, avg_samples: 12.5, ips: 5.01888 samples/s, eta: 5:46:23
[2024/07/27 13:30:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:30:43] ppocr INFO: epoch: [706/1500], global_step: 2118, lr: 0.001000, loss: 1.412003, loss_shrink_maps: 0.729649, loss_threshold_maps: 0.526197, loss_binary_maps: 0.144845, avg_reader_cost: 2.37298 s, avg_batch_cost: 2.61342 s, avg_samples: 12.5, ips: 4.78301 samples/s, eta: 5:45:57
[2024/07/27 13:30:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:30:52] ppocr INFO: epoch: [707/1500], global_step: 2120, lr: 0.001000, loss: 1.412003, loss_shrink_maps: 0.729649, loss_threshold_maps: 0.526197, loss_binary_maps: 0.144845, avg_reader_cost: 1.43441 s, avg_batch_cost: 1.62381 s, avg_samples: 9.6, ips: 5.91203 samples/s, eta: 5:45:38
[2024/07/27 13:30:53] ppocr INFO: epoch: [707/1500], global_step: 2121, lr: 0.001000, loss: 1.399205, loss_shrink_maps: 0.722781, loss_threshold_maps: 0.519891, loss_binary_maps: 0.143241, avg_reader_cost: 0.85807 s, avg_batch_cost: 0.91357 s, avg_samples: 2.9, ips: 3.17435 samples/s, eta: 5:45:30
[2024/07/27 13:30:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:31:03] ppocr INFO: epoch: [708/1500], global_step: 2124, lr: 0.001000, loss: 1.367401, loss_shrink_maps: 0.711943, loss_threshold_maps: 0.518947, loss_binary_maps: 0.141625, avg_reader_cost: 2.25152 s, avg_batch_cost: 2.60504 s, avg_samples: 12.5, ips: 4.79840 samples/s, eta: 5:45:04
[2024/07/27 13:31:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:31:13] ppocr INFO: epoch: [709/1500], global_step: 2127, lr: 0.001000, loss: 1.367401, loss_shrink_maps: 0.711943, loss_threshold_maps: 0.515565, loss_binary_maps: 0.141625, avg_reader_cost: 2.26521 s, avg_batch_cost: 2.63345 s, avg_samples: 12.5, ips: 4.74663 samples/s, eta: 5:44:38
[2024/07/27 13:31:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:31:23] ppocr INFO: epoch: [710/1500], global_step: 2130, lr: 0.001000, loss: 1.325262, loss_shrink_maps: 0.684530, loss_threshold_maps: 0.505516, loss_binary_maps: 0.136388, avg_reader_cost: 2.24910 s, avg_batch_cost: 2.61678 s, avg_samples: 12.5, ips: 4.77687 samples/s, eta: 5:44:12
[2024/07/27 13:31:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:31:33] ppocr INFO: epoch: [711/1500], global_step: 2133, lr: 0.001000, loss: 1.301446, loss_shrink_maps: 0.653191, loss_threshold_maps: 0.506558, loss_binary_maps: 0.129653, avg_reader_cost: 2.42135 s, avg_batch_cost: 2.68156 s, avg_samples: 12.5, ips: 4.66146 samples/s, eta: 5:43:46
[2024/07/27 13:31:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:31:43] ppocr INFO: epoch: [712/1500], global_step: 2136, lr: 0.001000, loss: 1.286010, loss_shrink_maps: 0.635164, loss_threshold_maps: 0.506558, loss_binary_maps: 0.126000, avg_reader_cost: 2.38421 s, avg_batch_cost: 2.61772 s, avg_samples: 12.5, ips: 4.77516 samples/s, eta: 5:43:20
[2024/07/27 13:31:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:31:51] ppocr INFO: epoch: [713/1500], global_step: 2139, lr: 0.001000, loss: 1.273334, loss_shrink_maps: 0.632104, loss_threshold_maps: 0.503404, loss_binary_maps: 0.125822, avg_reader_cost: 2.02465 s, avg_batch_cost: 2.26760 s, avg_samples: 12.5, ips: 5.51243 samples/s, eta: 5:42:50
[2024/07/27 13:31:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:32:00] ppocr INFO: epoch: [714/1500], global_step: 2140, lr: 0.001000, loss: 1.273334, loss_shrink_maps: 0.627630, loss_threshold_maps: 0.503404, loss_binary_maps: 0.125222, avg_reader_cost: 0.70123 s, avg_batch_cost: 0.79452 s, avg_samples: 4.8, ips: 6.04138 samples/s, eta: 5:42:41
[2024/07/27 13:32:01] ppocr INFO: epoch: [714/1500], global_step: 2142, lr: 0.001000, loss: 1.287464, loss_shrink_maps: 0.639733, loss_threshold_maps: 0.505384, loss_binary_maps: 0.126729, avg_reader_cost: 1.68148 s, avg_batch_cost: 1.82967 s, avg_samples: 7.7, ips: 4.20841 samples/s, eta: 5:42:24
[2024/07/27 13:32:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:32:11] ppocr INFO: epoch: [715/1500], global_step: 2145, lr: 0.001000, loss: 1.303690, loss_shrink_maps: 0.658618, loss_threshold_maps: 0.507399, loss_binary_maps: 0.131144, avg_reader_cost: 2.31508 s, avg_batch_cost: 2.56048 s, avg_samples: 12.5, ips: 4.88189 samples/s, eta: 5:41:58
[2024/07/27 13:32:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:32:21] ppocr INFO: epoch: [716/1500], global_step: 2148, lr: 0.001000, loss: 1.298238, loss_shrink_maps: 0.645932, loss_threshold_maps: 0.507399, loss_binary_maps: 0.128510, avg_reader_cost: 2.30273 s, avg_batch_cost: 2.54287 s, avg_samples: 12.5, ips: 4.91572 samples/s, eta: 5:41:31
[2024/07/27 13:32:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:32:30] ppocr INFO: epoch: [717/1500], global_step: 2150, lr: 0.001000, loss: 1.296364, loss_shrink_maps: 0.643551, loss_threshold_maps: 0.507399, loss_binary_maps: 0.127766, avg_reader_cost: 1.32624 s, avg_batch_cost: 1.68417 s, avg_samples: 9.6, ips: 5.70013 samples/s, eta: 5:41:13
[2024/07/27 13:32:31] ppocr INFO: epoch: [717/1500], global_step: 2151, lr: 0.001000, loss: 1.298238, loss_shrink_maps: 0.645288, loss_threshold_maps: 0.507399, loss_binary_maps: 0.128510, avg_reader_cost: 0.88819 s, avg_batch_cost: 0.94318 s, avg_samples: 2.9, ips: 3.07472 samples/s, eta: 5:41:05
[2024/07/27 13:32:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:32:41] ppocr INFO: epoch: [718/1500], global_step: 2154, lr: 0.001000, loss: 1.302089, loss_shrink_maps: 0.658618, loss_threshold_maps: 0.506224, loss_binary_maps: 0.131144, avg_reader_cost: 2.51601 s, avg_batch_cost: 2.76704 s, avg_samples: 12.5, ips: 4.51746 samples/s, eta: 5:40:40
[2024/07/27 13:32:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:32:51] ppocr INFO: epoch: [719/1500], global_step: 2157, lr: 0.001000, loss: 1.347502, loss_shrink_maps: 0.701214, loss_threshold_maps: 0.514373, loss_binary_maps: 0.139089, avg_reader_cost: 2.25758 s, avg_batch_cost: 2.50278 s, avg_samples: 12.5, ips: 4.99445 samples/s, eta: 5:40:13
[2024/07/27 13:32:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:33:00] ppocr INFO: epoch: [720/1500], global_step: 2160, lr: 0.001000, loss: 1.378038, loss_shrink_maps: 0.712009, loss_threshold_maps: 0.517521, loss_binary_maps: 0.141373, avg_reader_cost: 2.22214 s, avg_batch_cost: 2.57284 s, avg_samples: 12.5, ips: 4.85845 samples/s, eta: 5:39:46

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:55,  9.04it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 17.10it/s]
eval model::   1%|▏         | 7/500 [00:00<00:25, 19.24it/s]
eval model::   2%|▏         | 9/500 [00:00<00:26, 18.79it/s]
eval model::   2%|▏         | 11/500 [00:00<00:31, 15.61it/s]
eval model::   3%|▎         | 13/500 [00:00<00:29, 16.67it/s]
eval model::   3%|▎         | 15/500 [00:00<00:28, 17.18it/s]
eval model::   4%|▎         | 18/500 [00:00<00:23, 20.21it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.88it/s]
eval model::   5%|▌         | 25/500 [00:01<00:21, 22.37it/s]
eval model::   6%|▌         | 28/500 [00:01<00:19, 23.88it/s]
eval model::   6%|▌         | 31/500 [00:01<00:22, 20.65it/s]
eval model::   7%|▋         | 34/500 [00:01<00:21, 21.38it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 22.02it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.10it/s]
eval model::   8%|▊         | 42/500 [00:02<00:29, 15.39it/s]
eval model::   9%|▉         | 45/500 [00:02<00:26, 17.10it/s]
eval model::   9%|▉         | 47/500 [00:02<00:28, 16.00it/s]
eval model::  10%|█         | 50/500 [00:02<00:25, 17.88it/s]
eval model::  11%|█         | 53/500 [00:02<00:23, 19.19it/s]
eval model::  11%|█         | 56/500 [00:03<00:23, 18.85it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 18.33it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.60it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.58it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.98it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:22, 19.06it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:22, 19.02it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:22, 18.64it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:21, 20.08it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:20, 20.99it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.31it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.75it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 19.99it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:23, 17.33it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 17.83it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.26it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.31it/s]
eval model::  20%|██        | 101/500 [00:05<00:21, 18.96it/s]
eval model::  21%|██        | 104/500 [00:05<00:19, 20.51it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 22.32it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 22.51it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 21.43it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:19, 19.88it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:18, 21.08it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.07it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:21, 17.57it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:19, 18.94it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:21, 17.57it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 17.16it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:18, 19.41it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.24it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.71it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:15, 22.47it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:17, 20.50it/s]
eval model::  30%|███       | 150/500 [00:07<00:15, 22.09it/s]
eval model::  31%|███       | 153/500 [00:07<00:16, 20.93it/s]
eval model::  31%|███       | 156/500 [00:08<00:15, 22.42it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:15, 21.32it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:20, 16.32it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:18, 18.29it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:17, 19.17it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 19.31it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 16.44it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.52it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.65it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:17, 17.68it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 21.61it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 22.15it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:13, 22.57it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 20.72it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.04it/s]
eval model::  40%|████      | 201/500 [00:10<00:15, 19.88it/s]
eval model::  41%|████      | 204/500 [00:10<00:14, 20.80it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:23, 12.26it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:22, 13.21it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:21, 13.51it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:20, 14.25it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:23, 12.10it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:23, 12.27it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 14.85it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:19, 14.38it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.90it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.12it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 22.87it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:11, 22.84it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.84it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:13, 18.54it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.03it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.79it/s]
eval model::  50%|█████     | 251/500 [00:13<00:12, 20.27it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.02it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 20.39it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:11, 21.47it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 21.75it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:11, 19.62it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.88it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 20.42it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.49it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.55it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:11, 19.73it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 21.10it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:18, 11.70it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:13, 15.30it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:13, 15.40it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:15, 13.40it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 16.15it/s]
eval model::  60%|██████    | 302/500 [00:16<00:12, 16.18it/s]
eval model::  61%|██████    | 305/500 [00:16<00:11, 17.65it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 19.34it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 20.86it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:09, 20.23it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:08, 20.66it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:09, 19.64it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:09, 17.77it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:09, 19.07it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:09, 18.58it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 19.51it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 22.31it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 19.98it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:10, 14.80it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:10, 15.40it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 18.45it/s]
eval model::  70%|███████   | 350/500 [00:18<00:07, 19.83it/s]
eval model::  71%|███████   | 353/500 [00:18<00:07, 20.16it/s]
eval model::  71%|███████   | 356/500 [00:19<00:07, 19.34it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:07, 19.96it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:08, 16.29it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:08, 15.98it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:08, 16.67it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 18.23it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:06, 18.96it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 20.82it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 23.99it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 22.22it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:04, 23.92it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:07, 14.53it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:06, 16.44it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 17.65it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 19.94it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 19.74it/s]
eval model::  81%|████████  | 403/500 [00:21<00:04, 20.55it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 21.03it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 22.32it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:03, 22.98it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:03, 22.99it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:03, 20.99it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:05, 13.77it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.47it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.10it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 20.16it/s]
eval model::  87%|████████▋ | 433/500 [00:23<00:03, 20.96it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:02, 23.98it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:02, 23.76it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 26.22it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 25.57it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 22.13it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 21.72it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 22.87it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 22.16it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 16.57it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 16.81it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:02, 15.96it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 16.40it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 18.73it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.72it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.62it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 21.15it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 21.68it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 21.83it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 23.04it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 19.08it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 20.54it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 22.02it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.00it/s]
[2024/07/27 13:33:27] ppocr INFO: cur metric, precision: 0.7223382045929019, recall: 0.666345690900337, hmean: 0.6932131229651891, fps: 45.56820783819796
[2024/07/27 13:33:27] ppocr INFO: best metric, hmean: 0.7032851511169513, precision: 0.7743055555555556, recall: 0.6441983630235917, fps: 43.729055311097895, best_epoch: 640
[2024/07/27 13:33:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:33:36] ppocr INFO: epoch: [721/1500], global_step: 2163, lr: 0.001000, loss: 1.378038, loss_shrink_maps: 0.712009, loss_threshold_maps: 0.518302, loss_binary_maps: 0.141373, avg_reader_cost: 2.15995 s, avg_batch_cost: 2.58630 s, avg_samples: 12.5, ips: 4.83316 samples/s, eta: 5:39:20
[2024/07/27 13:33:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:33:46] ppocr INFO: epoch: [722/1500], global_step: 2166, lr: 0.001000, loss: 1.263592, loss_shrink_maps: 0.649077, loss_threshold_maps: 0.497003, loss_binary_maps: 0.129003, avg_reader_cost: 2.37198 s, avg_batch_cost: 2.62385 s, avg_samples: 12.5, ips: 4.76400 samples/s, eta: 5:38:54
[2024/07/27 13:33:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:33:56] ppocr INFO: epoch: [723/1500], global_step: 2169, lr: 0.001000, loss: 1.339754, loss_shrink_maps: 0.694594, loss_threshold_maps: 0.506154, loss_binary_maps: 0.138188, avg_reader_cost: 2.20087 s, avg_batch_cost: 2.56623 s, avg_samples: 12.5, ips: 4.87095 samples/s, eta: 5:38:27
[2024/07/27 13:33:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:34:04] ppocr INFO: epoch: [724/1500], global_step: 2170, lr: 0.001000, loss: 1.353938, loss_shrink_maps: 0.708472, loss_threshold_maps: 0.517673, loss_binary_maps: 0.140828, avg_reader_cost: 0.56539 s, avg_batch_cost: 0.82227 s, avg_samples: 4.8, ips: 5.83751 samples/s, eta: 5:38:18
[2024/07/27 13:34:06] ppocr INFO: epoch: [724/1500], global_step: 2172, lr: 0.001000, loss: 1.311413, loss_shrink_maps: 0.669585, loss_threshold_maps: 0.498105, loss_binary_maps: 0.133305, avg_reader_cost: 1.73722 s, avg_batch_cost: 1.88555 s, avg_samples: 7.7, ips: 4.08369 samples/s, eta: 5:38:02
[2024/07/27 13:34:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:34:15] ppocr INFO: epoch: [725/1500], global_step: 2175, lr: 0.001000, loss: 1.319874, loss_shrink_maps: 0.671555, loss_threshold_maps: 0.498105, loss_binary_maps: 0.133930, avg_reader_cost: 2.26978 s, avg_batch_cost: 2.50710 s, avg_samples: 12.5, ips: 4.98584 samples/s, eta: 5:37:35
[2024/07/27 13:34:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:34:25] ppocr INFO: epoch: [726/1500], global_step: 2178, lr: 0.001000, loss: 1.315939, loss_shrink_maps: 0.671555, loss_threshold_maps: 0.495852, loss_binary_maps: 0.133930, avg_reader_cost: 2.34951 s, avg_batch_cost: 2.58457 s, avg_samples: 12.5, ips: 4.83640 samples/s, eta: 5:37:08
[2024/07/27 13:34:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:34:34] ppocr INFO: epoch: [727/1500], global_step: 2180, lr: 0.001000, loss: 1.315939, loss_shrink_maps: 0.668258, loss_threshold_maps: 0.496954, loss_binary_maps: 0.133197, avg_reader_cost: 1.44292 s, avg_batch_cost: 1.62417 s, avg_samples: 9.6, ips: 5.91071 samples/s, eta: 5:36:50
[2024/07/27 13:34:35] ppocr INFO: epoch: [727/1500], global_step: 2181, lr: 0.001000, loss: 1.292242, loss_shrink_maps: 0.659558, loss_threshold_maps: 0.495852, loss_binary_maps: 0.131374, avg_reader_cost: 0.85850 s, avg_batch_cost: 0.91339 s, avg_samples: 2.9, ips: 3.17499 samples/s, eta: 5:36:41
[2024/07/27 13:34:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:34:45] ppocr INFO: epoch: [728/1500], global_step: 2184, lr: 0.001000, loss: 1.249141, loss_shrink_maps: 0.628522, loss_threshold_maps: 0.495013, loss_binary_maps: 0.125022, avg_reader_cost: 2.19743 s, avg_batch_cost: 2.55166 s, avg_samples: 12.5, ips: 4.89877 samples/s, eta: 5:36:15
[2024/07/27 13:34:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:34:54] ppocr INFO: epoch: [729/1500], global_step: 2187, lr: 0.001000, loss: 1.287385, loss_shrink_maps: 0.654484, loss_threshold_maps: 0.495421, loss_binary_maps: 0.130609, avg_reader_cost: 2.28868 s, avg_batch_cost: 2.52713 s, avg_samples: 12.5, ips: 4.94633 samples/s, eta: 5:35:48
[2024/07/27 13:34:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:35:04] ppocr INFO: epoch: [730/1500], global_step: 2190, lr: 0.001000, loss: 1.278852, loss_shrink_maps: 0.643192, loss_threshold_maps: 0.496833, loss_binary_maps: 0.128213, avg_reader_cost: 2.21703 s, avg_batch_cost: 2.58263 s, avg_samples: 12.5, ips: 4.84003 samples/s, eta: 5:35:21
[2024/07/27 13:35:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:35:14] ppocr INFO: epoch: [731/1500], global_step: 2193, lr: 0.001000, loss: 1.278852, loss_shrink_maps: 0.643192, loss_threshold_maps: 0.500590, loss_binary_maps: 0.128213, avg_reader_cost: 2.22475 s, avg_batch_cost: 2.55981 s, avg_samples: 12.5, ips: 4.88318 samples/s, eta: 5:34:54
[2024/07/27 13:35:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:35:24] ppocr INFO: epoch: [732/1500], global_step: 2196, lr: 0.001000, loss: 1.271121, loss_shrink_maps: 0.638535, loss_threshold_maps: 0.497857, loss_binary_maps: 0.127207, avg_reader_cost: 2.22506 s, avg_batch_cost: 2.56070 s, avg_samples: 12.5, ips: 4.88148 samples/s, eta: 5:34:28
[2024/07/27 13:35:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:35:34] ppocr INFO: epoch: [733/1500], global_step: 2199, lr: 0.001000, loss: 1.278852, loss_shrink_maps: 0.643192, loss_threshold_maps: 0.500575, loss_binary_maps: 0.128213, avg_reader_cost: 2.35889 s, avg_batch_cost: 2.60886 s, avg_samples: 12.5, ips: 4.79137 samples/s, eta: 5:34:02
[2024/07/27 13:35:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:35:42] ppocr INFO: epoch: [734/1500], global_step: 2200, lr: 0.001000, loss: 1.278852, loss_shrink_maps: 0.643192, loss_threshold_maps: 0.500575, loss_binary_maps: 0.128213, avg_reader_cost: 0.56391 s, avg_batch_cost: 0.80167 s, avg_samples: 4.8, ips: 5.98751 samples/s, eta: 5:33:52
[2024/07/27 13:35:44] ppocr INFO: epoch: [734/1500], global_step: 2202, lr: 0.001000, loss: 1.294581, loss_shrink_maps: 0.664616, loss_threshold_maps: 0.504998, loss_binary_maps: 0.133137, avg_reader_cost: 1.69571 s, avg_batch_cost: 1.84309 s, avg_samples: 7.7, ips: 4.17776 samples/s, eta: 5:33:36
[2024/07/27 13:35:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:35:53] ppocr INFO: epoch: [735/1500], global_step: 2205, lr: 0.001000, loss: 1.325098, loss_shrink_maps: 0.680239, loss_threshold_maps: 0.513684, loss_binary_maps: 0.135533, avg_reader_cost: 2.29183 s, avg_batch_cost: 2.53164 s, avg_samples: 12.5, ips: 4.93751 samples/s, eta: 5:33:09
[2024/07/27 13:35:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:36:03] ppocr INFO: epoch: [736/1500], global_step: 2208, lr: 0.001000, loss: 1.362877, loss_shrink_maps: 0.690801, loss_threshold_maps: 0.518879, loss_binary_maps: 0.137128, avg_reader_cost: 2.26244 s, avg_batch_cost: 2.57596 s, avg_samples: 12.5, ips: 4.85255 samples/s, eta: 5:32:42
[2024/07/27 13:36:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:36:12] ppocr INFO: epoch: [737/1500], global_step: 2210, lr: 0.001000, loss: 1.362877, loss_shrink_maps: 0.690801, loss_threshold_maps: 0.529648, loss_binary_maps: 0.137128, avg_reader_cost: 1.46181 s, avg_batch_cost: 1.64666 s, avg_samples: 9.6, ips: 5.82999 samples/s, eta: 5:32:24
[2024/07/27 13:36:13] ppocr INFO: epoch: [737/1500], global_step: 2211, lr: 0.001000, loss: 1.362877, loss_shrink_maps: 0.690801, loss_threshold_maps: 0.529648, loss_binary_maps: 0.137128, avg_reader_cost: 0.86978 s, avg_batch_cost: 0.92516 s, avg_samples: 2.9, ips: 3.13459 samples/s, eta: 5:32:16
[2024/07/27 13:36:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:36:22] ppocr INFO: epoch: [738/1500], global_step: 2214, lr: 0.001000, loss: 1.362760, loss_shrink_maps: 0.691373, loss_threshold_maps: 0.531782, loss_binary_maps: 0.137395, avg_reader_cost: 2.20192 s, avg_batch_cost: 2.55223 s, avg_samples: 12.5, ips: 4.89768 samples/s, eta: 5:31:49
[2024/07/27 13:36:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:36:32] ppocr INFO: epoch: [739/1500], global_step: 2217, lr: 0.001000, loss: 1.384723, loss_shrink_maps: 0.705040, loss_threshold_maps: 0.534262, loss_binary_maps: 0.139926, avg_reader_cost: 2.34884 s, avg_batch_cost: 2.58685 s, avg_samples: 12.5, ips: 4.83212 samples/s, eta: 5:31:23
[2024/07/27 13:36:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:36:42] ppocr INFO: epoch: [740/1500], global_step: 2220, lr: 0.001000, loss: 1.362373, loss_shrink_maps: 0.691373, loss_threshold_maps: 0.530012, loss_binary_maps: 0.137395, avg_reader_cost: 2.32689 s, avg_batch_cost: 2.57215 s, avg_samples: 12.5, ips: 4.85974 samples/s, eta: 5:30:56

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:54,  9.14it/s]
eval model::   1%|          | 3/500 [00:00<00:37, 13.15it/s]
eval model::   1%|          | 6/500 [00:00<00:32, 15.33it/s]
eval model::   2%|▏         | 9/500 [00:00<00:26, 18.27it/s]
eval model::   2%|▏         | 11/500 [00:00<00:29, 16.66it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.47it/s]
eval model::   3%|▎         | 16/500 [00:01<00:33, 14.54it/s]
eval model::   4%|▍         | 19/500 [00:01<00:28, 17.17it/s]
eval model::   4%|▍         | 21/500 [00:01<00:28, 16.84it/s]
eval model::   5%|▍         | 24/500 [00:01<00:24, 19.32it/s]
eval model::   5%|▌         | 27/500 [00:01<00:23, 20.12it/s]
eval model::   6%|▌         | 30/500 [00:01<00:22, 21.16it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 19.61it/s]
eval model::   7%|▋         | 36/500 [00:02<00:24, 18.84it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 17.74it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.72it/s]
eval model::   9%|▉         | 44/500 [00:02<00:26, 17.21it/s]
eval model::   9%|▉         | 46/500 [00:02<00:27, 16.42it/s]
eval model::  10%|▉         | 49/500 [00:02<00:26, 17.27it/s]
eval model::  10%|█         | 51/500 [00:02<00:27, 16.22it/s]
eval model::  11%|█         | 54/500 [00:03<00:24, 18.25it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:22, 19.57it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:21, 20.86it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.65it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 18.03it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 21.72it/s]
eval model::  15%|█▍        | 73/500 [00:04<00:20, 21.22it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:19, 21.96it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.49it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.60it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.64it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 19.96it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:24, 16.83it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:24, 16.49it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:22, 18.19it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.90it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.23it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.57it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.31it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:17, 22.39it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:19, 19.55it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:18, 20.52it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:21, 17.94it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.62it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 18.59it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 19.11it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:20, 18.41it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:21, 17.06it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:19, 18.42it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.49it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:19, 18.26it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 19.99it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:20, 17.28it/s]
eval model::  30%|███       | 150/500 [00:08<00:18, 18.56it/s]
eval model::  30%|███       | 152/500 [00:08<00:19, 18.15it/s]
eval model::  31%|███       | 155/500 [00:08<00:17, 20.08it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.32it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:22, 15.16it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:22, 15.32it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:19, 17.13it/s]
eval model::  34%|███▎      | 168/500 [00:09<00:19, 17.46it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:17, 18.99it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 15.98it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 16.19it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.68it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.58it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:19, 16.40it/s]
eval model::  37%|███▋      | 186/500 [00:10<00:17, 17.63it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:17, 17.82it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:17, 17.30it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:16, 19.16it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:16, 18.22it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 17.91it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 20.12it/s]
eval model::  41%|████      | 203/500 [00:10<00:13, 21.69it/s]
eval model::  41%|████      | 206/500 [00:11<00:19, 14.90it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:20, 13.99it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:21, 13.51it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 15.06it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:22, 12.84it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:22, 12.83it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:19, 14.08it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:23, 12.09it/s]
eval model::  45%|████▍     | 223/500 [00:12<00:20, 13.51it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:17, 15.23it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:14, 18.21it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 20.71it/s]
eval model::  47%|████▋     | 235/500 [00:13<00:12, 20.68it/s]
eval model::  48%|████▊     | 238/500 [00:13<00:11, 22.01it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:12, 21.51it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:16, 15.65it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:14, 17.10it/s]
eval model::  50%|█████     | 250/500 [00:13<00:13, 18.34it/s]
eval model::  51%|█████     | 253/500 [00:14<00:12, 20.17it/s]
eval model::  51%|█████     | 256/500 [00:14<00:12, 19.28it/s]
eval model::  52%|█████▏    | 259/500 [00:14<00:11, 21.37it/s]
eval model::  52%|█████▏    | 262/500 [00:14<00:10, 23.21it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:10, 22.28it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:10, 21.74it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:11, 19.87it/s]
eval model::  55%|█████▍    | 274/500 [00:15<00:10, 20.99it/s]
eval model::  55%|█████▌    | 277/500 [00:15<00:11, 19.23it/s]
eval model::  56%|█████▌    | 280/500 [00:15<00:11, 19.96it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:10, 20.75it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:16, 12.92it/s]
eval model::  58%|█████▊    | 289/500 [00:16<00:13, 15.29it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:12, 16.90it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:15, 13.35it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:11, 16.90it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 16.60it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.67it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:10, 18.17it/s]
eval model::  62%|██████▏   | 310/500 [00:17<00:10, 18.47it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:10, 18.07it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:09, 19.05it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:09, 19.89it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 19.98it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 19.98it/s]
eval model::  65%|██████▌   | 327/500 [00:18<00:08, 20.36it/s]
eval model::  66%|██████▌   | 330/500 [00:18<00:08, 19.26it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:08, 20.06it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:08, 19.69it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 18.10it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 14.69it/s]
eval model::  69%|██████▊   | 343/500 [00:19<00:09, 16.58it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:08, 17.87it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:07, 19.93it/s]
eval model::  70%|███████   | 352/500 [00:19<00:07, 20.33it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 19.75it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 19.95it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.07it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:07, 17.19it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:07, 17.41it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:07, 18.51it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:06, 19.93it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:05, 21.03it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 22.51it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:05, 22.44it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:04, 24.26it/s]
eval model::  77%|███████▋  | 387/500 [00:21<00:08, 13.59it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:07, 14.64it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:07, 15.51it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:07, 15.15it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 15.71it/s]
eval model::  80%|███████▉  | 398/500 [00:22<00:05, 18.10it/s]
eval model::  80%|████████  | 400/500 [00:22<00:06, 16.07it/s]
eval model::  81%|████████  | 403/500 [00:22<00:05, 17.87it/s]
eval model::  81%|████████  | 406/500 [00:22<00:04, 19.05it/s]
eval model::  82%|████████▏ | 409/500 [00:22<00:04, 21.61it/s]
eval model::  82%|████████▏ | 412/500 [00:22<00:03, 22.38it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:04, 20.72it/s]
eval model::  84%|████████▎ | 418/500 [00:23<00:04, 19.68it/s]
eval model::  84%|████████▍ | 421/500 [00:23<00:06, 13.10it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.59it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.37it/s]
eval model::  86%|████████▌ | 428/500 [00:23<00:04, 15.27it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:04, 15.54it/s]
eval model::  86%|████████▋ | 432/500 [00:24<00:04, 16.23it/s]
eval model::  87%|████████▋ | 435/500 [00:24<00:03, 18.90it/s]
eval model::  88%|████████▊ | 438/500 [00:24<00:02, 20.71it/s]
eval model::  88%|████████▊ | 441/500 [00:24<00:03, 19.25it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 20.86it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 20.85it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 18.63it/s]
eval model::  91%|█████████ | 453/500 [00:25<00:02, 20.13it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:02, 21.36it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:02, 20.26it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 16.35it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 16.76it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 16.73it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 17.41it/s]
eval model::  94%|█████████▍| 471/500 [00:26<00:01, 19.17it/s]
eval model::  95%|█████████▍| 473/500 [00:26<00:01, 19.32it/s]
eval model::  95%|█████████▌| 475/500 [00:26<00:01, 18.89it/s]
eval model::  96%|█████████▌| 478/500 [00:26<00:01, 21.26it/s]
eval model::  96%|█████████▌| 481/500 [00:26<00:00, 23.13it/s]
eval model::  97%|█████████▋| 484/500 [00:26<00:00, 22.79it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 24.00it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 24.36it/s]
eval model::  99%|█████████▊| 493/500 [00:27<00:00, 22.05it/s]
eval model::  99%|█████████▉| 496/500 [00:27<00:00, 22.74it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 25.11it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.34it/s]
[2024/07/27 13:37:09] ppocr INFO: cur metric, precision: 0.7627118644067796, recall: 0.6716417910447762, hmean: 0.7142857142857143, fps: 43.33575316801794
[2024/07/27 13:37:09] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 13:37:09] ppocr INFO: best metric, hmean: 0.7142857142857143, precision: 0.7627118644067796, recall: 0.6716417910447762, fps: 43.33575316801794, best_epoch: 740
[2024/07/27 13:37:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:37:18] ppocr INFO: epoch: [741/1500], global_step: 2223, lr: 0.001000, loss: 1.362373, loss_shrink_maps: 0.691373, loss_threshold_maps: 0.532127, loss_binary_maps: 0.137395, avg_reader_cost: 2.14513 s, avg_batch_cost: 2.38204 s, avg_samples: 12.5, ips: 5.24761 samples/s, eta: 5:30:28
[2024/07/27 13:37:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:37:28] ppocr INFO: epoch: [742/1500], global_step: 2226, lr: 0.001000, loss: 1.353238, loss_shrink_maps: 0.686898, loss_threshold_maps: 0.532127, loss_binary_maps: 0.136474, avg_reader_cost: 2.34346 s, avg_batch_cost: 2.58410 s, avg_samples: 12.5, ips: 4.83728 samples/s, eta: 5:30:01
[2024/07/27 13:37:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:37:38] ppocr INFO: epoch: [743/1500], global_step: 2229, lr: 0.001000, loss: 1.373469, loss_shrink_maps: 0.695754, loss_threshold_maps: 0.537944, loss_binary_maps: 0.138296, avg_reader_cost: 2.09766 s, avg_batch_cost: 2.52708 s, avg_samples: 12.5, ips: 4.94641 samples/s, eta: 5:29:34
[2024/07/27 13:37:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:37:46] ppocr INFO: epoch: [744/1500], global_step: 2230, lr: 0.001000, loss: 1.373469, loss_shrink_maps: 0.695754, loss_threshold_maps: 0.539541, loss_binary_maps: 0.138296, avg_reader_cost: 0.72523 s, avg_batch_cost: 0.82140 s, avg_samples: 4.8, ips: 5.84366 samples/s, eta: 5:29:25
[2024/07/27 13:37:48] ppocr INFO: epoch: [744/1500], global_step: 2232, lr: 0.001000, loss: 1.382242, loss_shrink_maps: 0.698318, loss_threshold_maps: 0.539541, loss_binary_maps: 0.138979, avg_reader_cost: 1.73464 s, avg_batch_cost: 1.88149 s, avg_samples: 7.7, ips: 4.09249 samples/s, eta: 5:29:09
[2024/07/27 13:37:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:37:57] ppocr INFO: epoch: [745/1500], global_step: 2235, lr: 0.001000, loss: 1.368193, loss_shrink_maps: 0.698318, loss_threshold_maps: 0.528313, loss_binary_maps: 0.138979, avg_reader_cost: 2.25676 s, avg_batch_cost: 2.60864 s, avg_samples: 12.5, ips: 4.79177 samples/s, eta: 5:28:43
[2024/07/27 13:37:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:38:07] ppocr INFO: epoch: [746/1500], global_step: 2238, lr: 0.001000, loss: 1.383972, loss_shrink_maps: 0.705716, loss_threshold_maps: 0.534402, loss_binary_maps: 0.140349, avg_reader_cost: 2.33167 s, avg_batch_cost: 2.57094 s, avg_samples: 12.5, ips: 4.86203 samples/s, eta: 5:28:16
[2024/07/27 13:38:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:38:16] ppocr INFO: epoch: [747/1500], global_step: 2240, lr: 0.001000, loss: 1.383972, loss_shrink_maps: 0.705716, loss_threshold_maps: 0.538452, loss_binary_maps: 0.140349, avg_reader_cost: 1.27618 s, avg_batch_cost: 1.64307 s, avg_samples: 9.6, ips: 5.84273 samples/s, eta: 5:27:58
[2024/07/27 13:38:17] ppocr INFO: epoch: [747/1500], global_step: 2241, lr: 0.001000, loss: 1.369786, loss_shrink_maps: 0.698318, loss_threshold_maps: 0.534402, loss_binary_maps: 0.138979, avg_reader_cost: 0.86769 s, avg_batch_cost: 0.92377 s, avg_samples: 2.9, ips: 3.13932 samples/s, eta: 5:27:50
[2024/07/27 13:38:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:38:27] ppocr INFO: epoch: [748/1500], global_step: 2244, lr: 0.001000, loss: 1.372734, loss_shrink_maps: 0.719784, loss_threshold_maps: 0.532824, loss_binary_maps: 0.143236, avg_reader_cost: 2.41953 s, avg_batch_cost: 2.65213 s, avg_samples: 12.5, ips: 4.71319 samples/s, eta: 5:27:24
[2024/07/27 13:38:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:38:37] ppocr INFO: epoch: [749/1500], global_step: 2247, lr: 0.001000, loss: 1.395385, loss_shrink_maps: 0.729901, loss_threshold_maps: 0.532824, loss_binary_maps: 0.145270, avg_reader_cost: 2.21782 s, avg_batch_cost: 2.61879 s, avg_samples: 12.5, ips: 4.77319 samples/s, eta: 5:26:58
[2024/07/27 13:38:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:38:47] ppocr INFO: epoch: [750/1500], global_step: 2250, lr: 0.001000, loss: 1.372734, loss_shrink_maps: 0.708398, loss_threshold_maps: 0.520221, loss_binary_maps: 0.140213, avg_reader_cost: 2.18937 s, avg_batch_cost: 2.59686 s, avg_samples: 12.5, ips: 4.81351 samples/s, eta: 5:26:32
[2024/07/27 13:38:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:38:57] ppocr INFO: epoch: [751/1500], global_step: 2253, lr: 0.001000, loss: 1.372734, loss_shrink_maps: 0.708398, loss_threshold_maps: 0.517996, loss_binary_maps: 0.140213, avg_reader_cost: 2.41097 s, avg_batch_cost: 2.65105 s, avg_samples: 12.5, ips: 4.71511 samples/s, eta: 5:26:06
[2024/07/27 13:38:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:39:06] ppocr INFO: epoch: [752/1500], global_step: 2256, lr: 0.001000, loss: 1.372596, loss_shrink_maps: 0.710579, loss_threshold_maps: 0.518687, loss_binary_maps: 0.141192, avg_reader_cost: 2.20645 s, avg_batch_cost: 2.56160 s, avg_samples: 12.5, ips: 4.87975 samples/s, eta: 5:25:39
[2024/07/27 13:39:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:39:16] ppocr INFO: epoch: [753/1500], global_step: 2259, lr: 0.001000, loss: 1.383744, loss_shrink_maps: 0.724471, loss_threshold_maps: 0.524783, loss_binary_maps: 0.144382, avg_reader_cost: 2.33878 s, avg_batch_cost: 2.58497 s, avg_samples: 12.5, ips: 4.83564 samples/s, eta: 5:25:13
[2024/07/27 13:39:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:39:24] ppocr INFO: epoch: [754/1500], global_step: 2260, lr: 0.001000, loss: 1.383744, loss_shrink_maps: 0.724471, loss_threshold_maps: 0.518687, loss_binary_maps: 0.144382, avg_reader_cost: 0.55726 s, avg_batch_cost: 0.75041 s, avg_samples: 4.8, ips: 6.39647 samples/s, eta: 5:25:03
[2024/07/27 13:39:26] ppocr INFO: epoch: [754/1500], global_step: 2262, lr: 0.001000, loss: 1.372172, loss_shrink_maps: 0.710579, loss_threshold_maps: 0.524570, loss_binary_maps: 0.141192, avg_reader_cost: 1.59312 s, avg_batch_cost: 1.74044 s, avg_samples: 7.7, ips: 4.42417 samples/s, eta: 5:24:45
[2024/07/27 13:39:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:39:36] ppocr INFO: epoch: [755/1500], global_step: 2265, lr: 0.001000, loss: 1.372172, loss_shrink_maps: 0.710579, loss_threshold_maps: 0.532494, loss_binary_maps: 0.141192, avg_reader_cost: 2.36602 s, avg_batch_cost: 2.60332 s, avg_samples: 12.5, ips: 4.80156 samples/s, eta: 5:24:19
[2024/07/27 13:39:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:39:46] ppocr INFO: epoch: [756/1500], global_step: 2268, lr: 0.001000, loss: 1.323145, loss_shrink_maps: 0.673677, loss_threshold_maps: 0.524570, loss_binary_maps: 0.134515, avg_reader_cost: 2.26522 s, avg_batch_cost: 2.66258 s, avg_samples: 12.5, ips: 4.69469 samples/s, eta: 5:23:54
[2024/07/27 13:39:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:39:55] ppocr INFO: epoch: [757/1500], global_step: 2270, lr: 0.001000, loss: 1.323145, loss_shrink_maps: 0.673677, loss_threshold_maps: 0.524570, loss_binary_maps: 0.134515, avg_reader_cost: 1.31631 s, avg_batch_cost: 1.61299 s, avg_samples: 9.6, ips: 5.95167 samples/s, eta: 5:23:35
[2024/07/27 13:39:55] ppocr INFO: epoch: [757/1500], global_step: 2271, lr: 0.001000, loss: 1.344811, loss_shrink_maps: 0.673844, loss_threshold_maps: 0.532494, loss_binary_maps: 0.133827, avg_reader_cost: 0.85274 s, avg_batch_cost: 0.90863 s, avg_samples: 2.9, ips: 3.19163 samples/s, eta: 5:23:27
[2024/07/27 13:39:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:40:05] ppocr INFO: epoch: [758/1500], global_step: 2274, lr: 0.001000, loss: 1.307874, loss_shrink_maps: 0.657161, loss_threshold_maps: 0.525476, loss_binary_maps: 0.130935, avg_reader_cost: 2.25684 s, avg_batch_cost: 2.64556 s, avg_samples: 12.5, ips: 4.72490 samples/s, eta: 5:23:01
[2024/07/27 13:40:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:40:15] ppocr INFO: epoch: [759/1500], global_step: 2277, lr: 0.001000, loss: 1.307874, loss_shrink_maps: 0.657161, loss_threshold_maps: 0.533844, loss_binary_maps: 0.130935, avg_reader_cost: 2.36560 s, avg_batch_cost: 2.60659 s, avg_samples: 12.5, ips: 4.79553 samples/s, eta: 5:22:35
[2024/07/27 13:40:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:40:25] ppocr INFO: epoch: [760/1500], global_step: 2280, lr: 0.001000, loss: 1.310020, loss_shrink_maps: 0.657161, loss_threshold_maps: 0.533844, loss_binary_maps: 0.130935, avg_reader_cost: 2.32775 s, avg_batch_cost: 2.57162 s, avg_samples: 12.5, ips: 4.86076 samples/s, eta: 5:22:08

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:57,  8.73it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.29it/s]
eval model::   1%|          | 6/500 [00:00<00:31, 15.54it/s]
eval model::   2%|▏         | 8/500 [00:00<00:31, 15.87it/s]
eval model::   2%|▏         | 11/500 [00:00<00:32, 15.11it/s]
eval model::   3%|▎         | 14/500 [00:00<00:30, 16.13it/s]
eval model::   4%|▎         | 18/500 [00:01<00:23, 20.57it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.28it/s]
eval model::   5%|▍         | 24/500 [00:01<00:24, 19.33it/s]
eval model::   5%|▌         | 26/500 [00:01<00:25, 18.64it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 19.77it/s]
eval model::   6%|▋         | 32/500 [00:01<00:26, 17.98it/s]
eval model::   7%|▋         | 34/500 [00:01<00:26, 17.59it/s]
eval model::   7%|▋         | 37/500 [00:02<00:24, 18.60it/s]
eval model::   8%|▊         | 39/500 [00:02<00:29, 15.47it/s]
eval model::   8%|▊         | 41/500 [00:02<00:29, 15.79it/s]
eval model::   9%|▊         | 43/500 [00:02<00:30, 15.15it/s]
eval model::   9%|▉         | 46/500 [00:02<00:29, 15.39it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.47it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 20.00it/s]
eval model::  11%|█         | 55/500 [00:03<00:20, 21.48it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:21, 20.92it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.82it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.89it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.28it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.70it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.07it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:20, 20.61it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:22, 18.53it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:21, 19.69it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:23, 17.71it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:23, 17.68it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:25, 15.91it/s]
eval model::  18%|█▊        | 90/500 [00:05<00:29, 14.10it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:26, 15.56it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:25, 16.19it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:22, 18.02it/s]
eval model::  20%|██        | 100/500 [00:05<00:22, 17.85it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 20.21it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.02it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.54it/s]
eval model::  22%|██▏       | 112/500 [00:06<00:18, 21.16it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:18, 20.96it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:17, 22.39it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 19.95it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 19.88it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.05it/s]
eval model::  26%|██▌       | 130/500 [00:07<00:19, 18.92it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:20, 17.58it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:18, 19.51it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.76it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.52it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.89it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.85it/s]
eval model::  30%|███       | 150/500 [00:08<00:17, 20.02it/s]
eval model::  31%|███       | 153/500 [00:08<00:17, 19.47it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:14, 23.02it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:22, 14.92it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:20, 16.20it/s]
eval model::  33%|███▎      | 166/500 [00:09<00:18, 17.71it/s]
eval model::  34%|███▍      | 169/500 [00:09<00:18, 17.63it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:17, 18.57it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:18, 17.80it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:16, 19.23it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:18, 17.18it/s]
eval model::  37%|███▋      | 184/500 [00:10<00:17, 18.57it/s]
eval model::  37%|███▋      | 187/500 [00:10<00:16, 18.76it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:16, 19.15it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:15, 20.33it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:15, 19.23it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 20.72it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 21.98it/s]
eval model::  41%|████      | 205/500 [00:11<00:21, 14.02it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:19, 14.99it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 15.09it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.59it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.27it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.18it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:16, 17.27it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:18, 14.99it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 18.01it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.90it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 22.43it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:11, 22.71it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:10, 23.99it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 18.67it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 19.94it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 19.56it/s]
eval model::  50%|█████     | 251/500 [00:13<00:12, 19.18it/s]
eval model::  51%|█████     | 254/500 [00:13<00:12, 20.15it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.48it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 23.45it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:10, 21.64it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:11, 19.79it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.35it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 20.09it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 18.82it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 20.16it/s]
eval model::  56%|█████▌    | 281/500 [00:15<00:10, 20.29it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:10, 20.90it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:17, 12.16it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:14, 14.64it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:13, 15.87it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:14, 14.26it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 16.02it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.76it/s]
eval model::  61%|██████    | 306/500 [00:16<00:11, 17.64it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:10, 18.41it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 20.22it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:08, 21.04it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:07, 23.05it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 20.66it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:09, 18.40it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 19.80it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 19.86it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:08, 19.99it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:08, 20.07it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:08, 18.54it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:11, 14.23it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 16.58it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 17.99it/s]
eval model::  70%|███████   | 351/500 [00:19<00:07, 20.78it/s]
eval model::  71%|███████   | 354/500 [00:19<00:06, 22.40it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 19.93it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:06, 20.69it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:06, 20.25it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 19.06it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 20.26it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:06, 20.40it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 18.95it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 20.38it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:06, 17.47it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:06, 18.55it/s]
eval model::  77%|███████▋  | 386/500 [00:21<00:10, 10.97it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:08, 13.54it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:07, 14.90it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 17.14it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 19.17it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 17.73it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 19.68it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 20.45it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 21.31it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:04, 20.89it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 19.53it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 18.47it/s]
eval model::  84%|████████▍ | 421/500 [00:23<00:05, 13.17it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.11it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.70it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 17.58it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 17.02it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 20.66it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 22.32it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 21.75it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 23.62it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 23.88it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 21.29it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 22.87it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 23.40it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 23.35it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 17.95it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 16.53it/s]
eval model::  93%|█████████▎| 467/500 [00:25<00:01, 17.02it/s]
eval model::  94%|█████████▍| 470/500 [00:25<00:01, 19.53it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 20.36it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 20.30it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:00, 21.26it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 19.84it/s]
eval model::  97%|█████████▋| 485/500 [00:26<00:00, 21.91it/s]
eval model::  98%|█████████▊| 488/500 [00:26<00:00, 21.97it/s]
eval model::  98%|█████████▊| 491/500 [00:26<00:00, 21.41it/s]
eval model::  99%|█████████▉| 494/500 [00:26<00:00, 20.34it/s]
eval model::  99%|█████████▉| 497/500 [00:26<00:00, 20.93it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 21.29it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.64it/s]
[2024/07/27 13:40:52] ppocr INFO: cur metric, precision: 0.7556512378902045, recall: 0.6759749638902263, hmean: 0.7135959339263024, fps: 45.444833450798015
[2024/07/27 13:40:52] ppocr INFO: best metric, hmean: 0.7142857142857143, precision: 0.7627118644067796, recall: 0.6716417910447762, fps: 43.33575316801794, best_epoch: 740
[2024/07/27 13:40:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:41:01] ppocr INFO: epoch: [761/1500], global_step: 2283, lr: 0.001000, loss: 1.302773, loss_shrink_maps: 0.645857, loss_threshold_maps: 0.523027, loss_binary_maps: 0.128722, avg_reader_cost: 2.11312 s, avg_batch_cost: 2.47911 s, avg_samples: 12.5, ips: 5.04214 samples/s, eta: 5:21:41
[2024/07/27 13:41:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:41:11] ppocr INFO: epoch: [762/1500], global_step: 2286, lr: 0.001000, loss: 1.317220, loss_shrink_maps: 0.657161, loss_threshold_maps: 0.521656, loss_binary_maps: 0.130935, avg_reader_cost: 2.21479 s, avg_batch_cost: 2.59287 s, avg_samples: 12.5, ips: 4.82092 samples/s, eta: 5:21:14
[2024/07/27 13:41:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:41:21] ppocr INFO: epoch: [763/1500], global_step: 2289, lr: 0.001000, loss: 1.317220, loss_shrink_maps: 0.657161, loss_threshold_maps: 0.521656, loss_binary_maps: 0.130935, avg_reader_cost: 2.45592 s, avg_batch_cost: 2.70173 s, avg_samples: 12.5, ips: 4.62667 samples/s, eta: 5:20:49
[2024/07/27 13:41:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:41:29] ppocr INFO: epoch: [764/1500], global_step: 2290, lr: 0.001000, loss: 1.317220, loss_shrink_maps: 0.657161, loss_threshold_maps: 0.521656, loss_binary_maps: 0.130935, avg_reader_cost: 0.55675 s, avg_batch_cost: 0.80983 s, avg_samples: 4.8, ips: 5.92714 samples/s, eta: 5:20:40
[2024/07/27 13:41:31] ppocr INFO: epoch: [764/1500], global_step: 2292, lr: 0.001000, loss: 1.325607, loss_shrink_maps: 0.667958, loss_threshold_maps: 0.519836, loss_binary_maps: 0.132418, avg_reader_cost: 1.71140 s, avg_batch_cost: 1.85833 s, avg_samples: 7.7, ips: 4.14352 samples/s, eta: 5:20:24
[2024/07/27 13:41:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:41:40] ppocr INFO: epoch: [765/1500], global_step: 2295, lr: 0.001000, loss: 1.336895, loss_shrink_maps: 0.679370, loss_threshold_maps: 0.521207, loss_binary_maps: 0.134669, avg_reader_cost: 2.29374 s, avg_batch_cost: 2.52694 s, avg_samples: 12.5, ips: 4.94669 samples/s, eta: 5:19:57
[2024/07/27 13:41:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:41:50] ppocr INFO: epoch: [766/1500], global_step: 2298, lr: 0.001000, loss: 1.336895, loss_shrink_maps: 0.679370, loss_threshold_maps: 0.514758, loss_binary_maps: 0.134669, avg_reader_cost: 2.36618 s, avg_batch_cost: 2.60631 s, avg_samples: 12.5, ips: 4.79606 samples/s, eta: 5:19:31
[2024/07/27 13:41:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:42:00] ppocr INFO: epoch: [767/1500], global_step: 2300, lr: 0.001000, loss: 1.325607, loss_shrink_maps: 0.659728, loss_threshold_maps: 0.508156, loss_binary_maps: 0.130744, avg_reader_cost: 1.38186 s, avg_batch_cost: 1.71223 s, avg_samples: 9.6, ips: 5.60672 samples/s, eta: 5:19:13
[2024/07/27 13:42:00] ppocr INFO: epoch: [767/1500], global_step: 2301, lr: 0.001000, loss: 1.336895, loss_shrink_maps: 0.679370, loss_threshold_maps: 0.508156, loss_binary_maps: 0.134669, avg_reader_cost: 0.90240 s, avg_batch_cost: 0.95827 s, avg_samples: 2.9, ips: 3.02630 samples/s, eta: 5:19:05
[2024/07/27 13:42:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:42:10] ppocr INFO: epoch: [768/1500], global_step: 2304, lr: 0.001000, loss: 1.336895, loss_shrink_maps: 0.679370, loss_threshold_maps: 0.508156, loss_binary_maps: 0.134669, avg_reader_cost: 2.35678 s, avg_batch_cost: 2.59877 s, avg_samples: 12.5, ips: 4.80997 samples/s, eta: 5:18:39
[2024/07/27 13:42:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:42:20] ppocr INFO: epoch: [769/1500], global_step: 2307, lr: 0.001000, loss: 1.333692, loss_shrink_maps: 0.692216, loss_threshold_maps: 0.508156, loss_binary_maps: 0.137986, avg_reader_cost: 2.35490 s, avg_batch_cost: 2.60937 s, avg_samples: 12.5, ips: 4.79043 samples/s, eta: 5:18:13
[2024/07/27 13:42:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:42:30] ppocr INFO: epoch: [770/1500], global_step: 2310, lr: 0.001000, loss: 1.321404, loss_shrink_maps: 0.680065, loss_threshold_maps: 0.493714, loss_binary_maps: 0.135078, avg_reader_cost: 2.42994 s, avg_batch_cost: 2.67862 s, avg_samples: 12.5, ips: 4.66659 samples/s, eta: 5:17:47
[2024/07/27 13:42:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:42:40] ppocr INFO: epoch: [771/1500], global_step: 2313, lr: 0.001000, loss: 1.307244, loss_shrink_maps: 0.676019, loss_threshold_maps: 0.492140, loss_binary_maps: 0.134281, avg_reader_cost: 2.24933 s, avg_batch_cost: 2.51302 s, avg_samples: 12.5, ips: 4.97409 samples/s, eta: 5:17:20
[2024/07/27 13:42:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:42:50] ppocr INFO: epoch: [772/1500], global_step: 2316, lr: 0.001000, loss: 1.284238, loss_shrink_maps: 0.642678, loss_threshold_maps: 0.491171, loss_binary_maps: 0.127661, avg_reader_cost: 2.39418 s, avg_batch_cost: 2.63484 s, avg_samples: 12.5, ips: 4.74412 samples/s, eta: 5:16:54
[2024/07/27 13:42:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:43:00] ppocr INFO: epoch: [773/1500], global_step: 2319, lr: 0.001000, loss: 1.288594, loss_shrink_maps: 0.647888, loss_threshold_maps: 0.491171, loss_binary_maps: 0.128714, avg_reader_cost: 2.23840 s, avg_batch_cost: 2.60485 s, avg_samples: 12.5, ips: 4.79874 samples/s, eta: 5:16:28
[2024/07/27 13:43:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:43:08] ppocr INFO: epoch: [774/1500], global_step: 2320, lr: 0.001000, loss: 1.289417, loss_shrink_maps: 0.658377, loss_threshold_maps: 0.492140, loss_binary_maps: 0.131247, avg_reader_cost: 0.55489 s, avg_batch_cost: 0.78926 s, avg_samples: 4.8, ips: 6.08162 samples/s, eta: 5:16:18
[2024/07/27 13:43:09] ppocr INFO: epoch: [774/1500], global_step: 2322, lr: 0.001000, loss: 1.289417, loss_shrink_maps: 0.657839, loss_threshold_maps: 0.507100, loss_binary_maps: 0.130440, avg_reader_cost: 1.67070 s, avg_batch_cost: 1.81806 s, avg_samples: 7.7, ips: 4.23527 samples/s, eta: 5:16:02
[2024/07/27 13:43:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:43:19] ppocr INFO: epoch: [775/1500], global_step: 2325, lr: 0.001000, loss: 1.306293, loss_shrink_maps: 0.676019, loss_threshold_maps: 0.519884, loss_binary_maps: 0.134281, avg_reader_cost: 2.38080 s, avg_batch_cost: 2.62006 s, avg_samples: 12.5, ips: 4.77089 samples/s, eta: 5:15:36
[2024/07/27 13:43:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:43:29] ppocr INFO: epoch: [776/1500], global_step: 2328, lr: 0.001000, loss: 1.306293, loss_shrink_maps: 0.670644, loss_threshold_maps: 0.521930, loss_binary_maps: 0.132981, avg_reader_cost: 2.31246 s, avg_batch_cost: 2.69061 s, avg_samples: 12.5, ips: 4.64579 samples/s, eta: 5:15:10
[2024/07/27 13:43:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:43:38] ppocr INFO: epoch: [777/1500], global_step: 2330, lr: 0.001000, loss: 1.312800, loss_shrink_maps: 0.678526, loss_threshold_maps: 0.522664, loss_binary_maps: 0.134492, avg_reader_cost: 1.34728 s, avg_batch_cost: 1.53313 s, avg_samples: 9.6, ips: 6.26170 samples/s, eta: 5:14:51
[2024/07/27 13:43:38] ppocr INFO: epoch: [777/1500], global_step: 2331, lr: 0.001000, loss: 1.333119, loss_shrink_maps: 0.692445, loss_threshold_maps: 0.522664, loss_binary_maps: 0.137446, avg_reader_cost: 0.81294 s, avg_batch_cost: 0.86846 s, avg_samples: 2.9, ips: 3.33923 samples/s, eta: 5:14:42
[2024/07/27 13:43:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:43:48] ppocr INFO: epoch: [778/1500], global_step: 2334, lr: 0.001000, loss: 1.362524, loss_shrink_maps: 0.710297, loss_threshold_maps: 0.521930, loss_binary_maps: 0.140843, avg_reader_cost: 2.19044 s, avg_batch_cost: 2.53716 s, avg_samples: 12.5, ips: 4.92678 samples/s, eta: 5:14:15
[2024/07/27 13:43:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:43:58] ppocr INFO: epoch: [779/1500], global_step: 2337, lr: 0.001000, loss: 1.384550, loss_shrink_maps: 0.718400, loss_threshold_maps: 0.522152, loss_binary_maps: 0.142738, avg_reader_cost: 2.29293 s, avg_batch_cost: 2.53047 s, avg_samples: 12.5, ips: 4.93980 samples/s, eta: 5:13:49
[2024/07/27 13:43:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:44:08] ppocr INFO: epoch: [780/1500], global_step: 2340, lr: 0.001000, loss: 1.378044, loss_shrink_maps: 0.710297, loss_threshold_maps: 0.519884, loss_binary_maps: 0.140843, avg_reader_cost: 2.38080 s, avg_batch_cost: 2.62467 s, avg_samples: 12.5, ips: 4.76249 samples/s, eta: 5:13:23

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:56,  8.77it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.55it/s]
eval model::   1%|▏         | 7/500 [00:00<00:24, 20.51it/s]
eval model::   2%|▏         | 10/500 [00:00<00:27, 18.14it/s]
eval model::   2%|▏         | 12/500 [00:00<00:28, 16.89it/s]
eval model::   3%|▎         | 14/500 [00:00<00:31, 15.33it/s]
eval model::   3%|▎         | 17/500 [00:00<00:26, 18.28it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 18.99it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.56it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.24it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 20.62it/s]
eval model::   6%|▋         | 32/500 [00:01<00:25, 18.42it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.54it/s]
eval model::   7%|▋         | 36/500 [00:01<00:25, 18.42it/s]
eval model::   8%|▊         | 38/500 [00:02<00:25, 18.15it/s]
eval model::   8%|▊         | 40/500 [00:02<00:27, 16.66it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.41it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.32it/s]
eval model::   9%|▉         | 47/500 [00:02<00:27, 16.50it/s]
eval model::  10%|▉         | 49/500 [00:02<00:26, 16.92it/s]
eval model::  10%|█         | 51/500 [00:02<00:25, 17.61it/s]
eval model::  11%|█         | 54/500 [00:02<00:23, 18.74it/s]
eval model::  11%|█         | 56/500 [00:03<00:24, 18.46it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:25, 17.64it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.67it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.40it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.40it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.58it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 20.83it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:22, 18.62it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:23, 17.98it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:22, 18.88it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:20, 20.62it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:20, 20.22it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:25, 15.88it/s]
eval model::  18%|█▊        | 91/500 [00:05<00:26, 15.16it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:27, 14.88it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:26, 15.56it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:24, 16.19it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:23, 16.75it/s]
eval model::  20%|██        | 101/500 [00:05<00:23, 16.80it/s]
eval model::  21%|██        | 104/500 [00:05<00:19, 20.07it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.56it/s]
eval model::  22%|██▏       | 110/500 [00:06<00:18, 20.58it/s]
eval model::  23%|██▎       | 113/500 [00:06<00:19, 19.85it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:19, 19.23it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:18, 20.30it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:21, 17.87it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:21, 17.22it/s]
eval model::  25%|██▌       | 126/500 [00:07<00:22, 16.84it/s]
eval model::  26%|██▌       | 129/500 [00:07<00:20, 17.93it/s]
eval model::  26%|██▌       | 131/500 [00:07<00:21, 17.30it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:22, 16.02it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:21, 17.09it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:21, 16.96it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:20, 17.28it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:20, 17.43it/s]
eval model::  29%|██▉       | 145/500 [00:08<00:19, 17.83it/s]
eval model::  29%|██▉       | 147/500 [00:08<00:21, 16.13it/s]
eval model::  30%|███       | 150/500 [00:08<00:18, 19.27it/s]
eval model::  31%|███       | 153/500 [00:08<00:17, 19.41it/s]
eval model::  31%|███       | 156/500 [00:08<00:15, 21.86it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 20.50it/s]
eval model::  32%|███▏      | 162/500 [00:09<00:23, 14.55it/s]
eval model::  33%|███▎      | 165/500 [00:09<00:20, 16.00it/s]
eval model::  33%|███▎      | 167/500 [00:09<00:19, 16.66it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:17, 19.00it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:16, 19.26it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:21, 15.03it/s]
eval model::  36%|███▌      | 179/500 [00:10<00:19, 16.81it/s]
eval model::  36%|███▌      | 181/500 [00:10<00:20, 15.36it/s]
eval model::  37%|███▋      | 183/500 [00:10<00:19, 16.02it/s]
eval model::  37%|███▋      | 186/500 [00:10<00:17, 17.96it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:17, 17.38it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:16, 18.64it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:14, 21.13it/s]
eval model::  39%|███▉      | 197/500 [00:11<00:15, 19.60it/s]
eval model::  40%|████      | 200/500 [00:11<00:14, 21.13it/s]
eval model::  41%|████      | 203/500 [00:11<00:13, 22.63it/s]
eval model::  41%|████      | 206/500 [00:11<00:24, 11.98it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:23, 12.47it/s]
eval model::  42%|████▏     | 210/500 [00:12<00:23, 12.46it/s]
eval model::  43%|████▎     | 213/500 [00:12<00:20, 14.17it/s]
eval model::  43%|████▎     | 215/500 [00:12<00:25, 11.18it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:27, 10.46it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:24, 11.62it/s]
eval model::  44%|████▍     | 221/500 [00:13<00:24, 11.47it/s]
eval model::  45%|████▍     | 223/500 [00:13<00:21, 12.80it/s]
eval model::  45%|████▌     | 226/500 [00:13<00:18, 14.82it/s]
eval model::  46%|████▌     | 229/500 [00:13<00:16, 16.88it/s]
eval model::  46%|████▌     | 231/500 [00:13<00:15, 16.99it/s]
eval model::  47%|████▋     | 233/500 [00:13<00:15, 16.72it/s]
eval model::  47%|████▋     | 235/500 [00:13<00:15, 17.28it/s]
eval model::  47%|████▋     | 237/500 [00:13<00:15, 16.68it/s]
eval model::  48%|████▊     | 239/500 [00:14<00:15, 17.21it/s]
eval model::  48%|████▊     | 241/500 [00:14<00:14, 17.49it/s]
eval model::  49%|████▊     | 243/500 [00:14<00:17, 14.92it/s]
eval model::  49%|████▉     | 246/500 [00:14<00:14, 17.89it/s]
eval model::  50%|████▉     | 249/500 [00:14<00:14, 17.17it/s]
eval model::  50%|█████     | 252/500 [00:14<00:12, 19.92it/s]
eval model::  51%|█████     | 255/500 [00:14<00:11, 20.50it/s]
eval model::  52%|█████▏    | 258/500 [00:14<00:10, 22.38it/s]
eval model::  52%|█████▏    | 262/500 [00:15<00:09, 25.14it/s]
eval model::  53%|█████▎    | 265/500 [00:15<00:11, 20.73it/s]
eval model::  54%|█████▎    | 268/500 [00:15<00:10, 21.66it/s]
eval model::  54%|█████▍    | 271/500 [00:15<00:11, 20.55it/s]
eval model::  55%|█████▍    | 274/500 [00:15<00:10, 21.32it/s]
eval model::  55%|█████▌    | 277/500 [00:15<00:11, 18.89it/s]
eval model::  56%|█████▌    | 279/500 [00:16<00:11, 18.79it/s]
eval model::  56%|█████▋    | 282/500 [00:16<00:10, 21.23it/s]
eval model::  57%|█████▋    | 285/500 [00:16<00:18, 11.70it/s]
eval model::  58%|█████▊    | 288/500 [00:16<00:14, 14.25it/s]
eval model::  58%|█████▊    | 291/500 [00:16<00:12, 16.65it/s]
eval model::  59%|█████▉    | 294/500 [00:17<00:13, 15.43it/s]
eval model::  59%|█████▉    | 296/500 [00:17<00:15, 13.21it/s]
eval model::  60%|█████▉    | 299/500 [00:17<00:12, 15.91it/s]
eval model::  60%|██████    | 301/500 [00:17<00:14, 13.90it/s]
eval model::  61%|██████    | 304/500 [00:17<00:12, 15.35it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:11, 17.05it/s]
eval model::  62%|██████▏   | 309/500 [00:17<00:10, 17.65it/s]
eval model::  62%|██████▏   | 312/500 [00:18<00:09, 19.96it/s]
eval model::  63%|██████▎   | 315/500 [00:18<00:09, 19.89it/s]
eval model::  64%|██████▎   | 318/500 [00:18<00:08, 20.41it/s]
eval model::  64%|██████▍   | 321/500 [00:18<00:08, 21.14it/s]
eval model::  65%|██████▍   | 324/500 [00:18<00:08, 20.08it/s]
eval model::  65%|██████▌   | 327/500 [00:18<00:07, 21.76it/s]
eval model::  66%|██████▌   | 330/500 [00:18<00:07, 21.50it/s]
eval model::  67%|██████▋   | 333/500 [00:19<00:07, 21.00it/s]
eval model::  67%|██████▋   | 336/500 [00:19<00:07, 21.77it/s]
eval model::  68%|██████▊   | 339/500 [00:19<00:08, 18.98it/s]
eval model::  68%|██████▊   | 341/500 [00:19<00:11, 14.18it/s]
eval model::  69%|██████▊   | 343/500 [00:19<00:10, 15.21it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:08, 17.46it/s]
eval model::  70%|██████▉   | 349/500 [00:20<00:07, 19.53it/s]
eval model::  70%|███████   | 352/500 [00:20<00:07, 20.87it/s]
eval model::  71%|███████   | 355/500 [00:20<00:06, 21.26it/s]
eval model::  72%|███████▏  | 358/500 [00:20<00:06, 21.39it/s]
eval model::  72%|███████▏  | 361/500 [00:20<00:07, 19.06it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:07, 18.92it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:07, 18.39it/s]
eval model::  74%|███████▍  | 369/500 [00:21<00:06, 20.78it/s]
eval model::  74%|███████▍  | 372/500 [00:21<00:06, 18.30it/s]
eval model::  75%|███████▌  | 375/500 [00:21<00:06, 19.33it/s]
eval model::  76%|███████▌  | 378/500 [00:21<00:05, 21.49it/s]
eval model::  76%|███████▌  | 381/500 [00:21<00:05, 21.12it/s]
eval model::  77%|███████▋  | 384/500 [00:21<00:05, 23.15it/s]
eval model::  77%|███████▋  | 387/500 [00:22<00:08, 13.81it/s]
eval model::  78%|███████▊  | 390/500 [00:22<00:06, 15.84it/s]
eval model::  79%|███████▊  | 393/500 [00:22<00:06, 17.03it/s]
eval model::  79%|███████▉  | 396/500 [00:22<00:05, 18.07it/s]
eval model::  80%|███████▉  | 399/500 [00:22<00:04, 20.23it/s]
eval model::  80%|████████  | 402/500 [00:22<00:05, 18.95it/s]
eval model::  81%|████████  | 405/500 [00:22<00:04, 19.47it/s]
eval model::  82%|████████▏ | 408/500 [00:23<00:04, 19.47it/s]
eval model::  82%|████████▏ | 411/500 [00:23<00:04, 20.84it/s]
eval model::  83%|████████▎ | 414/500 [00:23<00:03, 22.17it/s]
eval model::  83%|████████▎ | 417/500 [00:23<00:03, 21.69it/s]
eval model::  84%|████████▍ | 420/500 [00:23<00:05, 14.97it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:04, 15.75it/s]
eval model::  85%|████████▍ | 424/500 [00:24<00:05, 15.16it/s]
eval model::  85%|████████▌ | 426/500 [00:24<00:04, 15.92it/s]
eval model::  86%|████████▌ | 428/500 [00:24<00:04, 16.65it/s]
eval model::  86%|████████▌ | 431/500 [00:24<00:03, 17.57it/s]
eval model::  87%|████████▋ | 434/500 [00:24<00:03, 20.22it/s]
eval model::  87%|████████▋ | 437/500 [00:24<00:02, 21.39it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:03, 19.09it/s]
eval model::  89%|████████▊ | 443/500 [00:24<00:02, 21.25it/s]
eval model::  89%|████████▉ | 446/500 [00:25<00:02, 21.57it/s]
eval model::  90%|████████▉ | 449/500 [00:25<00:02, 17.97it/s]
eval model::  90%|█████████ | 452/500 [00:25<00:02, 20.13it/s]
eval model::  91%|█████████ | 455/500 [00:25<00:02, 20.89it/s]
eval model::  92%|█████████▏| 458/500 [00:25<00:01, 22.48it/s]
eval model::  92%|█████████▏| 461/500 [00:25<00:01, 21.22it/s]
eval model::  93%|█████████▎| 464/500 [00:26<00:02, 16.94it/s]
eval model::  93%|█████████▎| 466/500 [00:26<00:02, 16.16it/s]
eval model::  94%|█████████▎| 468/500 [00:26<00:01, 16.86it/s]
eval model::  94%|█████████▍| 471/500 [00:26<00:01, 18.73it/s]
eval model::  95%|█████████▍| 473/500 [00:26<00:01, 17.93it/s]
eval model::  95%|█████████▌| 475/500 [00:26<00:01, 18.12it/s]
eval model::  96%|█████████▌| 478/500 [00:26<00:01, 19.44it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:01, 19.33it/s]
eval model::  97%|█████████▋| 483/500 [00:27<00:00, 20.41it/s]
eval model::  97%|█████████▋| 486/500 [00:27<00:00, 20.03it/s]
eval model::  98%|█████████▊| 489/500 [00:27<00:00, 20.88it/s]
eval model::  98%|█████████▊| 492/500 [00:27<00:00, 20.45it/s]
eval model::  99%|█████████▉| 495/500 [00:27<00:00, 20.82it/s]
eval model:: 100%|█████████▉| 499/500 [00:27<00:00, 24.20it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 17.96it/s]
[2024/07/27 13:44:36] ppocr INFO: cur metric, precision: 0.7142152023692004, recall: 0.6966779008184882, hmean: 0.7053375578844747, fps: 43.85500415829066
[2024/07/27 13:44:36] ppocr INFO: best metric, hmean: 0.7142857142857143, precision: 0.7627118644067796, recall: 0.6716417910447762, fps: 43.33575316801794, best_epoch: 740
[2024/07/27 13:44:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:44:45] ppocr INFO: epoch: [781/1500], global_step: 2343, lr: 0.001000, loss: 1.386170, loss_shrink_maps: 0.718954, loss_threshold_maps: 0.503484, loss_binary_maps: 0.142428, avg_reader_cost: 2.16863 s, avg_batch_cost: 2.45751 s, avg_samples: 12.5, ips: 5.08644 samples/s, eta: 5:12:55
[2024/07/27 13:44:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:44:54] ppocr INFO: epoch: [782/1500], global_step: 2346, lr: 0.001000, loss: 1.386170, loss_shrink_maps: 0.714050, loss_threshold_maps: 0.503484, loss_binary_maps: 0.142091, avg_reader_cost: 2.18705 s, avg_batch_cost: 2.54454 s, avg_samples: 12.5, ips: 4.91248 samples/s, eta: 5:12:28
[2024/07/27 13:44:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:45:04] ppocr INFO: epoch: [783/1500], global_step: 2349, lr: 0.001000, loss: 1.373016, loss_shrink_maps: 0.707927, loss_threshold_maps: 0.506501, loss_binary_maps: 0.140489, avg_reader_cost: 2.33824 s, avg_batch_cost: 2.57690 s, avg_samples: 12.5, ips: 4.85080 samples/s, eta: 5:12:02
[2024/07/27 13:45:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:45:13] ppocr INFO: epoch: [784/1500], global_step: 2350, lr: 0.001000, loss: 1.373016, loss_shrink_maps: 0.707927, loss_threshold_maps: 0.513728, loss_binary_maps: 0.140489, avg_reader_cost: 0.59730 s, avg_batch_cost: 0.79544 s, avg_samples: 4.8, ips: 6.03438 samples/s, eta: 5:11:53
[2024/07/27 13:45:14] ppocr INFO: epoch: [784/1500], global_step: 2352, lr: 0.001000, loss: 1.358009, loss_shrink_maps: 0.704189, loss_threshold_maps: 0.506162, loss_binary_maps: 0.140093, avg_reader_cost: 1.68336 s, avg_batch_cost: 1.83115 s, avg_samples: 7.7, ips: 4.20502 samples/s, eta: 5:11:36
[2024/07/27 13:45:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:45:24] ppocr INFO: epoch: [785/1500], global_step: 2355, lr: 0.001000, loss: 1.358009, loss_shrink_maps: 0.704189, loss_threshold_maps: 0.513728, loss_binary_maps: 0.140093, avg_reader_cost: 2.33117 s, avg_batch_cost: 2.57033 s, avg_samples: 12.5, ips: 4.86320 samples/s, eta: 5:11:09
[2024/07/27 13:45:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:45:34] ppocr INFO: epoch: [786/1500], global_step: 2358, lr: 0.001000, loss: 1.347768, loss_shrink_maps: 0.699875, loss_threshold_maps: 0.505980, loss_binary_maps: 0.138730, avg_reader_cost: 2.36381 s, avg_batch_cost: 2.61327 s, avg_samples: 12.5, ips: 4.78327 samples/s, eta: 5:10:43
[2024/07/27 13:45:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:45:43] ppocr INFO: epoch: [787/1500], global_step: 2360, lr: 0.001000, loss: 1.349906, loss_shrink_maps: 0.700404, loss_threshold_maps: 0.508996, loss_binary_maps: 0.139137, avg_reader_cost: 1.48951 s, avg_batch_cost: 1.67433 s, avg_samples: 9.6, ips: 5.73365 samples/s, eta: 5:10:25
[2024/07/27 13:45:44] ppocr INFO: epoch: [787/1500], global_step: 2361, lr: 0.001000, loss: 1.347768, loss_shrink_maps: 0.690157, loss_threshold_maps: 0.505980, loss_binary_maps: 0.136845, avg_reader_cost: 0.88329 s, avg_batch_cost: 0.93888 s, avg_samples: 2.9, ips: 3.08879 samples/s, eta: 5:10:17
[2024/07/27 13:45:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:45:54] ppocr INFO: epoch: [788/1500], global_step: 2364, lr: 0.001000, loss: 1.347768, loss_shrink_maps: 0.690157, loss_threshold_maps: 0.505871, loss_binary_maps: 0.136845, avg_reader_cost: 2.14428 s, avg_batch_cost: 2.51380 s, avg_samples: 12.5, ips: 4.97256 samples/s, eta: 5:09:50
[2024/07/27 13:45:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:46:03] ppocr INFO: epoch: [789/1500], global_step: 2367, lr: 0.001000, loss: 1.330250, loss_shrink_maps: 0.676225, loss_threshold_maps: 0.505871, loss_binary_maps: 0.134463, avg_reader_cost: 2.33524 s, avg_batch_cost: 2.58619 s, avg_samples: 12.5, ips: 4.83337 samples/s, eta: 5:09:24
[2024/07/27 13:46:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:46:13] ppocr INFO: epoch: [790/1500], global_step: 2370, lr: 0.001000, loss: 1.291572, loss_shrink_maps: 0.657999, loss_threshold_maps: 0.504912, loss_binary_maps: 0.130967, avg_reader_cost: 2.24034 s, avg_batch_cost: 2.63777 s, avg_samples: 12.5, ips: 4.73886 samples/s, eta: 5:08:58
[2024/07/27 13:46:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:46:24] ppocr INFO: epoch: [791/1500], global_step: 2373, lr: 0.001000, loss: 1.366368, loss_shrink_maps: 0.678924, loss_threshold_maps: 0.508476, loss_binary_maps: 0.135092, avg_reader_cost: 2.25008 s, avg_batch_cost: 2.67503 s, avg_samples: 12.5, ips: 4.67284 samples/s, eta: 5:08:33
[2024/07/27 13:46:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:46:33] ppocr INFO: epoch: [792/1500], global_step: 2376, lr: 0.001000, loss: 1.300022, loss_shrink_maps: 0.659923, loss_threshold_maps: 0.507354, loss_binary_maps: 0.131623, avg_reader_cost: 2.30470 s, avg_batch_cost: 2.55899 s, avg_samples: 12.5, ips: 4.88473 samples/s, eta: 5:08:06
[2024/07/27 13:46:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:46:43] ppocr INFO: epoch: [793/1500], global_step: 2379, lr: 0.001000, loss: 1.355994, loss_shrink_maps: 0.691650, loss_threshold_maps: 0.512648, loss_binary_maps: 0.137548, avg_reader_cost: 2.29487 s, avg_batch_cost: 2.54767 s, avg_samples: 12.5, ips: 4.90645 samples/s, eta: 5:07:39
[2024/07/27 13:46:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:46:51] ppocr INFO: epoch: [794/1500], global_step: 2380, lr: 0.001000, loss: 1.323181, loss_shrink_maps: 0.678924, loss_threshold_maps: 0.508476, loss_binary_maps: 0.135092, avg_reader_cost: 0.67585 s, avg_batch_cost: 0.77819 s, avg_samples: 4.8, ips: 6.16816 samples/s, eta: 5:07:30
[2024/07/27 13:46:53] ppocr INFO: epoch: [794/1500], global_step: 2382, lr: 0.001000, loss: 1.316232, loss_shrink_maps: 0.669987, loss_threshold_maps: 0.512648, loss_binary_maps: 0.133598, avg_reader_cost: 1.64885 s, avg_batch_cost: 1.79645 s, avg_samples: 7.7, ips: 4.28624 samples/s, eta: 5:07:13
[2024/07/27 13:46:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:47:03] ppocr INFO: epoch: [795/1500], global_step: 2385, lr: 0.001000, loss: 1.312738, loss_shrink_maps: 0.669987, loss_threshold_maps: 0.508476, loss_binary_maps: 0.133598, avg_reader_cost: 2.26795 s, avg_batch_cost: 2.63034 s, avg_samples: 12.5, ips: 4.75224 samples/s, eta: 5:06:47
[2024/07/27 13:47:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:47:12] ppocr INFO: epoch: [796/1500], global_step: 2388, lr: 0.001000, loss: 1.310576, loss_shrink_maps: 0.677724, loss_threshold_maps: 0.507155, loss_binary_maps: 0.134918, avg_reader_cost: 2.30914 s, avg_batch_cost: 2.57084 s, avg_samples: 12.5, ips: 4.86222 samples/s, eta: 5:06:20
[2024/07/27 13:47:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:47:21] ppocr INFO: epoch: [797/1500], global_step: 2390, lr: 0.001000, loss: 1.293746, loss_shrink_maps: 0.647075, loss_threshold_maps: 0.502995, loss_binary_maps: 0.128655, avg_reader_cost: 1.33377 s, avg_batch_cost: 1.60249 s, avg_samples: 9.6, ips: 5.99067 samples/s, eta: 5:06:02
[2024/07/27 13:47:22] ppocr INFO: epoch: [797/1500], global_step: 2391, lr: 0.001000, loss: 1.280976, loss_shrink_maps: 0.644340, loss_threshold_maps: 0.502988, loss_binary_maps: 0.127529, avg_reader_cost: 0.84756 s, avg_batch_cost: 0.90325 s, avg_samples: 2.9, ips: 3.21064 samples/s, eta: 5:05:53
[2024/07/27 13:47:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:47:32] ppocr INFO: epoch: [798/1500], global_step: 2394, lr: 0.001000, loss: 1.288780, loss_shrink_maps: 0.644340, loss_threshold_maps: 0.505591, loss_binary_maps: 0.127529, avg_reader_cost: 2.24166 s, avg_batch_cost: 2.60041 s, avg_samples: 12.5, ips: 4.80693 samples/s, eta: 5:05:27
[2024/07/27 13:47:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:47:42] ppocr INFO: epoch: [799/1500], global_step: 2397, lr: 0.001000, loss: 1.273118, loss_shrink_maps: 0.641131, loss_threshold_maps: 0.502988, loss_binary_maps: 0.127102, avg_reader_cost: 2.41607 s, avg_batch_cost: 2.65276 s, avg_samples: 12.5, ips: 4.71208 samples/s, eta: 5:05:01
[2024/07/27 13:47:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:47:52] ppocr INFO: epoch: [800/1500], global_step: 2400, lr: 0.001000, loss: 1.267214, loss_shrink_maps: 0.638685, loss_threshold_maps: 0.505591, loss_binary_maps: 0.126846, avg_reader_cost: 2.39154 s, avg_batch_cost: 2.64035 s, avg_samples: 12.5, ips: 4.73422 samples/s, eta: 5:04:36

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:02,  7.99it/s]
eval model::   1%|          | 4/500 [00:00<00:30, 16.06it/s]
eval model::   1%|          | 6/500 [00:00<00:29, 16.67it/s]
eval model::   2%|▏         | 8/500 [00:00<00:32, 15.27it/s]
eval model::   2%|▏         | 11/500 [00:00<00:32, 15.27it/s]
eval model::   3%|▎         | 13/500 [00:00<00:30, 16.18it/s]
eval model::   3%|▎         | 15/500 [00:00<00:31, 15.54it/s]
eval model::   4%|▎         | 18/500 [00:01<00:25, 18.90it/s]
eval model::   4%|▍         | 20/500 [00:01<00:26, 18.13it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.11it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 21.34it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.02it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.33it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.13it/s]
eval model::   7%|▋         | 37/500 [00:02<00:22, 20.34it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 19.11it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 18.13it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.26it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 17.29it/s]
eval model::  10%|█         | 50/500 [00:02<00:25, 17.66it/s]
eval model::  11%|█         | 53/500 [00:02<00:23, 18.79it/s]
eval model::  11%|█         | 56/500 [00:03<00:23, 18.55it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 18.44it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 16.10it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.35it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.23it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 21.14it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 20.76it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:19, 21.93it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.39it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.10it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:21, 19.62it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:23, 17.65it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:25, 15.86it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:24, 16.55it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:21, 19.09it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.50it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.79it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.73it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:17, 21.88it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 19.93it/s]
eval model::  23%|██▎       | 114/500 [00:06<00:19, 19.59it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:22, 17.40it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:21, 17.43it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:22, 16.86it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:23, 16.18it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:23, 16.02it/s]
eval model::  26%|██▌       | 129/500 [00:07<00:20, 18.03it/s]
eval model::  26%|██▌       | 131/500 [00:07<00:21, 17.19it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:21, 17.29it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:21, 17.34it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:20, 17.45it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:21, 17.17it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:19, 18.66it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:19, 18.18it/s]
eval model::  29%|██▉       | 147/500 [00:08<00:21, 16.55it/s]
eval model::  30%|██▉       | 149/500 [00:08<00:20, 17.29it/s]
eval model::  30%|███       | 151/500 [00:08<00:22, 15.41it/s]
eval model::  31%|███       | 155/500 [00:08<00:18, 19.16it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:16, 20.69it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:22, 15.28it/s]
eval model::  33%|███▎      | 163/500 [00:09<00:20, 16.09it/s]
eval model::  33%|███▎      | 166/500 [00:09<00:18, 17.98it/s]
eval model::  34%|███▎      | 168/500 [00:09<00:19, 17.12it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:19, 16.54it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:22, 14.36it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:22, 14.69it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:19, 16.55it/s]
eval model::  36%|███▌      | 181/500 [00:10<00:21, 15.04it/s]
eval model::  37%|███▋      | 184/500 [00:10<00:18, 17.04it/s]
eval model::  37%|███▋      | 187/500 [00:10<00:18, 16.99it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:17, 17.38it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:16, 18.65it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:17, 17.10it/s]
eval model::  39%|███▉      | 197/500 [00:11<00:17, 17.50it/s]
eval model::  40%|████      | 200/500 [00:11<00:15, 19.60it/s]
eval model::  41%|████      | 203/500 [00:11<00:14, 20.89it/s]
eval model::  41%|████      | 206/500 [00:11<00:23, 12.58it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:22, 12.95it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:21, 13.30it/s]
eval model::  43%|████▎     | 213/500 [00:12<00:19, 14.95it/s]
eval model::  43%|████▎     | 215/500 [00:12<00:21, 13.09it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:21, 13.31it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:19, 14.25it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:22, 12.61it/s]
eval model::  45%|████▍     | 223/500 [00:12<00:20, 13.50it/s]
eval model::  45%|████▌     | 226/500 [00:13<00:18, 15.11it/s]
eval model::  46%|████▌     | 230/500 [00:13<00:13, 19.65it/s]
eval model::  47%|████▋     | 233/500 [00:13<00:13, 19.71it/s]
eval model::  47%|████▋     | 236/500 [00:13<00:13, 18.93it/s]
eval model::  48%|████▊     | 239/500 [00:13<00:12, 20.36it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:14, 17.95it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:14, 18.22it/s]
eval model::  49%|████▉     | 247/500 [00:14<00:12, 19.75it/s]
eval model::  50%|█████     | 250/500 [00:14<00:12, 19.52it/s]
eval model::  51%|█████     | 254/500 [00:14<00:11, 22.33it/s]
eval model::  51%|█████▏    | 257/500 [00:14<00:10, 23.13it/s]
eval model::  52%|█████▏    | 260/500 [00:14<00:09, 24.58it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:09, 24.56it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:10, 21.66it/s]
eval model::  54%|█████▍    | 269/500 [00:15<00:11, 19.59it/s]
eval model::  54%|█████▍    | 272/500 [00:15<00:10, 21.44it/s]
eval model::  55%|█████▌    | 275/500 [00:15<00:11, 19.53it/s]
eval model::  56%|█████▌    | 278/500 [00:15<00:10, 21.41it/s]
eval model::  56%|█████▌    | 281/500 [00:15<00:09, 22.15it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:09, 22.87it/s]
eval model::  57%|█████▋    | 287/500 [00:16<00:16, 12.86it/s]
eval model::  58%|█████▊    | 290/500 [00:16<00:13, 15.48it/s]
eval model::  59%|█████▊    | 293/500 [00:16<00:13, 15.71it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:15, 13.49it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 15.60it/s]
eval model::  60%|██████    | 301/500 [00:17<00:14, 13.47it/s]
eval model::  61%|██████    | 304/500 [00:17<00:13, 15.08it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:11, 16.62it/s]
eval model::  62%|██████▏   | 309/500 [00:17<00:11, 17.15it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:10, 17.65it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:10, 18.49it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:09, 19.75it/s]
eval model::  64%|██████▍   | 321/500 [00:18<00:08, 20.48it/s]
eval model::  65%|██████▍   | 324/500 [00:18<00:09, 19.04it/s]
eval model::  65%|██████▌   | 327/500 [00:18<00:08, 20.00it/s]
eval model::  66%|██████▌   | 330/500 [00:18<00:08, 19.56it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:07, 21.10it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:07, 22.62it/s]
eval model::  68%|██████▊   | 339/500 [00:19<00:08, 20.12it/s]
eval model::  68%|██████▊   | 342/500 [00:19<00:10, 15.47it/s]
eval model::  69%|██████▉   | 345/500 [00:19<00:08, 17.74it/s]
eval model::  70%|██████▉   | 348/500 [00:19<00:08, 18.88it/s]
eval model::  70%|███████   | 351/500 [00:19<00:07, 19.52it/s]
eval model::  71%|███████   | 354/500 [00:19<00:06, 21.32it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 19.24it/s]
eval model::  72%|███████▏  | 360/500 [00:20<00:07, 19.22it/s]
eval model::  73%|███████▎  | 363/500 [00:20<00:07, 19.48it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:07, 18.12it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:06, 18.88it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:06, 18.71it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:06, 18.62it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 18.01it/s]
eval model::  76%|███████▌  | 378/500 [00:21<00:06, 20.17it/s]
eval model::  76%|███████▌  | 381/500 [00:21<00:06, 19.53it/s]
eval model::  77%|███████▋  | 384/500 [00:21<00:05, 21.23it/s]
eval model::  77%|███████▋  | 387/500 [00:21<00:09, 12.54it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:07, 14.85it/s]
eval model::  78%|███████▊  | 392/500 [00:22<00:06, 15.49it/s]
eval model::  79%|███████▉  | 395/500 [00:22<00:06, 17.01it/s]
eval model::  80%|███████▉  | 398/500 [00:22<00:05, 19.44it/s]
eval model::  80%|████████  | 401/500 [00:22<00:06, 16.12it/s]
eval model::  81%|████████  | 404/500 [00:22<00:05, 16.99it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 18.95it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 20.82it/s]
eval model::  83%|████████▎ | 413/500 [00:23<00:04, 20.98it/s]
eval model::  83%|████████▎ | 416/500 [00:23<00:04, 20.87it/s]
eval model::  84%|████████▍ | 419/500 [00:23<00:04, 19.80it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:06, 12.89it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 13.44it/s]
eval model::  85%|████████▌ | 426/500 [00:24<00:05, 13.26it/s]
eval model::  86%|████████▌ | 429/500 [00:24<00:04, 15.68it/s]
eval model::  86%|████████▌ | 431/500 [00:24<00:04, 15.43it/s]
eval model::  87%|████████▋ | 434/500 [00:24<00:03, 18.08it/s]
eval model::  87%|████████▋ | 437/500 [00:24<00:03, 19.98it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:03, 17.14it/s]
eval model::  89%|████████▊ | 443/500 [00:24<00:02, 19.69it/s]
eval model::  89%|████████▉ | 446/500 [00:25<00:02, 20.46it/s]
eval model::  90%|████████▉ | 449/500 [00:25<00:02, 19.76it/s]
eval model::  90%|█████████ | 452/500 [00:25<00:02, 21.89it/s]
eval model::  91%|█████████ | 455/500 [00:25<00:01, 23.62it/s]
eval model::  92%|█████████▏| 458/500 [00:25<00:01, 24.61it/s]
eval model::  92%|█████████▏| 461/500 [00:25<00:01, 24.37it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:01, 18.28it/s]
eval model::  93%|█████████▎| 467/500 [00:26<00:02, 16.36it/s]
eval model::  94%|█████████▍| 470/500 [00:26<00:01, 18.18it/s]
eval model::  95%|█████████▍| 473/500 [00:26<00:01, 18.59it/s]
eval model::  95%|█████████▌| 476/500 [00:26<00:01, 19.11it/s]
eval model::  96%|█████████▌| 479/500 [00:26<00:01, 19.58it/s]
eval model::  96%|█████████▋| 482/500 [00:26<00:00, 19.68it/s]
eval model::  97%|█████████▋| 485/500 [00:26<00:00, 21.25it/s]
eval model::  98%|█████████▊| 488/500 [00:27<00:00, 21.84it/s]
eval model::  98%|█████████▊| 491/500 [00:27<00:00, 19.89it/s]
eval model::  99%|█████████▉| 494/500 [00:27<00:00, 20.29it/s]
eval model::  99%|█████████▉| 497/500 [00:27<00:00, 21.40it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 21.70it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.06it/s]
[2024/07/27 13:48:20] ppocr INFO: cur metric, precision: 0.7083544303797469, recall: 0.673567645642754, hmean: 0.6905231984205331, fps: 44.58056628436093
[2024/07/27 13:48:20] ppocr INFO: best metric, hmean: 0.7142857142857143, precision: 0.7627118644067796, recall: 0.6716417910447762, fps: 43.33575316801794, best_epoch: 740
[2024/07/27 13:48:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:48:28] ppocr INFO: epoch: [801/1500], global_step: 2403, lr: 0.001000, loss: 1.267214, loss_shrink_maps: 0.638685, loss_threshold_maps: 0.506042, loss_binary_maps: 0.126846, avg_reader_cost: 2.04428 s, avg_batch_cost: 2.38128 s, avg_samples: 12.5, ips: 5.24927 samples/s, eta: 5:04:08
[2024/07/27 13:48:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:48:38] ppocr INFO: epoch: [802/1500], global_step: 2406, lr: 0.001000, loss: 1.331274, loss_shrink_maps: 0.690519, loss_threshold_maps: 0.512359, loss_binary_maps: 0.137175, avg_reader_cost: 2.37633 s, avg_batch_cost: 2.62304 s, avg_samples: 12.5, ips: 4.76547 samples/s, eta: 5:03:42
[2024/07/27 13:48:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:48:48] ppocr INFO: epoch: [803/1500], global_step: 2409, lr: 0.001000, loss: 1.324939, loss_shrink_maps: 0.679163, loss_threshold_maps: 0.512359, loss_binary_maps: 0.134763, avg_reader_cost: 2.27179 s, avg_batch_cost: 2.52051 s, avg_samples: 12.5, ips: 4.95932 samples/s, eta: 5:03:15
[2024/07/27 13:48:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:48:56] ppocr INFO: epoch: [804/1500], global_step: 2410, lr: 0.001000, loss: 1.324939, loss_shrink_maps: 0.679163, loss_threshold_maps: 0.512359, loss_binary_maps: 0.134763, avg_reader_cost: 0.68963 s, avg_batch_cost: 0.78156 s, avg_samples: 4.8, ips: 6.14157 samples/s, eta: 5:03:05
[2024/07/27 13:48:58] ppocr INFO: epoch: [804/1500], global_step: 2412, lr: 0.001000, loss: 1.360917, loss_shrink_maps: 0.689702, loss_threshold_maps: 0.518610, loss_binary_maps: 0.137564, avg_reader_cost: 1.65562 s, avg_batch_cost: 1.80384 s, avg_samples: 7.7, ips: 4.26867 samples/s, eta: 5:02:48
[2024/07/27 13:48:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:49:08] ppocr INFO: epoch: [805/1500], global_step: 2415, lr: 0.001000, loss: 1.370457, loss_shrink_maps: 0.694572, loss_threshold_maps: 0.520483, loss_binary_maps: 0.138695, avg_reader_cost: 2.47214 s, avg_batch_cost: 2.71104 s, avg_samples: 12.5, ips: 4.61077 samples/s, eta: 5:02:23
[2024/07/27 13:49:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:49:18] ppocr INFO: epoch: [806/1500], global_step: 2418, lr: 0.001000, loss: 1.370457, loss_shrink_maps: 0.694572, loss_threshold_maps: 0.518900, loss_binary_maps: 0.138695, avg_reader_cost: 2.38495 s, avg_batch_cost: 2.62349 s, avg_samples: 12.5, ips: 4.76464 samples/s, eta: 5:01:57
[2024/07/27 13:49:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:49:27] ppocr INFO: epoch: [807/1500], global_step: 2420, lr: 0.001000, loss: 1.379099, loss_shrink_maps: 0.701816, loss_threshold_maps: 0.521863, loss_binary_maps: 0.139945, avg_reader_cost: 1.51298 s, avg_batch_cost: 1.69401 s, avg_samples: 9.6, ips: 5.66701 samples/s, eta: 5:01:39
[2024/07/27 13:49:28] ppocr INFO: epoch: [807/1500], global_step: 2421, lr: 0.001000, loss: 1.379099, loss_shrink_maps: 0.701816, loss_threshold_maps: 0.530808, loss_binary_maps: 0.139945, avg_reader_cost: 0.89311 s, avg_batch_cost: 0.94902 s, avg_samples: 2.9, ips: 3.05578 samples/s, eta: 5:01:31
[2024/07/27 13:49:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:49:38] ppocr INFO: epoch: [808/1500], global_step: 2424, lr: 0.001000, loss: 1.370457, loss_shrink_maps: 0.694572, loss_threshold_maps: 0.521863, loss_binary_maps: 0.138695, avg_reader_cost: 2.39392 s, avg_batch_cost: 2.63406 s, avg_samples: 12.5, ips: 4.74553 samples/s, eta: 5:01:05
[2024/07/27 13:49:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:49:48] ppocr INFO: epoch: [809/1500], global_step: 2427, lr: 0.001000, loss: 1.333085, loss_shrink_maps: 0.677057, loss_threshold_maps: 0.506142, loss_binary_maps: 0.134827, avg_reader_cost: 2.22942 s, avg_batch_cost: 2.59386 s, avg_samples: 12.5, ips: 4.81908 samples/s, eta: 5:00:39
[2024/07/27 13:49:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:49:58] ppocr INFO: epoch: [810/1500], global_step: 2430, lr: 0.001000, loss: 1.311206, loss_shrink_maps: 0.670725, loss_threshold_maps: 0.499660, loss_binary_maps: 0.133679, avg_reader_cost: 2.21266 s, avg_batch_cost: 2.59520 s, avg_samples: 12.5, ips: 4.81658 samples/s, eta: 5:00:13
[2024/07/27 13:49:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:50:06] ppocr INFO: epoch: [811/1500], global_step: 2433, lr: 0.001000, loss: 1.226560, loss_shrink_maps: 0.638791, loss_threshold_maps: 0.483449, loss_binary_maps: 0.126728, avg_reader_cost: 1.98685 s, avg_batch_cost: 2.26267 s, avg_samples: 12.5, ips: 5.52444 samples/s, eta: 4:59:44
[2024/07/27 13:50:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:50:16] ppocr INFO: epoch: [812/1500], global_step: 2436, lr: 0.001000, loss: 1.226560, loss_shrink_maps: 0.643609, loss_threshold_maps: 0.486716, loss_binary_maps: 0.127977, avg_reader_cost: 2.27407 s, avg_batch_cost: 2.63235 s, avg_samples: 12.5, ips: 4.74861 samples/s, eta: 4:59:18
[2024/07/27 13:50:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:50:26] ppocr INFO: epoch: [813/1500], global_step: 2439, lr: 0.001000, loss: 1.285423, loss_shrink_maps: 0.655614, loss_threshold_maps: 0.495404, loss_binary_maps: 0.130272, avg_reader_cost: 2.19542 s, avg_batch_cost: 2.56031 s, avg_samples: 12.5, ips: 4.88223 samples/s, eta: 4:58:51
[2024/07/27 13:50:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:50:33] ppocr INFO: epoch: [814/1500], global_step: 2440, lr: 0.001000, loss: 1.262433, loss_shrink_maps: 0.648153, loss_threshold_maps: 0.492326, loss_binary_maps: 0.128867, avg_reader_cost: 0.57742 s, avg_batch_cost: 0.66536 s, avg_samples: 4.8, ips: 7.21414 samples/s, eta: 4:58:41
[2024/07/27 13:50:35] ppocr INFO: epoch: [814/1500], global_step: 2442, lr: 0.001000, loss: 1.189010, loss_shrink_maps: 0.591105, loss_threshold_maps: 0.486716, loss_binary_maps: 0.117480, avg_reader_cost: 1.50016 s, avg_batch_cost: 1.64827 s, avg_samples: 7.7, ips: 4.67157 samples/s, eta: 4:58:23
[2024/07/27 13:50:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:50:45] ppocr INFO: epoch: [815/1500], global_step: 2445, lr: 0.001000, loss: 1.239133, loss_shrink_maps: 0.616538, loss_threshold_maps: 0.493698, loss_binary_maps: 0.122531, avg_reader_cost: 2.21612 s, avg_batch_cost: 2.59088 s, avg_samples: 12.5, ips: 4.82461 samples/s, eta: 4:57:56
[2024/07/27 13:50:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:50:55] ppocr INFO: epoch: [816/1500], global_step: 2448, lr: 0.001000, loss: 1.280312, loss_shrink_maps: 0.643307, loss_threshold_maps: 0.495404, loss_binary_maps: 0.127938, avg_reader_cost: 2.38216 s, avg_batch_cost: 2.65480 s, avg_samples: 12.5, ips: 4.70845 samples/s, eta: 4:57:31
[2024/07/27 13:50:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:51:04] ppocr INFO: epoch: [817/1500], global_step: 2450, lr: 0.001000, loss: 1.280312, loss_shrink_maps: 0.643307, loss_threshold_maps: 0.497556, loss_binary_maps: 0.127938, avg_reader_cost: 1.46458 s, avg_batch_cost: 1.64393 s, avg_samples: 9.6, ips: 5.83965 samples/s, eta: 4:57:13
[2024/07/27 13:51:05] ppocr INFO: epoch: [817/1500], global_step: 2451, lr: 0.001000, loss: 1.285423, loss_shrink_maps: 0.650769, loss_threshold_maps: 0.502676, loss_binary_maps: 0.129409, avg_reader_cost: 0.86794 s, avg_batch_cost: 0.92352 s, avg_samples: 2.9, ips: 3.14015 samples/s, eta: 4:57:04
[2024/07/27 13:51:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:51:15] ppocr INFO: epoch: [818/1500], global_step: 2454, lr: 0.001000, loss: 1.301170, loss_shrink_maps: 0.650621, loss_threshold_maps: 0.507805, loss_binary_maps: 0.128874, avg_reader_cost: 2.29458 s, avg_batch_cost: 2.53467 s, avg_samples: 12.5, ips: 4.93161 samples/s, eta: 4:56:38
[2024/07/27 13:51:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:51:25] ppocr INFO: epoch: [819/1500], global_step: 2457, lr: 0.001000, loss: 1.307455, loss_shrink_maps: 0.655821, loss_threshold_maps: 0.513971, loss_binary_maps: 0.129353, avg_reader_cost: 2.23339 s, avg_batch_cost: 2.60909 s, avg_samples: 12.5, ips: 4.79093 samples/s, eta: 4:56:11
[2024/07/27 13:51:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:51:35] ppocr INFO: epoch: [820/1500], global_step: 2460, lr: 0.001000, loss: 1.316978, loss_shrink_maps: 0.661287, loss_threshold_maps: 0.511114, loss_binary_maps: 0.130518, avg_reader_cost: 2.37238 s, avg_batch_cost: 2.61559 s, avg_samples: 12.5, ips: 4.77904 samples/s, eta: 4:55:45

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:07,  7.36it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 16.55it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.53it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.42it/s]
eval model::   2%|▏         | 11/500 [00:00<00:29, 16.68it/s]
eval model::   3%|▎         | 13/500 [00:00<00:28, 17.24it/s]
eval model::   3%|▎         | 15/500 [00:00<00:27, 17.73it/s]
eval model::   4%|▎         | 18/500 [00:00<00:23, 20.83it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.62it/s]
eval model::   5%|▍         | 24/500 [00:01<00:21, 21.76it/s]
eval model::   5%|▌         | 27/500 [00:01<00:21, 22.07it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 22.22it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 20.16it/s]
eval model::   7%|▋         | 36/500 [00:01<00:23, 20.00it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 17.74it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 17.69it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 19.79it/s]
eval model::  10%|▉         | 48/500 [00:02<00:23, 19.35it/s]
eval model::  10%|█         | 51/500 [00:02<00:21, 20.74it/s]
eval model::  11%|█         | 54/500 [00:02<00:21, 21.16it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:20, 21.12it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:22, 19.37it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:29, 14.92it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:28, 15.43it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:23, 18.42it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 20.87it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 21.13it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.49it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:21, 19.90it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.28it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.50it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:22, 18.25it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:25, 16.05it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:24, 16.74it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:21, 18.91it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.76it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.22it/s]
eval model::  21%|██        | 106/500 [00:05<00:16, 23.40it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:15, 24.63it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:17, 22.61it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:17, 21.45it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:17, 22.01it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 20.13it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 20.03it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:17, 21.04it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:20, 18.28it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.96it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:17, 20.62it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:16, 21.88it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:16, 21.65it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.20it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.31it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.18it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 19.76it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 21.17it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 21.12it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:18, 17.97it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:18, 18.12it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 19.15it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:15, 20.74it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:16, 19.62it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:21, 15.20it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.48it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:19, 16.60it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.82it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:16, 18.66it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:16, 18.73it/s]
eval model::  39%|███▉      | 194/500 [00:09<00:14, 20.49it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.87it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 20.35it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 20.96it/s]
eval model::  41%|████      | 206/500 [00:10<00:23, 12.69it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:22, 13.21it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 13.88it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.18it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.24it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.82it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 15.81it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:19, 14.25it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.80it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.09it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 22.04it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 20.54it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:12, 20.88it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:15, 17.00it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:13, 18.96it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 19.94it/s]
eval model::  50%|█████     | 251/500 [00:13<00:13, 18.41it/s]
eval model::  51%|█████     | 253/500 [00:13<00:13, 18.66it/s]
eval model::  51%|█████     | 256/500 [00:13<00:12, 19.39it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:12, 18.99it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 19.91it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:12, 19.20it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:11, 20.00it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:11, 20.09it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:10, 21.23it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 19.43it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:10, 20.47it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:10, 21.55it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:17, 12.56it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:14, 14.79it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 16.53it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:14, 13.81it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:12, 16.32it/s]
eval model::  60%|██████    | 301/500 [00:16<00:12, 15.92it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 17.46it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:09, 19.81it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:08, 21.12it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 21.34it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:09, 18.92it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:09, 19.75it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 17.86it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:09, 18.08it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 19.47it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 19.37it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:08, 19.05it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 20.88it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 18.68it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 15.55it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:08, 17.94it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 20.00it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.94it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 21.95it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.55it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 22.02it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.68it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.14it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.87it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:07, 18.25it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 19.22it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 18.64it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 20.67it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 21.56it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 20.34it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:05, 20.96it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:08, 13.21it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:06, 15.59it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 17.35it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:04, 21.10it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 18.60it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 19.53it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 19.81it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 21.44it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:04, 20.69it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 20.68it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 20.07it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 14.26it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 15.13it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.38it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 17.43it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:03, 17.59it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 21.61it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 22.68it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 22.34it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 22.16it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 22.89it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 20.41it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 21.71it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 22.54it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 21.70it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 17.15it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 17.54it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:02, 16.22it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 16.78it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.42it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.22it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 19.54it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 20.42it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 21.22it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 21.12it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 20.97it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 17.78it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 18.43it/s]
eval model::  99%|█████████▉| 497/500 [00:26<00:00, 18.25it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.86it/s]
[2024/07/27 13:52:01] ppocr INFO: cur metric, precision: 0.714210802307289, recall: 0.6557534906114588, hmean: 0.6837349397590361, fps: 45.171169631596904
[2024/07/27 13:52:01] ppocr INFO: best metric, hmean: 0.7142857142857143, precision: 0.7627118644067796, recall: 0.6716417910447762, fps: 43.33575316801794, best_epoch: 740
[2024/07/27 13:52:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:52:11] ppocr INFO: epoch: [821/1500], global_step: 2463, lr: 0.001000, loss: 1.307455, loss_shrink_maps: 0.655821, loss_threshold_maps: 0.511114, loss_binary_maps: 0.129353, avg_reader_cost: 2.38330 s, avg_batch_cost: 2.64060 s, avg_samples: 12.5, ips: 4.73378 samples/s, eta: 4:55:20
[2024/07/27 13:52:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:52:21] ppocr INFO: epoch: [822/1500], global_step: 2466, lr: 0.001000, loss: 1.289168, loss_shrink_maps: 0.647129, loss_threshold_maps: 0.507177, loss_binary_maps: 0.128014, avg_reader_cost: 2.15494 s, avg_batch_cost: 2.52274 s, avg_samples: 12.5, ips: 4.95493 samples/s, eta: 4:54:53
[2024/07/27 13:52:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:52:30] ppocr INFO: epoch: [823/1500], global_step: 2469, lr: 0.001000, loss: 1.297737, loss_shrink_maps: 0.655821, loss_threshold_maps: 0.505786, loss_binary_maps: 0.129353, avg_reader_cost: 2.27693 s, avg_batch_cost: 2.62946 s, avg_samples: 12.5, ips: 4.75383 samples/s, eta: 4:54:27
[2024/07/27 13:52:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:52:39] ppocr INFO: epoch: [824/1500], global_step: 2470, lr: 0.001000, loss: 1.307455, loss_shrink_maps: 0.664444, loss_threshold_maps: 0.505786, loss_binary_maps: 0.131575, avg_reader_cost: 0.56775 s, avg_batch_cost: 0.78328 s, avg_samples: 4.8, ips: 6.12805 samples/s, eta: 4:54:17
[2024/07/27 13:52:40] ppocr INFO: epoch: [824/1500], global_step: 2472, lr: 0.001000, loss: 1.297737, loss_shrink_maps: 0.663187, loss_threshold_maps: 0.503929, loss_binary_maps: 0.131185, avg_reader_cost: 1.65864 s, avg_batch_cost: 1.80613 s, avg_samples: 7.7, ips: 4.26325 samples/s, eta: 4:54:01
[2024/07/27 13:52:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:52:50] ppocr INFO: epoch: [825/1500], global_step: 2475, lr: 0.001000, loss: 1.302764, loss_shrink_maps: 0.663005, loss_threshold_maps: 0.503929, loss_binary_maps: 0.132317, avg_reader_cost: 2.21315 s, avg_batch_cost: 2.57857 s, avg_samples: 12.5, ips: 4.84765 samples/s, eta: 4:53:34
[2024/07/27 13:52:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:53:00] ppocr INFO: epoch: [826/1500], global_step: 2478, lr: 0.001000, loss: 1.290877, loss_shrink_maps: 0.654662, loss_threshold_maps: 0.505411, loss_binary_maps: 0.130377, avg_reader_cost: 2.26765 s, avg_batch_cost: 2.50550 s, avg_samples: 12.5, ips: 4.98902 samples/s, eta: 4:53:07
[2024/07/27 13:53:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:53:09] ppocr INFO: epoch: [827/1500], global_step: 2480, lr: 0.001000, loss: 1.290877, loss_shrink_maps: 0.654662, loss_threshold_maps: 0.502781, loss_binary_maps: 0.130377, avg_reader_cost: 1.35655 s, avg_batch_cost: 1.66802 s, avg_samples: 9.6, ips: 5.75533 samples/s, eta: 4:52:49
[2024/07/27 13:53:09] ppocr INFO: epoch: [827/1500], global_step: 2481, lr: 0.001000, loss: 1.290877, loss_shrink_maps: 0.654662, loss_threshold_maps: 0.499732, loss_binary_maps: 0.130377, avg_reader_cost: 0.88024 s, avg_batch_cost: 0.93588 s, avg_samples: 2.9, ips: 3.09869 samples/s, eta: 4:52:41
[2024/07/27 13:53:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:53:19] ppocr INFO: epoch: [828/1500], global_step: 2484, lr: 0.001000, loss: 1.307119, loss_shrink_maps: 0.668178, loss_threshold_maps: 0.505411, loss_binary_maps: 0.132651, avg_reader_cost: 2.32483 s, avg_batch_cost: 2.60326 s, avg_samples: 12.5, ips: 4.80166 samples/s, eta: 4:52:15
[2024/07/27 13:53:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:53:29] ppocr INFO: epoch: [829/1500], global_step: 2487, lr: 0.001000, loss: 1.308745, loss_shrink_maps: 0.668271, loss_threshold_maps: 0.506261, loss_binary_maps: 0.132651, avg_reader_cost: 2.32747 s, avg_batch_cost: 2.56422 s, avg_samples: 12.5, ips: 4.87479 samples/s, eta: 4:51:49
[2024/07/27 13:53:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:53:39] ppocr INFO: epoch: [830/1500], global_step: 2490, lr: 0.001000, loss: 1.295904, loss_shrink_maps: 0.654662, loss_threshold_maps: 0.504127, loss_binary_maps: 0.130377, avg_reader_cost: 2.36144 s, avg_batch_cost: 2.60251 s, avg_samples: 12.5, ips: 4.80306 samples/s, eta: 4:51:22
[2024/07/27 13:53:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:53:49] ppocr INFO: epoch: [831/1500], global_step: 2493, lr: 0.001000, loss: 1.297530, loss_shrink_maps: 0.659104, loss_threshold_maps: 0.501497, loss_binary_maps: 0.131525, avg_reader_cost: 2.25136 s, avg_batch_cost: 2.62785 s, avg_samples: 12.5, ips: 4.75674 samples/s, eta: 4:50:56
[2024/07/27 13:53:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:53:58] ppocr INFO: epoch: [832/1500], global_step: 2496, lr: 0.001000, loss: 1.297530, loss_shrink_maps: 0.662054, loss_threshold_maps: 0.504201, loss_binary_maps: 0.131852, avg_reader_cost: 2.29511 s, avg_batch_cost: 2.53403 s, avg_samples: 12.5, ips: 4.93286 samples/s, eta: 4:50:30
[2024/07/27 13:53:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:54:08] ppocr INFO: epoch: [833/1500], global_step: 2499, lr: 0.001000, loss: 1.308745, loss_shrink_maps: 0.668271, loss_threshold_maps: 0.504201, loss_binary_maps: 0.132536, avg_reader_cost: 2.27330 s, avg_batch_cost: 2.63165 s, avg_samples: 12.5, ips: 4.74988 samples/s, eta: 4:50:04
[2024/07/27 13:54:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:54:17] ppocr INFO: epoch: [834/1500], global_step: 2500, lr: 0.001000, loss: 1.316647, loss_shrink_maps: 0.668271, loss_threshold_maps: 0.504201, loss_binary_maps: 0.132536, avg_reader_cost: 0.69114 s, avg_batch_cost: 0.78479 s, avg_samples: 4.8, ips: 6.11632 samples/s, eta: 4:49:54
[2024/07/27 13:54:18] ppocr INFO: epoch: [834/1500], global_step: 2502, lr: 0.001000, loss: 1.316647, loss_shrink_maps: 0.668271, loss_threshold_maps: 0.504201, loss_binary_maps: 0.132536, avg_reader_cost: 1.66172 s, avg_batch_cost: 1.80882 s, avg_samples: 7.7, ips: 4.25692 samples/s, eta: 4:49:38
[2024/07/27 13:54:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:54:28] ppocr INFO: epoch: [835/1500], global_step: 2505, lr: 0.001000, loss: 1.308280, loss_shrink_maps: 0.671342, loss_threshold_maps: 0.491722, loss_binary_maps: 0.133430, avg_reader_cost: 2.29750 s, avg_batch_cost: 2.53346 s, avg_samples: 12.5, ips: 4.93395 samples/s, eta: 4:49:11
[2024/07/27 13:54:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:54:38] ppocr INFO: epoch: [836/1500], global_step: 2508, lr: 0.001000, loss: 1.328063, loss_shrink_maps: 0.694848, loss_threshold_maps: 0.484352, loss_binary_maps: 0.138096, avg_reader_cost: 2.23563 s, avg_batch_cost: 2.58979 s, avg_samples: 12.5, ips: 4.82664 samples/s, eta: 4:48:45
[2024/07/27 13:54:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:54:47] ppocr INFO: epoch: [837/1500], global_step: 2510, lr: 0.001000, loss: 1.328063, loss_shrink_maps: 0.694848, loss_threshold_maps: 0.484352, loss_binary_maps: 0.138096, avg_reader_cost: 1.51747 s, avg_batch_cost: 1.69910 s, avg_samples: 9.6, ips: 5.65004 samples/s, eta: 4:48:27
[2024/07/27 13:54:48] ppocr INFO: epoch: [837/1500], global_step: 2511, lr: 0.001000, loss: 1.328063, loss_shrink_maps: 0.694848, loss_threshold_maps: 0.493066, loss_binary_maps: 0.138096, avg_reader_cost: 0.89575 s, avg_batch_cost: 0.95086 s, avg_samples: 2.9, ips: 3.04987 samples/s, eta: 4:48:19
[2024/07/27 13:54:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:54:58] ppocr INFO: epoch: [838/1500], global_step: 2514, lr: 0.001000, loss: 1.328063, loss_shrink_maps: 0.703177, loss_threshold_maps: 0.498539, loss_binary_maps: 0.139580, avg_reader_cost: 2.33180 s, avg_batch_cost: 2.58118 s, avg_samples: 12.5, ips: 4.84274 samples/s, eta: 4:47:53
[2024/07/27 13:54:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:55:08] ppocr INFO: epoch: [839/1500], global_step: 2517, lr: 0.001000, loss: 1.328063, loss_shrink_maps: 0.703177, loss_threshold_maps: 0.500088, loss_binary_maps: 0.139580, avg_reader_cost: 2.44021 s, avg_batch_cost: 2.67889 s, avg_samples: 12.5, ips: 4.66612 samples/s, eta: 4:47:27
[2024/07/27 13:55:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:55:17] ppocr INFO: epoch: [840/1500], global_step: 2520, lr: 0.001000, loss: 1.316078, loss_shrink_maps: 0.683490, loss_threshold_maps: 0.498539, loss_binary_maps: 0.135827, avg_reader_cost: 2.24728 s, avg_batch_cost: 2.48876 s, avg_samples: 12.5, ips: 5.02258 samples/s, eta: 4:47:00

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:55,  8.93it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.42it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.69it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 18.06it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.64it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 18.82it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.29it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.33it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.67it/s]
eval model::   5%|▌         | 26/500 [00:01<00:25, 18.76it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.43it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.25it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.58it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 19.55it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 18.24it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 17.78it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.35it/s]
eval model::   9%|▉         | 47/500 [00:02<00:24, 18.28it/s]
eval model::  10%|█         | 50/500 [00:02<00:22, 19.83it/s]
eval model::  11%|█         | 54/500 [00:02<00:19, 22.86it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:20, 21.58it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:23, 19.02it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:29, 14.91it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:27, 15.69it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:21, 19.85it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:19, 21.54it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:21, 19.98it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:21, 19.33it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:20, 20.53it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:20, 20.60it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:20, 20.32it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:23, 17.37it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:23, 17.39it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 17.75it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 20.03it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.91it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.53it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.42it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.61it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 20.04it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 20.85it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:18, 20.91it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.59it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 20.04it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 19.09it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:18, 20.34it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:18, 20.01it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:16, 21.45it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:16, 21.44it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:16, 21.58it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 21.21it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 19.17it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 18.73it/s]
eval model::  30%|███       | 152/500 [00:07<00:20, 16.94it/s]
eval model::  31%|███       | 155/500 [00:07<00:18, 18.45it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:17, 19.91it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:21, 15.46it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:19, 17.47it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 18.15it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:17, 19.26it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:17, 18.36it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:19, 16.34it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:19, 16.21it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:19, 16.71it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:21, 15.07it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:20, 15.61it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:18, 17.43it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:18, 17.25it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:15, 19.58it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:14, 21.82it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.16it/s]
eval model::  40%|████      | 200/500 [00:10<00:15, 19.62it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 20.75it/s]
eval model::  41%|████      | 206/500 [00:10<00:20, 14.57it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:19, 14.62it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 14.73it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.78it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.69it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.82it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.27it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:18, 15.08it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.92it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.17it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.02it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 20.62it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:12, 20.69it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:14, 17.93it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:13, 18.71it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 19.71it/s]
eval model::  50%|█████     | 251/500 [00:13<00:13, 18.84it/s]
eval model::  51%|█████     | 254/500 [00:13<00:12, 19.15it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:12, 20.12it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 21.99it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 22.58it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:11, 20.34it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.70it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 20.46it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.23it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 20.70it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.97it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:09, 22.17it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:17, 12.51it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:13, 15.90it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.43it/s]
eval model::  59%|█████▉    | 297/500 [00:16<00:13, 14.61it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 15.67it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.79it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 18.83it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 20.14it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 19.40it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 18.97it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:08, 20.52it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 20.99it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 20.00it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 20.61it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 19.28it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:08, 20.69it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:07, 23.17it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:09, 17.41it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:08, 19.26it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 20.23it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.48it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 21.38it/s]
eval model::  71%|███████   | 355/500 [00:18<00:07, 19.28it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 19.69it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 17.65it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:08, 16.78it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 17.92it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 19.74it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 18.44it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 21.83it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 20.51it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 22.11it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:08, 14.16it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 16.18it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:05, 18.04it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:05, 19.92it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:04, 21.28it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 18.71it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 20.25it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 22.15it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 24.30it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 22.93it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:03, 21.98it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 15.56it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 15.97it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.59it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.97it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 20.52it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 20.25it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:02, 21.70it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 23.34it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 22.95it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 22.48it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 21.11it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.63it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 21.32it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 22.39it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 21.20it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 17.85it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.75it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.01it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 19.81it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.87it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.26it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 20.53it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 21.19it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 19.21it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 20.83it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 18.45it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 18.55it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 21.86it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.06it/s]
[2024/07/27 13:55:44] ppocr INFO: cur metric, precision: 0.732650739476678, recall: 0.6201251805488686, hmean: 0.6717079530638852, fps: 43.76186539825858
[2024/07/27 13:55:44] ppocr INFO: best metric, hmean: 0.7142857142857143, precision: 0.7627118644067796, recall: 0.6716417910447762, fps: 43.33575316801794, best_epoch: 740
[2024/07/27 13:55:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:55:53] ppocr INFO: epoch: [841/1500], global_step: 2523, lr: 0.001000, loss: 1.278918, loss_shrink_maps: 0.658058, loss_threshold_maps: 0.486851, loss_binary_maps: 0.130707, avg_reader_cost: 2.41686 s, avg_batch_cost: 2.72913 s, avg_samples: 12.5, ips: 4.58022 samples/s, eta: 4:46:35
[2024/07/27 13:55:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:56:03] ppocr INFO: epoch: [842/1500], global_step: 2526, lr: 0.001000, loss: 1.278918, loss_shrink_maps: 0.658058, loss_threshold_maps: 0.498539, loss_binary_maps: 0.130707, avg_reader_cost: 2.14931 s, avg_batch_cost: 2.61164 s, avg_samples: 12.5, ips: 4.78626 samples/s, eta: 4:46:09
[2024/07/27 13:56:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:56:12] ppocr INFO: epoch: [843/1500], global_step: 2529, lr: 0.001000, loss: 1.263496, loss_shrink_maps: 0.648119, loss_threshold_maps: 0.499257, loss_binary_maps: 0.128722, avg_reader_cost: 2.09867 s, avg_batch_cost: 2.37301 s, avg_samples: 12.5, ips: 5.26758 samples/s, eta: 4:45:41
[2024/07/27 13:56:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:56:21] ppocr INFO: epoch: [844/1500], global_step: 2530, lr: 0.001000, loss: 1.270769, loss_shrink_maps: 0.651737, loss_threshold_maps: 0.500769, loss_binary_maps: 0.129458, avg_reader_cost: 0.57770 s, avg_batch_cost: 0.80891 s, avg_samples: 4.8, ips: 5.93392 samples/s, eta: 4:45:32
[2024/07/27 13:56:22] ppocr INFO: epoch: [844/1500], global_step: 2532, lr: 0.001000, loss: 1.256458, loss_shrink_maps: 0.642327, loss_threshold_maps: 0.500030, loss_binary_maps: 0.127331, avg_reader_cost: 1.70997 s, avg_batch_cost: 1.85740 s, avg_samples: 7.7, ips: 4.14559 samples/s, eta: 4:45:15
[2024/07/27 13:56:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:56:32] ppocr INFO: epoch: [845/1500], global_step: 2535, lr: 0.001000, loss: 1.242716, loss_shrink_maps: 0.636744, loss_threshold_maps: 0.498642, loss_binary_maps: 0.126380, avg_reader_cost: 2.22040 s, avg_batch_cost: 2.58124 s, avg_samples: 12.5, ips: 4.84263 samples/s, eta: 4:44:49
[2024/07/27 13:56:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:56:42] ppocr INFO: epoch: [846/1500], global_step: 2538, lr: 0.001000, loss: 1.268552, loss_shrink_maps: 0.648119, loss_threshold_maps: 0.503638, loss_binary_maps: 0.128791, avg_reader_cost: 2.26498 s, avg_batch_cost: 2.50405 s, avg_samples: 12.5, ips: 4.99192 samples/s, eta: 4:44:22
[2024/07/27 13:56:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:56:51] ppocr INFO: epoch: [847/1500], global_step: 2540, lr: 0.001000, loss: 1.268552, loss_shrink_maps: 0.648119, loss_threshold_maps: 0.503638, loss_binary_maps: 0.128791, avg_reader_cost: 1.36096 s, avg_batch_cost: 1.69603 s, avg_samples: 9.6, ips: 5.66028 samples/s, eta: 4:44:04
[2024/07/27 13:56:52] ppocr INFO: epoch: [847/1500], global_step: 2541, lr: 0.001000, loss: 1.282590, loss_shrink_maps: 0.651737, loss_threshold_maps: 0.506775, loss_binary_maps: 0.129458, avg_reader_cost: 0.89470 s, avg_batch_cost: 0.94971 s, avg_samples: 2.9, ips: 3.05356 samples/s, eta: 4:43:56
[2024/07/27 13:56:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:57:02] ppocr INFO: epoch: [848/1500], global_step: 2544, lr: 0.001000, loss: 1.291143, loss_shrink_maps: 0.652554, loss_threshold_maps: 0.506701, loss_binary_maps: 0.129550, avg_reader_cost: 2.19697 s, avg_batch_cost: 2.60083 s, avg_samples: 12.5, ips: 4.80616 samples/s, eta: 4:43:30
[2024/07/27 13:57:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:57:11] ppocr INFO: epoch: [849/1500], global_step: 2547, lr: 0.001000, loss: 1.291143, loss_shrink_maps: 0.650873, loss_threshold_maps: 0.503260, loss_binary_maps: 0.129078, avg_reader_cost: 2.19103 s, avg_batch_cost: 2.53953 s, avg_samples: 12.5, ips: 4.92217 samples/s, eta: 4:43:03
[2024/07/27 13:57:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:57:21] ppocr INFO: epoch: [850/1500], global_step: 2550, lr: 0.001000, loss: 1.293882, loss_shrink_maps: 0.656134, loss_threshold_maps: 0.506701, loss_binary_maps: 0.129935, avg_reader_cost: 2.25458 s, avg_batch_cost: 2.49982 s, avg_samples: 12.5, ips: 5.00036 samples/s, eta: 4:42:36
[2024/07/27 13:57:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:57:31] ppocr INFO: epoch: [851/1500], global_step: 2553, lr: 0.001000, loss: 1.293882, loss_shrink_maps: 0.656134, loss_threshold_maps: 0.506701, loss_binary_maps: 0.129935, avg_reader_cost: 2.38390 s, avg_batch_cost: 2.63241 s, avg_samples: 12.5, ips: 4.74850 samples/s, eta: 4:42:11
[2024/07/27 13:57:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:57:41] ppocr INFO: epoch: [852/1500], global_step: 2556, lr: 0.001000, loss: 1.293882, loss_shrink_maps: 0.656134, loss_threshold_maps: 0.506701, loss_binary_maps: 0.129935, avg_reader_cost: 2.26290 s, avg_batch_cost: 2.49738 s, avg_samples: 12.5, ips: 5.00525 samples/s, eta: 4:41:44
[2024/07/27 13:57:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:57:50] ppocr INFO: epoch: [853/1500], global_step: 2559, lr: 0.001000, loss: 1.304868, loss_shrink_maps: 0.661409, loss_threshold_maps: 0.506701, loss_binary_maps: 0.131237, avg_reader_cost: 2.23090 s, avg_batch_cost: 2.58769 s, avg_samples: 12.5, ips: 4.83056 samples/s, eta: 4:41:17
[2024/07/27 13:57:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:57:59] ppocr INFO: epoch: [854/1500], global_step: 2560, lr: 0.001000, loss: 1.293882, loss_shrink_maps: 0.661168, loss_threshold_maps: 0.503212, loss_binary_maps: 0.131237, avg_reader_cost: 0.69986 s, avg_batch_cost: 0.79061 s, avg_samples: 4.8, ips: 6.07124 samples/s, eta: 4:41:08
[2024/07/27 13:58:00] ppocr INFO: epoch: [854/1500], global_step: 2562, lr: 0.001000, loss: 1.293882, loss_shrink_maps: 0.661168, loss_threshold_maps: 0.502976, loss_binary_maps: 0.131237, avg_reader_cost: 1.67315 s, avg_batch_cost: 1.81995 s, avg_samples: 7.7, ips: 4.23087 samples/s, eta: 4:40:51
[2024/07/27 13:58:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:58:10] ppocr INFO: epoch: [855/1500], global_step: 2565, lr: 0.001000, loss: 1.333309, loss_shrink_maps: 0.689501, loss_threshold_maps: 0.503772, loss_binary_maps: 0.137309, avg_reader_cost: 2.19692 s, avg_batch_cost: 2.53205 s, avg_samples: 12.5, ips: 4.93670 samples/s, eta: 4:40:25
[2024/07/27 13:58:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:58:20] ppocr INFO: epoch: [856/1500], global_step: 2568, lr: 0.001000, loss: 1.313372, loss_shrink_maps: 0.688211, loss_threshold_maps: 0.496425, loss_binary_maps: 0.137307, avg_reader_cost: 2.21673 s, avg_batch_cost: 2.58808 s, avg_samples: 12.5, ips: 4.82983 samples/s, eta: 4:39:58
[2024/07/27 13:58:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:58:29] ppocr INFO: epoch: [857/1500], global_step: 2570, lr: 0.001000, loss: 1.292124, loss_shrink_maps: 0.673823, loss_threshold_maps: 0.494500, loss_binary_maps: 0.134129, avg_reader_cost: 1.43364 s, avg_batch_cost: 1.69768 s, avg_samples: 9.6, ips: 5.65477 samples/s, eta: 4:39:41
[2024/07/27 13:58:30] ppocr INFO: epoch: [857/1500], global_step: 2571, lr: 0.001000, loss: 1.292124, loss_shrink_maps: 0.673823, loss_threshold_maps: 0.494500, loss_binary_maps: 0.134129, avg_reader_cost: 0.89573 s, avg_batch_cost: 0.95128 s, avg_samples: 2.9, ips: 3.04852 samples/s, eta: 4:39:33
[2024/07/27 13:58:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:58:40] ppocr INFO: epoch: [858/1500], global_step: 2574, lr: 0.001000, loss: 1.313372, loss_shrink_maps: 0.688211, loss_threshold_maps: 0.498029, loss_binary_maps: 0.137307, avg_reader_cost: 2.37690 s, avg_batch_cost: 2.63134 s, avg_samples: 12.5, ips: 4.75042 samples/s, eta: 4:39:07
[2024/07/27 13:58:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:58:50] ppocr INFO: epoch: [859/1500], global_step: 2577, lr: 0.001000, loss: 1.298388, loss_shrink_maps: 0.679338, loss_threshold_maps: 0.495184, loss_binary_maps: 0.135499, avg_reader_cost: 2.25883 s, avg_batch_cost: 2.59209 s, avg_samples: 12.5, ips: 4.82236 samples/s, eta: 4:38:41
[2024/07/27 13:58:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:58:59] ppocr INFO: epoch: [860/1500], global_step: 2580, lr: 0.001000, loss: 1.273996, loss_shrink_maps: 0.659165, loss_threshold_maps: 0.485401, loss_binary_maps: 0.131198, avg_reader_cost: 2.26097 s, avg_batch_cost: 2.59842 s, avg_samples: 12.5, ips: 4.81062 samples/s, eta: 4:38:14

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:03,  7.86it/s]
eval model::   1%|          | 4/500 [00:00<00:34, 14.52it/s]
eval model::   1%|▏         | 7/500 [00:00<00:29, 16.75it/s]
eval model::   2%|▏         | 9/500 [00:00<00:29, 16.76it/s]
eval model::   2%|▏         | 11/500 [00:00<00:29, 16.71it/s]
eval model::   3%|▎         | 14/500 [00:00<00:28, 17.31it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.48it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 19.07it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 21.21it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 21.27it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 22.00it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 20.13it/s]
eval model::   7%|▋         | 35/500 [00:01<00:22, 20.81it/s]
eval model::   8%|▊         | 38/500 [00:01<00:22, 20.66it/s]
eval model::   8%|▊         | 41/500 [00:02<00:22, 20.00it/s]
eval model::   9%|▉         | 44/500 [00:02<00:23, 19.04it/s]
eval model::   9%|▉         | 46/500 [00:02<00:26, 17.37it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 20.01it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 19.64it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 19.57it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 17.85it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.17it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.57it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.39it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.94it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 22.29it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:18, 22.72it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.70it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 21.85it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:21, 19.80it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:23, 17.76it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:23, 17.29it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:24, 16.75it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:23, 17.29it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:21, 18.77it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.50it/s]
eval model::  20%|██        | 102/500 [00:05<00:18, 21.05it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 22.77it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:16, 23.44it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 21.45it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 21.13it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:18, 20.29it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 21.02it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.64it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.60it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:20, 18.28it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:22, 16.35it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:23, 15.67it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:20, 17.43it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.31it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.27it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.33it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 18.81it/s]
eval model::  30%|███       | 151/500 [00:07<00:21, 16.15it/s]
eval model::  31%|███       | 154/500 [00:08<00:19, 17.63it/s]
eval model::  31%|███       | 156/500 [00:08<00:19, 17.82it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:18, 18.44it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:22, 14.81it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:21, 15.74it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:18, 17.74it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:19, 17.12it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:18, 17.95it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 16.19it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 15.68it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:19, 16.88it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.62it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 17.94it/s]
eval model::  37%|███▋      | 187/500 [00:10<00:16, 18.96it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:15, 19.61it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:14, 21.84it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:14, 20.86it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:13, 21.79it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 22.06it/s]
eval model::  41%|████      | 205/500 [00:11<00:18, 15.79it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:17, 16.24it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 15.83it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.56it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.91it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.77it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 15.74it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:19, 14.54it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:15, 17.48it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:14, 19.28it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 20.95it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 21.46it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 22.93it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 23.07it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:12, 20.87it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:11, 22.13it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:12, 19.52it/s]
eval model::  51%|█████     | 253/500 [00:13<00:10, 22.53it/s]
eval model::  51%|█████     | 256/500 [00:13<00:10, 22.73it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 23.20it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 21.55it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:11, 19.61it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:11, 20.51it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:11, 20.03it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:12, 18.00it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:13, 16.82it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 18.97it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 20.51it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:20, 10.63it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:16, 13.09it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:13, 15.73it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.76it/s]
eval model::  59%|█████▉    | 297/500 [00:16<00:13, 15.40it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 16.46it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 16.92it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 17.31it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:09, 19.59it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.94it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 21.74it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:08, 20.87it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:07, 22.72it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.76it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:07, 22.74it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:07, 21.62it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:07, 22.19it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:06, 23.67it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:07, 21.19it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:09, 16.67it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:08, 18.83it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:07, 20.49it/s]
eval model::  70%|███████   | 351/500 [00:18<00:06, 23.45it/s]
eval model::  71%|███████   | 354/500 [00:18<00:05, 24.35it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:06, 20.97it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:06, 21.84it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:06, 19.96it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 19.06it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 20.48it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 21.00it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 20.38it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 20.47it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:06, 17.74it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 19.43it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:08, 13.05it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:07, 14.97it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 15.82it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 17.20it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 19.06it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 16.85it/s]
eval model::  81%|████████  | 404/500 [00:21<00:05, 18.26it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 19.42it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 21.24it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:03, 21.82it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:03, 22.01it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:03, 20.34it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 15.01it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.39it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.15it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:04, 17.53it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 16.07it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 20.04it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 23.21it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 22.81it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 22.10it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 20.52it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.67it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 20.75it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:02, 20.85it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 20.62it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 17.46it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:01, 17.75it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.79it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.38it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 19.09it/s]
eval model::  95%|█████████▌| 475/500 [00:25<00:01, 19.02it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:01, 19.83it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 20.07it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 19.57it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 20.56it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 21.68it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 20.26it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 20.60it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 21.55it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.93it/s]
[2024/07/27 13:59:26] ppocr INFO: cur metric, precision: 0.7822857142857143, recall: 0.6591237361579201, hmean: 0.7154429056702378, fps: 44.393764002497214
[2024/07/27 13:59:26] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 13:59:26] ppocr INFO: best metric, hmean: 0.7154429056702378, precision: 0.7822857142857143, recall: 0.6591237361579201, fps: 44.393764002497214, best_epoch: 860
[2024/07/27 13:59:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:59:35] ppocr INFO: epoch: [861/1500], global_step: 2583, lr: 0.001000, loss: 1.273996, loss_shrink_maps: 0.659165, loss_threshold_maps: 0.481068, loss_binary_maps: 0.131198, avg_reader_cost: 2.11415 s, avg_batch_cost: 2.45683 s, avg_samples: 12.5, ips: 5.08786 samples/s, eta: 4:37:47
[2024/07/27 13:59:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:59:45] ppocr INFO: epoch: [862/1500], global_step: 2586, lr: 0.001000, loss: 1.287828, loss_shrink_maps: 0.656214, loss_threshold_maps: 0.485401, loss_binary_maps: 0.130521, avg_reader_cost: 2.32493 s, avg_batch_cost: 2.56215 s, avg_samples: 12.5, ips: 4.87871 samples/s, eta: 4:37:21
[2024/07/27 13:59:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 13:59:54] ppocr INFO: epoch: [863/1500], global_step: 2589, lr: 0.001000, loss: 1.286369, loss_shrink_maps: 0.654314, loss_threshold_maps: 0.496499, loss_binary_maps: 0.129337, avg_reader_cost: 2.17517 s, avg_batch_cost: 2.53247 s, avg_samples: 12.5, ips: 4.93590 samples/s, eta: 4:36:54
[2024/07/27 13:59:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:00:02] ppocr INFO: epoch: [864/1500], global_step: 2590, lr: 0.001000, loss: 1.279100, loss_shrink_maps: 0.650014, loss_threshold_maps: 0.496499, loss_binary_maps: 0.128974, avg_reader_cost: 0.56148 s, avg_batch_cost: 0.71401 s, avg_samples: 4.8, ips: 6.72257 samples/s, eta: 4:36:44
[2024/07/27 14:00:03] ppocr INFO: epoch: [864/1500], global_step: 2592, lr: 0.001000, loss: 1.266727, loss_shrink_maps: 0.645082, loss_threshold_maps: 0.496499, loss_binary_maps: 0.128476, avg_reader_cost: 1.52100 s, avg_batch_cost: 1.66875 s, avg_samples: 7.7, ips: 4.61423 samples/s, eta: 4:36:26
[2024/07/27 14:00:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:00:13] ppocr INFO: epoch: [865/1500], global_step: 2595, lr: 0.001000, loss: 1.252878, loss_shrink_maps: 0.639675, loss_threshold_maps: 0.481068, loss_binary_maps: 0.126738, avg_reader_cost: 2.43955 s, avg_batch_cost: 2.69095 s, avg_samples: 12.5, ips: 4.64520 samples/s, eta: 4:36:01
[2024/07/27 14:00:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:00:23] ppocr INFO: epoch: [866/1500], global_step: 2598, lr: 0.001000, loss: 1.236236, loss_shrink_maps: 0.625682, loss_threshold_maps: 0.490225, loss_binary_maps: 0.124167, avg_reader_cost: 2.41432 s, avg_batch_cost: 2.65631 s, avg_samples: 12.5, ips: 4.70578 samples/s, eta: 4:35:35
[2024/07/27 14:00:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:00:33] ppocr INFO: epoch: [867/1500], global_step: 2600, lr: 0.001000, loss: 1.252345, loss_shrink_maps: 0.633728, loss_threshold_maps: 0.490428, loss_binary_maps: 0.126123, avg_reader_cost: 1.49239 s, avg_batch_cost: 1.67931 s, avg_samples: 9.6, ips: 5.71663 samples/s, eta: 4:35:17
[2024/07/27 14:00:33] ppocr INFO: epoch: [867/1500], global_step: 2601, lr: 0.001000, loss: 1.258118, loss_shrink_maps: 0.642339, loss_threshold_maps: 0.490726, loss_binary_maps: 0.127507, avg_reader_cost: 0.88580 s, avg_batch_cost: 0.94137 s, avg_samples: 2.9, ips: 3.08062 samples/s, eta: 4:35:09
[2024/07/27 14:00:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:00:43] ppocr INFO: epoch: [868/1500], global_step: 2604, lr: 0.001000, loss: 1.238719, loss_shrink_maps: 0.622947, loss_threshold_maps: 0.490225, loss_binary_maps: 0.123879, avg_reader_cost: 2.26874 s, avg_batch_cost: 2.50402 s, avg_samples: 12.5, ips: 4.99196 samples/s, eta: 4:34:42
[2024/07/27 14:00:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:00:53] ppocr INFO: epoch: [869/1500], global_step: 2607, lr: 0.001000, loss: 1.238719, loss_shrink_maps: 0.622947, loss_threshold_maps: 0.490225, loss_binary_maps: 0.123879, avg_reader_cost: 2.35283 s, avg_batch_cost: 2.60501 s, avg_samples: 12.5, ips: 4.79844 samples/s, eta: 4:34:16
[2024/07/27 14:00:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:01:03] ppocr INFO: epoch: [870/1500], global_step: 2610, lr: 0.001000, loss: 1.232104, loss_shrink_maps: 0.617324, loss_threshold_maps: 0.490225, loss_binary_maps: 0.122635, avg_reader_cost: 2.28781 s, avg_batch_cost: 2.69485 s, avg_samples: 12.5, ips: 4.63848 samples/s, eta: 4:33:51
[2024/07/27 14:01:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:01:13] ppocr INFO: epoch: [871/1500], global_step: 2613, lr: 0.001000, loss: 1.241204, loss_shrink_maps: 0.622947, loss_threshold_maps: 0.490225, loss_binary_maps: 0.123879, avg_reader_cost: 2.17150 s, avg_batch_cost: 2.58158 s, avg_samples: 12.5, ips: 4.84199 samples/s, eta: 4:33:24
[2024/07/27 14:01:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:01:22] ppocr INFO: epoch: [872/1500], global_step: 2616, lr: 0.001000, loss: 1.263036, loss_shrink_maps: 0.636760, loss_threshold_maps: 0.494946, loss_binary_maps: 0.126721, avg_reader_cost: 2.33561 s, avg_batch_cost: 2.58780 s, avg_samples: 12.5, ips: 4.83037 samples/s, eta: 4:32:58
[2024/07/27 14:01:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:01:32] ppocr INFO: epoch: [873/1500], global_step: 2619, lr: 0.001000, loss: 1.258510, loss_shrink_maps: 0.629004, loss_threshold_maps: 0.498044, loss_binary_maps: 0.125337, avg_reader_cost: 2.33286 s, avg_batch_cost: 2.57700 s, avg_samples: 12.5, ips: 4.85060 samples/s, eta: 4:32:32
[2024/07/27 14:01:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:01:41] ppocr INFO: epoch: [874/1500], global_step: 2620, lr: 0.001000, loss: 1.281586, loss_shrink_maps: 0.637155, loss_threshold_maps: 0.508413, loss_binary_maps: 0.126753, avg_reader_cost: 0.59345 s, avg_batch_cost: 0.79699 s, avg_samples: 4.8, ips: 6.02263 samples/s, eta: 4:32:23
[2024/07/27 14:01:42] ppocr INFO: epoch: [874/1500], global_step: 2622, lr: 0.001000, loss: 1.256716, loss_shrink_maps: 0.629004, loss_threshold_maps: 0.496016, loss_binary_maps: 0.124952, avg_reader_cost: 1.68586 s, avg_batch_cost: 1.83345 s, avg_samples: 7.7, ips: 4.19973 samples/s, eta: 4:32:06
[2024/07/27 14:01:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:01:52] ppocr INFO: epoch: [875/1500], global_step: 2625, lr: 0.001000, loss: 1.283140, loss_shrink_maps: 0.644839, loss_threshold_maps: 0.498188, loss_binary_maps: 0.128528, avg_reader_cost: 2.39745 s, avg_batch_cost: 2.63397 s, avg_samples: 12.5, ips: 4.74568 samples/s, eta: 4:31:40
[2024/07/27 14:01:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:02:02] ppocr INFO: epoch: [876/1500], global_step: 2628, lr: 0.001000, loss: 1.296812, loss_shrink_maps: 0.649320, loss_threshold_maps: 0.507487, loss_binary_maps: 0.128948, avg_reader_cost: 2.31353 s, avg_batch_cost: 2.55367 s, avg_samples: 12.5, ips: 4.89492 samples/s, eta: 4:31:14
[2024/07/27 14:02:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:02:11] ppocr INFO: epoch: [877/1500], global_step: 2630, lr: 0.001000, loss: 1.284221, loss_shrink_maps: 0.644839, loss_threshold_maps: 0.500763, loss_binary_maps: 0.128495, avg_reader_cost: 1.33160 s, avg_batch_cost: 1.63666 s, avg_samples: 9.6, ips: 5.86561 samples/s, eta: 4:30:56
[2024/07/27 14:02:12] ppocr INFO: epoch: [877/1500], global_step: 2631, lr: 0.001000, loss: 1.273389, loss_shrink_maps: 0.642887, loss_threshold_maps: 0.500763, loss_binary_maps: 0.128060, avg_reader_cost: 0.86436 s, avg_batch_cost: 0.92017 s, avg_samples: 2.9, ips: 3.15159 samples/s, eta: 4:30:47
[2024/07/27 14:02:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:02:21] ppocr INFO: epoch: [878/1500], global_step: 2634, lr: 0.001000, loss: 1.287061, loss_shrink_maps: 0.644839, loss_threshold_maps: 0.511920, loss_binary_maps: 0.128495, avg_reader_cost: 2.38264 s, avg_batch_cost: 2.62084 s, avg_samples: 12.5, ips: 4.76946 samples/s, eta: 4:30:21
[2024/07/27 14:02:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:02:31] ppocr INFO: epoch: [879/1500], global_step: 2637, lr: 0.001000, loss: 1.287061, loss_shrink_maps: 0.644839, loss_threshold_maps: 0.511920, loss_binary_maps: 0.128495, avg_reader_cost: 2.25315 s, avg_batch_cost: 2.51202 s, avg_samples: 12.5, ips: 4.97607 samples/s, eta: 4:29:54
[2024/07/27 14:02:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:02:41] ppocr INFO: epoch: [880/1500], global_step: 2640, lr: 0.001000, loss: 1.273389, loss_shrink_maps: 0.644114, loss_threshold_maps: 0.500763, loss_binary_maps: 0.128479, avg_reader_cost: 2.18738 s, avg_batch_cost: 2.53573 s, avg_samples: 12.5, ips: 4.92954 samples/s, eta: 4:29:28

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:55,  8.97it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.05it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.37it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.37it/s]
eval model::   2%|▏         | 11/500 [00:00<00:25, 18.83it/s]
eval model::   3%|▎         | 13/500 [00:00<00:27, 18.03it/s]
eval model::   3%|▎         | 15/500 [00:00<00:28, 16.96it/s]
eval model::   4%|▎         | 18/500 [00:00<00:24, 19.43it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 18.84it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 21.12it/s]
eval model::   5%|▌         | 26/500 [00:01<00:21, 21.64it/s]
eval model::   6%|▌         | 29/500 [00:01<00:20, 22.87it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 20.15it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 18.88it/s]
eval model::   8%|▊         | 38/500 [00:02<00:25, 18.40it/s]
eval model::   8%|▊         | 40/500 [00:02<00:27, 16.98it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.00it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.33it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 18.08it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.16it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 20.32it/s]
eval model::  11%|█         | 56/500 [00:02<00:21, 20.97it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:22, 19.21it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.17it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.16it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.21it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.12it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.02it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 20.54it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.55it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 23.07it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 20.99it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 20.45it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:20, 19.54it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:20, 19.54it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.59it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 22.20it/s]
eval model::  20%|██        | 102/500 [00:05<00:18, 21.79it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 20.84it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.27it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 20.60it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:17, 22.03it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:19, 19.98it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:21, 17.76it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 19.86it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.73it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 19.01it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.75it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.68it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.34it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:19, 18.46it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.74it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.02it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.30it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 20.36it/s]
eval model::  31%|███       | 156/500 [00:07<00:15, 21.91it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 20.96it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:18, 18.54it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:18, 18.18it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 18.35it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:18, 17.80it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:19, 17.29it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 16.35it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.25it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.46it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:18, 17.26it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:15, 19.90it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 20.04it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:15, 20.22it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:14, 21.82it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:14, 20.87it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:13, 22.25it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 21.07it/s]
eval model::  41%|████      | 205/500 [00:10<00:19, 14.86it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:18, 15.69it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:17, 16.17it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:18, 15.88it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.92it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.72it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.58it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.04it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:19, 14.52it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:15, 17.72it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:15, 18.19it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 22.01it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 20.93it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 21.97it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 21.89it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:13, 19.51it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:12, 21.15it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:13, 19.25it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 22.26it/s]
eval model::  51%|█████     | 256/500 [00:13<00:10, 22.36it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:09, 24.77it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 22.23it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:11, 20.01it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 20.24it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:10, 21.82it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.13it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.89it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:11, 19.85it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 20.05it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:18, 11.36it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:14, 14.83it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:13, 15.58it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:14, 13.62it/s]
eval model::  60%|██████    | 300/500 [00:15<00:12, 15.73it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.35it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 18.88it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 20.73it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 20.38it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 19.90it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 21.20it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 21.47it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 21.44it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 22.51it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:07, 21.61it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 22.03it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:06, 23.79it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 21.67it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 15.79it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 18.09it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:07, 19.48it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 22.51it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 22.20it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 21.68it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:06, 21.35it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.81it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.33it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 18.97it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 18.88it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 18.34it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 18.10it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 22.77it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 20.72it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:05, 21.93it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:08, 12.55it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:07, 14.52it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 15.32it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:06, 17.23it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 19.74it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 19.13it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 20.00it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.98it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 23.71it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 22.28it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 19.47it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 13.75it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 14.13it/s]
eval model::  85%|████████▌ | 425/500 [00:22<00:04, 15.53it/s]
eval model::  86%|████████▌ | 428/500 [00:22<00:04, 17.69it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 19.32it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:02, 22.59it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 25.07it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 24.07it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 25.06it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 23.67it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 22.43it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:01, 23.18it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 23.92it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 21.09it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 15.99it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 14.66it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:02, 14.73it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 17.12it/s]
eval model::  95%|█████████▍| 473/500 [00:24<00:01, 18.59it/s]
eval model::  95%|█████████▌| 476/500 [00:24<00:01, 20.03it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:01, 19.91it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 20.23it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 22.29it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 21.83it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 21.17it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 19.34it/s]
eval model::  99%|█████████▉| 497/500 [00:25<00:00, 19.94it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 21.20it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.16it/s]
[2024/07/27 14:03:07] ppocr INFO: cur metric, precision: 0.7548913043478261, recall: 0.6687530091478093, hmean: 0.7092162369160071, fps: 45.657436854920455
[2024/07/27 14:03:07] ppocr INFO: best metric, hmean: 0.7154429056702378, precision: 0.7822857142857143, recall: 0.6591237361579201, fps: 44.393764002497214, best_epoch: 860
[2024/07/27 14:03:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:03:17] ppocr INFO: epoch: [881/1500], global_step: 2643, lr: 0.001000, loss: 1.342170, loss_shrink_maps: 0.671811, loss_threshold_maps: 0.522285, loss_binary_maps: 0.133278, avg_reader_cost: 2.39422 s, avg_batch_cost: 2.83735 s, avg_samples: 12.5, ips: 4.40552 samples/s, eta: 4:29:03
[2024/07/27 14:03:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:03:27] ppocr INFO: epoch: [882/1500], global_step: 2646, lr: 0.001000, loss: 1.220131, loss_shrink_maps: 0.622015, loss_threshold_maps: 0.495034, loss_binary_maps: 0.123985, avg_reader_cost: 2.34058 s, avg_batch_cost: 2.57677 s, avg_samples: 12.5, ips: 4.85103 samples/s, eta: 4:28:37
[2024/07/27 14:03:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:03:37] ppocr INFO: epoch: [883/1500], global_step: 2649, lr: 0.001000, loss: 1.205146, loss_shrink_maps: 0.619025, loss_threshold_maps: 0.485295, loss_binary_maps: 0.123627, avg_reader_cost: 2.22532 s, avg_batch_cost: 2.58225 s, avg_samples: 12.5, ips: 4.84074 samples/s, eta: 4:28:11
[2024/07/27 14:03:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:03:45] ppocr INFO: epoch: [884/1500], global_step: 2650, lr: 0.001000, loss: 1.203629, loss_shrink_maps: 0.619025, loss_threshold_maps: 0.471531, loss_binary_maps: 0.123627, avg_reader_cost: 0.69053 s, avg_batch_cost: 0.81013 s, avg_samples: 4.8, ips: 5.92494 samples/s, eta: 4:28:02
[2024/07/27 14:03:47] ppocr INFO: epoch: [884/1500], global_step: 2652, lr: 0.001000, loss: 1.196864, loss_shrink_maps: 0.611248, loss_threshold_maps: 0.471531, loss_binary_maps: 0.121921, avg_reader_cost: 1.71311 s, avg_batch_cost: 1.85994 s, avg_samples: 7.7, ips: 4.13993 samples/s, eta: 4:27:45
[2024/07/27 14:03:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:03:56] ppocr INFO: epoch: [885/1500], global_step: 2655, lr: 0.001000, loss: 1.182082, loss_shrink_maps: 0.595143, loss_threshold_maps: 0.471531, loss_binary_maps: 0.118405, avg_reader_cost: 2.21801 s, avg_batch_cost: 2.58120 s, avg_samples: 12.5, ips: 4.84271 samples/s, eta: 4:27:19
[2024/07/27 14:03:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:04:07] ppocr INFO: epoch: [886/1500], global_step: 2658, lr: 0.001000, loss: 1.151308, loss_shrink_maps: 0.578979, loss_threshold_maps: 0.460676, loss_binary_maps: 0.115169, avg_reader_cost: 2.32685 s, avg_batch_cost: 2.71230 s, avg_samples: 12.5, ips: 4.60864 samples/s, eta: 4:26:54
[2024/07/27 14:04:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:04:16] ppocr INFO: epoch: [887/1500], global_step: 2660, lr: 0.001000, loss: 1.149090, loss_shrink_maps: 0.574148, loss_threshold_maps: 0.454773, loss_binary_maps: 0.114001, avg_reader_cost: 1.55027 s, avg_batch_cost: 1.74791 s, avg_samples: 9.6, ips: 5.49228 samples/s, eta: 4:26:36
[2024/07/27 14:04:17] ppocr INFO: epoch: [887/1500], global_step: 2661, lr: 0.001000, loss: 1.149090, loss_shrink_maps: 0.574148, loss_threshold_maps: 0.454773, loss_binary_maps: 0.114001, avg_reader_cost: 0.92044 s, avg_batch_cost: 0.97562 s, avg_samples: 2.9, ips: 2.97248 samples/s, eta: 4:26:28
[2024/07/27 14:04:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:04:27] ppocr INFO: epoch: [888/1500], global_step: 2664, lr: 0.001000, loss: 1.149090, loss_shrink_maps: 0.574148, loss_threshold_maps: 0.454773, loss_binary_maps: 0.114001, avg_reader_cost: 2.33030 s, avg_batch_cost: 2.57253 s, avg_samples: 12.5, ips: 4.85902 samples/s, eta: 4:26:02
[2024/07/27 14:04:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:04:36] ppocr INFO: epoch: [889/1500], global_step: 2667, lr: 0.001000, loss: 1.151683, loss_shrink_maps: 0.587365, loss_threshold_maps: 0.464592, loss_binary_maps: 0.116931, avg_reader_cost: 2.22219 s, avg_batch_cost: 2.57817 s, avg_samples: 12.5, ips: 4.84839 samples/s, eta: 4:25:36
[2024/07/27 14:04:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:04:46] ppocr INFO: epoch: [890/1500], global_step: 2670, lr: 0.001000, loss: 1.185316, loss_shrink_maps: 0.589235, loss_threshold_maps: 0.491543, loss_binary_maps: 0.117639, avg_reader_cost: 2.27204 s, avg_batch_cost: 2.51073 s, avg_samples: 12.5, ips: 4.97864 samples/s, eta: 4:25:09
[2024/07/27 14:04:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:04:55] ppocr INFO: epoch: [891/1500], global_step: 2673, lr: 0.001000, loss: 1.188132, loss_shrink_maps: 0.589235, loss_threshold_maps: 0.485728, loss_binary_maps: 0.117639, avg_reader_cost: 2.34853 s, avg_batch_cost: 2.58892 s, avg_samples: 12.5, ips: 4.82826 samples/s, eta: 4:24:43
[2024/07/27 14:04:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:05:06] ppocr INFO: epoch: [892/1500], global_step: 2676, lr: 0.001000, loss: 1.249764, loss_shrink_maps: 0.627532, loss_threshold_maps: 0.495367, loss_binary_maps: 0.124741, avg_reader_cost: 2.32150 s, avg_batch_cost: 2.70834 s, avg_samples: 12.5, ips: 4.61537 samples/s, eta: 4:24:17
[2024/07/27 14:05:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:05:16] ppocr INFO: epoch: [893/1500], global_step: 2679, lr: 0.001000, loss: 1.291969, loss_shrink_maps: 0.657550, loss_threshold_maps: 0.504244, loss_binary_maps: 0.130831, avg_reader_cost: 2.25759 s, avg_batch_cost: 2.62962 s, avg_samples: 12.5, ips: 4.75353 samples/s, eta: 4:23:51
[2024/07/27 14:05:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:05:24] ppocr INFO: epoch: [894/1500], global_step: 2680, lr: 0.001000, loss: 1.291969, loss_shrink_maps: 0.657550, loss_threshold_maps: 0.509144, loss_binary_maps: 0.130831, avg_reader_cost: 0.65783 s, avg_batch_cost: 0.75207 s, avg_samples: 4.8, ips: 6.38240 samples/s, eta: 4:23:42
[2024/07/27 14:05:25] ppocr INFO: epoch: [894/1500], global_step: 2682, lr: 0.001000, loss: 1.291969, loss_shrink_maps: 0.657550, loss_threshold_maps: 0.509144, loss_binary_maps: 0.130831, avg_reader_cost: 1.59687 s, avg_batch_cost: 1.74396 s, avg_samples: 7.7, ips: 4.41524 samples/s, eta: 4:23:25
[2024/07/27 14:05:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:05:35] ppocr INFO: epoch: [895/1500], global_step: 2685, lr: 0.001000, loss: 1.267692, loss_shrink_maps: 0.640106, loss_threshold_maps: 0.508576, loss_binary_maps: 0.127039, avg_reader_cost: 2.40831 s, avg_batch_cost: 2.65501 s, avg_samples: 12.5, ips: 4.70808 samples/s, eta: 4:22:59
[2024/07/27 14:05:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:05:45] ppocr INFO: epoch: [896/1500], global_step: 2688, lr: 0.001000, loss: 1.258737, loss_shrink_maps: 0.640106, loss_threshold_maps: 0.501182, loss_binary_maps: 0.127039, avg_reader_cost: 2.39887 s, avg_batch_cost: 2.64364 s, avg_samples: 12.5, ips: 4.72833 samples/s, eta: 4:22:33
[2024/07/27 14:05:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:05:55] ppocr INFO: epoch: [897/1500], global_step: 2690, lr: 0.001000, loss: 1.270703, loss_shrink_maps: 0.642196, loss_threshold_maps: 0.502546, loss_binary_maps: 0.127435, avg_reader_cost: 1.37866 s, avg_batch_cost: 1.69397 s, avg_samples: 9.6, ips: 5.66715 samples/s, eta: 4:22:15
[2024/07/27 14:05:55] ppocr INFO: epoch: [897/1500], global_step: 2691, lr: 0.001000, loss: 1.290825, loss_shrink_maps: 0.648998, loss_threshold_maps: 0.508252, loss_binary_maps: 0.129211, avg_reader_cost: 0.89373 s, avg_batch_cost: 0.94875 s, avg_samples: 2.9, ips: 3.05667 samples/s, eta: 4:22:07
[2024/07/27 14:05:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:06:05] ppocr INFO: epoch: [898/1500], global_step: 2694, lr: 0.001000, loss: 1.257593, loss_shrink_maps: 0.631553, loss_threshold_maps: 0.503182, loss_binary_maps: 0.125419, avg_reader_cost: 2.18185 s, avg_batch_cost: 2.55205 s, avg_samples: 12.5, ips: 4.89803 samples/s, eta: 4:21:41
[2024/07/27 14:06:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:06:15] ppocr INFO: epoch: [899/1500], global_step: 2697, lr: 0.001000, loss: 1.257593, loss_shrink_maps: 0.641718, loss_threshold_maps: 0.498542, loss_binary_maps: 0.127784, avg_reader_cost: 2.30602 s, avg_batch_cost: 2.54711 s, avg_samples: 12.5, ips: 4.90753 samples/s, eta: 4:21:14
[2024/07/27 14:06:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:06:24] ppocr INFO: epoch: [900/1500], global_step: 2700, lr: 0.001000, loss: 1.253112, loss_shrink_maps: 0.641718, loss_threshold_maps: 0.493428, loss_binary_maps: 0.127784, avg_reader_cost: 2.23645 s, avg_batch_cost: 2.62910 s, avg_samples: 12.5, ips: 4.75448 samples/s, eta: 4:20:48

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:59,  8.43it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.17it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.75it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.60it/s]
eval model::   2%|▏         | 10/500 [00:00<00:27, 18.09it/s]
eval model::   2%|▏         | 12/500 [00:00<00:26, 18.11it/s]
eval model::   3%|▎         | 14/500 [00:00<00:29, 16.58it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 20.03it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.52it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.56it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.00it/s]
eval model::   6%|▌         | 28/500 [00:01<00:26, 17.88it/s]
eval model::   6%|▌         | 31/500 [00:01<00:27, 17.15it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.52it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 19.85it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.22it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.41it/s]
eval model::   9%|▉         | 44/500 [00:02<00:26, 16.89it/s]
eval model::   9%|▉         | 46/500 [00:02<00:28, 15.90it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.25it/s]
eval model::  10%|█         | 51/500 [00:02<00:24, 18.66it/s]
eval model::  11%|█         | 54/500 [00:02<00:21, 20.67it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:22, 19.47it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:24, 18.17it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:29, 14.79it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:29, 14.60it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:28, 15.13it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:24, 17.51it/s]
eval model::  14%|█▍        | 72/500 [00:04<00:23, 18.18it/s]
eval model::  15%|█▍        | 74/500 [00:04<00:25, 16.75it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:23, 17.68it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:20, 20.09it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:19, 21.73it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:20, 20.20it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.22it/s]
eval model::  18%|█▊        | 91/500 [00:05<00:24, 17.04it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:23, 16.99it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:22, 18.09it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.66it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.43it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.72it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:16, 23.10it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:16, 23.85it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:16, 22.85it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:16, 22.72it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:19, 19.49it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:21, 17.73it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 18.02it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 19.40it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:22, 16.58it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:20, 17.92it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:20, 17.69it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:19, 18.15it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.63it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.29it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.35it/s]
eval model::  30%|███       | 150/500 [00:08<00:18, 19.33it/s]
eval model::  30%|███       | 152/500 [00:08<00:18, 19.05it/s]
eval model::  31%|███       | 156/500 [00:08<00:15, 22.91it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:15, 22.20it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:18, 18.42it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:17, 18.75it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 19.10it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:15, 20.97it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:16, 19.63it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.33it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.28it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:18, 16.90it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:16, 19.38it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:16, 18.95it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:16, 18.64it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:15, 20.25it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.73it/s]
eval model::  40%|████      | 200/500 [00:10<00:15, 19.92it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 20.67it/s]
eval model::  41%|████      | 206/500 [00:11<00:20, 14.07it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:21, 13.59it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 14.07it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 15.06it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.92it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.63it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:18, 15.34it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:22, 12.46it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:17, 15.50it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 19.87it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 21.62it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 22.89it/s]
eval model::  48%|████▊     | 240/500 [00:13<00:10, 24.32it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:13, 18.64it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:12, 20.56it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:13, 18.95it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 20.90it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.64it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 20.33it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:11, 20.98it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:11, 20.91it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 20.73it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 19.36it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 19.68it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 18.41it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:11, 19.41it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 20.31it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.30it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.67it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 17.30it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:11, 17.33it/s]
eval model::  59%|█████▉    | 297/500 [00:16<00:13, 15.06it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 15.84it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.69it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 17.74it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.07it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:10, 17.86it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:10, 18.73it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:09, 19.42it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:09, 19.03it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:09, 19.89it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:09, 18.61it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 19.51it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 19.21it/s]
eval model::  66%|██████▋   | 332/500 [00:18<00:07, 21.31it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:06, 23.84it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:06, 23.99it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:08, 19.02it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 21.25it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:06, 22.38it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 22.58it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 20.91it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 20.85it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 19.24it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.67it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:06, 19.21it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.70it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:06, 20.88it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 21.27it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 21.58it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:04, 23.47it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 14.44it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:06, 16.31it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 16.89it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 16.59it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:06, 17.03it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:06, 16.57it/s]
eval model::  80%|████████  | 401/500 [00:21<00:06, 15.84it/s]
eval model::  81%|████████  | 403/500 [00:21<00:05, 16.70it/s]
eval model::  81%|████████  | 406/500 [00:21<00:05, 18.63it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:04, 18.78it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:04, 20.43it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:04, 20.79it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 19.17it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 18.63it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:06, 12.67it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.70it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:04, 15.09it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:03, 17.82it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 16.51it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 18.80it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:03, 19.95it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:03, 19.29it/s]
eval model::  89%|████████▊ | 443/500 [00:23<00:02, 21.45it/s]
eval model::  89%|████████▉ | 446/500 [00:24<00:02, 21.36it/s]
eval model::  90%|████████▉ | 449/500 [00:24<00:02, 19.06it/s]
eval model::  90%|█████████ | 452/500 [00:24<00:02, 18.85it/s]
eval model::  91%|█████████ | 455/500 [00:24<00:02, 19.01it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:02, 20.25it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:02, 19.23it/s]
eval model::  93%|█████████▎| 463/500 [00:25<00:02, 16.04it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 15.19it/s]
eval model::  93%|█████████▎| 467/500 [00:25<00:02, 16.04it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.99it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.66it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.09it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 20.68it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 20.45it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 20.27it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 21.67it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 19.56it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 20.16it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 22.34it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.64it/s]
[2024/07/27 14:06:51] ppocr INFO: cur metric, precision: 0.766951566951567, recall: 0.6480500722195475, hmean: 0.7025052192066806, fps: 42.7353551947244
[2024/07/27 14:06:51] ppocr INFO: best metric, hmean: 0.7154429056702378, precision: 0.7822857142857143, recall: 0.6591237361579201, fps: 44.393764002497214, best_epoch: 860
[2024/07/27 14:06:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:07:00] ppocr INFO: epoch: [901/1500], global_step: 2703, lr: 0.001000, loss: 1.278390, loss_shrink_maps: 0.650514, loss_threshold_maps: 0.493428, loss_binary_maps: 0.129554, avg_reader_cost: 2.18321 s, avg_batch_cost: 2.42211 s, avg_samples: 12.5, ips: 5.16078 samples/s, eta: 4:20:21
[2024/07/27 14:07:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:07:10] ppocr INFO: epoch: [902/1500], global_step: 2706, lr: 0.001000, loss: 1.278390, loss_shrink_maps: 0.650514, loss_threshold_maps: 0.491774, loss_binary_maps: 0.129554, avg_reader_cost: 2.17512 s, avg_batch_cost: 2.63590 s, avg_samples: 12.5, ips: 4.74221 samples/s, eta: 4:19:55
[2024/07/27 14:07:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:07:20] ppocr INFO: epoch: [903/1500], global_step: 2709, lr: 0.001000, loss: 1.270680, loss_shrink_maps: 0.643333, loss_threshold_maps: 0.496321, loss_binary_maps: 0.127931, avg_reader_cost: 2.23763 s, avg_batch_cost: 2.57591 s, avg_samples: 12.5, ips: 4.85266 samples/s, eta: 4:19:29
[2024/07/27 14:07:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:07:28] ppocr INFO: epoch: [904/1500], global_step: 2710, lr: 0.001000, loss: 1.257115, loss_shrink_maps: 0.643333, loss_threshold_maps: 0.490972, loss_binary_maps: 0.127931, avg_reader_cost: 0.61084 s, avg_batch_cost: 0.78153 s, avg_samples: 4.8, ips: 6.14176 samples/s, eta: 4:19:19
[2024/07/27 14:07:30] ppocr INFO: epoch: [904/1500], global_step: 2712, lr: 0.001000, loss: 1.239546, loss_shrink_maps: 0.630027, loss_threshold_maps: 0.484054, loss_binary_maps: 0.125118, avg_reader_cost: 1.65492 s, avg_batch_cost: 1.80168 s, avg_samples: 7.7, ips: 4.27379 samples/s, eta: 4:19:02
[2024/07/27 14:07:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:07:39] ppocr INFO: epoch: [905/1500], global_step: 2715, lr: 0.001000, loss: 1.257115, loss_shrink_maps: 0.630027, loss_threshold_maps: 0.496321, loss_binary_maps: 0.125118, avg_reader_cost: 2.22798 s, avg_batch_cost: 2.57299 s, avg_samples: 12.5, ips: 4.85816 samples/s, eta: 4:18:36
[2024/07/27 14:07:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:07:49] ppocr INFO: epoch: [906/1500], global_step: 2718, lr: 0.001000, loss: 1.240538, loss_shrink_maps: 0.619162, loss_threshold_maps: 0.489590, loss_binary_maps: 0.123687, avg_reader_cost: 2.21896 s, avg_batch_cost: 2.56093 s, avg_samples: 12.5, ips: 4.88104 samples/s, eta: 4:18:10
[2024/07/27 14:07:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:07:58] ppocr INFO: epoch: [907/1500], global_step: 2720, lr: 0.001000, loss: 1.257115, loss_shrink_maps: 0.630027, loss_threshold_maps: 0.494136, loss_binary_maps: 0.125118, avg_reader_cost: 1.33708 s, avg_batch_cost: 1.64259 s, avg_samples: 9.6, ips: 5.84443 samples/s, eta: 4:17:52
[2024/07/27 14:07:59] ppocr INFO: epoch: [907/1500], global_step: 2721, lr: 0.001000, loss: 1.257115, loss_shrink_maps: 0.630027, loss_threshold_maps: 0.496926, loss_binary_maps: 0.125118, avg_reader_cost: 0.86808 s, avg_batch_cost: 0.92297 s, avg_samples: 2.9, ips: 3.14203 samples/s, eta: 4:17:43
[2024/07/27 14:08:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:08:09] ppocr INFO: epoch: [908/1500], global_step: 2724, lr: 0.001000, loss: 1.256775, loss_shrink_maps: 0.627255, loss_threshold_maps: 0.496926, loss_binary_maps: 0.124645, avg_reader_cost: 2.32964 s, avg_batch_cost: 2.56909 s, avg_samples: 12.5, ips: 4.86553 samples/s, eta: 4:17:17
[2024/07/27 14:08:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:08:18] ppocr INFO: epoch: [909/1500], global_step: 2727, lr: 0.001000, loss: 1.254886, loss_shrink_maps: 0.623235, loss_threshold_maps: 0.498849, loss_binary_maps: 0.124604, avg_reader_cost: 2.38412 s, avg_batch_cost: 2.61712 s, avg_samples: 12.5, ips: 4.77624 samples/s, eta: 4:16:51
[2024/07/27 14:08:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:08:28] ppocr INFO: epoch: [910/1500], global_step: 2730, lr: 0.001000, loss: 1.254886, loss_shrink_maps: 0.623235, loss_threshold_maps: 0.500734, loss_binary_maps: 0.124604, avg_reader_cost: 2.20556 s, avg_batch_cost: 2.52590 s, avg_samples: 12.5, ips: 4.94873 samples/s, eta: 4:16:24
[2024/07/27 14:08:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:08:38] ppocr INFO: epoch: [911/1500], global_step: 2733, lr: 0.001000, loss: 1.290775, loss_shrink_maps: 0.647521, loss_threshold_maps: 0.514917, loss_binary_maps: 0.128901, avg_reader_cost: 2.19956 s, avg_batch_cost: 2.58793 s, avg_samples: 12.5, ips: 4.83011 samples/s, eta: 4:15:58
[2024/07/27 14:08:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:08:48] ppocr INFO: epoch: [912/1500], global_step: 2736, lr: 0.001000, loss: 1.307988, loss_shrink_maps: 0.644757, loss_threshold_maps: 0.520882, loss_binary_maps: 0.127979, avg_reader_cost: 2.16694 s, avg_batch_cost: 2.52566 s, avg_samples: 12.5, ips: 4.94920 samples/s, eta: 4:15:32
[2024/07/27 14:08:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:08:57] ppocr INFO: epoch: [913/1500], global_step: 2739, lr: 0.001000, loss: 1.307988, loss_shrink_maps: 0.644757, loss_threshold_maps: 0.530770, loss_binary_maps: 0.127979, avg_reader_cost: 2.23045 s, avg_batch_cost: 2.58394 s, avg_samples: 12.5, ips: 4.83757 samples/s, eta: 4:15:05
[2024/07/27 14:08:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:09:06] ppocr INFO: epoch: [914/1500], global_step: 2740, lr: 0.001000, loss: 1.307988, loss_shrink_maps: 0.644757, loss_threshold_maps: 0.530770, loss_binary_maps: 0.127979, avg_reader_cost: 0.69729 s, avg_batch_cost: 0.79775 s, avg_samples: 4.8, ips: 6.01693 samples/s, eta: 4:14:56
[2024/07/27 14:09:07] ppocr INFO: epoch: [914/1500], global_step: 2742, lr: 0.001000, loss: 1.294910, loss_shrink_maps: 0.643604, loss_threshold_maps: 0.520882, loss_binary_maps: 0.127720, avg_reader_cost: 1.68745 s, avg_batch_cost: 1.83374 s, avg_samples: 7.7, ips: 4.19907 samples/s, eta: 4:14:40
[2024/07/27 14:09:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:09:17] ppocr INFO: epoch: [915/1500], global_step: 2745, lr: 0.001000, loss: 1.311152, loss_shrink_maps: 0.654354, loss_threshold_maps: 0.527721, loss_binary_maps: 0.130366, avg_reader_cost: 2.21171 s, avg_batch_cost: 2.56009 s, avg_samples: 12.5, ips: 4.88264 samples/s, eta: 4:14:13
[2024/07/27 14:09:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:09:27] ppocr INFO: epoch: [916/1500], global_step: 2748, lr: 0.001000, loss: 1.311152, loss_shrink_maps: 0.654354, loss_threshold_maps: 0.524441, loss_binary_maps: 0.130366, avg_reader_cost: 2.19930 s, avg_batch_cost: 2.53804 s, avg_samples: 12.5, ips: 4.92506 samples/s, eta: 4:13:47
[2024/07/27 14:09:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:09:36] ppocr INFO: epoch: [917/1500], global_step: 2750, lr: 0.001000, loss: 1.331597, loss_shrink_maps: 0.685020, loss_threshold_maps: 0.519452, loss_binary_maps: 0.136518, avg_reader_cost: 1.46481 s, avg_batch_cost: 1.66692 s, avg_samples: 9.6, ips: 5.75912 samples/s, eta: 4:13:29
[2024/07/27 14:09:36] ppocr INFO: epoch: [917/1500], global_step: 2751, lr: 0.001000, loss: 1.331597, loss_shrink_maps: 0.685020, loss_threshold_maps: 0.515746, loss_binary_maps: 0.136518, avg_reader_cost: 0.87923 s, avg_batch_cost: 0.93448 s, avg_samples: 2.9, ips: 3.10332 samples/s, eta: 4:13:20
[2024/07/27 14:09:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:09:46] ppocr INFO: epoch: [918/1500], global_step: 2754, lr: 0.001000, loss: 1.368693, loss_shrink_maps: 0.708725, loss_threshold_maps: 0.517117, loss_binary_maps: 0.141079, avg_reader_cost: 2.32934 s, avg_batch_cost: 2.56789 s, avg_samples: 12.5, ips: 4.86780 samples/s, eta: 4:12:54
[2024/07/27 14:09:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:09:56] ppocr INFO: epoch: [919/1500], global_step: 2757, lr: 0.001000, loss: 1.331670, loss_shrink_maps: 0.696560, loss_threshold_maps: 0.502141, loss_binary_maps: 0.138400, avg_reader_cost: 2.20657 s, avg_batch_cost: 2.58357 s, avg_samples: 12.5, ips: 4.83826 samples/s, eta: 4:12:28
[2024/07/27 14:09:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:10:06] ppocr INFO: epoch: [920/1500], global_step: 2760, lr: 0.001000, loss: 1.331376, loss_shrink_maps: 0.694342, loss_threshold_maps: 0.502141, loss_binary_maps: 0.138178, avg_reader_cost: 2.27967 s, avg_batch_cost: 2.65665 s, avg_samples: 12.5, ips: 4.70517 samples/s, eta: 4:12:02

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:50,  9.96it/s]
eval model::   1%|          | 3/500 [00:00<00:32, 15.49it/s]
eval model::   1%|          | 5/500 [00:00<00:32, 15.46it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 18.02it/s]
eval model::   2%|▏         | 11/500 [00:00<00:25, 19.14it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 18.98it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.46it/s]
eval model::   4%|▍         | 20/500 [00:01<00:27, 17.58it/s]
eval model::   5%|▍         | 23/500 [00:01<00:25, 19.04it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 20.38it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 20.88it/s]
eval model::   6%|▋         | 32/500 [00:01<00:25, 18.40it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.59it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 19.92it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.51it/s]
eval model::   8%|▊         | 42/500 [00:02<00:28, 16.17it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 17.64it/s]
eval model::   9%|▉         | 47/500 [00:02<00:29, 15.57it/s]
eval model::  10%|▉         | 49/500 [00:02<00:28, 15.80it/s]
eval model::  10%|█         | 51/500 [00:02<00:28, 15.98it/s]
eval model::  11%|█         | 54/500 [00:02<00:23, 19.06it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:22, 19.38it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:24, 18.03it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:29, 14.75it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:29, 14.75it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:24, 17.79it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:22, 19.29it/s]
eval model::  15%|█▍        | 73/500 [00:04<00:21, 20.27it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:19, 21.93it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:18, 22.46it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:17, 23.23it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 21.41it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 19.66it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:23, 17.74it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:23, 17.13it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:23, 17.02it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:21, 19.00it/s]
eval model::  20%|██        | 100/500 [00:05<00:23, 17.31it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 20.08it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 21.92it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:16, 23.30it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:16, 23.98it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:15, 24.00it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 21.45it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.73it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:22, 16.94it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:20, 18.31it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:22, 16.35it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:22, 16.50it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:19, 18.55it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:19, 18.60it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:19, 18.87it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.87it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.49it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 19.03it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 20.60it/s]
eval model::  31%|███       | 153/500 [00:08<00:16, 20.51it/s]
eval model::  31%|███       | 156/500 [00:08<00:15, 22.23it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:14, 23.08it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:20, 16.67it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:19, 17.21it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 18.28it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 19.65it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:17, 18.50it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:20, 15.81it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:21, 15.35it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:22, 14.31it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:19, 16.56it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.97it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:16, 19.43it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:15, 19.83it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:16, 18.97it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.67it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 20.76it/s]
eval model::  41%|████      | 204/500 [00:10<00:12, 23.50it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:20, 14.33it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:19, 14.67it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 13.91it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:21, 13.34it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:24, 11.87it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:22, 12.31it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:20, 13.74it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:20, 13.56it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:17, 16.12it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.52it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 19.77it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.23it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.83it/s]
eval model::  48%|████▊     | 239/500 [00:13<00:11, 23.13it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 18.45it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 19.84it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 20.25it/s]
eval model::  50%|█████     | 251/500 [00:13<00:12, 20.73it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.50it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.74it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:10, 23.36it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:10, 21.80it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 21.06it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 20.86it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:10, 20.93it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:11, 18.98it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:11, 19.68it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:09, 21.84it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.37it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.76it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.72it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:12, 16.55it/s]
eval model::  59%|█████▉    | 297/500 [00:16<00:13, 14.79it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 16.22it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.89it/s]
eval model::  61%|██████    | 306/500 [00:16<00:11, 17.40it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:10, 18.42it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:10, 18.04it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:09, 18.59it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:08, 20.81it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 20.64it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 20.26it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 22.42it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:07, 22.77it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 23.26it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:06, 24.24it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:06, 23.12it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:09, 17.49it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:07, 19.83it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:07, 21.13it/s]
eval model::  70%|███████   | 351/500 [00:18<00:06, 22.44it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 22.23it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 22.15it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:06, 19.91it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.10it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.99it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 20.26it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 20.66it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:05, 21.46it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:04, 24.46it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 22.60it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:04, 23.38it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:08, 12.72it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:07, 14.49it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 15.85it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 18.06it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 17.24it/s]
eval model::  81%|████████  | 403/500 [00:21<00:05, 18.64it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 20.39it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 22.53it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:03, 22.49it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:04, 20.67it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 19.49it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:05, 13.46it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 14.20it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:04, 14.98it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 16.95it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 16.50it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 19.15it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:02, 21.47it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:02, 20.48it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 23.45it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 23.77it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 22.67it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:01, 24.02it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 23.69it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 22.80it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 18.53it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 17.14it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.84it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 21.14it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 20.37it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:00, 23.31it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 22.24it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 22.07it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 20.41it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 21.21it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 19.56it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 20.48it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 24.08it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.94it/s]
[2024/07/27 14:10:32] ppocr INFO: cur metric, precision: 0.7458445040214478, recall: 0.6697159364467983, hmean: 0.7057331303906647, fps: 46.09005744466407
[2024/07/27 14:10:32] ppocr INFO: best metric, hmean: 0.7154429056702378, precision: 0.7822857142857143, recall: 0.6591237361579201, fps: 44.393764002497214, best_epoch: 860
[2024/07/27 14:10:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:10:41] ppocr INFO: epoch: [921/1500], global_step: 2763, lr: 0.001000, loss: 1.311065, loss_shrink_maps: 0.679477, loss_threshold_maps: 0.494801, loss_binary_maps: 0.135208, avg_reader_cost: 2.09319 s, avg_batch_cost: 2.45396 s, avg_samples: 12.5, ips: 5.09381 samples/s, eta: 4:11:35
[2024/07/27 14:10:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:10:51] ppocr INFO: epoch: [922/1500], global_step: 2766, lr: 0.001000, loss: 1.330857, loss_shrink_maps: 0.687115, loss_threshold_maps: 0.496248, loss_binary_maps: 0.136103, avg_reader_cost: 2.32745 s, avg_batch_cost: 2.56806 s, avg_samples: 12.5, ips: 4.86749 samples/s, eta: 4:11:09
[2024/07/27 14:10:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:11:00] ppocr INFO: epoch: [923/1500], global_step: 2769, lr: 0.001000, loss: 1.311065, loss_shrink_maps: 0.676077, loss_threshold_maps: 0.497954, loss_binary_maps: 0.134402, avg_reader_cost: 2.17514 s, avg_batch_cost: 2.53644 s, avg_samples: 12.5, ips: 4.92816 samples/s, eta: 4:10:42
[2024/07/27 14:11:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:11:09] ppocr INFO: epoch: [924/1500], global_step: 2770, lr: 0.001000, loss: 1.311065, loss_shrink_maps: 0.676077, loss_threshold_maps: 0.499000, loss_binary_maps: 0.134402, avg_reader_cost: 0.57765 s, avg_batch_cost: 0.76025 s, avg_samples: 4.8, ips: 6.31368 samples/s, eta: 4:10:33
[2024/07/27 14:11:10] ppocr INFO: epoch: [924/1500], global_step: 2772, lr: 0.001000, loss: 1.311065, loss_shrink_maps: 0.676077, loss_threshold_maps: 0.496234, loss_binary_maps: 0.134402, avg_reader_cost: 1.61276 s, avg_batch_cost: 1.75981 s, avg_samples: 7.7, ips: 4.37548 samples/s, eta: 4:10:16
[2024/07/27 14:11:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:11:20] ppocr INFO: epoch: [925/1500], global_step: 2775, lr: 0.001000, loss: 1.316096, loss_shrink_maps: 0.681653, loss_threshold_maps: 0.493198, loss_binary_maps: 0.135181, avg_reader_cost: 2.33631 s, avg_batch_cost: 2.57625 s, avg_samples: 12.5, ips: 4.85202 samples/s, eta: 4:09:49
[2024/07/27 14:11:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:11:29] ppocr INFO: epoch: [926/1500], global_step: 2778, lr: 0.001000, loss: 1.332232, loss_shrink_maps: 0.683714, loss_threshold_maps: 0.497003, loss_binary_maps: 0.135297, avg_reader_cost: 2.10952 s, avg_batch_cost: 2.41965 s, avg_samples: 12.5, ips: 5.16603 samples/s, eta: 4:09:22
[2024/07/27 14:11:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:11:38] ppocr INFO: epoch: [927/1500], global_step: 2780, lr: 0.001000, loss: 1.326892, loss_shrink_maps: 0.677057, loss_threshold_maps: 0.497003, loss_binary_maps: 0.134267, avg_reader_cost: 1.44388 s, avg_batch_cost: 1.62513 s, avg_samples: 9.6, ips: 5.90720 samples/s, eta: 4:09:04
[2024/07/27 14:11:39] ppocr INFO: epoch: [927/1500], global_step: 2781, lr: 0.001000, loss: 1.326892, loss_shrink_maps: 0.677057, loss_threshold_maps: 0.497003, loss_binary_maps: 0.134267, avg_reader_cost: 0.85876 s, avg_batch_cost: 0.91416 s, avg_samples: 2.9, ips: 3.17230 samples/s, eta: 4:08:56
[2024/07/27 14:11:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:11:49] ppocr INFO: epoch: [928/1500], global_step: 2784, lr: 0.001000, loss: 1.336408, loss_shrink_maps: 0.683714, loss_threshold_maps: 0.501494, loss_binary_maps: 0.135297, avg_reader_cost: 2.39213 s, avg_batch_cost: 2.63390 s, avg_samples: 12.5, ips: 4.74581 samples/s, eta: 4:08:30
[2024/07/27 14:11:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:11:58] ppocr INFO: epoch: [929/1500], global_step: 2787, lr: 0.001000, loss: 1.299656, loss_shrink_maps: 0.670047, loss_threshold_maps: 0.497003, loss_binary_maps: 0.133362, avg_reader_cost: 2.30149 s, avg_batch_cost: 2.54321 s, avg_samples: 12.5, ips: 4.91504 samples/s, eta: 4:08:03
[2024/07/27 14:11:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:12:08] ppocr INFO: epoch: [930/1500], global_step: 2790, lr: 0.001000, loss: 1.299656, loss_shrink_maps: 0.665823, loss_threshold_maps: 0.495762, loss_binary_maps: 0.132300, avg_reader_cost: 2.15161 s, avg_batch_cost: 2.48316 s, avg_samples: 12.5, ips: 5.03390 samples/s, eta: 4:07:37
[2024/07/27 14:12:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:12:18] ppocr INFO: epoch: [931/1500], global_step: 2793, lr: 0.001000, loss: 1.288958, loss_shrink_maps: 0.659201, loss_threshold_maps: 0.496246, loss_binary_maps: 0.130577, avg_reader_cost: 2.28081 s, avg_batch_cost: 2.52122 s, avg_samples: 12.5, ips: 4.95792 samples/s, eta: 4:07:10
[2024/07/27 14:12:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:12:27] ppocr INFO: epoch: [932/1500], global_step: 2796, lr: 0.001000, loss: 1.250689, loss_shrink_maps: 0.634184, loss_threshold_maps: 0.494932, loss_binary_maps: 0.126408, avg_reader_cost: 2.23900 s, avg_batch_cost: 2.62720 s, avg_samples: 12.5, ips: 4.75792 samples/s, eta: 4:06:44
[2024/07/27 14:12:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:12:37] ppocr INFO: epoch: [933/1500], global_step: 2799, lr: 0.001000, loss: 1.242049, loss_shrink_maps: 0.622702, loss_threshold_maps: 0.494047, loss_binary_maps: 0.123850, avg_reader_cost: 2.21617 s, avg_batch_cost: 2.56016 s, avg_samples: 12.5, ips: 4.88251 samples/s, eta: 4:06:18
[2024/07/27 14:12:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:12:46] ppocr INFO: epoch: [934/1500], global_step: 2800, lr: 0.001000, loss: 1.248148, loss_shrink_maps: 0.634184, loss_threshold_maps: 0.492898, loss_binary_maps: 0.126408, avg_reader_cost: 0.57146 s, avg_batch_cost: 0.78432 s, avg_samples: 4.8, ips: 6.11992 samples/s, eta: 4:06:08
[2024/07/27 14:12:47] ppocr INFO: epoch: [934/1500], global_step: 2802, lr: 0.001000, loss: 1.248148, loss_shrink_maps: 0.634184, loss_threshold_maps: 0.490488, loss_binary_maps: 0.126408, avg_reader_cost: 1.66125 s, avg_batch_cost: 1.80876 s, avg_samples: 7.7, ips: 4.25706 samples/s, eta: 4:05:52
[2024/07/27 14:12:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:12:57] ppocr INFO: epoch: [935/1500], global_step: 2805, lr: 0.001000, loss: 1.233405, loss_shrink_maps: 0.617668, loss_threshold_maps: 0.485827, loss_binary_maps: 0.122348, avg_reader_cost: 2.25438 s, avg_batch_cost: 2.60692 s, avg_samples: 12.5, ips: 4.79493 samples/s, eta: 4:05:25
[2024/07/27 14:12:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:13:07] ppocr INFO: epoch: [936/1500], global_step: 2808, lr: 0.001000, loss: 1.241431, loss_shrink_maps: 0.623607, loss_threshold_maps: 0.483774, loss_binary_maps: 0.123712, avg_reader_cost: 2.22885 s, avg_batch_cost: 2.62715 s, avg_samples: 12.5, ips: 4.75800 samples/s, eta: 4:05:00
[2024/07/27 14:13:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:13:16] ppocr INFO: epoch: [937/1500], global_step: 2810, lr: 0.001000, loss: 1.241431, loss_shrink_maps: 0.623607, loss_threshold_maps: 0.485827, loss_binary_maps: 0.123712, avg_reader_cost: 1.51112 s, avg_batch_cost: 1.69372 s, avg_samples: 9.6, ips: 5.66801 samples/s, eta: 4:04:42
[2024/07/27 14:13:17] ppocr INFO: epoch: [937/1500], global_step: 2811, lr: 0.001000, loss: 1.239504, loss_shrink_maps: 0.617668, loss_threshold_maps: 0.485827, loss_binary_maps: 0.122348, avg_reader_cost: 0.89370 s, avg_batch_cost: 0.94834 s, avg_samples: 2.9, ips: 3.05799 samples/s, eta: 4:04:34
[2024/07/27 14:13:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:13:27] ppocr INFO: epoch: [938/1500], global_step: 2814, lr: 0.001000, loss: 1.239572, loss_shrink_maps: 0.628442, loss_threshold_maps: 0.485827, loss_binary_maps: 0.124868, avg_reader_cost: 2.25266 s, avg_batch_cost: 2.63976 s, avg_samples: 12.5, ips: 4.73528 samples/s, eta: 4:04:08
[2024/07/27 14:13:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:13:37] ppocr INFO: epoch: [939/1500], global_step: 2817, lr: 0.001000, loss: 1.250114, loss_shrink_maps: 0.641924, loss_threshold_maps: 0.487256, loss_binary_maps: 0.127596, avg_reader_cost: 2.33108 s, avg_batch_cost: 2.57692 s, avg_samples: 12.5, ips: 4.85076 samples/s, eta: 4:03:42
[2024/07/27 14:13:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:13:47] ppocr INFO: epoch: [940/1500], global_step: 2820, lr: 0.001000, loss: 1.255361, loss_shrink_maps: 0.633588, loss_threshold_maps: 0.487256, loss_binary_maps: 0.125763, avg_reader_cost: 2.28776 s, avg_batch_cost: 2.68948 s, avg_samples: 12.5, ips: 4.64774 samples/s, eta: 4:03:16

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:00,  8.27it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 17.07it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.52it/s]
eval model::   2%|▏         | 9/500 [00:00<00:24, 20.04it/s]
eval model::   2%|▏         | 11/500 [00:00<00:30, 16.13it/s]
eval model::   3%|▎         | 14/500 [00:00<00:28, 17.34it/s]
eval model::   3%|▎         | 17/500 [00:00<00:26, 18.48it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 19.15it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 21.17it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 20.35it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 21.05it/s]
eval model::   6%|▋         | 32/500 [00:01<00:26, 17.90it/s]
eval model::   7%|▋         | 34/500 [00:01<00:27, 16.77it/s]
eval model::   7%|▋         | 36/500 [00:01<00:26, 17.23it/s]
eval model::   8%|▊         | 39/500 [00:02<00:26, 17.68it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.70it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 17.78it/s]
eval model::   9%|▉         | 47/500 [00:02<00:28, 16.13it/s]
eval model::  10%|█         | 50/500 [00:02<00:26, 17.09it/s]
eval model::  11%|█         | 53/500 [00:02<00:23, 18.74it/s]
eval model::  11%|█         | 56/500 [00:03<00:23, 19.17it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 18.74it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.41it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 14.76it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:25, 16.82it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:22, 19.31it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.05it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:21, 19.90it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:21, 19.99it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 20.98it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:21, 19.04it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.74it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:23, 17.27it/s]
eval model::  18%|█▊        | 91/500 [00:05<00:23, 17.36it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:24, 16.59it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:22, 18.36it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.99it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.36it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.70it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:17, 21.83it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 21.10it/s]
eval model::  23%|██▎       | 114/500 [00:06<00:17, 22.11it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:20, 19.02it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:23, 16.01it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:20, 18.07it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:22, 16.48it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:19, 18.71it/s]
eval model::  26%|██▌       | 131/500 [00:07<00:19, 18.74it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:20, 17.81it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:20, 17.92it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:20, 17.95it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.17it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:19, 18.22it/s]
eval model::  29%|██▉       | 147/500 [00:08<00:21, 16.59it/s]
eval model::  30%|███       | 150/500 [00:08<00:19, 18.10it/s]
eval model::  30%|███       | 152/500 [00:08<00:20, 17.31it/s]
eval model::  31%|███       | 155/500 [00:08<00:17, 19.77it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:16, 21.25it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:24, 13.73it/s]
eval model::  33%|███▎      | 163/500 [00:09<00:22, 14.80it/s]
eval model::  33%|███▎      | 166/500 [00:09<00:19, 17.49it/s]
eval model::  34%|███▍      | 169/500 [00:09<00:18, 18.26it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:17, 18.99it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:20, 15.52it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:19, 16.25it/s]
eval model::  36%|███▌      | 180/500 [00:10<00:22, 14.52it/s]
eval model::  36%|███▋      | 182/500 [00:10<00:20, 15.31it/s]
eval model::  37%|███▋      | 185/500 [00:10<00:17, 18.35it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:17, 17.83it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:16, 19.27it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:14, 21.67it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:15, 19.42it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 21.87it/s]
eval model::  41%|████      | 205/500 [00:11<00:21, 13.72it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:20, 14.54it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:19, 15.19it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 15.15it/s]
eval model::  43%|████▎     | 214/500 [00:11<00:16, 17.25it/s]
eval model::  43%|████▎     | 216/500 [00:12<00:20, 13.91it/s]
eval model::  44%|████▎     | 218/500 [00:12<00:22, 12.58it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:20, 13.89it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:17, 15.93it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.37it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:14, 19.31it/s]
eval model::  46%|████▋     | 232/500 [00:13<00:12, 21.07it/s]
eval model::  47%|████▋     | 235/500 [00:13<00:12, 20.61it/s]
eval model::  48%|████▊     | 238/500 [00:13<00:11, 22.09it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:11, 21.82it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:12, 19.97it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:12, 20.74it/s]
eval model::  50%|█████     | 250/500 [00:13<00:12, 19.36it/s]
eval model::  50%|█████     | 252/500 [00:14<00:13, 18.47it/s]
eval model::  51%|█████     | 255/500 [00:14<00:12, 19.80it/s]
eval model::  52%|█████▏    | 258/500 [00:14<00:10, 22.08it/s]
eval model::  52%|█████▏    | 262/500 [00:14<00:09, 24.67it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:10, 23.33it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:10, 22.29it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:11, 20.08it/s]
eval model::  55%|█████▍    | 274/500 [00:15<00:10, 20.67it/s]
eval model::  55%|█████▌    | 277/500 [00:15<00:11, 18.94it/s]
eval model::  56%|█████▌    | 280/500 [00:15<00:11, 19.93it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:10, 21.67it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:17, 12.31it/s]
eval model::  58%|█████▊    | 289/500 [00:16<00:14, 14.08it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:14, 14.80it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:13, 14.92it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:15, 12.87it/s]
eval model::  60%|██████    | 300/500 [00:16<00:13, 14.96it/s]
eval model::  60%|██████    | 302/500 [00:16<00:12, 15.54it/s]
eval model::  61%|██████    | 304/500 [00:17<00:12, 15.81it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:10, 17.60it/s]
eval model::  62%|██████▏   | 309/500 [00:17<00:10, 18.07it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:10, 18.26it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:09, 19.70it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:08, 20.65it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 21.53it/s]
eval model::  65%|██████▍   | 324/500 [00:18<00:08, 19.78it/s]
eval model::  65%|██████▌   | 327/500 [00:18<00:08, 21.06it/s]
eval model::  66%|██████▌   | 330/500 [00:18<00:08, 19.93it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:07, 20.93it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:07, 21.77it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:07, 21.20it/s]
eval model::  68%|██████▊   | 342/500 [00:19<00:10, 15.78it/s]
eval model::  69%|██████▉   | 345/500 [00:19<00:08, 18.34it/s]
eval model::  70%|██████▉   | 348/500 [00:19<00:07, 19.43it/s]
eval model::  70%|███████   | 351/500 [00:19<00:07, 21.19it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 21.64it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 22.14it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 19.12it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:07, 17.86it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:07, 18.23it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:06, 19.40it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:06, 18.54it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 19.50it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 22.41it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 19.96it/s]
eval model::  77%|███████▋  | 385/500 [00:21<00:05, 20.63it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:08, 12.65it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:08, 13.57it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 15.74it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 16.20it/s]
eval model::  80%|███████▉  | 398/500 [00:22<00:05, 18.01it/s]
eval model::  80%|████████  | 401/500 [00:22<00:06, 16.27it/s]
eval model::  81%|████████  | 404/500 [00:22<00:05, 17.30it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 18.76it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 20.70it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:04, 21.00it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 19.70it/s]
eval model::  84%|████████▍ | 419/500 [00:23<00:04, 19.61it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:05, 13.98it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.38it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.67it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 17.17it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 15.86it/s]
eval model::  87%|████████▋ | 434/500 [00:24<00:03, 18.44it/s]
eval model::  87%|████████▋ | 437/500 [00:24<00:03, 19.93it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:03, 18.66it/s]
eval model::  89%|████████▊ | 443/500 [00:24<00:02, 20.22it/s]
eval model::  89%|████████▉ | 446/500 [00:24<00:02, 19.52it/s]
eval model::  90%|████████▉ | 449/500 [00:24<00:02, 18.45it/s]
eval model::  90%|█████████ | 452/500 [00:25<00:02, 19.26it/s]
eval model::  91%|█████████ | 455/500 [00:25<00:02, 19.82it/s]
eval model::  92%|█████████▏| 458/500 [00:25<00:01, 21.55it/s]
eval model::  92%|█████████▏| 461/500 [00:25<00:01, 22.57it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 16.98it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 16.15it/s]
eval model::  94%|█████████▍| 469/500 [00:25<00:01, 18.01it/s]
eval model::  94%|█████████▍| 472/500 [00:26<00:01, 19.29it/s]
eval model::  95%|█████████▌| 475/500 [00:26<00:01, 17.95it/s]
eval model::  96%|█████████▌| 478/500 [00:26<00:01, 19.29it/s]
eval model::  96%|█████████▌| 481/500 [00:26<00:00, 20.44it/s]
eval model::  97%|█████████▋| 484/500 [00:26<00:00, 21.37it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 19.12it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 21.30it/s]
eval model::  99%|█████████▊| 493/500 [00:27<00:00, 20.54it/s]
eval model::  99%|█████████▉| 496/500 [00:27<00:00, 20.31it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 24.56it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.28it/s]
[2024/07/27 14:14:14] ppocr INFO: cur metric, precision: 0.7219115404168785, recall: 0.6836783822821377, hmean: 0.702274975272008, fps: 43.763510117517484
[2024/07/27 14:14:14] ppocr INFO: best metric, hmean: 0.7154429056702378, precision: 0.7822857142857143, recall: 0.6591237361579201, fps: 44.393764002497214, best_epoch: 860
[2024/07/27 14:14:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:14:23] ppocr INFO: epoch: [941/1500], global_step: 2823, lr: 0.001000, loss: 1.274460, loss_shrink_maps: 0.637102, loss_threshold_maps: 0.489500, loss_binary_maps: 0.126916, avg_reader_cost: 2.05845 s, avg_batch_cost: 2.29326 s, avg_samples: 12.5, ips: 5.45076 samples/s, eta: 4:02:48
[2024/07/27 14:14:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:14:33] ppocr INFO: epoch: [942/1500], global_step: 2826, lr: 0.001000, loss: 1.255361, loss_shrink_maps: 0.635455, loss_threshold_maps: 0.490231, loss_binary_maps: 0.126463, avg_reader_cost: 2.44507 s, avg_batch_cost: 2.73684 s, avg_samples: 12.5, ips: 4.56731 samples/s, eta: 4:02:23
[2024/07/27 14:14:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:14:43] ppocr INFO: epoch: [943/1500], global_step: 2829, lr: 0.001000, loss: 1.235157, loss_shrink_maps: 0.631098, loss_threshold_maps: 0.485402, loss_binary_maps: 0.125321, avg_reader_cost: 2.23688 s, avg_batch_cost: 2.60006 s, avg_samples: 12.5, ips: 4.80758 samples/s, eta: 4:01:57
[2024/07/27 14:14:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:14:50] ppocr INFO: epoch: [944/1500], global_step: 2830, lr: 0.001000, loss: 1.245699, loss_shrink_maps: 0.633588, loss_threshold_maps: 0.485402, loss_binary_maps: 0.125763, avg_reader_cost: 0.57512 s, avg_batch_cost: 0.70068 s, avg_samples: 4.8, ips: 6.85048 samples/s, eta: 4:01:47
[2024/07/27 14:14:52] ppocr INFO: epoch: [944/1500], global_step: 2832, lr: 0.001000, loss: 1.255361, loss_shrink_maps: 0.635455, loss_threshold_maps: 0.490231, loss_binary_maps: 0.126463, avg_reader_cost: 1.49459 s, avg_batch_cost: 1.64205 s, avg_samples: 7.7, ips: 4.68926 samples/s, eta: 4:01:29
[2024/07/27 14:14:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:15:01] ppocr INFO: epoch: [945/1500], global_step: 2835, lr: 0.001000, loss: 1.269792, loss_shrink_maps: 0.640067, loss_threshold_maps: 0.492622, loss_binary_maps: 0.127214, avg_reader_cost: 2.29290 s, avg_batch_cost: 2.53552 s, avg_samples: 12.5, ips: 4.92995 samples/s, eta: 4:01:03
[2024/07/27 14:15:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:15:11] ppocr INFO: epoch: [946/1500], global_step: 2838, lr: 0.001000, loss: 1.269792, loss_shrink_maps: 0.642054, loss_threshold_maps: 0.496024, loss_binary_maps: 0.127539, avg_reader_cost: 2.35512 s, avg_batch_cost: 2.61068 s, avg_samples: 12.5, ips: 4.78802 samples/s, eta: 4:00:37
[2024/07/27 14:15:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:15:20] ppocr INFO: epoch: [947/1500], global_step: 2840, lr: 0.001000, loss: 1.282941, loss_shrink_maps: 0.645970, loss_threshold_maps: 0.503682, loss_binary_maps: 0.128605, avg_reader_cost: 1.46879 s, avg_batch_cost: 1.65098 s, avg_samples: 9.6, ips: 5.81472 samples/s, eta: 4:00:19
[2024/07/27 14:15:21] ppocr INFO: epoch: [947/1500], global_step: 2841, lr: 0.001000, loss: 1.282941, loss_shrink_maps: 0.645970, loss_threshold_maps: 0.503682, loss_binary_maps: 0.128605, avg_reader_cost: 0.87178 s, avg_batch_cost: 0.92712 s, avg_samples: 2.9, ips: 3.12797 samples/s, eta: 4:00:10
[2024/07/27 14:15:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:15:31] ppocr INFO: epoch: [948/1500], global_step: 2844, lr: 0.001000, loss: 1.298204, loss_shrink_maps: 0.653381, loss_threshold_maps: 0.517540, loss_binary_maps: 0.130231, avg_reader_cost: 2.24423 s, avg_batch_cost: 2.60261 s, avg_samples: 12.5, ips: 4.80287 samples/s, eta: 3:59:44
[2024/07/27 14:15:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:15:40] ppocr INFO: epoch: [949/1500], global_step: 2847, lr: 0.001000, loss: 1.298204, loss_shrink_maps: 0.653381, loss_threshold_maps: 0.517540, loss_binary_maps: 0.130231, avg_reader_cost: 2.13954 s, avg_batch_cost: 2.48384 s, avg_samples: 12.5, ips: 5.03253 samples/s, eta: 3:59:18
[2024/07/27 14:15:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:15:50] ppocr INFO: epoch: [950/1500], global_step: 2850, lr: 0.001000, loss: 1.308818, loss_shrink_maps: 0.660671, loss_threshold_maps: 0.518958, loss_binary_maps: 0.132034, avg_reader_cost: 2.26189 s, avg_batch_cost: 2.50465 s, avg_samples: 12.5, ips: 4.99073 samples/s, eta: 3:58:51
[2024/07/27 14:15:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:16:00] ppocr INFO: epoch: [951/1500], global_step: 2853, lr: 0.001000, loss: 1.267454, loss_shrink_maps: 0.649688, loss_threshold_maps: 0.502072, loss_binary_maps: 0.129876, avg_reader_cost: 2.38712 s, avg_batch_cost: 2.63399 s, avg_samples: 12.5, ips: 4.74566 samples/s, eta: 3:58:25
[2024/07/27 14:16:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:16:10] ppocr INFO: epoch: [952/1500], global_step: 2856, lr: 0.001000, loss: 1.247860, loss_shrink_maps: 0.645293, loss_threshold_maps: 0.496883, loss_binary_maps: 0.128499, avg_reader_cost: 2.36477 s, avg_batch_cost: 2.63927 s, avg_samples: 12.5, ips: 4.73615 samples/s, eta: 3:57:59
[2024/07/27 14:16:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:16:19] ppocr INFO: epoch: [953/1500], global_step: 2859, lr: 0.001000, loss: 1.246385, loss_shrink_maps: 0.641076, loss_threshold_maps: 0.485952, loss_binary_maps: 0.127429, avg_reader_cost: 2.21020 s, avg_batch_cost: 2.58080 s, avg_samples: 12.5, ips: 4.84345 samples/s, eta: 3:57:33
[2024/07/27 14:16:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:16:28] ppocr INFO: epoch: [954/1500], global_step: 2860, lr: 0.001000, loss: 1.242879, loss_shrink_maps: 0.632489, loss_threshold_maps: 0.480622, loss_binary_maps: 0.125879, avg_reader_cost: 0.53180 s, avg_batch_cost: 0.80439 s, avg_samples: 4.8, ips: 5.96724 samples/s, eta: 3:57:24
[2024/07/27 14:16:29] ppocr INFO: epoch: [954/1500], global_step: 2862, lr: 0.001000, loss: 1.242879, loss_shrink_maps: 0.632489, loss_threshold_maps: 0.480622, loss_binary_maps: 0.125879, avg_reader_cost: 1.70048 s, avg_batch_cost: 1.84658 s, avg_samples: 7.7, ips: 4.16988 samples/s, eta: 3:57:07
[2024/07/27 14:16:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:16:39] ppocr INFO: epoch: [955/1500], global_step: 2865, lr: 0.001000, loss: 1.233722, loss_shrink_maps: 0.622718, loss_threshold_maps: 0.480591, loss_binary_maps: 0.122688, avg_reader_cost: 2.16793 s, avg_batch_cost: 2.50826 s, avg_samples: 12.5, ips: 4.98354 samples/s, eta: 3:56:40
[2024/07/27 14:16:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:16:49] ppocr INFO: epoch: [956/1500], global_step: 2868, lr: 0.001000, loss: 1.246385, loss_shrink_maps: 0.632489, loss_threshold_maps: 0.486117, loss_binary_maps: 0.125879, avg_reader_cost: 2.24467 s, avg_batch_cost: 2.48650 s, avg_samples: 12.5, ips: 5.02714 samples/s, eta: 3:56:14
[2024/07/27 14:16:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:16:58] ppocr INFO: epoch: [957/1500], global_step: 2870, lr: 0.001000, loss: 1.243017, loss_shrink_maps: 0.623907, loss_threshold_maps: 0.486117, loss_binary_maps: 0.123938, avg_reader_cost: 1.39471 s, avg_batch_cost: 1.70330 s, avg_samples: 9.6, ips: 5.63611 samples/s, eta: 3:55:56
[2024/07/27 14:16:58] ppocr INFO: epoch: [957/1500], global_step: 2871, lr: 0.001000, loss: 1.243017, loss_shrink_maps: 0.623907, loss_threshold_maps: 0.486117, loss_binary_maps: 0.123938, avg_reader_cost: 0.89770 s, avg_batch_cost: 0.95366 s, avg_samples: 2.9, ips: 3.04092 samples/s, eta: 3:55:48
[2024/07/27 14:16:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:17:08] ppocr INFO: epoch: [958/1500], global_step: 2874, lr: 0.001000, loss: 1.271217, loss_shrink_maps: 0.627850, loss_threshold_maps: 0.506138, loss_binary_maps: 0.124617, avg_reader_cost: 2.15897 s, avg_batch_cost: 2.52748 s, avg_samples: 12.5, ips: 4.94563 samples/s, eta: 3:55:21
[2024/07/27 14:17:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:17:18] ppocr INFO: epoch: [959/1500], global_step: 2877, lr: 0.001000, loss: 1.291694, loss_shrink_maps: 0.646124, loss_threshold_maps: 0.511089, loss_binary_maps: 0.127980, avg_reader_cost: 2.32807 s, avg_batch_cost: 2.56118 s, avg_samples: 12.5, ips: 4.88056 samples/s, eta: 3:54:55
[2024/07/27 14:17:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:17:28] ppocr INFO: epoch: [960/1500], global_step: 2880, lr: 0.001000, loss: 1.267579, loss_shrink_maps: 0.645990, loss_threshold_maps: 0.493961, loss_binary_maps: 0.127980, avg_reader_cost: 2.25857 s, avg_batch_cost: 2.61926 s, avg_samples: 12.5, ips: 4.77235 samples/s, eta: 3:54:29

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:05,  7.61it/s]
eval model::   1%|          | 4/500 [00:00<00:30, 16.16it/s]
eval model::   1%|▏         | 7/500 [00:00<00:26, 18.56it/s]
eval model::   2%|▏         | 9/500 [00:00<00:26, 18.31it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 18.03it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.66it/s]
eval model::   3%|▎         | 17/500 [00:00<00:25, 19.19it/s]
eval model::   4%|▍         | 19/500 [00:01<00:26, 18.35it/s]
eval model::   4%|▍         | 21/500 [00:01<00:27, 17.38it/s]
eval model::   5%|▍         | 24/500 [00:01<00:24, 19.73it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.16it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.01it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 20.04it/s]
eval model::   7%|▋         | 35/500 [00:01<00:22, 20.52it/s]
eval model::   8%|▊         | 38/500 [00:01<00:21, 21.60it/s]
eval model::   8%|▊         | 41/500 [00:02<00:21, 21.38it/s]
eval model::   9%|▉         | 44/500 [00:02<00:24, 18.65it/s]
eval model::   9%|▉         | 46/500 [00:02<00:25, 17.97it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 20.27it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 21.07it/s]
eval model::  11%|█         | 55/500 [00:02<00:19, 22.67it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:20, 21.72it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:24, 17.60it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:24, 17.81it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:20, 21.41it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 21.57it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 20.07it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:20, 20.27it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:21, 19.16it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:21, 19.25it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:21, 19.28it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.70it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:24, 16.57it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:24, 16.58it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:24, 16.35it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:23, 17.08it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:20, 19.23it/s]
eval model::  20%|██        | 100/500 [00:05<00:21, 18.23it/s]
eval model::  20%|██        | 102/500 [00:05<00:21, 18.33it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 20.60it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:17, 21.88it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 20.95it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:17, 22.06it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:19, 19.79it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.56it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 20.63it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:22, 16.31it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:20, 18.54it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 17.46it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:18, 19.65it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.35it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.26it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.57it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:21, 16.41it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 18.48it/s]
eval model::  31%|███       | 153/500 [00:08<00:19, 17.90it/s]
eval model::  31%|███       | 156/500 [00:08<00:18, 18.87it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:17, 18.96it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:22, 14.84it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:20, 16.75it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:19, 17.43it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:17, 19.13it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:16, 19.79it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.37it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.27it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.28it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:18, 16.72it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.66it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:16, 19.17it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:16, 18.31it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:17, 17.48it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:17, 17.01it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:17, 17.20it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:17, 17.50it/s]
eval model::  40%|████      | 201/500 [00:10<00:16, 18.14it/s]
eval model::  41%|████      | 204/500 [00:10<00:14, 20.97it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:21, 13.41it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:21, 13.57it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:21, 13.45it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:20, 14.33it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:22, 12.63it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:23, 12.16it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:21, 12.99it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:24, 11.55it/s]
eval model::  45%|████▍     | 223/500 [00:12<00:21, 12.61it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:19, 14.35it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:14, 18.33it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:14, 18.49it/s]
eval model::  47%|████▋     | 235/500 [00:13<00:14, 17.98it/s]
eval model::  48%|████▊     | 238/500 [00:13<00:13, 19.29it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:12, 19.94it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:14, 17.38it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:12, 19.67it/s]
eval model::  50%|█████     | 250/500 [00:13<00:12, 20.38it/s]
eval model::  51%|█████     | 254/500 [00:13<00:10, 23.71it/s]
eval model::  51%|█████▏    | 257/500 [00:14<00:09, 25.02it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:09, 26.52it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:09, 24.69it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:10, 23.09it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:10, 22.95it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:09, 22.89it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:10, 21.57it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:10, 20.51it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 20.48it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:18, 11.93it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:16, 13.10it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:13, 15.68it/s]
eval model::  59%|█████▊    | 293/500 [00:16<00:12, 16.73it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:14, 13.87it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:13, 15.38it/s]
eval model::  60%|██████    | 301/500 [00:16<00:13, 14.51it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 15.18it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:11, 16.54it/s]
eval model::  62%|██████▏   | 309/500 [00:17<00:11, 16.74it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:10, 17.93it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:09, 19.06it/s]
eval model::  63%|██████▎   | 317/500 [00:17<00:09, 18.98it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:09, 19.97it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:09, 19.51it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:08, 21.38it/s]
eval model::  66%|██████▌   | 329/500 [00:18<00:08, 19.68it/s]
eval model::  66%|██████▋   | 332/500 [00:18<00:08, 20.35it/s]
eval model::  67%|██████▋   | 335/500 [00:18<00:07, 21.61it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 19.39it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:10, 15.24it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 17.17it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:09, 17.08it/s]
eval model::  70%|██████▉   | 348/500 [00:19<00:08, 17.58it/s]
eval model::  70%|███████   | 351/500 [00:19<00:07, 19.22it/s]
eval model::  71%|███████   | 354/500 [00:19<00:07, 20.63it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 19.22it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:06, 20.30it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 19.18it/s]
eval model::  73%|███████▎  | 365/500 [00:20<00:08, 16.86it/s]
eval model::  73%|███████▎  | 367/500 [00:20<00:08, 15.16it/s]
eval model::  74%|███████▍  | 370/500 [00:20<00:07, 17.64it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 16.65it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 18.71it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:06, 20.32it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:06, 19.44it/s]
eval model::  77%|███████▋  | 385/500 [00:21<00:05, 22.88it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:08, 13.12it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:07, 15.19it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 15.49it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 17.51it/s]
eval model::  80%|████████  | 400/500 [00:22<00:05, 18.30it/s]
eval model::  81%|████████  | 403/500 [00:22<00:05, 19.38it/s]
eval model::  81%|████████  | 406/500 [00:22<00:05, 18.50it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:05, 18.21it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:04, 19.78it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:04, 20.74it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 19.71it/s]
eval model::  84%|████████▍ | 420/500 [00:23<00:05, 13.39it/s]
eval model::  85%|████████▍ | 423/500 [00:23<00:05, 15.34it/s]
eval model::  85%|████████▌ | 425/500 [00:23<00:05, 13.59it/s]
eval model::  86%|████████▌ | 428/500 [00:23<00:04, 15.48it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 16.77it/s]
eval model::  87%|████████▋ | 433/500 [00:24<00:03, 17.15it/s]
eval model::  87%|████████▋ | 436/500 [00:24<00:03, 19.09it/s]
eval model::  88%|████████▊ | 439/500 [00:24<00:02, 20.79it/s]
eval model::  88%|████████▊ | 442/500 [00:24<00:02, 20.13it/s]
eval model::  89%|████████▉ | 445/500 [00:24<00:02, 21.81it/s]
eval model::  90%|████████▉ | 448/500 [00:24<00:02, 21.72it/s]
eval model::  90%|█████████ | 451/500 [00:24<00:02, 20.48it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 21.63it/s]
eval model::  91%|█████████▏| 457/500 [00:25<00:02, 20.76it/s]
eval model::  92%|█████████▏| 460/500 [00:25<00:01, 20.55it/s]
eval model::  93%|█████████▎| 463/500 [00:25<00:02, 17.96it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 16.03it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 17.58it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.35it/s]
eval model::  95%|█████████▍| 474/500 [00:26<00:01, 18.50it/s]
eval model::  96%|█████████▌| 478/500 [00:26<00:01, 21.48it/s]
eval model::  96%|█████████▌| 481/500 [00:26<00:00, 23.07it/s]
eval model::  97%|█████████▋| 484/500 [00:26<00:00, 23.93it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 23.01it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 23.21it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 19.39it/s]
eval model::  99%|█████████▉| 496/500 [00:27<00:00, 20.98it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 25.04it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.41it/s]
[2024/07/27 14:17:55] ppocr INFO: cur metric, precision: 0.7747163695299838, recall: 0.6904188733750601, hmean: 0.7301425661914459, fps: 43.582139874688714
[2024/07/27 14:17:55] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 14:17:55] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 14:17:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:18:03] ppocr INFO: epoch: [961/1500], global_step: 2883, lr: 0.001000, loss: 1.284311, loss_shrink_maps: 0.647198, loss_threshold_maps: 0.506316, loss_binary_maps: 0.128436, avg_reader_cost: 1.91082 s, avg_batch_cost: 2.18826 s, avg_samples: 12.5, ips: 5.71230 samples/s, eta: 3:54:01
[2024/07/27 14:18:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:18:13] ppocr INFO: epoch: [962/1500], global_step: 2886, lr: 0.001000, loss: 1.284311, loss_shrink_maps: 0.647198, loss_threshold_maps: 0.504749, loss_binary_maps: 0.128805, avg_reader_cost: 2.17919 s, avg_batch_cost: 2.55354 s, avg_samples: 12.5, ips: 4.89516 samples/s, eta: 3:53:34
[2024/07/27 14:18:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:18:22] ppocr INFO: epoch: [963/1500], global_step: 2889, lr: 0.001000, loss: 1.276412, loss_shrink_maps: 0.646124, loss_threshold_maps: 0.500961, loss_binary_maps: 0.128436, avg_reader_cost: 2.32726 s, avg_batch_cost: 2.57316 s, avg_samples: 12.5, ips: 4.85785 samples/s, eta: 3:53:08
[2024/07/27 14:18:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:18:31] ppocr INFO: epoch: [964/1500], global_step: 2890, lr: 0.001000, loss: 1.276412, loss_shrink_maps: 0.646124, loss_threshold_maps: 0.500961, loss_binary_maps: 0.128436, avg_reader_cost: 0.68074 s, avg_batch_cost: 0.76786 s, avg_samples: 4.8, ips: 6.25111 samples/s, eta: 3:52:59
[2024/07/27 14:18:32] ppocr INFO: epoch: [964/1500], global_step: 2892, lr: 0.001000, loss: 1.276412, loss_shrink_maps: 0.647198, loss_threshold_maps: 0.500961, loss_binary_maps: 0.128805, avg_reader_cost: 1.62779 s, avg_batch_cost: 1.77527 s, avg_samples: 7.7, ips: 4.33736 samples/s, eta: 3:52:42
[2024/07/27 14:18:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:18:42] ppocr INFO: epoch: [965/1500], global_step: 2895, lr: 0.001000, loss: 1.276412, loss_shrink_maps: 0.648361, loss_threshold_maps: 0.500049, loss_binary_maps: 0.129198, avg_reader_cost: 2.30959 s, avg_batch_cost: 2.55640 s, avg_samples: 12.5, ips: 4.88968 samples/s, eta: 3:52:16
[2024/07/27 14:18:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:18:51] ppocr INFO: epoch: [966/1500], global_step: 2898, lr: 0.001000, loss: 1.276412, loss_shrink_maps: 0.648361, loss_threshold_maps: 0.496686, loss_binary_maps: 0.129198, avg_reader_cost: 2.09908 s, avg_batch_cost: 2.41441 s, avg_samples: 12.5, ips: 5.17725 samples/s, eta: 3:51:48
[2024/07/27 14:18:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:19:00] ppocr INFO: epoch: [967/1500], global_step: 2900, lr: 0.001000, loss: 1.302665, loss_shrink_maps: 0.676338, loss_threshold_maps: 0.501973, loss_binary_maps: 0.134259, avg_reader_cost: 1.36198 s, avg_batch_cost: 1.67630 s, avg_samples: 9.6, ips: 5.72691 samples/s, eta: 3:51:31
[2024/07/27 14:19:01] ppocr INFO: epoch: [967/1500], global_step: 2901, lr: 0.001000, loss: 1.283151, loss_shrink_maps: 0.665312, loss_threshold_maps: 0.500049, loss_binary_maps: 0.132772, avg_reader_cost: 0.88499 s, avg_batch_cost: 0.93984 s, avg_samples: 2.9, ips: 3.08564 samples/s, eta: 3:51:22
[2024/07/27 14:19:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:19:10] ppocr INFO: epoch: [968/1500], global_step: 2904, lr: 0.001000, loss: 1.262326, loss_shrink_maps: 0.659014, loss_threshold_maps: 0.498243, loss_binary_maps: 0.131281, avg_reader_cost: 2.19252 s, avg_batch_cost: 2.55427 s, avg_samples: 12.5, ips: 4.89376 samples/s, eta: 3:50:56
[2024/07/27 14:19:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:19:20] ppocr INFO: epoch: [969/1500], global_step: 2907, lr: 0.001000, loss: 1.262326, loss_shrink_maps: 0.651053, loss_threshold_maps: 0.493048, loss_binary_maps: 0.129324, avg_reader_cost: 2.31730 s, avg_batch_cost: 2.55738 s, avg_samples: 12.5, ips: 4.88782 samples/s, eta: 3:50:30
[2024/07/27 14:19:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:19:30] ppocr INFO: epoch: [970/1500], global_step: 2910, lr: 0.001000, loss: 1.305824, loss_shrink_maps: 0.677163, loss_threshold_maps: 0.507131, loss_binary_maps: 0.134872, avg_reader_cost: 2.28247 s, avg_batch_cost: 2.53046 s, avg_samples: 12.5, ips: 4.93982 samples/s, eta: 3:50:03
[2024/07/27 14:19:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:19:39] ppocr INFO: epoch: [971/1500], global_step: 2913, lr: 0.001000, loss: 1.296692, loss_shrink_maps: 0.669959, loss_threshold_maps: 0.502700, loss_binary_maps: 0.133349, avg_reader_cost: 2.18750 s, avg_batch_cost: 2.55686 s, avg_samples: 12.5, ips: 4.88880 samples/s, eta: 3:49:37
[2024/07/27 14:19:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:19:49] ppocr INFO: epoch: [972/1500], global_step: 2916, lr: 0.001000, loss: 1.290502, loss_shrink_maps: 0.662462, loss_threshold_maps: 0.502700, loss_binary_maps: 0.131670, avg_reader_cost: 2.27723 s, avg_batch_cost: 2.51663 s, avg_samples: 12.5, ips: 4.96696 samples/s, eta: 3:49:11
[2024/07/27 14:19:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:19:59] ppocr INFO: epoch: [973/1500], global_step: 2919, lr: 0.001000, loss: 1.267419, loss_shrink_maps: 0.647977, loss_threshold_maps: 0.502700, loss_binary_maps: 0.128870, avg_reader_cost: 2.36195 s, avg_batch_cost: 2.59890 s, avg_samples: 12.5, ips: 4.80973 samples/s, eta: 3:48:44
[2024/07/27 14:20:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:20:07] ppocr INFO: epoch: [974/1500], global_step: 2920, lr: 0.001000, loss: 1.244677, loss_shrink_maps: 0.633492, loss_threshold_maps: 0.499019, loss_binary_maps: 0.126180, avg_reader_cost: 0.68225 s, avg_batch_cost: 0.77405 s, avg_samples: 4.8, ips: 6.20118 samples/s, eta: 3:48:35
[2024/07/27 14:20:09] ppocr INFO: epoch: [974/1500], global_step: 2922, lr: 0.001000, loss: 1.271362, loss_shrink_maps: 0.647977, loss_threshold_maps: 0.504038, loss_binary_maps: 0.128870, avg_reader_cost: 1.64008 s, avg_batch_cost: 1.78737 s, avg_samples: 7.7, ips: 4.30800 samples/s, eta: 3:48:18
[2024/07/27 14:20:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:20:18] ppocr INFO: epoch: [975/1500], global_step: 2925, lr: 0.001000, loss: 1.244677, loss_shrink_maps: 0.626358, loss_threshold_maps: 0.499019, loss_binary_maps: 0.124645, avg_reader_cost: 2.21341 s, avg_batch_cost: 2.58751 s, avg_samples: 12.5, ips: 4.83089 samples/s, eta: 3:47:52
[2024/07/27 14:20:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:20:28] ppocr INFO: epoch: [976/1500], global_step: 2928, lr: 0.001000, loss: 1.219362, loss_shrink_maps: 0.606180, loss_threshold_maps: 0.491089, loss_binary_maps: 0.120670, avg_reader_cost: 2.37290 s, avg_batch_cost: 2.61996 s, avg_samples: 12.5, ips: 4.77107 samples/s, eta: 3:47:26
[2024/07/27 14:20:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:20:38] ppocr INFO: epoch: [977/1500], global_step: 2930, lr: 0.001000, loss: 1.219362, loss_shrink_maps: 0.606180, loss_threshold_maps: 0.490272, loss_binary_maps: 0.120670, avg_reader_cost: 1.35321 s, avg_batch_cost: 1.67974 s, avg_samples: 9.6, ips: 5.71517 samples/s, eta: 3:47:08
[2024/07/27 14:20:38] ppocr INFO: epoch: [977/1500], global_step: 2931, lr: 0.001000, loss: 1.234817, loss_shrink_maps: 0.616795, loss_threshold_maps: 0.496060, loss_binary_maps: 0.122618, avg_reader_cost: 0.88624 s, avg_batch_cost: 0.94176 s, avg_samples: 2.9, ips: 3.07935 samples/s, eta: 3:47:00
[2024/07/27 14:20:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:20:48] ppocr INFO: epoch: [978/1500], global_step: 2934, lr: 0.001000, loss: 1.252359, loss_shrink_maps: 0.627096, loss_threshold_maps: 0.500357, loss_binary_maps: 0.124778, avg_reader_cost: 2.36671 s, avg_batch_cost: 2.60394 s, avg_samples: 12.5, ips: 4.80041 samples/s, eta: 3:46:34
[2024/07/27 14:20:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:20:58] ppocr INFO: epoch: [979/1500], global_step: 2937, lr: 0.001000, loss: 1.226932, loss_shrink_maps: 0.616795, loss_threshold_maps: 0.496060, loss_binary_maps: 0.122618, avg_reader_cost: 2.41291 s, avg_batch_cost: 2.66819 s, avg_samples: 12.5, ips: 4.68482 samples/s, eta: 3:46:08
[2024/07/27 14:20:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:21:08] ppocr INFO: epoch: [980/1500], global_step: 2940, lr: 0.001000, loss: 1.226932, loss_shrink_maps: 0.619084, loss_threshold_maps: 0.484629, loss_binary_maps: 0.123199, avg_reader_cost: 2.29148 s, avg_batch_cost: 2.52721 s, avg_samples: 12.5, ips: 4.94616 samples/s, eta: 3:45:42

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:57,  8.65it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 17.92it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.06it/s]
eval model::   2%|▏         | 8/500 [00:00<00:26, 18.33it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.79it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.94it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.97it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.47it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.63it/s]
eval model::   5%|▌         | 26/500 [00:01<00:21, 21.63it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 21.73it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.24it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 19.05it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 21.24it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.87it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 17.88it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.19it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 17.40it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.10it/s]
eval model::  11%|█         | 53/500 [00:02<00:23, 19.06it/s]
eval model::  11%|█         | 56/500 [00:02<00:23, 19.02it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 18.24it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:29, 14.96it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 14.95it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 19.41it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:18, 22.81it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:19, 21.58it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:20, 20.58it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:19, 22.00it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:18, 22.17it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:20, 20.06it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:24, 17.02it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:25, 15.75it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:25, 16.04it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:22, 17.71it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:21, 18.33it/s]
eval model::  20%|██        | 101/500 [00:05<00:22, 17.74it/s]
eval model::  21%|██        | 104/500 [00:05<00:20, 19.50it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.51it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 22.61it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:17, 21.57it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:19, 19.69it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:18, 20.96it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.84it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:20, 18.17it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 20.35it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 19.32it/s]
eval model::  27%|██▋       | 134/500 [00:07<00:19, 18.82it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:18, 19.97it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 19.54it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 20.31it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:18, 19.57it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 18.81it/s]
eval model::  30%|███       | 151/500 [00:07<00:18, 18.88it/s]
eval model::  31%|███       | 155/500 [00:08<00:15, 21.98it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.71it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:23, 14.52it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:21, 15.39it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:18, 17.62it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:19, 17.02it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:19, 17.20it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 16.73it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.58it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.12it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.55it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:19, 16.50it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.05it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:15, 20.10it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:15, 20.41it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:16, 18.03it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:17, 17.29it/s]
eval model::  40%|████      | 200/500 [00:10<00:15, 19.58it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 20.31it/s]
eval model::  41%|████      | 206/500 [00:11<00:21, 13.93it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:20, 13.98it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:22, 13.15it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.55it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.57it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.51it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:18, 14.80it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.30it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.45it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:14, 18.77it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:13, 19.25it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:13, 19.19it/s]
eval model::  48%|████▊     | 238/500 [00:13<00:12, 20.45it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:12, 21.45it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:12, 19.71it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:12, 20.30it/s]
eval model::  50%|█████     | 250/500 [00:13<00:12, 19.62it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.17it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 22.48it/s]
eval model::  52%|█████▏    | 260/500 [00:14<00:10, 23.17it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:10, 23.00it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:10, 21.39it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 19.60it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 19.66it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 18.78it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.95it/s]
eval model::  56%|█████▌    | 281/500 [00:15<00:10, 21.28it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:09, 22.28it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:18, 11.64it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:14, 14.16it/s]
eval model::  59%|█████▊    | 293/500 [00:16<00:13, 14.83it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:17, 11.92it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 15.89it/s]
eval model::  60%|██████    | 302/500 [00:16<00:12, 15.53it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 15.71it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 17.93it/s]
eval model::  62%|██████▏   | 310/500 [00:17<00:10, 18.75it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:09, 19.47it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:10, 18.12it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:09, 20.01it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 19.43it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 21.04it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 19.96it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:08, 20.77it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:08, 20.72it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 19.09it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:11, 14.24it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:10, 15.67it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 18.16it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:07, 20.16it/s]
eval model::  70%|███████   | 352/500 [00:19<00:07, 20.14it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 19.65it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 19.38it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.02it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.92it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:07, 17.21it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:07, 18.69it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:06, 18.60it/s]
eval model::  75%|███████▍  | 374/500 [00:20<00:06, 19.99it/s]
eval model::  75%|███████▌  | 377/500 [00:20<00:05, 21.96it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 21.11it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:04, 24.57it/s]
eval model::  77%|███████▋  | 387/500 [00:21<00:07, 14.31it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:06, 16.01it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 16.91it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 17.55it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 19.28it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 18.88it/s]
eval model::  81%|████████  | 405/500 [00:22<00:04, 20.36it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:04, 22.04it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:04, 21.98it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 21.87it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:03, 21.33it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.63it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:05, 15.13it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.66it/s]
eval model::  85%|████████▌ | 427/500 [00:23<00:04, 16.74it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 18.99it/s]
eval model::  87%|████████▋ | 433/500 [00:23<00:03, 19.74it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:03, 21.04it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 21.88it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 21.82it/s]
eval model::  89%|████████▉ | 445/500 [00:24<00:02, 22.20it/s]
eval model::  90%|████████▉ | 448/500 [00:24<00:02, 22.15it/s]
eval model::  90%|█████████ | 451/500 [00:24<00:02, 22.66it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:01, 23.33it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:01, 24.26it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 21.85it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 17.49it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 16.37it/s]
eval model::  94%|█████████▍| 469/500 [00:25<00:01, 17.87it/s]
eval model::  94%|█████████▍| 472/500 [00:25<00:01, 19.11it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.18it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.17it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:01, 19.91it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 20.77it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 20.07it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 20.88it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 19.54it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 20.28it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 22.12it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.66it/s]
[2024/07/27 14:21:35] ppocr INFO: cur metric, precision: 0.7678762006403416, recall: 0.6928261916225325, hmean: 0.7284231840040496, fps: 45.08591318252024
[2024/07/27 14:21:35] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 14:21:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:21:44] ppocr INFO: epoch: [981/1500], global_step: 2943, lr: 0.001000, loss: 1.205666, loss_shrink_maps: 0.610115, loss_threshold_maps: 0.472353, loss_binary_maps: 0.121250, avg_reader_cost: 2.34249 s, avg_batch_cost: 2.59314 s, avg_samples: 12.5, ips: 4.82041 samples/s, eta: 3:45:16
[2024/07/27 14:21:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:21:54] ppocr INFO: epoch: [982/1500], global_step: 2946, lr: 0.001000, loss: 1.216745, loss_shrink_maps: 0.619822, loss_threshold_maps: 0.473486, loss_binary_maps: 0.123333, avg_reader_cost: 2.21437 s, avg_batch_cost: 2.56795 s, avg_samples: 12.5, ips: 4.86770 samples/s, eta: 3:44:50
[2024/07/27 14:21:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:22:04] ppocr INFO: epoch: [983/1500], global_step: 2949, lr: 0.001000, loss: 1.212477, loss_shrink_maps: 0.614335, loss_threshold_maps: 0.472353, loss_binary_maps: 0.122311, avg_reader_cost: 2.36984 s, avg_batch_cost: 2.60931 s, avg_samples: 12.5, ips: 4.79053 samples/s, eta: 3:44:24
[2024/07/27 14:22:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:22:12] ppocr INFO: epoch: [984/1500], global_step: 2950, lr: 0.001000, loss: 1.205666, loss_shrink_maps: 0.602180, loss_threshold_maps: 0.471374, loss_binary_maps: 0.120060, avg_reader_cost: 0.51619 s, avg_batch_cost: 0.75651 s, avg_samples: 4.8, ips: 6.34495 samples/s, eta: 3:44:14
[2024/07/27 14:22:13] ppocr INFO: epoch: [984/1500], global_step: 2952, lr: 0.001000, loss: 1.179520, loss_shrink_maps: 0.587328, loss_threshold_maps: 0.469749, loss_binary_maps: 0.116867, avg_reader_cost: 1.60541 s, avg_batch_cost: 1.75267 s, avg_samples: 7.7, ips: 4.39329 samples/s, eta: 3:43:57
[2024/07/27 14:22:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:22:23] ppocr INFO: epoch: [985/1500], global_step: 2955, lr: 0.001000, loss: 1.154585, loss_shrink_maps: 0.570910, loss_threshold_maps: 0.459555, loss_binary_maps: 0.113832, avg_reader_cost: 2.38068 s, avg_batch_cost: 2.65803 s, avg_samples: 12.5, ips: 4.70273 samples/s, eta: 3:43:31
[2024/07/27 14:22:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:22:33] ppocr INFO: epoch: [986/1500], global_step: 2958, lr: 0.001000, loss: 1.179520, loss_shrink_maps: 0.602180, loss_threshold_maps: 0.466736, loss_binary_maps: 0.120060, avg_reader_cost: 2.32070 s, avg_batch_cost: 2.56470 s, avg_samples: 12.5, ips: 4.87386 samples/s, eta: 3:43:05
[2024/07/27 14:22:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:22:42] ppocr INFO: epoch: [987/1500], global_step: 2960, lr: 0.001000, loss: 1.159312, loss_shrink_maps: 0.581816, loss_threshold_maps: 0.467985, loss_binary_maps: 0.115890, avg_reader_cost: 1.41005 s, avg_batch_cost: 1.62390 s, avg_samples: 9.6, ips: 5.91170 samples/s, eta: 3:42:47
[2024/07/27 14:22:43] ppocr INFO: epoch: [987/1500], global_step: 2961, lr: 0.001000, loss: 1.163423, loss_shrink_maps: 0.581816, loss_threshold_maps: 0.474219, loss_binary_maps: 0.115890, avg_reader_cost: 0.85819 s, avg_batch_cost: 0.91397 s, avg_samples: 2.9, ips: 3.17298 samples/s, eta: 3:42:39
[2024/07/27 14:22:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:22:52] ppocr INFO: epoch: [988/1500], global_step: 2964, lr: 0.001000, loss: 1.208546, loss_shrink_maps: 0.611675, loss_threshold_maps: 0.480968, loss_binary_maps: 0.122289, avg_reader_cost: 2.20443 s, avg_batch_cost: 2.57353 s, avg_samples: 12.5, ips: 4.85714 samples/s, eta: 3:42:12
[2024/07/27 14:22:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:23:02] ppocr INFO: epoch: [989/1500], global_step: 2967, lr: 0.001000, loss: 1.183740, loss_shrink_maps: 0.594101, loss_threshold_maps: 0.480968, loss_binary_maps: 0.118479, avg_reader_cost: 2.21682 s, avg_batch_cost: 2.60614 s, avg_samples: 12.5, ips: 4.79636 samples/s, eta: 3:41:46
[2024/07/27 14:23:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:23:12] ppocr INFO: epoch: [990/1500], global_step: 2970, lr: 0.001000, loss: 1.205669, loss_shrink_maps: 0.611675, loss_threshold_maps: 0.480968, loss_binary_maps: 0.122289, avg_reader_cost: 2.22390 s, avg_batch_cost: 2.60320 s, avg_samples: 12.5, ips: 4.80178 samples/s, eta: 3:41:20
[2024/07/27 14:23:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:23:22] ppocr INFO: epoch: [991/1500], global_step: 2973, lr: 0.001000, loss: 1.195568, loss_shrink_maps: 0.602139, loss_threshold_maps: 0.480968, loss_binary_maps: 0.120010, avg_reader_cost: 2.16846 s, avg_batch_cost: 2.50458 s, avg_samples: 12.5, ips: 4.99085 samples/s, eta: 3:40:54
[2024/07/27 14:23:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:23:32] ppocr INFO: epoch: [992/1500], global_step: 2976, lr: 0.001000, loss: 1.195568, loss_shrink_maps: 0.602139, loss_threshold_maps: 0.484420, loss_binary_maps: 0.120010, avg_reader_cost: 2.37069 s, avg_batch_cost: 2.61166 s, avg_samples: 12.5, ips: 4.78623 samples/s, eta: 3:40:28
[2024/07/27 14:23:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:23:41] ppocr INFO: epoch: [993/1500], global_step: 2979, lr: 0.001000, loss: 1.198214, loss_shrink_maps: 0.598650, loss_threshold_maps: 0.484420, loss_binary_maps: 0.118371, avg_reader_cost: 2.12395 s, avg_batch_cost: 2.58663 s, avg_samples: 12.5, ips: 4.83254 samples/s, eta: 3:40:02
[2024/07/27 14:23:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:23:49] ppocr INFO: epoch: [994/1500], global_step: 2980, lr: 0.001000, loss: 1.198214, loss_shrink_maps: 0.598650, loss_threshold_maps: 0.478033, loss_binary_maps: 0.118371, avg_reader_cost: 0.66324 s, avg_batch_cost: 0.76835 s, avg_samples: 4.8, ips: 6.24717 samples/s, eta: 3:39:53
[2024/07/27 14:23:51] ppocr INFO: epoch: [994/1500], global_step: 2982, lr: 0.001000, loss: 1.211191, loss_shrink_maps: 0.604468, loss_threshold_maps: 0.478033, loss_binary_maps: 0.120280, avg_reader_cost: 1.62864 s, avg_batch_cost: 1.77564 s, avg_samples: 7.7, ips: 4.33646 samples/s, eta: 3:39:35
[2024/07/27 14:23:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:24:00] ppocr INFO: epoch: [995/1500], global_step: 2985, lr: 0.001000, loss: 1.206183, loss_shrink_maps: 0.605355, loss_threshold_maps: 0.472286, loss_binary_maps: 0.120132, avg_reader_cost: 2.11198 s, avg_batch_cost: 2.43061 s, avg_samples: 12.5, ips: 5.14274 samples/s, eta: 3:39:08
[2024/07/27 14:24:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:24:10] ppocr INFO: epoch: [996/1500], global_step: 2988, lr: 0.001000, loss: 1.212802, loss_shrink_maps: 0.605355, loss_threshold_maps: 0.476618, loss_binary_maps: 0.120132, avg_reader_cost: 2.30899 s, avg_batch_cost: 2.56220 s, avg_samples: 12.5, ips: 4.87861 samples/s, eta: 3:38:42
[2024/07/27 14:24:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:24:19] ppocr INFO: epoch: [997/1500], global_step: 2990, lr: 0.001000, loss: 1.233700, loss_shrink_maps: 0.633833, loss_threshold_maps: 0.482594, loss_binary_maps: 0.126131, avg_reader_cost: 1.38828 s, avg_batch_cost: 1.57544 s, avg_samples: 9.6, ips: 6.09354 samples/s, eta: 3:38:24
[2024/07/27 14:24:19] ppocr INFO: epoch: [997/1500], global_step: 2991, lr: 0.001000, loss: 1.250026, loss_shrink_maps: 0.658620, loss_threshold_maps: 0.482594, loss_binary_maps: 0.130892, avg_reader_cost: 0.83364 s, avg_batch_cost: 0.88895 s, avg_samples: 2.9, ips: 3.26227 samples/s, eta: 3:38:15
[2024/07/27 14:24:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:24:29] ppocr INFO: epoch: [998/1500], global_step: 2994, lr: 0.001000, loss: 1.275238, loss_shrink_maps: 0.658620, loss_threshold_maps: 0.487976, loss_binary_maps: 0.130892, avg_reader_cost: 2.20365 s, avg_batch_cost: 2.55986 s, avg_samples: 12.5, ips: 4.88308 samples/s, eta: 3:37:49
[2024/07/27 14:24:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:24:39] ppocr INFO: epoch: [999/1500], global_step: 2997, lr: 0.001000, loss: 1.225560, loss_shrink_maps: 0.618750, loss_threshold_maps: 0.481797, loss_binary_maps: 0.123068, avg_reader_cost: 2.29731 s, avg_batch_cost: 2.57847 s, avg_samples: 12.5, ips: 4.84784 samples/s, eta: 3:37:23
[2024/07/27 14:24:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:24:48] ppocr INFO: epoch: [1000/1500], global_step: 3000, lr: 0.001000, loss: 1.275238, loss_shrink_maps: 0.649573, loss_threshold_maps: 0.483742, loss_binary_maps: 0.129528, avg_reader_cost: 2.20274 s, avg_batch_cost: 2.57930 s, avg_samples: 12.5, ips: 4.84627 samples/s, eta: 3:36:57

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:37, 13.36it/s]
eval model::   1%|          | 5/500 [00:00<00:23, 21.06it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.36it/s]
eval model::   2%|▏         | 11/500 [00:00<00:31, 15.53it/s]
eval model::   3%|▎         | 13/500 [00:00<00:30, 15.90it/s]
eval model::   3%|▎         | 15/500 [00:00<00:31, 15.49it/s]
eval model::   4%|▎         | 18/500 [00:01<00:27, 17.22it/s]
eval model::   4%|▍         | 20/500 [00:01<00:27, 17.33it/s]
eval model::   5%|▍         | 24/500 [00:01<00:22, 21.22it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 20.78it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 21.85it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 19.50it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 19.30it/s]
eval model::   8%|▊         | 38/500 [00:02<00:24, 18.58it/s]
eval model::   8%|▊         | 40/500 [00:02<00:27, 16.81it/s]
eval model::   8%|▊         | 42/500 [00:02<00:29, 15.78it/s]
eval model::   9%|▉         | 44/500 [00:02<00:27, 16.38it/s]
eval model::   9%|▉         | 46/500 [00:02<00:28, 16.08it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.64it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 19.48it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 20.64it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:22, 19.78it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.52it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.19it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:21, 20.04it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 21.73it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 21.18it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:20, 20.99it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.08it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 20.60it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 21.33it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.41it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.29it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:23, 17.41it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:21, 18.42it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:19, 20.37it/s]
eval model::  20%|██        | 101/500 [00:05<00:20, 19.61it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 23.02it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:16, 23.09it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:17, 21.94it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:16, 23.45it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:18, 20.69it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:20, 18.74it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:20, 18.63it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.35it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 19.95it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 18.53it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.61it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:18, 19.96it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.50it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.92it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.76it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 17.95it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 19.38it/s]
eval model::  31%|███       | 153/500 [00:08<00:19, 17.49it/s]
eval model::  31%|███       | 156/500 [00:08<00:18, 18.64it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:18, 18.54it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:23, 14.55it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:22, 14.68it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:21, 15.60it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:21, 15.57it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:18, 17.82it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:16, 19.35it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:21, 15.26it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:18, 17.40it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:21, 15.07it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:20, 15.58it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:19, 16.51it/s]
eval model::  37%|███▋      | 186/500 [00:10<00:18, 17.10it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:16, 18.83it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:16, 18.27it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:15, 19.92it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.46it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 21.07it/s]
eval model::  41%|████      | 205/500 [00:11<00:20, 14.20it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:19, 14.84it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 14.59it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.81it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.94it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:21, 13.12it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:19, 14.36it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:21, 13.16it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:18, 15.20it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:17, 16.08it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 20.04it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:13, 19.24it/s]
eval model::  47%|████▋     | 235/500 [00:13<00:13, 18.93it/s]
eval model::  48%|████▊     | 238/500 [00:13<00:13, 19.63it/s]
eval model::  48%|████▊     | 240/500 [00:13<00:13, 19.68it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:15, 16.25it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:13, 18.35it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:13, 18.47it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:14, 17.46it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 19.63it/s]
eval model::  51%|█████     | 255/500 [00:14<00:11, 21.26it/s]
eval model::  52%|█████▏    | 258/500 [00:14<00:11, 21.28it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:10, 22.37it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:11, 20.31it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 19.76it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:12, 18.25it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.08it/s]
eval model::  55%|█████▌    | 275/500 [00:15<00:12, 17.67it/s]
eval model::  55%|█████▌    | 277/500 [00:15<00:12, 17.87it/s]
eval model::  56%|█████▌    | 280/500 [00:15<00:11, 19.76it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:10, 19.98it/s]
eval model::  57%|█████▋    | 286/500 [00:16<00:19, 10.94it/s]
eval model::  58%|█████▊    | 288/500 [00:16<00:17, 12.10it/s]
eval model::  58%|█████▊    | 291/500 [00:16<00:13, 15.02it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:13, 15.09it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:16, 12.70it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:13, 14.60it/s]
eval model::  60%|██████    | 301/500 [00:16<00:14, 14.01it/s]
eval model::  61%|██████    | 304/500 [00:17<00:12, 15.57it/s]
eval model::  61%|██████    | 306/500 [00:17<00:11, 16.32it/s]
eval model::  62%|██████▏   | 308/500 [00:17<00:11, 17.09it/s]
eval model::  62%|██████▏   | 311/500 [00:17<00:10, 18.71it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:10, 18.49it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:10, 16.87it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:09, 18.35it/s]
eval model::  64%|██████▍   | 321/500 [00:18<00:09, 18.74it/s]
eval model::  65%|██████▍   | 323/500 [00:18<00:10, 17.08it/s]
eval model::  65%|██████▌   | 326/500 [00:18<00:09, 19.32it/s]
eval model::  66%|██████▌   | 328/500 [00:18<00:08, 19.26it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:08, 19.23it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:07, 21.35it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 21.00it/s]
eval model::  68%|██████▊   | 340/500 [00:19<00:10, 14.72it/s]
eval model::  69%|██████▊   | 343/500 [00:19<00:09, 16.21it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:08, 17.60it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:07, 19.13it/s]
eval model::  70%|███████   | 352/500 [00:19<00:07, 20.30it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 18.75it/s]
eval model::  72%|███████▏  | 358/500 [00:20<00:07, 19.72it/s]
eval model::  72%|███████▏  | 361/500 [00:20<00:07, 17.93it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:07, 17.51it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:07, 17.69it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:06, 18.82it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:06, 18.81it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:05, 21.93it/s]
eval model::  76%|███████▌  | 378/500 [00:21<00:05, 22.66it/s]
eval model::  76%|███████▌  | 381/500 [00:21<00:06, 19.59it/s]
eval model::  77%|███████▋  | 385/500 [00:21<00:05, 22.71it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:07, 14.20it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:06, 15.89it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 16.23it/s]
eval model::  79%|███████▉  | 395/500 [00:22<00:06, 15.60it/s]
eval model::  80%|███████▉  | 398/500 [00:22<00:05, 17.88it/s]
eval model::  80%|████████  | 400/500 [00:22<00:05, 16.70it/s]
eval model::  80%|████████  | 402/500 [00:22<00:05, 16.38it/s]
eval model::  81%|████████  | 404/500 [00:22<00:05, 16.11it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:05, 17.80it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 20.04it/s]
eval model::  83%|████████▎ | 413/500 [00:23<00:04, 20.75it/s]
eval model::  83%|████████▎ | 416/500 [00:23<00:03, 21.67it/s]
eval model::  84%|████████▍ | 419/500 [00:23<00:03, 20.26it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:05, 15.28it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 15.17it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:04, 15.34it/s]
eval model::  86%|████████▌ | 429/500 [00:24<00:03, 18.03it/s]
eval model::  86%|████████▌ | 431/500 [00:24<00:04, 16.73it/s]
eval model::  87%|████████▋ | 434/500 [00:24<00:03, 19.04it/s]
eval model::  87%|████████▋ | 437/500 [00:24<00:03, 19.75it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:03, 17.94it/s]
eval model::  89%|████████▊ | 443/500 [00:24<00:02, 19.65it/s]
eval model::  89%|████████▉ | 446/500 [00:24<00:02, 20.59it/s]
eval model::  90%|████████▉ | 449/500 [00:25<00:02, 19.01it/s]
eval model::  91%|█████████ | 453/500 [00:25<00:02, 21.97it/s]
eval model::  91%|█████████▏| 457/500 [00:25<00:01, 25.30it/s]
eval model::  92%|█████████▏| 460/500 [00:25<00:01, 23.75it/s]
eval model::  93%|█████████▎| 463/500 [00:25<00:02, 18.13it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:01, 17.06it/s]
eval model::  94%|█████████▎| 468/500 [00:26<00:01, 16.57it/s]
eval model::  94%|█████████▍| 472/500 [00:26<00:01, 19.52it/s]
eval model::  95%|█████████▌| 475/500 [00:26<00:01, 20.58it/s]
eval model::  96%|█████████▌| 478/500 [00:26<00:00, 22.55it/s]
eval model::  96%|█████████▌| 481/500 [00:26<00:00, 23.77it/s]
eval model::  97%|█████████▋| 484/500 [00:26<00:00, 24.04it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 19.46it/s]
eval model::  98%|█████████▊| 490/500 [00:27<00:00, 20.64it/s]
eval model::  99%|█████████▊| 493/500 [00:27<00:00, 17.07it/s]
eval model::  99%|█████████▉| 496/500 [00:27<00:00, 18.25it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.19it/s]
[2024/07/27 14:25:16] ppocr INFO: cur metric, precision: 0.7417721518987341, recall: 0.7053442465093885, hmean: 0.7230997038499506, fps: 44.1903043427176
[2024/07/27 14:25:16] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 14:25:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:25:25] ppocr INFO: epoch: [1001/1500], global_step: 3003, lr: 0.001000, loss: 1.240090, loss_shrink_maps: 0.633969, loss_threshold_maps: 0.481797, loss_binary_maps: 0.126263, avg_reader_cost: 2.13190 s, avg_batch_cost: 2.44840 s, avg_samples: 12.5, ips: 5.10537 samples/s, eta: 3:36:30
[2024/07/27 14:25:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:25:35] ppocr INFO: epoch: [1002/1500], global_step: 3006, lr: 0.001000, loss: 1.225560, loss_shrink_maps: 0.623559, loss_threshold_maps: 0.474738, loss_binary_maps: 0.123616, avg_reader_cost: 2.19406 s, avg_batch_cost: 2.55684 s, avg_samples: 12.5, ips: 4.88884 samples/s, eta: 3:36:04
[2024/07/27 14:25:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:25:44] ppocr INFO: epoch: [1003/1500], global_step: 3009, lr: 0.001000, loss: 1.240090, loss_shrink_maps: 0.638495, loss_threshold_maps: 0.475996, loss_binary_maps: 0.126796, avg_reader_cost: 2.18034 s, avg_batch_cost: 2.52507 s, avg_samples: 12.5, ips: 4.95035 samples/s, eta: 3:35:37
[2024/07/27 14:25:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:25:53] ppocr INFO: epoch: [1004/1500], global_step: 3010, lr: 0.001000, loss: 1.240090, loss_shrink_maps: 0.638495, loss_threshold_maps: 0.475996, loss_binary_maps: 0.126796, avg_reader_cost: 0.70696 s, avg_batch_cost: 0.80637 s, avg_samples: 4.8, ips: 5.95263 samples/s, eta: 3:35:28
[2024/07/27 14:25:54] ppocr INFO: epoch: [1004/1500], global_step: 3012, lr: 0.001000, loss: 1.225560, loss_shrink_maps: 0.623559, loss_threshold_maps: 0.483742, loss_binary_maps: 0.123616, avg_reader_cost: 1.70487 s, avg_batch_cost: 1.85104 s, avg_samples: 7.7, ips: 4.15983 samples/s, eta: 3:35:12
[2024/07/27 14:25:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:26:04] ppocr INFO: epoch: [1005/1500], global_step: 3015, lr: 0.001000, loss: 1.252391, loss_shrink_maps: 0.638495, loss_threshold_maps: 0.478235, loss_binary_maps: 0.126796, avg_reader_cost: 2.21310 s, avg_batch_cost: 2.59446 s, avg_samples: 12.5, ips: 4.81796 samples/s, eta: 3:34:46
[2024/07/27 14:26:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:26:14] ppocr INFO: epoch: [1006/1500], global_step: 3018, lr: 0.001000, loss: 1.243346, loss_shrink_maps: 0.640047, loss_threshold_maps: 0.469726, loss_binary_maps: 0.126871, avg_reader_cost: 2.22947 s, avg_batch_cost: 2.58663 s, avg_samples: 12.5, ips: 4.83254 samples/s, eta: 3:34:19
[2024/07/27 14:26:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:26:23] ppocr INFO: epoch: [1007/1500], global_step: 3020, lr: 0.001000, loss: 1.243346, loss_shrink_maps: 0.640047, loss_threshold_maps: 0.469726, loss_binary_maps: 0.126871, avg_reader_cost: 1.46951 s, avg_batch_cost: 1.64734 s, avg_samples: 9.6, ips: 5.82759 samples/s, eta: 3:34:02
[2024/07/27 14:26:24] ppocr INFO: epoch: [1007/1500], global_step: 3021, lr: 0.001000, loss: 1.243346, loss_shrink_maps: 0.640047, loss_threshold_maps: 0.469726, loss_binary_maps: 0.126871, avg_reader_cost: 0.87055 s, avg_batch_cost: 0.92582 s, avg_samples: 2.9, ips: 3.13236 samples/s, eta: 3:33:53
[2024/07/27 14:26:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:26:33] ppocr INFO: epoch: [1008/1500], global_step: 3024, lr: 0.001000, loss: 1.294126, loss_shrink_maps: 0.669738, loss_threshold_maps: 0.481114, loss_binary_maps: 0.132293, avg_reader_cost: 2.31711 s, avg_batch_cost: 2.55078 s, avg_samples: 12.5, ips: 4.90046 samples/s, eta: 3:33:27
[2024/07/27 14:26:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:26:43] ppocr INFO: epoch: [1009/1500], global_step: 3027, lr: 0.001000, loss: 1.290283, loss_shrink_maps: 0.649969, loss_threshold_maps: 0.483041, loss_binary_maps: 0.129416, avg_reader_cost: 2.36566 s, avg_batch_cost: 2.61854 s, avg_samples: 12.5, ips: 4.77365 samples/s, eta: 3:33:01
[2024/07/27 14:26:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:26:53] ppocr INFO: epoch: [1010/1500], global_step: 3030, lr: 0.001000, loss: 1.202179, loss_shrink_maps: 0.616576, loss_threshold_maps: 0.473263, loss_binary_maps: 0.122461, avg_reader_cost: 2.26633 s, avg_batch_cost: 2.65642 s, avg_samples: 12.5, ips: 4.70558 samples/s, eta: 3:32:35
[2024/07/27 14:26:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:27:03] ppocr INFO: epoch: [1011/1500], global_step: 3033, lr: 0.001000, loss: 1.202179, loss_shrink_maps: 0.616576, loss_threshold_maps: 0.459301, loss_binary_maps: 0.122461, avg_reader_cost: 2.24059 s, avg_batch_cost: 2.60086 s, avg_samples: 12.5, ips: 4.80610 samples/s, eta: 3:32:09
[2024/07/27 14:27:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:27:13] ppocr INFO: epoch: [1012/1500], global_step: 3036, lr: 0.001000, loss: 1.232040, loss_shrink_maps: 0.624681, loss_threshold_maps: 0.473937, loss_binary_maps: 0.124256, avg_reader_cost: 2.26557 s, avg_batch_cost: 2.65670 s, avg_samples: 12.5, ips: 4.70508 samples/s, eta: 3:31:43
[2024/07/27 14:27:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:27:23] ppocr INFO: epoch: [1013/1500], global_step: 3039, lr: 0.001000, loss: 1.202179, loss_shrink_maps: 0.616576, loss_threshold_maps: 0.472551, loss_binary_maps: 0.122461, avg_reader_cost: 2.44506 s, avg_batch_cost: 2.68667 s, avg_samples: 12.5, ips: 4.65260 samples/s, eta: 3:31:18
[2024/07/27 14:27:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:27:32] ppocr INFO: epoch: [1014/1500], global_step: 3040, lr: 0.001000, loss: 1.202179, loss_shrink_maps: 0.616576, loss_threshold_maps: 0.472551, loss_binary_maps: 0.122461, avg_reader_cost: 0.68122 s, avg_batch_cost: 0.81173 s, avg_samples: 4.8, ips: 5.91333 samples/s, eta: 3:31:09
[2024/07/27 14:27:33] ppocr INFO: epoch: [1014/1500], global_step: 3042, lr: 0.001000, loss: 1.189902, loss_shrink_maps: 0.606275, loss_threshold_maps: 0.472551, loss_binary_maps: 0.120524, avg_reader_cost: 1.71567 s, avg_batch_cost: 1.86126 s, avg_samples: 7.7, ips: 4.13699 samples/s, eta: 3:30:52
[2024/07/27 14:27:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:27:43] ppocr INFO: epoch: [1015/1500], global_step: 3045, lr: 0.001000, loss: 1.186328, loss_shrink_maps: 0.602014, loss_threshold_maps: 0.473895, loss_binary_maps: 0.119675, avg_reader_cost: 2.32990 s, avg_batch_cost: 2.57987 s, avg_samples: 12.5, ips: 4.84520 samples/s, eta: 3:30:26
[2024/07/27 14:27:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:27:53] ppocr INFO: epoch: [1016/1500], global_step: 3048, lr: 0.001000, loss: 1.186328, loss_shrink_maps: 0.606275, loss_threshold_maps: 0.473336, loss_binary_maps: 0.120524, avg_reader_cost: 2.19367 s, avg_batch_cost: 2.53088 s, avg_samples: 12.5, ips: 4.93900 samples/s, eta: 3:30:00
[2024/07/27 14:27:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:28:02] ppocr INFO: epoch: [1017/1500], global_step: 3050, lr: 0.001000, loss: 1.202004, loss_shrink_maps: 0.609697, loss_threshold_maps: 0.480195, loss_binary_maps: 0.121614, avg_reader_cost: 1.33446 s, avg_batch_cost: 1.66759 s, avg_samples: 9.6, ips: 5.75681 samples/s, eta: 3:29:42
[2024/07/27 14:28:03] ppocr INFO: epoch: [1017/1500], global_step: 3051, lr: 0.001000, loss: 1.217510, loss_shrink_maps: 0.618505, loss_threshold_maps: 0.482780, loss_binary_maps: 0.122840, avg_reader_cost: 0.87998 s, avg_batch_cost: 0.93602 s, avg_samples: 2.9, ips: 3.09824 samples/s, eta: 3:29:34
[2024/07/27 14:28:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:28:12] ppocr INFO: epoch: [1018/1500], global_step: 3054, lr: 0.001000, loss: 1.217510, loss_shrink_maps: 0.618505, loss_threshold_maps: 0.482780, loss_binary_maps: 0.122840, avg_reader_cost: 2.15767 s, avg_batch_cost: 2.52597 s, avg_samples: 12.5, ips: 4.94859 samples/s, eta: 3:29:07
[2024/07/27 14:28:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:28:22] ppocr INFO: epoch: [1019/1500], global_step: 3057, lr: 0.001000, loss: 1.211473, loss_shrink_maps: 0.600759, loss_threshold_maps: 0.482780, loss_binary_maps: 0.119369, avg_reader_cost: 2.16172 s, avg_batch_cost: 2.49174 s, avg_samples: 12.5, ips: 5.01657 samples/s, eta: 3:28:41
[2024/07/27 14:28:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:28:32] ppocr INFO: epoch: [1020/1500], global_step: 3060, lr: 0.001000, loss: 1.217510, loss_shrink_maps: 0.615306, loss_threshold_maps: 0.490382, loss_binary_maps: 0.121993, avg_reader_cost: 2.15067 s, avg_batch_cost: 2.54665 s, avg_samples: 12.5, ips: 4.90841 samples/s, eta: 3:28:14

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:58,  8.55it/s]
eval model::   1%|          | 4/500 [00:00<00:34, 14.30it/s]
eval model::   1%|          | 6/500 [00:00<00:35, 13.92it/s]
eval model::   2%|▏         | 9/500 [00:00<00:27, 17.89it/s]
eval model::   2%|▏         | 11/500 [00:00<00:32, 15.25it/s]
eval model::   3%|▎         | 13/500 [00:00<00:31, 15.66it/s]
eval model::   3%|▎         | 15/500 [00:01<00:33, 14.49it/s]
eval model::   4%|▍         | 19/500 [00:01<00:26, 18.23it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 18.59it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.42it/s]
eval model::   5%|▌         | 27/500 [00:01<00:23, 19.90it/s]
eval model::   6%|▌         | 30/500 [00:01<00:22, 20.87it/s]
eval model::   7%|▋         | 33/500 [00:01<00:24, 18.93it/s]
eval model::   7%|▋         | 35/500 [00:02<00:26, 17.35it/s]
eval model::   8%|▊         | 38/500 [00:02<00:25, 18.30it/s]
eval model::   8%|▊         | 40/500 [00:02<00:28, 16.10it/s]
eval model::   8%|▊         | 42/500 [00:02<00:29, 15.78it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.34it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 17.11it/s]
eval model::  10%|█         | 50/500 [00:02<00:25, 17.69it/s]
eval model::  11%|█         | 53/500 [00:03<00:23, 18.93it/s]
eval model::  11%|█         | 56/500 [00:03<00:23, 18.96it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 18.56it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 15.99it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.27it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.88it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:23, 18.70it/s]
eval model::  14%|█▍        | 72/500 [00:04<00:20, 20.52it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:20, 20.51it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:21, 19.76it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 21.28it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:21, 19.20it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:23, 17.71it/s]
eval model::  18%|█▊        | 89/500 [00:05<00:25, 16.42it/s]
eval model::  18%|█▊        | 91/500 [00:05<00:25, 16.05it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:24, 16.29it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:21, 18.40it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:21, 18.70it/s]
eval model::  20%|██        | 100/500 [00:05<00:21, 18.61it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 20.20it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.03it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 21.82it/s]
eval model::  22%|██▏       | 112/500 [00:06<00:19, 20.03it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:19, 20.12it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:18, 20.34it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:20, 18.83it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.39it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.11it/s]
eval model::  26%|██▌       | 129/500 [00:07<00:20, 17.89it/s]
eval model::  26%|██▌       | 131/500 [00:07<00:21, 17.53it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:22, 15.96it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:22, 16.49it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:22, 15.96it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:19, 18.18it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:18, 19.39it/s]
eval model::  29%|██▉       | 145/500 [00:08<00:20, 17.50it/s]
eval model::  29%|██▉       | 147/500 [00:08<00:20, 17.02it/s]
eval model::  30%|███       | 150/500 [00:08<00:18, 18.89it/s]
eval model::  30%|███       | 152/500 [00:08<00:19, 18.23it/s]
eval model::  31%|███       | 155/500 [00:08<00:16, 20.57it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.20it/s]
eval model::  32%|███▏      | 161/500 [00:09<00:24, 13.75it/s]
eval model::  33%|███▎      | 163/500 [00:09<00:22, 14.75it/s]
eval model::  33%|███▎      | 166/500 [00:09<00:19, 16.93it/s]
eval model::  34%|███▍      | 169/500 [00:09<00:20, 16.30it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:20, 15.68it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 16.42it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 15.64it/s]
eval model::  36%|███▌      | 179/500 [00:10<00:17, 18.14it/s]
eval model::  36%|███▌      | 181/500 [00:10<00:19, 16.57it/s]
eval model::  37%|███▋      | 183/500 [00:10<00:18, 16.99it/s]
eval model::  37%|███▋      | 186/500 [00:10<00:16, 18.99it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:16, 18.88it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:15, 19.92it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:13, 22.04it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:12, 23.57it/s]
eval model::  40%|████      | 201/500 [00:11<00:12, 24.37it/s]
eval model::  41%|████      | 204/500 [00:11<00:11, 25.81it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:18, 15.69it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 14.83it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.86it/s]
eval model::  43%|████▎     | 215/500 [00:12<00:21, 13.38it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:21, 13.15it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:20, 13.78it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:22, 12.30it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:18, 14.85it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:17, 15.60it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:14, 18.13it/s]
eval model::  46%|████▋     | 232/500 [00:13<00:13, 20.51it/s]
eval model::  47%|████▋     | 235/500 [00:13<00:13, 19.74it/s]
eval model::  48%|████▊     | 238/500 [00:13<00:12, 20.51it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:12, 21.01it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:13, 18.53it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:13, 18.81it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:13, 18.70it/s]
eval model::  50%|█████     | 252/500 [00:14<00:12, 20.01it/s]
eval model::  51%|█████     | 255/500 [00:14<00:11, 20.49it/s]
eval model::  52%|█████▏    | 258/500 [00:14<00:12, 19.57it/s]
eval model::  52%|█████▏    | 260/500 [00:14<00:12, 19.61it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:11, 20.15it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:12, 19.17it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 19.37it/s]
eval model::  54%|█████▍    | 272/500 [00:15<00:10, 21.21it/s]
eval model::  55%|█████▌    | 275/500 [00:15<00:11, 20.22it/s]
eval model::  56%|█████▌    | 278/500 [00:15<00:10, 20.93it/s]
eval model::  56%|█████▌    | 281/500 [00:15<00:09, 21.97it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:09, 23.60it/s]
eval model::  57%|█████▋    | 287/500 [00:16<00:19, 10.72it/s]
eval model::  58%|█████▊    | 290/500 [00:16<00:16, 12.99it/s]
eval model::  59%|█████▊    | 293/500 [00:16<00:14, 14.10it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:18, 11.36it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:14, 14.02it/s]
eval model::  60%|██████    | 300/500 [00:17<00:15, 13.21it/s]
eval model::  60%|██████    | 302/500 [00:17<00:14, 14.03it/s]
eval model::  61%|██████    | 304/500 [00:17<00:13, 14.14it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:11, 16.63it/s]
eval model::  62%|██████▏   | 309/500 [00:17<00:11, 17.35it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:10, 17.43it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:09, 18.63it/s]
eval model::  64%|██████▍   | 319/500 [00:18<00:08, 21.61it/s]
eval model::  64%|██████▍   | 322/500 [00:18<00:08, 20.06it/s]
eval model::  65%|██████▌   | 325/500 [00:18<00:08, 19.61it/s]
eval model::  66%|██████▌   | 328/500 [00:18<00:08, 19.14it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:08, 20.39it/s]
eval model::  67%|██████▋   | 335/500 [00:18<00:06, 23.58it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:07, 21.28it/s]
eval model::  68%|██████▊   | 341/500 [00:19<00:10, 15.79it/s]
eval model::  69%|██████▊   | 343/500 [00:19<00:09, 16.03it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:08, 18.00it/s]
eval model::  70%|██████▉   | 348/500 [00:19<00:08, 18.36it/s]
eval model::  70%|███████   | 351/500 [00:19<00:07, 19.18it/s]
eval model::  71%|███████   | 354/500 [00:19<00:06, 21.06it/s]
eval model::  71%|███████▏  | 357/500 [00:20<00:07, 20.13it/s]
eval model::  72%|███████▏  | 360/500 [00:20<00:06, 21.05it/s]
eval model::  73%|███████▎  | 363/500 [00:20<00:07, 19.48it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:07, 18.66it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:06, 20.05it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 17.82it/s]
eval model::  75%|███████▌  | 375/500 [00:21<00:06, 17.90it/s]
eval model::  76%|███████▌  | 379/500 [00:21<00:05, 21.14it/s]
eval model::  76%|███████▋  | 382/500 [00:21<00:05, 20.04it/s]
eval model::  77%|███████▋  | 385/500 [00:21<00:05, 21.66it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:08, 12.49it/s]
eval model::  78%|███████▊  | 391/500 [00:22<00:07, 14.41it/s]
eval model::  79%|███████▉  | 394/500 [00:22<00:06, 15.53it/s]
eval model::  79%|███████▉  | 396/500 [00:22<00:06, 16.32it/s]
eval model::  80%|███████▉  | 399/500 [00:22<00:05, 18.80it/s]
eval model::  80%|████████  | 402/500 [00:22<00:05, 17.69it/s]
eval model::  81%|████████  | 405/500 [00:22<00:05, 18.90it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:04, 20.38it/s]
eval model::  82%|████████▏ | 411/500 [00:23<00:04, 20.96it/s]
eval model::  83%|████████▎ | 414/500 [00:23<00:04, 20.72it/s]
eval model::  83%|████████▎ | 417/500 [00:23<00:03, 21.16it/s]
eval model::  84%|████████▍ | 420/500 [00:23<00:05, 15.22it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:05, 15.48it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.67it/s]
eval model::  85%|████████▌ | 426/500 [00:24<00:04, 15.25it/s]
eval model::  86%|████████▌ | 429/500 [00:24<00:04, 17.28it/s]
eval model::  86%|████████▌ | 431/500 [00:24<00:04, 16.81it/s]
eval model::  87%|████████▋ | 434/500 [00:24<00:03, 18.67it/s]
eval model::  87%|████████▋ | 437/500 [00:24<00:03, 20.47it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:03, 17.55it/s]
eval model::  89%|████████▊ | 443/500 [00:24<00:03, 18.88it/s]
eval model::  89%|████████▉ | 445/500 [00:25<00:03, 17.05it/s]
eval model::  90%|████████▉ | 448/500 [00:25<00:03, 16.54it/s]
eval model::  90%|█████████ | 450/500 [00:25<00:03, 16.19it/s]
eval model::  91%|█████████ | 453/500 [00:25<00:02, 17.66it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:02, 18.73it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:02, 19.98it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 17.29it/s]
eval model::  93%|█████████▎| 464/500 [00:26<00:02, 17.26it/s]
eval model::  93%|█████████▎| 466/500 [00:26<00:02, 16.34it/s]
eval model::  94%|█████████▎| 468/500 [00:26<00:01, 16.27it/s]
eval model::  94%|█████████▍| 471/500 [00:26<00:01, 19.23it/s]
eval model::  95%|█████████▍| 474/500 [00:26<00:01, 18.74it/s]
eval model::  96%|█████████▌| 478/500 [00:26<00:01, 21.56it/s]
eval model::  96%|█████████▌| 481/500 [00:26<00:00, 22.34it/s]
eval model::  97%|█████████▋| 484/500 [00:27<00:00, 23.35it/s]
eval model::  97%|█████████▋| 487/500 [00:27<00:00, 23.01it/s]
eval model::  98%|█████████▊| 491/500 [00:27<00:00, 24.20it/s]
eval model::  99%|█████████▉| 494/500 [00:27<00:00, 22.95it/s]
eval model::  99%|█████████▉| 497/500 [00:27<00:00, 24.29it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.09it/s]
[2024/07/27 14:28:59] ppocr INFO: cur metric, precision: 0.7501290655653072, recall: 0.699566682715455, hmean: 0.7239661185849527, fps: 43.98590464047862
[2024/07/27 14:28:59] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 14:28:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:29:07] ppocr INFO: epoch: [1021/1500], global_step: 3063, lr: 0.001000, loss: 1.229111, loss_shrink_maps: 0.622291, loss_threshold_maps: 0.486940, loss_binary_maps: 0.123990, avg_reader_cost: 1.93663 s, avg_batch_cost: 2.20194 s, avg_samples: 12.5, ips: 5.67680 samples/s, eta: 3:27:46
[2024/07/27 14:29:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:29:17] ppocr INFO: epoch: [1022/1500], global_step: 3066, lr: 0.001000, loss: 1.192654, loss_shrink_maps: 0.596691, loss_threshold_maps: 0.478425, loss_binary_maps: 0.119101, avg_reader_cost: 2.35167 s, avg_batch_cost: 2.58944 s, avg_samples: 12.5, ips: 4.82730 samples/s, eta: 3:27:20
[2024/07/27 14:29:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:29:27] ppocr INFO: epoch: [1023/1500], global_step: 3069, lr: 0.001000, loss: 1.192654, loss_shrink_maps: 0.589406, loss_threshold_maps: 0.478425, loss_binary_maps: 0.117183, avg_reader_cost: 2.15044 s, avg_batch_cost: 2.47955 s, avg_samples: 12.5, ips: 5.04125 samples/s, eta: 3:26:54
[2024/07/27 14:29:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:29:35] ppocr INFO: epoch: [1024/1500], global_step: 3070, lr: 0.001000, loss: 1.162001, loss_shrink_maps: 0.581674, loss_threshold_maps: 0.472788, loss_binary_maps: 0.115517, avg_reader_cost: 0.58296 s, avg_batch_cost: 0.81549 s, avg_samples: 4.8, ips: 5.88600 samples/s, eta: 3:26:45
[2024/07/27 14:29:37] ppocr INFO: epoch: [1024/1500], global_step: 3072, lr: 0.001000, loss: 1.165262, loss_shrink_maps: 0.581674, loss_threshold_maps: 0.476662, loss_binary_maps: 0.115517, avg_reader_cost: 1.72452 s, avg_batch_cost: 1.87285 s, avg_samples: 7.7, ips: 4.11139 samples/s, eta: 3:26:28
[2024/07/27 14:29:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:29:47] ppocr INFO: epoch: [1025/1500], global_step: 3075, lr: 0.001000, loss: 1.182492, loss_shrink_maps: 0.589406, loss_threshold_maps: 0.477968, loss_binary_maps: 0.117183, avg_reader_cost: 2.23424 s, avg_batch_cost: 2.59989 s, avg_samples: 12.5, ips: 4.80790 samples/s, eta: 3:26:02
[2024/07/27 14:29:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:29:56] ppocr INFO: epoch: [1026/1500], global_step: 3078, lr: 0.001000, loss: 1.183162, loss_shrink_maps: 0.595141, loss_threshold_maps: 0.477968, loss_binary_maps: 0.118291, avg_reader_cost: 2.23681 s, avg_batch_cost: 2.62836 s, avg_samples: 12.5, ips: 4.75582 samples/s, eta: 3:25:36
[2024/07/27 14:29:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:30:06] ppocr INFO: epoch: [1027/1500], global_step: 3080, lr: 0.001000, loss: 1.197108, loss_shrink_maps: 0.601233, loss_threshold_maps: 0.482800, loss_binary_maps: 0.119546, avg_reader_cost: 1.33041 s, avg_batch_cost: 1.63936 s, avg_samples: 9.6, ips: 5.85593 samples/s, eta: 3:25:18
[2024/07/27 14:30:06] ppocr INFO: epoch: [1027/1500], global_step: 3081, lr: 0.001000, loss: 1.211528, loss_shrink_maps: 0.604572, loss_threshold_maps: 0.482800, loss_binary_maps: 0.120358, avg_reader_cost: 0.86548 s, avg_batch_cost: 0.92151 s, avg_samples: 2.9, ips: 3.14701 samples/s, eta: 3:25:10
[2024/07/27 14:30:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:30:16] ppocr INFO: epoch: [1028/1500], global_step: 3084, lr: 0.001000, loss: 1.211528, loss_shrink_maps: 0.601233, loss_threshold_maps: 0.482800, loss_binary_maps: 0.119546, avg_reader_cost: 2.26398 s, avg_batch_cost: 2.62650 s, avg_samples: 12.5, ips: 4.75918 samples/s, eta: 3:24:44
[2024/07/27 14:30:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:30:26] ppocr INFO: epoch: [1029/1500], global_step: 3087, lr: 0.001000, loss: 1.211528, loss_shrink_maps: 0.604880, loss_threshold_maps: 0.482800, loss_binary_maps: 0.120286, avg_reader_cost: 2.25531 s, avg_batch_cost: 2.67564 s, avg_samples: 12.5, ips: 4.67178 samples/s, eta: 3:24:18
[2024/07/27 14:30:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:30:36] ppocr INFO: epoch: [1030/1500], global_step: 3090, lr: 0.001000, loss: 1.221957, loss_shrink_maps: 0.624478, loss_threshold_maps: 0.484644, loss_binary_maps: 0.124070, avg_reader_cost: 2.38543 s, avg_batch_cost: 2.64139 s, avg_samples: 12.5, ips: 4.73236 samples/s, eta: 3:23:52
[2024/07/27 14:30:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:30:46] ppocr INFO: epoch: [1031/1500], global_step: 3093, lr: 0.001000, loss: 1.227928, loss_shrink_maps: 0.631201, loss_threshold_maps: 0.477456, loss_binary_maps: 0.125232, avg_reader_cost: 2.36030 s, avg_batch_cost: 2.60445 s, avg_samples: 12.5, ips: 4.79947 samples/s, eta: 3:23:26
[2024/07/27 14:30:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:30:56] ppocr INFO: epoch: [1032/1500], global_step: 3096, lr: 0.001000, loss: 1.235188, loss_shrink_maps: 0.634706, loss_threshold_maps: 0.484644, loss_binary_maps: 0.126099, avg_reader_cost: 2.33368 s, avg_batch_cost: 2.57232 s, avg_samples: 12.5, ips: 4.85942 samples/s, eta: 3:23:00
[2024/07/27 14:30:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:31:06] ppocr INFO: epoch: [1033/1500], global_step: 3099, lr: 0.001000, loss: 1.227928, loss_shrink_maps: 0.630202, loss_threshold_maps: 0.477456, loss_binary_maps: 0.125232, avg_reader_cost: 2.30333 s, avg_batch_cost: 2.70378 s, avg_samples: 12.5, ips: 4.62315 samples/s, eta: 3:22:35
[2024/07/27 14:31:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:31:14] ppocr INFO: epoch: [1034/1500], global_step: 3100, lr: 0.001000, loss: 1.210755, loss_shrink_maps: 0.616269, loss_threshold_maps: 0.472026, loss_binary_maps: 0.122460, avg_reader_cost: 0.59043 s, avg_batch_cost: 0.73126 s, avg_samples: 4.8, ips: 6.56401 samples/s, eta: 3:22:25
[2024/07/27 14:31:15] ppocr INFO: epoch: [1034/1500], global_step: 3102, lr: 0.001000, loss: 1.227928, loss_shrink_maps: 0.630202, loss_threshold_maps: 0.486666, loss_binary_maps: 0.125232, avg_reader_cost: 1.55468 s, avg_batch_cost: 1.70082 s, avg_samples: 7.7, ips: 4.52723 samples/s, eta: 3:22:08
[2024/07/27 14:31:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:31:25] ppocr INFO: epoch: [1035/1500], global_step: 3105, lr: 0.001000, loss: 1.227928, loss_shrink_maps: 0.630202, loss_threshold_maps: 0.478982, loss_binary_maps: 0.125232, avg_reader_cost: 2.33225 s, avg_batch_cost: 2.56674 s, avg_samples: 12.5, ips: 4.86999 samples/s, eta: 3:21:42
[2024/07/27 14:31:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:31:34] ppocr INFO: epoch: [1036/1500], global_step: 3108, lr: 0.001000, loss: 1.227928, loss_shrink_maps: 0.630202, loss_threshold_maps: 0.483720, loss_binary_maps: 0.125232, avg_reader_cost: 2.27417 s, avg_batch_cost: 2.52240 s, avg_samples: 12.5, ips: 4.95559 samples/s, eta: 3:21:15
[2024/07/27 14:31:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:31:44] ppocr INFO: epoch: [1037/1500], global_step: 3110, lr: 0.001000, loss: 1.239456, loss_shrink_maps: 0.634706, loss_threshold_maps: 0.481148, loss_binary_maps: 0.125232, avg_reader_cost: 1.38056 s, avg_batch_cost: 1.66574 s, avg_samples: 9.6, ips: 5.76321 samples/s, eta: 3:20:58
[2024/07/27 14:31:44] ppocr INFO: epoch: [1037/1500], global_step: 3111, lr: 0.001000, loss: 1.256772, loss_shrink_maps: 0.639432, loss_threshold_maps: 0.487764, loss_binary_maps: 0.126099, avg_reader_cost: 0.87901 s, avg_batch_cost: 0.93453 s, avg_samples: 2.9, ips: 3.10317 samples/s, eta: 3:20:49
[2024/07/27 14:31:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:31:54] ppocr INFO: epoch: [1038/1500], global_step: 3114, lr: 0.001000, loss: 1.340306, loss_shrink_maps: 0.717342, loss_threshold_maps: 0.499173, loss_binary_maps: 0.129085, avg_reader_cost: 2.20282 s, avg_batch_cost: 2.57903 s, avg_samples: 12.5, ips: 4.84678 samples/s, eta: 3:20:23
[2024/07/27 14:31:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:32:03] ppocr INFO: epoch: [1039/1500], global_step: 3117, lr: 0.001000, loss: 1.399750, loss_shrink_maps: 0.752291, loss_threshold_maps: 0.520080, loss_binary_maps: 0.138792, avg_reader_cost: 2.14340 s, avg_batch_cost: 2.47992 s, avg_samples: 12.5, ips: 5.04049 samples/s, eta: 3:19:57
[2024/07/27 14:32:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:32:13] ppocr INFO: epoch: [1040/1500], global_step: 3120, lr: 0.001000, loss: 1.520162, loss_shrink_maps: 0.789766, loss_threshold_maps: 0.543626, loss_binary_maps: 0.153932, avg_reader_cost: 2.14476 s, avg_batch_cost: 2.44888 s, avg_samples: 12.5, ips: 5.10436 samples/s, eta: 3:19:30

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:00,  8.30it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.27it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.91it/s]
eval model::   2%|▏         | 8/500 [00:00<00:26, 18.46it/s]
eval model::   2%|▏         | 11/500 [00:00<00:24, 19.98it/s]
eval model::   3%|▎         | 13/500 [00:00<00:25, 19.39it/s]
eval model::   3%|▎         | 16/500 [00:00<00:24, 19.74it/s]
eval model::   4%|▎         | 18/500 [00:00<00:24, 19.78it/s]
eval model::   4%|▍         | 20/500 [00:01<00:26, 18.22it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 19.97it/s]
eval model::   5%|▌         | 25/500 [00:01<00:25, 18.40it/s]
eval model::   5%|▌         | 27/500 [00:01<00:25, 18.79it/s]
eval model::   6%|▌         | 30/500 [00:01<00:24, 19.50it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.31it/s]
eval model::   7%|▋         | 35/500 [00:01<00:22, 20.37it/s]
eval model::   8%|▊         | 38/500 [00:01<00:21, 21.08it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.81it/s]
eval model::   9%|▊         | 43/500 [00:02<00:23, 19.58it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.32it/s]
eval model::   9%|▉         | 47/500 [00:02<00:24, 18.85it/s]
eval model::  10%|▉         | 49/500 [00:02<00:25, 17.88it/s]
eval model::  10%|█         | 51/500 [00:02<00:24, 18.26it/s]
eval model::  11%|█         | 54/500 [00:02<00:22, 19.59it/s]
eval model::  11%|█         | 56/500 [00:02<00:23, 18.60it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:21, 20.79it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:23, 18.83it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:23, 18.79it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:19, 22.57it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 21.13it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:21, 19.47it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:21, 19.74it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:21, 19.48it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:21, 19.35it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:21, 19.02it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.02it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:21, 19.48it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:20, 19.54it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:20, 20.21it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:20, 19.46it/s]
eval model::  20%|██        | 101/500 [00:05<00:19, 20.83it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 21.30it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.20it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 21.19it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 21.09it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:17, 21.54it/s]
eval model::  24%|██▍       | 119/500 [00:05<00:17, 22.35it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:17, 21.89it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:18, 20.50it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:17, 20.89it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:17, 20.89it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:17, 20.86it/s]
eval model::  27%|██▋       | 137/500 [00:06<00:18, 19.73it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:19, 18.97it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.29it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 19.96it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 19.22it/s]
eval model::  30%|███       | 151/500 [00:07<00:18, 18.49it/s]
eval model::  31%|███       | 154/500 [00:07<00:16, 20.74it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:15, 21.49it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:18, 18.78it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:17, 19.65it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:15, 21.40it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:15, 20.77it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:15, 21.19it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:15, 21.07it/s]
eval model::  36%|███▌      | 178/500 [00:08<00:15, 20.29it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:16, 19.91it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:16, 19.62it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.43it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.13it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 20.85it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:13, 22.46it/s]
eval model::  40%|███▉      | 198/500 [00:09<00:13, 22.23it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 21.49it/s]
eval model::  41%|████      | 204/500 [00:10<00:14, 20.90it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:18, 16.22it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:17, 16.59it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:18, 15.96it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:17, 16.61it/s]
eval model::  43%|████▎     | 215/500 [00:10<00:17, 16.03it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:17, 15.80it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:17, 16.02it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:16, 16.44it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:14, 19.19it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:14, 18.36it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:12, 22.40it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:11, 23.35it/s]
eval model::  47%|████▋     | 236/500 [00:11<00:11, 22.76it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.19it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 21.45it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:11, 23.13it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 21.96it/s]
eval model::  50%|█████     | 251/500 [00:12<00:10, 22.71it/s]
eval model::  51%|█████     | 254/500 [00:12<00:11, 21.30it/s]
eval model::  51%|█████▏    | 257/500 [00:12<00:11, 20.29it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:12, 19.79it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:12, 18.80it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:12, 18.25it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:11, 19.48it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:11, 20.53it/s]
eval model::  55%|█████▍    | 274/500 [00:13<00:11, 19.28it/s]
eval model::  55%|█████▌    | 277/500 [00:13<00:11, 20.27it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:10, 21.17it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:10, 21.70it/s]
eval model::  57%|█████▋    | 286/500 [00:14<00:11, 18.39it/s]
eval model::  58%|█████▊    | 289/500 [00:14<00:10, 20.34it/s]
eval model::  58%|█████▊    | 292/500 [00:14<00:09, 21.09it/s]
eval model::  59%|█████▉    | 295/500 [00:14<00:10, 19.20it/s]
eval model::  60%|█████▉    | 298/500 [00:14<00:10, 20.15it/s]
eval model::  60%|██████    | 301/500 [00:15<00:10, 19.23it/s]
eval model::  61%|██████    | 304/500 [00:15<00:09, 20.27it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:09, 21.29it/s]
eval model::  62%|██████▏   | 310/500 [00:15<00:08, 21.11it/s]
eval model::  63%|██████▎   | 313/500 [00:15<00:08, 21.34it/s]
eval model::  63%|██████▎   | 316/500 [00:15<00:08, 20.66it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 20.66it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:08, 20.95it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:08, 21.39it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:08, 19.42it/s]
eval model::  66%|██████▌   | 331/500 [00:16<00:08, 20.28it/s]
eval model::  67%|██████▋   | 334/500 [00:16<00:08, 20.63it/s]
eval model::  67%|██████▋   | 337/500 [00:16<00:08, 19.13it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 16.18it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:08, 18.53it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 20.67it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:07, 21.35it/s]
eval model::  70%|███████   | 352/500 [00:17<00:07, 20.59it/s]
eval model::  71%|███████   | 355/500 [00:17<00:07, 18.90it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:07, 19.52it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 19.69it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.89it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:06, 19.89it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:06, 20.27it/s]
eval model::  75%|███████▍  | 373/500 [00:18<00:06, 19.81it/s]
eval model::  75%|███████▌  | 376/500 [00:18<00:05, 20.76it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 20.90it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:06, 18.74it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:06, 18.42it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:07, 15.92it/s]
eval model::  78%|███████▊  | 388/500 [00:19<00:06, 16.27it/s]
eval model::  78%|███████▊  | 391/500 [00:19<00:05, 18.27it/s]
eval model::  79%|███████▉  | 394/500 [00:19<00:05, 19.16it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 19.02it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:05, 18.39it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 17.91it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 20.45it/s]
eval model::  81%|████████  | 406/500 [00:20<00:05, 18.39it/s]
eval model::  82%|████████▏ | 409/500 [00:20<00:04, 18.98it/s]
eval model::  82%|████████▏ | 411/500 [00:20<00:04, 19.18it/s]
eval model::  83%|████████▎ | 414/500 [00:20<00:04, 21.17it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 20.73it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 18.37it/s]
eval model::  85%|████████▍ | 423/500 [00:21<00:03, 19.68it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:03, 18.88it/s]
eval model::  86%|████████▌ | 429/500 [00:21<00:03, 20.49it/s]
eval model::  86%|████████▋ | 432/500 [00:21<00:03, 20.99it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 19.78it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:03, 19.31it/s]
eval model::  88%|████████▊ | 440/500 [00:22<00:03, 17.42it/s]
eval model::  89%|████████▊ | 443/500 [00:22<00:02, 19.90it/s]
eval model::  89%|████████▉ | 446/500 [00:22<00:02, 18.94it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 17.93it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 16.89it/s]
eval model::  91%|█████████ | 453/500 [00:22<00:02, 18.85it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:02, 19.40it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:02, 20.09it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:02, 18.18it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:01, 18.70it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 20.89it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 19.05it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 18.22it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 19.97it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:01, 19.59it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 19.41it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 19.11it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 19.79it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 18.59it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 19.79it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 21.89it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.73it/s]
[2024/07/27 14:32:38] ppocr INFO: cur metric, precision: 0.7542147293700089, recall: 0.4092441020702937, hmean: 0.5305867665418227, fps: 44.32687928330005
[2024/07/27 14:32:38] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 14:32:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:32:47] ppocr INFO: epoch: [1041/1500], global_step: 3123, lr: 0.001000, loss: 1.587472, loss_shrink_maps: 0.829006, loss_threshold_maps: 0.540659, loss_binary_maps: 0.154715, avg_reader_cost: 2.13127 s, avg_batch_cost: 2.42845 s, avg_samples: 12.5, ips: 5.14732 samples/s, eta: 3:19:03
[2024/07/27 14:32:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:32:57] ppocr INFO: epoch: [1042/1500], global_step: 3126, lr: 0.001000, loss: 1.650586, loss_shrink_maps: 0.874009, loss_threshold_maps: 0.568582, loss_binary_maps: 0.164186, avg_reader_cost: 2.28198 s, avg_batch_cost: 2.52059 s, avg_samples: 12.5, ips: 4.95915 samples/s, eta: 3:18:37
[2024/07/27 14:32:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:33:06] ppocr INFO: epoch: [1043/1500], global_step: 3129, lr: 0.001000, loss: 1.650586, loss_shrink_maps: 0.850014, loss_threshold_maps: 0.568582, loss_binary_maps: 0.161309, avg_reader_cost: 2.36006 s, avg_batch_cost: 2.59792 s, avg_samples: 12.5, ips: 4.81153 samples/s, eta: 3:18:11
[2024/07/27 14:33:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:33:15] ppocr INFO: epoch: [1044/1500], global_step: 3130, lr: 0.001000, loss: 1.650586, loss_shrink_maps: 0.850014, loss_threshold_maps: 0.568582, loss_binary_maps: 0.161309, avg_reader_cost: 0.53974 s, avg_batch_cost: 0.75997 s, avg_samples: 4.8, ips: 6.31604 samples/s, eta: 3:18:02
[2024/07/27 14:33:16] ppocr INFO: epoch: [1044/1500], global_step: 3132, lr: 0.001000, loss: 1.606895, loss_shrink_maps: 0.829006, loss_threshold_maps: 0.568582, loss_binary_maps: 0.154715, avg_reader_cost: 1.61206 s, avg_batch_cost: 1.75927 s, avg_samples: 7.7, ips: 4.37681 samples/s, eta: 3:17:44
[2024/07/27 14:33:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:33:26] ppocr INFO: epoch: [1045/1500], global_step: 3135, lr: 0.001000, loss: 1.498324, loss_shrink_maps: 0.790626, loss_threshold_maps: 0.557578, loss_binary_maps: 0.147329, avg_reader_cost: 2.36282 s, avg_batch_cost: 2.59620 s, avg_samples: 12.5, ips: 4.81474 samples/s, eta: 3:17:18
[2024/07/27 14:33:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:33:36] ppocr INFO: epoch: [1046/1500], global_step: 3138, lr: 0.001000, loss: 1.463379, loss_shrink_maps: 0.772871, loss_threshold_maps: 0.550387, loss_binary_maps: 0.145892, avg_reader_cost: 2.31146 s, avg_batch_cost: 2.55222 s, avg_samples: 12.5, ips: 4.89771 samples/s, eta: 3:16:52
[2024/07/27 14:33:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:33:45] ppocr INFO: epoch: [1047/1500], global_step: 3140, lr: 0.001000, loss: 1.434247, loss_shrink_maps: 0.738310, loss_threshold_maps: 0.540659, loss_binary_maps: 0.142875, avg_reader_cost: 1.36937 s, avg_batch_cost: 1.64782 s, avg_samples: 9.6, ips: 5.82587 samples/s, eta: 3:16:34
[2024/07/27 14:33:45] ppocr INFO: epoch: [1047/1500], global_step: 3141, lr: 0.001000, loss: 1.425404, loss_shrink_maps: 0.732450, loss_threshold_maps: 0.540286, loss_binary_maps: 0.140770, avg_reader_cost: 0.87011 s, avg_batch_cost: 0.92548 s, avg_samples: 2.9, ips: 3.13351 samples/s, eta: 3:16:26
[2024/07/27 14:33:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:33:55] ppocr INFO: epoch: [1048/1500], global_step: 3144, lr: 0.001000, loss: 1.400400, loss_shrink_maps: 0.717962, loss_threshold_maps: 0.532896, loss_binary_maps: 0.138593, avg_reader_cost: 2.26921 s, avg_batch_cost: 2.67104 s, avg_samples: 12.5, ips: 4.67983 samples/s, eta: 3:16:00
[2024/07/27 14:33:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:34:05] ppocr INFO: epoch: [1049/1500], global_step: 3147, lr: 0.001000, loss: 1.377586, loss_shrink_maps: 0.713650, loss_threshold_maps: 0.529260, loss_binary_maps: 0.138224, avg_reader_cost: 2.38188 s, avg_batch_cost: 2.63457 s, avg_samples: 12.5, ips: 4.74461 samples/s, eta: 3:15:34
[2024/07/27 14:34:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:34:15] ppocr INFO: epoch: [1050/1500], global_step: 3150, lr: 0.001000, loss: 1.431347, loss_shrink_maps: 0.743988, loss_threshold_maps: 0.538569, loss_binary_maps: 0.144234, avg_reader_cost: 2.20693 s, avg_batch_cost: 2.61781 s, avg_samples: 12.5, ips: 4.77498 samples/s, eta: 3:15:08
[2024/07/27 14:34:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:34:25] ppocr INFO: epoch: [1051/1500], global_step: 3153, lr: 0.001000, loss: 1.394655, loss_shrink_maps: 0.717962, loss_threshold_maps: 0.526044, loss_binary_maps: 0.139767, avg_reader_cost: 2.44939 s, avg_batch_cost: 2.69128 s, avg_samples: 12.5, ips: 4.64463 samples/s, eta: 3:14:43
[2024/07/27 14:34:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:34:35] ppocr INFO: epoch: [1052/1500], global_step: 3156, lr: 0.001000, loss: 1.416249, loss_shrink_maps: 0.735547, loss_threshold_maps: 0.529657, loss_binary_maps: 0.143803, avg_reader_cost: 2.42317 s, avg_batch_cost: 2.66152 s, avg_samples: 12.5, ips: 4.69656 samples/s, eta: 3:14:17
[2024/07/27 14:34:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:34:45] ppocr INFO: epoch: [1053/1500], global_step: 3159, lr: 0.001000, loss: 1.345102, loss_shrink_maps: 0.690514, loss_threshold_maps: 0.522990, loss_binary_maps: 0.134954, avg_reader_cost: 2.34351 s, avg_batch_cost: 2.59382 s, avg_samples: 12.5, ips: 4.81915 samples/s, eta: 3:13:51
[2024/07/27 14:34:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:34:53] ppocr INFO: epoch: [1054/1500], global_step: 3160, lr: 0.001000, loss: 1.345102, loss_shrink_maps: 0.690514, loss_threshold_maps: 0.522990, loss_binary_maps: 0.134954, avg_reader_cost: 0.56146 s, avg_batch_cost: 0.77227 s, avg_samples: 4.8, ips: 6.21544 samples/s, eta: 3:13:42
[2024/07/27 14:34:55] ppocr INFO: epoch: [1054/1500], global_step: 3162, lr: 0.001000, loss: 1.384376, loss_shrink_maps: 0.715040, loss_threshold_maps: 0.525726, loss_binary_maps: 0.139076, avg_reader_cost: 1.63615 s, avg_batch_cost: 1.78216 s, avg_samples: 7.7, ips: 4.32059 samples/s, eta: 3:13:25
[2024/07/27 14:34:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:35:05] ppocr INFO: epoch: [1055/1500], global_step: 3165, lr: 0.001000, loss: 1.363674, loss_shrink_maps: 0.715094, loss_threshold_maps: 0.525149, loss_binary_maps: 0.139076, avg_reader_cost: 2.22840 s, avg_batch_cost: 2.59225 s, avg_samples: 12.5, ips: 4.82206 samples/s, eta: 3:12:59
[2024/07/27 14:35:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:35:14] ppocr INFO: epoch: [1056/1500], global_step: 3168, lr: 0.001000, loss: 1.352611, loss_shrink_maps: 0.693566, loss_threshold_maps: 0.524617, loss_binary_maps: 0.134428, avg_reader_cost: 2.35449 s, avg_batch_cost: 2.59402 s, avg_samples: 12.5, ips: 4.81878 samples/s, eta: 3:12:32
[2024/07/27 14:35:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:35:24] ppocr INFO: epoch: [1057/1500], global_step: 3170, lr: 0.001000, loss: 1.326459, loss_shrink_maps: 0.677909, loss_threshold_maps: 0.517232, loss_binary_maps: 0.133428, avg_reader_cost: 1.44912 s, avg_batch_cost: 1.65012 s, avg_samples: 9.6, ips: 5.81775 samples/s, eta: 3:12:15
[2024/07/27 14:35:24] ppocr INFO: epoch: [1057/1500], global_step: 3171, lr: 0.001000, loss: 1.326459, loss_shrink_maps: 0.677909, loss_threshold_maps: 0.517232, loss_binary_maps: 0.133428, avg_reader_cost: 0.87140 s, avg_batch_cost: 0.92708 s, avg_samples: 2.9, ips: 3.12811 samples/s, eta: 3:12:06
[2024/07/27 14:35:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:35:34] ppocr INFO: epoch: [1058/1500], global_step: 3174, lr: 0.001000, loss: 1.315058, loss_shrink_maps: 0.668438, loss_threshold_maps: 0.503904, loss_binary_maps: 0.131378, avg_reader_cost: 2.38029 s, avg_batch_cost: 2.62924 s, avg_samples: 12.5, ips: 4.75423 samples/s, eta: 3:11:40
[2024/07/27 14:35:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:35:44] ppocr INFO: epoch: [1059/1500], global_step: 3177, lr: 0.001000, loss: 1.338674, loss_shrink_maps: 0.680908, loss_threshold_maps: 0.524617, loss_binary_maps: 0.133823, avg_reader_cost: 2.37479 s, avg_batch_cost: 2.62548 s, avg_samples: 12.5, ips: 4.76104 samples/s, eta: 3:11:15
[2024/07/27 14:35:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:35:54] ppocr INFO: epoch: [1060/1500], global_step: 3180, lr: 0.001000, loss: 1.352611, loss_shrink_maps: 0.693566, loss_threshold_maps: 0.509531, loss_binary_maps: 0.134797, avg_reader_cost: 2.18189 s, avg_batch_cost: 2.54426 s, avg_samples: 12.5, ips: 4.91301 samples/s, eta: 3:10:48

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:54,  9.14it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.16it/s]
eval model::   1%|▏         | 7/500 [00:00<00:23, 20.85it/s]
eval model::   2%|▏         | 10/500 [00:00<00:22, 21.34it/s]
eval model::   3%|▎         | 13/500 [00:00<00:27, 17.85it/s]
eval model::   3%|▎         | 16/500 [00:00<00:24, 19.93it/s]
eval model::   4%|▍         | 19/500 [00:00<00:23, 20.84it/s]
eval model::   4%|▍         | 22/500 [00:01<00:22, 21.05it/s]
eval model::   5%|▌         | 25/500 [00:01<00:22, 20.72it/s]
eval model::   6%|▌         | 28/500 [00:01<00:22, 21.15it/s]
eval model::   6%|▌         | 31/500 [00:01<00:23, 19.79it/s]
eval model::   7%|▋         | 34/500 [00:01<00:23, 19.60it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 21.35it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.35it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.18it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.92it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 17.71it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 18.94it/s]
eval model::  11%|█         | 53/500 [00:02<00:22, 20.24it/s]
eval model::  11%|█         | 56/500 [00:02<00:22, 19.39it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:22, 20.01it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:27, 15.92it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:26, 16.48it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 19.02it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:21, 20.03it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 21.39it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:18, 22.64it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:16, 25.14it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.70it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.38it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.55it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:23, 17.64it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:22, 18.21it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:20, 20.09it/s]
eval model::  20%|██        | 101/500 [00:05<00:19, 20.83it/s]
eval model::  21%|██        | 104/500 [00:05<00:17, 22.28it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:16, 23.94it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:16, 24.32it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:17, 22.30it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:17, 21.89it/s]
eval model::  24%|██▍       | 119/500 [00:05<00:18, 20.66it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.61it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:19, 19.59it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:17, 21.38it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:17, 20.62it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:17, 20.58it/s]
eval model::  27%|██▋       | 137/500 [00:06<00:18, 19.65it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 19.64it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 20.20it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 20.21it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:18, 18.80it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 18.28it/s]
eval model::  31%|███       | 154/500 [00:07<00:16, 20.36it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:15, 22.33it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:22, 15.17it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:20, 16.74it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 18.98it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 18.83it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:16, 19.35it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:17, 18.49it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:17, 18.39it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:18, 17.24it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:17, 18.48it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 20.16it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.15it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:15, 20.47it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:14, 20.48it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.17it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.78it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 21.91it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:18, 15.50it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:19, 15.23it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:18, 15.27it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:17, 16.10it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.76it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.97it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:18, 15.23it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:18, 14.71it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:15, 17.63it/s]
eval model::  45%|████▌     | 227/500 [00:11<00:13, 19.97it/s]
eval model::  46%|████▌     | 231/500 [00:11<00:11, 24.04it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:11, 22.69it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 23.12it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 22.63it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:13, 19.57it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:12, 21.02it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:11, 21.04it/s]
eval model::  50%|█████     | 252/500 [00:12<00:11, 21.95it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 20.82it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 20.51it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 20.74it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:11, 20.27it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:12, 19.32it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:12, 18.91it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:11, 20.72it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:13, 16.90it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 17.32it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:12, 17.80it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 20.02it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:18, 11.69it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.17it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.82it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.55it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:12, 16.42it/s]
eval model::  60%|██████    | 300/500 [00:15<00:12, 16.50it/s]
eval model::  61%|██████    | 303/500 [00:15<00:10, 18.45it/s]
eval model::  61%|██████    | 306/500 [00:15<00:10, 19.31it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 19.99it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:08, 21.00it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 21.65it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:07, 22.85it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 22.23it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 20.51it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:07, 22.40it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:07, 22.70it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 23.46it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 22.34it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:08, 20.01it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:09, 15.92it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:08, 18.30it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 18.96it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 20.25it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.46it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 21.50it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:06, 20.40it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.40it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:07, 18.18it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 19.05it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 18.46it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 20.72it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 22.66it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:05, 20.80it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:04, 23.49it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 14.38it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 16.61it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 17.03it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 18.41it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:05, 20.15it/s]
eval model::  80%|████████  | 402/500 [00:20<00:05, 19.40it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 20.37it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 22.07it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 22.45it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 21.55it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 20.71it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:05, 15.12it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 15.76it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 16.14it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:04, 17.67it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 18.86it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 20.64it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 22.13it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 23.67it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 23.59it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 25.08it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 24.18it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.58it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 22.92it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 23.59it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 21.49it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:02, 16.45it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.66it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.10it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.63it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 20.60it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 22.24it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 23.12it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 23.22it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 21.65it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 22.61it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 19.39it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 18.33it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 20.33it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.53it/s]
[2024/07/27 14:36:19] ppocr INFO: cur metric, precision: 0.7777777777777778, recall: 0.6336061627347135, hmean: 0.6983284690899443, fps: 45.32204794560884
[2024/07/27 14:36:19] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 14:36:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:36:28] ppocr INFO: epoch: [1061/1500], global_step: 3183, lr: 0.001000, loss: 1.323514, loss_shrink_maps: 0.678633, loss_threshold_maps: 0.500147, loss_binary_maps: 0.133591, avg_reader_cost: 2.20531 s, avg_batch_cost: 2.46293 s, avg_samples: 12.5, ips: 5.07526 samples/s, eta: 3:10:22
[2024/07/27 14:36:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:36:38] ppocr INFO: epoch: [1062/1500], global_step: 3186, lr: 0.001000, loss: 1.307670, loss_shrink_maps: 0.668438, loss_threshold_maps: 0.500147, loss_binary_maps: 0.131172, avg_reader_cost: 2.23232 s, avg_batch_cost: 2.61112 s, avg_samples: 12.5, ips: 4.78722 samples/s, eta: 3:09:56
[2024/07/27 14:36:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:36:47] ppocr INFO: epoch: [1063/1500], global_step: 3189, lr: 0.001000, loss: 1.267994, loss_shrink_maps: 0.651916, loss_threshold_maps: 0.495866, loss_binary_maps: 0.127623, avg_reader_cost: 2.17417 s, avg_batch_cost: 2.48466 s, avg_samples: 12.5, ips: 5.03087 samples/s, eta: 3:09:29
[2024/07/27 14:36:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:36:56] ppocr INFO: epoch: [1064/1500], global_step: 3190, lr: 0.001000, loss: 1.257244, loss_shrink_maps: 0.636791, loss_threshold_maps: 0.493663, loss_binary_maps: 0.125541, avg_reader_cost: 0.68455 s, avg_batch_cost: 0.77984 s, avg_samples: 4.8, ips: 6.15513 samples/s, eta: 3:09:20
[2024/07/27 14:36:57] ppocr INFO: epoch: [1064/1500], global_step: 3192, lr: 0.001000, loss: 1.296477, loss_shrink_maps: 0.667977, loss_threshold_maps: 0.495866, loss_binary_maps: 0.130412, avg_reader_cost: 1.65177 s, avg_batch_cost: 1.79948 s, avg_samples: 7.7, ips: 4.27901 samples/s, eta: 3:09:03
[2024/07/27 14:36:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:37:07] ppocr INFO: epoch: [1065/1500], global_step: 3195, lr: 0.001000, loss: 1.296477, loss_shrink_maps: 0.667977, loss_threshold_maps: 0.495866, loss_binary_maps: 0.130200, avg_reader_cost: 2.21650 s, avg_batch_cost: 2.58702 s, avg_samples: 12.5, ips: 4.83181 samples/s, eta: 3:08:37
[2024/07/27 14:37:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:37:17] ppocr INFO: epoch: [1066/1500], global_step: 3198, lr: 0.001000, loss: 1.250027, loss_shrink_maps: 0.633131, loss_threshold_maps: 0.489308, loss_binary_maps: 0.124281, avg_reader_cost: 2.40122 s, avg_batch_cost: 2.64263 s, avg_samples: 12.5, ips: 4.73013 samples/s, eta: 3:08:11
[2024/07/27 14:37:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:37:27] ppocr INFO: epoch: [1067/1500], global_step: 3200, lr: 0.001000, loss: 1.243519, loss_shrink_maps: 0.633131, loss_threshold_maps: 0.485026, loss_binary_maps: 0.124281, avg_reader_cost: 1.48228 s, avg_batch_cost: 1.67551 s, avg_samples: 9.6, ips: 5.72959 samples/s, eta: 3:07:54
[2024/07/27 14:37:27] ppocr INFO: epoch: [1067/1500], global_step: 3201, lr: 0.001000, loss: 1.233100, loss_shrink_maps: 0.627787, loss_threshold_maps: 0.481842, loss_binary_maps: 0.123637, avg_reader_cost: 0.88382 s, avg_batch_cost: 0.93893 s, avg_samples: 2.9, ips: 3.08863 samples/s, eta: 3:07:45
[2024/07/27 14:37:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:37:38] ppocr INFO: epoch: [1068/1500], global_step: 3204, lr: 0.001000, loss: 1.226412, loss_shrink_maps: 0.633131, loss_threshold_maps: 0.478361, loss_binary_maps: 0.124281, avg_reader_cost: 2.29127 s, avg_batch_cost: 2.68582 s, avg_samples: 12.5, ips: 4.65408 samples/s, eta: 3:07:20
[2024/07/27 14:37:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:37:48] ppocr INFO: epoch: [1069/1500], global_step: 3207, lr: 0.001000, loss: 1.234631, loss_shrink_maps: 0.637684, loss_threshold_maps: 0.485026, loss_binary_maps: 0.125783, avg_reader_cost: 2.27498 s, avg_batch_cost: 2.63749 s, avg_samples: 12.5, ips: 4.73936 samples/s, eta: 3:06:54
[2024/07/27 14:37:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:37:57] ppocr INFO: epoch: [1070/1500], global_step: 3210, lr: 0.001000, loss: 1.234631, loss_shrink_maps: 0.637684, loss_threshold_maps: 0.482803, loss_binary_maps: 0.125783, avg_reader_cost: 2.40531 s, avg_batch_cost: 2.64433 s, avg_samples: 12.5, ips: 4.72709 samples/s, eta: 3:06:28
[2024/07/27 14:37:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:38:07] ppocr INFO: epoch: [1071/1500], global_step: 3213, lr: 0.001000, loss: 1.234631, loss_shrink_maps: 0.637684, loss_threshold_maps: 0.486711, loss_binary_maps: 0.125783, avg_reader_cost: 2.27901 s, avg_batch_cost: 2.51930 s, avg_samples: 12.5, ips: 4.96170 samples/s, eta: 3:06:01
[2024/07/27 14:38:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:38:17] ppocr INFO: epoch: [1072/1500], global_step: 3216, lr: 0.001000, loss: 1.236831, loss_shrink_maps: 0.637684, loss_threshold_maps: 0.486711, loss_binary_maps: 0.125783, avg_reader_cost: 2.24113 s, avg_batch_cost: 2.60601 s, avg_samples: 12.5, ips: 4.79660 samples/s, eta: 3:05:35
[2024/07/27 14:38:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:38:27] ppocr INFO: epoch: [1073/1500], global_step: 3219, lr: 0.001000, loss: 1.214935, loss_shrink_maps: 0.628662, loss_threshold_maps: 0.480793, loss_binary_maps: 0.123909, avg_reader_cost: 2.18733 s, avg_batch_cost: 2.67186 s, avg_samples: 12.5, ips: 4.67838 samples/s, eta: 3:05:10
[2024/07/27 14:38:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:38:35] ppocr INFO: epoch: [1074/1500], global_step: 3220, lr: 0.001000, loss: 1.216465, loss_shrink_maps: 0.628662, loss_threshold_maps: 0.484893, loss_binary_maps: 0.123909, avg_reader_cost: 0.53605 s, avg_batch_cost: 0.79239 s, avg_samples: 4.8, ips: 6.05762 samples/s, eta: 3:05:01
[2024/07/27 14:38:36] ppocr INFO: epoch: [1074/1500], global_step: 3222, lr: 0.001000, loss: 1.220240, loss_shrink_maps: 0.628662, loss_threshold_maps: 0.484893, loss_binary_maps: 0.123909, avg_reader_cost: 1.67805 s, avg_batch_cost: 1.82641 s, avg_samples: 7.7, ips: 4.21592 samples/s, eta: 3:04:44
[2024/07/27 14:38:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:38:46] ppocr INFO: epoch: [1075/1500], global_step: 3225, lr: 0.001000, loss: 1.208762, loss_shrink_maps: 0.619640, loss_threshold_maps: 0.484893, loss_binary_maps: 0.122421, avg_reader_cost: 2.16642 s, avg_batch_cost: 2.49493 s, avg_samples: 12.5, ips: 5.01017 samples/s, eta: 3:04:17
[2024/07/27 14:38:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:38:56] ppocr INFO: epoch: [1076/1500], global_step: 3228, lr: 0.001000, loss: 1.242027, loss_shrink_maps: 0.622928, loss_threshold_maps: 0.484893, loss_binary_maps: 0.123256, avg_reader_cost: 2.44785 s, avg_batch_cost: 2.69047 s, avg_samples: 12.5, ips: 4.64603 samples/s, eta: 3:03:52
[2024/07/27 14:38:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:39:06] ppocr INFO: epoch: [1077/1500], global_step: 3230, lr: 0.001000, loss: 1.283639, loss_shrink_maps: 0.642418, loss_threshold_maps: 0.488435, loss_binary_maps: 0.126868, avg_reader_cost: 1.50450 s, avg_batch_cost: 1.68917 s, avg_samples: 9.6, ips: 5.68327 samples/s, eta: 3:03:34
[2024/07/27 14:39:06] ppocr INFO: epoch: [1077/1500], global_step: 3231, lr: 0.001000, loss: 1.290807, loss_shrink_maps: 0.646269, loss_threshold_maps: 0.488435, loss_binary_maps: 0.126868, avg_reader_cost: 0.89072 s, avg_batch_cost: 0.94627 s, avg_samples: 2.9, ips: 3.06465 samples/s, eta: 3:03:26
[2024/07/27 14:39:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:39:16] ppocr INFO: epoch: [1078/1500], global_step: 3234, lr: 0.001000, loss: 1.242027, loss_shrink_maps: 0.624349, loss_threshold_maps: 0.480793, loss_binary_maps: 0.124101, avg_reader_cost: 2.25290 s, avg_batch_cost: 2.62801 s, avg_samples: 12.5, ips: 4.75645 samples/s, eta: 3:03:00
[2024/07/27 14:39:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:39:26] ppocr INFO: epoch: [1079/1500], global_step: 3237, lr: 0.001000, loss: 1.265655, loss_shrink_maps: 0.652225, loss_threshold_maps: 0.479607, loss_binary_maps: 0.128659, avg_reader_cost: 2.25988 s, avg_batch_cost: 2.62805 s, avg_samples: 12.5, ips: 4.75638 samples/s, eta: 3:02:34
[2024/07/27 14:39:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:39:36] ppocr INFO: epoch: [1080/1500], global_step: 3240, lr: 0.001000, loss: 1.251863, loss_shrink_maps: 0.649910, loss_threshold_maps: 0.483149, loss_binary_maps: 0.128659, avg_reader_cost: 2.35089 s, avg_batch_cost: 2.59338 s, avg_samples: 12.5, ips: 4.81997 samples/s, eta: 3:02:08

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:57,  8.72it/s]
eval model::   1%|          | 4/500 [00:00<00:30, 16.34it/s]
eval model::   1%|▏         | 7/500 [00:00<00:25, 19.41it/s]
eval model::   2%|▏         | 9/500 [00:00<00:25, 19.36it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.41it/s]
eval model::   3%|▎         | 13/500 [00:00<00:25, 18.75it/s]
eval model::   3%|▎         | 16/500 [00:00<00:22, 21.19it/s]
eval model::   4%|▍         | 19/500 [00:00<00:21, 22.73it/s]
eval model::   4%|▍         | 22/500 [00:01<00:20, 22.99it/s]
eval model::   5%|▌         | 25/500 [00:01<00:21, 21.87it/s]
eval model::   6%|▌         | 28/500 [00:01<00:21, 22.27it/s]
eval model::   6%|▌         | 31/500 [00:01<00:20, 22.75it/s]
eval model::   7%|▋         | 34/500 [00:01<00:21, 21.99it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 22.03it/s]
eval model::   8%|▊         | 40/500 [00:01<00:23, 19.97it/s]
eval model::   9%|▊         | 43/500 [00:02<00:23, 19.51it/s]
eval model::   9%|▉         | 46/500 [00:02<00:25, 17.83it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 19.55it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 20.23it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.41it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:22, 19.48it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 16.23it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 16.00it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.39it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.61it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 21.61it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 21.09it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:20, 20.78it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.58it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.61it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 19.81it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:20, 19.69it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:20, 20.03it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 21.20it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:19, 20.67it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.48it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.67it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:17, 21.78it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:20, 18.83it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:19, 19.86it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:18, 20.29it/s]
eval model::  24%|██▍       | 120/500 [00:05<00:18, 20.03it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 20.16it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 18.45it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.91it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.89it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.28it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:17, 20.70it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.82it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.49it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 17.80it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.95it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 19.85it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:15, 22.85it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:18, 17.95it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:17, 19.59it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:15, 21.48it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 20.56it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:15, 20.54it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:16, 20.00it/s]
eval model::  36%|███▌      | 178/500 [00:08<00:14, 21.62it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:16, 19.61it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:14, 21.54it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:13, 22.81it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:13, 22.70it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:12, 24.40it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:14, 21.71it/s]
eval model::  40%|███▉      | 199/500 [00:09<00:13, 22.63it/s]
eval model::  40%|████      | 202/500 [00:09<00:12, 23.18it/s]
eval model::  41%|████      | 205/500 [00:10<00:18, 16.01it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:17, 16.28it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:17, 16.47it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:16, 17.28it/s]
eval model::  43%|████▎     | 215/500 [00:10<00:17, 16.10it/s]
eval model::  43%|████▎     | 217/500 [00:10<00:19, 14.66it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:18, 14.95it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:19, 14.53it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:15, 17.35it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:15, 17.94it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:13, 20.56it/s]
eval model::  46%|████▋     | 232/500 [00:11<00:11, 22.52it/s]
eval model::  47%|████▋     | 235/500 [00:11<00:11, 22.24it/s]
eval model::  48%|████▊     | 238/500 [00:11<00:11, 23.00it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 22.22it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:12, 20.28it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:12, 19.79it/s]
eval model::  50%|█████     | 250/500 [00:12<00:12, 20.49it/s]
eval model::  51%|█████     | 253/500 [00:12<00:11, 20.83it/s]
eval model::  51%|█████     | 256/500 [00:12<00:11, 21.20it/s]
eval model::  52%|█████▏    | 259/500 [00:12<00:10, 22.97it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 21.52it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:11, 20.88it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:11, 21.09it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:11, 20.50it/s]
eval model::  55%|█████▍    | 274/500 [00:13<00:10, 21.33it/s]
eval model::  55%|█████▌    | 277/500 [00:13<00:11, 18.86it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 18.84it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 19.59it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:18, 11.90it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:16, 13.04it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:12, 16.98it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.34it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:16, 12.29it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:13, 14.68it/s]
eval model::  60%|██████    | 301/500 [00:15<00:13, 14.72it/s]
eval model::  61%|██████    | 304/500 [00:15<00:11, 16.84it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:10, 18.58it/s]
eval model::  62%|██████▏   | 310/500 [00:15<00:09, 20.00it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 21.28it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 21.52it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 22.03it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:09, 18.83it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:09, 19.23it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:08, 19.30it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:09, 18.64it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:08, 20.54it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 21.24it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 20.72it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:10, 15.70it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:08, 18.20it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:07, 20.55it/s]
eval model::  70%|███████   | 351/500 [00:18<00:06, 22.13it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 22.91it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 22.57it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:06, 21.49it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:06, 19.47it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:06, 19.75it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:06, 21.16it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:05, 21.29it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 22.18it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 22.01it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 20.91it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 21.09it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:08, 13.61it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:07, 15.47it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:06, 17.32it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:04, 20.87it/s]
eval model::  80%|████████  | 401/500 [00:20<00:04, 20.83it/s]
eval model::  81%|████████  | 404/500 [00:20<00:04, 21.43it/s]
eval model::  81%|████████▏ | 407/500 [00:20<00:04, 22.01it/s]
eval model::  82%|████████▏ | 410/500 [00:20<00:03, 23.26it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:04, 21.39it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:04, 19.09it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 17.35it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:06, 12.47it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:05, 14.78it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:04, 16.70it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 18.96it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 20.72it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 21.62it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 22.57it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 22.89it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 23.33it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 22.71it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 22.68it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:01, 23.19it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 23.58it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 21.94it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 19.03it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:01, 17.76it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 19.60it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 21.08it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 20.47it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 20.30it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 20.27it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 19.27it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 18.32it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 19.92it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 17.56it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 17.81it/s]
eval model::  99%|█████████▉| 497/500 [00:25<00:00, 19.15it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.98it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.55it/s]
[2024/07/27 14:40:01] ppocr INFO: cur metric, precision: 0.7830130192188469, recall: 0.608088589311507, hmean: 0.6845528455284553, fps: 44.999275600650655
[2024/07/27 14:40:01] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 14:40:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:40:11] ppocr INFO: epoch: [1081/1500], global_step: 3243, lr: 0.001000, loss: 1.244452, loss_shrink_maps: 0.643593, loss_threshold_maps: 0.473215, loss_binary_maps: 0.127747, avg_reader_cost: 2.35316 s, avg_batch_cost: 2.71280 s, avg_samples: 12.5, ips: 4.60779 samples/s, eta: 3:01:42
[2024/07/27 14:40:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:40:21] ppocr INFO: epoch: [1082/1500], global_step: 3246, lr: 0.001000, loss: 1.251863, loss_shrink_maps: 0.649910, loss_threshold_maps: 0.485692, loss_binary_maps: 0.128801, avg_reader_cost: 2.35540 s, avg_batch_cost: 2.59497 s, avg_samples: 12.5, ips: 4.81702 samples/s, eta: 3:01:16
[2024/07/27 14:40:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:40:31] ppocr INFO: epoch: [1083/1500], global_step: 3249, lr: 0.001000, loss: 1.251667, loss_shrink_maps: 0.649475, loss_threshold_maps: 0.476143, loss_binary_maps: 0.128307, avg_reader_cost: 2.24102 s, avg_batch_cost: 2.59531 s, avg_samples: 12.5, ips: 4.81638 samples/s, eta: 3:00:50
[2024/07/27 14:40:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:40:39] ppocr INFO: epoch: [1084/1500], global_step: 3250, lr: 0.001000, loss: 1.244858, loss_shrink_maps: 0.643593, loss_threshold_maps: 0.476143, loss_binary_maps: 0.127728, avg_reader_cost: 0.53449 s, avg_batch_cost: 0.80933 s, avg_samples: 4.8, ips: 5.93085 samples/s, eta: 3:00:41
[2024/07/27 14:40:41] ppocr INFO: epoch: [1084/1500], global_step: 3252, lr: 0.001000, loss: 1.244858, loss_shrink_maps: 0.643593, loss_threshold_maps: 0.476143, loss_binary_maps: 0.127728, avg_reader_cost: 1.71110 s, avg_batch_cost: 1.85882 s, avg_samples: 7.7, ips: 4.14241 samples/s, eta: 3:00:24
[2024/07/27 14:40:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:40:51] ppocr INFO: epoch: [1085/1500], global_step: 3255, lr: 0.001000, loss: 1.243948, loss_shrink_maps: 0.637679, loss_threshold_maps: 0.474284, loss_binary_maps: 0.126864, avg_reader_cost: 2.28083 s, avg_batch_cost: 2.70463 s, avg_samples: 12.5, ips: 4.62171 samples/s, eta: 2:59:59
[2024/07/27 14:40:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:41:00] ppocr INFO: epoch: [1086/1500], global_step: 3258, lr: 0.001000, loss: 1.206394, loss_shrink_maps: 0.618691, loss_threshold_maps: 0.479167, loss_binary_maps: 0.121100, avg_reader_cost: 2.19281 s, avg_batch_cost: 2.46826 s, avg_samples: 12.5, ips: 5.06430 samples/s, eta: 2:59:32
[2024/07/27 14:41:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:41:09] ppocr INFO: epoch: [1087/1500], global_step: 3260, lr: 0.001000, loss: 1.206394, loss_shrink_maps: 0.618691, loss_threshold_maps: 0.479167, loss_binary_maps: 0.121100, avg_reader_cost: 1.38580 s, avg_batch_cost: 1.56906 s, avg_samples: 9.6, ips: 6.11831 samples/s, eta: 2:59:14
[2024/07/27 14:41:10] ppocr INFO: epoch: [1087/1500], global_step: 3261, lr: 0.001000, loss: 1.233927, loss_shrink_maps: 0.629157, loss_threshold_maps: 0.479968, loss_binary_maps: 0.123868, avg_reader_cost: 0.83048 s, avg_batch_cost: 0.88638 s, avg_samples: 2.9, ips: 3.27175 samples/s, eta: 2:59:06
[2024/07/27 14:41:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:41:20] ppocr INFO: epoch: [1088/1500], global_step: 3264, lr: 0.001000, loss: 1.228386, loss_shrink_maps: 0.626886, loss_threshold_maps: 0.479167, loss_binary_maps: 0.123633, avg_reader_cost: 2.34258 s, avg_batch_cost: 2.62455 s, avg_samples: 12.5, ips: 4.76273 samples/s, eta: 2:58:40
[2024/07/27 14:41:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:41:29] ppocr INFO: epoch: [1089/1500], global_step: 3267, lr: 0.001000, loss: 1.228386, loss_shrink_maps: 0.632137, loss_threshold_maps: 0.479968, loss_binary_maps: 0.124107, avg_reader_cost: 2.24952 s, avg_batch_cost: 2.48570 s, avg_samples: 12.5, ips: 5.02877 samples/s, eta: 2:58:13
[2024/07/27 14:41:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:41:39] ppocr INFO: epoch: [1090/1500], global_step: 3270, lr: 0.001000, loss: 1.245888, loss_shrink_maps: 0.632137, loss_threshold_maps: 0.481183, loss_binary_maps: 0.124107, avg_reader_cost: 2.15644 s, avg_batch_cost: 2.47367 s, avg_samples: 12.5, ips: 5.05323 samples/s, eta: 2:57:47
[2024/07/27 14:41:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:41:48] ppocr INFO: epoch: [1091/1500], global_step: 3273, lr: 0.001000, loss: 1.245888, loss_shrink_maps: 0.632137, loss_threshold_maps: 0.481183, loss_binary_maps: 0.124107, avg_reader_cost: 2.16007 s, avg_batch_cost: 2.51967 s, avg_samples: 12.5, ips: 4.96096 samples/s, eta: 2:57:20
[2024/07/27 14:41:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:41:58] ppocr INFO: epoch: [1092/1500], global_step: 3276, lr: 0.001000, loss: 1.261286, loss_shrink_maps: 0.633086, loss_threshold_maps: 0.487644, loss_binary_maps: 0.124768, avg_reader_cost: 2.41255 s, avg_batch_cost: 2.66323 s, avg_samples: 12.5, ips: 4.69355 samples/s, eta: 2:56:55
[2024/07/27 14:41:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:42:08] ppocr INFO: epoch: [1093/1500], global_step: 3279, lr: 0.001000, loss: 1.245888, loss_shrink_maps: 0.632137, loss_threshold_maps: 0.479581, loss_binary_maps: 0.124107, avg_reader_cost: 2.20235 s, avg_batch_cost: 2.56764 s, avg_samples: 12.5, ips: 4.86829 samples/s, eta: 2:56:29
[2024/07/27 14:42:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:42:16] ppocr INFO: epoch: [1094/1500], global_step: 3280, lr: 0.001000, loss: 1.228059, loss_shrink_maps: 0.626473, loss_threshold_maps: 0.475088, loss_binary_maps: 0.123320, avg_reader_cost: 0.69372 s, avg_batch_cost: 0.78481 s, avg_samples: 4.8, ips: 6.11610 samples/s, eta: 2:56:20
[2024/07/27 14:42:17] ppocr INFO: epoch: [1094/1500], global_step: 3282, lr: 0.001000, loss: 1.205277, loss_shrink_maps: 0.619651, loss_threshold_maps: 0.468524, loss_binary_maps: 0.122076, avg_reader_cost: 1.66224 s, avg_batch_cost: 1.80887 s, avg_samples: 7.7, ips: 4.25680 samples/s, eta: 2:56:02
[2024/07/27 14:42:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:42:27] ppocr INFO: epoch: [1095/1500], global_step: 3285, lr: 0.001000, loss: 1.199754, loss_shrink_maps: 0.616571, loss_threshold_maps: 0.465047, loss_binary_maps: 0.121203, avg_reader_cost: 2.34288 s, avg_batch_cost: 2.60304 s, avg_samples: 12.5, ips: 4.80208 samples/s, eta: 2:55:36
[2024/07/27 14:42:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:42:37] ppocr INFO: epoch: [1096/1500], global_step: 3288, lr: 0.001000, loss: 1.205277, loss_shrink_maps: 0.620703, loss_threshold_maps: 0.472576, loss_binary_maps: 0.121951, avg_reader_cost: 2.27934 s, avg_batch_cost: 2.64921 s, avg_samples: 12.5, ips: 4.71839 samples/s, eta: 2:55:11
[2024/07/27 14:42:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:42:47] ppocr INFO: epoch: [1097/1500], global_step: 3290, lr: 0.001000, loss: 1.205277, loss_shrink_maps: 0.621343, loss_threshold_maps: 0.472576, loss_binary_maps: 0.121951, avg_reader_cost: 1.45302 s, avg_batch_cost: 1.63459 s, avg_samples: 9.6, ips: 5.87304 samples/s, eta: 2:54:53
[2024/07/27 14:42:47] ppocr INFO: epoch: [1097/1500], global_step: 3291, lr: 0.001000, loss: 1.225060, loss_shrink_maps: 0.621343, loss_threshold_maps: 0.479268, loss_binary_maps: 0.122550, avg_reader_cost: 0.86360 s, avg_batch_cost: 0.91919 s, avg_samples: 2.9, ips: 3.15494 samples/s, eta: 2:54:44
[2024/07/27 14:42:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:42:57] ppocr INFO: epoch: [1098/1500], global_step: 3294, lr: 0.001000, loss: 1.226036, loss_shrink_maps: 0.624959, loss_threshold_maps: 0.484747, loss_binary_maps: 0.123461, avg_reader_cost: 2.38159 s, avg_batch_cost: 2.63554 s, avg_samples: 12.5, ips: 4.74287 samples/s, eta: 2:54:19
[2024/07/27 14:42:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:43:07] ppocr INFO: epoch: [1099/1500], global_step: 3297, lr: 0.001000, loss: 1.226036, loss_shrink_maps: 0.624959, loss_threshold_maps: 0.484747, loss_binary_maps: 0.123461, avg_reader_cost: 2.25415 s, avg_batch_cost: 2.64389 s, avg_samples: 12.5, ips: 4.72789 samples/s, eta: 2:53:53
[2024/07/27 14:43:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:43:17] ppocr INFO: epoch: [1100/1500], global_step: 3300, lr: 0.001000, loss: 1.244105, loss_shrink_maps: 0.626826, loss_threshold_maps: 0.489882, loss_binary_maps: 0.123838, avg_reader_cost: 2.16453 s, avg_batch_cost: 2.53453 s, avg_samples: 12.5, ips: 4.93187 samples/s, eta: 2:53:26

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:58,  8.49it/s]
eval model::   1%|          | 3/500 [00:00<00:41, 11.97it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.21it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.28it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.59it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 18.77it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.96it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.61it/s]
eval model::   5%|▍         | 24/500 [00:01<00:21, 22.13it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 20.90it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 21.66it/s]
eval model::   7%|▋         | 33/500 [00:01<00:25, 18.33it/s]
eval model::   7%|▋         | 35/500 [00:01<00:26, 17.82it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 20.03it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.18it/s]
eval model::   9%|▊         | 43/500 [00:02<00:25, 17.76it/s]
eval model::   9%|▉         | 46/500 [00:02<00:25, 17.54it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 19.25it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 20.94it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.79it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 19.03it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.77it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:23, 18.41it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:21, 20.38it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 22.03it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:18, 22.76it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:18, 23.24it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:18, 22.52it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 20.28it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 21.02it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.24it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.15it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.12it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:21, 18.47it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:21, 19.01it/s]
eval model::  20%|██        | 100/500 [00:05<00:21, 18.38it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 21.03it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.45it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:19, 19.70it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:20, 18.84it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:20, 18.62it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:19, 19.64it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:20, 18.28it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:21, 17.69it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.73it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.79it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.85it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.42it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.60it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.94it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 22.14it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 19.26it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.03it/s]
eval model::  31%|███       | 153/500 [00:07<00:20, 17.18it/s]
eval model::  31%|███       | 156/500 [00:08<00:17, 19.33it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 20.23it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:20, 16.61it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:19, 17.20it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 18.12it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 19.61it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:16, 19.79it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.81it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 18.95it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:18, 17.20it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:16, 19.68it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 20.70it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:14, 21.08it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:13, 22.13it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:14, 20.73it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 21.27it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 21.00it/s]
eval model::  41%|████      | 205/500 [00:10<00:19, 14.97it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:19, 15.26it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 14.07it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.69it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.91it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.57it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 15.75it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:19, 13.99it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 17.10it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 20.49it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 20.78it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.44it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.02it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:14, 17.89it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:12, 19.89it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 19.48it/s]
eval model::  50%|█████     | 251/500 [00:13<00:12, 20.18it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 22.10it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 23.80it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 22.42it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 21.90it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:12, 19.28it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 19.74it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 20.56it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.16it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 18.38it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 19.98it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:10, 20.73it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:17, 12.03it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:14, 14.66it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 16.70it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:14, 13.95it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:11, 17.70it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 17.34it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 17.87it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 19.26it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 19.39it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:09, 19.24it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:09, 20.10it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:09, 18.80it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:10, 16.29it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:09, 18.17it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:09, 18.38it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:10, 15.90it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:10, 16.55it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:08, 18.53it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:08, 19.50it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:11, 14.30it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.60it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 19.17it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.76it/s]
eval model::  70%|███████   | 352/500 [00:18<00:07, 20.81it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.50it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 20.07it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 19.71it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 19.22it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:06, 20.94it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:05, 21.91it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 20.79it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:06, 20.48it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 22.10it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 20.30it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:08, 13.98it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 17.24it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 18.38it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 19.11it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:04, 20.94it/s]
eval model::  80%|████████  | 402/500 [00:21<00:04, 20.42it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 21.19it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 22.65it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 23.63it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 23.94it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:03, 21.49it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 15.28it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:04, 16.98it/s]
eval model::  85%|████████▌ | 425/500 [00:22<00:04, 15.18it/s]
eval model::  86%|████████▌ | 428/500 [00:22<00:04, 16.31it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:04, 16.99it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 19.86it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 21.83it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 20.83it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 22.25it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 23.16it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 20.25it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 21.52it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 22.25it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 22.15it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 16.86it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 17.20it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:02, 15.73it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 16.38it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 18.65it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 17.57it/s]
eval model::  95%|█████████▌| 475/500 [00:25<00:01, 17.83it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:00, 21.51it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 21.50it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 23.38it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 21.97it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 19.94it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 20.94it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 21.93it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.03it/s]
[2024/07/27 14:43:43] ppocr INFO: cur metric, precision: 0.7910879629629629, recall: 0.6581608088589311, hmean: 0.7185282522996057, fps: 45.07907300154631
[2024/07/27 14:43:43] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 14:43:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:43:52] ppocr INFO: epoch: [1101/1500], global_step: 3303, lr: 0.001000, loss: 1.249068, loss_shrink_maps: 0.629452, loss_threshold_maps: 0.496749, loss_binary_maps: 0.124810, avg_reader_cost: 2.29735 s, avg_batch_cost: 2.54349 s, avg_samples: 12.5, ips: 4.91451 samples/s, eta: 2:53:00
[2024/07/27 14:43:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:44:02] ppocr INFO: epoch: [1102/1500], global_step: 3306, lr: 0.001000, loss: 1.249068, loss_shrink_maps: 0.629452, loss_threshold_maps: 0.494011, loss_binary_maps: 0.124810, avg_reader_cost: 2.42307 s, avg_batch_cost: 2.69427 s, avg_samples: 12.5, ips: 4.63948 samples/s, eta: 2:52:34
[2024/07/27 14:44:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:44:12] ppocr INFO: epoch: [1103/1500], global_step: 3309, lr: 0.001000, loss: 1.249068, loss_shrink_maps: 0.633149, loss_threshold_maps: 0.489830, loss_binary_maps: 0.125253, avg_reader_cost: 2.21279 s, avg_batch_cost: 2.57380 s, avg_samples: 12.5, ips: 4.85664 samples/s, eta: 2:52:08
[2024/07/27 14:44:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:44:21] ppocr INFO: epoch: [1104/1500], global_step: 3310, lr: 0.001000, loss: 1.240769, loss_shrink_maps: 0.626826, loss_threshold_maps: 0.488064, loss_binary_maps: 0.123838, avg_reader_cost: 0.55327 s, avg_batch_cost: 0.82102 s, avg_samples: 4.8, ips: 5.84635 samples/s, eta: 2:52:00
[2024/07/27 14:44:22] ppocr INFO: epoch: [1104/1500], global_step: 3312, lr: 0.001000, loss: 1.240769, loss_shrink_maps: 0.632979, loss_threshold_maps: 0.488064, loss_binary_maps: 0.125224, avg_reader_cost: 1.73454 s, avg_batch_cost: 1.88181 s, avg_samples: 7.7, ips: 4.09180 samples/s, eta: 2:51:43
[2024/07/27 14:44:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:44:32] ppocr INFO: epoch: [1105/1500], global_step: 3315, lr: 0.001000, loss: 1.240769, loss_shrink_maps: 0.632979, loss_threshold_maps: 0.483890, loss_binary_maps: 0.125224, avg_reader_cost: 2.11152 s, avg_batch_cost: 2.44994 s, avg_samples: 12.5, ips: 5.10218 samples/s, eta: 2:51:16
[2024/07/27 14:44:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:44:42] ppocr INFO: epoch: [1106/1500], global_step: 3318, lr: 0.001000, loss: 1.226875, loss_shrink_maps: 0.614264, loss_threshold_maps: 0.478819, loss_binary_maps: 0.121216, avg_reader_cost: 2.24414 s, avg_batch_cost: 2.62309 s, avg_samples: 12.5, ips: 4.76537 samples/s, eta: 2:50:50
[2024/07/27 14:44:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:44:51] ppocr INFO: epoch: [1107/1500], global_step: 3320, lr: 0.001000, loss: 1.226875, loss_shrink_maps: 0.614264, loss_threshold_maps: 0.478819, loss_binary_maps: 0.121216, avg_reader_cost: 1.35791 s, avg_batch_cost: 1.69187 s, avg_samples: 9.6, ips: 5.67418 samples/s, eta: 2:50:33
[2024/07/27 14:44:52] ppocr INFO: epoch: [1107/1500], global_step: 3321, lr: 0.001000, loss: 1.243387, loss_shrink_maps: 0.627295, loss_threshold_maps: 0.478819, loss_binary_maps: 0.123576, avg_reader_cost: 0.89204 s, avg_batch_cost: 0.94733 s, avg_samples: 2.9, ips: 3.06122 samples/s, eta: 2:50:24
[2024/07/27 14:44:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:45:02] ppocr INFO: epoch: [1108/1500], global_step: 3324, lr: 0.001000, loss: 1.243387, loss_shrink_maps: 0.627295, loss_threshold_maps: 0.478177, loss_binary_maps: 0.123576, avg_reader_cost: 2.28287 s, avg_batch_cost: 2.70505 s, avg_samples: 12.5, ips: 4.62099 samples/s, eta: 2:49:59
[2024/07/27 14:45:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:45:11] ppocr INFO: epoch: [1109/1500], global_step: 3327, lr: 0.001000, loss: 1.196200, loss_shrink_maps: 0.596071, loss_threshold_maps: 0.475320, loss_binary_maps: 0.117909, avg_reader_cost: 2.34030 s, avg_batch_cost: 2.58039 s, avg_samples: 12.5, ips: 4.84424 samples/s, eta: 2:49:33
[2024/07/27 14:45:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:45:21] ppocr INFO: epoch: [1110/1500], global_step: 3330, lr: 0.001000, loss: 1.162649, loss_shrink_maps: 0.585231, loss_threshold_maps: 0.465912, loss_binary_maps: 0.115793, avg_reader_cost: 2.23080 s, avg_batch_cost: 2.62252 s, avg_samples: 12.5, ips: 4.76641 samples/s, eta: 2:49:07
[2024/07/27 14:45:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:45:31] ppocr INFO: epoch: [1111/1500], global_step: 3333, lr: 0.001000, loss: 1.162649, loss_shrink_maps: 0.585231, loss_threshold_maps: 0.466155, loss_binary_maps: 0.115793, avg_reader_cost: 2.41675 s, avg_batch_cost: 2.66489 s, avg_samples: 12.5, ips: 4.69062 samples/s, eta: 2:48:41
[2024/07/27 14:45:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:45:42] ppocr INFO: epoch: [1112/1500], global_step: 3336, lr: 0.001000, loss: 1.162248, loss_shrink_maps: 0.588499, loss_threshold_maps: 0.466155, loss_binary_maps: 0.115990, avg_reader_cost: 2.39162 s, avg_batch_cost: 2.63348 s, avg_samples: 12.5, ips: 4.74656 samples/s, eta: 2:48:15
[2024/07/27 14:45:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:45:51] ppocr INFO: epoch: [1113/1500], global_step: 3339, lr: 0.001000, loss: 1.203479, loss_shrink_maps: 0.607625, loss_threshold_maps: 0.477404, loss_binary_maps: 0.119757, avg_reader_cost: 2.32277 s, avg_batch_cost: 2.56527 s, avg_samples: 12.5, ips: 4.87278 samples/s, eta: 2:47:49
[2024/07/27 14:45:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:46:00] ppocr INFO: epoch: [1114/1500], global_step: 3340, lr: 0.001000, loss: 1.254982, loss_shrink_maps: 0.638453, loss_threshold_maps: 0.480981, loss_binary_maps: 0.125543, avg_reader_cost: 0.68212 s, avg_batch_cost: 0.78909 s, avg_samples: 4.8, ips: 6.08292 samples/s, eta: 2:47:40
[2024/07/27 14:46:01] ppocr INFO: epoch: [1114/1500], global_step: 3342, lr: 0.001000, loss: 1.203479, loss_shrink_maps: 0.607625, loss_threshold_maps: 0.483125, loss_binary_maps: 0.119757, avg_reader_cost: 1.67021 s, avg_batch_cost: 1.81749 s, avg_samples: 7.7, ips: 4.23661 samples/s, eta: 2:47:23
[2024/07/27 14:46:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:46:11] ppocr INFO: epoch: [1115/1500], global_step: 3345, lr: 0.001000, loss: 1.217206, loss_shrink_maps: 0.615025, loss_threshold_maps: 0.483125, loss_binary_maps: 0.121597, avg_reader_cost: 2.29906 s, avg_batch_cost: 2.56408 s, avg_samples: 12.5, ips: 4.87504 samples/s, eta: 2:46:57
[2024/07/27 14:46:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:46:21] ppocr INFO: epoch: [1116/1500], global_step: 3348, lr: 0.001000, loss: 1.217206, loss_shrink_maps: 0.623131, loss_threshold_maps: 0.483125, loss_binary_maps: 0.122388, avg_reader_cost: 2.31719 s, avg_batch_cost: 2.56271 s, avg_samples: 12.5, ips: 4.87764 samples/s, eta: 2:46:30
[2024/07/27 14:46:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:46:30] ppocr INFO: epoch: [1117/1500], global_step: 3350, lr: 0.001000, loss: 1.191055, loss_shrink_maps: 0.613893, loss_threshold_maps: 0.473937, loss_binary_maps: 0.120643, avg_reader_cost: 1.38289 s, avg_batch_cost: 1.64598 s, avg_samples: 9.6, ips: 5.83239 samples/s, eta: 2:46:13
[2024/07/27 14:46:30] ppocr INFO: epoch: [1117/1500], global_step: 3351, lr: 0.001000, loss: 1.202501, loss_shrink_maps: 0.615161, loss_threshold_maps: 0.475790, loss_binary_maps: 0.120643, avg_reader_cost: 0.86921 s, avg_batch_cost: 0.92462 s, avg_samples: 2.9, ips: 3.13642 samples/s, eta: 2:46:04
[2024/07/27 14:46:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:46:41] ppocr INFO: epoch: [1118/1500], global_step: 3354, lr: 0.001000, loss: 1.202501, loss_shrink_maps: 0.615161, loss_threshold_maps: 0.476392, loss_binary_maps: 0.120643, avg_reader_cost: 2.41063 s, avg_batch_cost: 2.64286 s, avg_samples: 12.5, ips: 4.72972 samples/s, eta: 2:45:38
[2024/07/27 14:46:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:46:50] ppocr INFO: epoch: [1119/1500], global_step: 3357, lr: 0.001000, loss: 1.191055, loss_shrink_maps: 0.607055, loss_threshold_maps: 0.475564, loss_binary_maps: 0.119656, avg_reader_cost: 2.22862 s, avg_batch_cost: 2.57827 s, avg_samples: 12.5, ips: 4.84821 samples/s, eta: 2:45:12
[2024/07/27 14:46:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:47:00] ppocr INFO: epoch: [1120/1500], global_step: 3360, lr: 0.001000, loss: 1.177236, loss_shrink_maps: 0.594231, loss_threshold_maps: 0.473110, loss_binary_maps: 0.117768, avg_reader_cost: 2.33824 s, avg_batch_cost: 2.57763 s, avg_samples: 12.5, ips: 4.84942 samples/s, eta: 2:44:46

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:02,  8.04it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 17.80it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.34it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 18.08it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 16.88it/s]
eval model::   3%|▎         | 13/500 [00:00<00:28, 17.16it/s]
eval model::   3%|▎         | 15/500 [00:00<00:27, 17.80it/s]
eval model::   4%|▎         | 18/500 [00:00<00:24, 19.85it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.57it/s]
eval model::   5%|▍         | 24/500 [00:01<00:22, 21.51it/s]
eval model::   5%|▌         | 27/500 [00:01<00:21, 21.80it/s]
eval model::   6%|▌         | 30/500 [00:01<00:23, 19.70it/s]
eval model::   7%|▋         | 33/500 [00:01<00:22, 20.39it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 18.80it/s]
eval model::   8%|▊         | 38/500 [00:02<00:24, 18.91it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 17.88it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.07it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.71it/s]
eval model::   9%|▉         | 47/500 [00:02<00:27, 16.47it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.22it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 20.85it/s]
eval model::  11%|█         | 56/500 [00:02<00:21, 21.11it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:21, 20.54it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:25, 17.18it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:26, 16.51it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:21, 20.06it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 21.36it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:20, 20.43it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:20, 20.17it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:19, 21.93it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:19, 21.28it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:20, 20.31it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 17.91it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:24, 16.61it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:24, 16.66it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:21, 18.68it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.09it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.02it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 22.33it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:16, 23.15it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:17, 22.51it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:17, 22.35it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:17, 21.58it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.26it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 20.89it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.02it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 18.82it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:22, 16.06it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:19, 18.55it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.10it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.81it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.63it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 19.04it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.79it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 20.19it/s]
eval model::  31%|███       | 156/500 [00:08<00:16, 20.37it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:19, 17.69it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:23, 14.15it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:24, 14.01it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:22, 14.73it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:21, 15.69it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:18, 17.48it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:19, 17.00it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 15.95it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 16.05it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.67it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.75it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:18, 17.43it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:17, 17.97it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:17, 17.85it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:16, 18.30it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:17, 17.55it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:16, 18.07it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.67it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.61it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 21.55it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:21, 13.61it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:20, 13.98it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 14.20it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.58it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.00it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:23, 12.20it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:20, 13.49it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:24, 11.35it/s]
eval model::  45%|████▍     | 223/500 [00:12<00:23, 12.03it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:18, 14.42it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:14, 18.02it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:13, 19.88it/s]
eval model::  47%|████▋     | 236/500 [00:13<00:12, 20.41it/s]
eval model::  48%|████▊     | 239/500 [00:13<00:11, 22.35it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 18.68it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.09it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 20.95it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 21.41it/s]
eval model::  51%|█████     | 254/500 [00:13<00:10, 22.91it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 23.96it/s]
eval model::  52%|█████▏    | 260/500 [00:14<00:09, 25.36it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:09, 24.92it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:10, 21.54it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 20.49it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:10, 21.19it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 20.31it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 21.47it/s]
eval model::  56%|█████▌    | 281/500 [00:15<00:09, 22.65it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:09, 23.63it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:18, 11.62it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:16, 12.74it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:14, 13.99it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:15, 12.90it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:17, 11.65it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:14, 14.35it/s]
eval model::  60%|██████    | 301/500 [00:16<00:15, 12.83it/s]
eval model::  61%|██████    | 304/500 [00:16<00:13, 14.52it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:11, 16.99it/s]
eval model::  62%|██████▏   | 309/500 [00:17<00:10, 17.51it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:09, 18.97it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:09, 19.00it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:08, 20.38it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 20.55it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:09, 19.21it/s]
eval model::  65%|██████▌   | 327/500 [00:18<00:08, 21.02it/s]
eval model::  66%|██████▌   | 330/500 [00:18<00:08, 20.69it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:08, 20.79it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:07, 21.75it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:07, 21.63it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:09, 16.25it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 18.20it/s]
eval model::  70%|██████▉   | 348/500 [00:19<00:07, 19.26it/s]
eval model::  70%|███████   | 351/500 [00:19<00:07, 20.72it/s]
eval model::  71%|███████   | 354/500 [00:19<00:06, 22.26it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 19.99it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:06, 20.91it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 19.27it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:07, 17.90it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:07, 18.52it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:06, 18.87it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 22.11it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 22.11it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:04, 24.55it/s]
eval model::  77%|███████▋  | 387/500 [00:21<00:07, 14.25it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:06, 16.15it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 16.85it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:06, 16.70it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 17.80it/s]
eval model::  80%|████████  | 401/500 [00:21<00:06, 16.26it/s]
eval model::  81%|████████  | 404/500 [00:22<00:05, 18.19it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 19.33it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 21.02it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:04, 21.37it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 20.51it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:03, 20.78it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:05, 15.18it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.64it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.47it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 16.65it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 16.23it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 18.26it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:03, 20.01it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:03, 19.29it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 22.16it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 23.01it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 21.23it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:01, 23.65it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 23.08it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 20.80it/s]
eval model::  93%|█████████▎| 463/500 [00:25<00:02, 16.23it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 15.35it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 16.96it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 18.96it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.17it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.63it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:00, 22.21it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 22.96it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 21.68it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 20.80it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 19.86it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 21.42it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 25.05it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.57it/s]
[2024/07/27 14:47:27] ppocr INFO: cur metric, precision: 0.7657807308970099, recall: 0.6658642272508426, hmean: 0.7123358228174093, fps: 44.7780426615207
[2024/07/27 14:47:27] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 14:47:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:47:36] ppocr INFO: epoch: [1121/1500], global_step: 3363, lr: 0.001000, loss: 1.202501, loss_shrink_maps: 0.613279, loss_threshold_maps: 0.475564, loss_binary_maps: 0.120200, avg_reader_cost: 2.17243 s, avg_batch_cost: 2.40901 s, avg_samples: 12.5, ips: 5.18886 samples/s, eta: 2:44:20
[2024/07/27 14:47:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:47:46] ppocr INFO: epoch: [1122/1500], global_step: 3366, lr: 0.001000, loss: 1.253095, loss_shrink_maps: 0.638708, loss_threshold_maps: 0.486001, loss_binary_maps: 0.124748, avg_reader_cost: 2.20781 s, avg_batch_cost: 2.55942 s, avg_samples: 12.5, ips: 4.88391 samples/s, eta: 2:43:53
[2024/07/27 14:47:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:47:56] ppocr INFO: epoch: [1123/1500], global_step: 3369, lr: 0.001000, loss: 1.270302, loss_shrink_maps: 0.656367, loss_threshold_maps: 0.490362, loss_binary_maps: 0.129339, avg_reader_cost: 2.25456 s, avg_batch_cost: 2.63280 s, avg_samples: 12.5, ips: 4.74779 samples/s, eta: 2:43:28
[2024/07/27 14:47:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:48:04] ppocr INFO: epoch: [1124/1500], global_step: 3370, lr: 0.001000, loss: 1.270302, loss_shrink_maps: 0.656367, loss_threshold_maps: 0.490362, loss_binary_maps: 0.129339, avg_reader_cost: 0.57187 s, avg_batch_cost: 0.77520 s, avg_samples: 4.8, ips: 6.19193 samples/s, eta: 2:43:19
[2024/07/27 14:48:05] ppocr INFO: epoch: [1124/1500], global_step: 3372, lr: 0.001000, loss: 1.270302, loss_shrink_maps: 0.656367, loss_threshold_maps: 0.490362, loss_binary_maps: 0.129339, avg_reader_cost: 1.64302 s, avg_batch_cost: 1.78975 s, avg_samples: 7.7, ips: 4.30228 samples/s, eta: 2:43:01
[2024/07/27 14:48:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:48:15] ppocr INFO: epoch: [1125/1500], global_step: 3375, lr: 0.001000, loss: 1.270302, loss_shrink_maps: 0.656367, loss_threshold_maps: 0.501618, loss_binary_maps: 0.129339, avg_reader_cost: 2.42472 s, avg_batch_cost: 2.66328 s, avg_samples: 12.5, ips: 4.69347 samples/s, eta: 2:42:36
[2024/07/27 14:48:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:48:25] ppocr INFO: epoch: [1126/1500], global_step: 3378, lr: 0.001000, loss: 1.286990, loss_shrink_maps: 0.657829, loss_threshold_maps: 0.501618, loss_binary_maps: 0.129969, avg_reader_cost: 2.25002 s, avg_batch_cost: 2.62432 s, avg_samples: 12.5, ips: 4.76313 samples/s, eta: 2:42:10
[2024/07/27 14:48:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:48:34] ppocr INFO: epoch: [1127/1500], global_step: 3380, lr: 0.001000, loss: 1.286990, loss_shrink_maps: 0.657829, loss_threshold_maps: 0.501618, loss_binary_maps: 0.129969, avg_reader_cost: 1.30409 s, avg_batch_cost: 1.60038 s, avg_samples: 9.6, ips: 5.99857 samples/s, eta: 2:41:52
[2024/07/27 14:48:35] ppocr INFO: epoch: [1127/1500], global_step: 3381, lr: 0.001000, loss: 1.286990, loss_shrink_maps: 0.657829, loss_threshold_maps: 0.496145, loss_binary_maps: 0.129969, avg_reader_cost: 0.84654 s, avg_batch_cost: 0.90174 s, avg_samples: 2.9, ips: 3.21601 samples/s, eta: 2:41:43
[2024/07/27 14:48:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:48:44] ppocr INFO: epoch: [1128/1500], global_step: 3384, lr: 0.001000, loss: 1.244908, loss_shrink_maps: 0.634740, loss_threshold_maps: 0.476427, loss_binary_maps: 0.125616, avg_reader_cost: 2.14798 s, avg_batch_cost: 2.46500 s, avg_samples: 12.5, ips: 5.07100 samples/s, eta: 2:41:17
[2024/07/27 14:48:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:48:55] ppocr INFO: epoch: [1129/1500], global_step: 3387, lr: 0.001000, loss: 1.161734, loss_shrink_maps: 0.581033, loss_threshold_maps: 0.465822, loss_binary_maps: 0.115789, avg_reader_cost: 2.48042 s, avg_batch_cost: 2.71807 s, avg_samples: 12.5, ips: 4.59885 samples/s, eta: 2:40:51
[2024/07/27 14:48:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:49:04] ppocr INFO: epoch: [1130/1500], global_step: 3390, lr: 0.001000, loss: 1.220297, loss_shrink_maps: 0.620290, loss_threshold_maps: 0.469077, loss_binary_maps: 0.122498, avg_reader_cost: 2.20501 s, avg_batch_cost: 2.54289 s, avg_samples: 12.5, ips: 4.91566 samples/s, eta: 2:40:25
[2024/07/27 14:49:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:49:14] ppocr INFO: epoch: [1131/1500], global_step: 3393, lr: 0.001000, loss: 1.228444, loss_shrink_maps: 0.613439, loss_threshold_maps: 0.471131, loss_binary_maps: 0.121189, avg_reader_cost: 2.37242 s, avg_batch_cost: 2.62073 s, avg_samples: 12.5, ips: 4.76967 samples/s, eta: 2:39:59
[2024/07/27 14:49:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:49:24] ppocr INFO: epoch: [1132/1500], global_step: 3396, lr: 0.001000, loss: 1.161734, loss_shrink_maps: 0.581033, loss_threshold_maps: 0.469077, loss_binary_maps: 0.115789, avg_reader_cost: 2.21494 s, avg_batch_cost: 2.52207 s, avg_samples: 12.5, ips: 4.95624 samples/s, eta: 2:39:33
[2024/07/27 14:49:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:49:34] ppocr INFO: epoch: [1133/1500], global_step: 3399, lr: 0.001000, loss: 1.178330, loss_shrink_maps: 0.603343, loss_threshold_maps: 0.469077, loss_binary_maps: 0.118915, avg_reader_cost: 2.31732 s, avg_batch_cost: 2.55342 s, avg_samples: 12.5, ips: 4.89540 samples/s, eta: 2:39:07
[2024/07/27 14:49:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:49:42] ppocr INFO: epoch: [1134/1500], global_step: 3400, lr: 0.001000, loss: 1.178330, loss_shrink_maps: 0.603343, loss_threshold_maps: 0.465440, loss_binary_maps: 0.118915, avg_reader_cost: 0.59733 s, avg_batch_cost: 0.76026 s, avg_samples: 4.8, ips: 6.31367 samples/s, eta: 2:38:58
[2024/07/27 14:49:43] ppocr INFO: epoch: [1134/1500], global_step: 3402, lr: 0.001000, loss: 1.194952, loss_shrink_maps: 0.609851, loss_threshold_maps: 0.459083, loss_binary_maps: 0.120490, avg_reader_cost: 1.61289 s, avg_batch_cost: 1.75949 s, avg_samples: 7.7, ips: 4.37626 samples/s, eta: 2:38:40
[2024/07/27 14:49:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:49:53] ppocr INFO: epoch: [1135/1500], global_step: 3405, lr: 0.001000, loss: 1.224246, loss_shrink_maps: 0.613537, loss_threshold_maps: 0.469747, loss_binary_maps: 0.121189, avg_reader_cost: 2.32166 s, avg_batch_cost: 2.58296 s, avg_samples: 12.5, ips: 4.83940 samples/s, eta: 2:38:14
[2024/07/27 14:49:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:50:03] ppocr INFO: epoch: [1136/1500], global_step: 3408, lr: 0.001000, loss: 1.224246, loss_shrink_maps: 0.613537, loss_threshold_maps: 0.467493, loss_binary_maps: 0.121189, avg_reader_cost: 2.23620 s, avg_batch_cost: 2.54769 s, avg_samples: 12.5, ips: 4.90641 samples/s, eta: 2:37:48
[2024/07/27 14:50:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:50:12] ppocr INFO: epoch: [1137/1500], global_step: 3410, lr: 0.001000, loss: 1.206864, loss_shrink_maps: 0.613537, loss_threshold_maps: 0.459079, loss_binary_maps: 0.121113, avg_reader_cost: 1.44483 s, avg_batch_cost: 1.73587 s, avg_samples: 9.6, ips: 5.53036 samples/s, eta: 2:37:31
[2024/07/27 14:50:13] ppocr INFO: epoch: [1137/1500], global_step: 3411, lr: 0.001000, loss: 1.206864, loss_shrink_maps: 0.618029, loss_threshold_maps: 0.459079, loss_binary_maps: 0.122010, avg_reader_cost: 0.91435 s, avg_batch_cost: 0.96953 s, avg_samples: 2.9, ips: 2.99114 samples/s, eta: 2:37:22
[2024/07/27 14:50:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:50:23] ppocr INFO: epoch: [1138/1500], global_step: 3414, lr: 0.001000, loss: 1.194952, loss_shrink_maps: 0.615919, loss_threshold_maps: 0.454425, loss_binary_maps: 0.121613, avg_reader_cost: 2.24892 s, avg_batch_cost: 2.60698 s, avg_samples: 12.5, ips: 4.79481 samples/s, eta: 2:36:56
[2024/07/27 14:50:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:50:32] ppocr INFO: epoch: [1139/1500], global_step: 3417, lr: 0.001000, loss: 1.254062, loss_shrink_maps: 0.636544, loss_threshold_maps: 0.467748, loss_binary_maps: 0.125762, avg_reader_cost: 2.18170 s, avg_batch_cost: 2.52876 s, avg_samples: 12.5, ips: 4.94313 samples/s, eta: 2:36:30
[2024/07/27 14:50:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:50:42] ppocr INFO: epoch: [1140/1500], global_step: 3420, lr: 0.001000, loss: 1.276793, loss_shrink_maps: 0.642946, loss_threshold_maps: 0.478260, loss_binary_maps: 0.127362, avg_reader_cost: 2.29482 s, avg_batch_cost: 2.53825 s, avg_samples: 12.5, ips: 4.92464 samples/s, eta: 2:36:04

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.73it/s]
eval model::   1%|          | 3/500 [00:00<00:33, 14.82it/s]
eval model::   1%|          | 5/500 [00:00<00:29, 16.93it/s]
eval model::   2%|▏         | 8/500 [00:00<00:25, 19.31it/s]
eval model::   2%|▏         | 11/500 [00:00<00:25, 19.33it/s]
eval model::   3%|▎         | 13/500 [00:00<00:25, 19.23it/s]
eval model::   3%|▎         | 15/500 [00:00<00:25, 19.13it/s]
eval model::   4%|▎         | 18/500 [00:00<00:22, 21.66it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.46it/s]
eval model::   5%|▍         | 24/500 [00:01<00:21, 21.64it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 21.29it/s]
eval model::   6%|▌         | 30/500 [00:01<00:22, 20.74it/s]
eval model::   7%|▋         | 33/500 [00:01<00:22, 20.34it/s]
eval model::   7%|▋         | 36/500 [00:01<00:23, 19.70it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.57it/s]
eval model::   8%|▊         | 40/500 [00:02<00:27, 16.74it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.22it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.30it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 17.91it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.71it/s]
eval model::  11%|█         | 53/500 [00:02<00:22, 20.22it/s]
eval model::  11%|█         | 56/500 [00:02<00:22, 19.52it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 17.93it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:24, 18.06it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:30, 14.46it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:30, 14.30it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:25, 16.71it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:22, 19.38it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:22, 19.30it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:20, 20.27it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:21, 19.60it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 20.53it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 19.80it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 18.81it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.01it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.44it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:22, 18.32it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:19, 20.46it/s]
eval model::  20%|██        | 101/500 [00:05<00:18, 21.42it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 20.89it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.22it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 21.69it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 21.30it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:18, 20.69it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:18, 20.19it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:19, 19.33it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.65it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.69it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:20, 18.13it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:21, 17.19it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:22, 16.59it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:19, 18.62it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.80it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:16, 21.19it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 21.38it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 18.80it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 18.24it/s]
eval model::  31%|███       | 155/500 [00:08<00:16, 21.11it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 21.56it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:22, 14.94it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:20, 16.72it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:19, 17.29it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:17, 19.01it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:17, 18.62it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 15.91it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.12it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:18, 16.78it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.31it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:16, 19.40it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:17, 17.39it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:17, 17.91it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.14it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 20.34it/s]
eval model::  41%|████      | 203/500 [00:10<00:13, 22.27it/s]
eval model::  41%|████      | 206/500 [00:11<00:18, 15.55it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:18, 15.39it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 15.26it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.84it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.66it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 14.02it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 15.85it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.70it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 16.76it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:14, 18.22it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:13, 19.78it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:13, 20.01it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 20.87it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 22.20it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:15, 16.92it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:13, 19.11it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:13, 18.54it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 20.26it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.49it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:10, 22.41it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:09, 24.33it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:11, 20.96it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:11, 21.02it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:11, 19.68it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:11, 19.93it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:13, 17.12it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:12, 17.56it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 20.09it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:18, 11.59it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.25it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.51it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.08it/s]
eval model::  59%|█████▉    | 297/500 [00:16<00:14, 14.11it/s]
eval model::  60%|██████    | 300/500 [00:16<00:13, 14.69it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 16.47it/s]
eval model::  61%|██████    | 305/500 [00:16<00:11, 16.26it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:11, 17.41it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 19.23it/s]
eval model::  63%|██████▎   | 314/500 [00:17<00:09, 18.98it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:10, 18.17it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 20.52it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 18.76it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:09, 18.98it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 20.70it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 19.24it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:07, 20.97it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 22.61it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:09, 16.03it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:08, 17.55it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 19.55it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.34it/s]
eval model::  70%|███████   | 352/500 [00:19<00:07, 21.08it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 19.22it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 19.58it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:08, 17.36it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.71it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.41it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 18.80it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:06, 19.07it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:06, 18.59it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 20.68it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 22.86it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 20.47it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:05, 22.56it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:08, 13.57it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:07, 15.46it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 16.55it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 19.02it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 18.66it/s]
eval model::  81%|████████  | 403/500 [00:21<00:04, 19.52it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 19.93it/s]
eval model::  82%|████████▏ | 409/500 [00:22<00:04, 21.53it/s]
eval model::  82%|████████▏ | 412/500 [00:22<00:03, 22.26it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:04, 20.15it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 18.88it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 13.82it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:04, 15.87it/s]
eval model::  85%|████████▌ | 425/500 [00:23<00:04, 15.08it/s]
eval model::  86%|████████▌ | 428/500 [00:23<00:04, 17.43it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:03, 17.31it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 20.28it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 21.72it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 20.52it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 22.01it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 22.80it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 19.81it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 21.01it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 22.59it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 23.31it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 17.73it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 16.56it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 18.35it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.67it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.99it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:01, 21.98it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 21.76it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 22.61it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 21.40it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 22.28it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 21.29it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 20.49it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 23.95it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.77it/s]
[2024/07/27 14:51:09] ppocr INFO: cur metric, precision: 0.7557826788596019, recall: 0.6764564275397208, hmean: 0.7139227642276423, fps: 44.80655525591726
[2024/07/27 14:51:09] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 14:51:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:51:18] ppocr INFO: epoch: [1141/1500], global_step: 3423, lr: 0.001000, loss: 1.254217, loss_shrink_maps: 0.624958, loss_threshold_maps: 0.484593, loss_binary_maps: 0.123792, avg_reader_cost: 2.17629 s, avg_batch_cost: 2.49501 s, avg_samples: 12.5, ips: 5.01001 samples/s, eta: 2:35:38
[2024/07/27 14:51:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:51:28] ppocr INFO: epoch: [1142/1500], global_step: 3426, lr: 0.001000, loss: 1.234817, loss_shrink_maps: 0.617324, loss_threshold_maps: 0.478260, loss_binary_maps: 0.122290, avg_reader_cost: 2.21001 s, avg_batch_cost: 2.59121 s, avg_samples: 12.5, ips: 4.82400 samples/s, eta: 2:35:12
[2024/07/27 14:51:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:51:38] ppocr INFO: epoch: [1143/1500], global_step: 3429, lr: 0.001000, loss: 1.234817, loss_shrink_maps: 0.617324, loss_threshold_maps: 0.484593, loss_binary_maps: 0.122290, avg_reader_cost: 2.31323 s, avg_batch_cost: 2.71015 s, avg_samples: 12.5, ips: 4.61230 samples/s, eta: 2:34:46
[2024/07/27 14:51:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:51:46] ppocr INFO: epoch: [1144/1500], global_step: 3430, lr: 0.001000, loss: 1.254160, loss_shrink_maps: 0.624958, loss_threshold_maps: 0.491439, loss_binary_maps: 0.123792, avg_reader_cost: 0.70817 s, avg_batch_cost: 0.82127 s, avg_samples: 4.8, ips: 5.84464 samples/s, eta: 2:34:37
[2024/07/27 14:51:48] ppocr INFO: epoch: [1144/1500], global_step: 3432, lr: 0.001000, loss: 1.248402, loss_shrink_maps: 0.618429, loss_threshold_maps: 0.493123, loss_binary_maps: 0.122787, avg_reader_cost: 1.73466 s, avg_batch_cost: 1.88150 s, avg_samples: 7.7, ips: 4.09248 samples/s, eta: 2:34:20
[2024/07/27 14:51:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:51:58] ppocr INFO: epoch: [1145/1500], global_step: 3435, lr: 0.001000, loss: 1.248402, loss_shrink_maps: 0.622469, loss_threshold_maps: 0.493123, loss_binary_maps: 0.123792, avg_reader_cost: 2.40131 s, avg_batch_cost: 2.63778 s, avg_samples: 12.5, ips: 4.73883 samples/s, eta: 2:33:54
[2024/07/27 14:51:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:52:08] ppocr INFO: epoch: [1146/1500], global_step: 3438, lr: 0.001000, loss: 1.249191, loss_shrink_maps: 0.630194, loss_threshold_maps: 0.488772, loss_binary_maps: 0.124968, avg_reader_cost: 2.31490 s, avg_batch_cost: 2.55708 s, avg_samples: 12.5, ips: 4.88840 samples/s, eta: 2:33:28
[2024/07/27 14:52:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:52:17] ppocr INFO: epoch: [1147/1500], global_step: 3440, lr: 0.001000, loss: 1.233131, loss_shrink_maps: 0.618429, loss_threshold_maps: 0.488772, loss_binary_maps: 0.122020, avg_reader_cost: 1.49172 s, avg_batch_cost: 1.71952 s, avg_samples: 9.6, ips: 5.58296 samples/s, eta: 2:33:11
[2024/07/27 14:52:17] ppocr INFO: epoch: [1147/1500], global_step: 3441, lr: 0.001000, loss: 1.233131, loss_shrink_maps: 0.618429, loss_threshold_maps: 0.485390, loss_binary_maps: 0.122020, avg_reader_cost: 0.90592 s, avg_batch_cost: 0.96209 s, avg_samples: 2.9, ips: 3.01426 samples/s, eta: 2:33:02
[2024/07/27 14:52:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:52:27] ppocr INFO: epoch: [1148/1500], global_step: 3444, lr: 0.001000, loss: 1.214881, loss_shrink_maps: 0.613343, loss_threshold_maps: 0.479419, loss_binary_maps: 0.121433, avg_reader_cost: 2.26674 s, avg_batch_cost: 2.50642 s, avg_samples: 12.5, ips: 4.98720 samples/s, eta: 2:32:36
[2024/07/27 14:52:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:52:37] ppocr INFO: epoch: [1149/1500], global_step: 3447, lr: 0.001000, loss: 1.224410, loss_shrink_maps: 0.619034, loss_threshold_maps: 0.484051, loss_binary_maps: 0.122800, avg_reader_cost: 2.23673 s, avg_batch_cost: 2.59262 s, avg_samples: 12.5, ips: 4.82137 samples/s, eta: 2:32:10
[2024/07/27 14:52:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:52:47] ppocr INFO: epoch: [1150/1500], global_step: 3450, lr: 0.001000, loss: 1.224410, loss_shrink_maps: 0.619034, loss_threshold_maps: 0.484051, loss_binary_maps: 0.122800, avg_reader_cost: 2.21386 s, avg_batch_cost: 2.54295 s, avg_samples: 12.5, ips: 4.91555 samples/s, eta: 2:31:44
[2024/07/27 14:52:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:52:56] ppocr INFO: epoch: [1151/1500], global_step: 3453, lr: 0.001000, loss: 1.203739, loss_shrink_maps: 0.608348, loss_threshold_maps: 0.472788, loss_binary_maps: 0.120008, avg_reader_cost: 2.30410 s, avg_batch_cost: 2.54603 s, avg_samples: 12.5, ips: 4.90960 samples/s, eta: 2:31:18
[2024/07/27 14:52:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:53:06] ppocr INFO: epoch: [1152/1500], global_step: 3456, lr: 0.001000, loss: 1.192946, loss_shrink_maps: 0.589100, loss_threshold_maps: 0.467330, loss_binary_maps: 0.116465, avg_reader_cost: 2.33727 s, avg_batch_cost: 2.57774 s, avg_samples: 12.5, ips: 4.84921 samples/s, eta: 2:30:52
[2024/07/27 14:53:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:53:16] ppocr INFO: epoch: [1153/1500], global_step: 3459, lr: 0.001000, loss: 1.169292, loss_shrink_maps: 0.575732, loss_threshold_maps: 0.467330, loss_binary_maps: 0.114323, avg_reader_cost: 2.27028 s, avg_batch_cost: 2.63745 s, avg_samples: 12.5, ips: 4.73942 samples/s, eta: 2:30:26
[2024/07/27 14:53:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:53:24] ppocr INFO: epoch: [1154/1500], global_step: 3460, lr: 0.001000, loss: 1.169292, loss_shrink_maps: 0.583160, loss_threshold_maps: 0.467330, loss_binary_maps: 0.115656, avg_reader_cost: 0.56858 s, avg_batch_cost: 0.76013 s, avg_samples: 4.8, ips: 6.31474 samples/s, eta: 2:30:17
[2024/07/27 14:53:26] ppocr INFO: epoch: [1154/1500], global_step: 3462, lr: 0.001000, loss: 1.194558, loss_shrink_maps: 0.602527, loss_threshold_maps: 0.469728, loss_binary_maps: 0.119073, avg_reader_cost: 1.61300 s, avg_batch_cost: 1.76050 s, avg_samples: 7.7, ips: 4.37377 samples/s, eta: 2:29:59
[2024/07/27 14:53:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:53:36] ppocr INFO: epoch: [1155/1500], global_step: 3465, lr: 0.001000, loss: 1.202304, loss_shrink_maps: 0.602950, loss_threshold_maps: 0.476676, loss_binary_maps: 0.119899, avg_reader_cost: 2.35747 s, avg_batch_cost: 2.68568 s, avg_samples: 12.5, ips: 4.65432 samples/s, eta: 2:29:34
[2024/07/27 14:53:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:53:45] ppocr INFO: epoch: [1156/1500], global_step: 3468, lr: 0.001000, loss: 1.202304, loss_shrink_maps: 0.602950, loss_threshold_maps: 0.473341, loss_binary_maps: 0.119899, avg_reader_cost: 2.33184 s, avg_batch_cost: 2.58617 s, avg_samples: 12.5, ips: 4.83340 samples/s, eta: 2:29:08
[2024/07/27 14:53:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:53:54] ppocr INFO: epoch: [1157/1500], global_step: 3470, lr: 0.001000, loss: 1.192619, loss_shrink_maps: 0.595961, loss_threshold_maps: 0.466021, loss_binary_maps: 0.118462, avg_reader_cost: 1.27818 s, avg_batch_cost: 1.51438 s, avg_samples: 9.6, ips: 6.33923 samples/s, eta: 2:28:50
[2024/07/27 14:53:54] ppocr INFO: epoch: [1157/1500], global_step: 3471, lr: 0.001000, loss: 1.202304, loss_shrink_maps: 0.602950, loss_threshold_maps: 0.473341, loss_binary_maps: 0.119899, avg_reader_cost: 0.80346 s, avg_batch_cost: 0.85900 s, avg_samples: 2.9, ips: 3.37603 samples/s, eta: 2:28:41
[2024/07/27 14:53:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:54:04] ppocr INFO: epoch: [1158/1500], global_step: 3474, lr: 0.001000, loss: 1.206597, loss_shrink_maps: 0.612005, loss_threshold_maps: 0.473341, loss_binary_maps: 0.121158, avg_reader_cost: 2.44615 s, avg_batch_cost: 2.69338 s, avg_samples: 12.5, ips: 4.64101 samples/s, eta: 2:28:15
[2024/07/27 14:54:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:54:14] ppocr INFO: epoch: [1159/1500], global_step: 3477, lr: 0.001000, loss: 1.219628, loss_shrink_maps: 0.621368, loss_threshold_maps: 0.473341, loss_binary_maps: 0.122362, avg_reader_cost: 2.25470 s, avg_batch_cost: 2.62718 s, avg_samples: 12.5, ips: 4.75795 samples/s, eta: 2:27:49
[2024/07/27 14:54:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:54:24] ppocr INFO: epoch: [1160/1500], global_step: 3480, lr: 0.001000, loss: 1.213080, loss_shrink_maps: 0.615238, loss_threshold_maps: 0.461436, loss_binary_maps: 0.121433, avg_reader_cost: 2.16432 s, avg_batch_cost: 2.50258 s, avg_samples: 12.5, ips: 4.99484 samples/s, eta: 2:27:23

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:57,  8.64it/s]
eval model::   1%|          | 3/500 [00:00<00:33, 14.70it/s]
eval model::   1%|          | 6/500 [00:00<00:29, 16.71it/s]
eval model::   2%|▏         | 8/500 [00:00<00:31, 15.47it/s]
eval model::   2%|▏         | 10/500 [00:00<00:29, 16.62it/s]
eval model::   2%|▏         | 12/500 [00:00<00:31, 15.54it/s]
eval model::   3%|▎         | 14/500 [00:00<00:30, 15.99it/s]
eval model::   3%|▎         | 17/500 [00:01<00:25, 19.15it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.39it/s]
eval model::   5%|▍         | 23/500 [00:01<00:24, 19.18it/s]
eval model::   5%|▌         | 25/500 [00:01<00:25, 18.85it/s]
eval model::   6%|▌         | 28/500 [00:01<00:22, 21.00it/s]
eval model::   6%|▌         | 31/500 [00:01<00:23, 20.11it/s]
eval model::   7%|▋         | 34/500 [00:01<00:22, 21.11it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.94it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 17.89it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.36it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.87it/s]
eval model::   9%|▉         | 47/500 [00:02<00:29, 15.47it/s]
eval model::  10%|█         | 50/500 [00:02<00:26, 17.19it/s]
eval model::  11%|█         | 53/500 [00:02<00:24, 18.48it/s]
eval model::  11%|█         | 56/500 [00:03<00:23, 18.73it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 18.62it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.63it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.69it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 19.24it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 21.39it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 22.17it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:20, 20.72it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 21.08it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.40it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 20.85it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:24, 16.94it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:24, 16.86it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:24, 16.70it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:23, 17.18it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:21, 18.65it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.28it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 19.99it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.65it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:17, 22.07it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:17, 21.83it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:17, 22.01it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:18, 20.58it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 20.49it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:16, 22.58it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 18.33it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.81it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 17.35it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:19, 19.21it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.61it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.43it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.75it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.38it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.12it/s]
eval model::  31%|███       | 153/500 [00:08<00:17, 19.71it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:15, 22.48it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:20, 16.47it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:18, 18.44it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:16, 20.09it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:15, 21.87it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:15, 20.75it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:17, 18.74it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.40it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:18, 17.40it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 20.29it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.08it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.45it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 20.93it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.21it/s]
eval model::  40%|████      | 201/500 [00:10<00:15, 19.81it/s]
eval model::  41%|████      | 204/500 [00:10<00:14, 20.55it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:22, 12.82it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:21, 13.37it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 13.95it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.88it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 12.97it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.39it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 15.69it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.48it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 17.10it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 20.77it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 22.19it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:11, 23.08it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:10, 23.75it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 20.33it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:11, 21.60it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:10, 23.11it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 21.08it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.37it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 22.89it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 23.09it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 23.59it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:10, 21.86it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 20.29it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:10, 21.03it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.73it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.10it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:11, 19.70it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 20.41it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:20, 10.48it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:16, 12.95it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:14, 14.55it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:16, 12.64it/s]
eval model::  60%|██████    | 300/500 [00:16<00:13, 14.79it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 16.87it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 18.13it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:10, 19.04it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 19.73it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 20.31it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 21.97it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:07, 22.73it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 20.86it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 22.35it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 20.83it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 21.22it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 22.25it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:07, 20.20it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 15.75it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 17.30it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 17.47it/s]
eval model::  70%|███████   | 350/500 [00:18<00:07, 19.61it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 21.01it/s]
eval model::  71%|███████   | 356/500 [00:18<00:07, 19.23it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:06, 20.34it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:07, 18.96it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.73it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.26it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.78it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:05, 22.77it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 23.24it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 23.02it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:04, 25.18it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 14.20it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 15.72it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 17.27it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 17.88it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 19.12it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 18.82it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 19.24it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 20.93it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 21.58it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:04, 21.04it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 19.69it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 15.69it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 15.99it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.64it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.33it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 18.54it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 17.53it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 20.14it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:03, 20.31it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:03, 19.49it/s]
eval model::  89%|████████▊ | 443/500 [00:23<00:02, 19.60it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 19.09it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 19.20it/s]
eval model::  90%|████████▉ | 449/500 [00:23<00:02, 18.51it/s]
eval model::  90%|█████████ | 452/500 [00:23<00:02, 21.46it/s]
eval model::  91%|█████████ | 455/500 [00:24<00:01, 23.74it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 25.86it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:01, 20.81it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:01, 18.52it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 19.24it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.07it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.13it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 19.19it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:01, 19.86it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 21.17it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 20.55it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 21.14it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 19.51it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 20.12it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 23.40it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.01it/s]
[2024/07/27 14:54:50] ppocr INFO: cur metric, precision: 0.7411194833153929, recall: 0.6629754453538758, hmean: 0.6998729351969504, fps: 45.64836031633566
[2024/07/27 14:54:50] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 14:54:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:55:00] ppocr INFO: epoch: [1161/1500], global_step: 3483, lr: 0.001000, loss: 1.221597, loss_shrink_maps: 0.619819, loss_threshold_maps: 0.468756, loss_binary_maps: 0.122282, avg_reader_cost: 2.30291 s, avg_batch_cost: 2.74029 s, avg_samples: 12.5, ips: 4.56156 samples/s, eta: 2:26:57
[2024/07/27 14:55:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:55:09] ppocr INFO: epoch: [1162/1500], global_step: 3486, lr: 0.001000, loss: 1.215049, loss_shrink_maps: 0.614866, loss_threshold_maps: 0.472882, loss_binary_maps: 0.121981, avg_reader_cost: 2.04987 s, avg_batch_cost: 2.32192 s, avg_samples: 12.5, ips: 5.38348 samples/s, eta: 2:26:31
[2024/07/27 14:55:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:55:19] ppocr INFO: epoch: [1163/1500], global_step: 3489, lr: 0.001000, loss: 1.230917, loss_shrink_maps: 0.620438, loss_threshold_maps: 0.472637, loss_binary_maps: 0.122353, avg_reader_cost: 2.26314 s, avg_batch_cost: 2.50030 s, avg_samples: 12.5, ips: 4.99939 samples/s, eta: 2:26:04
[2024/07/27 14:55:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:55:27] ppocr INFO: epoch: [1164/1500], global_step: 3490, lr: 0.001000, loss: 1.240201, loss_shrink_maps: 0.627338, loss_threshold_maps: 0.479509, loss_binary_maps: 0.124112, avg_reader_cost: 0.56444 s, avg_batch_cost: 0.78627 s, avg_samples: 4.8, ips: 6.10474 samples/s, eta: 2:25:55
[2024/07/27 14:55:29] ppocr INFO: epoch: [1164/1500], global_step: 3492, lr: 0.001000, loss: 1.230917, loss_shrink_maps: 0.620438, loss_threshold_maps: 0.472637, loss_binary_maps: 0.122353, avg_reader_cost: 1.66496 s, avg_batch_cost: 1.81138 s, avg_samples: 7.7, ips: 4.25091 samples/s, eta: 2:25:38
[2024/07/27 14:55:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:55:38] ppocr INFO: epoch: [1165/1500], global_step: 3495, lr: 0.001000, loss: 1.230917, loss_shrink_maps: 0.620438, loss_threshold_maps: 0.470897, loss_binary_maps: 0.122353, avg_reader_cost: 2.22551 s, avg_batch_cost: 2.59153 s, avg_samples: 12.5, ips: 4.82340 samples/s, eta: 2:25:12
[2024/07/27 14:55:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:55:48] ppocr INFO: epoch: [1166/1500], global_step: 3498, lr: 0.001000, loss: 1.218295, loss_shrink_maps: 0.610740, loss_threshold_maps: 0.470897, loss_binary_maps: 0.121235, avg_reader_cost: 2.37917 s, avg_batch_cost: 2.61894 s, avg_samples: 12.5, ips: 4.77293 samples/s, eta: 2:24:46
[2024/07/27 14:55:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:55:58] ppocr INFO: epoch: [1167/1500], global_step: 3500, lr: 0.001000, loss: 1.221869, loss_shrink_maps: 0.614866, loss_threshold_maps: 0.473131, loss_binary_maps: 0.121981, avg_reader_cost: 1.35289 s, avg_batch_cost: 1.68486 s, avg_samples: 9.6, ips: 5.69782 samples/s, eta: 2:24:29
[2024/07/27 14:55:58] ppocr INFO: epoch: [1167/1500], global_step: 3501, lr: 0.001000, loss: 1.218295, loss_shrink_maps: 0.610740, loss_threshold_maps: 0.470897, loss_binary_maps: 0.121235, avg_reader_cost: 0.88834 s, avg_batch_cost: 0.94392 s, avg_samples: 2.9, ips: 3.07231 samples/s, eta: 2:24:20
[2024/07/27 14:55:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:56:08] ppocr INFO: epoch: [1168/1500], global_step: 3504, lr: 0.001000, loss: 1.210720, loss_shrink_maps: 0.608897, loss_threshold_maps: 0.467809, loss_binary_maps: 0.119855, avg_reader_cost: 2.21930 s, avg_batch_cost: 2.62186 s, avg_samples: 12.5, ips: 4.76760 samples/s, eta: 2:23:54
[2024/07/27 14:56:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:56:18] ppocr INFO: epoch: [1169/1500], global_step: 3507, lr: 0.001000, loss: 1.143430, loss_shrink_maps: 0.566532, loss_threshold_maps: 0.454691, loss_binary_maps: 0.112364, avg_reader_cost: 2.16675 s, avg_batch_cost: 2.51481 s, avg_samples: 12.5, ips: 4.97055 samples/s, eta: 2:23:28
[2024/07/27 14:56:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:56:27] ppocr INFO: epoch: [1170/1500], global_step: 3510, lr: 0.001000, loss: 1.179922, loss_shrink_maps: 0.582749, loss_threshold_maps: 0.453216, loss_binary_maps: 0.116047, avg_reader_cost: 2.29149 s, avg_batch_cost: 2.52538 s, avg_samples: 12.5, ips: 4.94975 samples/s, eta: 2:23:02
[2024/07/27 14:56:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:56:37] ppocr INFO: epoch: [1171/1500], global_step: 3513, lr: 0.001000, loss: 1.195772, loss_shrink_maps: 0.593594, loss_threshold_maps: 0.468303, loss_binary_maps: 0.118283, avg_reader_cost: 2.18891 s, avg_batch_cost: 2.55239 s, avg_samples: 12.5, ips: 4.89737 samples/s, eta: 2:22:36
[2024/07/27 14:56:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:56:47] ppocr INFO: epoch: [1172/1500], global_step: 3516, lr: 0.001000, loss: 1.149080, loss_shrink_maps: 0.573060, loss_threshold_maps: 0.449105, loss_binary_maps: 0.113401, avg_reader_cost: 2.41311 s, avg_batch_cost: 2.66090 s, avg_samples: 12.5, ips: 4.69766 samples/s, eta: 2:22:10
[2024/07/27 14:56:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:56:57] ppocr INFO: epoch: [1173/1500], global_step: 3519, lr: 0.001000, loss: 1.149080, loss_shrink_maps: 0.573060, loss_threshold_maps: 0.453155, loss_binary_maps: 0.113401, avg_reader_cost: 2.16251 s, avg_batch_cost: 2.50924 s, avg_samples: 12.5, ips: 4.98160 samples/s, eta: 2:21:44
[2024/07/27 14:56:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:57:05] ppocr INFO: epoch: [1174/1500], global_step: 3520, lr: 0.001000, loss: 1.149080, loss_shrink_maps: 0.573060, loss_threshold_maps: 0.453155, loss_binary_maps: 0.113401, avg_reader_cost: 0.54710 s, avg_batch_cost: 0.77087 s, avg_samples: 4.8, ips: 6.22671 samples/s, eta: 2:21:35
[2024/07/27 14:57:06] ppocr INFO: epoch: [1174/1500], global_step: 3522, lr: 0.001000, loss: 1.162063, loss_shrink_maps: 0.578004, loss_threshold_maps: 0.461120, loss_binary_maps: 0.115263, avg_reader_cost: 1.63377 s, avg_batch_cost: 1.78117 s, avg_samples: 7.7, ips: 4.32300 samples/s, eta: 2:21:17
[2024/07/27 14:57:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:57:16] ppocr INFO: epoch: [1175/1500], global_step: 3525, lr: 0.001000, loss: 1.163071, loss_shrink_maps: 0.581714, loss_threshold_maps: 0.468630, loss_binary_maps: 0.115925, avg_reader_cost: 2.34502 s, avg_batch_cost: 2.61212 s, avg_samples: 12.5, ips: 4.78539 samples/s, eta: 2:20:52
[2024/07/27 14:57:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:57:26] ppocr INFO: epoch: [1176/1500], global_step: 3528, lr: 0.001000, loss: 1.195772, loss_shrink_maps: 0.593594, loss_threshold_maps: 0.469673, loss_binary_maps: 0.118283, avg_reader_cost: 2.19542 s, avg_batch_cost: 2.52310 s, avg_samples: 12.5, ips: 4.95423 samples/s, eta: 2:20:25
[2024/07/27 14:57:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:57:35] ppocr INFO: epoch: [1177/1500], global_step: 3530, lr: 0.001000, loss: 1.178922, loss_shrink_maps: 0.588849, loss_threshold_maps: 0.465298, loss_binary_maps: 0.117499, avg_reader_cost: 1.29624 s, avg_batch_cost: 1.56514 s, avg_samples: 9.6, ips: 6.13364 samples/s, eta: 2:20:07
[2024/07/27 14:57:35] ppocr INFO: epoch: [1177/1500], global_step: 3531, lr: 0.001000, loss: 1.178922, loss_shrink_maps: 0.588849, loss_threshold_maps: 0.465298, loss_binary_maps: 0.117499, avg_reader_cost: 0.82858 s, avg_batch_cost: 0.88406 s, avg_samples: 2.9, ips: 3.28030 samples/s, eta: 2:19:59
[2024/07/27 14:57:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:57:45] ppocr INFO: epoch: [1178/1500], global_step: 3534, lr: 0.001000, loss: 1.215057, loss_shrink_maps: 0.614903, loss_threshold_maps: 0.461808, loss_binary_maps: 0.122776, avg_reader_cost: 2.21319 s, avg_batch_cost: 2.45286 s, avg_samples: 12.5, ips: 5.09610 samples/s, eta: 2:19:32
[2024/07/27 14:57:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:57:55] ppocr INFO: epoch: [1179/1500], global_step: 3537, lr: 0.001000, loss: 1.222784, loss_shrink_maps: 0.625365, loss_threshold_maps: 0.471665, loss_binary_maps: 0.124115, avg_reader_cost: 2.20228 s, avg_batch_cost: 2.55545 s, avg_samples: 12.5, ips: 4.89150 samples/s, eta: 2:19:06
[2024/07/27 14:57:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:58:04] ppocr INFO: epoch: [1180/1500], global_step: 3540, lr: 0.001000, loss: 1.215057, loss_shrink_maps: 0.605815, loss_threshold_maps: 0.465298, loss_binary_maps: 0.121168, avg_reader_cost: 2.30658 s, avg_batch_cost: 2.54397 s, avg_samples: 12.5, ips: 4.91357 samples/s, eta: 2:18:40

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.74it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.43it/s]
eval model::   1%|          | 6/500 [00:00<00:30, 16.23it/s]
eval model::   2%|▏         | 8/500 [00:00<00:31, 15.87it/s]
eval model::   2%|▏         | 11/500 [00:00<00:29, 16.38it/s]
eval model::   3%|▎         | 13/500 [00:00<00:29, 16.75it/s]
eval model::   3%|▎         | 15/500 [00:00<00:28, 17.21it/s]
eval model::   4%|▎         | 18/500 [00:01<00:24, 19.45it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.37it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 22.27it/s]
eval model::   5%|▌         | 26/500 [00:01<00:21, 22.44it/s]
eval model::   6%|▌         | 29/500 [00:01<00:19, 23.81it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 19.82it/s]
eval model::   7%|▋         | 35/500 [00:01<00:22, 20.52it/s]
eval model::   8%|▊         | 38/500 [00:01<00:22, 20.78it/s]
eval model::   8%|▊         | 41/500 [00:02<00:22, 20.53it/s]
eval model::   9%|▉         | 44/500 [00:02<00:24, 18.83it/s]
eval model::   9%|▉         | 46/500 [00:02<00:26, 17.27it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 19.03it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 19.91it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 20.25it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 18.12it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.32it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.45it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:20, 20.69it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 22.50it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 21.48it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.47it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:18, 22.72it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.00it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 21.69it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.24it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:23, 17.40it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:21, 19.18it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:19, 20.69it/s]
eval model::  20%|██        | 100/500 [00:05<00:19, 20.42it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 21.27it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 20.24it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:19, 20.15it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 20.54it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:20, 18.67it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:19, 19.92it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:20, 18.76it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.65it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 18.90it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 20.74it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.91it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:17, 20.43it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.66it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.52it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.92it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.16it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.94it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 18.85it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 21.20it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:17, 19.10it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:23, 14.68it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:21, 15.43it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:20, 16.53it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:17, 18.42it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:17, 18.29it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:18, 17.54it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:17, 18.80it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:19, 16.80it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 18.95it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 20.67it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.91it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:13, 22.43it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 21.24it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:15, 19.40it/s]
eval model::  40%|████      | 201/500 [00:10<00:15, 19.62it/s]
eval model::  41%|████      | 205/500 [00:10<00:18, 15.58it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:18, 16.13it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 15.93it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:16, 17.01it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.41it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.90it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:18, 15.10it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:19, 14.60it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:16, 16.89it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.52it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:13, 19.68it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 21.85it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 20.95it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 21.89it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 21.76it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:12, 19.98it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:12, 20.55it/s]
eval model::  50%|█████     | 250/500 [00:13<00:12, 19.40it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 21.11it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 20.39it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 21.96it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 20.68it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:12, 19.30it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:11, 20.69it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:11, 20.39it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:10, 21.39it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 17.39it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:12, 17.11it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 19.01it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:19, 11.04it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:15, 13.32it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 16.83it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:14, 14.40it/s]
eval model::  60%|█████▉    | 298/500 [00:15<00:12, 16.19it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 16.02it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.82it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 18.20it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 19.65it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 20.78it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 20.45it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 21.75it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 21.00it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 20.52it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 21.07it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 19.93it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 21.54it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 23.35it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 20.87it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 15.58it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 17.32it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 18.79it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 20.60it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 22.06it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 22.74it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:06, 20.61it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:06, 20.57it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:06, 20.32it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 21.54it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 20.32it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 21.28it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 22.48it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:06, 18.65it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:05, 20.43it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:09, 12.10it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:07, 14.02it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:06, 15.44it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 17.94it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 18.76it/s]
eval model::  81%|████████  | 403/500 [00:21<00:04, 19.68it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 21.00it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 21.67it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:03, 23.24it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:03, 22.71it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 19.29it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:05, 14.17it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 15.12it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.47it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 18.83it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 19.91it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:02, 21.70it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 22.92it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 20.61it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 21.15it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 21.91it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 22.58it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:01, 24.10it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 24.89it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 21.74it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 17.98it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.96it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.49it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 19.55it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 18.88it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.45it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 22.06it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 22.03it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 20.31it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 21.59it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 20.36it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 20.67it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 23.50it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.15it/s]
[2024/07/27 14:58:30] ppocr INFO: cur metric, precision: 0.7855946398659966, recall: 0.6774193548387096, hmean: 0.7275077559462254, fps: 45.654781000070706
[2024/07/27 14:58:30] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 14:58:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:58:40] ppocr INFO: epoch: [1181/1500], global_step: 3543, lr: 0.001000, loss: 1.204971, loss_shrink_maps: 0.604840, loss_threshold_maps: 0.462821, loss_binary_maps: 0.120493, avg_reader_cost: 2.32559 s, avg_batch_cost: 2.61847 s, avg_samples: 12.5, ips: 4.77379 samples/s, eta: 2:18:14
[2024/07/27 14:58:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:58:50] ppocr INFO: epoch: [1182/1500], global_step: 3546, lr: 0.001000, loss: 1.191955, loss_shrink_maps: 0.604365, loss_threshold_maps: 0.460122, loss_binary_maps: 0.119488, avg_reader_cost: 2.33673 s, avg_batch_cost: 2.59705 s, avg_samples: 12.5, ips: 4.81316 samples/s, eta: 2:17:48
[2024/07/27 14:58:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:58:59] ppocr INFO: epoch: [1183/1500], global_step: 3549, lr: 0.001000, loss: 1.157947, loss_shrink_maps: 0.580123, loss_threshold_maps: 0.454594, loss_binary_maps: 0.115127, avg_reader_cost: 2.10728 s, avg_batch_cost: 2.44047 s, avg_samples: 12.5, ips: 5.12197 samples/s, eta: 2:17:22
[2024/07/27 14:59:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:59:07] ppocr INFO: epoch: [1184/1500], global_step: 3550, lr: 0.001000, loss: 1.157947, loss_shrink_maps: 0.580123, loss_threshold_maps: 0.454594, loss_binary_maps: 0.115127, avg_reader_cost: 0.57165 s, avg_batch_cost: 0.77401 s, avg_samples: 4.8, ips: 6.20146 samples/s, eta: 2:17:13
[2024/07/27 14:59:09] ppocr INFO: epoch: [1184/1500], global_step: 3552, lr: 0.001000, loss: 1.157426, loss_shrink_maps: 0.574250, loss_threshold_maps: 0.454594, loss_binary_maps: 0.113846, avg_reader_cost: 1.64013 s, avg_batch_cost: 1.78628 s, avg_samples: 7.7, ips: 4.31063 samples/s, eta: 2:16:56
[2024/07/27 14:59:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:59:19] ppocr INFO: epoch: [1185/1500], global_step: 3555, lr: 0.001000, loss: 1.126954, loss_shrink_maps: 0.565983, loss_threshold_maps: 0.449193, loss_binary_maps: 0.112669, avg_reader_cost: 2.47593 s, avg_batch_cost: 2.72025 s, avg_samples: 12.5, ips: 4.59516 samples/s, eta: 2:16:30
[2024/07/27 14:59:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:59:29] ppocr INFO: epoch: [1186/1500], global_step: 3558, lr: 0.001000, loss: 1.130230, loss_shrink_maps: 0.571662, loss_threshold_maps: 0.452653, loss_binary_maps: 0.113458, avg_reader_cost: 2.17190 s, avg_batch_cost: 2.51301 s, avg_samples: 12.5, ips: 4.97412 samples/s, eta: 2:16:04
[2024/07/27 14:59:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:59:38] ppocr INFO: epoch: [1187/1500], global_step: 3560, lr: 0.001000, loss: 1.130230, loss_shrink_maps: 0.571662, loss_threshold_maps: 0.449208, loss_binary_maps: 0.113458, avg_reader_cost: 1.47642 s, avg_batch_cost: 1.66256 s, avg_samples: 9.6, ips: 5.77421 samples/s, eta: 2:15:46
[2024/07/27 14:59:39] ppocr INFO: epoch: [1187/1500], global_step: 3561, lr: 0.001000, loss: 1.119740, loss_shrink_maps: 0.565983, loss_threshold_maps: 0.445010, loss_binary_maps: 0.112669, avg_reader_cost: 0.87734 s, avg_batch_cost: 0.93281 s, avg_samples: 2.9, ips: 3.10890 samples/s, eta: 2:15:38
[2024/07/27 14:59:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:59:48] ppocr INFO: epoch: [1188/1500], global_step: 3564, lr: 0.001000, loss: 1.130230, loss_shrink_maps: 0.571662, loss_threshold_maps: 0.455035, loss_binary_maps: 0.113378, avg_reader_cost: 2.15236 s, avg_batch_cost: 2.52648 s, avg_samples: 12.5, ips: 4.94760 samples/s, eta: 2:15:12
[2024/07/27 14:59:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 14:59:58] ppocr INFO: epoch: [1189/1500], global_step: 3567, lr: 0.001000, loss: 1.173868, loss_shrink_maps: 0.583312, loss_threshold_maps: 0.456752, loss_binary_maps: 0.115200, avg_reader_cost: 2.17176 s, avg_batch_cost: 2.49879 s, avg_samples: 12.5, ips: 5.00242 samples/s, eta: 2:14:45
[2024/07/27 14:59:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:00:07] ppocr INFO: epoch: [1190/1500], global_step: 3570, lr: 0.001000, loss: 1.193723, loss_shrink_maps: 0.597397, loss_threshold_maps: 0.470643, loss_binary_maps: 0.117921, avg_reader_cost: 2.29836 s, avg_batch_cost: 2.53645 s, avg_samples: 12.5, ips: 4.92814 samples/s, eta: 2:14:19
[2024/07/27 15:00:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:00:17] ppocr INFO: epoch: [1191/1500], global_step: 3573, lr: 0.001000, loss: 1.197404, loss_shrink_maps: 0.603180, loss_threshold_maps: 0.470643, loss_binary_maps: 0.119666, avg_reader_cost: 2.15392 s, avg_batch_cost: 2.49461 s, avg_samples: 12.5, ips: 5.01080 samples/s, eta: 2:13:53
[2024/07/27 15:00:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:00:26] ppocr INFO: epoch: [1192/1500], global_step: 3576, lr: 0.001000, loss: 1.214568, loss_shrink_maps: 0.617075, loss_threshold_maps: 0.488024, loss_binary_maps: 0.122919, avg_reader_cost: 2.14085 s, avg_batch_cost: 2.50785 s, avg_samples: 12.5, ips: 4.98435 samples/s, eta: 2:13:27
[2024/07/27 15:00:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:00:36] ppocr INFO: epoch: [1193/1500], global_step: 3579, lr: 0.001000, loss: 1.193723, loss_shrink_maps: 0.603180, loss_threshold_maps: 0.467216, loss_binary_maps: 0.119643, avg_reader_cost: 2.23652 s, avg_batch_cost: 2.57706 s, avg_samples: 12.5, ips: 4.85050 samples/s, eta: 2:13:01
[2024/07/27 15:00:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:00:44] ppocr INFO: epoch: [1194/1500], global_step: 3580, lr: 0.001000, loss: 1.187792, loss_shrink_maps: 0.594519, loss_threshold_maps: 0.454465, loss_binary_maps: 0.117786, avg_reader_cost: 0.66475 s, avg_batch_cost: 0.75108 s, avg_samples: 4.8, ips: 6.39076 samples/s, eta: 2:12:52
[2024/07/27 15:00:46] ppocr INFO: epoch: [1194/1500], global_step: 3582, lr: 0.001000, loss: 1.187792, loss_shrink_maps: 0.594519, loss_threshold_maps: 0.454465, loss_binary_maps: 0.117786, avg_reader_cost: 1.59417 s, avg_batch_cost: 1.74133 s, avg_samples: 7.7, ips: 4.42191 samples/s, eta: 2:12:34
[2024/07/27 15:00:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:00:55] ppocr INFO: epoch: [1195/1500], global_step: 3585, lr: 0.001000, loss: 1.178772, loss_shrink_maps: 0.594519, loss_threshold_maps: 0.453670, loss_binary_maps: 0.117786, avg_reader_cost: 2.16798 s, avg_batch_cost: 2.49098 s, avg_samples: 12.5, ips: 5.01810 samples/s, eta: 2:12:08
[2024/07/27 15:00:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:01:05] ppocr INFO: epoch: [1196/1500], global_step: 3588, lr: 0.001000, loss: 1.191473, loss_shrink_maps: 0.604007, loss_threshold_maps: 0.460974, loss_binary_maps: 0.120250, avg_reader_cost: 2.32998 s, avg_batch_cost: 2.57251 s, avg_samples: 12.5, ips: 4.85906 samples/s, eta: 2:11:42
[2024/07/27 15:01:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:01:14] ppocr INFO: epoch: [1197/1500], global_step: 3590, lr: 0.001000, loss: 1.140346, loss_shrink_maps: 0.581566, loss_threshold_maps: 0.452975, loss_binary_maps: 0.115737, avg_reader_cost: 1.50069 s, avg_batch_cost: 1.68830 s, avg_samples: 9.6, ips: 5.68620 samples/s, eta: 2:11:25
[2024/07/27 15:01:15] ppocr INFO: epoch: [1197/1500], global_step: 3591, lr: 0.001000, loss: 1.140346, loss_shrink_maps: 0.581566, loss_threshold_maps: 0.458954, loss_binary_maps: 0.115737, avg_reader_cost: 0.89034 s, avg_batch_cost: 0.94586 s, avg_samples: 2.9, ips: 3.06599 samples/s, eta: 2:11:16
[2024/07/27 15:01:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:01:25] ppocr INFO: epoch: [1198/1500], global_step: 3594, lr: 0.001000, loss: 1.202980, loss_shrink_maps: 0.611198, loss_threshold_maps: 0.470199, loss_binary_maps: 0.121389, avg_reader_cost: 2.37002 s, avg_batch_cost: 2.60921 s, avg_samples: 12.5, ips: 4.79071 samples/s, eta: 2:10:50
[2024/07/27 15:01:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:01:35] ppocr INFO: epoch: [1199/1500], global_step: 3597, lr: 0.001000, loss: 1.181874, loss_shrink_maps: 0.591022, loss_threshold_maps: 0.468457, loss_binary_maps: 0.117725, avg_reader_cost: 2.23910 s, avg_batch_cost: 2.62336 s, avg_samples: 12.5, ips: 4.76489 samples/s, eta: 2:10:24
[2024/07/27 15:01:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:01:44] ppocr INFO: epoch: [1200/1500], global_step: 3600, lr: 0.001000, loss: 1.202980, loss_shrink_maps: 0.611198, loss_threshold_maps: 0.470723, loss_binary_maps: 0.121389, avg_reader_cost: 2.20313 s, avg_batch_cost: 2.51728 s, avg_samples: 12.5, ips: 4.96568 samples/s, eta: 2:09:58

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:55,  9.00it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 17.79it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.10it/s]
eval model::   2%|▏         | 8/500 [00:00<00:26, 18.48it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.23it/s]
eval model::   3%|▎         | 13/500 [00:00<00:28, 17.05it/s]
eval model::   3%|▎         | 15/500 [00:00<00:29, 16.53it/s]
eval model::   4%|▎         | 18/500 [00:01<00:25, 18.88it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 19.03it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 21.08it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 20.38it/s]
eval model::   6%|▌         | 30/500 [00:01<00:19, 23.63it/s]
eval model::   7%|▋         | 33/500 [00:01<00:22, 20.70it/s]
eval model::   7%|▋         | 36/500 [00:01<00:22, 20.64it/s]
eval model::   8%|▊         | 39/500 [00:02<00:24, 18.89it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.52it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 19.89it/s]
eval model::  10%|▉         | 48/500 [00:02<00:26, 17.22it/s]
eval model::  10%|█         | 50/500 [00:02<00:25, 17.53it/s]
eval model::  11%|█         | 53/500 [00:02<00:23, 19.33it/s]
eval model::  11%|█         | 56/500 [00:02<00:21, 20.38it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:23, 18.69it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.59it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.35it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.41it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.13it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 21.87it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:21, 20.13it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.21it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.64it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.25it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.21it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.22it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:22, 18.50it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:20, 19.51it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:19, 20.30it/s]
eval model::  20%|██        | 101/500 [00:05<00:20, 19.05it/s]
eval model::  21%|██        | 104/500 [00:05<00:19, 20.69it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.34it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:19, 20.17it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:19, 20.23it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:19, 19.35it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:18, 20.50it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:19, 19.07it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:21, 17.26it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:23, 16.22it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 18.68it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:20, 17.64it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:20, 18.04it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:19, 18.80it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:19, 18.65it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:19, 18.59it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 20.93it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 20.12it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:17, 19.75it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 18.21it/s]
eval model::  31%|███       | 155/500 [00:08<00:15, 21.59it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.42it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:19, 16.97it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:17, 19.06it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 19.55it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:15, 21.08it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:16, 20.02it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.90it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.61it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.75it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 18.29it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 18.54it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:16, 18.63it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:15, 19.82it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:16, 18.82it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 20.23it/s]
eval model::  40%|████      | 202/500 [00:10<00:15, 19.35it/s]
eval model::  41%|████      | 205/500 [00:10<00:19, 14.84it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:18, 15.61it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:18, 15.74it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 14.53it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.81it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.62it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.82it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.21it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:18, 15.12it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:14, 19.03it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.67it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.70it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 20.53it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:12, 21.64it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:14, 17.72it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:13, 19.32it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 20.29it/s]
eval model::  50%|█████     | 251/500 [00:13<00:12, 20.63it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.74it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.87it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 23.58it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:09, 24.14it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:10, 22.53it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 20.47it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:10, 21.17it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.32it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 21.34it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.19it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 19.79it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:19, 10.95it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:14, 14.35it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:13, 14.94it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:16, 12.63it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:13, 14.75it/s]
eval model::  60%|██████    | 301/500 [00:16<00:13, 14.32it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 16.29it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.30it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 19.19it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 19.91it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:09, 20.05it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 22.12it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.74it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:07, 22.06it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 21.13it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:07, 21.66it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 23.61it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:07, 23.05it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 15.94it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:08, 17.91it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:07, 20.61it/s]
eval model::  70%|███████   | 350/500 [00:18<00:06, 22.52it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 23.33it/s]
eval model::  71%|███████   | 356/500 [00:18<00:06, 22.63it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:06, 23.45it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:06, 19.82it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 18.57it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 17.17it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 18.61it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 18.62it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 20.36it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 23.60it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 22.55it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:07, 14.42it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 16.50it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 17.76it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 18.61it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:04, 20.52it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 19.41it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 20.71it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 20.41it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 21.36it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:04, 19.86it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 19.38it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 19.03it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.39it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 15.06it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 14.90it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.79it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 18.29it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:04, 17.10it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 20.63it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 22.45it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 22.15it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 23.88it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 22.58it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 23.23it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:01, 24.34it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:01, 26.32it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 24.41it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:01, 18.11it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 18.76it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 21.72it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.60it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.65it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 20.60it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 21.77it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 22.99it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 23.98it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 21.25it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 20.89it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 22.68it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.20it/s]
[2024/07/27 15:02:10] ppocr INFO: cur metric, precision: 0.7494703389830508, recall: 0.6812710640346654, hmean: 0.7137452711223202, fps: 44.90074549960995
[2024/07/27 15:02:10] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:02:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:02:11] ppocr INFO: save model in ./output/db_mv3/iter_epoch_1200
[2024/07/27 15:02:21] ppocr INFO: epoch: [1201/1500], global_step: 3603, lr: 0.001000, loss: 1.210829, loss_shrink_maps: 0.620955, loss_threshold_maps: 0.470199, loss_binary_maps: 0.123012, avg_reader_cost: 2.41219 s, avg_batch_cost: 2.79327 s, avg_samples: 12.5, ips: 4.47505 samples/s, eta: 2:09:32
[2024/07/27 15:02:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:02:30] ppocr INFO: epoch: [1202/1500], global_step: 3606, lr: 0.001000, loss: 1.210829, loss_shrink_maps: 0.620955, loss_threshold_maps: 0.470723, loss_binary_maps: 0.123012, avg_reader_cost: 2.05176 s, avg_batch_cost: 2.46461 s, avg_samples: 12.5, ips: 5.07180 samples/s, eta: 2:09:06
[2024/07/27 15:02:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:02:40] ppocr INFO: epoch: [1203/1500], global_step: 3609, lr: 0.001000, loss: 1.222667, loss_shrink_maps: 0.618954, loss_threshold_maps: 0.475583, loss_binary_maps: 0.123294, avg_reader_cost: 2.22188 s, avg_batch_cost: 2.60271 s, avg_samples: 12.5, ips: 4.80269 samples/s, eta: 2:08:40
[2024/07/27 15:02:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:02:48] ppocr INFO: epoch: [1204/1500], global_step: 3610, lr: 0.001000, loss: 1.222667, loss_shrink_maps: 0.618954, loss_threshold_maps: 0.475583, loss_binary_maps: 0.123294, avg_reader_cost: 0.56826 s, avg_batch_cost: 0.77787 s, avg_samples: 4.8, ips: 6.17066 samples/s, eta: 2:08:31
[2024/07/27 15:02:50] ppocr INFO: epoch: [1204/1500], global_step: 3612, lr: 0.001000, loss: 1.211168, loss_shrink_maps: 0.610592, loss_threshold_maps: 0.472748, loss_binary_maps: 0.121710, avg_reader_cost: 1.64792 s, avg_batch_cost: 1.79527 s, avg_samples: 7.7, ips: 4.28904 samples/s, eta: 2:08:14
[2024/07/27 15:02:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:03:00] ppocr INFO: epoch: [1205/1500], global_step: 3615, lr: 0.001000, loss: 1.187751, loss_shrink_maps: 0.595964, loss_threshold_maps: 0.468980, loss_binary_maps: 0.118608, avg_reader_cost: 2.23917 s, avg_batch_cost: 2.57881 s, avg_samples: 12.5, ips: 4.84720 samples/s, eta: 2:07:48
[2024/07/27 15:03:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:03:10] ppocr INFO: epoch: [1206/1500], global_step: 3618, lr: 0.001000, loss: 1.177054, loss_shrink_maps: 0.597902, loss_threshold_maps: 0.455270, loss_binary_maps: 0.118414, avg_reader_cost: 2.44212 s, avg_batch_cost: 2.68151 s, avg_samples: 12.5, ips: 4.66156 samples/s, eta: 2:07:22
[2024/07/27 15:03:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:03:19] ppocr INFO: epoch: [1207/1500], global_step: 3620, lr: 0.001000, loss: 1.167846, loss_shrink_maps: 0.597902, loss_threshold_maps: 0.459313, loss_binary_maps: 0.118414, avg_reader_cost: 1.52385 s, avg_batch_cost: 1.70953 s, avg_samples: 9.6, ips: 5.61557 samples/s, eta: 2:07:05
[2024/07/27 15:03:20] ppocr INFO: epoch: [1207/1500], global_step: 3621, lr: 0.001000, loss: 1.170976, loss_shrink_maps: 0.597902, loss_threshold_maps: 0.472856, loss_binary_maps: 0.118414, avg_reader_cost: 0.90086 s, avg_batch_cost: 0.95626 s, avg_samples: 2.9, ips: 3.03265 samples/s, eta: 2:06:56
[2024/07/27 15:03:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:03:29] ppocr INFO: epoch: [1208/1500], global_step: 3624, lr: 0.001000, loss: 1.155033, loss_shrink_maps: 0.595234, loss_threshold_maps: 0.472856, loss_binary_maps: 0.118414, avg_reader_cost: 2.16576 s, avg_batch_cost: 2.50840 s, avg_samples: 12.5, ips: 4.98326 samples/s, eta: 2:06:30
[2024/07/27 15:03:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:03:39] ppocr INFO: epoch: [1209/1500], global_step: 3627, lr: 0.001000, loss: 1.155033, loss_shrink_maps: 0.592731, loss_threshold_maps: 0.471393, loss_binary_maps: 0.117763, avg_reader_cost: 2.21408 s, avg_batch_cost: 2.58737 s, avg_samples: 12.5, ips: 4.83116 samples/s, eta: 2:06:04
[2024/07/27 15:03:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:03:49] ppocr INFO: epoch: [1210/1500], global_step: 3630, lr: 0.001000, loss: 1.167582, loss_shrink_maps: 0.594368, loss_threshold_maps: 0.468852, loss_binary_maps: 0.118266, avg_reader_cost: 2.29232 s, avg_batch_cost: 2.68161 s, avg_samples: 12.5, ips: 4.66138 samples/s, eta: 2:05:38
[2024/07/27 15:03:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:03:59] ppocr INFO: epoch: [1211/1500], global_step: 3633, lr: 0.001000, loss: 1.179579, loss_shrink_maps: 0.595454, loss_threshold_maps: 0.471393, loss_binary_maps: 0.118527, avg_reader_cost: 2.25193 s, avg_batch_cost: 2.66165 s, avg_samples: 12.5, ips: 4.69634 samples/s, eta: 2:05:12
[2024/07/27 15:04:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:04:09] ppocr INFO: epoch: [1212/1500], global_step: 3636, lr: 0.001000, loss: 1.182974, loss_shrink_maps: 0.595454, loss_threshold_maps: 0.475581, loss_binary_maps: 0.118578, avg_reader_cost: 2.30269 s, avg_batch_cost: 2.70852 s, avg_samples: 12.5, ips: 4.61506 samples/s, eta: 2:04:47
[2024/07/27 15:04:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:04:19] ppocr INFO: epoch: [1213/1500], global_step: 3639, lr: 0.001000, loss: 1.202208, loss_shrink_maps: 0.602948, loss_threshold_maps: 0.479162, loss_binary_maps: 0.119890, avg_reader_cost: 2.15135 s, avg_batch_cost: 2.51577 s, avg_samples: 12.5, ips: 4.96865 samples/s, eta: 2:04:21
[2024/07/27 15:04:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:04:27] ppocr INFO: epoch: [1214/1500], global_step: 3640, lr: 0.001000, loss: 1.202208, loss_shrink_maps: 0.602948, loss_threshold_maps: 0.477264, loss_binary_maps: 0.119890, avg_reader_cost: 0.51434 s, avg_batch_cost: 0.77805 s, avg_samples: 4.8, ips: 6.16927 samples/s, eta: 2:04:12
[2024/07/27 15:04:28] ppocr INFO: epoch: [1214/1500], global_step: 3642, lr: 0.001000, loss: 1.202208, loss_shrink_maps: 0.602948, loss_threshold_maps: 0.475152, loss_binary_maps: 0.119690, avg_reader_cost: 1.64752 s, avg_batch_cost: 1.79442 s, avg_samples: 7.7, ips: 4.29108 samples/s, eta: 2:03:54
[2024/07/27 15:04:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:04:38] ppocr INFO: epoch: [1215/1500], global_step: 3645, lr: 0.001000, loss: 1.179579, loss_shrink_maps: 0.592952, loss_threshold_maps: 0.470928, loss_binary_maps: 0.117875, avg_reader_cost: 2.31135 s, avg_batch_cost: 2.56093 s, avg_samples: 12.5, ips: 4.88104 samples/s, eta: 2:03:28
[2024/07/27 15:04:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:04:48] ppocr INFO: epoch: [1216/1500], global_step: 3648, lr: 0.001000, loss: 1.124130, loss_shrink_maps: 0.560264, loss_threshold_maps: 0.471785, loss_binary_maps: 0.111871, avg_reader_cost: 2.35028 s, avg_batch_cost: 2.59273 s, avg_samples: 12.5, ips: 4.82117 samples/s, eta: 2:03:02
[2024/07/27 15:04:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:04:57] ppocr INFO: epoch: [1217/1500], global_step: 3650, lr: 0.001000, loss: 1.097090, loss_shrink_maps: 0.556590, loss_threshold_maps: 0.457613, loss_binary_maps: 0.109749, avg_reader_cost: 1.33471 s, avg_batch_cost: 1.63011 s, avg_samples: 9.6, ips: 5.88918 samples/s, eta: 2:02:45
[2024/07/27 15:04:58] ppocr INFO: epoch: [1217/1500], global_step: 3651, lr: 0.001000, loss: 1.091683, loss_shrink_maps: 0.548639, loss_threshold_maps: 0.437388, loss_binary_maps: 0.108379, avg_reader_cost: 0.86148 s, avg_batch_cost: 0.91659 s, avg_samples: 2.9, ips: 3.16389 samples/s, eta: 2:02:36
[2024/07/27 15:04:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:05:07] ppocr INFO: epoch: [1218/1500], global_step: 3654, lr: 0.001000, loss: 1.097090, loss_shrink_maps: 0.556590, loss_threshold_maps: 0.439243, loss_binary_maps: 0.109749, avg_reader_cost: 2.17373 s, avg_batch_cost: 2.60175 s, avg_samples: 12.5, ips: 4.80446 samples/s, eta: 2:02:10
[2024/07/27 15:05:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:05:17] ppocr INFO: epoch: [1219/1500], global_step: 3657, lr: 0.001000, loss: 1.107791, loss_shrink_maps: 0.561658, loss_threshold_maps: 0.440479, loss_binary_maps: 0.112159, avg_reader_cost: 2.25269 s, avg_batch_cost: 2.60128 s, avg_samples: 12.5, ips: 4.80532 samples/s, eta: 2:01:44
[2024/07/27 15:05:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:05:27] ppocr INFO: epoch: [1220/1500], global_step: 3660, lr: 0.001000, loss: 1.118920, loss_shrink_maps: 0.561658, loss_threshold_maps: 0.443556, loss_binary_maps: 0.112100, avg_reader_cost: 2.32840 s, avg_batch_cost: 2.56671 s, avg_samples: 12.5, ips: 4.87005 samples/s, eta: 2:01:18

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:02,  7.96it/s]
eval model::   1%|          | 3/500 [00:00<00:35, 13.81it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.03it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.94it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.57it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.65it/s]
eval model::   4%|▎         | 18/500 [00:00<00:21, 22.51it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 18.97it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.37it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 20.89it/s]
eval model::   6%|▌         | 30/500 [00:01<00:22, 21.14it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 19.47it/s]
eval model::   7%|▋         | 36/500 [00:01<00:25, 18.56it/s]
eval model::   8%|▊         | 39/500 [00:02<00:26, 17.52it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.82it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.31it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 17.66it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.32it/s]
eval model::  11%|█         | 53/500 [00:02<00:22, 19.45it/s]
eval model::  11%|█         | 56/500 [00:02<00:22, 20.12it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:24, 18.09it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.51it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 14.94it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.59it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 19.99it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 22.01it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:18, 23.33it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:17, 24.10it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:16, 24.92it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.84it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 20.61it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.30it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:20, 19.44it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.85it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.80it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.03it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.87it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:19, 20.14it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:22, 17.09it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:21, 17.61it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:21, 17.50it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:22, 16.53it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:21, 17.88it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:23, 16.03it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:20, 18.54it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:20, 17.92it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 17.32it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:19, 19.04it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.15it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:16, 21.16it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.62it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:20, 17.61it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 19.23it/s]
eval model::  31%|███       | 153/500 [00:08<00:18, 19.07it/s]
eval model::  31%|███       | 156/500 [00:08<00:16, 20.65it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 20.09it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:21, 15.57it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:20, 16.12it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:20, 16.52it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:18, 17.89it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:18, 17.30it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 17.09it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.54it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.42it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:18, 17.03it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:16, 19.39it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 20.36it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:15, 20.23it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:15, 19.85it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:16, 18.06it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:15, 19.94it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 21.70it/s]
eval model::  41%|████      | 205/500 [00:11<00:22, 13.03it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:20, 14.34it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 14.56it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.87it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.66it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.38it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:18, 15.20it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:22, 12.42it/s]
eval model::  45%|████▍     | 223/500 [00:12<00:20, 13.51it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:17, 15.74it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:15, 17.66it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:13, 20.06it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:14, 17.74it/s]
eval model::  48%|████▊     | 238/500 [00:13<00:13, 19.20it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:13, 18.77it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:16, 15.55it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:15, 16.00it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:15, 16.64it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:14, 16.81it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 19.38it/s]
eval model::  51%|█████     | 255/500 [00:14<00:12, 19.74it/s]
eval model::  52%|█████▏    | 258/500 [00:14<00:11, 21.72it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:10, 23.75it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:11, 21.42it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 20.60it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 19.40it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 19.46it/s]
eval model::  55%|█████▌    | 275/500 [00:15<00:12, 18.34it/s]
eval model::  56%|█████▌    | 278/500 [00:15<00:10, 20.42it/s]
eval model::  56%|█████▌    | 281/500 [00:15<00:09, 21.95it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:09, 23.33it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:20, 10.62it/s]
eval model::  58%|█████▊    | 290/500 [00:16<00:16, 12.70it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:15, 13.29it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:15, 13.09it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:17, 11.98it/s]
eval model::  60%|██████    | 300/500 [00:16<00:14, 14.02it/s]
eval model::  60%|██████    | 302/500 [00:16<00:13, 14.27it/s]
eval model::  61%|██████    | 304/500 [00:17<00:13, 14.36it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:11, 16.68it/s]
eval model::  62%|██████▏   | 310/500 [00:17<00:10, 18.11it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:09, 19.91it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 20.22it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 21.70it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 19.56it/s]
eval model::  65%|██████▌   | 325/500 [00:18<00:08, 20.26it/s]
eval model::  66%|██████▌   | 328/500 [00:18<00:08, 20.83it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:08, 19.93it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:07, 21.45it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 20.06it/s]
eval model::  68%|██████▊   | 340/500 [00:19<00:11, 14.00it/s]
eval model::  68%|██████▊   | 342/500 [00:19<00:10, 14.85it/s]
eval model::  69%|██████▉   | 345/500 [00:19<00:09, 16.86it/s]
eval model::  69%|██████▉   | 347/500 [00:19<00:09, 16.55it/s]
eval model::  70%|███████   | 350/500 [00:19<00:08, 18.47it/s]
eval model::  71%|███████   | 354/500 [00:19<00:06, 22.02it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 19.38it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:06, 20.26it/s]
eval model::  73%|███████▎  | 363/500 [00:20<00:07, 17.77it/s]
eval model::  73%|███████▎  | 365/500 [00:20<00:07, 16.94it/s]
eval model::  73%|███████▎  | 367/500 [00:20<00:08, 15.58it/s]
eval model::  74%|███████▍  | 370/500 [00:20<00:07, 17.56it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 17.65it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 19.29it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 20.97it/s]
eval model::  76%|███████▌  | 381/500 [00:21<00:06, 19.12it/s]
eval model::  77%|███████▋  | 384/500 [00:21<00:05, 21.47it/s]
eval model::  77%|███████▋  | 387/500 [00:21<00:08, 13.15it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:07, 15.08it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:06, 15.84it/s]
eval model::  79%|███████▉  | 394/500 [00:22<00:06, 16.27it/s]
eval model::  79%|███████▉  | 396/500 [00:22<00:06, 16.51it/s]
eval model::  80%|███████▉  | 399/500 [00:22<00:05, 19.08it/s]
eval model::  80%|████████  | 402/500 [00:22<00:05, 19.18it/s]
eval model::  81%|████████  | 405/500 [00:22<00:04, 21.32it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:04, 22.61it/s]
eval model::  82%|████████▏ | 412/500 [00:22<00:03, 25.46it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:03, 22.90it/s]
eval model::  84%|████████▎ | 418/500 [00:23<00:04, 20.07it/s]
eval model::  84%|████████▍ | 421/500 [00:23<00:05, 13.56it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.34it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:04, 14.82it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 17.12it/s]
eval model::  86%|████████▌ | 431/500 [00:24<00:04, 15.82it/s]
eval model::  87%|████████▋ | 435/500 [00:24<00:03, 18.94it/s]
eval model::  88%|████████▊ | 438/500 [00:24<00:03, 19.73it/s]
eval model::  88%|████████▊ | 441/500 [00:24<00:02, 19.99it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 21.65it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 23.17it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 20.22it/s]
eval model::  91%|█████████ | 453/500 [00:25<00:02, 22.18it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:02, 20.63it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:02, 18.97it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 15.81it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 15.55it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 14.93it/s]
eval model::  94%|█████████▎| 468/500 [00:26<00:02, 15.89it/s]
eval model::  94%|█████████▍| 471/500 [00:26<00:01, 18.06it/s]
eval model::  95%|█████████▍| 474/500 [00:26<00:01, 17.46it/s]
eval model::  95%|█████████▌| 477/500 [00:26<00:01, 19.81it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:00, 21.16it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 21.30it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 20.20it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 21.76it/s]
eval model::  98%|█████████▊| 492/500 [00:27<00:00, 20.12it/s]
eval model::  99%|█████████▉| 495/500 [00:27<00:00, 22.01it/s]
eval model:: 100%|█████████▉| 499/500 [00:27<00:00, 25.99it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.24it/s]
[2024/07/27 15:05:54] ppocr INFO: cur metric, precision: 0.7439664218258132, recall: 0.6827154549831488, hmean: 0.7120261109716295, fps: 43.624911201553495
[2024/07/27 15:05:54] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:05:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:06:03] ppocr INFO: epoch: [1221/1500], global_step: 3663, lr: 0.001000, loss: 1.118920, loss_shrink_maps: 0.561658, loss_threshold_maps: 0.443556, loss_binary_maps: 0.112100, avg_reader_cost: 2.11424 s, avg_batch_cost: 2.37154 s, avg_samples: 12.5, ips: 5.27084 samples/s, eta: 2:00:52
[2024/07/27 15:06:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:06:12] ppocr INFO: epoch: [1222/1500], global_step: 3666, lr: 0.001000, loss: 1.143639, loss_shrink_maps: 0.577752, loss_threshold_maps: 0.452289, loss_binary_maps: 0.114408, avg_reader_cost: 2.05061 s, avg_batch_cost: 2.40381 s, avg_samples: 12.5, ips: 5.20009 samples/s, eta: 2:00:25
[2024/07/27 15:06:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:06:21] ppocr INFO: epoch: [1223/1500], global_step: 3669, lr: 0.001000, loss: 1.143639, loss_shrink_maps: 0.577752, loss_threshold_maps: 0.445870, loss_binary_maps: 0.114408, avg_reader_cost: 2.15366 s, avg_batch_cost: 2.49514 s, avg_samples: 12.5, ips: 5.00973 samples/s, eta: 1:59:59
[2024/07/27 15:06:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:06:30] ppocr INFO: epoch: [1224/1500], global_step: 3670, lr: 0.001000, loss: 1.136540, loss_shrink_maps: 0.566351, loss_threshold_maps: 0.445870, loss_binary_maps: 0.112425, avg_reader_cost: 0.56606 s, avg_batch_cost: 0.79781 s, avg_samples: 4.8, ips: 6.01645 samples/s, eta: 1:59:50
[2024/07/27 15:06:31] ppocr INFO: epoch: [1224/1500], global_step: 3672, lr: 0.001000, loss: 1.136540, loss_shrink_maps: 0.567747, loss_threshold_maps: 0.448020, loss_binary_maps: 0.112425, avg_reader_cost: 1.68727 s, avg_batch_cost: 1.83389 s, avg_samples: 7.7, ips: 4.19873 samples/s, eta: 1:59:33
[2024/07/27 15:06:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:06:41] ppocr INFO: epoch: [1225/1500], global_step: 3675, lr: 0.001000, loss: 1.166560, loss_shrink_maps: 0.573624, loss_threshold_maps: 0.460884, loss_binary_maps: 0.113639, avg_reader_cost: 2.20695 s, avg_batch_cost: 2.53553 s, avg_samples: 12.5, ips: 4.92993 samples/s, eta: 1:59:07
[2024/07/27 15:06:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:06:51] ppocr INFO: epoch: [1226/1500], global_step: 3678, lr: 0.001000, loss: 1.169978, loss_shrink_maps: 0.575307, loss_threshold_maps: 0.468133, loss_binary_maps: 0.113878, avg_reader_cost: 2.34162 s, avg_batch_cost: 2.58080 s, avg_samples: 12.5, ips: 4.84346 samples/s, eta: 1:58:41
[2024/07/27 15:06:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:07:00] ppocr INFO: epoch: [1227/1500], global_step: 3680, lr: 0.001000, loss: 1.175131, loss_shrink_maps: 0.589967, loss_threshold_maps: 0.467637, loss_binary_maps: 0.116424, avg_reader_cost: 1.39253 s, avg_batch_cost: 1.57312 s, avg_samples: 9.6, ips: 6.10254 samples/s, eta: 1:58:23
[2024/07/27 15:07:00] ppocr INFO: epoch: [1227/1500], global_step: 3681, lr: 0.001000, loss: 1.169978, loss_shrink_maps: 0.575307, loss_threshold_maps: 0.460884, loss_binary_maps: 0.113878, avg_reader_cost: 0.83288 s, avg_batch_cost: 0.88808 s, avg_samples: 2.9, ips: 3.26548 samples/s, eta: 1:58:15
[2024/07/27 15:07:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:07:10] ppocr INFO: epoch: [1228/1500], global_step: 3684, lr: 0.001000, loss: 1.175131, loss_shrink_maps: 0.589967, loss_threshold_maps: 0.487047, loss_binary_maps: 0.116424, avg_reader_cost: 2.38162 s, avg_batch_cost: 2.62153 s, avg_samples: 12.5, ips: 4.76821 samples/s, eta: 1:57:49
[2024/07/27 15:07:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:07:20] ppocr INFO: epoch: [1229/1500], global_step: 3687, lr: 0.001000, loss: 1.192978, loss_shrink_maps: 0.612560, loss_threshold_maps: 0.491220, loss_binary_maps: 0.121170, avg_reader_cost: 2.37733 s, avg_batch_cost: 2.62515 s, avg_samples: 12.5, ips: 4.76163 samples/s, eta: 1:57:23
[2024/07/27 15:07:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:07:30] ppocr INFO: epoch: [1230/1500], global_step: 3690, lr: 0.001000, loss: 1.304942, loss_shrink_maps: 0.656577, loss_threshold_maps: 0.498689, loss_binary_maps: 0.130156, avg_reader_cost: 2.24110 s, avg_batch_cost: 2.62272 s, avg_samples: 12.5, ips: 4.76604 samples/s, eta: 1:56:57
[2024/07/27 15:07:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:07:40] ppocr INFO: epoch: [1231/1500], global_step: 3693, lr: 0.001000, loss: 1.309391, loss_shrink_maps: 0.667039, loss_threshold_maps: 0.509824, loss_binary_maps: 0.132823, avg_reader_cost: 2.36238 s, avg_batch_cost: 2.60324 s, avg_samples: 12.5, ips: 4.80170 samples/s, eta: 1:56:31
[2024/07/27 15:07:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:07:49] ppocr INFO: epoch: [1232/1500], global_step: 3696, lr: 0.001000, loss: 1.295664, loss_shrink_maps: 0.652536, loss_threshold_maps: 0.499480, loss_binary_maps: 0.129449, avg_reader_cost: 2.29876 s, avg_batch_cost: 2.53763 s, avg_samples: 12.5, ips: 4.92586 samples/s, eta: 1:56:05
[2024/07/27 15:07:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:07:59] ppocr INFO: epoch: [1233/1500], global_step: 3699, lr: 0.001000, loss: 1.295664, loss_shrink_maps: 0.652759, loss_threshold_maps: 0.503402, loss_binary_maps: 0.129902, avg_reader_cost: 2.31009 s, avg_batch_cost: 2.55248 s, avg_samples: 12.5, ips: 4.89721 samples/s, eta: 1:55:39
[2024/07/27 15:08:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:08:08] ppocr INFO: epoch: [1234/1500], global_step: 3700, lr: 0.001000, loss: 1.275480, loss_shrink_maps: 0.650964, loss_threshold_maps: 0.503402, loss_binary_maps: 0.129449, avg_reader_cost: 0.67562 s, avg_batch_cost: 0.80193 s, avg_samples: 4.8, ips: 5.98556 samples/s, eta: 1:55:30
[2024/07/27 15:08:09] ppocr INFO: epoch: [1234/1500], global_step: 3702, lr: 0.001000, loss: 1.296530, loss_shrink_maps: 0.652759, loss_threshold_maps: 0.509824, loss_binary_maps: 0.129902, avg_reader_cost: 1.69595 s, avg_batch_cost: 1.84290 s, avg_samples: 7.7, ips: 4.17819 samples/s, eta: 1:55:13
[2024/07/27 15:08:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:08:19] ppocr INFO: epoch: [1235/1500], global_step: 3705, lr: 0.001000, loss: 1.256276, loss_shrink_maps: 0.640015, loss_threshold_maps: 0.497102, loss_binary_maps: 0.126963, avg_reader_cost: 2.32316 s, avg_batch_cost: 2.57481 s, avg_samples: 12.5, ips: 4.85472 samples/s, eta: 1:54:47
[2024/07/27 15:08:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:08:29] ppocr INFO: epoch: [1236/1500], global_step: 3708, lr: 0.001000, loss: 1.248795, loss_shrink_maps: 0.633500, loss_threshold_maps: 0.491570, loss_binary_maps: 0.125674, avg_reader_cost: 2.28051 s, avg_batch_cost: 2.64315 s, avg_samples: 12.5, ips: 4.72921 samples/s, eta: 1:54:21
[2024/07/27 15:08:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:08:38] ppocr INFO: epoch: [1237/1500], global_step: 3710, lr: 0.001000, loss: 1.242170, loss_shrink_maps: 0.630183, loss_threshold_maps: 0.483017, loss_binary_maps: 0.124704, avg_reader_cost: 1.44738 s, avg_batch_cost: 1.63186 s, avg_samples: 9.6, ips: 5.88285 samples/s, eta: 1:54:03
[2024/07/27 15:08:39] ppocr INFO: epoch: [1237/1500], global_step: 3711, lr: 0.001000, loss: 1.244223, loss_shrink_maps: 0.632620, loss_threshold_maps: 0.485694, loss_binary_maps: 0.125301, avg_reader_cost: 0.86210 s, avg_batch_cost: 0.91765 s, avg_samples: 2.9, ips: 3.16024 samples/s, eta: 1:53:55
[2024/07/27 15:08:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:08:48] ppocr INFO: epoch: [1238/1500], global_step: 3714, lr: 0.001000, loss: 1.242170, loss_shrink_maps: 0.630183, loss_threshold_maps: 0.482782, loss_binary_maps: 0.124704, avg_reader_cost: 2.36851 s, avg_batch_cost: 2.60162 s, avg_samples: 12.5, ips: 4.80471 samples/s, eta: 1:53:29
[2024/07/27 15:08:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:08:58] ppocr INFO: epoch: [1239/1500], global_step: 3717, lr: 0.001000, loss: 1.227440, loss_shrink_maps: 0.618781, loss_threshold_maps: 0.478419, loss_binary_maps: 0.122922, avg_reader_cost: 2.26623 s, avg_batch_cost: 2.51563 s, avg_samples: 12.5, ips: 4.96893 samples/s, eta: 1:53:03
[2024/07/27 15:08:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:09:07] ppocr INFO: epoch: [1240/1500], global_step: 3720, lr: 0.001000, loss: 1.225920, loss_shrink_maps: 0.618847, loss_threshold_maps: 0.472736, loss_binary_maps: 0.122860, avg_reader_cost: 2.07314 s, avg_batch_cost: 2.32975 s, avg_samples: 12.5, ips: 5.36539 samples/s, eta: 1:52:36

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:59,  8.45it/s]
eval model::   1%|          | 3/500 [00:00<00:34, 14.25it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.75it/s]
eval model::   2%|▏         | 8/500 [00:00<00:29, 16.84it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.04it/s]
eval model::   3%|▎         | 13/500 [00:00<00:28, 16.83it/s]
eval model::   3%|▎         | 16/500 [00:00<00:24, 19.37it/s]
eval model::   4%|▎         | 18/500 [00:01<00:24, 19.33it/s]
eval model::   4%|▍         | 20/500 [00:01<00:26, 18.44it/s]
eval model::   5%|▍         | 24/500 [00:01<00:21, 22.53it/s]
eval model::   5%|▌         | 27/500 [00:01<00:21, 21.91it/s]
eval model::   6%|▌         | 30/500 [00:01<00:20, 22.65it/s]
eval model::   7%|▋         | 33/500 [00:01<00:22, 20.49it/s]
eval model::   7%|▋         | 36/500 [00:01<00:22, 20.81it/s]
eval model::   8%|▊         | 39/500 [00:02<00:23, 20.02it/s]
eval model::   8%|▊         | 42/500 [00:02<00:23, 19.47it/s]
eval model::   9%|▉         | 46/500 [00:02<00:24, 18.32it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 20.08it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 20.85it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.85it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:21, 20.58it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 15.72it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 15.04it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.74it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 19.60it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 19.96it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 20.40it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 20.46it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.08it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:21, 19.48it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.16it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:21, 18.79it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 17.83it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 17.73it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:22, 18.11it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:21, 18.52it/s]
eval model::  20%|██        | 100/500 [00:05<00:22, 18.01it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 19.98it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.42it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 21.53it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:20, 18.76it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:20, 18.41it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:23, 16.16it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:21, 17.61it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:21, 17.29it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:22, 16.73it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:22, 16.81it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 18.64it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:20, 17.75it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:21, 16.90it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:18, 19.22it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:19, 18.63it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:20, 17.81it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:18, 19.13it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:19, 18.02it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:20, 16.84it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 19.28it/s]
eval model::  30%|███       | 152/500 [00:08<00:17, 19.41it/s]
eval model::  31%|███       | 155/500 [00:08<00:15, 21.99it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.24it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:22, 15.13it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:21, 15.74it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:21, 15.72it/s]
eval model::  33%|███▎      | 167/500 [00:09<00:21, 15.43it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:18, 17.68it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:18, 17.92it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:18, 18.07it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.36it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.18it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.14it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 18.35it/s]
eval model::  37%|███▋      | 187/500 [00:10<00:16, 18.67it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:16, 19.05it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:16, 18.68it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 20.30it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.39it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 21.43it/s]
eval model::  41%|████      | 205/500 [00:11<00:17, 17.01it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:17, 17.10it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:17, 17.02it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:16, 17.55it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.16it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.88it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 16.92it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:17, 15.69it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:14, 19.50it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:11, 22.78it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 22.66it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.39it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:12, 21.59it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:14, 17.99it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.00it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:13, 18.10it/s]
eval model::  50%|█████     | 250/500 [00:13<00:15, 16.41it/s]
eval model::  51%|█████     | 253/500 [00:13<00:13, 18.42it/s]
eval model::  51%|█████     | 256/500 [00:13<00:12, 19.07it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:11, 20.76it/s]
eval model::  52%|█████▏    | 262/500 [00:14<00:11, 20.35it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:11, 20.23it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:11, 21.05it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:10, 20.85it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:10, 20.70it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 18.66it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:10, 20.43it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:10, 21.10it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:19, 11.06it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:16, 13.03it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:14, 14.75it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:13, 14.95it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:16, 12.47it/s]
eval model::  60%|██████    | 300/500 [00:16<00:13, 14.76it/s]
eval model::  61%|██████    | 303/500 [00:16<00:12, 16.38it/s]
eval model::  61%|██████    | 305/500 [00:16<00:11, 16.68it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:10, 17.74it/s]
eval model::  62%|██████▏   | 311/500 [00:17<00:10, 18.80it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:10, 18.60it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:10, 17.49it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:10, 17.81it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:09, 18.28it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 17.82it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 20.78it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 19.79it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:08, 19.89it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:07, 21.55it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 22.23it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 14.61it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.02it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 18.63it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:07, 19.53it/s]
eval model::  70%|███████   | 352/500 [00:19<00:07, 20.65it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 19.63it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 19.86it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.74it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.76it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.64it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:06, 19.70it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:06, 19.19it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 20.82it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 22.85it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:05, 21.08it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 22.79it/s]
eval model::  77%|███████▋  | 387/500 [00:21<00:08, 12.87it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:07, 14.16it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:07, 14.71it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 16.81it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 19.08it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 18.55it/s]
eval model::  81%|████████  | 404/500 [00:22<00:04, 20.53it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 21.95it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:03, 23.73it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:03, 23.96it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:03, 23.00it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:03, 21.72it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 16.06it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.30it/s]
eval model::  85%|████████▌ | 427/500 [00:23<00:04, 16.14it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 17.75it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 17.43it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 19.03it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:03, 20.56it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:03, 19.52it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 20.72it/s]
eval model::  90%|████████▉ | 448/500 [00:24<00:02, 22.46it/s]
eval model::  90%|█████████ | 451/500 [00:24<00:02, 21.99it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 22.00it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 22.49it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 23.31it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 19.25it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:01, 18.44it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 18.52it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 20.03it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.72it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:00, 22.36it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 23.44it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 24.13it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 22.62it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 24.33it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 21.83it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 21.98it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 25.39it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.79it/s]
[2024/07/27 15:09:34] ppocr INFO: cur metric, precision: 0.7351716961498439, recall: 0.6803081367356765, hmean: 0.7066766691672918, fps: 44.764177865562615
[2024/07/27 15:09:34] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:09:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:09:42] ppocr INFO: epoch: [1241/1500], global_step: 3723, lr: 0.001000, loss: 1.171553, loss_shrink_maps: 0.593676, loss_threshold_maps: 0.470032, loss_binary_maps: 0.118950, avg_reader_cost: 2.19475 s, avg_batch_cost: 2.43643 s, avg_samples: 12.5, ips: 5.13046 samples/s, eta: 1:52:10
[2024/07/27 15:09:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:09:52] ppocr INFO: epoch: [1242/1500], global_step: 3726, lr: 0.001000, loss: 1.168512, loss_shrink_maps: 0.580488, loss_threshold_maps: 0.470032, loss_binary_maps: 0.115184, avg_reader_cost: 2.29650 s, avg_batch_cost: 2.60039 s, avg_samples: 12.5, ips: 4.80698 samples/s, eta: 1:51:44
[2024/07/27 15:09:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:10:03] ppocr INFO: epoch: [1243/1500], global_step: 3729, lr: 0.001000, loss: 1.179242, loss_shrink_maps: 0.591329, loss_threshold_maps: 0.470032, loss_binary_maps: 0.117992, avg_reader_cost: 2.41028 s, avg_batch_cost: 2.75427 s, avg_samples: 12.5, ips: 4.53840 samples/s, eta: 1:51:18
[2024/07/27 15:10:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:10:11] ppocr INFO: epoch: [1244/1500], global_step: 3730, lr: 0.001000, loss: 1.164345, loss_shrink_maps: 0.580488, loss_threshold_maps: 0.470032, loss_binary_maps: 0.115184, avg_reader_cost: 0.57896 s, avg_batch_cost: 0.77520 s, avg_samples: 4.8, ips: 6.19194 samples/s, eta: 1:51:09
[2024/07/27 15:10:12] ppocr INFO: epoch: [1244/1500], global_step: 3732, lr: 0.001000, loss: 1.140878, loss_shrink_maps: 0.579375, loss_threshold_maps: 0.465838, loss_binary_maps: 0.114615, avg_reader_cost: 1.64340 s, avg_batch_cost: 1.79113 s, avg_samples: 7.7, ips: 4.29896 samples/s, eta: 1:50:52
[2024/07/27 15:10:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:10:22] ppocr INFO: epoch: [1245/1500], global_step: 3735, lr: 0.001000, loss: 1.140878, loss_shrink_maps: 0.579375, loss_threshold_maps: 0.465838, loss_binary_maps: 0.114615, avg_reader_cost: 2.33342 s, avg_batch_cost: 2.58398 s, avg_samples: 12.5, ips: 4.83749 samples/s, eta: 1:50:26
[2024/07/27 15:10:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:10:32] ppocr INFO: epoch: [1246/1500], global_step: 3738, lr: 0.001000, loss: 1.162910, loss_shrink_maps: 0.579375, loss_threshold_maps: 0.467147, loss_binary_maps: 0.114615, avg_reader_cost: 2.31864 s, avg_batch_cost: 2.57669 s, avg_samples: 12.5, ips: 4.85118 samples/s, eta: 1:50:00
[2024/07/27 15:10:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:10:42] ppocr INFO: epoch: [1247/1500], global_step: 3740, lr: 0.001000, loss: 1.140878, loss_shrink_maps: 0.570048, loss_threshold_maps: 0.464982, loss_binary_maps: 0.112976, avg_reader_cost: 1.34446 s, avg_batch_cost: 1.65810 s, avg_samples: 9.6, ips: 5.78976 samples/s, eta: 1:49:42
[2024/07/27 15:10:42] ppocr INFO: epoch: [1247/1500], global_step: 3741, lr: 0.001000, loss: 1.162910, loss_shrink_maps: 0.578866, loss_threshold_maps: 0.469532, loss_binary_maps: 0.114615, avg_reader_cost: 0.87506 s, avg_batch_cost: 0.93044 s, avg_samples: 2.9, ips: 3.11680 samples/s, eta: 1:49:34
[2024/07/27 15:10:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:10:52] ppocr INFO: epoch: [1248/1500], global_step: 3744, lr: 0.001000, loss: 1.180975, loss_shrink_maps: 0.584805, loss_threshold_maps: 0.482045, loss_binary_maps: 0.116051, avg_reader_cost: 2.42161 s, avg_batch_cost: 2.66475 s, avg_samples: 12.5, ips: 4.69087 samples/s, eta: 1:49:08
[2024/07/27 15:10:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:11:02] ppocr INFO: epoch: [1249/1500], global_step: 3747, lr: 0.001000, loss: 1.180975, loss_shrink_maps: 0.589217, loss_threshold_maps: 0.482498, loss_binary_maps: 0.116482, avg_reader_cost: 2.17570 s, avg_batch_cost: 2.49170 s, avg_samples: 12.5, ips: 5.01665 samples/s, eta: 1:48:42
[2024/07/27 15:11:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:11:11] ppocr INFO: epoch: [1250/1500], global_step: 3750, lr: 0.001000, loss: 1.182179, loss_shrink_maps: 0.589217, loss_threshold_maps: 0.482498, loss_binary_maps: 0.116482, avg_reader_cost: 2.18559 s, avg_batch_cost: 2.50925 s, avg_samples: 12.5, ips: 4.98157 samples/s, eta: 1:48:16
[2024/07/27 15:11:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:11:21] ppocr INFO: epoch: [1251/1500], global_step: 3753, lr: 0.001000, loss: 1.184267, loss_shrink_maps: 0.592897, loss_threshold_maps: 0.489489, loss_binary_maps: 0.118011, avg_reader_cost: 2.23320 s, avg_batch_cost: 2.47310 s, avg_samples: 12.5, ips: 5.05438 samples/s, eta: 1:47:49
[2024/07/27 15:11:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:11:30] ppocr INFO: epoch: [1252/1500], global_step: 3756, lr: 0.001000, loss: 1.184055, loss_shrink_maps: 0.592897, loss_threshold_maps: 0.483808, loss_binary_maps: 0.118011, avg_reader_cost: 2.17798 s, avg_batch_cost: 2.51829 s, avg_samples: 12.5, ips: 4.96369 samples/s, eta: 1:47:23
[2024/07/27 15:11:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:11:40] ppocr INFO: epoch: [1253/1500], global_step: 3759, lr: 0.001000, loss: 1.231843, loss_shrink_maps: 0.615666, loss_threshold_maps: 0.483808, loss_binary_maps: 0.122637, avg_reader_cost: 2.21682 s, avg_batch_cost: 2.56964 s, avg_samples: 12.5, ips: 4.86450 samples/s, eta: 1:46:57
[2024/07/27 15:11:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:11:48] ppocr INFO: epoch: [1254/1500], global_step: 3760, lr: 0.001000, loss: 1.256707, loss_shrink_maps: 0.631304, loss_threshold_maps: 0.485686, loss_binary_maps: 0.125161, avg_reader_cost: 0.64829 s, avg_batch_cost: 0.74279 s, avg_samples: 4.8, ips: 6.46210 samples/s, eta: 1:46:48
[2024/07/27 15:11:50] ppocr INFO: epoch: [1254/1500], global_step: 3762, lr: 0.001000, loss: 1.184055, loss_shrink_maps: 0.592897, loss_threshold_maps: 0.475757, loss_binary_maps: 0.118011, avg_reader_cost: 1.57776 s, avg_batch_cost: 1.72497 s, avg_samples: 7.7, ips: 4.46385 samples/s, eta: 1:46:31
[2024/07/27 15:11:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:11:59] ppocr INFO: epoch: [1255/1500], global_step: 3765, lr: 0.001000, loss: 1.202360, loss_shrink_maps: 0.608658, loss_threshold_maps: 0.480401, loss_binary_maps: 0.121308, avg_reader_cost: 2.33138 s, avg_batch_cost: 2.57191 s, avg_samples: 12.5, ips: 4.86020 samples/s, eta: 1:46:05
[2024/07/27 15:12:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:12:09] ppocr INFO: epoch: [1256/1500], global_step: 3768, lr: 0.001000, loss: 1.170915, loss_shrink_maps: 0.577786, loss_threshold_maps: 0.471321, loss_binary_maps: 0.114911, avg_reader_cost: 2.23215 s, avg_batch_cost: 2.58523 s, avg_samples: 12.5, ips: 4.83516 samples/s, eta: 1:45:39
[2024/07/27 15:12:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:12:18] ppocr INFO: epoch: [1257/1500], global_step: 3770, lr: 0.001000, loss: 1.179398, loss_shrink_maps: 0.589981, loss_threshold_maps: 0.472738, loss_binary_maps: 0.117000, avg_reader_cost: 1.36975 s, avg_batch_cost: 1.67753 s, avg_samples: 9.6, ips: 5.72271 samples/s, eta: 1:45:22
[2024/07/27 15:12:19] ppocr INFO: epoch: [1257/1500], global_step: 3771, lr: 0.001000, loss: 1.170915, loss_shrink_maps: 0.577786, loss_threshold_maps: 0.467981, loss_binary_maps: 0.114911, avg_reader_cost: 0.88519 s, avg_batch_cost: 0.94046 s, avg_samples: 2.9, ips: 3.08360 samples/s, eta: 1:45:13
[2024/07/27 15:12:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:12:29] ppocr INFO: epoch: [1258/1500], global_step: 3774, lr: 0.001000, loss: 1.179398, loss_shrink_maps: 0.591206, loss_threshold_maps: 0.472417, loss_binary_maps: 0.117615, avg_reader_cost: 2.35080 s, avg_batch_cost: 2.59066 s, avg_samples: 12.5, ips: 4.82502 samples/s, eta: 1:44:47
[2024/07/27 15:12:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:12:39] ppocr INFO: epoch: [1259/1500], global_step: 3777, lr: 0.001000, loss: 1.165974, loss_shrink_maps: 0.579011, loss_threshold_maps: 0.459841, loss_binary_maps: 0.115526, avg_reader_cost: 2.25294 s, avg_batch_cost: 2.64269 s, avg_samples: 12.5, ips: 4.73002 samples/s, eta: 1:44:21
[2024/07/27 15:12:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:12:49] ppocr INFO: epoch: [1260/1500], global_step: 3780, lr: 0.001000, loss: 1.165974, loss_shrink_maps: 0.579011, loss_threshold_maps: 0.458555, loss_binary_maps: 0.115199, avg_reader_cost: 2.34970 s, avg_batch_cost: 2.60629 s, avg_samples: 12.5, ips: 4.79609 samples/s, eta: 1:43:55

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:59,  8.36it/s]
eval model::   1%|          | 4/500 [00:00<00:30, 16.25it/s]
eval model::   1%|          | 6/500 [00:00<00:32, 14.98it/s]
eval model::   2%|▏         | 9/500 [00:00<00:28, 17.32it/s]
eval model::   2%|▏         | 11/500 [00:00<00:29, 16.31it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.84it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.55it/s]
eval model::   4%|▍         | 19/500 [00:01<00:26, 18.37it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 18.64it/s]
eval model::   5%|▍         | 23/500 [00:01<00:26, 18.28it/s]
eval model::   5%|▌         | 25/500 [00:01<00:25, 18.34it/s]
eval model::   5%|▌         | 27/500 [00:01<00:25, 18.69it/s]
eval model::   6%|▌         | 30/500 [00:01<00:23, 19.91it/s]
eval model::   6%|▋         | 32/500 [00:01<00:26, 17.80it/s]
eval model::   7%|▋         | 34/500 [00:01<00:27, 17.24it/s]
eval model::   7%|▋         | 37/500 [00:02<00:24, 18.91it/s]
eval model::   8%|▊         | 39/500 [00:02<00:24, 18.58it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 17.75it/s]
eval model::   9%|▉         | 44/500 [00:02<00:25, 18.08it/s]
eval model::   9%|▉         | 46/500 [00:02<00:26, 17.07it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.54it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 19.03it/s]
eval model::  11%|█         | 55/500 [00:03<00:23, 19.32it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 18.08it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 15.74it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.35it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:25, 17.34it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:22, 19.19it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 19.92it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:22, 19.29it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:22, 19.14it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:20, 20.98it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:18, 22.03it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:19, 21.33it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:21, 19.13it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 17.99it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:22, 18.43it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 20.08it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.89it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.88it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 22.16it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:17, 22.30it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 21.03it/s]
eval model::  23%|██▎       | 114/500 [00:06<00:17, 21.51it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:18, 21.00it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.91it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 20.75it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 17.83it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 19.47it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:21, 16.86it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:19, 18.87it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.93it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.57it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 20.99it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 19.40it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 21.32it/s]
eval model::  31%|███       | 153/500 [00:08<00:16, 20.84it/s]
eval model::  31%|███       | 156/500 [00:08<00:15, 21.97it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 20.67it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:18, 18.03it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:19, 17.67it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:18, 18.05it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:19, 16.89it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:18, 17.45it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 16.74it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:17, 18.56it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:20, 15.87it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:19, 16.64it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:16, 18.58it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:17, 17.65it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:16, 18.83it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:14, 21.23it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:15, 20.16it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 20.87it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 20.88it/s]
eval model::  41%|████      | 205/500 [00:10<00:16, 17.71it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:16, 17.59it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:17, 16.82it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:16, 17.25it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.01it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.73it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 16.87it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:19, 14.62it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 17.16it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:14, 18.44it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:13, 20.56it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:14, 18.65it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:13, 20.18it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:12, 20.52it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:14, 17.99it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:13, 18.85it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:13, 18.62it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 21.02it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.78it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:10, 22.91it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 22.38it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:11, 21.16it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 21.08it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:12, 18.39it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 20.18it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 17.68it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 18.48it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 19.14it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.27it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.21it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:13, 15.83it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:12, 16.34it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:15, 13.22it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:12, 16.13it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 16.00it/s]
eval model::  61%|██████    | 303/500 [00:16<00:10, 18.20it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 18.29it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 19.79it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 20.39it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 19.48it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:08, 21.60it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 21.41it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 19.90it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 20.16it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 19.40it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:08, 18.91it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:08, 20.49it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 19.85it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:10, 14.71it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 17.24it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 17.72it/s]
eval model::  70%|███████   | 350/500 [00:18<00:08, 18.69it/s]
eval model::  71%|███████   | 353/500 [00:18<00:07, 20.31it/s]
eval model::  71%|███████   | 356/500 [00:19<00:08, 16.72it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:07, 17.91it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:08, 16.77it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:08, 16.36it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 16.90it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 18.37it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 18.14it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 19.04it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 22.00it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:06, 19.62it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:07, 15.45it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:06, 18.22it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:05, 19.30it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:04, 21.13it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:04, 22.87it/s]
eval model::  80%|████████  | 402/500 [00:21<00:04, 20.16it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 21.45it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.36it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 22.38it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 22.37it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:03, 20.82it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 15.17it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 15.54it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.76it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.75it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 17.59it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 16.35it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 20.21it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 22.12it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:03, 18.40it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 19.11it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 20.38it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 19.44it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 21.66it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 22.12it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 22.71it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 18.98it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.67it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 18.01it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 20.42it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.70it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 18.93it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 20.06it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 20.22it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 20.83it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 20.82it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 19.17it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 19.97it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 23.55it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.81it/s]
[2024/07/27 15:13:15] ppocr INFO: cur metric, precision: 0.7638660076880834, recall: 0.6697159364467983, hmean: 0.7136993329912775, fps: 43.5550968906054
[2024/07/27 15:13:15] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:13:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:13:25] ppocr INFO: epoch: [1261/1500], global_step: 3783, lr: 0.001000, loss: 1.165974, loss_shrink_maps: 0.576516, loss_threshold_maps: 0.465636, loss_binary_maps: 0.114542, avg_reader_cost: 2.23557 s, avg_batch_cost: 2.64438 s, avg_samples: 12.5, ips: 4.72700 samples/s, eta: 1:43:29
[2024/07/27 15:13:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:13:35] ppocr INFO: epoch: [1262/1500], global_step: 3786, lr: 0.001000, loss: 1.131496, loss_shrink_maps: 0.561227, loss_threshold_maps: 0.465636, loss_binary_maps: 0.111638, avg_reader_cost: 2.23967 s, avg_batch_cost: 2.60761 s, avg_samples: 12.5, ips: 4.79366 samples/s, eta: 1:43:03
[2024/07/27 15:13:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:13:45] ppocr INFO: epoch: [1263/1500], global_step: 3789, lr: 0.001000, loss: 1.169877, loss_shrink_maps: 0.584146, loss_threshold_maps: 0.465050, loss_binary_maps: 0.116062, avg_reader_cost: 2.39605 s, avg_batch_cost: 2.64004 s, avg_samples: 12.5, ips: 4.73478 samples/s, eta: 1:42:37
[2024/07/27 15:13:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:13:53] ppocr INFO: epoch: [1264/1500], global_step: 3790, lr: 0.001000, loss: 1.169877, loss_shrink_maps: 0.583272, loss_threshold_maps: 0.465050, loss_binary_maps: 0.116062, avg_reader_cost: 0.58040 s, avg_batch_cost: 0.80113 s, avg_samples: 4.8, ips: 5.99157 samples/s, eta: 1:42:29
[2024/07/27 15:13:55] ppocr INFO: epoch: [1264/1500], global_step: 3792, lr: 0.001000, loss: 1.182084, loss_shrink_maps: 0.584668, loss_threshold_maps: 0.468505, loss_binary_maps: 0.116184, avg_reader_cost: 1.69458 s, avg_batch_cost: 1.84183 s, avg_samples: 7.7, ips: 4.18062 samples/s, eta: 1:42:12
[2024/07/27 15:13:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:14:04] ppocr INFO: epoch: [1265/1500], global_step: 3795, lr: 0.001000, loss: 1.125871, loss_shrink_maps: 0.560163, loss_threshold_maps: 0.458191, loss_binary_maps: 0.111352, avg_reader_cost: 2.21354 s, avg_batch_cost: 2.58311 s, avg_samples: 12.5, ips: 4.83912 samples/s, eta: 1:41:46
[2024/07/27 15:14:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:14:14] ppocr INFO: epoch: [1266/1500], global_step: 3798, lr: 0.001000, loss: 1.164252, loss_shrink_maps: 0.582208, loss_threshold_maps: 0.463966, loss_binary_maps: 0.115449, avg_reader_cost: 2.17423 s, avg_batch_cost: 2.53651 s, avg_samples: 12.5, ips: 4.92803 samples/s, eta: 1:41:19
[2024/07/27 15:14:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:14:23] ppocr INFO: epoch: [1267/1500], global_step: 3800, lr: 0.001000, loss: 1.118720, loss_shrink_maps: 0.551578, loss_threshold_maps: 0.458191, loss_binary_maps: 0.109587, avg_reader_cost: 1.36711 s, avg_batch_cost: 1.68689 s, avg_samples: 9.6, ips: 5.69094 samples/s, eta: 1:41:02
[2024/07/27 15:14:24] ppocr INFO: epoch: [1267/1500], global_step: 3801, lr: 0.001000, loss: 1.112257, loss_shrink_maps: 0.551578, loss_threshold_maps: 0.455144, loss_binary_maps: 0.109587, avg_reader_cost: 0.88947 s, avg_batch_cost: 0.94482 s, avg_samples: 2.9, ips: 3.06936 samples/s, eta: 1:40:54
[2024/07/27 15:14:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:14:34] ppocr INFO: epoch: [1268/1500], global_step: 3804, lr: 0.001000, loss: 1.144351, loss_shrink_maps: 0.572373, loss_threshold_maps: 0.458191, loss_binary_maps: 0.113398, avg_reader_cost: 2.33527 s, avg_batch_cost: 2.56815 s, avg_samples: 12.5, ips: 4.86732 samples/s, eta: 1:40:27
[2024/07/27 15:14:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:14:44] ppocr INFO: epoch: [1269/1500], global_step: 3807, lr: 0.001000, loss: 1.118743, loss_shrink_maps: 0.561835, loss_threshold_maps: 0.455144, loss_binary_maps: 0.111293, avg_reader_cost: 2.32787 s, avg_batch_cost: 2.73082 s, avg_samples: 12.5, ips: 4.57738 samples/s, eta: 1:40:02
[2024/07/27 15:14:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:14:54] ppocr INFO: epoch: [1270/1500], global_step: 3810, lr: 0.001000, loss: 1.091332, loss_shrink_maps: 0.547631, loss_threshold_maps: 0.445518, loss_binary_maps: 0.108339, avg_reader_cost: 2.36720 s, avg_batch_cost: 2.59996 s, avg_samples: 12.5, ips: 4.80777 samples/s, eta: 1:39:36
[2024/07/27 15:14:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:15:03] ppocr INFO: epoch: [1271/1500], global_step: 3813, lr: 0.001000, loss: 1.118743, loss_shrink_maps: 0.561835, loss_threshold_maps: 0.445624, loss_binary_maps: 0.111293, avg_reader_cost: 2.22906 s, avg_batch_cost: 2.46516 s, avg_samples: 12.5, ips: 5.07067 samples/s, eta: 1:39:10
[2024/07/27 15:15:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:15:13] ppocr INFO: epoch: [1272/1500], global_step: 3816, lr: 0.001000, loss: 1.086339, loss_shrink_maps: 0.554481, loss_threshold_maps: 0.431515, loss_binary_maps: 0.110567, avg_reader_cost: 2.39335 s, avg_batch_cost: 2.64196 s, avg_samples: 12.5, ips: 4.73134 samples/s, eta: 1:38:44
[2024/07/27 15:15:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:15:23] ppocr INFO: epoch: [1273/1500], global_step: 3819, lr: 0.001000, loss: 1.121573, loss_shrink_maps: 0.559842, loss_threshold_maps: 0.442868, loss_binary_maps: 0.110670, avg_reader_cost: 2.24052 s, avg_batch_cost: 2.60696 s, avg_samples: 12.5, ips: 4.79487 samples/s, eta: 1:38:18
[2024/07/27 15:15:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:15:31] ppocr INFO: epoch: [1274/1500], global_step: 3820, lr: 0.001000, loss: 1.121573, loss_shrink_maps: 0.559842, loss_threshold_maps: 0.442868, loss_binary_maps: 0.110670, avg_reader_cost: 0.55962 s, avg_batch_cost: 0.77770 s, avg_samples: 4.8, ips: 6.17205 samples/s, eta: 1:38:09
[2024/07/27 15:15:33] ppocr INFO: epoch: [1274/1500], global_step: 3822, lr: 0.001000, loss: 1.143460, loss_shrink_maps: 0.567817, loss_threshold_maps: 0.453239, loss_binary_maps: 0.112365, avg_reader_cost: 1.64772 s, avg_batch_cost: 1.79505 s, avg_samples: 7.7, ips: 4.28958 samples/s, eta: 1:37:52
[2024/07/27 15:15:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:15:42] ppocr INFO: epoch: [1275/1500], global_step: 3825, lr: 0.001000, loss: 1.143460, loss_shrink_maps: 0.567817, loss_threshold_maps: 0.453239, loss_binary_maps: 0.112365, avg_reader_cost: 2.35368 s, avg_batch_cost: 2.61146 s, avg_samples: 12.5, ips: 4.78659 samples/s, eta: 1:37:26
[2024/07/27 15:15:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:15:53] ppocr INFO: epoch: [1276/1500], global_step: 3828, lr: 0.001000, loss: 1.152261, loss_shrink_maps: 0.568457, loss_threshold_maps: 0.465354, loss_binary_maps: 0.112267, avg_reader_cost: 2.41061 s, avg_batch_cost: 2.65672 s, avg_samples: 12.5, ips: 4.70505 samples/s, eta: 1:37:00
[2024/07/27 15:15:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:16:02] ppocr INFO: epoch: [1277/1500], global_step: 3830, lr: 0.001000, loss: 1.152261, loss_shrink_maps: 0.563624, loss_threshold_maps: 0.465354, loss_binary_maps: 0.111563, avg_reader_cost: 1.46658 s, avg_batch_cost: 1.65490 s, avg_samples: 9.6, ips: 5.80094 samples/s, eta: 1:36:42
[2024/07/27 15:16:02] ppocr INFO: epoch: [1277/1500], global_step: 3831, lr: 0.001000, loss: 1.161759, loss_shrink_maps: 0.566905, loss_threshold_maps: 0.465354, loss_binary_maps: 0.112489, avg_reader_cost: 0.87347 s, avg_batch_cost: 0.92898 s, avg_samples: 2.9, ips: 3.12172 samples/s, eta: 1:36:34
[2024/07/27 15:16:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:16:12] ppocr INFO: epoch: [1278/1500], global_step: 3834, lr: 0.001000, loss: 1.137641, loss_shrink_maps: 0.556066, loss_threshold_maps: 0.456888, loss_binary_maps: 0.110309, avg_reader_cost: 2.20416 s, avg_batch_cost: 2.54801 s, avg_samples: 12.5, ips: 4.90578 samples/s, eta: 1:36:08
[2024/07/27 15:16:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:16:21] ppocr INFO: epoch: [1279/1500], global_step: 3837, lr: 0.001000, loss: 1.137641, loss_shrink_maps: 0.556066, loss_threshold_maps: 0.453549, loss_binary_maps: 0.110309, avg_reader_cost: 2.17365 s, avg_batch_cost: 2.50952 s, avg_samples: 12.5, ips: 4.98104 samples/s, eta: 1:35:42
[2024/07/27 15:16:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:16:31] ppocr INFO: epoch: [1280/1500], global_step: 3840, lr: 0.001000, loss: 1.149928, loss_shrink_maps: 0.563624, loss_threshold_maps: 0.456888, loss_binary_maps: 0.111563, avg_reader_cost: 2.34619 s, avg_batch_cost: 2.59738 s, avg_samples: 12.5, ips: 4.81254 samples/s, eta: 1:35:16

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:58,  8.52it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.71it/s]
eval model::   1%|▏         | 7/500 [00:00<00:23, 20.79it/s]
eval model::   2%|▏         | 10/500 [00:00<00:25, 19.43it/s]
eval model::   2%|▏         | 12/500 [00:00<00:27, 18.04it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.94it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.33it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 18.71it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 19.92it/s]
eval model::   5%|▌         | 26/500 [00:01<00:26, 17.82it/s]
eval model::   6%|▌         | 29/500 [00:01<00:24, 18.95it/s]
eval model::   6%|▌         | 31/500 [00:01<00:26, 17.82it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.62it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 19.37it/s]
eval model::   8%|▊         | 39/500 [00:02<00:26, 17.29it/s]
eval model::   8%|▊         | 41/500 [00:02<00:27, 16.98it/s]
eval model::   9%|▊         | 43/500 [00:02<00:29, 15.35it/s]
eval model::   9%|▉         | 46/500 [00:02<00:27, 16.24it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 18.84it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 20.55it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 20.65it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 18.20it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 15.75it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.69it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.13it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:23, 18.71it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:22, 19.36it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:23, 18.25it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:22, 18.49it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.19it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.87it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 20.43it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.27it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:22, 18.21it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.98it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.69it/s]
eval model::  20%|██        | 102/500 [00:05<00:18, 21.17it/s]
eval model::  21%|██        | 106/500 [00:05<00:16, 23.32it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:16, 23.18it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:16, 22.98it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:20, 18.56it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:20, 18.95it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:20, 18.85it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.67it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 18.29it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.76it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:21, 17.45it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:18, 19.22it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.42it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.56it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.73it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:20, 17.47it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 19.11it/s]
eval model::  31%|███       | 153/500 [00:08<00:19, 17.51it/s]
eval model::  31%|███       | 156/500 [00:08<00:18, 18.31it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:18, 18.28it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:23, 14.50it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:22, 14.68it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:19, 17.20it/s]
eval model::  34%|███▎      | 168/500 [00:09<00:19, 17.15it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:17, 18.66it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:18, 17.65it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.25it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.28it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:18, 17.19it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:18, 17.48it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:17, 18.13it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:18, 17.24it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:17, 17.80it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:16, 18.88it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:16, 18.20it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:13, 21.90it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 21.03it/s]
eval model::  41%|████      | 205/500 [00:11<00:19, 15.32it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:18, 16.17it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:17, 16.43it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:16, 17.45it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.75it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:17, 15.74it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 18.25it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:17, 16.32it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:13, 19.63it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:12, 21.75it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 23.39it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.88it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.00it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:14, 18.08it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:11, 21.37it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:11, 21.89it/s]
eval model::  50%|█████     | 252/500 [00:13<00:10, 22.89it/s]
eval model::  51%|█████     | 255/500 [00:13<00:10, 22.28it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 21.42it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 20.66it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:12, 19.29it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 19.68it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:13, 17.65it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 18.97it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.91it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 18.32it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 19.54it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:10, 20.37it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:17, 12.04it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:14, 14.57it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 16.61it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:15, 13.54it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:12, 16.15it/s]
eval model::  60%|██████    | 301/500 [00:16<00:12, 16.05it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 17.41it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.97it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.18it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 20.69it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 19.20it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 21.07it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.26it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:07, 22.53it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:07, 22.63it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:06, 24.19it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:06, 25.22it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 22.47it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:09, 16.82it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 18.65it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:07, 19.78it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 20.94it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.43it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 21.95it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:06, 20.20it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.59it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 18.14it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 20.02it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 19.34it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 20.51it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 22.14it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:06, 19.81it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 21.83it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:08, 13.67it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 16.03it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 17.82it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 19.21it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:04, 21.12it/s]
eval model::  80%|████████  | 402/500 [00:21<00:04, 20.01it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 21.27it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.61it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 22.39it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 21.54it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 20.70it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 13.65it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 14.07it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 14.15it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.04it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:04, 17.59it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:03, 17.54it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 21.17it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 21.47it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:03, 19.52it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 21.65it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 21.18it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 19.15it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 19.93it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:02, 21.68it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 22.81it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 17.39it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 16.89it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:02, 16.27it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 16.12it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 17.87it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 17.81it/s]
eval model::  95%|█████████▌| 475/500 [00:25<00:01, 18.17it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:01, 20.32it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 21.25it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 22.25it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 20.56it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 22.06it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 19.30it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 18.36it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 21.63it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.90it/s]
[2024/07/27 15:16:58] ppocr INFO: cur metric, precision: 0.7604501607717041, recall: 0.6831969186326432, hmean: 0.7197565305604868, fps: 45.21531483716131
[2024/07/27 15:16:58] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:16:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:17:07] ppocr INFO: epoch: [1281/1500], global_step: 3843, lr: 0.001000, loss: 1.149928, loss_shrink_maps: 0.563624, loss_threshold_maps: 0.475884, loss_binary_maps: 0.111563, avg_reader_cost: 2.32959 s, avg_batch_cost: 2.59986 s, avg_samples: 12.5, ips: 4.80796 samples/s, eta: 1:34:50
[2024/07/27 15:17:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:17:17] ppocr INFO: epoch: [1282/1500], global_step: 3846, lr: 0.001000, loss: 1.131420, loss_shrink_maps: 0.557214, loss_threshold_maps: 0.456888, loss_binary_maps: 0.110657, avg_reader_cost: 2.15014 s, avg_batch_cost: 2.48315 s, avg_samples: 12.5, ips: 5.03394 samples/s, eta: 1:34:23
[2024/07/27 15:17:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:17:26] ppocr INFO: epoch: [1283/1500], global_step: 3849, lr: 0.001000, loss: 1.131420, loss_shrink_maps: 0.558393, loss_threshold_maps: 0.456759, loss_binary_maps: 0.111163, avg_reader_cost: 2.14481 s, avg_batch_cost: 2.46136 s, avg_samples: 12.5, ips: 5.07849 samples/s, eta: 1:33:57
[2024/07/27 15:17:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:17:34] ppocr INFO: epoch: [1284/1500], global_step: 3850, lr: 0.001000, loss: 1.131420, loss_shrink_maps: 0.558393, loss_threshold_maps: 0.451252, loss_binary_maps: 0.111163, avg_reader_cost: 0.67607 s, avg_batch_cost: 0.77991 s, avg_samples: 4.8, ips: 6.15458 samples/s, eta: 1:33:48
[2024/07/27 15:17:36] ppocr INFO: epoch: [1284/1500], global_step: 3852, lr: 0.001000, loss: 1.129999, loss_shrink_maps: 0.558393, loss_threshold_maps: 0.451655, loss_binary_maps: 0.111163, avg_reader_cost: 1.65217 s, avg_batch_cost: 1.79890 s, avg_samples: 7.7, ips: 4.28039 samples/s, eta: 1:33:31
[2024/07/27 15:17:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:17:45] ppocr INFO: epoch: [1285/1500], global_step: 3855, lr: 0.001000, loss: 1.145912, loss_shrink_maps: 0.587743, loss_threshold_maps: 0.467522, loss_binary_maps: 0.116967, avg_reader_cost: 2.36358 s, avg_batch_cost: 2.60586 s, avg_samples: 12.5, ips: 4.79688 samples/s, eta: 1:33:05
[2024/07/27 15:17:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:17:55] ppocr INFO: epoch: [1286/1500], global_step: 3858, lr: 0.001000, loss: 1.133687, loss_shrink_maps: 0.566500, loss_threshold_maps: 0.459763, loss_binary_maps: 0.112512, avg_reader_cost: 2.21261 s, avg_batch_cost: 2.60739 s, avg_samples: 12.5, ips: 4.79407 samples/s, eta: 1:32:39
[2024/07/27 15:17:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:18:04] ppocr INFO: epoch: [1287/1500], global_step: 3860, lr: 0.001000, loss: 1.129999, loss_shrink_maps: 0.566255, loss_threshold_maps: 0.453732, loss_binary_maps: 0.111981, avg_reader_cost: 1.41826 s, avg_batch_cost: 1.60135 s, avg_samples: 9.6, ips: 5.99496 samples/s, eta: 1:32:22
[2024/07/27 15:18:05] ppocr INFO: epoch: [1287/1500], global_step: 3861, lr: 0.001000, loss: 1.133687, loss_shrink_maps: 0.568979, loss_threshold_maps: 0.459763, loss_binary_maps: 0.112725, avg_reader_cost: 0.84727 s, avg_batch_cost: 0.90333 s, avg_samples: 2.9, ips: 3.21035 samples/s, eta: 1:32:13
[2024/07/27 15:18:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:18:14] ppocr INFO: epoch: [1288/1500], global_step: 3864, lr: 0.001000, loss: 1.144694, loss_shrink_maps: 0.582079, loss_threshold_maps: 0.457634, loss_binary_maps: 0.115821, avg_reader_cost: 2.15855 s, avg_batch_cost: 2.50537 s, avg_samples: 12.5, ips: 4.98928 samples/s, eta: 1:31:47
[2024/07/27 15:18:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:18:25] ppocr INFO: epoch: [1289/1500], global_step: 3867, lr: 0.001000, loss: 1.160678, loss_shrink_maps: 0.599398, loss_threshold_maps: 0.455624, loss_binary_maps: 0.119030, avg_reader_cost: 2.28053 s, avg_batch_cost: 2.64067 s, avg_samples: 12.5, ips: 4.73364 samples/s, eta: 1:31:21
[2024/07/27 15:18:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:18:34] ppocr INFO: epoch: [1290/1500], global_step: 3870, lr: 0.001000, loss: 1.169204, loss_shrink_maps: 0.605446, loss_threshold_maps: 0.453584, loss_binary_maps: 0.119887, avg_reader_cost: 2.12516 s, avg_batch_cost: 2.43301 s, avg_samples: 12.5, ips: 5.13767 samples/s, eta: 1:30:55
[2024/07/27 15:18:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:18:43] ppocr INFO: epoch: [1291/1500], global_step: 3873, lr: 0.001000, loss: 1.169204, loss_shrink_maps: 0.607209, loss_threshold_maps: 0.453584, loss_binary_maps: 0.120253, avg_reader_cost: 2.11906 s, avg_batch_cost: 2.38439 s, avg_samples: 12.5, ips: 5.24242 samples/s, eta: 1:30:29
[2024/07/27 15:18:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:18:52] ppocr INFO: epoch: [1292/1500], global_step: 3876, lr: 0.001000, loss: 1.162888, loss_shrink_maps: 0.588644, loss_threshold_maps: 0.453584, loss_binary_maps: 0.116738, avg_reader_cost: 2.24824 s, avg_batch_cost: 2.49082 s, avg_samples: 12.5, ips: 5.01843 samples/s, eta: 1:30:02
[2024/07/27 15:18:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:19:03] ppocr INFO: epoch: [1293/1500], global_step: 3879, lr: 0.001000, loss: 1.182442, loss_shrink_maps: 0.604443, loss_threshold_maps: 0.456231, loss_binary_maps: 0.119562, avg_reader_cost: 2.48143 s, avg_batch_cost: 2.72136 s, avg_samples: 12.5, ips: 4.59330 samples/s, eta: 1:29:37
[2024/07/27 15:19:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:19:11] ppocr INFO: epoch: [1294/1500], global_step: 3880, lr: 0.001000, loss: 1.201440, loss_shrink_maps: 0.606720, loss_threshold_maps: 0.460452, loss_binary_maps: 0.120253, avg_reader_cost: 0.56761 s, avg_batch_cost: 0.77644 s, avg_samples: 4.8, ips: 6.18205 samples/s, eta: 1:29:28
[2024/07/27 15:19:13] ppocr INFO: epoch: [1294/1500], global_step: 3882, lr: 0.001000, loss: 1.182442, loss_shrink_maps: 0.604132, loss_threshold_maps: 0.456231, loss_binary_maps: 0.119714, avg_reader_cost: 1.64470 s, avg_batch_cost: 1.79209 s, avg_samples: 7.7, ips: 4.29667 samples/s, eta: 1:29:11
[2024/07/27 15:19:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:19:22] ppocr INFO: epoch: [1295/1500], global_step: 3885, lr: 0.001000, loss: 1.182442, loss_shrink_maps: 0.597012, loss_threshold_maps: 0.474499, loss_binary_maps: 0.118039, avg_reader_cost: 2.24890 s, avg_batch_cost: 2.48572 s, avg_samples: 12.5, ips: 5.02873 samples/s, eta: 1:28:44
[2024/07/27 15:19:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:19:32] ppocr INFO: epoch: [1296/1500], global_step: 3888, lr: 0.001000, loss: 1.176126, loss_shrink_maps: 0.588644, loss_threshold_maps: 0.474499, loss_binary_maps: 0.116738, avg_reader_cost: 2.31615 s, avg_batch_cost: 2.57986 s, avg_samples: 12.5, ips: 4.84523 samples/s, eta: 1:28:18
[2024/07/27 15:19:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:19:41] ppocr INFO: epoch: [1297/1500], global_step: 3890, lr: 0.001000, loss: 1.176126, loss_shrink_maps: 0.588644, loss_threshold_maps: 0.473520, loss_binary_maps: 0.116738, avg_reader_cost: 1.52244 s, avg_batch_cost: 1.71651 s, avg_samples: 9.6, ips: 5.59275 samples/s, eta: 1:28:01
[2024/07/27 15:19:42] ppocr INFO: epoch: [1297/1500], global_step: 3891, lr: 0.001000, loss: 1.137159, loss_shrink_maps: 0.578248, loss_threshold_maps: 0.471064, loss_binary_maps: 0.114969, avg_reader_cost: 0.90449 s, avg_batch_cost: 0.96016 s, avg_samples: 2.9, ips: 3.02032 samples/s, eta: 1:27:53
[2024/07/27 15:19:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:19:51] ppocr INFO: epoch: [1298/1500], global_step: 3894, lr: 0.001000, loss: 1.137159, loss_shrink_maps: 0.578248, loss_threshold_maps: 0.462477, loss_binary_maps: 0.114969, avg_reader_cost: 2.29077 s, avg_batch_cost: 2.53995 s, avg_samples: 12.5, ips: 4.92135 samples/s, eta: 1:27:27
[2024/07/27 15:19:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:20:02] ppocr INFO: epoch: [1299/1500], global_step: 3897, lr: 0.001000, loss: 1.116169, loss_shrink_maps: 0.565624, loss_threshold_maps: 0.460486, loss_binary_maps: 0.112813, avg_reader_cost: 2.44710 s, avg_batch_cost: 2.68789 s, avg_samples: 12.5, ips: 4.65048 samples/s, eta: 1:27:01
[2024/07/27 15:20:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:20:11] ppocr INFO: epoch: [1300/1500], global_step: 3900, lr: 0.001000, loss: 1.111190, loss_shrink_maps: 0.553546, loss_threshold_maps: 0.456178, loss_binary_maps: 0.109779, avg_reader_cost: 2.15731 s, avg_batch_cost: 2.46744 s, avg_samples: 12.5, ips: 5.06598 samples/s, eta: 1:26:35

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:59,  8.34it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.18it/s]
eval model::   1%|▏         | 7/500 [00:00<00:24, 19.83it/s]
eval model::   2%|▏         | 9/500 [00:00<00:25, 19.46it/s]
eval model::   2%|▏         | 11/500 [00:00<00:31, 15.76it/s]
eval model::   3%|▎         | 13/500 [00:00<00:31, 15.33it/s]
eval model::   3%|▎         | 15/500 [00:00<00:30, 15.73it/s]
eval model::   4%|▎         | 18/500 [00:01<00:26, 17.88it/s]
eval model::   4%|▍         | 20/500 [00:01<00:28, 17.12it/s]
eval model::   5%|▍         | 23/500 [00:01<00:24, 19.19it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 19.92it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.24it/s]
eval model::   6%|▋         | 32/500 [00:01<00:25, 18.65it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 18.82it/s]
eval model::   7%|▋         | 37/500 [00:02<00:22, 20.56it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.78it/s]
eval model::   9%|▊         | 43/500 [00:02<00:23, 19.23it/s]
eval model::   9%|▉         | 46/500 [00:02<00:26, 17.40it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.45it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 19.60it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.21it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:22, 19.50it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 16.01it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 16.12it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.70it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 21.15it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 21.73it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:19, 21.26it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:18, 22.27it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.97it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.12it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 20.12it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.29it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.11it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.39it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:21, 18.79it/s]
eval model::  20%|██        | 101/500 [00:05<00:21, 18.48it/s]
eval model::  21%|██        | 104/500 [00:05<00:19, 20.34it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 22.02it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 22.68it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:17, 22.60it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:19, 19.62it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 21.47it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.76it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:21, 17.61it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:20, 17.86it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:20, 18.26it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:21, 17.04it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:22, 16.29it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:20, 17.63it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:20, 17.73it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:20, 17.68it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:18, 19.63it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.71it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.09it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 18.95it/s]
eval model::  30%|███       | 152/500 [00:07<00:18, 19.02it/s]
eval model::  31%|███       | 156/500 [00:08<00:15, 22.54it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:15, 22.33it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:19, 17.54it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:18, 18.44it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 18.74it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 18.93it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:18, 18.16it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 16.69it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.76it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.10it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.22it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:19, 16.21it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.84it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.15it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:14, 20.74it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 20.40it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.28it/s]
eval model::  40%|████      | 201/500 [00:10<00:15, 19.58it/s]
eval model::  41%|████      | 205/500 [00:10<00:19, 14.88it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:18, 15.54it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 14.86it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.48it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.47it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 12.87it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.39it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.36it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.72it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 20.18it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:13, 20.38it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:13, 19.58it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:12, 21.25it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:15, 16.14it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:14, 17.64it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:13, 18.76it/s]
eval model::  50%|█████     | 251/500 [00:13<00:14, 17.15it/s]
eval model::  51%|█████     | 254/500 [00:13<00:13, 18.26it/s]
eval model::  51%|█████     | 256/500 [00:13<00:13, 18.58it/s]
eval model::  52%|█████▏    | 259/500 [00:14<00:11, 20.91it/s]
eval model::  52%|█████▏    | 262/500 [00:14<00:11, 21.49it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:11, 19.83it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:11, 20.51it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:11, 20.61it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:10, 21.42it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 19.04it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:12, 18.28it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 19.86it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:19, 11.10it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:15, 13.32it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:13, 15.48it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:13, 14.98it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:15, 12.84it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 15.54it/s]
eval model::  60%|██████    | 301/500 [00:16<00:14, 14.11it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 15.22it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 17.66it/s]
eval model::  62%|██████▏   | 309/500 [00:17<00:10, 18.01it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:10, 18.09it/s]
eval model::  63%|██████▎   | 314/500 [00:17<00:10, 18.18it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:11, 16.45it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:09, 18.51it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 18.78it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 20.41it/s]
eval model::  66%|██████▌   | 328/500 [00:18<00:09, 18.79it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:08, 19.63it/s]
eval model::  67%|██████▋   | 335/500 [00:18<00:07, 22.97it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 19.92it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:09, 16.17it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.40it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:08, 18.98it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:07, 20.89it/s]
eval model::  70%|███████   | 352/500 [00:19<00:07, 20.52it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 18.63it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 18.75it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:07, 19.86it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 17.97it/s]
eval model::  73%|███████▎  | 365/500 [00:20<00:07, 17.50it/s]
eval model::  73%|███████▎  | 367/500 [00:20<00:08, 14.96it/s]
eval model::  74%|███████▍  | 370/500 [00:20<00:07, 17.03it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 17.12it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 19.13it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 24.01it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 22.47it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:04, 23.31it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:08, 13.74it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:06, 15.97it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 17.02it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 19.19it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 18.09it/s]
eval model::  81%|████████  | 403/500 [00:22<00:05, 18.18it/s]
eval model::  81%|████████  | 406/500 [00:22<00:05, 18.79it/s]
eval model::  82%|████████▏ | 409/500 [00:22<00:04, 20.07it/s]
eval model::  82%|████████▏ | 412/500 [00:22<00:03, 22.12it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:03, 22.34it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:03, 21.98it/s]
eval model::  84%|████████▍ | 421/500 [00:23<00:05, 13.99it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.12it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.69it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 16.57it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 15.50it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 19.12it/s]
eval model::  88%|████████▊ | 438/500 [00:24<00:03, 20.16it/s]
eval model::  88%|████████▊ | 441/500 [00:24<00:03, 18.34it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 19.70it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 20.90it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 20.39it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 22.06it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 22.73it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:01, 22.91it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 18.16it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 15.56it/s]
eval model::  93%|█████████▎| 467/500 [00:25<00:02, 15.74it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 18.77it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.45it/s]
eval model::  95%|█████████▌| 477/500 [00:26<00:01, 20.35it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:00, 21.54it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 22.30it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 21.08it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 21.61it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 19.80it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 21.05it/s]
eval model:: 100%|█████████▉| 499/500 [00:27<00:00, 24.29it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.46it/s]
[2024/07/27 15:20:38] ppocr INFO: cur metric, precision: 0.7227319062181448, recall: 0.6827154549831488, hmean: 0.7021539985144838, fps: 44.38254242193704
[2024/07/27 15:20:38] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:20:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:20:47] ppocr INFO: epoch: [1301/1500], global_step: 3903, lr: 0.001000, loss: 1.111190, loss_shrink_maps: 0.549394, loss_threshold_maps: 0.456178, loss_binary_maps: 0.109479, avg_reader_cost: 2.06085 s, avg_batch_cost: 2.35833 s, avg_samples: 12.5, ips: 5.30037 samples/s, eta: 1:26:08
[2024/07/27 15:20:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:20:56] ppocr INFO: epoch: [1302/1500], global_step: 3906, lr: 0.001000, loss: 1.106836, loss_shrink_maps: 0.537837, loss_threshold_maps: 0.439203, loss_binary_maps: 0.107253, avg_reader_cost: 2.28278 s, avg_batch_cost: 2.55281 s, avg_samples: 12.5, ips: 4.89657 samples/s, eta: 1:25:42
[2024/07/27 15:20:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:21:06] ppocr INFO: epoch: [1303/1500], global_step: 3909, lr: 0.001000, loss: 1.111190, loss_shrink_maps: 0.554420, loss_threshold_maps: 0.439996, loss_binary_maps: 0.110220, avg_reader_cost: 2.37278 s, avg_batch_cost: 2.61166 s, avg_samples: 12.5, ips: 4.78623 samples/s, eta: 1:25:16
[2024/07/27 15:21:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:21:15] ppocr INFO: epoch: [1304/1500], global_step: 3910, lr: 0.001000, loss: 1.116169, loss_shrink_maps: 0.556182, loss_threshold_maps: 0.447217, loss_binary_maps: 0.110898, avg_reader_cost: 0.56200 s, avg_batch_cost: 0.77235 s, avg_samples: 4.8, ips: 6.21481 samples/s, eta: 1:25:07
[2024/07/27 15:21:16] ppocr INFO: epoch: [1304/1500], global_step: 3912, lr: 0.001000, loss: 1.159606, loss_shrink_maps: 0.574412, loss_threshold_maps: 0.453521, loss_binary_maps: 0.114109, avg_reader_cost: 1.63704 s, avg_batch_cost: 1.78249 s, avg_samples: 7.7, ips: 4.31979 samples/s, eta: 1:24:50
[2024/07/27 15:21:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:21:26] ppocr INFO: epoch: [1305/1500], global_step: 3915, lr: 0.001000, loss: 1.159606, loss_shrink_maps: 0.576914, loss_threshold_maps: 0.458374, loss_binary_maps: 0.114699, avg_reader_cost: 2.29703 s, avg_batch_cost: 2.53785 s, avg_samples: 12.5, ips: 4.92543 samples/s, eta: 1:24:24
[2024/07/27 15:21:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:21:35] ppocr INFO: epoch: [1306/1500], global_step: 3918, lr: 0.001000, loss: 1.154426, loss_shrink_maps: 0.576914, loss_threshold_maps: 0.454913, loss_binary_maps: 0.114699, avg_reader_cost: 2.33805 s, avg_batch_cost: 2.57670 s, avg_samples: 12.5, ips: 4.85117 samples/s, eta: 1:23:58
[2024/07/27 15:21:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:21:45] ppocr INFO: epoch: [1307/1500], global_step: 3920, lr: 0.001000, loss: 1.163433, loss_shrink_maps: 0.581112, loss_threshold_maps: 0.454913, loss_binary_maps: 0.115361, avg_reader_cost: 1.43257 s, avg_batch_cost: 1.61817 s, avg_samples: 9.6, ips: 5.93261 samples/s, eta: 1:23:41
[2024/07/27 15:21:45] ppocr INFO: epoch: [1307/1500], global_step: 3921, lr: 0.001000, loss: 1.163433, loss_shrink_maps: 0.581112, loss_threshold_maps: 0.454913, loss_binary_maps: 0.115361, avg_reader_cost: 0.85584 s, avg_batch_cost: 0.91069 s, avg_samples: 2.9, ips: 3.18439 samples/s, eta: 1:23:32
[2024/07/27 15:21:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:21:55] ppocr INFO: epoch: [1308/1500], global_step: 3924, lr: 0.001000, loss: 1.163433, loss_shrink_maps: 0.581112, loss_threshold_maps: 0.459170, loss_binary_maps: 0.115361, avg_reader_cost: 2.37471 s, avg_batch_cost: 2.62077 s, avg_samples: 12.5, ips: 4.76959 samples/s, eta: 1:23:06
[2024/07/27 15:21:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:22:05] ppocr INFO: epoch: [1309/1500], global_step: 3927, lr: 0.001000, loss: 1.161251, loss_shrink_maps: 0.577259, loss_threshold_maps: 0.459170, loss_binary_maps: 0.114699, avg_reader_cost: 2.18009 s, avg_batch_cost: 2.59226 s, avg_samples: 12.5, ips: 4.82205 samples/s, eta: 1:22:40
[2024/07/27 15:22:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:22:15] ppocr INFO: epoch: [1310/1500], global_step: 3930, lr: 0.001000, loss: 1.148396, loss_shrink_maps: 0.575357, loss_threshold_maps: 0.450903, loss_binary_maps: 0.114321, avg_reader_cost: 2.24609 s, avg_batch_cost: 2.61566 s, avg_samples: 12.5, ips: 4.77892 samples/s, eta: 1:22:14
[2024/07/27 15:22:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:22:25] ppocr INFO: epoch: [1311/1500], global_step: 3933, lr: 0.001000, loss: 1.118981, loss_shrink_maps: 0.557111, loss_threshold_maps: 0.444163, loss_binary_maps: 0.110523, avg_reader_cost: 2.38659 s, avg_batch_cost: 2.65471 s, avg_samples: 12.5, ips: 4.70862 samples/s, eta: 1:21:48
[2024/07/27 15:22:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:22:35] ppocr INFO: epoch: [1312/1500], global_step: 3936, lr: 0.001000, loss: 1.118981, loss_shrink_maps: 0.557111, loss_threshold_maps: 0.444163, loss_binary_maps: 0.110464, avg_reader_cost: 2.25839 s, avg_batch_cost: 2.64049 s, avg_samples: 12.5, ips: 4.73398 samples/s, eta: 1:21:22
[2024/07/27 15:22:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:22:45] ppocr INFO: epoch: [1313/1500], global_step: 3939, lr: 0.001000, loss: 1.143445, loss_shrink_maps: 0.566877, loss_threshold_maps: 0.455160, loss_binary_maps: 0.112408, avg_reader_cost: 2.19911 s, avg_batch_cost: 2.56808 s, avg_samples: 12.5, ips: 4.86744 samples/s, eta: 1:20:56
[2024/07/27 15:22:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:22:53] ppocr INFO: epoch: [1314/1500], global_step: 3940, lr: 0.001000, loss: 1.154135, loss_shrink_maps: 0.578382, loss_threshold_maps: 0.466229, loss_binary_maps: 0.114613, avg_reader_cost: 0.67526 s, avg_batch_cost: 0.77942 s, avg_samples: 4.8, ips: 6.15843 samples/s, eta: 1:20:48
[2024/07/27 15:22:54] ppocr INFO: epoch: [1314/1500], global_step: 3942, lr: 0.001000, loss: 1.154135, loss_shrink_maps: 0.569556, loss_threshold_maps: 0.471662, loss_binary_maps: 0.112888, avg_reader_cost: 1.65114 s, avg_batch_cost: 1.79823 s, avg_samples: 7.7, ips: 4.28198 samples/s, eta: 1:20:30
[2024/07/27 15:22:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:23:04] ppocr INFO: epoch: [1315/1500], global_step: 3945, lr: 0.001000, loss: 1.145455, loss_shrink_maps: 0.579475, loss_threshold_maps: 0.444976, loss_binary_maps: 0.115185, avg_reader_cost: 2.35255 s, avg_batch_cost: 2.59904 s, avg_samples: 12.5, ips: 4.80946 samples/s, eta: 1:20:04
[2024/07/27 15:23:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:23:14] ppocr INFO: epoch: [1316/1500], global_step: 3948, lr: 0.001000, loss: 1.145455, loss_shrink_maps: 0.579475, loss_threshold_maps: 0.453595, loss_binary_maps: 0.115185, avg_reader_cost: 2.36378 s, avg_batch_cost: 2.60136 s, avg_samples: 12.5, ips: 4.80518 samples/s, eta: 1:19:38
[2024/07/27 15:23:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:23:23] ppocr INFO: epoch: [1317/1500], global_step: 3950, lr: 0.001000, loss: 1.154142, loss_shrink_maps: 0.579475, loss_threshold_maps: 0.461875, loss_binary_maps: 0.115185, avg_reader_cost: 1.31620 s, avg_batch_cost: 1.61623 s, avg_samples: 9.6, ips: 5.93975 samples/s, eta: 1:19:21
[2024/07/27 15:23:23] ppocr INFO: epoch: [1317/1500], global_step: 3951, lr: 0.001000, loss: 1.178124, loss_shrink_maps: 0.585112, loss_threshold_maps: 0.471662, loss_binary_maps: 0.116292, avg_reader_cost: 0.85428 s, avg_batch_cost: 0.90974 s, avg_samples: 2.9, ips: 3.18774 samples/s, eta: 1:19:12
[2024/07/27 15:23:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:23:33] ppocr INFO: epoch: [1318/1500], global_step: 3954, lr: 0.001000, loss: 1.119716, loss_shrink_maps: 0.571997, loss_threshold_maps: 0.443299, loss_binary_maps: 0.113856, avg_reader_cost: 2.36851 s, avg_batch_cost: 2.60917 s, avg_samples: 12.5, ips: 4.79079 samples/s, eta: 1:18:46
[2024/07/27 15:23:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:23:43] ppocr INFO: epoch: [1319/1500], global_step: 3957, lr: 0.001000, loss: 1.121383, loss_shrink_maps: 0.566237, loss_threshold_maps: 0.443299, loss_binary_maps: 0.112551, avg_reader_cost: 2.29930 s, avg_batch_cost: 2.54367 s, avg_samples: 12.5, ips: 4.91415 samples/s, eta: 1:18:20
[2024/07/27 15:23:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:23:53] ppocr INFO: epoch: [1320/1500], global_step: 3960, lr: 0.001000, loss: 1.100638, loss_shrink_maps: 0.550936, loss_threshold_maps: 0.433819, loss_binary_maps: 0.109479, avg_reader_cost: 2.42347 s, avg_batch_cost: 2.67614 s, avg_samples: 12.5, ips: 4.67091 samples/s, eta: 1:17:54

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:55,  8.92it/s]
eval model::   1%|          | 3/500 [00:00<00:33, 14.91it/s]
eval model::   1%|          | 6/500 [00:00<00:23, 21.14it/s]
eval model::   2%|▏         | 9/500 [00:00<00:22, 21.41it/s]
eval model::   2%|▏         | 12/500 [00:00<00:25, 19.23it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.72it/s]
eval model::   4%|▎         | 18/500 [00:00<00:21, 22.35it/s]
eval model::   4%|▍         | 21/500 [00:01<00:21, 22.50it/s]
eval model::   5%|▌         | 25/500 [00:01<00:20, 23.60it/s]
eval model::   6%|▌         | 28/500 [00:01<00:19, 24.26it/s]
eval model::   6%|▌         | 31/500 [00:01<00:21, 22.21it/s]
eval model::   7%|▋         | 34/500 [00:01<00:21, 21.75it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 21.96it/s]
eval model::   8%|▊         | 40/500 [00:01<00:23, 19.78it/s]
eval model::   9%|▊         | 43/500 [00:02<00:23, 19.20it/s]
eval model::   9%|▉         | 46/500 [00:02<00:24, 18.78it/s]
eval model::  10%|▉         | 49/500 [00:02<00:21, 20.87it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 21.12it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.25it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:22, 19.80it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 15.73it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.90it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:21, 20.24it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 21.92it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 22.24it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:18, 22.34it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:17, 23.53it/s]
eval model::  16%|█▋        | 82/500 [00:03<00:17, 23.39it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:17, 23.65it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 20.32it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:24, 16.37it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:24, 16.88it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:20, 19.57it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:18, 21.60it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.60it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 22.24it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.64it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 21.39it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:17, 22.61it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:17, 21.71it/s]
eval model::  24%|██▍       | 120/500 [00:05<00:18, 20.97it/s]
eval model::  25%|██▍       | 123/500 [00:05<00:17, 21.04it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.45it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 19.33it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 17.28it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:19, 18.45it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:17, 20.25it/s]
eval model::  28%|██▊       | 141/500 [00:06<00:17, 20.12it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.13it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:20, 17.29it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:19, 17.65it/s]
eval model::  30%|███       | 151/500 [00:07<00:21, 16.31it/s]
eval model::  31%|███       | 154/500 [00:07<00:18, 19.16it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:17, 19.76it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:23, 14.68it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:20, 16.24it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:18, 17.94it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:19, 16.91it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:19, 16.69it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:22, 14.58it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:21, 15.29it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.15it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.78it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 18.33it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:17, 18.40it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:16, 19.10it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:14, 20.81it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:15, 19.98it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:13, 21.57it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 21.20it/s]
eval model::  41%|████      | 205/500 [00:10<00:22, 13.02it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:20, 14.11it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:20, 14.52it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 14.51it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.31it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 12.98it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.31it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:19, 14.40it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:21, 12.72it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:18, 15.29it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:15, 17.92it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 21.11it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:13, 20.41it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 21.64it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 22.20it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:13, 18.90it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:12, 20.71it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:13, 19.01it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 20.69it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 20.60it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 20.41it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:12, 19.16it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:12, 18.50it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:14, 16.36it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:13, 17.13it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:14, 15.76it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 17.89it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:14, 15.84it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:14, 15.65it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:13, 15.98it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:11, 18.99it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:18, 11.79it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:15, 13.74it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:13, 15.70it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:12, 16.30it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:15, 13.35it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:13, 15.32it/s]
eval model::  60%|██████    | 300/500 [00:16<00:14, 13.75it/s]
eval model::  61%|██████    | 303/500 [00:16<00:12, 15.61it/s]
eval model::  61%|██████    | 305/500 [00:16<00:12, 16.01it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:10, 17.86it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 19.86it/s]
eval model::  63%|██████▎   | 314/500 [00:17<00:09, 19.49it/s]
eval model::  63%|██████▎   | 317/500 [00:17<00:08, 20.61it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:08, 20.76it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:08, 20.38it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:08, 20.93it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 20.70it/s]
eval model::  66%|██████▋   | 332/500 [00:18<00:08, 20.40it/s]
eval model::  67%|██████▋   | 335/500 [00:18<00:07, 22.31it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:07, 20.54it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:09, 16.06it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.40it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:09, 16.84it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 17.05it/s]
eval model::  70%|███████   | 351/500 [00:19<00:08, 18.44it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 18.68it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 18.58it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:07, 19.65it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:07, 17.91it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.29it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.70it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:06, 19.30it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 18.14it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 19.54it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 22.75it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 21.25it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:05, 22.72it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:07, 14.88it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:06, 16.49it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 16.96it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 17.17it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 19.32it/s]
eval model::  80%|████████  | 401/500 [00:21<00:06, 16.36it/s]
eval model::  81%|████████  | 404/500 [00:21<00:05, 18.28it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 19.88it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 20.65it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:04, 20.77it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:03, 21.12it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:03, 20.76it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 16.05it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:04, 15.51it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.77it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 16.94it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 16.42it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 19.44it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 20.99it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:03, 19.65it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 21.24it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 22.36it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 20.93it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 21.89it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 22.59it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 24.38it/s]
eval model::  93%|█████████▎| 463/500 [00:25<00:02, 17.71it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:01, 17.36it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 17.13it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.29it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.67it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.49it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 21.09it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 21.14it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 19.88it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 20.92it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 19.63it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 20.71it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 24.23it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.69it/s]
[2024/07/27 15:24:20] ppocr INFO: cur metric, precision: 0.7466039707419018, recall: 0.6880115551275878, hmean: 0.7161112503132047, fps: 44.60784484725058
[2024/07/27 15:24:20] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:24:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:24:29] ppocr INFO: epoch: [1321/1500], global_step: 3963, lr: 0.001000, loss: 1.102305, loss_shrink_maps: 0.550936, loss_threshold_maps: 0.443997, loss_binary_maps: 0.109479, avg_reader_cost: 2.08630 s, avg_batch_cost: 2.42417 s, avg_samples: 12.5, ips: 5.15640 samples/s, eta: 1:17:28
[2024/07/27 15:24:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:24:39] ppocr INFO: epoch: [1322/1500], global_step: 3966, lr: 0.001000, loss: 1.098995, loss_shrink_maps: 0.550936, loss_threshold_maps: 0.444410, loss_binary_maps: 0.109479, avg_reader_cost: 2.28711 s, avg_batch_cost: 2.57610 s, avg_samples: 12.5, ips: 4.85229 samples/s, eta: 1:17:02
[2024/07/27 15:24:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:24:49] ppocr INFO: epoch: [1323/1500], global_step: 3969, lr: 0.001000, loss: 1.112331, loss_shrink_maps: 0.554495, loss_threshold_maps: 0.444410, loss_binary_maps: 0.110138, avg_reader_cost: 2.25880 s, avg_batch_cost: 2.61909 s, avg_samples: 12.5, ips: 4.77265 samples/s, eta: 1:16:36
[2024/07/27 15:24:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:24:57] ppocr INFO: epoch: [1324/1500], global_step: 3970, lr: 0.001000, loss: 1.098995, loss_shrink_maps: 0.550333, loss_threshold_maps: 0.444410, loss_binary_maps: 0.109329, avg_reader_cost: 0.67330 s, avg_batch_cost: 0.76636 s, avg_samples: 4.8, ips: 6.26340 samples/s, eta: 1:16:28
[2024/07/27 15:24:58] ppocr INFO: epoch: [1324/1500], global_step: 3972, lr: 0.001000, loss: 1.098995, loss_shrink_maps: 0.550333, loss_threshold_maps: 0.444410, loss_binary_maps: 0.109329, avg_reader_cost: 1.62475 s, avg_batch_cost: 1.77151 s, avg_samples: 7.7, ips: 4.34657 samples/s, eta: 1:16:10
[2024/07/27 15:24:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:25:08] ppocr INFO: epoch: [1325/1500], global_step: 3975, lr: 0.001000, loss: 1.098995, loss_shrink_maps: 0.550333, loss_threshold_maps: 0.444410, loss_binary_maps: 0.109329, avg_reader_cost: 2.33803 s, avg_batch_cost: 2.57523 s, avg_samples: 12.5, ips: 4.85393 samples/s, eta: 1:15:44
[2024/07/27 15:25:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:25:18] ppocr INFO: epoch: [1326/1500], global_step: 3978, lr: 0.001000, loss: 1.095740, loss_shrink_maps: 0.550333, loss_threshold_maps: 0.451444, loss_binary_maps: 0.109329, avg_reader_cost: 2.22501 s, avg_batch_cost: 2.59432 s, avg_samples: 12.5, ips: 4.81823 samples/s, eta: 1:15:18
[2024/07/27 15:25:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:25:27] ppocr INFO: epoch: [1327/1500], global_step: 3980, lr: 0.001000, loss: 1.108275, loss_shrink_maps: 0.565538, loss_threshold_maps: 0.451581, loss_binary_maps: 0.112153, avg_reader_cost: 1.47420 s, avg_batch_cost: 1.65768 s, avg_samples: 9.6, ips: 5.79123 samples/s, eta: 1:15:01
[2024/07/27 15:25:28] ppocr INFO: epoch: [1327/1500], global_step: 3981, lr: 0.001000, loss: 1.108275, loss_shrink_maps: 0.565538, loss_threshold_maps: 0.451581, loss_binary_maps: 0.112153, avg_reader_cost: 0.87465 s, avg_batch_cost: 0.93004 s, avg_samples: 2.9, ips: 3.11814 samples/s, eta: 1:14:52
[2024/07/27 15:25:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:25:38] ppocr INFO: epoch: [1328/1500], global_step: 3984, lr: 0.001000, loss: 1.108275, loss_shrink_maps: 0.565538, loss_threshold_maps: 0.453294, loss_binary_maps: 0.112153, avg_reader_cost: 2.27156 s, avg_batch_cost: 2.64268 s, avg_samples: 12.5, ips: 4.73005 samples/s, eta: 1:14:26
[2024/07/27 15:25:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:25:47] ppocr INFO: epoch: [1329/1500], global_step: 3987, lr: 0.001000, loss: 1.066260, loss_shrink_maps: 0.528523, loss_threshold_maps: 0.423481, loss_binary_maps: 0.105056, avg_reader_cost: 2.31634 s, avg_batch_cost: 2.55916 s, avg_samples: 12.5, ips: 4.88441 samples/s, eta: 1:14:00
[2024/07/27 15:25:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:25:57] ppocr INFO: epoch: [1330/1500], global_step: 3990, lr: 0.001000, loss: 1.066260, loss_shrink_maps: 0.534384, loss_threshold_maps: 0.427288, loss_binary_maps: 0.106226, avg_reader_cost: 2.19386 s, avg_batch_cost: 2.53522 s, avg_samples: 12.5, ips: 4.93053 samples/s, eta: 1:13:34
[2024/07/27 15:25:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:26:07] ppocr INFO: epoch: [1331/1500], global_step: 3993, lr: 0.001000, loss: 1.121674, loss_shrink_maps: 0.557551, loss_threshold_maps: 0.435983, loss_binary_maps: 0.110377, avg_reader_cost: 2.38225 s, avg_batch_cost: 2.64752 s, avg_samples: 12.5, ips: 4.72141 samples/s, eta: 1:13:08
[2024/07/27 15:26:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:26:17] ppocr INFO: epoch: [1332/1500], global_step: 3996, lr: 0.001000, loss: 1.170353, loss_shrink_maps: 0.583288, loss_threshold_maps: 0.464990, loss_binary_maps: 0.115081, avg_reader_cost: 2.21374 s, avg_batch_cost: 2.57855 s, avg_samples: 12.5, ips: 4.84768 samples/s, eta: 1:12:42
[2024/07/27 15:26:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:26:27] ppocr INFO: epoch: [1333/1500], global_step: 3999, lr: 0.001000, loss: 1.188952, loss_shrink_maps: 0.616422, loss_threshold_maps: 0.466980, loss_binary_maps: 0.122935, avg_reader_cost: 2.31317 s, avg_batch_cost: 2.55235 s, avg_samples: 12.5, ips: 4.89745 samples/s, eta: 1:12:16
[2024/07/27 15:26:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:26:35] ppocr INFO: epoch: [1334/1500], global_step: 4000, lr: 0.001000, loss: 1.188952, loss_shrink_maps: 0.616422, loss_threshold_maps: 0.466980, loss_binary_maps: 0.122935, avg_reader_cost: 0.73097 s, avg_batch_cost: 0.83124 s, avg_samples: 4.8, ips: 5.77454 samples/s, eta: 1:12:08
[2024/07/27 15:26:37] ppocr INFO: epoch: [1334/1500], global_step: 4002, lr: 0.001000, loss: 1.151787, loss_shrink_maps: 0.589087, loss_threshold_maps: 0.459683, loss_binary_maps: 0.117197, avg_reader_cost: 1.75509 s, avg_batch_cost: 1.90177 s, avg_samples: 7.7, ips: 4.04887 samples/s, eta: 1:11:51
[2024/07/27 15:26:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:26:47] ppocr INFO: epoch: [1335/1500], global_step: 4005, lr: 0.001000, loss: 1.124742, loss_shrink_maps: 0.558524, loss_threshold_maps: 0.455650, loss_binary_maps: 0.111062, avg_reader_cost: 2.23358 s, avg_batch_cost: 2.59274 s, avg_samples: 12.5, ips: 4.82115 samples/s, eta: 1:11:25
[2024/07/27 15:26:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:26:57] ppocr INFO: epoch: [1336/1500], global_step: 4008, lr: 0.001000, loss: 1.119180, loss_shrink_maps: 0.556900, loss_threshold_maps: 0.455650, loss_binary_maps: 0.110933, avg_reader_cost: 2.40163 s, avg_batch_cost: 2.66285 s, avg_samples: 12.5, ips: 4.69423 samples/s, eta: 1:10:59
[2024/07/27 15:26:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:27:06] ppocr INFO: epoch: [1337/1500], global_step: 4010, lr: 0.001000, loss: 1.119180, loss_shrink_maps: 0.556900, loss_threshold_maps: 0.455650, loss_binary_maps: 0.110933, avg_reader_cost: 1.35103 s, avg_batch_cost: 1.68081 s, avg_samples: 9.6, ips: 5.71152 samples/s, eta: 1:10:41
[2024/07/27 15:27:07] ppocr INFO: epoch: [1337/1500], global_step: 4011, lr: 0.001000, loss: 1.112613, loss_shrink_maps: 0.552571, loss_threshold_maps: 0.453171, loss_binary_maps: 0.109846, avg_reader_cost: 0.88693 s, avg_batch_cost: 0.94200 s, avg_samples: 2.9, ips: 3.07857 samples/s, eta: 1:10:33
[2024/07/27 15:27:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:27:16] ppocr INFO: epoch: [1338/1500], global_step: 4014, lr: 0.001000, loss: 1.081614, loss_shrink_maps: 0.527185, loss_threshold_maps: 0.445548, loss_binary_maps: 0.104826, avg_reader_cost: 2.17761 s, avg_batch_cost: 2.55205 s, avg_samples: 12.5, ips: 4.89802 samples/s, eta: 1:10:07
[2024/07/27 15:27:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:27:26] ppocr INFO: epoch: [1339/1500], global_step: 4017, lr: 0.001000, loss: 1.085667, loss_shrink_maps: 0.530602, loss_threshold_maps: 0.445548, loss_binary_maps: 0.105448, avg_reader_cost: 2.38366 s, avg_batch_cost: 2.63889 s, avg_samples: 12.5, ips: 4.73685 samples/s, eta: 1:09:41
[2024/07/27 15:27:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:27:36] ppocr INFO: epoch: [1340/1500], global_step: 4020, lr: 0.001000, loss: 1.059188, loss_shrink_maps: 0.516768, loss_threshold_maps: 0.445548, loss_binary_maps: 0.102827, avg_reader_cost: 2.17814 s, avg_batch_cost: 2.52312 s, avg_samples: 12.5, ips: 4.95418 samples/s, eta: 1:09:15

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:01,  8.17it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 17.02it/s]
eval model::   1%|▏         | 7/500 [00:00<00:25, 19.14it/s]
eval model::   2%|▏         | 9/500 [00:00<00:27, 17.99it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.65it/s]
eval model::   3%|▎         | 13/500 [00:00<00:26, 18.25it/s]
eval model::   3%|▎         | 16/500 [00:00<00:23, 20.21it/s]
eval model::   4%|▍         | 19/500 [00:00<00:21, 22.07it/s]
eval model::   4%|▍         | 22/500 [00:01<00:21, 22.31it/s]
eval model::   5%|▌         | 25/500 [00:01<00:22, 21.35it/s]
eval model::   6%|▌         | 28/500 [00:01<00:21, 22.17it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.68it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.56it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 18.87it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 18.41it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 18.01it/s]
eval model::   9%|▉         | 44/500 [00:02<00:25, 17.91it/s]
eval model::   9%|▉         | 46/500 [00:02<00:27, 16.31it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 19.09it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 18.96it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 19.57it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:26, 16.57it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:29, 14.95it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.22it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:23, 18.77it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:21, 20.18it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 21.09it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:19, 21.75it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.45it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.53it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 21.35it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.34it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.14it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.07it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.93it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.42it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.70it/s]
eval model::  21%|██        | 106/500 [00:05<00:16, 23.48it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:16, 23.05it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:17, 21.99it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:18, 20.37it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:18, 20.83it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 20.29it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 18.94it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 18.75it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.72it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:21, 17.22it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:21, 17.15it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:18, 19.43it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.25it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.43it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.02it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 18.49it/s]
eval model::  30%|███       | 151/500 [00:07<00:18, 18.38it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 21.21it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.49it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:19, 17.60it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:18, 18.02it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.30it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:15, 20.81it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:16, 20.20it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:18, 17.96it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:16, 19.11it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:18, 17.17it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:16, 19.34it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 19.96it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:15, 20.61it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:15, 20.45it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:15, 19.17it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 20.14it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 20.34it/s]
eval model::  41%|████      | 205/500 [00:10<00:20, 14.55it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:18, 15.82it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 15.97it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.67it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:22, 12.44it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.50it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.19it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:20, 13.32it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 16.46it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:15, 17.21it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:14, 18.93it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:13, 20.28it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 20.33it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:13, 20.01it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:15, 17.10it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:13, 18.57it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 19.88it/s]
eval model::  50%|█████     | 251/500 [00:13<00:12, 19.37it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 20.74it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.20it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 22.38it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 22.13it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:11, 20.22it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 19.41it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 20.34it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.50it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 18.52it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 19.85it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:10, 20.99it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:18, 11.60it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:15, 13.88it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:13, 15.65it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:15, 13.06it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:13, 15.27it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 15.58it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.67it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 18.32it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:10, 18.95it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 19.45it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 19.14it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:09, 19.87it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 20.29it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 19.67it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 20.97it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 20.78it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 21.26it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 22.60it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:07, 21.75it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:09, 16.77it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 18.24it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 18.28it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 19.96it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 21.93it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 20.10it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:06, 20.22it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 18.30it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 17.56it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:08, 16.42it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:07, 18.47it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 20.40it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 21.57it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 23.10it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 20.64it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:07, 14.51it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 16.88it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:05, 18.08it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:05, 19.35it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:04, 21.42it/s]
eval model::  80%|████████  | 402/500 [00:21<00:04, 20.40it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 20.12it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 20.34it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 21.01it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 21.68it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 20.45it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.37it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:04, 16.33it/s]
eval model::  85%|████████▌ | 425/500 [00:22<00:05, 14.96it/s]
eval model::  86%|████████▌ | 428/500 [00:22<00:04, 16.99it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:03, 18.38it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:02, 21.99it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 23.55it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 21.48it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 23.11it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 21.35it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 18.96it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 21.16it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:02, 20.46it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:02, 19.50it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 16.97it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 17.33it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:02, 16.55it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 16.90it/s]
eval model::  94%|█████████▍| 472/500 [00:25<00:01, 20.11it/s]
eval model::  95%|█████████▌| 475/500 [00:25<00:01, 20.62it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:00, 22.27it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 22.20it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 21.35it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 21.34it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 22.61it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 19.94it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 19.29it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 22.83it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.96it/s]
[2024/07/27 15:28:02] ppocr INFO: cur metric, precision: 0.7521786492374728, recall: 0.6649012999518537, hmean: 0.705852287247636, fps: 44.85282197265395
[2024/07/27 15:28:02] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:28:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:28:12] ppocr INFO: epoch: [1341/1500], global_step: 4023, lr: 0.001000, loss: 1.076834, loss_shrink_maps: 0.526975, loss_threshold_maps: 0.446613, loss_binary_maps: 0.104891, avg_reader_cost: 2.30687 s, avg_batch_cost: 2.65960 s, avg_samples: 12.5, ips: 4.69996 samples/s, eta: 1:08:49
[2024/07/27 15:28:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:28:22] ppocr INFO: epoch: [1342/1500], global_step: 4026, lr: 0.001000, loss: 1.093821, loss_shrink_maps: 0.549546, loss_threshold_maps: 0.443581, loss_binary_maps: 0.108505, avg_reader_cost: 2.36268 s, avg_batch_cost: 2.61403 s, avg_samples: 12.5, ips: 4.78190 samples/s, eta: 1:08:23
[2024/07/27 15:28:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:28:32] ppocr INFO: epoch: [1343/1500], global_step: 4029, lr: 0.001000, loss: 1.093821, loss_shrink_maps: 0.549546, loss_threshold_maps: 0.439787, loss_binary_maps: 0.108505, avg_reader_cost: 2.36966 s, avg_batch_cost: 2.62480 s, avg_samples: 12.5, ips: 4.76226 samples/s, eta: 1:07:57
[2024/07/27 15:28:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:28:40] ppocr INFO: epoch: [1344/1500], global_step: 4030, lr: 0.001000, loss: 1.093821, loss_shrink_maps: 0.541558, loss_threshold_maps: 0.439787, loss_binary_maps: 0.107006, avg_reader_cost: 0.70130 s, avg_batch_cost: 0.79164 s, avg_samples: 4.8, ips: 6.06336 samples/s, eta: 1:07:48
[2024/07/27 15:28:42] ppocr INFO: epoch: [1344/1500], global_step: 4032, lr: 0.001000, loss: 1.113257, loss_shrink_maps: 0.549546, loss_threshold_maps: 0.447973, loss_binary_maps: 0.108505, avg_reader_cost: 1.67545 s, avg_batch_cost: 1.82256 s, avg_samples: 7.7, ips: 4.22482 samples/s, eta: 1:07:31
[2024/07/27 15:28:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:28:52] ppocr INFO: epoch: [1345/1500], global_step: 4035, lr: 0.001000, loss: 1.134255, loss_shrink_maps: 0.555572, loss_threshold_maps: 0.447973, loss_binary_maps: 0.110535, avg_reader_cost: 2.24970 s, avg_batch_cost: 2.61555 s, avg_samples: 12.5, ips: 4.77910 samples/s, eta: 1:07:05
[2024/07/27 15:28:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:29:02] ppocr INFO: epoch: [1346/1500], global_step: 4038, lr: 0.001000, loss: 1.123406, loss_shrink_maps: 0.549546, loss_threshold_maps: 0.444179, loss_binary_maps: 0.108505, avg_reader_cost: 2.30830 s, avg_batch_cost: 2.56532 s, avg_samples: 12.5, ips: 4.87269 samples/s, eta: 1:06:39
[2024/07/27 15:29:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:29:11] ppocr INFO: epoch: [1347/1500], global_step: 4040, lr: 0.001000, loss: 1.123406, loss_shrink_maps: 0.549546, loss_threshold_maps: 0.444179, loss_binary_maps: 0.108505, avg_reader_cost: 1.52190 s, avg_batch_cost: 1.71936 s, avg_samples: 9.6, ips: 5.58348 samples/s, eta: 1:06:22
[2024/07/27 15:29:12] ppocr INFO: epoch: [1347/1500], global_step: 4041, lr: 0.001000, loss: 1.107413, loss_shrink_maps: 0.541558, loss_threshold_maps: 0.447973, loss_binary_maps: 0.107006, avg_reader_cost: 0.90584 s, avg_batch_cost: 0.96157 s, avg_samples: 2.9, ips: 3.01592 samples/s, eta: 1:06:13
[2024/07/27 15:29:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:29:22] ppocr INFO: epoch: [1348/1500], global_step: 4044, lr: 0.001000, loss: 1.129250, loss_shrink_maps: 0.558302, loss_threshold_maps: 0.464580, loss_binary_maps: 0.111592, avg_reader_cost: 2.46291 s, avg_batch_cost: 2.70827 s, avg_samples: 12.5, ips: 4.61548 samples/s, eta: 1:05:47
[2024/07/27 15:29:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:29:32] ppocr INFO: epoch: [1349/1500], global_step: 4047, lr: 0.001000, loss: 1.123406, loss_shrink_maps: 0.548285, loss_threshold_maps: 0.466198, loss_binary_maps: 0.109483, avg_reader_cost: 2.22761 s, avg_batch_cost: 2.58565 s, avg_samples: 12.5, ips: 4.83437 samples/s, eta: 1:05:21
[2024/07/27 15:29:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:29:42] ppocr INFO: epoch: [1350/1500], global_step: 4050, lr: 0.001000, loss: 1.137400, loss_shrink_maps: 0.569335, loss_threshold_maps: 0.466198, loss_binary_maps: 0.113402, avg_reader_cost: 2.27349 s, avg_batch_cost: 2.63642 s, avg_samples: 12.5, ips: 4.74128 samples/s, eta: 1:04:55
[2024/07/27 15:29:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:29:52] ppocr INFO: epoch: [1351/1500], global_step: 4053, lr: 0.001000, loss: 1.142968, loss_shrink_maps: 0.586973, loss_threshold_maps: 0.456405, loss_binary_maps: 0.117086, avg_reader_cost: 2.18571 s, avg_batch_cost: 2.53784 s, avg_samples: 12.5, ips: 4.92545 samples/s, eta: 1:04:29
[2024/07/27 15:29:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:30:01] ppocr INFO: epoch: [1352/1500], global_step: 4056, lr: 0.001000, loss: 1.134353, loss_shrink_maps: 0.585392, loss_threshold_maps: 0.456405, loss_binary_maps: 0.116945, avg_reader_cost: 2.16304 s, avg_batch_cost: 2.49446 s, avg_samples: 12.5, ips: 5.01111 samples/s, eta: 1:04:03
[2024/07/27 15:30:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:30:10] ppocr INFO: epoch: [1353/1500], global_step: 4059, lr: 0.001000, loss: 1.119951, loss_shrink_maps: 0.567068, loss_threshold_maps: 0.448092, loss_binary_maps: 0.113313, avg_reader_cost: 2.04876 s, avg_batch_cost: 2.32922 s, avg_samples: 12.5, ips: 5.36660 samples/s, eta: 1:03:37
[2024/07/27 15:30:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:30:19] ppocr INFO: epoch: [1354/1500], global_step: 4060, lr: 0.001000, loss: 1.119951, loss_shrink_maps: 0.567068, loss_threshold_maps: 0.448092, loss_binary_maps: 0.113313, avg_reader_cost: 0.69008 s, avg_batch_cost: 0.77583 s, avg_samples: 4.8, ips: 6.18693 samples/s, eta: 1:03:28
[2024/07/27 15:30:20] ppocr INFO: epoch: [1354/1500], global_step: 4062, lr: 0.001000, loss: 1.105432, loss_shrink_maps: 0.550663, loss_threshold_maps: 0.445528, loss_binary_maps: 0.109751, avg_reader_cost: 1.64421 s, avg_batch_cost: 1.79170 s, avg_samples: 7.7, ips: 4.29759 samples/s, eta: 1:03:11
[2024/07/27 15:30:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:30:30] ppocr INFO: epoch: [1355/1500], global_step: 4065, lr: 0.001000, loss: 1.134319, loss_shrink_maps: 0.577209, loss_threshold_maps: 0.446722, loss_binary_maps: 0.115164, avg_reader_cost: 2.25471 s, avg_batch_cost: 2.60420 s, avg_samples: 12.5, ips: 4.79994 samples/s, eta: 1:02:45
[2024/07/27 15:30:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:30:40] ppocr INFO: epoch: [1356/1500], global_step: 4068, lr: 0.001000, loss: 1.134319, loss_shrink_maps: 0.577209, loss_threshold_maps: 0.448092, loss_binary_maps: 0.115164, avg_reader_cost: 2.29239 s, avg_batch_cost: 2.52857 s, avg_samples: 12.5, ips: 4.94351 samples/s, eta: 1:02:19
[2024/07/27 15:30:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:30:49] ppocr INFO: epoch: [1357/1500], global_step: 4070, lr: 0.001000, loss: 1.134319, loss_shrink_maps: 0.577209, loss_threshold_maps: 0.449773, loss_binary_maps: 0.115164, avg_reader_cost: 1.51185 s, avg_batch_cost: 1.71042 s, avg_samples: 9.6, ips: 5.61264 samples/s, eta: 1:02:02
[2024/07/27 15:30:50] ppocr INFO: epoch: [1357/1500], global_step: 4071, lr: 0.001000, loss: 1.134319, loss_shrink_maps: 0.577209, loss_threshold_maps: 0.456789, loss_binary_maps: 0.115164, avg_reader_cost: 0.90213 s, avg_batch_cost: 0.95700 s, avg_samples: 2.9, ips: 3.03030 samples/s, eta: 1:01:53
[2024/07/27 15:30:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:31:00] ppocr INFO: epoch: [1358/1500], global_step: 4074, lr: 0.001000, loss: 1.103323, loss_shrink_maps: 0.548764, loss_threshold_maps: 0.456789, loss_binary_maps: 0.108902, avg_reader_cost: 2.45638 s, avg_batch_cost: 2.71806 s, avg_samples: 12.5, ips: 4.59887 samples/s, eta: 1:01:27
[2024/07/27 15:31:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:31:10] ppocr INFO: epoch: [1359/1500], global_step: 4077, lr: 0.001000, loss: 1.180944, loss_shrink_maps: 0.579874, loss_threshold_maps: 0.473954, loss_binary_maps: 0.115374, avg_reader_cost: 2.32014 s, avg_batch_cost: 2.56086 s, avg_samples: 12.5, ips: 4.88117 samples/s, eta: 1:01:01
[2024/07/27 15:31:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:31:20] ppocr INFO: epoch: [1360/1500], global_step: 4080, lr: 0.001000, loss: 1.194380, loss_shrink_maps: 0.601796, loss_threshold_maps: 0.473954, loss_binary_maps: 0.119985, avg_reader_cost: 2.28766 s, avg_batch_cost: 2.65730 s, avg_samples: 12.5, ips: 4.70402 samples/s, eta: 1:00:35

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:01,  8.11it/s]
eval model::   1%|          | 3/500 [00:00<00:34, 14.22it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.89it/s]
eval model::   2%|▏         | 8/500 [00:00<00:25, 19.13it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.72it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 18.82it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.77it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.43it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 21.91it/s]
eval model::   5%|▌         | 26/500 [00:01<00:21, 21.68it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 22.07it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 19.69it/s]
eval model::   7%|▋         | 35/500 [00:01<00:23, 19.39it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.86it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.32it/s]
eval model::   9%|▊         | 43/500 [00:02<00:25, 17.82it/s]
eval model::   9%|▉         | 46/500 [00:02<00:25, 17.63it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 18.90it/s]
eval model::  10%|█         | 51/500 [00:02<00:23, 19.01it/s]
eval model::  11%|█         | 54/500 [00:02<00:22, 19.48it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:21, 20.73it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:22, 19.64it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:30, 14.55it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:29, 14.66it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:25, 17.24it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:23, 18.16it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 19.70it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:20, 20.79it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:21, 19.86it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.61it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.90it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:19, 21.65it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:21, 18.70it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.81it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:20, 20.15it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.33it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.33it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 20.92it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.77it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 21.27it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:17, 22.43it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:17, 21.93it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.17it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.11it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.81it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.66it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 17.32it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:19, 19.02it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:19, 18.95it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.12it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 20.92it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:17, 20.32it/s]
eval model::  30%|███       | 151/500 [00:07<00:17, 19.42it/s]
eval model::  31%|███       | 154/500 [00:07<00:16, 21.60it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:14, 23.23it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:18, 18.36it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:17, 19.60it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.75it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:18, 18.23it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 18.28it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:19, 16.77it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.16it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.53it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.48it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 18.58it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 19.05it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:15, 20.16it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:14, 21.76it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:15, 19.09it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 20.67it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 20.03it/s]
eval model::  41%|████      | 205/500 [00:10<00:21, 13.52it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:20, 14.49it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 14.61it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.91it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.26it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.21it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:19, 14.44it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:22, 12.42it/s]
eval model::  45%|████▍     | 223/500 [00:12<00:20, 13.57it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:17, 15.75it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:15, 17.95it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:13, 19.50it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:14, 18.55it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:13, 19.16it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:13, 19.77it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:15, 16.89it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:13, 18.62it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:13, 18.29it/s]
eval model::  50%|█████     | 251/500 [00:13<00:13, 18.58it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.18it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.95it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 23.26it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:09, 24.16it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:10, 21.47it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 19.95it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 20.15it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.51it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.13it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.63it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:10, 21.19it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:18, 11.72it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:14, 14.05it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:14, 14.81it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:13, 15.67it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:15, 13.43it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 16.50it/s]
eval model::  60%|██████    | 301/500 [00:16<00:14, 13.83it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 15.21it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:11, 16.93it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:10, 18.28it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:10, 18.08it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:10, 18.36it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:08, 20.48it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 21.31it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:09, 19.46it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 20.58it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 19.45it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:08, 19.42it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:08, 19.66it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:10, 16.05it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:12, 12.82it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:10, 15.15it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 17.29it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 17.69it/s]
eval model::  70%|███████   | 351/500 [00:19<00:08, 18.04it/s]
eval model::  71%|███████   | 354/500 [00:19<00:07, 20.17it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 18.03it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:07, 18.26it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:08, 16.44it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:08, 16.65it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:08, 16.39it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:07, 18.27it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 17.67it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 19.15it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 22.78it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 22.26it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:04, 23.08it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:08, 13.80it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:07, 14.53it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:07, 15.36it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 17.10it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 18.66it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 17.14it/s]
eval model::  81%|████████  | 404/500 [00:22<00:05, 18.51it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 19.75it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 21.13it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:03, 22.07it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:03, 21.17it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 19.79it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:05, 13.83it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 13.80it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.11it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 16.58it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 15.50it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 18.44it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:03, 20.03it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:03, 19.42it/s]
eval model::  89%|████████▊ | 443/500 [00:24<00:02, 20.85it/s]
eval model::  89%|████████▉ | 446/500 [00:24<00:02, 20.27it/s]
eval model::  90%|████████▉ | 449/500 [00:24<00:02, 18.19it/s]
eval model::  90%|█████████ | 452/500 [00:24<00:02, 20.13it/s]
eval model::  91%|█████████ | 455/500 [00:24<00:02, 21.89it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:02, 20.88it/s]
eval model::  92%|█████████▏| 461/500 [00:25<00:01, 19.54it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 14.91it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 15.16it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:02, 15.76it/s]
eval model::  94%|█████████▍| 472/500 [00:25<00:01, 19.54it/s]
eval model::  95%|█████████▌| 475/500 [00:25<00:01, 19.91it/s]
eval model::  96%|█████████▌| 478/500 [00:26<00:01, 21.69it/s]
eval model::  96%|█████████▌| 481/500 [00:26<00:00, 22.63it/s]
eval model::  97%|█████████▋| 484/500 [00:26<00:00, 23.16it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 22.53it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 22.45it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 20.88it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 22.29it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 25.57it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.54it/s]
[2024/07/27 15:31:47] ppocr INFO: cur metric, precision: 0.7461333333333333, recall: 0.673567645642754, hmean: 0.707995951417004, fps: 44.495262446689914
[2024/07/27 15:31:47] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:31:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:31:56] ppocr INFO: epoch: [1361/1500], global_step: 4083, lr: 0.001000, loss: 1.180944, loss_shrink_maps: 0.579874, loss_threshold_maps: 0.480018, loss_binary_maps: 0.115374, avg_reader_cost: 2.18653 s, avg_batch_cost: 2.42761 s, avg_samples: 12.5, ips: 5.14909 samples/s, eta: 1:00:09
[2024/07/27 15:31:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:32:05] ppocr INFO: epoch: [1362/1500], global_step: 4086, lr: 0.001000, loss: 1.170079, loss_shrink_maps: 0.571111, loss_threshold_maps: 0.473674, loss_binary_maps: 0.113701, avg_reader_cost: 2.22182 s, avg_batch_cost: 2.57392 s, avg_samples: 12.5, ips: 4.85640 samples/s, eta: 0:59:43
[2024/07/27 15:32:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:32:15] ppocr INFO: epoch: [1363/1500], global_step: 4089, lr: 0.001000, loss: 1.123381, loss_shrink_maps: 0.555612, loss_threshold_maps: 0.449198, loss_binary_maps: 0.110270, avg_reader_cost: 2.33385 s, avg_batch_cost: 2.57087 s, avg_samples: 12.5, ips: 4.86216 samples/s, eta: 0:59:17
[2024/07/27 15:32:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:32:23] ppocr INFO: epoch: [1364/1500], global_step: 4090, lr: 0.001000, loss: 1.123381, loss_shrink_maps: 0.554450, loss_threshold_maps: 0.449198, loss_binary_maps: 0.110270, avg_reader_cost: 0.55496 s, avg_batch_cost: 0.71608 s, avg_samples: 4.8, ips: 6.70321 samples/s, eta: 0:59:08
[2024/07/27 15:32:24] ppocr INFO: epoch: [1364/1500], global_step: 4092, lr: 0.001000, loss: 1.123381, loss_shrink_maps: 0.554450, loss_threshold_maps: 0.449198, loss_binary_maps: 0.110270, avg_reader_cost: 1.52362 s, avg_batch_cost: 1.67021 s, avg_samples: 7.7, ips: 4.61019 samples/s, eta: 0:58:51
[2024/07/27 15:32:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:32:34] ppocr INFO: epoch: [1365/1500], global_step: 4095, lr: 0.001000, loss: 1.123381, loss_shrink_maps: 0.554450, loss_threshold_maps: 0.449198, loss_binary_maps: 0.110270, avg_reader_cost: 2.32915 s, avg_batch_cost: 2.62107 s, avg_samples: 12.5, ips: 4.76904 samples/s, eta: 0:58:25
[2024/07/27 15:32:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:32:44] ppocr INFO: epoch: [1366/1500], global_step: 4098, lr: 0.001000, loss: 1.134361, loss_shrink_maps: 0.555188, loss_threshold_maps: 0.457201, loss_binary_maps: 0.110795, avg_reader_cost: 2.15056 s, avg_batch_cost: 2.50167 s, avg_samples: 12.5, ips: 4.99666 samples/s, eta: 0:57:59
[2024/07/27 15:32:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:32:53] ppocr INFO: epoch: [1367/1500], global_step: 4100, lr: 0.001000, loss: 1.107824, loss_shrink_maps: 0.548862, loss_threshold_maps: 0.452677, loss_binary_maps: 0.109154, avg_reader_cost: 1.35327 s, avg_batch_cost: 1.67135 s, avg_samples: 9.6, ips: 5.74385 samples/s, eta: 0:57:42
[2024/07/27 15:32:54] ppocr INFO: epoch: [1367/1500], global_step: 4101, lr: 0.001000, loss: 1.085856, loss_shrink_maps: 0.542938, loss_threshold_maps: 0.434478, loss_binary_maps: 0.107861, avg_reader_cost: 0.88207 s, avg_batch_cost: 0.93744 s, avg_samples: 2.9, ips: 3.09354 samples/s, eta: 0:57:33
[2024/07/27 15:32:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:33:03] ppocr INFO: epoch: [1368/1500], global_step: 4104, lr: 0.001000, loss: 1.097685, loss_shrink_maps: 0.548862, loss_threshold_maps: 0.452677, loss_binary_maps: 0.109154, avg_reader_cost: 2.22380 s, avg_batch_cost: 2.55437 s, avg_samples: 12.5, ips: 4.89357 samples/s, eta: 0:57:07
[2024/07/27 15:33:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:33:13] ppocr INFO: epoch: [1369/1500], global_step: 4107, lr: 0.001000, loss: 1.124548, loss_shrink_maps: 0.557144, loss_threshold_maps: 0.454408, loss_binary_maps: 0.111158, avg_reader_cost: 2.44103 s, avg_batch_cost: 2.67276 s, avg_samples: 12.5, ips: 4.67680 samples/s, eta: 0:56:41
[2024/07/27 15:33:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:33:23] ppocr INFO: epoch: [1370/1500], global_step: 4110, lr: 0.001000, loss: 1.138470, loss_shrink_maps: 0.568280, loss_threshold_maps: 0.458308, loss_binary_maps: 0.112654, avg_reader_cost: 2.22540 s, avg_batch_cost: 2.59852 s, avg_samples: 12.5, ips: 4.81042 samples/s, eta: 0:56:15
[2024/07/27 15:33:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:33:33] ppocr INFO: epoch: [1371/1500], global_step: 4113, lr: 0.001000, loss: 1.124548, loss_shrink_maps: 0.561016, loss_threshold_maps: 0.454408, loss_binary_maps: 0.111457, avg_reader_cost: 2.42290 s, avg_batch_cost: 2.66644 s, avg_samples: 12.5, ips: 4.68789 samples/s, eta: 0:55:49
[2024/07/27 15:33:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:33:43] ppocr INFO: epoch: [1372/1500], global_step: 4116, lr: 0.001000, loss: 1.138470, loss_shrink_maps: 0.568280, loss_threshold_maps: 0.450067, loss_binary_maps: 0.112654, avg_reader_cost: 2.15672 s, avg_batch_cost: 2.54429 s, avg_samples: 12.5, ips: 4.91297 samples/s, eta: 0:55:23
[2024/07/27 15:33:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:33:53] ppocr INFO: epoch: [1373/1500], global_step: 4119, lr: 0.001000, loss: 1.106847, loss_shrink_maps: 0.557839, loss_threshold_maps: 0.448021, loss_binary_maps: 0.111001, avg_reader_cost: 2.19780 s, avg_batch_cost: 2.56351 s, avg_samples: 12.5, ips: 4.87612 samples/s, eta: 0:54:57
[2024/07/27 15:33:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:34:01] ppocr INFO: epoch: [1374/1500], global_step: 4120, lr: 0.001000, loss: 1.106847, loss_shrink_maps: 0.557839, loss_threshold_maps: 0.448021, loss_binary_maps: 0.111001, avg_reader_cost: 0.70380 s, avg_batch_cost: 0.79621 s, avg_samples: 4.8, ips: 6.02859 samples/s, eta: 0:54:49
[2024/07/27 15:34:03] ppocr INFO: epoch: [1374/1500], global_step: 4122, lr: 0.001000, loss: 1.106847, loss_shrink_maps: 0.553970, loss_threshold_maps: 0.448021, loss_binary_maps: 0.110649, avg_reader_cost: 1.68449 s, avg_batch_cost: 1.83144 s, avg_samples: 7.7, ips: 4.20435 samples/s, eta: 0:54:31
[2024/07/27 15:34:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:34:13] ppocr INFO: epoch: [1375/1500], global_step: 4125, lr: 0.001000, loss: 1.101380, loss_shrink_maps: 0.547288, loss_threshold_maps: 0.440002, loss_binary_maps: 0.109632, avg_reader_cost: 2.21536 s, avg_batch_cost: 2.62372 s, avg_samples: 12.5, ips: 4.76423 samples/s, eta: 0:54:05
[2024/07/27 15:34:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:34:22] ppocr INFO: epoch: [1376/1500], global_step: 4128, lr: 0.001000, loss: 1.093426, loss_shrink_maps: 0.546124, loss_threshold_maps: 0.440002, loss_binary_maps: 0.108823, avg_reader_cost: 2.16203 s, avg_batch_cost: 2.47755 s, avg_samples: 12.5, ips: 5.04531 samples/s, eta: 0:53:39
[2024/07/27 15:34:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:34:31] ppocr INFO: epoch: [1377/1500], global_step: 4130, lr: 0.001000, loss: 1.076703, loss_shrink_maps: 0.530695, loss_threshold_maps: 0.438918, loss_binary_maps: 0.105360, avg_reader_cost: 1.32647 s, avg_batch_cost: 1.63169 s, avg_samples: 9.6, ips: 5.88348 samples/s, eta: 0:53:22
[2024/07/27 15:34:32] ppocr INFO: epoch: [1377/1500], global_step: 4131, lr: 0.001000, loss: 1.069787, loss_shrink_maps: 0.525272, loss_threshold_maps: 0.438918, loss_binary_maps: 0.104547, avg_reader_cost: 0.86194 s, avg_batch_cost: 0.91768 s, avg_samples: 2.9, ips: 3.16015 samples/s, eta: 0:53:13
[2024/07/27 15:34:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:34:42] ppocr INFO: epoch: [1378/1500], global_step: 4134, lr: 0.001000, loss: 1.069787, loss_shrink_maps: 0.525272, loss_threshold_maps: 0.438918, loss_binary_maps: 0.104547, avg_reader_cost: 2.48004 s, avg_batch_cost: 2.73217 s, avg_samples: 12.5, ips: 4.57512 samples/s, eta: 0:52:48
[2024/07/27 15:34:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:34:52] ppocr INFO: epoch: [1379/1500], global_step: 4137, lr: 0.001000, loss: 1.072990, loss_shrink_maps: 0.525272, loss_threshold_maps: 0.442194, loss_binary_maps: 0.104547, avg_reader_cost: 2.18984 s, avg_batch_cost: 2.58745 s, avg_samples: 12.5, ips: 4.83101 samples/s, eta: 0:52:22
[2024/07/27 15:34:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:35:02] ppocr INFO: epoch: [1380/1500], global_step: 4140, lr: 0.001000, loss: 1.072990, loss_shrink_maps: 0.533859, loss_threshold_maps: 0.441113, loss_binary_maps: 0.106055, avg_reader_cost: 2.38861 s, avg_batch_cost: 2.63483 s, avg_samples: 12.5, ips: 4.74414 samples/s, eta: 0:51:56

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:56,  8.77it/s]
eval model::   1%|          | 3/500 [00:00<00:36, 13.79it/s]
eval model::   1%|          | 6/500 [00:00<00:25, 19.27it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 18.04it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.03it/s]
eval model::   3%|▎         | 13/500 [00:00<00:27, 17.78it/s]
eval model::   3%|▎         | 15/500 [00:00<00:27, 17.96it/s]
eval model::   4%|▎         | 18/500 [00:00<00:23, 20.90it/s]
eval model::   4%|▍         | 21/500 [00:01<00:23, 20.69it/s]
eval model::   5%|▍         | 24/500 [00:01<00:21, 22.33it/s]
eval model::   5%|▌         | 27/500 [00:01<00:23, 20.33it/s]
eval model::   6%|▌         | 30/500 [00:01<00:22, 20.85it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 19.59it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 18.84it/s]
eval model::   8%|▊         | 38/500 [00:02<00:26, 17.70it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.36it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.22it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.12it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 17.07it/s]
eval model::  10%|█         | 50/500 [00:02<00:25, 17.57it/s]
eval model::  10%|█         | 52/500 [00:02<00:25, 17.44it/s]
eval model::  11%|█         | 55/500 [00:02<00:23, 18.77it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:23, 18.79it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:27, 16.30it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:30, 14.25it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:30, 14.42it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.54it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:22, 19.22it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 19.86it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:22, 18.51it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:21, 19.57it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:21, 19.85it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:21, 19.32it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.54it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:23, 17.54it/s]
eval model::  18%|█▊        | 91/500 [00:05<00:24, 17.02it/s]
eval model::  19%|█▉        | 94/500 [00:05<00:21, 18.98it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:19, 20.38it/s]
eval model::  20%|██        | 100/500 [00:05<00:20, 19.68it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.02it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.05it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.39it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 20.58it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:19, 20.13it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:17, 21.53it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:19, 19.72it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:21, 17.54it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.33it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:20, 17.73it/s]
eval model::  26%|██▌       | 130/500 [00:07<00:23, 15.95it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:24, 15.09it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:21, 16.64it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:20, 17.34it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:20, 17.74it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 18.89it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.13it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.14it/s]
eval model::  30%|███       | 150/500 [00:08<00:18, 19.16it/s]
eval model::  30%|███       | 152/500 [00:08<00:20, 16.61it/s]
eval model::  31%|███       | 155/500 [00:08<00:17, 19.53it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 21.76it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:22, 15.31it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:19, 16.96it/s]
eval model::  33%|███▎      | 167/500 [00:09<00:18, 18.00it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:16, 19.74it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 16.88it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:17, 18.19it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.10it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:21, 15.10it/s]
eval model::  37%|███▋      | 184/500 [00:10<00:18, 16.99it/s]
eval model::  37%|███▋      | 186/500 [00:10<00:17, 17.62it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:16, 18.70it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:16, 18.91it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.91it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.73it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 21.51it/s]
eval model::  41%|████      | 204/500 [00:10<00:12, 23.23it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:18, 15.88it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:18, 15.43it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:18, 15.50it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.78it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 14.23it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:20, 13.79it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:17, 16.14it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.58it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:17, 16.04it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:17, 15.79it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:14, 18.65it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:14, 18.58it/s]
eval model::  47%|████▋     | 235/500 [00:13<00:14, 18.63it/s]
eval model::  48%|████▊     | 238/500 [00:13<00:12, 20.93it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:12, 21.44it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:14, 18.04it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:13, 19.14it/s]
eval model::  50%|█████     | 250/500 [00:13<00:13, 18.62it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.55it/s]
eval model::  51%|█████▏    | 257/500 [00:14<00:11, 21.80it/s]
eval model::  52%|█████▏    | 260/500 [00:14<00:10, 23.05it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:10, 22.34it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:11, 20.14it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.52it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 19.83it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.06it/s]
eval model::  55%|█████▌    | 277/500 [00:15<00:11, 19.00it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:11, 19.01it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:11, 19.77it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:20, 10.64it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:16, 12.99it/s]
eval model::  58%|█████▊    | 291/500 [00:16<00:13, 15.73it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:13, 15.66it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:15, 13.08it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 16.00it/s]
eval model::  60%|██████    | 302/500 [00:16<00:12, 15.57it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 15.25it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:11, 17.30it/s]
eval model::  62%|██████▏   | 310/500 [00:17<00:10, 18.88it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:09, 19.84it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 18.92it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:09, 19.95it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 19.38it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:09, 19.43it/s]
eval model::  65%|██████▌   | 327/500 [00:18<00:08, 21.00it/s]
eval model::  66%|██████▌   | 330/500 [00:18<00:08, 20.39it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:07, 21.38it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:07, 22.47it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:07, 21.73it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 15.76it/s]
eval model::  69%|██████▉   | 345/500 [00:19<00:08, 17.66it/s]
eval model::  70%|██████▉   | 348/500 [00:19<00:08, 18.86it/s]
eval model::  70%|███████   | 351/500 [00:19<00:07, 19.23it/s]
eval model::  71%|███████   | 354/500 [00:19<00:07, 20.67it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 17.94it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:07, 19.82it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 17.87it/s]
eval model::  73%|███████▎  | 365/500 [00:20<00:07, 18.28it/s]
eval model::  73%|███████▎  | 367/500 [00:20<00:07, 17.82it/s]
eval model::  74%|███████▍  | 370/500 [00:20<00:06, 19.30it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 18.10it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 19.12it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 21.15it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:05, 20.24it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:04, 23.86it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:08, 13.50it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:07, 15.30it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 16.09it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 17.81it/s]
eval model::  80%|████████  | 400/500 [00:22<00:05, 16.72it/s]
eval model::  80%|████████  | 402/500 [00:22<00:05, 17.04it/s]
eval model::  81%|████████  | 404/500 [00:22<00:05, 17.35it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 18.66it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 20.78it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:04, 20.91it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 20.73it/s]
eval model::  84%|████████▍ | 419/500 [00:23<00:04, 19.28it/s]
eval model::  84%|████████▍ | 421/500 [00:23<00:05, 13.96it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:04, 15.72it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:04, 16.05it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:03, 18.51it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 18.31it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 20.07it/s]
eval model::  88%|████████▊ | 438/500 [00:24<00:02, 20.93it/s]
eval model::  88%|████████▊ | 441/500 [00:24<00:03, 18.36it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 19.45it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 20.51it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 18.13it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 19.81it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:02, 19.74it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:01, 20.78it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 16.40it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 16.85it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 15.13it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:02, 15.74it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 18.27it/s]
eval model::  95%|█████████▍| 473/500 [00:26<00:01, 17.57it/s]
eval model::  95%|█████████▌| 475/500 [00:26<00:01, 17.20it/s]
eval model::  96%|█████████▌| 478/500 [00:26<00:01, 18.79it/s]
eval model::  96%|█████████▌| 481/500 [00:26<00:00, 20.44it/s]
eval model::  97%|█████████▋| 484/500 [00:26<00:00, 21.08it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 20.72it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 21.67it/s]
eval model::  99%|█████████▊| 493/500 [00:27<00:00, 18.81it/s]
eval model::  99%|█████████▉| 496/500 [00:27<00:00, 20.18it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 23.72it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.31it/s]
[2024/07/27 15:35:29] ppocr INFO: cur metric, precision: 0.7505175983436853, recall: 0.6981222917669716, hmean: 0.7233724120728362, fps: 44.24035771970459
[2024/07/27 15:35:29] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:35:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:35:38] ppocr INFO: epoch: [1381/1500], global_step: 4143, lr: 0.001000, loss: 1.076651, loss_shrink_maps: 0.534230, loss_threshold_maps: 0.443033, loss_binary_maps: 0.106690, avg_reader_cost: 2.11704 s, avg_batch_cost: 2.44807 s, avg_samples: 12.5, ips: 5.10607 samples/s, eta: 0:51:30
[2024/07/27 15:35:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:35:48] ppocr INFO: epoch: [1382/1500], global_step: 4146, lr: 0.001000, loss: 1.086060, loss_shrink_maps: 0.539482, loss_threshold_maps: 0.446155, loss_binary_maps: 0.107442, avg_reader_cost: 2.17741 s, avg_batch_cost: 2.49658 s, avg_samples: 12.5, ips: 5.00686 samples/s, eta: 0:51:03
[2024/07/27 15:35:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:35:58] ppocr INFO: epoch: [1383/1500], global_step: 4149, lr: 0.001000, loss: 1.116190, loss_shrink_maps: 0.546283, loss_threshold_maps: 0.450000, loss_binary_maps: 0.108858, avg_reader_cost: 2.42542 s, avg_batch_cost: 2.66418 s, avg_samples: 12.5, ips: 4.69188 samples/s, eta: 0:50:38
[2024/07/27 15:35:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:36:07] ppocr INFO: epoch: [1384/1500], global_step: 4150, lr: 0.001000, loss: 1.116190, loss_shrink_maps: 0.546283, loss_threshold_maps: 0.450000, loss_binary_maps: 0.108858, avg_reader_cost: 0.57450 s, avg_batch_cost: 0.79730 s, avg_samples: 4.8, ips: 6.02030 samples/s, eta: 0:50:29
[2024/07/27 15:36:08] ppocr INFO: epoch: [1384/1500], global_step: 4152, lr: 0.001000, loss: 1.116019, loss_shrink_maps: 0.551963, loss_threshold_maps: 0.444799, loss_binary_maps: 0.110182, avg_reader_cost: 1.68705 s, avg_batch_cost: 1.83459 s, avg_samples: 7.7, ips: 4.19711 samples/s, eta: 0:50:12
[2024/07/27 15:36:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:36:18] ppocr INFO: epoch: [1385/1500], global_step: 4155, lr: 0.001000, loss: 1.093412, loss_shrink_maps: 0.539634, loss_threshold_maps: 0.439031, loss_binary_maps: 0.107364, avg_reader_cost: 2.25388 s, avg_batch_cost: 2.63433 s, avg_samples: 12.5, ips: 4.74504 samples/s, eta: 0:49:46
[2024/07/27 15:36:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:36:28] ppocr INFO: epoch: [1386/1500], global_step: 4158, lr: 0.001000, loss: 1.098653, loss_shrink_maps: 0.539634, loss_threshold_maps: 0.440409, loss_binary_maps: 0.107471, avg_reader_cost: 2.25886 s, avg_batch_cost: 2.49967 s, avg_samples: 12.5, ips: 5.00067 samples/s, eta: 0:49:20
[2024/07/27 15:36:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:36:37] ppocr INFO: epoch: [1387/1500], global_step: 4160, lr: 0.001000, loss: 1.098653, loss_shrink_maps: 0.539634, loss_threshold_maps: 0.440409, loss_binary_maps: 0.107471, avg_reader_cost: 1.32984 s, avg_batch_cost: 1.63964 s, avg_samples: 9.6, ips: 5.85494 samples/s, eta: 0:49:02
[2024/07/27 15:36:37] ppocr INFO: epoch: [1387/1500], global_step: 4161, lr: 0.001000, loss: 1.093412, loss_shrink_maps: 0.535726, loss_threshold_maps: 0.439031, loss_binary_maps: 0.106475, avg_reader_cost: 0.86632 s, avg_batch_cost: 0.92144 s, avg_samples: 2.9, ips: 3.14725 samples/s, eta: 0:48:54
[2024/07/27 15:36:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:36:47] ppocr INFO: epoch: [1388/1500], global_step: 4164, lr: 0.001000, loss: 1.102282, loss_shrink_maps: 0.544678, loss_threshold_maps: 0.442165, loss_binary_maps: 0.108543, avg_reader_cost: 2.21545 s, avg_batch_cost: 2.58871 s, avg_samples: 12.5, ips: 4.82866 samples/s, eta: 0:48:28
[2024/07/27 15:36:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:36:57] ppocr INFO: epoch: [1389/1500], global_step: 4167, lr: 0.001000, loss: 1.098695, loss_shrink_maps: 0.542520, loss_threshold_maps: 0.439031, loss_binary_maps: 0.107980, avg_reader_cost: 2.32135 s, avg_batch_cost: 2.55765 s, avg_samples: 12.5, ips: 4.88731 samples/s, eta: 0:48:02
[2024/07/27 15:36:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:37:07] ppocr INFO: epoch: [1390/1500], global_step: 4170, lr: 0.001000, loss: 1.111392, loss_shrink_maps: 0.555083, loss_threshold_maps: 0.442056, loss_binary_maps: 0.110112, avg_reader_cost: 2.30086 s, avg_batch_cost: 2.67670 s, avg_samples: 12.5, ips: 4.66993 samples/s, eta: 0:47:36
[2024/07/27 15:37:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:37:17] ppocr INFO: epoch: [1391/1500], global_step: 4173, lr: 0.001000, loss: 1.127388, loss_shrink_maps: 0.566232, loss_threshold_maps: 0.446293, loss_binary_maps: 0.112556, avg_reader_cost: 2.39501 s, avg_batch_cost: 2.65336 s, avg_samples: 12.5, ips: 4.71101 samples/s, eta: 0:47:10
[2024/07/27 15:37:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:37:27] ppocr INFO: epoch: [1392/1500], global_step: 4176, lr: 0.001000, loss: 1.137492, loss_shrink_maps: 0.571832, loss_threshold_maps: 0.452297, loss_binary_maps: 0.113836, avg_reader_cost: 2.26192 s, avg_batch_cost: 2.50004 s, avg_samples: 12.5, ips: 4.99992 samples/s, eta: 0:46:44
[2024/07/27 15:37:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:37:37] ppocr INFO: epoch: [1393/1500], global_step: 4179, lr: 0.001000, loss: 1.127778, loss_shrink_maps: 0.571832, loss_threshold_maps: 0.438499, loss_binary_maps: 0.113836, avg_reader_cost: 2.32031 s, avg_batch_cost: 2.55926 s, avg_samples: 12.5, ips: 4.88422 samples/s, eta: 0:46:18
[2024/07/27 15:37:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:37:45] ppocr INFO: epoch: [1394/1500], global_step: 4180, lr: 0.001000, loss: 1.127778, loss_shrink_maps: 0.571832, loss_threshold_maps: 0.442102, loss_binary_maps: 0.113836, avg_reader_cost: 0.56872 s, avg_batch_cost: 0.78129 s, avg_samples: 4.8, ips: 6.14366 samples/s, eta: 0:46:09
[2024/07/27 15:37:46] ppocr INFO: epoch: [1394/1500], global_step: 4182, lr: 0.001000, loss: 1.122814, loss_shrink_maps: 0.556816, loss_threshold_maps: 0.451195, loss_binary_maps: 0.110800, avg_reader_cost: 1.65463 s, avg_batch_cost: 1.80185 s, avg_samples: 7.7, ips: 4.27338 samples/s, eta: 0:45:52
[2024/07/27 15:37:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:37:56] ppocr INFO: epoch: [1395/1500], global_step: 4185, lr: 0.001000, loss: 1.099470, loss_shrink_maps: 0.541203, loss_threshold_maps: 0.443898, loss_binary_maps: 0.107631, avg_reader_cost: 2.31513 s, avg_batch_cost: 2.56169 s, avg_samples: 12.5, ips: 4.87960 samples/s, eta: 0:45:26
[2024/07/27 15:37:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:38:06] ppocr INFO: epoch: [1396/1500], global_step: 4188, lr: 0.001000, loss: 1.088266, loss_shrink_maps: 0.533390, loss_threshold_maps: 0.437886, loss_binary_maps: 0.106277, avg_reader_cost: 2.38906 s, avg_batch_cost: 2.62564 s, avg_samples: 12.5, ips: 4.76075 samples/s, eta: 0:45:00
[2024/07/27 15:38:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:38:15] ppocr INFO: epoch: [1397/1500], global_step: 4190, lr: 0.001000, loss: 1.088266, loss_shrink_maps: 0.536683, loss_threshold_maps: 0.437886, loss_binary_maps: 0.106831, avg_reader_cost: 1.32627 s, avg_batch_cost: 1.63106 s, avg_samples: 9.6, ips: 5.88573 samples/s, eta: 0:44:43
[2024/07/27 15:38:16] ppocr INFO: epoch: [1397/1500], global_step: 4191, lr: 0.001000, loss: 1.088266, loss_shrink_maps: 0.536683, loss_threshold_maps: 0.437886, loss_binary_maps: 0.106831, avg_reader_cost: 0.86187 s, avg_batch_cost: 0.91745 s, avg_samples: 2.9, ips: 3.16092 samples/s, eta: 0:44:34
[2024/07/27 15:38:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:38:26] ppocr INFO: epoch: [1398/1500], global_step: 4194, lr: 0.001000, loss: 1.087199, loss_shrink_maps: 0.539118, loss_threshold_maps: 0.435117, loss_binary_maps: 0.107631, avg_reader_cost: 2.25452 s, avg_batch_cost: 2.59289 s, avg_samples: 12.5, ips: 4.82087 samples/s, eta: 0:44:08
[2024/07/27 15:38:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:38:35] ppocr INFO: epoch: [1399/1500], global_step: 4197, lr: 0.001000, loss: 1.087199, loss_shrink_maps: 0.541692, loss_threshold_maps: 0.448715, loss_binary_maps: 0.108023, avg_reader_cost: 2.22438 s, avg_batch_cost: 2.46078 s, avg_samples: 12.5, ips: 5.07969 samples/s, eta: 0:43:42
[2024/07/27 15:38:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:38:45] ppocr INFO: epoch: [1400/1500], global_step: 4200, lr: 0.001000, loss: 1.119319, loss_shrink_maps: 0.548423, loss_threshold_maps: 0.448715, loss_binary_maps: 0.109147, avg_reader_cost: 2.22274 s, avg_batch_cost: 2.60979 s, avg_samples: 12.5, ips: 4.78966 samples/s, eta: 0:43:16

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.65it/s]
eval model::   1%|          | 4/500 [00:00<00:25, 19.74it/s]
eval model::   1%|          | 6/500 [00:00<00:25, 19.00it/s]
eval model::   2%|▏         | 8/500 [00:00<00:30, 16.27it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.82it/s]
eval model::   3%|▎         | 13/500 [00:00<00:27, 17.60it/s]
eval model::   3%|▎         | 15/500 [00:00<00:27, 17.55it/s]
eval model::   4%|▍         | 19/500 [00:01<00:23, 20.68it/s]
eval model::   4%|▍         | 22/500 [00:01<00:24, 19.23it/s]
eval model::   5%|▌         | 25/500 [00:01<00:24, 19.20it/s]
eval model::   6%|▌         | 28/500 [00:01<00:23, 20.41it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.21it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.11it/s]
eval model::   7%|▋         | 37/500 [00:01<00:24, 18.69it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 18.12it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.18it/s]
eval model::   9%|▉         | 44/500 [00:02<00:25, 17.67it/s]
eval model::   9%|▉         | 46/500 [00:02<00:28, 16.15it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 19.03it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 19.57it/s]
eval model::  11%|█         | 55/500 [00:02<00:23, 18.88it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 17.96it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.48it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.58it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.82it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:22, 19.53it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 19.87it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:21, 19.33it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 20.73it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.68it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 20.89it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.36it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:21, 18.86it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:20, 19.87it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 20.10it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:21, 18.94it/s]
eval model::  20%|██        | 101/500 [00:05<00:21, 18.67it/s]
eval model::  21%|██        | 103/500 [00:05<00:22, 17.47it/s]
eval model::  21%|██        | 106/500 [00:05<00:20, 19.22it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:19, 20.43it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 20.63it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:19, 20.22it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:17, 22.12it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:17, 22.03it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.54it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.36it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 18.71it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:21, 17.25it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:21, 16.83it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:19, 18.42it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:19, 18.66it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.09it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:19, 18.40it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:20, 17.33it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.63it/s]
eval model::  31%|███       | 153/500 [00:08<00:18, 19.03it/s]
eval model::  31%|███       | 156/500 [00:08<00:16, 21.07it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:15, 21.38it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:21, 15.73it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:20, 16.51it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:20, 16.50it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:18, 18.28it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:18, 18.15it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:18, 17.54it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.88it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.76it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.42it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:19, 16.07it/s]
eval model::  37%|███▋      | 186/500 [00:10<00:18, 17.39it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:16, 18.80it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:15, 20.48it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.60it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.86it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 21.52it/s]
eval model::  41%|████      | 205/500 [00:11<00:18, 16.32it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:17, 16.84it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:17, 16.64it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:16, 17.63it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.36it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.04it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 17.55it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:18, 15.05it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:15, 17.78it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:14, 19.01it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 21.14it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:13, 19.60it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 21.18it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 21.73it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:14, 18.32it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:12, 20.68it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:13, 18.82it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 19.92it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.23it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 21.73it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 21.57it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:10, 22.31it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 19.91it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 19.46it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:10, 20.95it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 18.33it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 19.97it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 21.16it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:18, 11.69it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:15, 13.92it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.59it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:13, 15.60it/s]
eval model::  59%|█████▉    | 297/500 [00:16<00:14, 14.30it/s]
eval model::  60%|██████    | 300/500 [00:16<00:13, 14.59it/s]
eval model::  60%|██████    | 302/500 [00:16<00:13, 15.19it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 15.60it/s]
eval model::  61%|██████    | 306/500 [00:16<00:12, 16.02it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:11, 16.75it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:10, 17.59it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:10, 17.52it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:09, 18.91it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:09, 19.01it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:09, 19.01it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:08, 20.02it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 20.16it/s]
eval model::  66%|██████▋   | 332/500 [00:18<00:07, 21.69it/s]
eval model::  67%|██████▋   | 335/500 [00:18<00:07, 22.20it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:07, 20.26it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:11, 14.05it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 16.42it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 17.47it/s]
eval model::  70%|███████   | 350/500 [00:19<00:08, 18.62it/s]
eval model::  71%|███████   | 353/500 [00:19<00:07, 20.66it/s]
eval model::  71%|███████   | 356/500 [00:19<00:07, 20.39it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:06, 21.42it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:06, 20.08it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:06, 19.76it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:06, 19.40it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:06, 19.52it/s]
eval model::  75%|███████▍  | 374/500 [00:20<00:06, 20.57it/s]
eval model::  75%|███████▌  | 377/500 [00:20<00:06, 20.19it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:06, 18.95it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 19.94it/s]
eval model::  77%|███████▋  | 386/500 [00:21<00:08, 13.21it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:07, 15.27it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:06, 16.78it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 15.81it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:06, 17.10it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 17.15it/s]
eval model::  80%|████████  | 401/500 [00:21<00:06, 16.31it/s]
eval model::  81%|████████  | 404/500 [00:22<00:05, 18.17it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 19.42it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 20.52it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:04, 20.72it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 19.23it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 19.03it/s]
eval model::  84%|████████▍ | 421/500 [00:23<00:06, 12.83it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.27it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:04, 15.25it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 17.34it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 15.90it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 18.88it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:03, 20.74it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:03, 19.30it/s]
eval model::  89%|████████▊ | 443/500 [00:24<00:02, 21.13it/s]
eval model::  89%|████████▉ | 446/500 [00:24<00:02, 21.64it/s]
eval model::  90%|████████▉ | 449/500 [00:24<00:02, 19.48it/s]
eval model::  90%|█████████ | 452/500 [00:24<00:02, 21.54it/s]
eval model::  91%|█████████ | 455/500 [00:24<00:01, 23.11it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:01, 23.23it/s]
eval model::  92%|█████████▏| 461/500 [00:25<00:01, 22.38it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 17.49it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 16.20it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 16.58it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.23it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.17it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:01, 21.06it/s]
eval model::  96%|█████████▌| 481/500 [00:26<00:00, 22.00it/s]
eval model::  97%|█████████▋| 484/500 [00:26<00:00, 22.43it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 22.08it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 23.00it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 20.77it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 21.94it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 25.40it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.59it/s]
[2024/07/27 15:39:12] ppocr INFO: cur metric, precision: 0.7654252362423568, recall: 0.6629754453538758, hmean: 0.7105263157894737, fps: 43.790199052224935
[2024/07/27 15:39:12] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:39:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:39:20] ppocr INFO: epoch: [1401/1500], global_step: 4203, lr: 0.001000, loss: 1.136887, loss_shrink_maps: 0.565376, loss_threshold_maps: 0.454899, loss_binary_maps: 0.112859, avg_reader_cost: 2.02366 s, avg_batch_cost: 2.30928 s, avg_samples: 12.5, ips: 5.41295 samples/s, eta: 0:42:50
[2024/07/27 15:39:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:39:30] ppocr INFO: epoch: [1402/1500], global_step: 4206, lr: 0.001000, loss: 1.136887, loss_shrink_maps: 0.574042, loss_threshold_maps: 0.464963, loss_binary_maps: 0.114032, avg_reader_cost: 2.33608 s, avg_batch_cost: 2.59176 s, avg_samples: 12.5, ips: 4.82298 samples/s, eta: 0:42:24
[2024/07/27 15:39:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:39:40] ppocr INFO: epoch: [1403/1500], global_step: 4209, lr: 0.001000, loss: 1.136887, loss_shrink_maps: 0.574042, loss_threshold_maps: 0.462786, loss_binary_maps: 0.114032, avg_reader_cost: 2.16093 s, avg_batch_cost: 2.48166 s, avg_samples: 12.5, ips: 5.03694 samples/s, eta: 0:41:58
[2024/07/27 15:39:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:39:48] ppocr INFO: epoch: [1404/1500], global_step: 4210, lr: 0.001000, loss: 1.136887, loss_shrink_maps: 0.574042, loss_threshold_maps: 0.461296, loss_binary_maps: 0.114032, avg_reader_cost: 0.56807 s, avg_batch_cost: 0.78107 s, avg_samples: 4.8, ips: 6.14543 samples/s, eta: 0:41:49
[2024/07/27 15:39:49] ppocr INFO: epoch: [1404/1500], global_step: 4212, lr: 0.001000, loss: 1.136887, loss_shrink_maps: 0.574042, loss_threshold_maps: 0.461296, loss_binary_maps: 0.114032, avg_reader_cost: 1.65404 s, avg_batch_cost: 1.80101 s, avg_samples: 7.7, ips: 4.27538 samples/s, eta: 0:41:32
[2024/07/27 15:39:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:39:59] ppocr INFO: epoch: [1405/1500], global_step: 4215, lr: 0.001000, loss: 1.136887, loss_shrink_maps: 0.574042, loss_threshold_maps: 0.461296, loss_binary_maps: 0.114032, avg_reader_cost: 2.30198 s, avg_batch_cost: 2.54184 s, avg_samples: 12.5, ips: 4.91770 samples/s, eta: 0:41:06
[2024/07/27 15:40:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:40:09] ppocr INFO: epoch: [1406/1500], global_step: 4218, lr: 0.001000, loss: 1.141885, loss_shrink_maps: 0.574042, loss_threshold_maps: 0.467423, loss_binary_maps: 0.114032, avg_reader_cost: 2.21852 s, avg_batch_cost: 2.59606 s, avg_samples: 12.5, ips: 4.81499 samples/s, eta: 0:40:40
[2024/07/27 15:40:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:40:18] ppocr INFO: epoch: [1407/1500], global_step: 4220, lr: 0.001000, loss: 1.141885, loss_shrink_maps: 0.570637, loss_threshold_maps: 0.467423, loss_binary_maps: 0.114032, avg_reader_cost: 1.31369 s, avg_batch_cost: 1.56302 s, avg_samples: 9.6, ips: 6.14194 samples/s, eta: 0:40:22
[2024/07/27 15:40:18] ppocr INFO: epoch: [1407/1500], global_step: 4221, lr: 0.001000, loss: 1.141885, loss_shrink_maps: 0.570637, loss_threshold_maps: 0.467423, loss_binary_maps: 0.114032, avg_reader_cost: 0.82794 s, avg_batch_cost: 0.88333 s, avg_samples: 2.9, ips: 3.28303 samples/s, eta: 0:40:14
[2024/07/27 15:40:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:40:28] ppocr INFO: epoch: [1408/1500], global_step: 4224, lr: 0.001000, loss: 1.165396, loss_shrink_maps: 0.578912, loss_threshold_maps: 0.470510, loss_binary_maps: 0.115325, avg_reader_cost: 2.12653 s, avg_batch_cost: 2.59168 s, avg_samples: 12.5, ips: 4.82313 samples/s, eta: 0:39:48
[2024/07/27 15:40:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:40:38] ppocr INFO: epoch: [1409/1500], global_step: 4227, lr: 0.001000, loss: 1.165396, loss_shrink_maps: 0.572674, loss_threshold_maps: 0.468613, loss_binary_maps: 0.113076, avg_reader_cost: 2.40064 s, avg_batch_cost: 2.64225 s, avg_samples: 12.5, ips: 4.73082 samples/s, eta: 0:39:22
[2024/07/27 15:40:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:40:48] ppocr INFO: epoch: [1410/1500], global_step: 4230, lr: 0.001000, loss: 1.137699, loss_shrink_maps: 0.564107, loss_threshold_maps: 0.459452, loss_binary_maps: 0.112380, avg_reader_cost: 2.28016 s, avg_batch_cost: 2.68060 s, avg_samples: 12.5, ips: 4.66314 samples/s, eta: 0:38:56
[2024/07/27 15:40:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:40:58] ppocr INFO: epoch: [1411/1500], global_step: 4233, lr: 0.001000, loss: 1.137699, loss_shrink_maps: 0.564406, loss_threshold_maps: 0.463303, loss_binary_maps: 0.112380, avg_reader_cost: 2.36874 s, avg_batch_cost: 2.62840 s, avg_samples: 12.5, ips: 4.75574 samples/s, eta: 0:38:30
[2024/07/27 15:40:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:41:07] ppocr INFO: epoch: [1412/1500], global_step: 4236, lr: 0.001000, loss: 1.121290, loss_shrink_maps: 0.550523, loss_threshold_maps: 0.443717, loss_binary_maps: 0.109464, avg_reader_cost: 2.21711 s, avg_batch_cost: 2.57730 s, avg_samples: 12.5, ips: 4.85004 samples/s, eta: 0:38:04
[2024/07/27 15:41:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:41:17] ppocr INFO: epoch: [1413/1500], global_step: 4239, lr: 0.001000, loss: 1.103602, loss_shrink_maps: 0.550523, loss_threshold_maps: 0.435510, loss_binary_maps: 0.109464, avg_reader_cost: 2.39771 s, avg_batch_cost: 2.63894 s, avg_samples: 12.5, ips: 4.73675 samples/s, eta: 0:37:38
[2024/07/27 15:41:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:41:26] ppocr INFO: epoch: [1414/1500], global_step: 4240, lr: 0.001000, loss: 1.120672, loss_shrink_maps: 0.557501, loss_threshold_maps: 0.439763, loss_binary_maps: 0.110749, avg_reader_cost: 0.70955 s, avg_batch_cost: 0.80321 s, avg_samples: 4.8, ips: 5.97600 samples/s, eta: 0:37:29
[2024/07/27 15:41:27] ppocr INFO: epoch: [1414/1500], global_step: 4242, lr: 0.001000, loss: 1.131062, loss_shrink_maps: 0.568932, loss_threshold_maps: 0.443717, loss_binary_maps: 0.112755, avg_reader_cost: 1.69908 s, avg_batch_cost: 1.84712 s, avg_samples: 7.7, ips: 4.16865 samples/s, eta: 0:37:12
[2024/07/27 15:41:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:41:37] ppocr INFO: epoch: [1415/1500], global_step: 4245, lr: 0.001000, loss: 1.133940, loss_shrink_maps: 0.575836, loss_threshold_maps: 0.447390, loss_binary_maps: 0.114387, avg_reader_cost: 2.17246 s, avg_batch_cost: 2.48820 s, avg_samples: 12.5, ips: 5.02371 samples/s, eta: 0:36:46
[2024/07/27 15:41:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:41:47] ppocr INFO: epoch: [1416/1500], global_step: 4248, lr: 0.001000, loss: 1.133940, loss_shrink_maps: 0.572202, loss_threshold_maps: 0.449558, loss_binary_maps: 0.113856, avg_reader_cost: 2.42763 s, avg_batch_cost: 2.67141 s, avg_samples: 12.5, ips: 4.67918 samples/s, eta: 0:36:20
[2024/07/27 15:41:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:41:56] ppocr INFO: epoch: [1417/1500], global_step: 4250, lr: 0.001000, loss: 1.138337, loss_shrink_maps: 0.572202, loss_threshold_maps: 0.450778, loss_binary_maps: 0.113856, avg_reader_cost: 1.32246 s, avg_batch_cost: 1.64118 s, avg_samples: 9.6, ips: 5.84945 samples/s, eta: 0:36:03
[2024/07/27 15:41:56] ppocr INFO: epoch: [1417/1500], global_step: 4251, lr: 0.001000, loss: 1.138337, loss_shrink_maps: 0.572202, loss_threshold_maps: 0.450778, loss_binary_maps: 0.113856, avg_reader_cost: 0.86661 s, avg_batch_cost: 0.92234 s, avg_samples: 2.9, ips: 3.14418 samples/s, eta: 0:35:54
[2024/07/27 15:41:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:42:06] ppocr INFO: epoch: [1418/1500], global_step: 4254, lr: 0.001000, loss: 1.140927, loss_shrink_maps: 0.576854, loss_threshold_maps: 0.446347, loss_binary_maps: 0.114750, avg_reader_cost: 2.15872 s, avg_batch_cost: 2.51411 s, avg_samples: 12.5, ips: 4.97194 samples/s, eta: 0:35:28
[2024/07/27 15:42:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:42:16] ppocr INFO: epoch: [1419/1500], global_step: 4257, lr: 0.001000, loss: 1.169256, loss_shrink_maps: 0.591681, loss_threshold_maps: 0.453842, loss_binary_maps: 0.117553, avg_reader_cost: 2.21730 s, avg_batch_cost: 2.57745 s, avg_samples: 12.5, ips: 4.84975 samples/s, eta: 0:35:02
[2024/07/27 15:42:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:42:25] ppocr INFO: epoch: [1420/1500], global_step: 4260, lr: 0.001000, loss: 1.155048, loss_shrink_maps: 0.577056, loss_threshold_maps: 0.449558, loss_binary_maps: 0.114750, avg_reader_cost: 2.18198 s, avg_batch_cost: 2.52567 s, avg_samples: 12.5, ips: 4.94918 samples/s, eta: 0:34:36

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:52,  9.42it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 17.82it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.21it/s]
eval model::   2%|▏         | 8/500 [00:00<00:29, 16.76it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.18it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.35it/s]
eval model::   4%|▎         | 18/500 [00:00<00:21, 22.46it/s]
eval model::   4%|▍         | 21/500 [00:01<00:22, 21.43it/s]
eval model::   5%|▍         | 24/500 [00:01<00:24, 19.20it/s]
eval model::   5%|▌         | 26/500 [00:01<00:26, 18.20it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 19.83it/s]
eval model::   6%|▋         | 32/500 [00:01<00:26, 17.58it/s]
eval model::   7%|▋         | 34/500 [00:01<00:29, 16.05it/s]
eval model::   7%|▋         | 36/500 [00:02<00:29, 15.93it/s]
eval model::   8%|▊         | 39/500 [00:02<00:26, 17.66it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.55it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.21it/s]
eval model::  10%|▉         | 48/500 [00:02<00:21, 20.89it/s]
eval model::  10%|█         | 51/500 [00:02<00:21, 21.05it/s]
eval model::  11%|█         | 54/500 [00:02<00:21, 20.71it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:21, 20.64it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:22, 19.73it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 14.67it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:23, 18.42it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:21, 19.89it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 19.50it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.53it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 22.04it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:17, 23.45it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.00it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.10it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:23, 17.19it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:23, 17.02it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:21, 19.06it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.35it/s]
eval model::  20%|██        | 102/500 [00:05<00:18, 21.91it/s]
eval model::  21%|██        | 106/500 [00:05<00:16, 23.28it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:16, 23.63it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 21.15it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:18, 20.53it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:17, 22.10it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 20.41it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.07it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 17.99it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.79it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 16.98it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:20, 18.22it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:19, 18.28it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 19.64it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:18, 19.38it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.10it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.22it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 19.22it/s]
eval model::  30%|███       | 152/500 [00:07<00:21, 16.31it/s]
eval model::  31%|███       | 155/500 [00:08<00:19, 18.10it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:16, 20.47it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:20, 16.46it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:18, 18.19it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:16, 19.79it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:15, 21.45it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:16, 20.16it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:17, 18.97it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:17, 18.81it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:19, 16.84it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:19, 16.69it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:16, 19.57it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:17, 18.07it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:17, 17.82it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:15, 20.44it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:16, 18.24it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:16, 17.90it/s]
eval model::  40%|████      | 202/500 [00:10<00:15, 19.45it/s]
eval model::  41%|████      | 205/500 [00:10<00:19, 15.09it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:18, 15.73it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 15.86it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:16, 16.89it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.49it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.94it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.11it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.47it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:18, 15.25it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:17, 15.71it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:15, 17.10it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:13, 19.13it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:14, 18.04it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:12, 20.76it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:13, 18.59it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:16, 15.16it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:14, 17.11it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:14, 17.72it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 19.11it/s]
eval model::  51%|█████     | 255/500 [00:13<00:12, 19.78it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:13, 18.36it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:12, 19.65it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:10, 21.77it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:10, 22.08it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:12, 18.66it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 19.99it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 17.64it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:12, 17.94it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:11, 19.76it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.50it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.76it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 17.00it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:16, 12.45it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:14, 13.98it/s]
eval model::  60%|██████    | 300/500 [00:16<00:14, 13.85it/s]
eval model::  61%|██████    | 303/500 [00:16<00:12, 15.29it/s]
eval model::  61%|██████    | 305/500 [00:16<00:13, 14.72it/s]
eval model::  62%|██████▏   | 308/500 [00:17<00:12, 15.75it/s]
eval model::  62%|██████▏   | 311/500 [00:17<00:10, 17.51it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:10, 17.92it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:10, 16.88it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:09, 18.50it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:09, 18.44it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:09, 18.40it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:08, 19.44it/s]
eval model::  66%|██████▌   | 328/500 [00:18<00:08, 19.32it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:07, 21.35it/s]
eval model::  67%|██████▋   | 335/500 [00:18<00:06, 24.07it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:07, 20.88it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:09, 16.87it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:08, 18.67it/s]
eval model::  69%|██████▉   | 347/500 [00:19<00:08, 18.85it/s]
eval model::  70%|███████   | 351/500 [00:19<00:06, 22.01it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 21.67it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 20.47it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 17.38it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 17.34it/s]
eval model::  73%|███████▎  | 365/500 [00:20<00:07, 16.91it/s]
eval model::  73%|███████▎  | 367/500 [00:20<00:07, 16.91it/s]
eval model::  74%|███████▍  | 370/500 [00:20<00:06, 19.31it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:05, 21.22it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 21.75it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 20.41it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 21.14it/s]
eval model::  77%|███████▋  | 386/500 [00:21<00:08, 12.97it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:07, 15.34it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:06, 16.59it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 17.34it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 19.58it/s]
eval model::  80%|████████  | 402/500 [00:22<00:05, 17.40it/s]
eval model::  81%|████████  | 404/500 [00:22<00:05, 16.82it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:05, 18.38it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:03, 22.58it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 23.06it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:03, 20.76it/s]
eval model::  84%|████████▍ | 420/500 [00:23<00:06, 13.22it/s]
eval model::  85%|████████▍ | 423/500 [00:23<00:05, 14.86it/s]
eval model::  85%|████████▌ | 425/500 [00:23<00:05, 14.24it/s]
eval model::  86%|████████▌ | 428/500 [00:23<00:04, 16.82it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 16.66it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 19.18it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:03, 20.98it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:02, 20.13it/s]
eval model::  89%|████████▊ | 443/500 [00:24<00:02, 21.63it/s]
eval model::  89%|████████▉ | 446/500 [00:24<00:02, 19.44it/s]
eval model::  90%|████████▉ | 449/500 [00:24<00:02, 18.33it/s]
eval model::  90%|█████████ | 452/500 [00:24<00:02, 20.23it/s]
eval model::  91%|█████████ | 455/500 [00:24<00:02, 20.52it/s]
eval model::  92%|█████████▏| 458/500 [00:25<00:01, 21.08it/s]
eval model::  92%|█████████▏| 461/500 [00:25<00:01, 19.66it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 15.02it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 15.38it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:02, 15.45it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 17.36it/s]
eval model::  95%|█████████▍| 474/500 [00:26<00:01, 16.89it/s]
eval model::  95%|█████████▌| 477/500 [00:26<00:01, 19.56it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:00, 21.79it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 22.73it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 24.34it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 23.09it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 20.67it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 22.27it/s]
eval model:: 100%|█████████▉| 499/500 [00:27<00:00, 26.36it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.46it/s]
[2024/07/27 15:42:52] ppocr INFO: cur metric, precision: 0.7767807066741447, recall: 0.6668271545498314, hmean: 0.7176165803108807, fps: 43.03829399948473
[2024/07/27 15:42:52] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:42:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:43:01] ppocr INFO: epoch: [1421/1500], global_step: 4263, lr: 0.001000, loss: 1.127459, loss_shrink_maps: 0.566016, loss_threshold_maps: 0.446776, loss_binary_maps: 0.112957, avg_reader_cost: 2.05178 s, avg_batch_cost: 2.28876 s, avg_samples: 12.5, ips: 5.46148 samples/s, eta: 0:34:10
[2024/07/27 15:43:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:43:10] ppocr INFO: epoch: [1422/1500], global_step: 4266, lr: 0.001000, loss: 1.175043, loss_shrink_maps: 0.590151, loss_threshold_maps: 0.462115, loss_binary_maps: 0.117330, avg_reader_cost: 2.16834 s, avg_batch_cost: 2.53848 s, avg_samples: 12.5, ips: 4.92421 samples/s, eta: 0:33:44
[2024/07/27 15:43:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:43:20] ppocr INFO: epoch: [1423/1500], global_step: 4269, lr: 0.001000, loss: 1.236532, loss_shrink_maps: 0.634415, loss_threshold_maps: 0.467562, loss_binary_maps: 0.126476, avg_reader_cost: 2.21003 s, avg_batch_cost: 2.57471 s, avg_samples: 12.5, ips: 4.85491 samples/s, eta: 0:33:18
[2024/07/27 15:43:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:43:29] ppocr INFO: epoch: [1424/1500], global_step: 4270, lr: 0.001000, loss: 1.222135, loss_shrink_maps: 0.616704, loss_threshold_maps: 0.467562, loss_binary_maps: 0.122746, avg_reader_cost: 0.63105 s, avg_batch_cost: 0.80390 s, avg_samples: 4.8, ips: 5.97089 samples/s, eta: 0:33:10
[2024/07/27 15:43:30] ppocr INFO: epoch: [1424/1500], global_step: 4272, lr: 0.001000, loss: 1.222135, loss_shrink_maps: 0.616704, loss_threshold_maps: 0.462115, loss_binary_maps: 0.122746, avg_reader_cost: 1.69957 s, avg_batch_cost: 1.84714 s, avg_samples: 7.7, ips: 4.16860 samples/s, eta: 0:32:52
[2024/07/27 15:43:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:43:40] ppocr INFO: epoch: [1425/1500], global_step: 4275, lr: 0.001000, loss: 1.170972, loss_shrink_maps: 0.586032, loss_threshold_maps: 0.462115, loss_binary_maps: 0.116635, avg_reader_cost: 2.21828 s, avg_batch_cost: 2.59749 s, avg_samples: 12.5, ips: 4.81235 samples/s, eta: 0:32:26
[2024/07/27 15:43:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:43:50] ppocr INFO: epoch: [1426/1500], global_step: 4278, lr: 0.001000, loss: 1.071304, loss_shrink_maps: 0.531075, loss_threshold_maps: 0.445174, loss_binary_maps: 0.105772, avg_reader_cost: 2.26918 s, avg_batch_cost: 2.51496 s, avg_samples: 12.5, ips: 4.97025 samples/s, eta: 0:32:00
[2024/07/27 15:43:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:43:59] ppocr INFO: epoch: [1427/1500], global_step: 4280, lr: 0.001000, loss: 1.119516, loss_shrink_maps: 0.558545, loss_threshold_maps: 0.445174, loss_binary_maps: 0.111204, avg_reader_cost: 1.47631 s, avg_batch_cost: 1.65947 s, avg_samples: 9.6, ips: 5.78497 samples/s, eta: 0:31:43
[2024/07/27 15:43:59] ppocr INFO: epoch: [1427/1500], global_step: 4281, lr: 0.001000, loss: 1.119516, loss_shrink_maps: 0.558545, loss_threshold_maps: 0.445174, loss_binary_maps: 0.111204, avg_reader_cost: 0.87591 s, avg_batch_cost: 0.93155 s, avg_samples: 2.9, ips: 3.11309 samples/s, eta: 0:31:34
[2024/07/27 15:44:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:44:09] ppocr INFO: epoch: [1428/1500], global_step: 4284, lr: 0.001000, loss: 1.088052, loss_shrink_maps: 0.555335, loss_threshold_maps: 0.439419, loss_binary_maps: 0.110427, avg_reader_cost: 2.27234 s, avg_batch_cost: 2.50877 s, avg_samples: 12.5, ips: 4.98252 samples/s, eta: 0:31:08
[2024/07/27 15:44:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:44:19] ppocr INFO: epoch: [1429/1500], global_step: 4287, lr: 0.001000, loss: 1.062987, loss_shrink_maps: 0.531075, loss_threshold_maps: 0.438825, loss_binary_maps: 0.105772, avg_reader_cost: 2.25033 s, avg_batch_cost: 2.62697 s, avg_samples: 12.5, ips: 4.75833 samples/s, eta: 0:30:42
[2024/07/27 15:44:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:44:29] ppocr INFO: epoch: [1430/1500], global_step: 4290, lr: 0.001000, loss: 1.060031, loss_shrink_maps: 0.523524, loss_threshold_maps: 0.438383, loss_binary_maps: 0.104158, avg_reader_cost: 2.34276 s, avg_batch_cost: 2.58313 s, avg_samples: 12.5, ips: 4.83909 samples/s, eta: 0:30:16
[2024/07/27 15:44:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:44:39] ppocr INFO: epoch: [1431/1500], global_step: 4293, lr: 0.001000, loss: 1.048769, loss_shrink_maps: 0.513510, loss_threshold_maps: 0.432705, loss_binary_maps: 0.102235, avg_reader_cost: 2.45115 s, avg_batch_cost: 2.68419 s, avg_samples: 12.5, ips: 4.65689 samples/s, eta: 0:29:51
[2024/07/27 15:44:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:44:49] ppocr INFO: epoch: [1432/1500], global_step: 4296, lr: 0.001000, loss: 1.049280, loss_shrink_maps: 0.514861, loss_threshold_maps: 0.437670, loss_binary_maps: 0.102629, avg_reader_cost: 2.35341 s, avg_batch_cost: 2.58886 s, avg_samples: 12.5, ips: 4.82839 samples/s, eta: 0:29:25
[2024/07/27 15:44:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:44:58] ppocr INFO: epoch: [1433/1500], global_step: 4299, lr: 0.001000, loss: 1.038018, loss_shrink_maps: 0.497276, loss_threshold_maps: 0.437670, loss_binary_maps: 0.098863, avg_reader_cost: 2.08674 s, avg_batch_cost: 2.36035 s, avg_samples: 12.5, ips: 5.29583 samples/s, eta: 0:28:59
[2024/07/27 15:44:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:45:06] ppocr INFO: epoch: [1434/1500], global_step: 4300, lr: 0.001000, loss: 1.038018, loss_shrink_maps: 0.496235, loss_threshold_maps: 0.437670, loss_binary_maps: 0.098624, avg_reader_cost: 0.56994 s, avg_batch_cost: 0.77791 s, avg_samples: 4.8, ips: 6.17040 samples/s, eta: 0:28:50
[2024/07/27 15:45:08] ppocr INFO: epoch: [1434/1500], global_step: 4302, lr: 0.001000, loss: 1.074345, loss_shrink_maps: 0.528287, loss_threshold_maps: 0.437670, loss_binary_maps: 0.104810, avg_reader_cost: 1.64791 s, avg_batch_cost: 1.79517 s, avg_samples: 7.7, ips: 4.28930 samples/s, eta: 0:28:33
[2024/07/27 15:45:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:45:18] ppocr INFO: epoch: [1435/1500], global_step: 4305, lr: 0.001000, loss: 1.077971, loss_shrink_maps: 0.528287, loss_threshold_maps: 0.440207, loss_binary_maps: 0.104810, avg_reader_cost: 2.21392 s, avg_batch_cost: 2.57659 s, avg_samples: 12.5, ips: 4.85137 samples/s, eta: 0:28:07
[2024/07/27 15:45:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:45:28] ppocr INFO: epoch: [1436/1500], global_step: 4308, lr: 0.001000, loss: 1.089524, loss_shrink_maps: 0.549326, loss_threshold_maps: 0.439644, loss_binary_maps: 0.109431, avg_reader_cost: 2.28419 s, avg_batch_cost: 2.66322 s, avg_samples: 12.5, ips: 4.69357 samples/s, eta: 0:27:41
[2024/07/27 15:45:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:45:37] ppocr INFO: epoch: [1437/1500], global_step: 4310, lr: 0.001000, loss: 1.083742, loss_shrink_maps: 0.548644, loss_threshold_maps: 0.437122, loss_binary_maps: 0.109431, avg_reader_cost: 1.46706 s, avg_batch_cost: 1.67440 s, avg_samples: 9.6, ips: 5.73341 samples/s, eta: 0:27:23
[2024/07/27 15:45:38] ppocr INFO: epoch: [1437/1500], global_step: 4311, lr: 0.001000, loss: 1.111999, loss_shrink_maps: 0.558595, loss_threshold_maps: 0.439776, loss_binary_maps: 0.111018, avg_reader_cost: 0.88319 s, avg_batch_cost: 0.93871 s, avg_samples: 2.9, ips: 3.08933 samples/s, eta: 0:27:15
[2024/07/27 15:45:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:45:48] ppocr INFO: epoch: [1438/1500], global_step: 4314, lr: 0.001000, loss: 1.128951, loss_shrink_maps: 0.555322, loss_threshold_maps: 0.447331, loss_binary_maps: 0.110819, avg_reader_cost: 2.34883 s, avg_batch_cost: 2.58416 s, avg_samples: 12.5, ips: 4.83717 samples/s, eta: 0:26:49
[2024/07/27 15:45:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:45:57] ppocr INFO: epoch: [1439/1500], global_step: 4317, lr: 0.001000, loss: 1.128951, loss_shrink_maps: 0.555322, loss_threshold_maps: 0.451383, loss_binary_maps: 0.110819, avg_reader_cost: 2.21371 s, avg_batch_cost: 2.56564 s, avg_samples: 12.5, ips: 4.87209 samples/s, eta: 0:26:23
[2024/07/27 15:45:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:46:06] ppocr INFO: epoch: [1440/1500], global_step: 4320, lr: 0.001000, loss: 1.154817, loss_shrink_maps: 0.569382, loss_threshold_maps: 0.453275, loss_binary_maps: 0.113641, avg_reader_cost: 1.95859 s, avg_batch_cost: 2.21485 s, avg_samples: 12.5, ips: 5.64373 samples/s, eta: 0:25:57

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:58,  8.60it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.49it/s]
eval model::   1%|▏         | 7/500 [00:00<00:23, 21.11it/s]
eval model::   2%|▏         | 10/500 [00:00<00:25, 19.33it/s]
eval model::   2%|▏         | 12/500 [00:00<00:25, 19.31it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.78it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.52it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 18.64it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.15it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.20it/s]
eval model::   6%|▌         | 29/500 [00:01<00:24, 19.11it/s]
eval model::   6%|▌         | 31/500 [00:01<00:27, 16.77it/s]
eval model::   7%|▋         | 34/500 [00:01<00:26, 17.29it/s]
eval model::   7%|▋         | 37/500 [00:01<00:24, 18.81it/s]
eval model::   8%|▊         | 39/500 [00:02<00:24, 18.79it/s]
eval model::   8%|▊         | 41/500 [00:02<00:24, 18.73it/s]
eval model::   9%|▊         | 43/500 [00:02<00:28, 15.99it/s]
eval model::   9%|▉         | 46/500 [00:02<00:25, 17.53it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 19.93it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 20.63it/s]
eval model::  11%|█         | 55/500 [00:02<00:19, 22.37it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:22, 19.84it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 16.03it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.50it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 19.27it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 19.73it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 20.40it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 21.22it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 20.76it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.32it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.20it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.75it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:23, 17.69it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:24, 16.75it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:21, 18.74it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:21, 19.01it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.79it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.23it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.40it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:17, 22.47it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:17, 22.12it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:16, 23.87it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:16, 22.61it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 20.14it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.68it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 19.49it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 21.11it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 18.29it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.83it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 21.13it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:16, 22.32it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 20.96it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.16it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.05it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 19.36it/s]
eval model::  31%|███       | 156/500 [00:07<00:17, 19.81it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:17, 19.30it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:23, 14.58it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:20, 16.64it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:19, 17.06it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:17, 18.65it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 18.25it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:17, 18.29it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:16, 20.00it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:17, 18.41it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:15, 20.03it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 22.19it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:13, 22.39it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.88it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 20.78it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:13, 22.51it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 22.09it/s]
eval model::  41%|████      | 204/500 [00:10<00:12, 22.77it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:17, 16.36it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:18, 15.78it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:18, 15.44it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.69it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 14.12it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 14.02it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 16.90it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:20, 13.64it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:15, 17.51it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.37it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 22.35it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.62it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.83it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:13, 19.11it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:12, 20.04it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:12, 20.97it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 20.91it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 22.03it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 22.61it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 22.08it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:11, 21.01it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:12, 18.93it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:12, 18.25it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 19.78it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:13, 17.11it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 18.52it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 18.55it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:11, 19.63it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:16, 12.64it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:13, 15.34it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 17.21it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:14, 13.71it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:11, 16.86it/s]
eval model::  60%|██████    | 302/500 [00:15<00:11, 17.13it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 18.59it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 19.76it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 20.68it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:09, 20.60it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:08, 21.09it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:08, 21.67it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:08, 19.99it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:07, 21.83it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:07, 21.38it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:07, 22.79it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:06, 24.24it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:07, 21.72it/s]
eval model::  68%|██████▊   | 341/500 [00:17<00:09, 16.85it/s]
eval model::  69%|██████▉   | 344/500 [00:17<00:08, 19.11it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:07, 20.07it/s]
eval model::  70%|███████   | 351/500 [00:18<00:06, 22.59it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 23.99it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:06, 21.11it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:07, 19.94it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:07, 18.52it/s]
eval model::  73%|███████▎  | 365/500 [00:18<00:07, 18.07it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 17.24it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.01it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:05, 21.75it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 23.77it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 23.44it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:06, 19.55it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:08, 13.53it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 16.67it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 17.97it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 19.40it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 21.01it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 20.49it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 22.12it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:03, 23.06it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 23.72it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 23.96it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 22.26it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:05, 14.12it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:04, 16.14it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.62it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 17.87it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 18.39it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:03, 20.67it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 21.22it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 20.89it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 20.95it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 20.73it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.68it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:01, 23.00it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 23.91it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 23.39it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 19.70it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 18.87it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.41it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.40it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.73it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 20.87it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 22.02it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 21.59it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 21.15it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 21.77it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 18.80it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 19.82it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 21.57it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.47it/s]
[2024/07/27 15:46:32] ppocr INFO: cur metric, precision: 0.7559888579387186, recall: 0.6533461723639865, hmean: 0.7009297520661156, fps: 46.054697463760355
[2024/07/27 15:46:32] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:46:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:46:42] ppocr INFO: epoch: [1441/1500], global_step: 4323, lr: 0.001000, loss: 1.109922, loss_shrink_maps: 0.547962, loss_threshold_maps: 0.446304, loss_binary_maps: 0.109448, avg_reader_cost: 2.59215 s, avg_batch_cost: 2.90663 s, avg_samples: 12.5, ips: 4.30051 samples/s, eta: 0:25:31
[2024/07/27 15:46:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:46:52] ppocr INFO: epoch: [1442/1500], global_step: 4326, lr: 0.001000, loss: 1.109922, loss_shrink_maps: 0.547856, loss_threshold_maps: 0.443782, loss_binary_maps: 0.109376, avg_reader_cost: 2.15283 s, avg_batch_cost: 2.50245 s, avg_samples: 12.5, ips: 4.99511 samples/s, eta: 0:25:05
[2024/07/27 15:46:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:47:02] ppocr INFO: epoch: [1443/1500], global_step: 4329, lr: 0.001000, loss: 1.090972, loss_shrink_maps: 0.529412, loss_threshold_maps: 0.443782, loss_binary_maps: 0.105212, avg_reader_cost: 2.36811 s, avg_batch_cost: 2.61038 s, avg_samples: 12.5, ips: 4.78857 samples/s, eta: 0:24:39
[2024/07/27 15:47:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:47:10] ppocr INFO: epoch: [1444/1500], global_step: 4330, lr: 0.001000, loss: 1.077344, loss_shrink_maps: 0.523760, loss_threshold_maps: 0.445280, loss_binary_maps: 0.104468, avg_reader_cost: 0.66141 s, avg_batch_cost: 0.77607 s, avg_samples: 4.8, ips: 6.18502 samples/s, eta: 0:24:30
[2024/07/27 15:47:12] ppocr INFO: epoch: [1444/1500], global_step: 4332, lr: 0.001000, loss: 1.034157, loss_shrink_maps: 0.511775, loss_threshold_maps: 0.429081, loss_binary_maps: 0.101512, avg_reader_cost: 1.64515 s, avg_batch_cost: 1.79314 s, avg_samples: 7.7, ips: 4.29415 samples/s, eta: 0:24:13
[2024/07/27 15:47:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:47:22] ppocr INFO: epoch: [1445/1500], global_step: 4335, lr: 0.001000, loss: 1.058041, loss_shrink_maps: 0.515863, loss_threshold_maps: 0.434832, loss_binary_maps: 0.102409, avg_reader_cost: 2.36371 s, avg_batch_cost: 2.63240 s, avg_samples: 12.5, ips: 4.74852 samples/s, eta: 0:23:47
[2024/07/27 15:47:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:47:31] ppocr INFO: epoch: [1446/1500], global_step: 4338, lr: 0.001000, loss: 1.065465, loss_shrink_maps: 0.525938, loss_threshold_maps: 0.434066, loss_binary_maps: 0.104354, avg_reader_cost: 2.36626 s, avg_batch_cost: 2.61380 s, avg_samples: 12.5, ips: 4.78231 samples/s, eta: 0:23:21
[2024/07/27 15:47:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:47:41] ppocr INFO: epoch: [1447/1500], global_step: 4340, lr: 0.001000, loss: 1.074230, loss_shrink_maps: 0.532996, loss_threshold_maps: 0.439161, loss_binary_maps: 0.106080, avg_reader_cost: 1.47571 s, avg_batch_cost: 1.65683 s, avg_samples: 9.6, ips: 5.79419 samples/s, eta: 0:23:04
[2024/07/27 15:47:41] ppocr INFO: epoch: [1447/1500], global_step: 4341, lr: 0.001000, loss: 1.065465, loss_shrink_maps: 0.525938, loss_threshold_maps: 0.434066, loss_binary_maps: 0.104354, avg_reader_cost: 0.87473 s, avg_batch_cost: 0.93040 s, avg_samples: 2.9, ips: 3.11693 samples/s, eta: 0:22:55
[2024/07/27 15:47:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:47:51] ppocr INFO: epoch: [1448/1500], global_step: 4344, lr: 0.001000, loss: 1.065465, loss_shrink_maps: 0.525938, loss_threshold_maps: 0.434066, loss_binary_maps: 0.104354, avg_reader_cost: 2.21954 s, avg_batch_cost: 2.45500 s, avg_samples: 12.5, ips: 5.09164 samples/s, eta: 0:22:29
[2024/07/27 15:47:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:48:00] ppocr INFO: epoch: [1449/1500], global_step: 4347, lr: 0.001000, loss: 1.067495, loss_shrink_maps: 0.530936, loss_threshold_maps: 0.430264, loss_binary_maps: 0.105188, avg_reader_cost: 2.17766 s, avg_batch_cost: 2.54258 s, avg_samples: 12.5, ips: 4.91627 samples/s, eta: 0:22:03
[2024/07/27 15:48:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:48:10] ppocr INFO: epoch: [1450/1500], global_step: 4350, lr: 0.001000, loss: 1.092816, loss_shrink_maps: 0.540264, loss_threshold_maps: 0.437783, loss_binary_maps: 0.107070, avg_reader_cost: 2.19432 s, avg_batch_cost: 2.53094 s, avg_samples: 12.5, ips: 4.93888 samples/s, eta: 0:21:37
[2024/07/27 15:48:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:48:20] ppocr INFO: epoch: [1451/1500], global_step: 4353, lr: 0.001000, loss: 1.092816, loss_shrink_maps: 0.550327, loss_threshold_maps: 0.437783, loss_binary_maps: 0.109271, avg_reader_cost: 2.46502 s, avg_batch_cost: 2.70952 s, avg_samples: 12.5, ips: 4.61336 samples/s, eta: 0:21:11
[2024/07/27 15:48:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:48:30] ppocr INFO: epoch: [1452/1500], global_step: 4356, lr: 0.001000, loss: 1.092816, loss_shrink_maps: 0.550327, loss_threshold_maps: 0.437783, loss_binary_maps: 0.109271, avg_reader_cost: 2.38601 s, avg_batch_cost: 2.62727 s, avg_samples: 12.5, ips: 4.75780 samples/s, eta: 0:20:45
[2024/07/27 15:48:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:48:40] ppocr INFO: epoch: [1453/1500], global_step: 4359, lr: 0.001000, loss: 1.112631, loss_shrink_maps: 0.556844, loss_threshold_maps: 0.440220, loss_binary_maps: 0.110968, avg_reader_cost: 2.26557 s, avg_batch_cost: 2.61779 s, avg_samples: 12.5, ips: 4.77502 samples/s, eta: 0:20:19
[2024/07/27 15:48:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:48:48] ppocr INFO: epoch: [1454/1500], global_step: 4360, lr: 0.001000, loss: 1.112631, loss_shrink_maps: 0.556844, loss_threshold_maps: 0.440220, loss_binary_maps: 0.110968, avg_reader_cost: 0.56648 s, avg_batch_cost: 0.79215 s, avg_samples: 4.8, ips: 6.05946 samples/s, eta: 0:20:11
[2024/07/27 15:48:50] ppocr INFO: epoch: [1454/1500], global_step: 4362, lr: 0.001000, loss: 1.125644, loss_shrink_maps: 0.568539, loss_threshold_maps: 0.443386, loss_binary_maps: 0.112903, avg_reader_cost: 1.67691 s, avg_batch_cost: 1.82451 s, avg_samples: 7.7, ips: 4.22030 samples/s, eta: 0:19:53
[2024/07/27 15:48:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:48:59] ppocr INFO: epoch: [1455/1500], global_step: 4365, lr: 0.001000, loss: 1.125644, loss_shrink_maps: 0.568864, loss_threshold_maps: 0.446876, loss_binary_maps: 0.113381, avg_reader_cost: 2.28791 s, avg_batch_cost: 2.52633 s, avg_samples: 12.5, ips: 4.94788 samples/s, eta: 0:19:27
[2024/07/27 15:49:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:49:09] ppocr INFO: epoch: [1456/1500], global_step: 4368, lr: 0.001000, loss: 1.157894, loss_shrink_maps: 0.594250, loss_threshold_maps: 0.448855, loss_binary_maps: 0.117942, avg_reader_cost: 2.26035 s, avg_batch_cost: 2.50622 s, avg_samples: 12.5, ips: 4.98760 samples/s, eta: 0:19:01
[2024/07/27 15:49:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:49:18] ppocr INFO: epoch: [1457/1500], global_step: 4370, lr: 0.001000, loss: 1.199100, loss_shrink_maps: 0.611210, loss_threshold_maps: 0.449991, loss_binary_maps: 0.121439, avg_reader_cost: 1.38790 s, avg_batch_cost: 1.62446 s, avg_samples: 9.6, ips: 5.90966 samples/s, eta: 0:18:44
[2024/07/27 15:49:19] ppocr INFO: epoch: [1457/1500], global_step: 4371, lr: 0.001000, loss: 1.199100, loss_shrink_maps: 0.611210, loss_threshold_maps: 0.450961, loss_binary_maps: 0.121439, avg_reader_cost: 0.85919 s, avg_batch_cost: 0.91474 s, avg_samples: 2.9, ips: 3.17031 samples/s, eta: 0:18:35
[2024/07/27 15:49:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:49:29] ppocr INFO: epoch: [1458/1500], global_step: 4374, lr: 0.001000, loss: 1.199151, loss_shrink_maps: 0.611210, loss_threshold_maps: 0.456777, loss_binary_maps: 0.121439, avg_reader_cost: 2.28635 s, avg_batch_cost: 2.66028 s, avg_samples: 12.5, ips: 4.69875 samples/s, eta: 0:18:10
[2024/07/27 15:49:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:49:39] ppocr INFO: epoch: [1459/1500], global_step: 4377, lr: 0.001000, loss: 1.192717, loss_shrink_maps: 0.611022, loss_threshold_maps: 0.466844, loss_binary_maps: 0.121267, avg_reader_cost: 2.24470 s, avg_batch_cost: 2.61736 s, avg_samples: 12.5, ips: 4.77580 samples/s, eta: 0:17:44
[2024/07/27 15:49:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:49:48] ppocr INFO: epoch: [1460/1500], global_step: 4380, lr: 0.001000, loss: 1.162829, loss_shrink_maps: 0.583481, loss_threshold_maps: 0.461217, loss_binary_maps: 0.115829, avg_reader_cost: 2.23945 s, avg_batch_cost: 2.60977 s, avg_samples: 12.5, ips: 4.78970 samples/s, eta: 0:17:18

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:03,  7.83it/s]
eval model::   1%|          | 3/500 [00:00<00:37, 13.37it/s]
eval model::   1%|          | 6/500 [00:00<00:29, 16.50it/s]
eval model::   2%|▏         | 8/500 [00:00<00:29, 16.50it/s]
eval model::   2%|▏         | 11/500 [00:00<00:33, 14.67it/s]
eval model::   3%|▎         | 13/500 [00:00<00:31, 15.53it/s]
eval model::   3%|▎         | 15/500 [00:00<00:31, 15.48it/s]
eval model::   4%|▎         | 18/500 [00:01<00:25, 18.77it/s]
eval model::   4%|▍         | 20/500 [00:01<00:26, 17.93it/s]
eval model::   5%|▍         | 23/500 [00:01<00:24, 19.69it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.25it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 21.12it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 18.78it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 19.01it/s]
eval model::   7%|▋         | 37/500 [00:02<00:22, 20.91it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.17it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.28it/s]
eval model::   9%|▉         | 44/500 [00:02<00:26, 17.50it/s]
eval model::   9%|▉         | 46/500 [00:02<00:26, 17.16it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.71it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 19.46it/s]
eval model::  11%|█         | 55/500 [00:03<00:21, 20.34it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 18.46it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 15.95it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.82it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 18.07it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 21.44it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 21.85it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:19, 21.52it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.16it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.22it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 20.99it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 19.84it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 17.90it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:23, 17.35it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.37it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.62it/s]
eval model::  20%|██        | 102/500 [00:05<00:21, 18.54it/s]
eval model::  21%|██        | 105/500 [00:05<00:20, 19.61it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:20, 18.83it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:20, 19.20it/s]
eval model::  23%|██▎       | 114/500 [00:06<00:18, 21.18it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:18, 20.81it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 20.99it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.67it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 18.88it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 20.35it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:20, 17.90it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:18, 20.00it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.35it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.83it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.01it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:20, 16.98it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 18.49it/s]
eval model::  30%|███       | 152/500 [00:08<00:20, 17.30it/s]
eval model::  31%|███       | 155/500 [00:08<00:17, 19.18it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 21.47it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:20, 16.46it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.05it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:19, 17.42it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 17.93it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:16, 19.68it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:16, 19.89it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.56it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.87it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:17, 18.03it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 19.95it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:15, 19.84it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:15, 20.04it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:16, 18.43it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.05it/s]
eval model::  40%|████      | 200/500 [00:10<00:15, 19.32it/s]
eval model::  41%|████      | 203/500 [00:10<00:13, 21.23it/s]
eval model::  41%|████      | 206/500 [00:11<00:19, 14.96it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:20, 14.58it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 13.98it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.55it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.45it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 12.91it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:18, 15.08it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:21, 13.14it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:17, 15.34it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:16, 16.06it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:14, 18.72it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:14, 18.05it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:13, 19.14it/s]
eval model::  48%|████▊     | 240/500 [00:13<00:13, 18.83it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:16, 16.02it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:14, 17.87it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:14, 17.44it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:14, 17.33it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 19.13it/s]
eval model::  51%|█████     | 255/500 [00:13<00:12, 20.31it/s]
eval model::  52%|█████▏    | 258/500 [00:14<00:11, 21.47it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:10, 21.87it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:10, 22.20it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:10, 21.62it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 20.15it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:10, 21.29it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 18.32it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:12, 18.36it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 20.21it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.41it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.66it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:11, 17.36it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:14, 13.77it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:12, 16.11it/s]
eval model::  60%|██████    | 301/500 [00:16<00:13, 14.92it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 16.12it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 17.82it/s]
eval model::  62%|██████▏   | 310/500 [00:17<00:10, 17.94it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:09, 19.03it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:10, 17.93it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:09, 19.45it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 18.62it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 20.75it/s]
eval model::  66%|██████▌   | 328/500 [00:18<00:08, 20.15it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:08, 20.43it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:07, 22.32it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 21.44it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:09, 16.08it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:08, 18.07it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 19.21it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:07, 20.50it/s]
eval model::  70%|███████   | 352/500 [00:19<00:07, 20.16it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 18.62it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 18.73it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:07, 19.25it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:07, 17.42it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:09, 15.03it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:09, 13.42it/s]
eval model::  74%|███████▎  | 368/500 [00:20<00:09, 14.17it/s]
eval model::  74%|███████▍  | 370/500 [00:20<00:08, 15.02it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:08, 14.87it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:07, 17.07it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 20.54it/s]
eval model::  76%|███████▋  | 382/500 [00:21<00:06, 18.37it/s]
eval model::  77%|███████▋  | 386/500 [00:21<00:08, 14.00it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:06, 16.22it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:06, 16.76it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 17.05it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 17.37it/s]
eval model::  80%|███████▉  | 399/500 [00:22<00:05, 18.69it/s]
eval model::  80%|████████  | 401/500 [00:22<00:05, 16.53it/s]
eval model::  81%|████████  | 404/500 [00:22<00:05, 18.42it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 19.42it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 20.88it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:04, 20.42it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 19.34it/s]
eval model::  84%|████████▍ | 419/500 [00:23<00:04, 19.73it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:05, 13.31it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 13.29it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 13.95it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 16.60it/s]
eval model::  86%|████████▌ | 431/500 [00:24<00:04, 15.85it/s]
eval model::  87%|████████▋ | 434/500 [00:24<00:03, 18.46it/s]
eval model::  87%|████████▋ | 437/500 [00:24<00:03, 19.45it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:03, 17.42it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 20.00it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 21.57it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 20.26it/s]
eval model::  91%|█████████ | 453/500 [00:25<00:02, 21.12it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:01, 22.17it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:01, 23.51it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 18.24it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 16.71it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 18.25it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.97it/s]
eval model::  95%|█████████▍| 474/500 [00:26<00:01, 18.09it/s]
eval model::  95%|█████████▌| 477/500 [00:26<00:01, 19.81it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:00, 20.52it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 21.71it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 21.94it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 23.47it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 21.15it/s]
eval model::  99%|█████████▉| 495/500 [00:27<00:00, 22.41it/s]
eval model:: 100%|█████████▉| 498/500 [00:27<00:00, 22.60it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.30it/s]
[2024/07/27 15:50:16] ppocr INFO: cur metric, precision: 0.7353383458646616, recall: 0.7063071738083775, hmean: 0.7205304518664046, fps: 44.423570433645146
[2024/07/27 15:50:16] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:50:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:50:25] ppocr INFO: epoch: [1461/1500], global_step: 4383, lr: 0.001000, loss: 1.159218, loss_shrink_maps: 0.575348, loss_threshold_maps: 0.463041, loss_binary_maps: 0.114751, avg_reader_cost: 2.17716 s, avg_batch_cost: 2.41548 s, avg_samples: 12.5, ips: 5.17497 samples/s, eta: 0:16:52
[2024/07/27 15:50:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:50:34] ppocr INFO: epoch: [1462/1500], global_step: 4386, lr: 0.001000, loss: 1.152912, loss_shrink_maps: 0.568585, loss_threshold_maps: 0.463041, loss_binary_maps: 0.113044, avg_reader_cost: 2.17646 s, avg_batch_cost: 2.53557 s, avg_samples: 12.5, ips: 4.92986 samples/s, eta: 0:16:26
[2024/07/27 15:50:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:50:44] ppocr INFO: epoch: [1463/1500], global_step: 4389, lr: 0.001000, loss: 1.152912, loss_shrink_maps: 0.568585, loss_threshold_maps: 0.461217, loss_binary_maps: 0.113044, avg_reader_cost: 2.39268 s, avg_batch_cost: 2.63753 s, avg_samples: 12.5, ips: 4.73929 samples/s, eta: 0:16:00
[2024/07/27 15:50:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:50:53] ppocr INFO: epoch: [1464/1500], global_step: 4390, lr: 0.001000, loss: 1.145163, loss_shrink_maps: 0.568585, loss_threshold_maps: 0.458312, loss_binary_maps: 0.113044, avg_reader_cost: 0.57731 s, avg_batch_cost: 0.79078 s, avg_samples: 4.8, ips: 6.06996 samples/s, eta: 0:15:51
[2024/07/27 15:50:54] ppocr INFO: epoch: [1464/1500], global_step: 4392, lr: 0.001000, loss: 1.150990, loss_shrink_maps: 0.572432, loss_threshold_maps: 0.458312, loss_binary_maps: 0.113932, avg_reader_cost: 1.67364 s, avg_batch_cost: 1.82049 s, avg_samples: 7.7, ips: 4.22963 samples/s, eta: 0:15:34
[2024/07/27 15:50:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:51:04] ppocr INFO: epoch: [1465/1500], global_step: 4395, lr: 0.001000, loss: 1.126365, loss_shrink_maps: 0.560901, loss_threshold_maps: 0.441190, loss_binary_maps: 0.111555, avg_reader_cost: 2.26333 s, avg_batch_cost: 2.63218 s, avg_samples: 12.5, ips: 4.74891 samples/s, eta: 0:15:08
[2024/07/27 15:51:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:51:14] ppocr INFO: epoch: [1466/1500], global_step: 4398, lr: 0.001000, loss: 1.085751, loss_shrink_maps: 0.535426, loss_threshold_maps: 0.436581, loss_binary_maps: 0.106351, avg_reader_cost: 2.42361 s, avg_batch_cost: 2.66496 s, avg_samples: 12.5, ips: 4.69050 samples/s, eta: 0:14:42
[2024/07/27 15:51:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:51:24] ppocr INFO: epoch: [1467/1500], global_step: 4400, lr: 0.001000, loss: 1.085751, loss_shrink_maps: 0.535893, loss_threshold_maps: 0.433094, loss_binary_maps: 0.106424, avg_reader_cost: 1.47897 s, avg_batch_cost: 1.67841 s, avg_samples: 9.6, ips: 5.71971 samples/s, eta: 0:14:25
[2024/07/27 15:51:24] ppocr INFO: epoch: [1467/1500], global_step: 4401, lr: 0.001000, loss: 1.085751, loss_shrink_maps: 0.535893, loss_threshold_maps: 0.435226, loss_binary_maps: 0.106424, avg_reader_cost: 0.88554 s, avg_batch_cost: 0.94071 s, avg_samples: 2.9, ips: 3.08277 samples/s, eta: 0:14:16
[2024/07/27 15:51:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:51:34] ppocr INFO: epoch: [1468/1500], global_step: 4404, lr: 0.001000, loss: 1.108399, loss_shrink_maps: 0.560901, loss_threshold_maps: 0.441073, loss_binary_maps: 0.111555, avg_reader_cost: 2.24074 s, avg_batch_cost: 2.58260 s, avg_samples: 12.5, ips: 4.84009 samples/s, eta: 0:13:50
[2024/07/27 15:51:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:51:44] ppocr INFO: epoch: [1469/1500], global_step: 4407, lr: 0.001000, loss: 1.131456, loss_shrink_maps: 0.577222, loss_threshold_maps: 0.435226, loss_binary_maps: 0.114862, avg_reader_cost: 2.31231 s, avg_batch_cost: 2.56861 s, avg_samples: 12.5, ips: 4.86644 samples/s, eta: 0:13:24
[2024/07/27 15:51:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:51:54] ppocr INFO: epoch: [1470/1500], global_step: 4410, lr: 0.001000, loss: 1.108399, loss_shrink_maps: 0.564992, loss_threshold_maps: 0.435226, loss_binary_maps: 0.112418, avg_reader_cost: 2.36236 s, avg_batch_cost: 2.59605 s, avg_samples: 12.5, ips: 4.81502 samples/s, eta: 0:12:58
[2024/07/27 15:51:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:52:04] ppocr INFO: epoch: [1471/1500], global_step: 4413, lr: 0.001000, loss: 1.105065, loss_shrink_maps: 0.555929, loss_threshold_maps: 0.435226, loss_binary_maps: 0.110700, avg_reader_cost: 2.16678 s, avg_batch_cost: 2.49253 s, avg_samples: 12.5, ips: 5.01499 samples/s, eta: 0:12:32
[2024/07/27 15:52:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:52:14] ppocr INFO: epoch: [1472/1500], global_step: 4416, lr: 0.001000, loss: 1.081304, loss_shrink_maps: 0.542917, loss_threshold_maps: 0.443319, loss_binary_maps: 0.107862, avg_reader_cost: 2.40075 s, avg_batch_cost: 2.63831 s, avg_samples: 12.5, ips: 4.73788 samples/s, eta: 0:12:06
[2024/07/27 15:52:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:52:23] ppocr INFO: epoch: [1473/1500], global_step: 4419, lr: 0.001000, loss: 1.076476, loss_shrink_maps: 0.547686, loss_threshold_maps: 0.435155, loss_binary_maps: 0.109238, avg_reader_cost: 2.25072 s, avg_batch_cost: 2.55568 s, avg_samples: 12.5, ips: 4.89106 samples/s, eta: 0:11:40
[2024/07/27 15:52:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:52:32] ppocr INFO: epoch: [1474/1500], global_step: 4420, lr: 0.001000, loss: 1.100618, loss_shrink_maps: 0.555929, loss_threshold_maps: 0.443319, loss_binary_maps: 0.110700, avg_reader_cost: 0.68686 s, avg_batch_cost: 0.77878 s, avg_samples: 4.8, ips: 6.16350 samples/s, eta: 0:11:32
[2024/07/27 15:52:33] ppocr INFO: epoch: [1474/1500], global_step: 4422, lr: 0.001000, loss: 1.105858, loss_shrink_maps: 0.555929, loss_threshold_maps: 0.445283, loss_binary_maps: 0.110700, avg_reader_cost: 1.64925 s, avg_batch_cost: 1.79613 s, avg_samples: 7.7, ips: 4.28700 samples/s, eta: 0:11:14
[2024/07/27 15:52:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:52:43] ppocr INFO: epoch: [1475/1500], global_step: 4425, lr: 0.001000, loss: 1.105858, loss_shrink_maps: 0.555929, loss_threshold_maps: 0.438288, loss_binary_maps: 0.110700, avg_reader_cost: 2.20863 s, avg_batch_cost: 2.57593 s, avg_samples: 12.5, ips: 4.85262 samples/s, eta: 0:10:48
[2024/07/27 15:52:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:52:52] ppocr INFO: epoch: [1476/1500], global_step: 4428, lr: 0.001000, loss: 1.113841, loss_shrink_maps: 0.559592, loss_threshold_maps: 0.443411, loss_binary_maps: 0.111490, avg_reader_cost: 2.15489 s, avg_batch_cost: 2.50619 s, avg_samples: 12.5, ips: 4.98764 samples/s, eta: 0:10:22
[2024/07/27 15:52:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:53:02] ppocr INFO: epoch: [1477/1500], global_step: 4430, lr: 0.001000, loss: 1.113841, loss_shrink_maps: 0.557529, loss_threshold_maps: 0.443411, loss_binary_maps: 0.111029, avg_reader_cost: 1.35169 s, avg_batch_cost: 1.65931 s, avg_samples: 9.6, ips: 5.78553 samples/s, eta: 0:10:05
[2024/07/27 15:53:02] ppocr INFO: epoch: [1477/1500], global_step: 4431, lr: 0.001000, loss: 1.120709, loss_shrink_maps: 0.565212, loss_threshold_maps: 0.445967, loss_binary_maps: 0.112633, avg_reader_cost: 0.87633 s, avg_batch_cost: 0.93129 s, avg_samples: 2.9, ips: 3.11395 samples/s, eta: 0:09:56
[2024/07/27 15:53:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:53:12] ppocr INFO: epoch: [1478/1500], global_step: 4434, lr: 0.001000, loss: 1.120132, loss_shrink_maps: 0.566748, loss_threshold_maps: 0.443411, loss_binary_maps: 0.112827, avg_reader_cost: 2.35066 s, avg_batch_cost: 2.59827 s, avg_samples: 12.5, ips: 4.81090 samples/s, eta: 0:09:30
[2024/07/27 15:53:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:53:22] ppocr INFO: epoch: [1479/1500], global_step: 4437, lr: 0.001000, loss: 1.129094, loss_shrink_maps: 0.566748, loss_threshold_maps: 0.443411, loss_binary_maps: 0.112827, avg_reader_cost: 2.14598 s, avg_batch_cost: 2.47923 s, avg_samples: 12.5, ips: 5.04188 samples/s, eta: 0:09:04
[2024/07/27 15:53:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:53:32] ppocr INFO: epoch: [1480/1500], global_step: 4440, lr: 0.001000, loss: 1.143530, loss_shrink_maps: 0.569638, loss_threshold_maps: 0.445967, loss_binary_maps: 0.113489, avg_reader_cost: 2.25668 s, avg_batch_cost: 2.62888 s, avg_samples: 12.5, ips: 4.75487 samples/s, eta: 0:08:39

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:58,  8.59it/s]
eval model::   1%|          | 3/500 [00:00<00:33, 14.74it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.70it/s]
eval model::   2%|▏         | 8/500 [00:00<00:26, 18.27it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.89it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.26it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.64it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.77it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 22.14it/s]
eval model::   5%|▌         | 26/500 [00:01<00:21, 22.37it/s]
eval model::   6%|▌         | 29/500 [00:01<00:20, 23.09it/s]
eval model::   6%|▋         | 32/500 [00:01<00:22, 21.23it/s]
eval model::   7%|▋         | 35/500 [00:01<00:22, 20.46it/s]
eval model::   8%|▊         | 38/500 [00:01<00:21, 21.64it/s]
eval model::   8%|▊         | 41/500 [00:02<00:22, 20.24it/s]
eval model::   9%|▉         | 44/500 [00:02<00:25, 17.91it/s]
eval model::   9%|▉         | 46/500 [00:02<00:27, 16.80it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 19.47it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 20.32it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.22it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:22, 19.49it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 15.76it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.72it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:21, 20.17it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 21.34it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 21.48it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 22.19it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:19, 21.63it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.46it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.42it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 20.26it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:21, 18.95it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.33it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.77it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:19, 20.99it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.46it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 22.23it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:17, 21.97it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 19.65it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 21.42it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:19, 19.79it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:20, 18.15it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 20.19it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.49it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:20, 18.28it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:20, 17.65it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:20, 17.61it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:19, 18.82it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:17, 20.59it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:16, 21.60it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 18.92it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 17.90it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 18.99it/s]
eval model::  30%|███       | 152/500 [00:07<00:19, 18.23it/s]
eval model::  31%|███       | 155/500 [00:07<00:17, 20.24it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:15, 22.64it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:23, 14.65it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:20, 16.48it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 17.56it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:18, 18.12it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:18, 17.63it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:20, 16.07it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:19, 16.75it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:18, 16.97it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:17, 18.21it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 19.77it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 19.71it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:15, 19.47it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.77it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.56it/s]
eval model::  40%|████      | 201/500 [00:10<00:15, 19.55it/s]
eval model::  41%|████      | 205/500 [00:10<00:21, 13.58it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:19, 14.63it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 14.96it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.49it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.93it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.69it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 17.01it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 14.94it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:15, 17.81it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:13, 19.84it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 22.25it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:13, 20.28it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 21.43it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 21.91it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:14, 17.50it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:12, 19.64it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:13, 19.22it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 21.24it/s]
eval model::  51%|█████     | 255/500 [00:13<00:10, 23.01it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:10, 24.03it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:09, 25.09it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:10, 21.92it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:10, 21.94it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 19.85it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:10, 20.93it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:11, 19.15it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 19.49it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 20.63it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.48it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.72it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.97it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 17.10it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:13, 14.78it/s]
eval model::  60%|██████    | 300/500 [00:15<00:12, 16.60it/s]
eval model::  61%|██████    | 303/500 [00:16<00:10, 18.38it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 18.29it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:10, 17.91it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:10, 18.42it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 19.22it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 21.04it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 21.21it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 20.36it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 21.93it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 21.10it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 22.38it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 22.49it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:08, 19.96it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 15.57it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 18.25it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 18.97it/s]
eval model::  70%|███████   | 352/500 [00:18<00:07, 20.46it/s]
eval model::  71%|███████   | 355/500 [00:18<00:07, 19.73it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:07, 20.05it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.15it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:08, 16.97it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:08, 16.03it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 18.16it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:07, 17.80it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 20.23it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 22.59it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 20.50it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:07, 14.81it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 17.85it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 18.29it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 17.74it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 18.87it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 19.02it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 19.41it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 20.43it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 22.02it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 22.43it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 22.59it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.87it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 15.52it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.32it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:04, 18.02it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 20.19it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 20.80it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 22.51it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 23.00it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 22.22it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 23.66it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 22.85it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.68it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 22.26it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 21.61it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 21.38it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 15.84it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 15.50it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:02, 16.23it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 18.59it/s]
eval model::  95%|█████████▍| 473/500 [00:24<00:01, 20.10it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 20.75it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 22.24it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 21.47it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 21.55it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 21.95it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 19.45it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 19.74it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 23.09it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.16it/s]
[2024/07/27 15:53:58] ppocr INFO: cur metric, precision: 0.736388140161725, recall: 0.6576793452094367, hmean: 0.6948118006103765, fps: 46.03030457737212
[2024/07/27 15:53:58] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:53:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:54:07] ppocr INFO: epoch: [1481/1500], global_step: 4443, lr: 0.001000, loss: 1.157223, loss_shrink_maps: 0.574434, loss_threshold_maps: 0.453570, loss_binary_maps: 0.114386, avg_reader_cost: 2.18118 s, avg_batch_cost: 2.53440 s, avg_samples: 12.5, ips: 4.93213 samples/s, eta: 0:08:13
[2024/07/27 15:54:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:54:17] ppocr INFO: epoch: [1482/1500], global_step: 4446, lr: 0.001000, loss: 1.143530, loss_shrink_maps: 0.566748, loss_threshold_maps: 0.445871, loss_binary_maps: 0.112870, avg_reader_cost: 2.23788 s, avg_batch_cost: 2.63370 s, avg_samples: 12.5, ips: 4.74617 samples/s, eta: 0:07:47
[2024/07/27 15:54:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:54:26] ppocr INFO: epoch: [1483/1500], global_step: 4449, lr: 0.001000, loss: 1.124606, loss_shrink_maps: 0.558903, loss_threshold_maps: 0.453004, loss_binary_maps: 0.111426, avg_reader_cost: 2.12139 s, avg_batch_cost: 2.41358 s, avg_samples: 12.5, ips: 5.17903 samples/s, eta: 0:07:21
[2024/07/27 15:54:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:54:35] ppocr INFO: epoch: [1484/1500], global_step: 4450, lr: 0.001000, loss: 1.124061, loss_shrink_maps: 0.558903, loss_threshold_maps: 0.453756, loss_binary_maps: 0.111426, avg_reader_cost: 0.70738 s, avg_batch_cost: 0.79817 s, avg_samples: 4.8, ips: 6.01376 samples/s, eta: 0:07:12
[2024/07/27 15:54:36] ppocr INFO: epoch: [1484/1500], global_step: 4452, lr: 0.001000, loss: 1.081774, loss_shrink_maps: 0.551739, loss_threshold_maps: 0.433245, loss_binary_maps: 0.110069, avg_reader_cost: 1.68827 s, avg_batch_cost: 1.83519 s, avg_samples: 7.7, ips: 4.19575 samples/s, eta: 0:06:55
[2024/07/27 15:54:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:54:46] ppocr INFO: epoch: [1485/1500], global_step: 4455, lr: 0.001000, loss: 1.073048, loss_shrink_maps: 0.543356, loss_threshold_maps: 0.438135, loss_binary_maps: 0.107950, avg_reader_cost: 2.32079 s, avg_batch_cost: 2.55774 s, avg_samples: 12.5, ips: 4.88712 samples/s, eta: 0:06:29
[2024/07/27 15:54:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:54:56] ppocr INFO: epoch: [1486/1500], global_step: 4458, lr: 0.001000, loss: 1.089019, loss_shrink_maps: 0.551421, loss_threshold_maps: 0.444445, loss_binary_maps: 0.109369, avg_reader_cost: 2.19417 s, avg_batch_cost: 2.55783 s, avg_samples: 12.5, ips: 4.88696 samples/s, eta: 0:06:03
[2024/07/27 15:54:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:55:05] ppocr INFO: epoch: [1487/1500], global_step: 4460, lr: 0.001000, loss: 1.073048, loss_shrink_maps: 0.545561, loss_threshold_maps: 0.434070, loss_binary_maps: 0.108116, avg_reader_cost: 1.48606 s, avg_batch_cost: 1.67783 s, avg_samples: 9.6, ips: 5.72167 samples/s, eta: 0:05:45
[2024/07/27 15:55:05] ppocr INFO: epoch: [1487/1500], global_step: 4461, lr: 0.001000, loss: 1.073048, loss_shrink_maps: 0.545561, loss_threshold_maps: 0.434070, loss_binary_maps: 0.108116, avg_reader_cost: 0.88491 s, avg_batch_cost: 0.94073 s, avg_samples: 2.9, ips: 3.08270 samples/s, eta: 0:05:37
[2024/07/27 15:55:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:55:15] ppocr INFO: epoch: [1488/1500], global_step: 4464, lr: 0.001000, loss: 1.089019, loss_shrink_maps: 0.550814, loss_threshold_maps: 0.440764, loss_binary_maps: 0.109153, avg_reader_cost: 2.29179 s, avg_batch_cost: 2.53243 s, avg_samples: 12.5, ips: 4.93596 samples/s, eta: 0:05:11
[2024/07/27 15:55:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:55:24] ppocr INFO: epoch: [1489/1500], global_step: 4467, lr: 0.001000, loss: 1.089019, loss_shrink_maps: 0.551570, loss_threshold_maps: 0.440764, loss_binary_maps: 0.109153, avg_reader_cost: 2.11378 s, avg_batch_cost: 2.42574 s, avg_samples: 12.5, ips: 5.15306 samples/s, eta: 0:04:45
[2024/07/27 15:55:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:55:34] ppocr INFO: epoch: [1490/1500], global_step: 4470, lr: 0.001000, loss: 1.098371, loss_shrink_maps: 0.551570, loss_threshold_maps: 0.446725, loss_binary_maps: 0.109153, avg_reader_cost: 2.42468 s, avg_batch_cost: 2.68455 s, avg_samples: 12.5, ips: 4.65627 samples/s, eta: 0:04:19
[2024/07/27 15:55:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:55:44] ppocr INFO: epoch: [1491/1500], global_step: 4473, lr: 0.001000, loss: 1.125745, loss_shrink_maps: 0.561838, loss_threshold_maps: 0.452581, loss_binary_maps: 0.111672, avg_reader_cost: 2.20117 s, avg_batch_cost: 2.55604 s, avg_samples: 12.5, ips: 4.89037 samples/s, eta: 0:03:53
[2024/07/27 15:55:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:55:54] ppocr INFO: epoch: [1492/1500], global_step: 4476, lr: 0.001000, loss: 1.162724, loss_shrink_maps: 0.573531, loss_threshold_maps: 0.457851, loss_binary_maps: 0.114240, avg_reader_cost: 2.30940 s, avg_batch_cost: 2.55310 s, avg_samples: 12.5, ips: 4.89602 samples/s, eta: 0:03:27
[2024/07/27 15:55:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:56:04] ppocr INFO: epoch: [1493/1500], global_step: 4479, lr: 0.001000, loss: 1.097078, loss_shrink_maps: 0.553248, loss_threshold_maps: 0.433422, loss_binary_maps: 0.109728, avg_reader_cost: 2.39364 s, avg_batch_cost: 2.63584 s, avg_samples: 12.5, ips: 4.74233 samples/s, eta: 0:03:01
[2024/07/27 15:56:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:56:12] ppocr INFO: epoch: [1494/1500], global_step: 4480, lr: 0.001000, loss: 1.097078, loss_shrink_maps: 0.553248, loss_threshold_maps: 0.433422, loss_binary_maps: 0.109728, avg_reader_cost: 0.56462 s, avg_batch_cost: 0.77386 s, avg_samples: 4.8, ips: 6.20263 samples/s, eta: 0:02:52
[2024/07/27 15:56:14] ppocr INFO: epoch: [1494/1500], global_step: 4482, lr: 0.001000, loss: 1.079382, loss_shrink_maps: 0.547297, loss_threshold_maps: 0.432049, loss_binary_maps: 0.108899, avg_reader_cost: 1.64118 s, avg_batch_cost: 1.78930 s, avg_samples: 7.7, ips: 4.30336 samples/s, eta: 0:02:35
[2024/07/27 15:56:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:56:23] ppocr INFO: epoch: [1495/1500], global_step: 4485, lr: 0.001000, loss: 1.099958, loss_shrink_maps: 0.557210, loss_threshold_maps: 0.438506, loss_binary_maps: 0.110208, avg_reader_cost: 2.17019 s, avg_batch_cost: 2.53162 s, avg_samples: 12.5, ips: 4.93754 samples/s, eta: 0:02:09
[2024/07/27 15:56:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:56:33] ppocr INFO: epoch: [1496/1500], global_step: 4488, lr: 0.001000, loss: 1.079382, loss_shrink_maps: 0.538719, loss_threshold_maps: 0.435981, loss_binary_maps: 0.106546, avg_reader_cost: 2.35863 s, avg_batch_cost: 2.59805 s, avg_samples: 12.5, ips: 4.81130 samples/s, eta: 0:01:43
[2024/07/27 15:56:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:56:43] ppocr INFO: epoch: [1497/1500], global_step: 4490, lr: 0.001000, loss: 1.075620, loss_shrink_maps: 0.538719, loss_threshold_maps: 0.427958, loss_binary_maps: 0.106546, avg_reader_cost: 1.47471 s, avg_batch_cost: 1.65681 s, avg_samples: 9.6, ips: 5.79428 samples/s, eta: 0:01:26
[2024/07/27 15:56:43] ppocr INFO: epoch: [1497/1500], global_step: 4491, lr: 0.001000, loss: 1.087519, loss_shrink_maps: 0.557210, loss_threshold_maps: 0.435981, loss_binary_maps: 0.110208, avg_reader_cost: 0.87474 s, avg_batch_cost: 0.93000 s, avg_samples: 2.9, ips: 3.11829 samples/s, eta: 0:01:17
[2024/07/27 15:56:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:56:52] ppocr INFO: epoch: [1498/1500], global_step: 4494, lr: 0.001000, loss: 1.075620, loss_shrink_maps: 0.526379, loss_threshold_maps: 0.435981, loss_binary_maps: 0.103872, avg_reader_cost: 2.15745 s, avg_batch_cost: 2.45796 s, avg_samples: 12.5, ips: 5.08551 samples/s, eta: 0:00:51
[2024/07/27 15:56:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:57:03] ppocr INFO: epoch: [1499/1500], global_step: 4497, lr: 0.001000, loss: 1.098361, loss_shrink_maps: 0.539089, loss_threshold_maps: 0.443743, loss_binary_maps: 0.107239, avg_reader_cost: 2.38678 s, avg_batch_cost: 2.62759 s, avg_samples: 12.5, ips: 4.75722 samples/s, eta: 0:00:25
[2024/07/27 15:57:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:57:12] ppocr INFO: epoch: [1500/1500], global_step: 4500, lr: 0.001000, loss: 1.114262, loss_shrink_maps: 0.553107, loss_threshold_maps: 0.445255, loss_binary_maps: 0.110493, avg_reader_cost: 2.37340 s, avg_batch_cost: 2.62435 s, avg_samples: 12.5, ips: 4.76308 samples/s, eta: 0:00:00

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:50,  9.96it/s]
eval model::   1%|          | 3/500 [00:00<00:31, 15.71it/s]
eval model::   1%|          | 6/500 [00:00<00:24, 19.93it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.26it/s]
eval model::   2%|▏         | 10/500 [00:00<00:27, 17.52it/s]
eval model::   2%|▏         | 12/500 [00:00<00:30, 15.83it/s]
eval model::   3%|▎         | 14/500 [00:00<00:30, 15.68it/s]
eval model::   3%|▎         | 17/500 [00:00<00:25, 19.09it/s]
eval model::   4%|▍         | 19/500 [00:01<00:25, 18.84it/s]
eval model::   4%|▍         | 21/500 [00:01<00:26, 18.21it/s]
eval model::   5%|▍         | 23/500 [00:01<00:26, 18.02it/s]
eval model::   5%|▌         | 25/500 [00:01<00:27, 17.57it/s]
eval model::   6%|▌         | 28/500 [00:01<00:25, 18.70it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.51it/s]
eval model::   7%|▋         | 33/500 [00:01<00:25, 18.60it/s]
eval model::   7%|▋         | 35/500 [00:02<00:29, 15.79it/s]
eval model::   8%|▊         | 38/500 [00:02<00:27, 16.60it/s]
eval model::   8%|▊         | 40/500 [00:02<00:27, 16.83it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.01it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.66it/s]
eval model::  10%|▉         | 48/500 [00:02<00:22, 19.90it/s]
eval model::  10%|█         | 51/500 [00:02<00:21, 20.77it/s]
eval model::  11%|█         | 54/500 [00:02<00:20, 21.74it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:20, 21.76it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:21, 20.69it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.10it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:26, 16.53it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:22, 18.93it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.21it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:19, 21.30it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 22.12it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:17, 23.97it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 23.00it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:19, 20.66it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.26it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.68it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:19, 20.26it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.64it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.15it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 22.15it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:17, 22.76it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 19.90it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 20.51it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:21, 17.94it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:21, 17.89it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:19, 19.17it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.54it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:20, 17.91it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:22, 16.49it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:24, 14.88it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:22, 16.20it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:21, 17.00it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:20, 17.96it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:20, 17.69it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:19, 18.12it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:21, 16.64it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:21, 16.11it/s]
eval model::  30%|███       | 150/500 [00:08<00:20, 16.95it/s]
eval model::  30%|███       | 152/500 [00:08<00:20, 16.85it/s]
eval model::  31%|███       | 155/500 [00:08<00:17, 19.79it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 21.50it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:22, 15.36it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:20, 16.07it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:18, 18.13it/s]
eval model::  34%|███▍      | 169/500 [00:09<00:18, 18.19it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:19, 17.24it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 15.83it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 15.96it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.29it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.52it/s]
eval model::  37%|███▋      | 183/500 [00:10<00:20, 15.63it/s]
eval model::  37%|███▋      | 186/500 [00:10<00:17, 17.53it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:16, 19.08it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:16, 19.25it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:14, 21.51it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:15, 19.09it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 20.70it/s]
eval model::  41%|████      | 203/500 [00:10<00:13, 21.52it/s]
eval model::  41%|████      | 206/500 [00:11<00:20, 14.30it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:20, 14.55it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 13.93it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.84it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.42it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:22, 12.55it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:19, 14.43it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:23, 11.82it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:19, 13.87it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:18, 14.73it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:16, 16.55it/s]
eval model::  47%|████▋     | 233/500 [00:13<00:15, 17.79it/s]
eval model::  47%|████▋     | 235/500 [00:13<00:14, 18.20it/s]
eval model::  48%|████▊     | 238/500 [00:13<00:13, 19.85it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:13, 19.32it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:15, 16.99it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:13, 19.32it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:13, 18.77it/s]
eval model::  50%|█████     | 251/500 [00:14<00:13, 19.00it/s]
eval model::  51%|█████     | 254/500 [00:14<00:12, 19.33it/s]
eval model::  51%|█████     | 256/500 [00:14<00:12, 19.04it/s]
eval model::  52%|█████▏    | 260/500 [00:14<00:10, 22.97it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:10, 22.20it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:11, 19.56it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 17.99it/s]
eval model::  54%|█████▍    | 272/500 [00:15<00:12, 18.07it/s]
eval model::  55%|█████▌    | 275/500 [00:15<00:12, 17.61it/s]
eval model::  56%|█████▌    | 278/500 [00:15<00:11, 19.67it/s]
eval model::  56%|█████▌    | 281/500 [00:15<00:10, 21.41it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:09, 22.39it/s]
eval model::  57%|█████▋    | 287/500 [00:16<00:19, 11.10it/s]
eval model::  58%|█████▊    | 290/500 [00:16<00:16, 12.96it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:16, 12.38it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:15, 13.14it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:17, 11.34it/s]
eval model::  60%|█████▉    | 299/500 [00:17<00:14, 13.69it/s]
eval model::  60%|██████    | 301/500 [00:17<00:15, 12.82it/s]
eval model::  61%|██████    | 304/500 [00:17<00:13, 14.55it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:11, 16.39it/s]
eval model::  62%|██████▏   | 309/500 [00:17<00:11, 16.06it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:10, 17.65it/s]
eval model::  63%|██████▎   | 315/500 [00:18<00:11, 16.66it/s]
eval model::  64%|██████▎   | 318/500 [00:18<00:09, 18.62it/s]
eval model::  64%|██████▍   | 321/500 [00:18<00:09, 19.41it/s]
eval model::  65%|██████▍   | 324/500 [00:18<00:08, 20.45it/s]
eval model::  65%|██████▌   | 327/500 [00:18<00:08, 21.56it/s]
eval model::  66%|██████▌   | 330/500 [00:18<00:07, 21.32it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:07, 21.09it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:07, 22.53it/s]
eval model::  68%|██████▊   | 339/500 [00:19<00:07, 20.84it/s]
eval model::  68%|██████▊   | 342/500 [00:19<00:10, 15.70it/s]
eval model::  69%|██████▉   | 345/500 [00:19<00:09, 16.93it/s]
eval model::  69%|██████▉   | 347/500 [00:19<00:09, 16.90it/s]
eval model::  70%|███████   | 350/500 [00:19<00:08, 18.74it/s]
eval model::  71%|███████   | 353/500 [00:19<00:07, 19.58it/s]
eval model::  71%|███████   | 356/500 [00:20<00:07, 18.11it/s]
eval model::  72%|███████▏  | 359/500 [00:20<00:07, 19.48it/s]
eval model::  72%|███████▏  | 362/500 [00:20<00:08, 16.95it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:08, 15.85it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:08, 14.93it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:07, 16.92it/s]
eval model::  74%|███████▍  | 372/500 [00:21<00:07, 17.29it/s]
eval model::  75%|███████▌  | 375/500 [00:21<00:06, 18.58it/s]
eval model::  76%|███████▌  | 379/500 [00:21<00:05, 21.82it/s]
eval model::  76%|███████▋  | 382/500 [00:21<00:06, 18.81it/s]
eval model::  77%|███████▋  | 385/500 [00:21<00:05, 19.81it/s]
eval model::  78%|███████▊  | 388/500 [00:22<00:09, 11.54it/s]
eval model::  78%|███████▊  | 391/500 [00:22<00:08, 13.48it/s]
eval model::  79%|███████▉  | 394/500 [00:22<00:07, 15.12it/s]
eval model::  79%|███████▉  | 397/500 [00:22<00:05, 17.67it/s]
eval model::  80%|████████  | 400/500 [00:22<00:05, 17.70it/s]
eval model::  81%|████████  | 403/500 [00:22<00:05, 18.01it/s]
eval model::  81%|████████  | 406/500 [00:23<00:04, 20.14it/s]
eval model::  82%|████████▏ | 409/500 [00:23<00:04, 21.17it/s]
eval model::  82%|████████▏ | 412/500 [00:23<00:04, 21.83it/s]
eval model::  83%|████████▎ | 415/500 [00:23<00:03, 21.45it/s]
eval model::  84%|████████▎ | 418/500 [00:23<00:04, 18.83it/s]
eval model::  84%|████████▍ | 421/500 [00:24<00:06, 12.85it/s]
eval model::  85%|████████▍ | 424/500 [00:24<00:05, 13.11it/s]
eval model::  85%|████████▌ | 426/500 [00:24<00:05, 13.83it/s]
eval model::  86%|████████▌ | 429/500 [00:24<00:04, 16.61it/s]
eval model::  86%|████████▌ | 431/500 [00:24<00:04, 15.71it/s]
eval model::  87%|████████▋ | 434/500 [00:24<00:03, 18.20it/s]
eval model::  88%|████████▊ | 438/500 [00:24<00:02, 21.58it/s]
eval model::  88%|████████▊ | 441/500 [00:25<00:02, 20.33it/s]
eval model::  89%|████████▉ | 444/500 [00:25<00:02, 21.53it/s]
eval model::  89%|████████▉ | 447/500 [00:25<00:02, 23.34it/s]
eval model::  90%|█████████ | 450/500 [00:25<00:02, 20.19it/s]
eval model::  91%|█████████ | 453/500 [00:25<00:02, 20.76it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:02, 20.18it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:01, 21.04it/s]
eval model::  92%|█████████▏| 462/500 [00:26<00:02, 15.85it/s]
eval model::  93%|█████████▎| 464/500 [00:26<00:02, 16.13it/s]
eval model::  93%|█████████▎| 466/500 [00:26<00:02, 16.18it/s]
eval model::  94%|█████████▍| 469/500 [00:26<00:01, 18.37it/s]
eval model::  94%|█████████▍| 472/500 [00:26<00:01, 20.16it/s]
eval model::  95%|█████████▌| 475/500 [00:26<00:01, 22.30it/s]
eval model::  96%|█████████▌| 479/500 [00:26<00:00, 26.10it/s]
eval model::  96%|█████████▋| 482/500 [00:27<00:00, 26.71it/s]
eval model::  97%|█████████▋| 486/500 [00:27<00:00, 28.01it/s]
eval model::  98%|█████████▊| 490/500 [00:27<00:00, 30.80it/s]
eval model::  99%|█████████▉| 494/500 [00:27<00:00, 27.59it/s]
eval model:: 100%|█████████▉| 498/500 [00:27<00:00, 29.52it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.13it/s]
[2024/07/27 15:57:40] ppocr INFO: cur metric, precision: 0.7100445324096981, recall: 0.6909003370245547, hmean: 0.7003416300634455, fps: 44.518711926072115
[2024/07/27 15:57:40] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
[2024/07/27 15:57:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 15:57:40] ppocr INFO: best metric, hmean: 0.7301425661914459, precision: 0.7747163695299838, recall: 0.6904188733750601, fps: 43.582139874688714, best_epoch: 960
I0727 15:57:42.215958  3063 tcp_store.cc:289] receive shutdown event and so quit from MasterDaemon run loop
[2024/07/27 21:45:51] ppocr INFO: Architecture : 
[2024/07/27 21:45:51] ppocr INFO:     Backbone : 
[2024/07/27 21:45:51] ppocr INFO:         model_name : large
[2024/07/27 21:45:51] ppocr INFO:         name : MobileNetV3
[2024/07/27 21:45:51] ppocr INFO:         scale : 0.5
[2024/07/27 21:45:51] ppocr INFO:     Head : 
[2024/07/27 21:45:51] ppocr INFO:         k : 50
[2024/07/27 21:45:51] ppocr INFO:         name : DBHead
[2024/07/27 21:45:51] ppocr INFO:     Neck : 
[2024/07/27 21:45:51] ppocr INFO:         name : DBFPN
[2024/07/27 21:45:51] ppocr INFO:         out_channels : 256
[2024/07/27 21:45:51] ppocr INFO:     Transform : None
[2024/07/27 21:45:51] ppocr INFO:     algorithm : DB
[2024/07/27 21:45:51] ppocr INFO:     model_type : det
[2024/07/27 21:45:51] ppocr INFO: Eval : 
[2024/07/27 21:45:51] ppocr INFO:     dataset : 
[2024/07/27 21:45:51] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/07/27 21:45:51] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
[2024/07/27 21:45:51] ppocr INFO:         name : SimpleDataSet
[2024/07/27 21:45:51] ppocr INFO:         transforms : 
[2024/07/27 21:45:51] ppocr INFO:             DecodeImage : 
[2024/07/27 21:45:51] ppocr INFO:                 channel_first : False
[2024/07/27 21:45:51] ppocr INFO:                 img_mode : BGR
[2024/07/27 21:45:51] ppocr INFO:             DetLabelEncode : None
[2024/07/27 21:45:51] ppocr INFO:             DetResizeForTest : 
[2024/07/27 21:45:51] ppocr INFO:                 image_shape : [736, 1280]
[2024/07/27 21:45:51] ppocr INFO:             NormalizeImage : 
[2024/07/27 21:45:51] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/07/27 21:45:51] ppocr INFO:                 order : hwc
[2024/07/27 21:45:51] ppocr INFO:                 scale : 1./255.
[2024/07/27 21:45:51] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/07/27 21:45:51] ppocr INFO:             ToCHWImage : None
[2024/07/27 21:45:51] ppocr INFO:             KeepKeys : 
[2024/07/27 21:45:51] ppocr INFO:                 keep_keys : ['image', 'shape', 'polys', 'ignore_tags']
[2024/07/27 21:45:51] ppocr INFO:     loader : 
[2024/07/27 21:45:51] ppocr INFO:         batch_size_per_card : 1
[2024/07/27 21:45:51] ppocr INFO:         drop_last : False
[2024/07/27 21:45:51] ppocr INFO:         num_workers : 0
[2024/07/27 21:45:51] ppocr INFO:         shuffle : False
[2024/07/27 21:45:51] ppocr INFO:         use_shared_memory : False
[2024/07/27 21:45:51] ppocr INFO: Global : 
[2024/07/27 21:45:51] ppocr INFO:     cal_metric_during_train : False
[2024/07/27 21:45:51] ppocr INFO:     checkpoints : None
[2024/07/27 21:45:51] ppocr INFO:     distributed : True
[2024/07/27 21:45:51] ppocr INFO:     epoch_num : 1500
[2024/07/27 21:45:51] ppocr INFO:     eval_batch_step : [0, 60]
[2024/07/27 21:45:51] ppocr INFO:     infer_img : doc/imgs_en/img_10.jpg
[2024/07/27 21:45:51] ppocr INFO:     log_smooth_window : 20
[2024/07/27 21:45:51] ppocr INFO:     pretrained_model : ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/07/27 21:45:51] ppocr INFO:     print_batch_step : 10
[2024/07/27 21:45:51] ppocr INFO:     save_epoch_step : 1200
[2024/07/27 21:45:51] ppocr INFO:     save_inference_dir : None
[2024/07/27 21:45:51] ppocr INFO:     save_model_dir : ./output/db_mv3/
[2024/07/27 21:45:51] ppocr INFO:     save_res_path : ./output/det_db/predicts_db.txt
[2024/07/27 21:45:51] ppocr INFO:     use_gpu : True
[2024/07/27 21:45:51] ppocr INFO:     use_visualdl : False
[2024/07/27 21:45:51] ppocr INFO:     use_xpu : False
[2024/07/27 21:45:51] ppocr INFO: Loss : 
[2024/07/27 21:45:51] ppocr INFO:     alpha : 5
[2024/07/27 21:45:51] ppocr INFO:     balance_loss : True
[2024/07/27 21:45:51] ppocr INFO:     beta : 10
[2024/07/27 21:45:51] ppocr INFO:     main_loss_type : DiceLoss
[2024/07/27 21:45:51] ppocr INFO:     name : DBLoss
[2024/07/27 21:45:51] ppocr INFO:     ohem_ratio : 3
[2024/07/27 21:45:51] ppocr INFO: Metric : 
[2024/07/27 21:45:51] ppocr INFO:     main_indicator : hmean
[2024/07/27 21:45:51] ppocr INFO:     name : DetMetric
[2024/07/27 21:45:51] ppocr INFO: Optimizer : 
[2024/07/27 21:45:51] ppocr INFO:     beta1 : 0.9
[2024/07/27 21:45:51] ppocr INFO:     beta2 : 0.999
[2024/07/27 21:45:51] ppocr INFO:     lr : 
[2024/07/27 21:45:51] ppocr INFO:         learning_rate : 0.001
[2024/07/27 21:45:51] ppocr INFO:     name : Adam
[2024/07/27 21:45:51] ppocr INFO:     regularizer : 
[2024/07/27 21:45:51] ppocr INFO:         factor : 0
[2024/07/27 21:45:51] ppocr INFO:         name : L2
[2024/07/27 21:45:51] ppocr INFO: PostProcess : 
[2024/07/27 21:45:51] ppocr INFO:     box_thresh : 0.6
[2024/07/27 21:45:51] ppocr INFO:     max_candidates : 1000
[2024/07/27 21:45:51] ppocr INFO:     name : DBPostProcess
[2024/07/27 21:45:51] ppocr INFO:     thresh : 0.3
[2024/07/27 21:45:51] ppocr INFO:     unclip_ratio : 1.5
[2024/07/27 21:45:51] ppocr INFO: Train : 
[2024/07/27 21:45:51] ppocr INFO:     dataset : 
[2024/07/27 21:45:51] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/07/27 21:45:51] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/07/27 21:45:51] ppocr INFO:         name : SimpleDataSet
[2024/07/27 21:45:51] ppocr INFO:         ratio_list : [1.0]
[2024/07/27 21:45:51] ppocr INFO:         transforms : 
[2024/07/27 21:45:51] ppocr INFO:             DecodeImage : 
[2024/07/27 21:45:51] ppocr INFO:                 channel_first : False
[2024/07/27 21:45:51] ppocr INFO:                 img_mode : BGR
[2024/07/27 21:45:51] ppocr INFO:             DetLabelEncode : None
[2024/07/27 21:45:51] ppocr INFO:             IaaAugment : 
[2024/07/27 21:45:51] ppocr INFO:                 augmenter_args : 
[2024/07/27 21:45:51] ppocr INFO:                     args : 
[2024/07/27 21:45:51] ppocr INFO:                         p : 0.5
[2024/07/27 21:45:51] ppocr INFO:                     type : Fliplr
[2024/07/27 21:45:51] ppocr INFO:                     args : 
[2024/07/27 21:45:51] ppocr INFO:                         rotate : [-10, 10]
[2024/07/27 21:45:51] ppocr INFO:                     type : Affine
[2024/07/27 21:45:51] ppocr INFO:                     args : 
[2024/07/27 21:45:51] ppocr INFO:                         size : [0.5, 3]
[2024/07/27 21:45:51] ppocr INFO:                     type : Resize
[2024/07/27 21:45:51] ppocr INFO:             EastRandomCropData : 
[2024/07/27 21:45:51] ppocr INFO:                 keep_ratio : True
[2024/07/27 21:45:51] ppocr INFO:                 max_tries : 50
[2024/07/27 21:45:51] ppocr INFO:                 size : [640, 640]
[2024/07/27 21:45:51] ppocr INFO:             MakeBorderMap : 
[2024/07/27 21:45:51] ppocr INFO:                 shrink_ratio : 0.4
[2024/07/27 21:45:51] ppocr INFO:                 thresh_max : 0.7
[2024/07/27 21:45:51] ppocr INFO:                 thresh_min : 0.3
[2024/07/27 21:45:51] ppocr INFO:             MakeShrinkMap : 
[2024/07/27 21:45:51] ppocr INFO:                 min_text_size : 8
[2024/07/27 21:45:51] ppocr INFO:                 shrink_ratio : 0.4
[2024/07/27 21:45:51] ppocr INFO:             NormalizeImage : 
[2024/07/27 21:45:51] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/07/27 21:45:51] ppocr INFO:                 order : hwc
[2024/07/27 21:45:51] ppocr INFO:                 scale : 1./255.
[2024/07/27 21:45:51] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/07/27 21:45:51] ppocr INFO:             ToCHWImage : None
[2024/07/27 21:45:51] ppocr INFO:             KeepKeys : 
[2024/07/27 21:45:51] ppocr INFO:                 keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask']
[2024/07/27 21:45:51] ppocr INFO:     loader : 
[2024/07/27 21:45:51] ppocr INFO:         batch_size_per_card : 48
[2024/07/27 21:45:51] ppocr INFO:         drop_last : False
[2024/07/27 21:45:51] ppocr INFO:         num_workers : 8
[2024/07/27 21:45:51] ppocr INFO:         shuffle : True
[2024/07/27 21:45:51] ppocr INFO:         use_shared_memory : False
[2024/07/27 21:45:51] ppocr INFO: profiler_options : None
[2024/07/27 21:45:51] ppocr INFO: train with paddle 2.5.2 and device Place(gpu:0)
======================= Modified FLAGS detected =======================
FLAGS(name='FLAGS_cudnn_batchnorm_spatial_persistent', current_value=True, default_value=False)
FLAGS(name='FLAGS_selected_gpus', current_value='0', default_value='')
=======================================================================
I0727 21:45:51.167779   256 tcp_utils.cc:181] The server starts to listen on IP_ANY:49703
I0727 21:45:51.167958   256 tcp_utils.cc:130] Successfully connected to 127.0.0.1:49703
I0727 21:45:54.286273   256 process_group_nccl.cc:120] ProcessGroupNCCL pg_timeout_ 1800000
[2024/07/27 21:45:54] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/07/27 21:45:54] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
W0727 21:45:54.298504   256 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 90.2, Driver API Version: 50724.2, Runtime API Version: 50724.2
[2024/07/27 21:45:55] ppocr INFO: load pretrain successful from ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/07/27 21:45:55] ppocr INFO: train dataloader has 3 iters
[2024/07/27 21:45:55] ppocr INFO: valid dataloader has 500 iters
[2024/07/27 21:45:55] ppocr INFO: During the training process, after the 0th iteration, an evaluation is run every 60 iterations
[2024/07/27 21:46:19] ppocr INFO: epoch: [1/1500], global_step: 3, lr: 0.001000, loss: 9.253895, loss_shrink_maps: 4.917219, loss_threshold_maps: 3.397514, loss_binary_maps: 0.984065, avg_reader_cost: 6.24634 s, avg_batch_cost: 6.90953 s, avg_samples: 12.5, ips: 1.80909 samples/s, eta: 1 day, 4:46:13
[2024/07/27 21:46:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:46:30] ppocr INFO: epoch: [2/1500], global_step: 6, lr: 0.001000, loss: 8.548260, loss_shrink_maps: 4.876904, loss_threshold_maps: 2.668770, loss_binary_maps: 0.979144, avg_reader_cost: 2.69399 s, avg_batch_cost: 2.99949 s, avg_samples: 12.5, ips: 4.16737 samples/s, eta: 20:36:58
[2024/07/27 21:46:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:46:42] ppocr INFO: epoch: [3/1500], global_step: 9, lr: 0.001000, loss: 7.702368, loss_shrink_maps: 4.852952, loss_threshold_maps: 1.932015, loss_binary_maps: 0.974952, avg_reader_cost: 2.82204 s, avg_batch_cost: 3.06487 s, avg_samples: 12.5, ips: 4.07847 samples/s, eta: 17:58:59
[2024/07/27 21:46:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:46:51] ppocr INFO: epoch: [4/1500], global_step: 10, lr: 0.001000, loss: 7.638932, loss_shrink_maps: 4.844220, loss_threshold_maps: 1.839804, loss_binary_maps: 0.971855, avg_reader_cost: 0.71370 s, avg_batch_cost: 0.87119 s, avg_samples: 4.8, ips: 5.50971 samples/s, eta: 17:16:04
[2024/07/27 21:46:53] ppocr INFO: epoch: [4/1500], global_step: 12, lr: 0.001000, loss: 7.301230, loss_shrink_maps: 4.834607, loss_threshold_maps: 1.516090, loss_binary_maps: 0.968031, avg_reader_cost: 1.83293 s, avg_batch_cost: 1.97747 s, avg_samples: 7.7, ips: 3.89386 samples/s, eta: 16:26:16
[2024/07/27 21:46:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:47:03] ppocr INFO: epoch: [5/1500], global_step: 15, lr: 0.001000, loss: 7.025673, loss_shrink_maps: 4.815832, loss_threshold_maps: 1.282615, loss_binary_maps: 0.963583, avg_reader_cost: 2.61890 s, avg_batch_cost: 2.85575 s, avg_samples: 12.5, ips: 4.37713 samples/s, eta: 15:30:48
[2024/07/27 21:47:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:47:14] ppocr INFO: epoch: [6/1500], global_step: 18, lr: 0.001000, loss: 7.002416, loss_shrink_maps: 4.797278, loss_threshold_maps: 1.202436, loss_binary_maps: 0.962072, avg_reader_cost: 2.65636 s, avg_batch_cost: 2.91135 s, avg_samples: 12.5, ips: 4.29354 samples/s, eta: 14:55:58
[2024/07/27 21:47:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:47:25] ppocr INFO: epoch: [7/1500], global_step: 20, lr: 0.001000, loss: 6.967031, loss_shrink_maps: 4.785719, loss_threshold_maps: 1.179879, loss_binary_maps: 0.959428, avg_reader_cost: 1.73273 s, avg_batch_cost: 1.91064 s, avg_samples: 9.6, ips: 5.02450 samples/s, eta: 14:37:20
[2024/07/27 21:47:25] ppocr INFO: epoch: [7/1500], global_step: 21, lr: 0.001000, loss: 6.919442, loss_shrink_maps: 4.782249, loss_threshold_maps: 1.174124, loss_binary_maps: 0.956609, avg_reader_cost: 1.00083 s, avg_batch_cost: 1.05568 s, avg_samples: 2.9, ips: 2.74705 samples/s, eta: 14:32:54
[2024/07/27 21:47:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:47:37] ppocr INFO: epoch: [8/1500], global_step: 24, lr: 0.001000, loss: 6.837163, loss_shrink_maps: 4.745248, loss_threshold_maps: 1.147013, loss_binary_maps: 0.947830, avg_reader_cost: 2.64389 s, avg_batch_cost: 2.91765 s, avg_samples: 12.5, ips: 4.28427 samples/s, eta: 14:13:58
[2024/07/27 21:47:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:47:48] ppocr INFO: epoch: [9/1500], global_step: 27, lr: 0.001000, loss: 6.719752, loss_shrink_maps: 4.652148, loss_threshold_maps: 1.122574, loss_binary_maps: 0.926876, avg_reader_cost: 2.75281 s, avg_batch_cost: 2.99654 s, avg_samples: 12.5, ips: 4.17148 samples/s, eta: 14:01:18
[2024/07/27 21:47:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:47:59] ppocr INFO: epoch: [10/1500], global_step: 30, lr: 0.001000, loss: 6.576698, loss_shrink_maps: 4.599428, loss_threshold_maps: 1.099971, loss_binary_maps: 0.887527, avg_reader_cost: 2.82482 s, avg_batch_cost: 3.07601 s, avg_samples: 12.5, ips: 4.06370 samples/s, eta: 13:53:03
[2024/07/27 21:48:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:48:10] ppocr INFO: epoch: [11/1500], global_step: 33, lr: 0.001000, loss: 6.473350, loss_shrink_maps: 4.528748, loss_threshold_maps: 1.060250, loss_binary_maps: 0.855419, avg_reader_cost: 2.74335 s, avg_batch_cost: 2.97632 s, avg_samples: 12.5, ips: 4.19982 samples/s, eta: 13:43:58
[2024/07/27 21:48:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:48:21] ppocr INFO: epoch: [12/1500], global_step: 36, lr: 0.001000, loss: 6.312004, loss_shrink_maps: 4.467349, loss_threshold_maps: 1.008221, loss_binary_maps: 0.814581, avg_reader_cost: 2.62160 s, avg_batch_cost: 2.97805 s, avg_samples: 12.5, ips: 4.19738 samples/s, eta: 13:36:20
[2024/07/27 21:48:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:48:33] ppocr INFO: epoch: [13/1500], global_step: 39, lr: 0.001000, loss: 5.777366, loss_shrink_maps: 4.126498, loss_threshold_maps: 0.981692, loss_binary_maps: 0.709848, avg_reader_cost: 2.72811 s, avg_batch_cost: 2.97097 s, avg_samples: 12.5, ips: 4.20739 samples/s, eta: 13:29:40
[2024/07/27 21:48:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:48:42] ppocr INFO: epoch: [14/1500], global_step: 40, lr: 0.001000, loss: 5.732084, loss_shrink_maps: 4.110692, loss_threshold_maps: 0.965224, loss_binary_maps: 0.697947, avg_reader_cost: 0.71389 s, avg_batch_cost: 0.86119 s, avg_samples: 4.8, ips: 5.57366 samples/s, eta: 13:25:15
[2024/07/27 21:48:43] ppocr INFO: epoch: [14/1500], global_step: 42, lr: 0.001000, loss: 5.605818, loss_shrink_maps: 4.004832, loss_threshold_maps: 0.955461, loss_binary_maps: 0.671300, avg_reader_cost: 1.81467 s, avg_batch_cost: 1.96145 s, avg_samples: 7.7, ips: 3.92567 samples/s, eta: 13:21:16
[2024/07/27 21:48:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:48:54] ppocr INFO: epoch: [15/1500], global_step: 45, lr: 0.001000, loss: 5.437328, loss_shrink_maps: 3.837244, loss_threshold_maps: 0.937092, loss_binary_maps: 0.636082, avg_reader_cost: 2.76506 s, avg_batch_cost: 3.01747 s, avg_samples: 12.5, ips: 4.14255 samples/s, eta: 13:17:08
[2024/07/27 21:48:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:49:06] ppocr INFO: epoch: [16/1500], global_step: 48, lr: 0.001000, loss: 5.305352, loss_shrink_maps: 3.746127, loss_threshold_maps: 0.928468, loss_binary_maps: 0.606087, avg_reader_cost: 2.67295 s, avg_batch_cost: 2.97316 s, avg_samples: 12.5, ips: 4.20428 samples/s, eta: 13:12:46
[2024/07/27 21:49:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:49:16] ppocr INFO: epoch: [17/1500], global_step: 50, lr: 0.001000, loss: 5.051848, loss_shrink_maps: 3.586887, loss_threshold_maps: 0.924119, loss_binary_maps: 0.557438, avg_reader_cost: 1.61005 s, avg_batch_cost: 1.86066 s, avg_samples: 9.6, ips: 5.15946 samples/s, eta: 13:08:19
[2024/07/27 21:49:16] ppocr INFO: epoch: [17/1500], global_step: 51, lr: 0.001000, loss: 4.901994, loss_shrink_maps: 3.467603, loss_threshold_maps: 0.922105, loss_binary_maps: 0.534717, avg_reader_cost: 0.97582 s, avg_batch_cost: 1.03054 s, avg_samples: 2.9, ips: 2.81406 samples/s, eta: 13:07:40
[2024/07/27 21:49:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:49:28] ppocr INFO: epoch: [18/1500], global_step: 54, lr: 0.001000, loss: 4.738723, loss_shrink_maps: 3.276343, loss_threshold_maps: 0.912072, loss_binary_maps: 0.507635, avg_reader_cost: 2.73928 s, avg_batch_cost: 3.00075 s, avg_samples: 12.5, ips: 4.16563 samples/s, eta: 13:04:35
[2024/07/27 21:49:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:49:39] ppocr INFO: epoch: [19/1500], global_step: 57, lr: 0.001000, loss: 4.423630, loss_shrink_maps: 3.062579, loss_threshold_maps: 0.903388, loss_binary_maps: 0.465144, avg_reader_cost: 2.60720 s, avg_batch_cost: 2.88348 s, avg_samples: 12.5, ips: 4.33504 samples/s, eta: 13:00:15
[2024/07/27 21:49:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:49:50] ppocr INFO: epoch: [20/1500], global_step: 60, lr: 0.001000, loss: 4.260386, loss_shrink_maps: 2.934451, loss_threshold_maps: 0.893398, loss_binary_maps: 0.455492, avg_reader_cost: 2.70225 s, avg_batch_cost: 3.02137 s, avg_samples: 12.5, ips: 4.13720 samples/s, eta: 12:58:00

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:54,  9.22it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 17.90it/s]
eval model::   1%|▏         | 7/500 [00:00<00:25, 19.06it/s]
eval model::   2%|▏         | 10/500 [00:00<00:23, 20.73it/s]
eval model::   3%|▎         | 13/500 [00:00<00:25, 19.03it/s]
eval model::   3%|▎         | 16/500 [00:00<00:23, 20.72it/s]
eval model::   4%|▍         | 19/500 [00:00<00:21, 21.97it/s]
eval model::   4%|▍         | 22/500 [00:01<00:20, 22.93it/s]
eval model::   5%|▌         | 25/500 [00:01<00:22, 21.08it/s]
eval model::   6%|▌         | 28/500 [00:01<00:21, 21.80it/s]
eval model::   6%|▌         | 31/500 [00:01<00:22, 20.84it/s]
eval model::   7%|▋         | 34/500 [00:01<00:20, 22.36it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 21.42it/s]
eval model::   8%|▊         | 40/500 [00:01<00:23, 19.31it/s]
eval model::   9%|▊         | 43/500 [00:02<00:23, 19.25it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.01it/s]
eval model::  10%|▉         | 48/500 [00:02<00:21, 20.70it/s]
eval model::  10%|█         | 51/500 [00:02<00:21, 20.84it/s]
eval model::  11%|█         | 54/500 [00:02<00:22, 20.11it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:25, 17.67it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:24, 17.97it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.82it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:23, 18.33it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 18.05it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:23, 18.49it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:22, 19.16it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:22, 19.08it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:20, 20.71it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:20, 20.78it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.72it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:17, 23.49it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:17, 24.15it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:16, 24.35it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:17, 23.74it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:16, 23.94it/s]
eval model::  20%|██        | 100/500 [00:04<00:17, 23.37it/s]
eval model::  21%|██        | 103/500 [00:04<00:16, 24.43it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 23.11it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:15, 24.68it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:16, 23.68it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:16, 22.93it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:17, 21.97it/s]
eval model::  24%|██▍       | 121/500 [00:05<00:16, 22.63it/s]
eval model::  25%|██▍       | 124/500 [00:05<00:16, 22.17it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:15, 23.71it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:17, 21.15it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:16, 22.47it/s]
eval model::  27%|██▋       | 137/500 [00:06<00:17, 21.04it/s]
eval model::  28%|██▊       | 140/500 [00:06<00:18, 19.75it/s]
eval model::  29%|██▊       | 143/500 [00:06<00:18, 19.43it/s]
eval model::  29%|██▉       | 146/500 [00:06<00:17, 20.74it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:17, 20.22it/s]
eval model::  30%|███       | 152/500 [00:07<00:18, 19.01it/s]
eval model::  31%|███       | 155/500 [00:07<00:17, 19.41it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:17, 19.53it/s]
eval model::  32%|███▏      | 159/500 [00:07<00:17, 19.07it/s]
eval model::  32%|███▏      | 161/500 [00:07<00:18, 18.30it/s]
eval model::  33%|███▎      | 164/500 [00:07<00:17, 19.58it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 19.37it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:15, 20.99it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:15, 20.52it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:16, 20.07it/s]
eval model::  36%|███▌      | 179/500 [00:08<00:15, 21.04it/s]
eval model::  36%|███▋      | 182/500 [00:08<00:15, 20.67it/s]
eval model::  37%|███▋      | 186/500 [00:08<00:14, 21.87it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.30it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.92it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:15, 19.91it/s]
eval model::  40%|███▉      | 198/500 [00:09<00:14, 20.93it/s]
eval model::  40%|████      | 201/500 [00:09<00:15, 19.62it/s]
eval model::  41%|████      | 204/500 [00:10<00:19, 15.18it/s]
eval model::  41%|████      | 206/500 [00:10<00:23, 12.33it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:21, 13.30it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:17, 16.25it/s]
eval model::  43%|████▎     | 214/500 [00:10<00:15, 19.03it/s]
eval model::  43%|████▎     | 217/500 [00:10<00:14, 19.09it/s]
eval model::  44%|████▍     | 221/500 [00:10<00:13, 21.25it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:13, 21.13it/s]
eval model::  45%|████▌     | 227/500 [00:11<00:13, 20.96it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:12, 20.78it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:12, 21.22it/s]
eval model::  47%|████▋     | 236/500 [00:11<00:12, 20.56it/s]
eval model::  48%|████▊     | 239/500 [00:11<00:13, 19.53it/s]
eval model::  48%|████▊     | 242/500 [00:11<00:13, 19.41it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:12, 20.91it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 21.51it/s]
eval model::  50%|█████     | 251/500 [00:12<00:12, 20.20it/s]
eval model::  51%|█████     | 254/500 [00:12<00:12, 19.56it/s]
eval model::  51%|█████     | 256/500 [00:12<00:13, 18.18it/s]
eval model::  52%|█████▏    | 259/500 [00:12<00:12, 19.93it/s]
eval model::  52%|█████▏    | 262/500 [00:12<00:12, 19.20it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:12, 19.29it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:10, 21.28it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:10, 21.67it/s]
eval model::  55%|█████▍    | 274/500 [00:13<00:10, 21.56it/s]
eval model::  55%|█████▌    | 277/500 [00:13<00:10, 21.59it/s]
eval model::  56%|█████▌    | 280/500 [00:13<00:09, 22.55it/s]
eval model::  57%|█████▋    | 283/500 [00:13<00:09, 21.75it/s]
eval model::  57%|█████▋    | 286/500 [00:14<00:10, 19.89it/s]
eval model::  58%|█████▊    | 289/500 [00:14<00:09, 21.72it/s]
eval model::  58%|█████▊    | 292/500 [00:14<00:09, 22.69it/s]
eval model::  59%|█████▉    | 295/500 [00:14<00:10, 19.62it/s]
eval model::  60%|█████▉    | 298/500 [00:14<00:09, 20.50it/s]
eval model::  60%|██████    | 301/500 [00:14<00:09, 21.12it/s]
eval model::  61%|██████    | 304/500 [00:14<00:08, 23.08it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:09, 21.37it/s]
eval model::  62%|██████▏   | 310/500 [00:15<00:08, 21.84it/s]
eval model::  63%|██████▎   | 313/500 [00:15<00:09, 20.74it/s]
eval model::  63%|██████▎   | 316/500 [00:15<00:08, 20.93it/s]
eval model::  64%|██████▍   | 319/500 [00:15<00:09, 19.53it/s]
eval model::  64%|██████▍   | 322/500 [00:15<00:09, 18.33it/s]
eval model::  65%|██████▍   | 324/500 [00:15<00:09, 18.35it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:08, 19.53it/s]
eval model::  66%|██████▌   | 329/500 [00:16<00:09, 18.96it/s]
eval model::  66%|██████▋   | 332/500 [00:16<00:08, 20.01it/s]
eval model::  67%|██████▋   | 335/500 [00:16<00:07, 20.88it/s]
eval model::  68%|██████▊   | 338/500 [00:16<00:08, 19.50it/s]
eval model::  68%|██████▊   | 340/500 [00:16<00:08, 17.83it/s]
eval model::  69%|██████▊   | 343/500 [00:16<00:07, 19.64it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 19.45it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:08, 18.68it/s]
eval model::  70%|███████   | 351/500 [00:17<00:07, 19.19it/s]
eval model::  71%|███████   | 353/500 [00:17<00:07, 19.29it/s]
eval model::  71%|███████   | 355/500 [00:17<00:08, 17.94it/s]
eval model::  71%|███████▏  | 357/500 [00:17<00:08, 17.50it/s]
eval model::  72%|███████▏  | 360/500 [00:17<00:07, 19.46it/s]
eval model::  72%|███████▏  | 362/500 [00:17<00:07, 19.06it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:08, 15.75it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:07, 18.48it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:06, 21.12it/s]
eval model::  75%|███████▍  | 373/500 [00:18<00:06, 20.33it/s]
eval model::  75%|███████▌  | 376/500 [00:18<00:06, 18.80it/s]
eval model::  76%|███████▌  | 379/500 [00:18<00:06, 19.60it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:06, 18.36it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 19.69it/s]
eval model::  78%|███████▊  | 388/500 [00:19<00:05, 19.65it/s]
eval model::  78%|███████▊  | 391/500 [00:19<00:05, 20.27it/s]
eval model::  79%|███████▉  | 394/500 [00:19<00:05, 19.77it/s]
eval model::  79%|███████▉  | 397/500 [00:19<00:05, 20.40it/s]
eval model::  80%|████████  | 400/500 [00:19<00:05, 19.57it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 19.57it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 19.18it/s]
eval model::  81%|████████▏ | 407/500 [00:20<00:04, 19.23it/s]
eval model::  82%|████████▏ | 410/500 [00:20<00:04, 19.18it/s]
eval model::  82%|████████▏ | 412/500 [00:20<00:04, 19.19it/s]
eval model::  83%|████████▎ | 415/500 [00:20<00:04, 20.43it/s]
eval model::  84%|████████▎ | 418/500 [00:20<00:04, 19.81it/s]
eval model::  84%|████████▍ | 420/500 [00:20<00:04, 18.78it/s]
eval model::  85%|████████▍ | 423/500 [00:21<00:03, 20.48it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:03, 18.79it/s]
eval model::  86%|████████▌ | 429/500 [00:21<00:03, 21.03it/s]
eval model::  86%|████████▋ | 432/500 [00:21<00:03, 21.33it/s]
eval model::  87%|████████▋ | 435/500 [00:21<00:03, 21.48it/s]
eval model::  88%|████████▊ | 438/500 [00:21<00:02, 21.56it/s]
eval model::  88%|████████▊ | 441/500 [00:21<00:02, 21.71it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 21.69it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 21.90it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 21.65it/s]
eval model::  91%|█████████ | 453/500 [00:22<00:02, 21.07it/s]
eval model::  91%|█████████ | 456/500 [00:22<00:02, 20.78it/s]
eval model::  92%|█████████▏| 459/500 [00:22<00:02, 19.79it/s]
eval model::  92%|█████████▏| 462/500 [00:22<00:01, 19.44it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 19.43it/s]
eval model::  93%|█████████▎| 466/500 [00:23<00:01, 19.28it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 18.60it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 20.58it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 19.98it/s]
eval model::  95%|█████████▌| 477/500 [00:23<00:01, 19.11it/s]
eval model::  96%|█████████▌| 480/500 [00:23<00:01, 19.85it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 19.79it/s]
eval model::  97%|█████████▋| 485/500 [00:24<00:00, 19.78it/s]
eval model::  98%|█████████▊| 488/500 [00:24<00:00, 20.63it/s]
eval model::  98%|█████████▊| 491/500 [00:24<00:00, 20.34it/s]
eval model::  99%|█████████▉| 494/500 [00:24<00:00, 20.42it/s]
eval model::  99%|█████████▉| 497/500 [00:24<00:00, 21.70it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 21.15it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 20.16it/s]
[2024/07/27 21:50:15] ppocr INFO: cur metric, precision: 0.5124481327800829, recall: 0.1189215214251324, hmean: 0.1930441578741696, fps: 45.79878372750227
[2024/07/27 21:50:15] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 21:50:15] ppocr INFO: best metric, hmean: 0.1930441578741696, precision: 0.5124481327800829, recall: 0.1189215214251324, fps: 45.79878372750227, best_epoch: 20
[2024/07/27 21:50:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:53:51] ppocr INFO: Architecture : 
[2024/07/27 21:53:51] ppocr INFO:     Backbone : 
[2024/07/27 21:53:51] ppocr INFO:         model_name : large
[2024/07/27 21:53:51] ppocr INFO:         name : MobileNetV3
[2024/07/27 21:53:51] ppocr INFO:         scale : 0.5
[2024/07/27 21:53:51] ppocr INFO:     Head : 
[2024/07/27 21:53:51] ppocr INFO:         k : 50
[2024/07/27 21:53:51] ppocr INFO:         name : DBHead
[2024/07/27 21:53:51] ppocr INFO:     Neck : 
[2024/07/27 21:53:51] ppocr INFO:         name : DBFPN
[2024/07/27 21:53:51] ppocr INFO:         out_channels : 256
[2024/07/27 21:53:51] ppocr INFO:     Transform : None
[2024/07/27 21:53:51] ppocr INFO:     algorithm : DB
[2024/07/27 21:53:51] ppocr INFO:     model_type : det
[2024/07/27 21:53:51] ppocr INFO: Eval : 
[2024/07/27 21:53:51] ppocr INFO:     dataset : 
[2024/07/27 21:53:51] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/07/27 21:53:51] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
[2024/07/27 21:53:51] ppocr INFO:         name : SimpleDataSet
[2024/07/27 21:53:51] ppocr INFO:         transforms : 
[2024/07/27 21:53:51] ppocr INFO:             DecodeImage : 
[2024/07/27 21:53:51] ppocr INFO:                 channel_first : False
[2024/07/27 21:53:51] ppocr INFO:                 img_mode : BGR
[2024/07/27 21:53:51] ppocr INFO:             DetLabelEncode : None
[2024/07/27 21:53:51] ppocr INFO:             DetResizeForTest : 
[2024/07/27 21:53:51] ppocr INFO:                 image_shape : [736, 1280]
[2024/07/27 21:53:51] ppocr INFO:             NormalizeImage : 
[2024/07/27 21:53:51] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/07/27 21:53:51] ppocr INFO:                 order : hwc
[2024/07/27 21:53:51] ppocr INFO:                 scale : 1./255.
[2024/07/27 21:53:51] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/07/27 21:53:51] ppocr INFO:             ToCHWImage : None
[2024/07/27 21:53:51] ppocr INFO:             KeepKeys : 
[2024/07/27 21:53:51] ppocr INFO:                 keep_keys : ['image', 'shape', 'polys', 'ignore_tags']
[2024/07/27 21:53:51] ppocr INFO:     loader : 
[2024/07/27 21:53:51] ppocr INFO:         batch_size_per_card : 1
[2024/07/27 21:53:51] ppocr INFO:         drop_last : False
[2024/07/27 21:53:51] ppocr INFO:         num_workers : 0
[2024/07/27 21:53:51] ppocr INFO:         shuffle : False
[2024/07/27 21:53:51] ppocr INFO:         use_shared_memory : False
[2024/07/27 21:53:51] ppocr INFO: Global : 
[2024/07/27 21:53:51] ppocr INFO:     cal_metric_during_train : False
[2024/07/27 21:53:51] ppocr INFO:     checkpoints : None
[2024/07/27 21:53:51] ppocr INFO:     distributed : True
[2024/07/27 21:53:51] ppocr INFO:     epoch_num : 200
[2024/07/27 21:53:51] ppocr INFO:     eval_batch_step : [0, 60]
[2024/07/27 21:53:51] ppocr INFO:     infer_img : doc/imgs_en/img_10.jpg
[2024/07/27 21:53:51] ppocr INFO:     log_smooth_window : 20
[2024/07/27 21:53:51] ppocr INFO:     pretrained_model : ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/07/27 21:53:51] ppocr INFO:     print_batch_step : 10
[2024/07/27 21:53:51] ppocr INFO:     save_epoch_step : 1200
[2024/07/27 21:53:51] ppocr INFO:     save_inference_dir : None
[2024/07/27 21:53:51] ppocr INFO:     save_model_dir : ./output/db_mv3/
[2024/07/27 21:53:51] ppocr INFO:     save_res_path : ./output/det_db/predicts_db.txt
[2024/07/27 21:53:51] ppocr INFO:     use_gpu : True
[2024/07/27 21:53:51] ppocr INFO:     use_visualdl : False
[2024/07/27 21:53:51] ppocr INFO:     use_xpu : False
[2024/07/27 21:53:51] ppocr INFO: Loss : 
[2024/07/27 21:53:51] ppocr INFO:     alpha : 5
[2024/07/27 21:53:51] ppocr INFO:     balance_loss : True
[2024/07/27 21:53:51] ppocr INFO:     beta : 10
[2024/07/27 21:53:51] ppocr INFO:     main_loss_type : DiceLoss
[2024/07/27 21:53:51] ppocr INFO:     name : DBLoss
[2024/07/27 21:53:51] ppocr INFO:     ohem_ratio : 3
[2024/07/27 21:53:51] ppocr INFO: Metric : 
[2024/07/27 21:53:51] ppocr INFO:     main_indicator : hmean
[2024/07/27 21:53:51] ppocr INFO:     name : DetMetric
[2024/07/27 21:53:51] ppocr INFO: Optimizer : 
[2024/07/27 21:53:51] ppocr INFO:     beta1 : 0.9
[2024/07/27 21:53:51] ppocr INFO:     beta2 : 0.999
[2024/07/27 21:53:51] ppocr INFO:     lr : 
[2024/07/27 21:53:51] ppocr INFO:         learning_rate : 0.001
[2024/07/27 21:53:51] ppocr INFO:     name : Adam
[2024/07/27 21:53:51] ppocr INFO:     regularizer : 
[2024/07/27 21:53:51] ppocr INFO:         factor : 0
[2024/07/27 21:53:51] ppocr INFO:         name : L2
[2024/07/27 21:53:51] ppocr INFO: PostProcess : 
[2024/07/27 21:53:51] ppocr INFO:     box_thresh : 0.6
[2024/07/27 21:53:51] ppocr INFO:     max_candidates : 1000
[2024/07/27 21:53:51] ppocr INFO:     name : DBPostProcess
[2024/07/27 21:53:51] ppocr INFO:     thresh : 0.3
[2024/07/27 21:53:51] ppocr INFO:     unclip_ratio : 1.5
[2024/07/27 21:53:51] ppocr INFO: Train : 
[2024/07/27 21:53:51] ppocr INFO:     dataset : 
[2024/07/27 21:53:51] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/07/27 21:53:51] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/07/27 21:53:51] ppocr INFO:         name : SimpleDataSet
[2024/07/27 21:53:51] ppocr INFO:         ratio_list : [1.0]
[2024/07/27 21:53:51] ppocr INFO:         transforms : 
[2024/07/27 21:53:51] ppocr INFO:             DecodeImage : 
[2024/07/27 21:53:51] ppocr INFO:                 channel_first : False
[2024/07/27 21:53:51] ppocr INFO:                 img_mode : BGR
[2024/07/27 21:53:51] ppocr INFO:             DetLabelEncode : None
[2024/07/27 21:53:51] ppocr INFO:             IaaAugment : 
[2024/07/27 21:53:51] ppocr INFO:                 augmenter_args : 
[2024/07/27 21:53:51] ppocr INFO:                     args : 
[2024/07/27 21:53:51] ppocr INFO:                         p : 0.5
[2024/07/27 21:53:51] ppocr INFO:                     type : Fliplr
[2024/07/27 21:53:51] ppocr INFO:                     args : 
[2024/07/27 21:53:51] ppocr INFO:                         rotate : [-10, 10]
[2024/07/27 21:53:51] ppocr INFO:                     type : Affine
[2024/07/27 21:53:51] ppocr INFO:                     args : 
[2024/07/27 21:53:51] ppocr INFO:                         size : [0.5, 3]
[2024/07/27 21:53:51] ppocr INFO:                     type : Resize
[2024/07/27 21:53:51] ppocr INFO:             EastRandomCropData : 
[2024/07/27 21:53:51] ppocr INFO:                 keep_ratio : True
[2024/07/27 21:53:51] ppocr INFO:                 max_tries : 50
[2024/07/27 21:53:51] ppocr INFO:                 size : [640, 640]
[2024/07/27 21:53:51] ppocr INFO:             MakeBorderMap : 
[2024/07/27 21:53:51] ppocr INFO:                 shrink_ratio : 0.4
[2024/07/27 21:53:51] ppocr INFO:                 thresh_max : 0.7
[2024/07/27 21:53:51] ppocr INFO:                 thresh_min : 0.3
[2024/07/27 21:53:51] ppocr INFO:             MakeShrinkMap : 
[2024/07/27 21:53:51] ppocr INFO:                 min_text_size : 8
[2024/07/27 21:53:51] ppocr INFO:                 shrink_ratio : 0.4
[2024/07/27 21:53:51] ppocr INFO:             NormalizeImage : 
[2024/07/27 21:53:51] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/07/27 21:53:51] ppocr INFO:                 order : hwc
[2024/07/27 21:53:51] ppocr INFO:                 scale : 1./255.
[2024/07/27 21:53:51] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/07/27 21:53:51] ppocr INFO:             ToCHWImage : None
[2024/07/27 21:53:51] ppocr INFO:             KeepKeys : 
[2024/07/27 21:53:51] ppocr INFO:                 keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask']
[2024/07/27 21:53:51] ppocr INFO:     loader : 
[2024/07/27 21:53:51] ppocr INFO:         batch_size_per_card : 48
[2024/07/27 21:53:51] ppocr INFO:         drop_last : False
[2024/07/27 21:53:51] ppocr INFO:         num_workers : 8
[2024/07/27 21:53:51] ppocr INFO:         shuffle : True
[2024/07/27 21:53:51] ppocr INFO:         use_shared_memory : False
[2024/07/27 21:53:51] ppocr INFO: profiler_options : None
[2024/07/27 21:53:51] ppocr INFO: train with paddle 2.5.2 and device Place(gpu:0)
======================= Modified FLAGS detected =======================
FLAGS(name='FLAGS_selected_gpus', current_value='0', default_value='')
FLAGS(name='FLAGS_cudnn_batchnorm_spatial_persistent', current_value=True, default_value=False)
=======================================================================
I0727 21:53:51.832775 33061 tcp_utils.cc:181] The server starts to listen on IP_ANY:49894
I0727 21:53:51.832955 33061 tcp_utils.cc:130] Successfully connected to 127.0.0.1:49894
I0727 21:53:54.952153 33061 process_group_nccl.cc:120] ProcessGroupNCCL pg_timeout_ 1800000
[2024/07/27 21:53:54] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/07/27 21:53:54] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
W0727 21:53:54.962349 33061 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 90.2, Driver API Version: 50724.2, Runtime API Version: 50724.2
[2024/07/27 21:53:56] ppocr INFO: load pretrain successful from ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/07/27 21:53:56] ppocr INFO: train dataloader has 3 iters
[2024/07/27 21:53:56] ppocr INFO: valid dataloader has 500 iters
[2024/07/27 21:53:56] ppocr INFO: During the training process, after the 0th iteration, an evaluation is run every 60 iterations
[2024/07/27 21:54:20] ppocr INFO: epoch: [1/200], global_step: 3, lr: 0.001000, loss: 9.229576, loss_shrink_maps: 4.929691, loss_threshold_maps: 3.358860, loss_binary_maps: 0.988369, avg_reader_cost: 6.09062 s, avg_batch_cost: 6.95484 s, avg_samples: 12.5, ips: 1.79731 samples/s, eta: 3:50:40
[2024/07/27 21:54:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:54:30] ppocr INFO: epoch: [2/200], global_step: 6, lr: 0.001000, loss: 8.496460, loss_shrink_maps: 4.870972, loss_threshold_maps: 2.625417, loss_binary_maps: 0.978406, avg_reader_cost: 2.45144 s, avg_batch_cost: 2.73668 s, avg_samples: 12.5, ips: 4.56757 samples/s, eta: 2:39:54
[2024/07/27 21:54:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:54:41] ppocr INFO: epoch: [3/200], global_step: 9, lr: 0.001000, loss: 7.642319, loss_shrink_maps: 4.851714, loss_threshold_maps: 1.814742, loss_binary_maps: 0.975864, avg_reader_cost: 2.72504 s, avg_batch_cost: 2.96752 s, avg_samples: 12.5, ips: 4.21228 samples/s, eta: 2:18:32
[2024/07/27 21:54:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:54:51] ppocr INFO: epoch: [4/200], global_step: 10, lr: 0.001000, loss: 7.458685, loss_shrink_maps: 4.852083, loss_threshold_maps: 1.637156, loss_binary_maps: 0.975520, avg_reader_cost: 0.80442 s, avg_batch_cost: 0.90679 s, avg_samples: 4.8, ips: 5.29342 samples/s, eta: 2:13:23
[2024/07/27 21:54:53] ppocr INFO: epoch: [4/200], global_step: 12, lr: 0.001000, loss: 7.204020, loss_shrink_maps: 4.849706, loss_threshold_maps: 1.382877, loss_binary_maps: 0.974764, avg_reader_cost: 1.90450 s, avg_batch_cost: 2.05016 s, avg_samples: 7.7, ips: 3.75580 samples/s, eta: 2:07:31
[2024/07/27 21:54:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:55:04] ppocr INFO: epoch: [5/200], global_step: 15, lr: 0.001000, loss: 7.079520, loss_shrink_maps: 4.842486, loss_threshold_maps: 1.268342, loss_binary_maps: 0.972995, avg_reader_cost: 2.69280 s, avg_batch_cost: 3.05492 s, avg_samples: 12.5, ips: 4.09176 samples/s, eta: 2:01:21
[2024/07/27 21:55:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:55:16] ppocr INFO: epoch: [6/200], global_step: 18, lr: 0.001000, loss: 6.991670, loss_shrink_maps: 4.836794, loss_threshold_maps: 1.191212, loss_binary_maps: 0.971500, avg_reader_cost: 2.63384 s, avg_batch_cost: 2.95434 s, avg_samples: 12.5, ips: 4.23107 samples/s, eta: 1:56:32
[2024/07/27 21:55:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:55:26] ppocr INFO: epoch: [7/200], global_step: 20, lr: 0.001000, loss: 6.974210, loss_shrink_maps: 4.820456, loss_threshold_maps: 1.172662, loss_binary_maps: 0.968736, avg_reader_cost: 1.62935 s, avg_batch_cost: 1.90411 s, avg_samples: 9.6, ips: 5.04173 samples/s, eta: 1:53:43
[2024/07/27 21:55:27] ppocr INFO: epoch: [7/200], global_step: 21, lr: 0.001000, loss: 6.927090, loss_shrink_maps: 4.794438, loss_threshold_maps: 1.166469, loss_binary_maps: 0.962212, avg_reader_cost: 0.99757 s, avg_batch_cost: 1.05228 s, avg_samples: 2.9, ips: 2.75591 samples/s, eta: 1:52:57
[2024/07/27 21:55:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:55:38] ppocr INFO: epoch: [8/200], global_step: 24, lr: 0.001000, loss: 6.816076, loss_shrink_maps: 4.727366, loss_threshold_maps: 1.147285, loss_binary_maps: 0.946213, avg_reader_cost: 2.71356 s, avg_batch_cost: 3.06665 s, avg_samples: 12.5, ips: 4.07611 samples/s, eta: 1:50:35
[2024/07/27 21:55:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:55:49] ppocr INFO: epoch: [9/200], global_step: 27, lr: 0.001000, loss: 6.643856, loss_shrink_maps: 4.625044, loss_threshold_maps: 1.124174, loss_binary_maps: 0.913743, avg_reader_cost: 2.68396 s, avg_batch_cost: 2.94215 s, avg_samples: 12.5, ips: 4.24860 samples/s, eta: 1:48:11
[2024/07/27 21:55:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:56:00] ppocr INFO: epoch: [10/200], global_step: 30, lr: 0.001000, loss: 6.507645, loss_shrink_maps: 4.530808, loss_threshold_maps: 1.082063, loss_binary_maps: 0.873636, avg_reader_cost: 2.67332 s, avg_batch_cost: 3.05944 s, avg_samples: 12.5, ips: 4.08572 samples/s, eta: 1:46:33
[2024/07/27 21:56:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:56:11] ppocr INFO: epoch: [11/200], global_step: 33, lr: 0.001000, loss: 6.339798, loss_shrink_maps: 4.463856, loss_threshold_maps: 1.058580, loss_binary_maps: 0.817362, avg_reader_cost: 2.77563 s, avg_batch_cost: 3.00958 s, avg_samples: 12.5, ips: 4.15340 samples/s, eta: 1:44:58
[2024/07/27 21:56:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:56:23] ppocr INFO: epoch: [12/200], global_step: 36, lr: 0.001000, loss: 6.005182, loss_shrink_maps: 4.290333, loss_threshold_maps: 0.979189, loss_binary_maps: 0.735661, avg_reader_cost: 2.73807 s, avg_batch_cost: 2.97514 s, avg_samples: 12.5, ips: 4.20148 samples/s, eta: 1:43:29
[2024/07/27 21:56:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:56:34] ppocr INFO: epoch: [13/200], global_step: 39, lr: 0.001000, loss: 5.733318, loss_shrink_maps: 4.100464, loss_threshold_maps: 0.947641, loss_binary_maps: 0.704538, avg_reader_cost: 2.82796 s, avg_batch_cost: 3.09998 s, avg_samples: 12.5, ips: 4.03228 samples/s, eta: 1:42:27
[2024/07/27 21:56:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:56:44] ppocr INFO: epoch: [14/200], global_step: 40, lr: 0.001000, loss: 5.650106, loss_shrink_maps: 4.015239, loss_threshold_maps: 0.944673, loss_binary_maps: 0.685875, avg_reader_cost: 0.82993 s, avg_batch_cost: 0.92379 s, avg_samples: 4.8, ips: 5.19600 samples/s, eta: 1:41:52
[2024/07/27 21:56:45] ppocr INFO: epoch: [14/200], global_step: 42, lr: 0.001000, loss: 5.449510, loss_shrink_maps: 3.883929, loss_threshold_maps: 0.926358, loss_binary_maps: 0.654370, avg_reader_cost: 1.93932 s, avg_batch_cost: 2.08489 s, avg_samples: 7.7, ips: 3.69325 samples/s, eta: 1:41:17
[2024/07/27 21:56:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:56:56] ppocr INFO: epoch: [15/200], global_step: 45, lr: 0.001000, loss: 5.201267, loss_shrink_maps: 3.699982, loss_threshold_maps: 0.898391, loss_binary_maps: 0.604805, avg_reader_cost: 2.63027 s, avg_batch_cost: 2.94674 s, avg_samples: 12.5, ips: 4.24198 samples/s, eta: 1:40:05
[2024/07/27 21:56:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:57:08] ppocr INFO: epoch: [16/200], global_step: 48, lr: 0.001000, loss: 4.751304, loss_shrink_maps: 3.388081, loss_threshold_maps: 0.884234, loss_binary_maps: 0.509737, avg_reader_cost: 2.74585 s, avg_batch_cost: 2.98926 s, avg_samples: 12.5, ips: 4.18164 samples/s, eta: 1:39:03
[2024/07/27 21:57:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:57:18] ppocr INFO: epoch: [17/200], global_step: 50, lr: 0.001000, loss: 4.610830, loss_shrink_maps: 3.255782, loss_threshold_maps: 0.879374, loss_binary_maps: 0.462134, avg_reader_cost: 1.64568 s, avg_batch_cost: 1.87156 s, avg_samples: 9.6, ips: 5.12942 samples/s, eta: 1:38:10
[2024/07/27 21:57:19] ppocr INFO: epoch: [17/200], global_step: 51, lr: 0.001000, loss: 4.538922, loss_shrink_maps: 3.192554, loss_threshold_maps: 0.876552, loss_binary_maps: 0.460564, avg_reader_cost: 0.98137 s, avg_batch_cost: 1.03611 s, avg_samples: 2.9, ips: 2.79894 samples/s, eta: 1:37:56
[2024/07/27 21:57:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:57:30] ppocr INFO: epoch: [18/200], global_step: 54, lr: 0.001000, loss: 4.296102, loss_shrink_maps: 2.981039, loss_threshold_maps: 0.876552, loss_binary_maps: 0.440266, avg_reader_cost: 2.74358 s, avg_batch_cost: 3.09119 s, avg_samples: 12.5, ips: 4.04375 samples/s, eta: 1:37:11
[2024/07/27 21:57:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:57:42] ppocr INFO: epoch: [19/200], global_step: 57, lr: 0.001000, loss: 4.100919, loss_shrink_maps: 2.765110, loss_threshold_maps: 0.879374, loss_binary_maps: 0.438362, avg_reader_cost: 2.78483 s, avg_batch_cost: 3.01724 s, avg_samples: 12.5, ips: 4.14286 samples/s, eta: 1:36:22
[2024/07/27 21:57:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:57:53] ppocr INFO: epoch: [20/200], global_step: 60, lr: 0.001000, loss: 3.940533, loss_shrink_maps: 2.656322, loss_threshold_maps: 0.870747, loss_binary_maps: 0.407147, avg_reader_cost: 2.78431 s, avg_batch_cost: 3.02102 s, avg_samples: 12.5, ips: 4.13768 samples/s, eta: 1:35:34

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:52,  9.52it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.64it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.58it/s]
eval model::   2%|▏         | 9/500 [00:00<00:23, 20.67it/s]
eval model::   2%|▏         | 12/500 [00:00<00:22, 21.42it/s]
eval model::   3%|▎         | 15/500 [00:00<00:20, 23.70it/s]
eval model::   4%|▍         | 19/500 [00:00<00:18, 25.40it/s]
eval model::   4%|▍         | 22/500 [00:00<00:19, 25.15it/s]
eval model::   5%|▌         | 25/500 [00:01<00:18, 26.27it/s]
eval model::   6%|▌         | 28/500 [00:01<00:18, 25.79it/s]
eval model::   6%|▌         | 31/500 [00:01<00:22, 20.46it/s]
eval model::   7%|▋         | 34/500 [00:01<00:23, 20.22it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.93it/s]
eval model::   8%|▊         | 40/500 [00:01<00:23, 19.51it/s]
eval model::   9%|▊         | 43/500 [00:02<00:25, 18.17it/s]
eval model::   9%|▉         | 46/500 [00:02<00:24, 18.50it/s]
eval model::  10%|█         | 50/500 [00:02<00:20, 21.79it/s]
eval model::  11%|█         | 53/500 [00:02<00:19, 22.39it/s]
eval model::  11%|█         | 56/500 [00:02<00:20, 21.34it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:19, 22.58it/s]
eval model::  12%|█▏        | 62/500 [00:02<00:20, 21.24it/s]
eval model::  13%|█▎        | 65/500 [00:03<00:19, 22.36it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:18, 23.09it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:21, 20.34it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:21, 19.59it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:20, 20.47it/s]
eval model::  16%|█▌        | 81/500 [00:03<00:17, 23.70it/s]
eval model::  17%|█▋        | 84/500 [00:03<00:18, 22.77it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:17, 23.22it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:18, 21.68it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:18, 21.61it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:18, 21.44it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:18, 21.16it/s]
eval model::  20%|██        | 102/500 [00:04<00:21, 18.94it/s]
eval model::  21%|██        | 105/500 [00:04<00:18, 21.01it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.15it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 20.75it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:17, 21.45it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:18, 20.58it/s]
eval model::  24%|██▍       | 120/500 [00:05<00:22, 16.80it/s]
eval model::  25%|██▍       | 123/500 [00:05<00:19, 19.30it/s]
eval model::  25%|██▌       | 126/500 [00:05<00:18, 19.86it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 20.61it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:18, 20.07it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:17, 20.37it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:16, 21.74it/s]
eval model::  28%|██▊       | 141/500 [00:06<00:16, 21.16it/s]
eval model::  29%|██▉       | 144/500 [00:06<00:16, 21.65it/s]
eval model::  29%|██▉       | 147/500 [00:06<00:16, 21.67it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 21.84it/s]
eval model::  31%|███       | 153/500 [00:07<00:16, 21.16it/s]
eval model::  31%|███       | 156/500 [00:07<00:17, 19.96it/s]
eval model::  32%|███▏      | 159/500 [00:07<00:18, 18.50it/s]
eval model::  32%|███▏      | 161/500 [00:07<00:19, 17.25it/s]
eval model::  33%|███▎      | 163/500 [00:07<00:20, 16.40it/s]
eval model::  33%|███▎      | 165/500 [00:07<00:20, 16.69it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:19, 16.96it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:17, 18.81it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 18.65it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:19, 16.81it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:17, 18.12it/s]
eval model::  36%|███▌      | 180/500 [00:08<00:17, 18.51it/s]
eval model::  37%|███▋      | 183/500 [00:08<00:15, 20.19it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 21.72it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.65it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:15, 20.01it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:15, 19.30it/s]
eval model::  40%|███▉      | 198/500 [00:09<00:15, 20.04it/s]
eval model::  40%|████      | 201/500 [00:09<00:15, 19.85it/s]
eval model::  41%|████      | 204/500 [00:10<00:18, 15.99it/s]
eval model::  41%|████      | 206/500 [00:10<00:23, 12.46it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:21, 13.70it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:19, 14.61it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:16, 17.51it/s]
eval model::  43%|████▎     | 215/500 [00:10<00:18, 15.18it/s]
eval model::  43%|████▎     | 217/500 [00:10<00:18, 15.59it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 18.55it/s]
eval model::  45%|████▍     | 223/500 [00:11<00:14, 18.64it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:13, 20.18it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:12, 22.24it/s]
eval model::  46%|████▋     | 232/500 [00:11<00:11, 22.60it/s]
eval model::  47%|████▋     | 235/500 [00:11<00:11, 22.32it/s]
eval model::  48%|████▊     | 238/500 [00:11<00:11, 23.54it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:12, 21.55it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:12, 21.22it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:12, 19.82it/s]
eval model::  50%|█████     | 250/500 [00:12<00:13, 17.97it/s]
eval model::  50%|█████     | 252/500 [00:12<00:13, 18.01it/s]
eval model::  51%|█████     | 255/500 [00:12<00:12, 19.68it/s]
eval model::  52%|█████▏    | 258/500 [00:12<00:11, 21.42it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 21.23it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:11, 19.86it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:11, 20.55it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:10, 21.09it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:10, 21.08it/s]
eval model::  55%|█████▌    | 276/500 [00:13<00:10, 21.94it/s]
eval model::  56%|█████▌    | 279/500 [00:13<00:10, 21.65it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:09, 21.85it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:12, 17.03it/s]
eval model::  58%|█████▊    | 288/500 [00:14<00:10, 19.29it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:10, 19.91it/s]
eval model::  59%|█████▉    | 294/500 [00:14<00:10, 18.97it/s]
eval model::  60%|█████▉    | 298/500 [00:14<00:09, 22.00it/s]
eval model::  60%|██████    | 301/500 [00:15<00:09, 21.13it/s]
eval model::  61%|██████    | 304/500 [00:15<00:08, 21.94it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:08, 22.34it/s]
eval model::  62%|██████▏   | 310/500 [00:15<00:09, 20.68it/s]
eval model::  63%|██████▎   | 313/500 [00:15<00:09, 20.14it/s]
eval model::  63%|██████▎   | 316/500 [00:15<00:09, 19.00it/s]
eval model::  64%|██████▍   | 319/500 [00:15<00:09, 20.02it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:09, 19.64it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:08, 20.23it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:09, 18.45it/s]
eval model::  66%|██████▌   | 331/500 [00:16<00:08, 19.46it/s]
eval model::  67%|██████▋   | 334/500 [00:16<00:07, 20.78it/s]
eval model::  67%|██████▋   | 337/500 [00:16<00:08, 20.36it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:08, 18.47it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:07, 20.10it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 19.85it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:07, 20.98it/s]
eval model::  70%|███████   | 352/500 [00:17<00:07, 20.48it/s]
eval model::  71%|███████   | 355/500 [00:17<00:08, 18.12it/s]
eval model::  71%|███████▏  | 357/500 [00:17<00:07, 18.38it/s]
eval model::  72%|███████▏  | 360/500 [00:17<00:06, 20.08it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:07, 18.94it/s]
eval model::  73%|███████▎  | 365/500 [00:18<00:07, 17.53it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:07, 16.96it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:07, 18.43it/s]
eval model::  75%|███████▍  | 373/500 [00:18<00:06, 20.35it/s]
eval model::  75%|███████▌  | 376/500 [00:18<00:06, 20.25it/s]
eval model::  76%|███████▌  | 379/500 [00:18<00:05, 21.54it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 21.84it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 19.89it/s]
eval model::  78%|███████▊  | 388/500 [00:19<00:06, 17.08it/s]
eval model::  78%|███████▊  | 391/500 [00:19<00:05, 18.63it/s]
eval model::  79%|███████▉  | 394/500 [00:19<00:05, 18.93it/s]
eval model::  79%|███████▉  | 397/500 [00:19<00:05, 20.45it/s]
eval model::  80%|████████  | 400/500 [00:20<00:04, 20.75it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 21.37it/s]
eval model::  81%|████████  | 406/500 [00:20<00:04, 20.44it/s]
eval model::  82%|████████▏ | 409/500 [00:20<00:04, 19.23it/s]
eval model::  82%|████████▏ | 411/500 [00:20<00:05, 16.53it/s]
eval model::  83%|████████▎ | 414/500 [00:20<00:04, 18.43it/s]
eval model::  83%|████████▎ | 416/500 [00:20<00:04, 17.15it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 17.64it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 16.77it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 17.48it/s]
eval model::  85%|████████▌ | 425/500 [00:21<00:04, 18.42it/s]
eval model::  86%|████████▌ | 429/500 [00:21<00:03, 20.85it/s]
eval model::  86%|████████▋ | 432/500 [00:21<00:03, 18.82it/s]
eval model::  87%|████████▋ | 435/500 [00:21<00:03, 19.92it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:03, 18.78it/s]
eval model::  88%|████████▊ | 440/500 [00:22<00:03, 17.83it/s]
eval model::  89%|████████▊ | 443/500 [00:22<00:02, 19.76it/s]
eval model::  89%|████████▉ | 446/500 [00:22<00:02, 19.02it/s]
eval model::  90%|████████▉ | 449/500 [00:22<00:02, 19.41it/s]
eval model::  90%|█████████ | 452/500 [00:22<00:02, 20.35it/s]
eval model::  91%|█████████ | 455/500 [00:22<00:02, 21.10it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:02, 20.54it/s]
eval model::  92%|█████████▏| 461/500 [00:23<00:01, 19.66it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 19.32it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:02, 16.45it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 18.09it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 20.47it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 20.67it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 19.97it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 20.22it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 20.84it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 20.35it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 20.25it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 19.09it/s]
eval model::  99%|█████████▉| 495/500 [00:24<00:00, 20.91it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 21.35it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.84it/s]
[2024/07/27 21:58:18] ppocr INFO: cur metric, precision: 0.39039039039039036, recall: 0.18777082330284064, hmean: 0.2535760728218466, fps: 44.81229986038038
[2024/07/27 21:58:18] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 21:58:18] ppocr INFO: best metric, hmean: 0.2535760728218466, precision: 0.39039039039039036, recall: 0.18777082330284064, fps: 44.81229986038038, best_epoch: 20
[2024/07/27 21:58:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:58:27] ppocr INFO: epoch: [21/200], global_step: 63, lr: 0.001000, loss: 3.815191, loss_shrink_maps: 2.574999, loss_threshold_maps: 0.866314, loss_binary_maps: 0.391925, avg_reader_cost: 1.97186 s, avg_batch_cost: 2.36125 s, avg_samples: 12.5, ips: 5.29380 samples/s, eta: 1:33:52
[2024/07/27 21:58:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:58:36] ppocr INFO: epoch: [22/200], global_step: 66, lr: 0.001000, loss: 3.759151, loss_shrink_maps: 2.515826, loss_threshold_maps: 0.863853, loss_binary_maps: 0.383742, avg_reader_cost: 2.05722 s, avg_batch_cost: 2.28605 s, avg_samples: 12.5, ips: 5.46794 samples/s, eta: 1:32:11
[2024/07/27 21:58:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:58:45] ppocr INFO: epoch: [23/200], global_step: 69, lr: 0.001000, loss: 3.717141, loss_shrink_maps: 2.431194, loss_threshold_maps: 0.860433, loss_binary_maps: 0.379031, avg_reader_cost: 1.92484 s, avg_batch_cost: 2.25099 s, avg_samples: 12.5, ips: 5.55310 samples/s, eta: 1:30:34
[2024/07/27 21:58:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:58:52] ppocr INFO: epoch: [24/200], global_step: 70, lr: 0.001000, loss: 3.687388, loss_shrink_maps: 2.416166, loss_threshold_maps: 0.857288, loss_binary_maps: 0.379031, avg_reader_cost: 0.60436 s, avg_batch_cost: 0.69816 s, avg_samples: 4.8, ips: 6.87520 samples/s, eta: 1:29:59
[2024/07/27 21:58:54] ppocr INFO: epoch: [24/200], global_step: 72, lr: 0.001000, loss: 3.534711, loss_shrink_maps: 2.330003, loss_threshold_maps: 0.857288, loss_binary_maps: 0.367913, avg_reader_cost: 1.48835 s, avg_batch_cost: 1.63475 s, avg_samples: 7.7, ips: 4.71021 samples/s, eta: 1:29:09
[2024/07/27 21:58:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:59:02] ppocr INFO: epoch: [25/200], global_step: 75, lr: 0.001000, loss: 3.495447, loss_shrink_maps: 2.274729, loss_threshold_maps: 0.859638, loss_binary_maps: 0.360350, avg_reader_cost: 1.79242 s, avg_batch_cost: 2.03285 s, avg_samples: 12.5, ips: 6.14900 samples/s, eta: 1:27:28
[2024/07/27 21:59:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:59:10] ppocr INFO: epoch: [26/200], global_step: 78, lr: 0.001000, loss: 3.362247, loss_shrink_maps: 2.179928, loss_threshold_maps: 0.837498, loss_binary_maps: 0.346018, avg_reader_cost: 2.02780 s, avg_batch_cost: 2.26702 s, avg_samples: 12.5, ips: 5.51384 samples/s, eta: 1:26:09
[2024/07/27 21:59:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:59:19] ppocr INFO: epoch: [27/200], global_step: 80, lr: 0.001000, loss: 3.276426, loss_shrink_maps: 2.083727, loss_threshold_maps: 0.836040, loss_binary_maps: 0.341723, avg_reader_cost: 1.29228 s, avg_batch_cost: 1.54865 s, avg_samples: 9.6, ips: 6.19893 samples/s, eta: 1:25:21
[2024/07/27 21:59:20] ppocr INFO: epoch: [27/200], global_step: 81, lr: 0.001000, loss: 3.217188, loss_shrink_maps: 2.067664, loss_threshold_maps: 0.836040, loss_binary_maps: 0.338815, avg_reader_cost: 0.82039 s, avg_batch_cost: 0.87530 s, avg_samples: 2.9, ips: 3.31316 samples/s, eta: 1:25:04
[2024/07/27 21:59:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:59:29] ppocr INFO: epoch: [28/200], global_step: 84, lr: 0.001000, loss: 3.169574, loss_shrink_maps: 1.980419, loss_threshold_maps: 0.831208, loss_binary_maps: 0.335330, avg_reader_cost: 2.02628 s, avg_batch_cost: 2.27625 s, avg_samples: 12.5, ips: 5.49149 samples/s, eta: 1:23:53
[2024/07/27 21:59:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:59:38] ppocr INFO: epoch: [29/200], global_step: 87, lr: 0.001000, loss: 3.097114, loss_shrink_maps: 1.921586, loss_threshold_maps: 0.837005, loss_binary_maps: 0.331543, avg_reader_cost: 2.08156 s, avg_batch_cost: 2.31322 s, avg_samples: 12.5, ips: 5.40373 samples/s, eta: 1:22:48
[2024/07/27 21:59:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:59:47] ppocr INFO: epoch: [30/200], global_step: 90, lr: 0.001000, loss: 3.049055, loss_shrink_maps: 1.868632, loss_threshold_maps: 0.837005, loss_binary_maps: 0.324984, avg_reader_cost: 2.04234 s, avg_batch_cost: 2.33335 s, avg_samples: 12.5, ips: 5.35709 samples/s, eta: 1:21:47
[2024/07/27 21:59:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 21:59:56] ppocr INFO: epoch: [31/200], global_step: 93, lr: 0.001000, loss: 2.890193, loss_shrink_maps: 1.806192, loss_threshold_maps: 0.822674, loss_binary_maps: 0.310852, avg_reader_cost: 1.98783 s, avg_batch_cost: 2.34485 s, avg_samples: 12.5, ips: 5.33084 samples/s, eta: 1:20:48
[2024/07/27 21:59:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:00:04] ppocr INFO: epoch: [32/200], global_step: 96, lr: 0.001000, loss: 2.820556, loss_shrink_maps: 1.771541, loss_threshold_maps: 0.810131, loss_binary_maps: 0.296296, avg_reader_cost: 1.98435 s, avg_batch_cost: 2.31518 s, avg_samples: 12.5, ips: 5.39914 samples/s, eta: 1:19:50
[2024/07/27 22:00:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:00:13] ppocr INFO: epoch: [33/200], global_step: 99, lr: 0.001000, loss: 2.937835, loss_shrink_maps: 1.811715, loss_threshold_maps: 0.815104, loss_binary_maps: 0.318851, avg_reader_cost: 1.89265 s, avg_batch_cost: 2.17797 s, avg_samples: 12.5, ips: 5.73929 samples/s, eta: 1:18:48
[2024/07/27 22:00:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:00:20] ppocr INFO: epoch: [34/200], global_step: 100, lr: 0.001000, loss: 2.960209, loss_shrink_maps: 1.835134, loss_threshold_maps: 0.815104, loss_binary_maps: 0.323584, avg_reader_cost: 0.49864 s, avg_batch_cost: 0.67689 s, avg_samples: 4.8, ips: 7.09121 samples/s, eta: 1:18:25
[2024/07/27 22:00:22] ppocr INFO: epoch: [34/200], global_step: 102, lr: 0.001000, loss: 2.960209, loss_shrink_maps: 1.835134, loss_threshold_maps: 0.815104, loss_binary_maps: 0.326258, avg_reader_cost: 1.44521 s, avg_batch_cost: 1.58990 s, avg_samples: 7.7, ips: 4.84306 samples/s, eta: 1:17:52
[2024/07/27 22:00:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:00:30] ppocr INFO: epoch: [35/200], global_step: 105, lr: 0.001000, loss: 2.943514, loss_shrink_maps: 1.811715, loss_threshold_maps: 0.810787, loss_binary_maps: 0.323121, avg_reader_cost: 1.94600 s, avg_batch_cost: 2.27375 s, avg_samples: 12.5, ips: 5.49752 samples/s, eta: 1:16:58
[2024/07/27 22:00:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:00:39] ppocr INFO: epoch: [36/200], global_step: 108, lr: 0.001000, loss: 2.699596, loss_shrink_maps: 1.617056, loss_threshold_maps: 0.800176, loss_binary_maps: 0.289466, avg_reader_cost: 1.97172 s, avg_batch_cost: 2.29633 s, avg_samples: 12.5, ips: 5.44348 samples/s, eta: 1:16:07
[2024/07/27 22:00:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:00:47] ppocr INFO: epoch: [37/200], global_step: 110, lr: 0.001000, loss: 2.687146, loss_shrink_maps: 1.605089, loss_threshold_maps: 0.804020, loss_binary_maps: 0.289181, avg_reader_cost: 1.25157 s, avg_batch_cost: 1.42869 s, avg_samples: 9.6, ips: 6.71943 samples/s, eta: 1:15:30
[2024/07/27 22:00:48] ppocr INFO: epoch: [37/200], global_step: 111, lr: 0.001000, loss: 2.699596, loss_shrink_maps: 1.617056, loss_threshold_maps: 0.807920, loss_binary_maps: 0.292439, avg_reader_cost: 0.75997 s, avg_batch_cost: 0.81472 s, avg_samples: 2.9, ips: 3.55951 samples/s, eta: 1:15:16
[2024/07/27 22:00:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:00:56] ppocr INFO: epoch: [38/200], global_step: 114, lr: 0.001000, loss: 2.699596, loss_shrink_maps: 1.617056, loss_threshold_maps: 0.804020, loss_binary_maps: 0.292439, avg_reader_cost: 1.85583 s, avg_batch_cost: 2.12509 s, avg_samples: 12.5, ips: 5.88210 samples/s, eta: 1:14:20
[2024/07/27 22:00:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:01:05] ppocr INFO: epoch: [39/200], global_step: 117, lr: 0.001000, loss: 2.699596, loss_shrink_maps: 1.617056, loss_threshold_maps: 0.804594, loss_binary_maps: 0.292439, avg_reader_cost: 2.06713 s, avg_batch_cost: 2.30512 s, avg_samples: 12.5, ips: 5.42271 samples/s, eta: 1:13:34
[2024/07/27 22:01:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:01:14] ppocr INFO: epoch: [40/200], global_step: 120, lr: 0.001000, loss: 2.699596, loss_shrink_maps: 1.609539, loss_threshold_maps: 0.798075, loss_binary_maps: 0.293217, avg_reader_cost: 1.94482 s, avg_batch_cost: 2.29720 s, avg_samples: 12.5, ips: 5.44141 samples/s, eta: 1:12:49

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:39, 12.61it/s]
eval model::   1%|          | 5/500 [00:00<00:25, 19.74it/s]
eval model::   2%|▏         | 8/500 [00:00<00:24, 19.88it/s]
eval model::   2%|▏         | 11/500 [00:00<00:24, 19.94it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 19.04it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.66it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 18.74it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.48it/s]
eval model::   5%|▌         | 26/500 [00:01<00:21, 21.67it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 22.12it/s]
eval model::   6%|▋         | 32/500 [00:01<00:22, 20.71it/s]
eval model::   7%|▋         | 35/500 [00:01<00:22, 20.27it/s]
eval model::   8%|▊         | 38/500 [00:01<00:24, 19.20it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.92it/s]
eval model::   8%|▊         | 42/500 [00:02<00:24, 18.91it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.30it/s]
eval model::  10%|▉         | 48/500 [00:02<00:23, 19.40it/s]
eval model::  10%|█         | 51/500 [00:02<00:21, 20.86it/s]
eval model::  11%|█         | 54/500 [00:02<00:23, 19.19it/s]
eval model::  11%|█         | 56/500 [00:02<00:23, 18.58it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:23, 18.44it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.38it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.70it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.14it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:22, 19.17it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:23, 18.47it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:20, 20.93it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:19, 21.19it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:18, 22.64it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:18, 22.06it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:19, 21.17it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:19, 21.25it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:20, 19.79it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:20, 19.76it/s]
eval model::  20%|█▉        | 98/500 [00:04<00:19, 20.89it/s]
eval model::  20%|██        | 101/500 [00:05<00:20, 19.08it/s]
eval model::  21%|██        | 104/500 [00:05<00:19, 20.35it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:20, 19.38it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:20, 18.82it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:20, 18.76it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 20.71it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:19, 19.79it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:21, 18.02it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:19, 19.42it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 18.77it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 20.54it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 17.46it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:19, 18.89it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.25it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.43it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:18, 19.29it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:18, 19.23it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 18.24it/s]
eval model::  30%|███       | 150/500 [00:07<00:19, 18.35it/s]
eval model::  30%|███       | 152/500 [00:07<00:18, 18.41it/s]
eval model::  31%|███       | 156/500 [00:07<00:15, 22.47it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:14, 23.40it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:16, 20.52it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:16, 20.70it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:16, 20.63it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:16, 20.00it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:16, 19.31it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:17, 18.46it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.50it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:17, 17.97it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:15, 19.83it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 20.70it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:15, 20.48it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:14, 20.98it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:14, 20.52it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:13, 21.90it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 21.18it/s]
eval model::  41%|████      | 205/500 [00:10<00:21, 13.94it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:19, 14.63it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:18, 15.36it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.48it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.67it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 14.06it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.16it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:17, 16.14it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:15, 17.75it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:14, 19.28it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 20.87it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 20.56it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 20.49it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:12, 21.44it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 20.05it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:11, 21.54it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:11, 21.95it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 19.63it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 20.58it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:12, 19.38it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 20.57it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 19.09it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:12, 18.90it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:12, 18.08it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 17.86it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.85it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.97it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:11, 19.01it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 20.19it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:13, 15.69it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:11, 17.92it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:10, 18.88it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:11, 17.12it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:10, 19.58it/s]
eval model::  60%|██████    | 302/500 [00:15<00:11, 16.95it/s]
eval model::  61%|██████    | 304/500 [00:15<00:11, 16.71it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.41it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:10, 18.51it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 20.02it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 20.26it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 20.44it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 20.27it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 19.67it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 20.62it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 20.27it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:08, 20.78it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 22.09it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 20.74it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:09, 17.05it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:08, 19.06it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:07, 20.63it/s]
eval model::  70%|███████   | 351/500 [00:18<00:06, 21.43it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 23.00it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:07, 20.20it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:07, 19.41it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:07, 18.26it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 17.43it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 16.69it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 18.85it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 18.88it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 19.50it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:06, 19.90it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:05, 20.96it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:05, 22.81it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:06, 17.16it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 18.33it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 20.13it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 19.75it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:05, 19.10it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 19.67it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 19.75it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 20.34it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 20.79it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:04, 20.60it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 19.59it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 18.92it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:04, 17.45it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 17.78it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:03, 19.91it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 21.36it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 22.12it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 23.39it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 24.00it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 22.88it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 21.87it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 20.29it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 19.69it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 20.31it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:02, 20.57it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 21.83it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:02, 18.02it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 15.48it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 17.24it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 18.82it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.19it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 21.58it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 21.79it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 22.13it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 19.46it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 20.66it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 21.04it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 21.78it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 23.32it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.52it/s]
[2024/07/27 22:01:39] ppocr INFO: cur metric, precision: 0.40350877192982454, recall: 0.2989889263360616, hmean: 0.3434734513274336, fps: 42.62244172103327
[2024/07/27 22:01:39] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 22:01:39] ppocr INFO: best metric, hmean: 0.3434734513274336, precision: 0.40350877192982454, recall: 0.2989889263360616, fps: 42.62244172103327, best_epoch: 40
[2024/07/27 22:01:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:01:47] ppocr INFO: epoch: [41/200], global_step: 123, lr: 0.001000, loss: 2.686909, loss_shrink_maps: 1.589560, loss_threshold_maps: 0.800308, loss_binary_maps: 0.293217, avg_reader_cost: 1.85827 s, avg_batch_cost: 2.13788 s, avg_samples: 12.5, ips: 5.84691 samples/s, eta: 1:11:59
[2024/07/27 22:01:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:01:56] ppocr INFO: epoch: [42/200], global_step: 126, lr: 0.001000, loss: 2.686909, loss_shrink_maps: 1.589560, loss_threshold_maps: 0.798075, loss_binary_maps: 0.295679, avg_reader_cost: 1.96262 s, avg_batch_cost: 2.30770 s, avg_samples: 12.5, ips: 5.41666 samples/s, eta: 1:11:16
[2024/07/27 22:01:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:02:05] ppocr INFO: epoch: [43/200], global_step: 129, lr: 0.001000, loss: 2.702756, loss_shrink_maps: 1.601358, loss_threshold_maps: 0.800624, loss_binary_maps: 0.300171, avg_reader_cost: 2.08082 s, avg_batch_cost: 2.31087 s, avg_samples: 12.5, ips: 5.40921 samples/s, eta: 1:10:35
[2024/07/27 22:02:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:02:12] ppocr INFO: epoch: [44/200], global_step: 130, lr: 0.001000, loss: 2.702756, loss_shrink_maps: 1.601358, loss_threshold_maps: 0.798075, loss_binary_maps: 0.300171, avg_reader_cost: 0.51735 s, avg_batch_cost: 0.64906 s, avg_samples: 4.8, ips: 7.39526 samples/s, eta: 1:10:17
[2024/07/27 22:02:14] ppocr INFO: epoch: [44/200], global_step: 132, lr: 0.001000, loss: 2.702756, loss_shrink_maps: 1.601484, loss_threshold_maps: 0.793968, loss_binary_maps: 0.302684, avg_reader_cost: 1.38974 s, avg_batch_cost: 1.53570 s, avg_samples: 7.7, ips: 5.01402 samples/s, eta: 1:09:50
[2024/07/27 22:02:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:02:22] ppocr INFO: epoch: [45/200], global_step: 135, lr: 0.001000, loss: 2.645467, loss_shrink_maps: 1.565739, loss_threshold_maps: 0.787574, loss_binary_maps: 0.292013, avg_reader_cost: 1.96921 s, avg_batch_cost: 2.30193 s, avg_samples: 12.5, ips: 5.43023 samples/s, eta: 1:09:09
[2024/07/27 22:02:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:02:31] ppocr INFO: epoch: [46/200], global_step: 138, lr: 0.001000, loss: 2.681845, loss_shrink_maps: 1.588754, loss_threshold_maps: 0.790676, loss_binary_maps: 0.295679, avg_reader_cost: 1.98367 s, avg_batch_cost: 2.32041 s, avg_samples: 12.5, ips: 5.38698 samples/s, eta: 1:08:31
[2024/07/27 22:02:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:02:40] ppocr INFO: epoch: [47/200], global_step: 140, lr: 0.001000, loss: 2.675375, loss_shrink_maps: 1.588293, loss_threshold_maps: 0.787574, loss_binary_maps: 0.297899, avg_reader_cost: 1.28952 s, avg_batch_cost: 1.46464 s, avg_samples: 9.6, ips: 6.55450 samples/s, eta: 1:08:03
[2024/07/27 22:02:40] ppocr INFO: epoch: [47/200], global_step: 141, lr: 0.001000, loss: 2.675375, loss_shrink_maps: 1.588293, loss_threshold_maps: 0.783192, loss_binary_maps: 0.297899, avg_reader_cost: 0.77789 s, avg_batch_cost: 0.83292 s, avg_samples: 2.9, ips: 3.48174 samples/s, eta: 1:07:52
[2024/07/27 22:02:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:02:49] ppocr INFO: epoch: [48/200], global_step: 144, lr: 0.001000, loss: 2.675375, loss_shrink_maps: 1.588293, loss_threshold_maps: 0.783192, loss_binary_maps: 0.298296, avg_reader_cost: 2.03986 s, avg_batch_cost: 2.34260 s, avg_samples: 12.5, ips: 5.33595 samples/s, eta: 1:07:15
[2024/07/27 22:02:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:02:58] ppocr INFO: epoch: [49/200], global_step: 147, lr: 0.001000, loss: 2.640125, loss_shrink_maps: 1.567560, loss_threshold_maps: 0.782926, loss_binary_maps: 0.294233, avg_reader_cost: 2.02971 s, avg_batch_cost: 2.27097 s, avg_samples: 12.5, ips: 5.50426 samples/s, eta: 1:06:37
[2024/07/27 22:02:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:03:07] ppocr INFO: epoch: [50/200], global_step: 150, lr: 0.001000, loss: 2.573725, loss_shrink_maps: 1.527524, loss_threshold_maps: 0.779611, loss_binary_maps: 0.283983, avg_reader_cost: 1.89948 s, avg_batch_cost: 2.17270 s, avg_samples: 12.5, ips: 5.75322 samples/s, eta: 1:05:56
[2024/07/27 22:03:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:03:15] ppocr INFO: epoch: [51/200], global_step: 153, lr: 0.001000, loss: 2.519914, loss_shrink_maps: 1.463194, loss_threshold_maps: 0.779611, loss_binary_maps: 0.274130, avg_reader_cost: 1.83674 s, avg_batch_cost: 2.09512 s, avg_samples: 12.5, ips: 5.96625 samples/s, eta: 1:05:14
[2024/07/27 22:03:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:03:24] ppocr INFO: epoch: [52/200], global_step: 156, lr: 0.001000, loss: 2.525271, loss_shrink_maps: 1.463194, loss_threshold_maps: 0.783825, loss_binary_maps: 0.274130, avg_reader_cost: 2.06537 s, avg_batch_cost: 2.29834 s, avg_samples: 12.5, ips: 5.43871 samples/s, eta: 1:04:38
[2024/07/27 22:03:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:03:33] ppocr INFO: epoch: [53/200], global_step: 159, lr: 0.001000, loss: 2.491115, loss_shrink_maps: 1.444094, loss_threshold_maps: 0.781400, loss_binary_maps: 0.271911, avg_reader_cost: 1.98110 s, avg_batch_cost: 2.27007 s, avg_samples: 12.5, ips: 5.50644 samples/s, eta: 1:04:02
[2024/07/27 22:03:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:03:40] ppocr INFO: epoch: [54/200], global_step: 160, lr: 0.001000, loss: 2.491115, loss_shrink_maps: 1.444094, loss_threshold_maps: 0.781400, loss_binary_maps: 0.271911, avg_reader_cost: 0.59896 s, avg_batch_cost: 0.69374 s, avg_samples: 4.8, ips: 6.91906 samples/s, eta: 1:03:49
[2024/07/27 22:03:42] ppocr INFO: epoch: [54/200], global_step: 162, lr: 0.001000, loss: 2.504121, loss_shrink_maps: 1.455217, loss_threshold_maps: 0.781400, loss_binary_maps: 0.274130, avg_reader_cost: 1.47897 s, avg_batch_cost: 1.62501 s, avg_samples: 7.7, ips: 4.73844 samples/s, eta: 1:03:28
[2024/07/27 22:03:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:03:50] ppocr INFO: epoch: [55/200], global_step: 165, lr: 0.001000, loss: 2.488252, loss_shrink_maps: 1.444094, loss_threshold_maps: 0.770367, loss_binary_maps: 0.272686, avg_reader_cost: 1.96025 s, avg_batch_cost: 2.27803 s, avg_samples: 12.5, ips: 5.48721 samples/s, eta: 1:02:53
[2024/07/27 22:03:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:03:59] ppocr INFO: epoch: [56/200], global_step: 168, lr: 0.001000, loss: 2.501258, loss_shrink_maps: 1.457934, loss_threshold_maps: 0.777779, loss_binary_maps: 0.275746, avg_reader_cost: 1.98856 s, avg_batch_cost: 2.27074 s, avg_samples: 12.5, ips: 5.50482 samples/s, eta: 1:02:19
[2024/07/27 22:04:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:04:08] ppocr INFO: epoch: [57/200], global_step: 170, lr: 0.001000, loss: 2.525271, loss_shrink_maps: 1.472741, loss_threshold_maps: 0.783142, loss_binary_maps: 0.276680, avg_reader_cost: 1.30511 s, avg_batch_cost: 1.48144 s, avg_samples: 9.6, ips: 6.48017 samples/s, eta: 1:01:55
[2024/07/27 22:04:08] ppocr INFO: epoch: [57/200], global_step: 171, lr: 0.001000, loss: 2.511354, loss_shrink_maps: 1.457934, loss_threshold_maps: 0.779521, loss_binary_maps: 0.276680, avg_reader_cost: 0.78639 s, avg_batch_cost: 0.84102 s, avg_samples: 2.9, ips: 3.44820 samples/s, eta: 1:01:46
[2024/07/27 22:04:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:04:17] ppocr INFO: epoch: [58/200], global_step: 174, lr: 0.001000, loss: 2.501111, loss_shrink_maps: 1.457934, loss_threshold_maps: 0.774532, loss_binary_maps: 0.276680, avg_reader_cost: 1.98187 s, avg_batch_cost: 2.30327 s, avg_samples: 12.5, ips: 5.42706 samples/s, eta: 1:01:13
[2024/07/27 22:04:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:04:26] ppocr INFO: epoch: [59/200], global_step: 177, lr: 0.001000, loss: 2.501111, loss_shrink_maps: 1.446010, loss_threshold_maps: 0.773697, loss_binary_maps: 0.276236, avg_reader_cost: 1.98645 s, avg_batch_cost: 2.33017 s, avg_samples: 12.5, ips: 5.36441 samples/s, eta: 1:00:41
[2024/07/27 22:04:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:04:35] ppocr INFO: epoch: [60/200], global_step: 180, lr: 0.001000, loss: 2.451399, loss_shrink_maps: 1.420337, loss_threshold_maps: 0.771193, loss_binary_maps: 0.270160, avg_reader_cost: 2.14910 s, avg_batch_cost: 2.38429 s, avg_samples: 12.5, ips: 5.24266 samples/s, eta: 1:00:11

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:50,  9.79it/s]
eval model::   1%|          | 3/500 [00:00<00:35, 14.14it/s]
eval model::   1%|          | 6/500 [00:00<00:24, 20.33it/s]
eval model::   2%|▏         | 9/500 [00:00<00:23, 21.32it/s]
eval model::   2%|▏         | 12/500 [00:00<00:24, 19.71it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 19.35it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.80it/s]
eval model::   4%|▍         | 19/500 [00:01<00:25, 18.53it/s]
eval model::   4%|▍         | 21/500 [00:01<00:27, 17.42it/s]
eval model::   5%|▍         | 24/500 [00:01<00:24, 19.64it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 20.99it/s]
eval model::   6%|▌         | 30/500 [00:01<00:22, 21.19it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 19.55it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 18.91it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.75it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.66it/s]
eval model::   8%|▊         | 42/500 [00:02<00:24, 18.74it/s]
eval model::   9%|▉         | 44/500 [00:02<00:24, 18.84it/s]
eval model::   9%|▉         | 46/500 [00:02<00:24, 18.22it/s]
eval model::  10%|▉         | 49/500 [00:02<00:21, 20.64it/s]
eval model::  10%|█         | 52/500 [00:02<00:20, 21.55it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 22.20it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:21, 20.24it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:23, 18.99it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:25, 17.34it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 19.19it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:18, 22.81it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 22.35it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:18, 22.70it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:18, 22.79it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:17, 24.10it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:17, 23.87it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:18, 22.64it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:20, 20.16it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:20, 19.59it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:19, 20.80it/s]
eval model::  20%|██        | 100/500 [00:04<00:19, 20.01it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 21.86it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 21.96it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.40it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 21.31it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:20, 19.22it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:19, 19.38it/s]
eval model::  24%|██▍       | 120/500 [00:05<00:20, 18.42it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 20.42it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:17, 20.78it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 19.39it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:17, 21.06it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:17, 20.89it/s]
eval model::  28%|██▊       | 139/500 [00:06<00:18, 19.90it/s]
eval model::  28%|██▊       | 142/500 [00:06<00:17, 20.99it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 21.92it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:15, 22.51it/s]
eval model::  30%|███       | 151/500 [00:07<00:16, 21.69it/s]
eval model::  31%|███       | 155/500 [00:07<00:14, 24.28it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:16, 20.94it/s]
eval model::  32%|███▏      | 161/500 [00:07<00:17, 19.07it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:16, 20.42it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:15, 21.47it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:15, 21.95it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:15, 20.60it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:17, 18.80it/s]
eval model::  36%|███▌      | 179/500 [00:08<00:15, 20.72it/s]
eval model::  36%|███▋      | 182/500 [00:08<00:15, 20.05it/s]
eval model::  37%|███▋      | 185/500 [00:08<00:14, 22.02it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:15, 20.33it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:15, 20.48it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:13, 22.44it/s]
eval model::  40%|███▉      | 198/500 [00:09<00:13, 22.67it/s]
eval model::  40%|████      | 201/500 [00:09<00:14, 21.04it/s]
eval model::  41%|████      | 204/500 [00:09<00:13, 22.70it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:20, 14.21it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:19, 14.88it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:18, 15.66it/s]
eval model::  43%|████▎     | 214/500 [00:10<00:15, 18.22it/s]
eval model::  43%|████▎     | 217/500 [00:10<00:19, 14.80it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 17.09it/s]
eval model::  45%|████▍     | 223/500 [00:11<00:15, 17.63it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:13, 19.70it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:12, 21.86it/s]
eval model::  46%|████▋     | 232/500 [00:11<00:11, 23.61it/s]
eval model::  47%|████▋     | 235/500 [00:11<00:11, 22.88it/s]
eval model::  48%|████▊     | 238/500 [00:11<00:11, 23.53it/s]
eval model::  48%|████▊     | 241/500 [00:11<00:10, 24.41it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:11, 22.26it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:11, 22.58it/s]
eval model::  50%|█████     | 250/500 [00:12<00:11, 21.26it/s]
eval model::  51%|█████     | 253/500 [00:12<00:10, 22.85it/s]
eval model::  51%|█████     | 256/500 [00:12<00:10, 24.08it/s]
eval model::  52%|█████▏    | 259/500 [00:12<00:10, 23.62it/s]
eval model::  52%|█████▏    | 262/500 [00:12<00:10, 22.02it/s]
eval model::  53%|█████▎    | 265/500 [00:12<00:11, 21.01it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:10, 21.35it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:11, 20.41it/s]
eval model::  55%|█████▍    | 274/500 [00:13<00:11, 20.46it/s]
eval model::  55%|█████▌    | 277/500 [00:13<00:10, 20.41it/s]
eval model::  56%|█████▌    | 280/500 [00:13<00:10, 20.94it/s]
eval model::  57%|█████▋    | 283/500 [00:13<00:10, 20.87it/s]
eval model::  57%|█████▋    | 286/500 [00:14<00:14, 14.29it/s]
eval model::  58%|█████▊    | 288/500 [00:14<00:13, 15.19it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:11, 17.82it/s]
eval model::  59%|█████▉    | 294/500 [00:14<00:11, 18.66it/s]
eval model::  59%|█████▉    | 297/500 [00:14<00:10, 19.21it/s]
eval model::  60%|██████    | 300/500 [00:14<00:11, 17.82it/s]
eval model::  61%|██████    | 303/500 [00:15<00:10, 19.29it/s]
eval model::  61%|██████    | 306/500 [00:15<00:09, 20.14it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:08, 21.97it/s]
eval model::  62%|██████▏   | 312/500 [00:15<00:08, 21.92it/s]
eval model::  63%|██████▎   | 315/500 [00:15<00:08, 21.81it/s]
eval model::  64%|██████▎   | 318/500 [00:15<00:07, 23.56it/s]
eval model::  64%|██████▍   | 321/500 [00:15<00:07, 23.98it/s]
eval model::  65%|██████▍   | 324/500 [00:15<00:07, 22.66it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:07, 23.62it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:07, 22.06it/s]
eval model::  67%|██████▋   | 333/500 [00:16<00:07, 21.76it/s]
eval model::  67%|██████▋   | 336/500 [00:16<00:07, 21.14it/s]
eval model::  68%|██████▊   | 339/500 [00:16<00:07, 21.25it/s]
eval model::  68%|██████▊   | 342/500 [00:16<00:08, 18.48it/s]
eval model::  69%|██████▉   | 345/500 [00:16<00:07, 20.44it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:06, 22.41it/s]
eval model::  70%|███████   | 351/500 [00:17<00:06, 21.80it/s]
eval model::  71%|███████   | 354/500 [00:17<00:06, 22.47it/s]
eval model::  71%|███████▏  | 357/500 [00:17<00:06, 20.64it/s]
eval model::  72%|███████▏  | 360/500 [00:17<00:07, 19.93it/s]
eval model::  73%|███████▎  | 363/500 [00:17<00:07, 18.38it/s]
eval model::  73%|███████▎  | 365/500 [00:17<00:07, 17.96it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:07, 16.67it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:06, 19.62it/s]
eval model::  75%|███████▍  | 373/500 [00:18<00:06, 20.41it/s]
eval model::  75%|███████▌  | 376/500 [00:18<00:05, 22.72it/s]
eval model::  76%|███████▌  | 379/500 [00:18<00:05, 22.84it/s]
eval model::  76%|███████▋  | 382/500 [00:18<00:05, 21.41it/s]
eval model::  77%|███████▋  | 385/500 [00:18<00:05, 20.74it/s]
eval model::  78%|███████▊  | 388/500 [00:19<00:07, 15.70it/s]
eval model::  78%|███████▊  | 391/500 [00:19<00:06, 17.59it/s]
eval model::  79%|███████▉  | 394/500 [00:19<00:05, 18.89it/s]
eval model::  79%|███████▉  | 397/500 [00:19<00:05, 20.32it/s]
eval model::  80%|████████  | 400/500 [00:19<00:04, 20.57it/s]
eval model::  81%|████████  | 403/500 [00:19<00:04, 22.18it/s]
eval model::  81%|████████  | 406/500 [00:19<00:03, 23.52it/s]
eval model::  82%|████████▏ | 410/500 [00:20<00:03, 25.29it/s]
eval model::  83%|████████▎ | 413/500 [00:20<00:03, 24.70it/s]
eval model::  83%|████████▎ | 416/500 [00:20<00:03, 21.43it/s]
eval model::  84%|████████▍ | 419/500 [00:20<00:03, 20.63it/s]
eval model::  84%|████████▍ | 422/500 [00:20<00:03, 19.78it/s]
eval model::  85%|████████▌ | 425/500 [00:20<00:04, 18.59it/s]
eval model::  86%|████████▌ | 428/500 [00:21<00:03, 20.46it/s]
eval model::  86%|████████▌ | 431/500 [00:21<00:03, 21.99it/s]
eval model::  87%|████████▋ | 434/500 [00:21<00:03, 21.09it/s]
eval model::  87%|████████▋ | 437/500 [00:21<00:02, 21.24it/s]
eval model::  88%|████████▊ | 440/500 [00:21<00:03, 19.44it/s]
eval model::  89%|████████▉ | 444/500 [00:21<00:02, 22.70it/s]
eval model::  89%|████████▉ | 447/500 [00:21<00:02, 22.46it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 21.79it/s]
eval model::  91%|█████████ | 453/500 [00:22<00:02, 21.51it/s]
eval model::  91%|█████████ | 456/500 [00:22<00:02, 19.67it/s]
eval model::  92%|█████████▏| 459/500 [00:22<00:02, 20.45it/s]
eval model::  92%|█████████▏| 462/500 [00:22<00:02, 18.08it/s]
eval model::  93%|█████████▎| 464/500 [00:22<00:02, 17.50it/s]
eval model::  93%|█████████▎| 466/500 [00:22<00:02, 16.52it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 17.08it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 18.86it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 18.49it/s]
eval model::  95%|█████████▌| 477/500 [00:23<00:01, 19.45it/s]
eval model::  96%|█████████▌| 479/500 [00:23<00:01, 19.40it/s]
eval model::  96%|█████████▋| 482/500 [00:23<00:00, 20.06it/s]
eval model::  97%|█████████▋| 485/500 [00:23<00:00, 21.14it/s]
eval model::  98%|█████████▊| 488/500 [00:24<00:00, 19.05it/s]
eval model::  98%|█████████▊| 491/500 [00:24<00:00, 19.88it/s]
eval model::  99%|█████████▉| 494/500 [00:24<00:00, 20.65it/s]
eval model::  99%|█████████▉| 497/500 [00:24<00:00, 21.17it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 22.54it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 20.36it/s]
[2024/07/27 22:05:00] ppocr INFO: cur metric, precision: 0.5563583815028902, recall: 0.37072701011073667, hmean: 0.44495810459404805, fps: 45.25903068570792
[2024/07/27 22:05:00] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 22:05:00] ppocr INFO: best metric, hmean: 0.44495810459404805, precision: 0.5563583815028902, recall: 0.37072701011073667, fps: 45.25903068570792, best_epoch: 60
[2024/07/27 22:05:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:05:09] ppocr INFO: epoch: [61/200], global_step: 183, lr: 0.001000, loss: 2.476644, loss_shrink_maps: 1.435521, loss_threshold_maps: 0.774654, loss_binary_maps: 0.273857, avg_reader_cost: 2.18337 s, avg_batch_cost: 2.50866 s, avg_samples: 12.5, ips: 4.98274 samples/s, eta: 0:59:43
[2024/07/27 22:05:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:05:18] ppocr INFO: epoch: [62/200], global_step: 186, lr: 0.001000, loss: 2.486739, loss_shrink_maps: 1.435521, loss_threshold_maps: 0.776781, loss_binary_maps: 0.274253, avg_reader_cost: 1.95192 s, avg_batch_cost: 2.28184 s, avg_samples: 12.5, ips: 5.47803 samples/s, eta: 0:59:11
[2024/07/27 22:05:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:05:26] ppocr INFO: epoch: [63/200], global_step: 189, lr: 0.001000, loss: 2.404141, loss_shrink_maps: 1.391876, loss_threshold_maps: 0.762153, loss_binary_maps: 0.268770, avg_reader_cost: 1.92758 s, avg_batch_cost: 2.22952 s, avg_samples: 12.5, ips: 5.60659 samples/s, eta: 0:58:38
[2024/07/27 22:05:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:05:34] ppocr INFO: epoch: [64/200], global_step: 190, lr: 0.001000, loss: 2.404141, loss_shrink_maps: 1.391876, loss_threshold_maps: 0.762153, loss_binary_maps: 0.268770, avg_reader_cost: 0.57087 s, avg_batch_cost: 0.68590 s, avg_samples: 4.8, ips: 6.99806 samples/s, eta: 0:58:25
[2024/07/27 22:05:35] ppocr INFO: epoch: [64/200], global_step: 192, lr: 0.001000, loss: 2.468278, loss_shrink_maps: 1.426937, loss_threshold_maps: 0.766311, loss_binary_maps: 0.272572, avg_reader_cost: 1.46327 s, avg_batch_cost: 1.60905 s, avg_samples: 7.7, ips: 4.78543 samples/s, eta: 0:58:06
[2024/07/27 22:05:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:05:43] ppocr INFO: epoch: [65/200], global_step: 195, lr: 0.001000, loss: 2.434873, loss_shrink_maps: 1.417364, loss_threshold_maps: 0.762153, loss_binary_maps: 0.271147, avg_reader_cost: 1.83815 s, avg_batch_cost: 2.07877 s, avg_samples: 12.5, ips: 6.01316 samples/s, eta: 0:57:30
[2024/07/27 22:05:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:05:52] ppocr INFO: epoch: [66/200], global_step: 198, lr: 0.001000, loss: 2.468278, loss_shrink_maps: 1.426937, loss_threshold_maps: 0.766311, loss_binary_maps: 0.272572, avg_reader_cost: 1.93143 s, avg_batch_cost: 2.26698 s, avg_samples: 12.5, ips: 5.51394 samples/s, eta: 0:56:59
[2024/07/27 22:05:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:06:01] ppocr INFO: epoch: [67/200], global_step: 200, lr: 0.001000, loss: 2.468278, loss_shrink_maps: 1.426937, loss_threshold_maps: 0.753095, loss_binary_maps: 0.272572, avg_reader_cost: 1.20835 s, avg_batch_cost: 1.48565 s, avg_samples: 9.6, ips: 6.46180 samples/s, eta: 0:56:38
[2024/07/27 22:06:01] ppocr INFO: epoch: [67/200], global_step: 201, lr: 0.001000, loss: 2.468278, loss_shrink_maps: 1.426937, loss_threshold_maps: 0.753095, loss_binary_maps: 0.272572, avg_reader_cost: 0.78837 s, avg_batch_cost: 0.84329 s, avg_samples: 2.9, ips: 3.43893 samples/s, eta: 0:56:29
[2024/07/27 22:06:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:06:10] ppocr INFO: epoch: [68/200], global_step: 204, lr: 0.001000, loss: 2.338977, loss_shrink_maps: 1.317709, loss_threshold_maps: 0.754752, loss_binary_maps: 0.251377, avg_reader_cost: 2.11115 s, avg_batch_cost: 2.35500 s, avg_samples: 12.5, ips: 5.30786 samples/s, eta: 0:56:00
[2024/07/27 22:06:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:06:19] ppocr INFO: epoch: [69/200], global_step: 207, lr: 0.001000, loss: 2.389712, loss_shrink_maps: 1.369361, loss_threshold_maps: 0.743948, loss_binary_maps: 0.263245, avg_reader_cost: 1.96632 s, avg_batch_cost: 2.29387 s, avg_samples: 12.5, ips: 5.44930 samples/s, eta: 0:55:30
[2024/07/27 22:06:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:06:28] ppocr INFO: epoch: [70/200], global_step: 210, lr: 0.001000, loss: 2.389712, loss_shrink_maps: 1.360695, loss_threshold_maps: 0.760145, loss_binary_maps: 0.262218, avg_reader_cost: 1.92477 s, avg_batch_cost: 2.24011 s, avg_samples: 12.5, ips: 5.58008 samples/s, eta: 0:54:58
[2024/07/27 22:06:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:06:36] ppocr INFO: epoch: [71/200], global_step: 213, lr: 0.001000, loss: 2.338977, loss_shrink_maps: 1.317709, loss_threshold_maps: 0.760145, loss_binary_maps: 0.250822, avg_reader_cost: 1.91137 s, avg_batch_cost: 2.21000 s, avg_samples: 12.5, ips: 5.65610 samples/s, eta: 0:54:27
[2024/07/27 22:06:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:06:45] ppocr INFO: epoch: [72/200], global_step: 216, lr: 0.001000, loss: 2.247943, loss_shrink_maps: 1.270615, loss_threshold_maps: 0.739267, loss_binary_maps: 0.243297, avg_reader_cost: 2.04814 s, avg_batch_cost: 2.28285 s, avg_samples: 12.5, ips: 5.47562 samples/s, eta: 0:53:57
[2024/07/27 22:06:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:06:54] ppocr INFO: epoch: [73/200], global_step: 219, lr: 0.001000, loss: 2.247943, loss_shrink_maps: 1.275753, loss_threshold_maps: 0.739267, loss_binary_maps: 0.243719, avg_reader_cost: 2.06836 s, avg_batch_cost: 2.30364 s, avg_samples: 12.5, ips: 5.42620 samples/s, eta: 0:53:28
[2024/07/27 22:06:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:07:01] ppocr INFO: epoch: [74/200], global_step: 220, lr: 0.001000, loss: 2.247943, loss_shrink_maps: 1.275753, loss_threshold_maps: 0.739267, loss_binary_maps: 0.243719, avg_reader_cost: 0.48067 s, avg_batch_cost: 0.69366 s, avg_samples: 4.8, ips: 6.91977 samples/s, eta: 0:53:17
[2024/07/27 22:07:03] ppocr INFO: epoch: [74/200], global_step: 222, lr: 0.001000, loss: 2.205292, loss_shrink_maps: 1.247208, loss_threshold_maps: 0.731227, loss_binary_maps: 0.241733, avg_reader_cost: 1.47853 s, avg_batch_cost: 1.62412 s, avg_samples: 7.7, ips: 4.74102 samples/s, eta: 0:52:59
[2024/07/27 22:07:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:07:12] ppocr INFO: epoch: [75/200], global_step: 225, lr: 0.001000, loss: 2.205292, loss_shrink_maps: 1.247208, loss_threshold_maps: 0.729604, loss_binary_maps: 0.241733, avg_reader_cost: 2.03301 s, avg_batch_cost: 2.27789 s, avg_samples: 12.5, ips: 5.48754 samples/s, eta: 0:52:30
[2024/07/27 22:07:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:07:21] ppocr INFO: epoch: [76/200], global_step: 228, lr: 0.001000, loss: 2.222640, loss_shrink_maps: 1.254155, loss_threshold_maps: 0.732510, loss_binary_maps: 0.243139, avg_reader_cost: 1.98527 s, avg_batch_cost: 2.29278 s, avg_samples: 12.5, ips: 5.45189 samples/s, eta: 0:52:01
[2024/07/27 22:07:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:07:29] ppocr INFO: epoch: [77/200], global_step: 230, lr: 0.001000, loss: 2.259202, loss_shrink_maps: 1.276727, loss_threshold_maps: 0.732510, loss_binary_maps: 0.247955, avg_reader_cost: 1.16023 s, avg_batch_cost: 1.46011 s, avg_samples: 9.6, ips: 6.57483 samples/s, eta: 0:51:41
[2024/07/27 22:07:29] ppocr INFO: epoch: [77/200], global_step: 231, lr: 0.001000, loss: 2.259202, loss_shrink_maps: 1.276727, loss_threshold_maps: 0.732510, loss_binary_maps: 0.247955, avg_reader_cost: 0.77546 s, avg_batch_cost: 0.83016 s, avg_samples: 2.9, ips: 3.49329 samples/s, eta: 0:51:32
[2024/07/27 22:07:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:07:38] ppocr INFO: epoch: [78/200], global_step: 234, lr: 0.001000, loss: 2.336891, loss_shrink_maps: 1.335312, loss_threshold_maps: 0.747950, loss_binary_maps: 0.258113, avg_reader_cost: 2.04770 s, avg_batch_cost: 2.29762 s, avg_samples: 12.5, ips: 5.44041 samples/s, eta: 0:51:04
[2024/07/27 22:07:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:07:47] ppocr INFO: epoch: [79/200], global_step: 237, lr: 0.001000, loss: 2.338432, loss_shrink_maps: 1.335312, loss_threshold_maps: 0.754063, loss_binary_maps: 0.258113, avg_reader_cost: 1.95611 s, avg_batch_cost: 2.26612 s, avg_samples: 12.5, ips: 5.51605 samples/s, eta: 0:50:35
[2024/07/27 22:07:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:07:56] ppocr INFO: epoch: [80/200], global_step: 240, lr: 0.001000, loss: 2.358754, loss_shrink_maps: 1.340635, loss_threshold_maps: 0.754063, loss_binary_maps: 0.258968, avg_reader_cost: 1.97658 s, avg_batch_cost: 2.29167 s, avg_samples: 12.5, ips: 5.45453 samples/s, eta: 0:50:07

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:02,  8.02it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.17it/s]
eval model::   1%|          | 6/500 [00:00<00:31, 15.46it/s]
eval model::   2%|▏         | 9/500 [00:00<00:26, 18.23it/s]
eval model::   2%|▏         | 11/500 [00:00<00:29, 16.52it/s]
eval model::   3%|▎         | 13/500 [00:00<00:28, 17.12it/s]
eval model::   3%|▎         | 15/500 [00:00<00:27, 17.69it/s]
eval model::   4%|▎         | 18/500 [00:01<00:24, 19.90it/s]
eval model::   4%|▍         | 21/500 [00:01<00:29, 16.32it/s]
eval model::   5%|▍         | 23/500 [00:01<00:27, 17.06it/s]
eval model::   5%|▌         | 26/500 [00:01<00:26, 18.18it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 19.68it/s]
eval model::   6%|▋         | 32/500 [00:01<00:25, 18.20it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.51it/s]
eval model::   7%|▋         | 36/500 [00:02<00:25, 18.49it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 17.78it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 17.74it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.04it/s]
eval model::  10%|▉         | 48/500 [00:02<00:23, 19.39it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 20.89it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.55it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:21, 20.63it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.29it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.77it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:20, 20.63it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 22.38it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 21.78it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:18, 22.58it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:18, 22.31it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.69it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 20.95it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 20.32it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:20, 20.04it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:19, 20.71it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:18, 21.61it/s]
eval model::  20%|██        | 100/500 [00:05<00:20, 19.63it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 20.34it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 20.17it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 20.92it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:20, 19.16it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:20, 19.07it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:19, 19.65it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:23, 16.38it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:19, 18.91it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 18.73it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.93it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:18, 19.43it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 20.19it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.98it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.08it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:18, 18.96it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:16, 21.04it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:16, 20.98it/s]
eval model::  30%|███       | 152/500 [00:07<00:18, 18.60it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 20.77it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:15, 22.41it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:16, 20.21it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:16, 20.11it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:15, 21.17it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:15, 20.86it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:17, 18.99it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:16, 19.11it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:15, 20.60it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:15, 20.09it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:13, 22.91it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:13, 22.87it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.11it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:14, 20.93it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:13, 22.15it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 21.42it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 21.67it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:21, 13.57it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:21, 13.69it/s]
eval model::  42%|████▏     | 212/500 [00:10<00:18, 15.77it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.20it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.13it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 17.29it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:16, 17.08it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:14, 19.19it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:13, 20.16it/s]
eval model::  46%|████▌     | 231/500 [00:11<00:12, 21.35it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 21.73it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 21.00it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:12, 21.53it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:13, 19.76it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:11, 21.66it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:11, 21.90it/s]
eval model::  50%|█████     | 252/500 [00:12<00:11, 21.62it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.25it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 20.23it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 20.34it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:11, 19.69it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:11, 20.64it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:10, 21.76it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:11, 20.22it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:11, 20.08it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:10, 20.32it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 21.28it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:14, 15.06it/s]
eval model::  58%|█████▊    | 288/500 [00:14<00:12, 17.20it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:10, 19.23it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:11, 17.82it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:11, 18.33it/s]
eval model::  60%|██████    | 300/500 [00:15<00:10, 18.99it/s]
eval model::  61%|██████    | 303/500 [00:15<00:09, 20.23it/s]
eval model::  61%|██████    | 306/500 [00:15<00:09, 20.03it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:09, 20.80it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:08, 20.96it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 20.71it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 22.13it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:07, 22.59it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:07, 22.71it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:07, 22.87it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:08, 21.21it/s]
eval model::  67%|██████▋   | 333/500 [00:16<00:07, 20.99it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:08, 18.89it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:08, 18.66it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:10, 15.80it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:08, 17.93it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 19.37it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:07, 20.20it/s]
eval model::  70%|███████   | 352/500 [00:17<00:07, 20.83it/s]
eval model::  71%|███████   | 355/500 [00:18<00:07, 19.92it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 20.29it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 19.35it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.39it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:07, 18.66it/s]
eval model::  74%|███████▍  | 369/500 [00:18<00:06, 19.34it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 20.05it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 20.52it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 21.00it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:06, 19.22it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:05, 19.73it/s]
eval model::  77%|███████▋  | 387/500 [00:19<00:07, 14.54it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 16.54it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 18.96it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 19.36it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 20.21it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 20.97it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 22.86it/s]
eval model::  82%|████████▏ | 408/500 [00:20<00:04, 22.76it/s]
eval model::  82%|████████▏ | 411/500 [00:20<00:04, 21.99it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:04, 20.59it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 20.37it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 17.03it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 17.07it/s]
eval model::  85%|████████▌ | 425/500 [00:21<00:04, 17.70it/s]
eval model::  86%|████████▌ | 428/500 [00:21<00:03, 19.75it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 21.47it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:02, 24.25it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 24.42it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 20.99it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 21.63it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 20.54it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 18.33it/s]
eval model::  90%|█████████ | 452/500 [00:23<00:02, 18.49it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:02, 19.11it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:02, 20.65it/s]
eval model::  92%|█████████▏| 461/500 [00:23<00:01, 20.09it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 18.81it/s]
eval model::  93%|█████████▎| 466/500 [00:23<00:02, 16.85it/s]
eval model::  94%|█████████▍| 470/500 [00:23<00:01, 20.90it/s]
eval model::  95%|█████████▍| 473/500 [00:24<00:01, 21.52it/s]
eval model::  95%|█████████▌| 476/500 [00:24<00:01, 22.33it/s]
eval model::  96%|█████████▌| 479/500 [00:24<00:00, 22.02it/s]
eval model::  96%|█████████▋| 482/500 [00:24<00:00, 21.24it/s]
eval model::  97%|█████████▋| 485/500 [00:24<00:00, 22.39it/s]
eval model::  98%|█████████▊| 488/500 [00:24<00:00, 20.62it/s]
eval model::  98%|█████████▊| 491/500 [00:24<00:00, 21.10it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 21.39it/s]
eval model::  99%|█████████▉| 497/500 [00:25<00:00, 21.10it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 22.35it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.72it/s]
[2024/07/27 22:08:21] ppocr INFO: cur metric, precision: 0.5277777777777778, recall: 0.3750601829561868, hmean: 0.4385026737967915, fps: 44.05197083622204
[2024/07/27 22:08:21] ppocr INFO: best metric, hmean: 0.44495810459404805, precision: 0.5563583815028902, recall: 0.37072701011073667, fps: 45.25903068570792, best_epoch: 60
[2024/07/27 22:08:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:08:30] ppocr INFO: epoch: [81/200], global_step: 243, lr: 0.001000, loss: 2.376284, loss_shrink_maps: 1.348319, loss_threshold_maps: 0.751474, loss_binary_maps: 0.262278, avg_reader_cost: 2.03740 s, avg_batch_cost: 2.27635 s, avg_samples: 12.5, ips: 5.49124 samples/s, eta: 0:49:38
[2024/07/27 22:08:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:08:38] ppocr INFO: epoch: [82/200], global_step: 246, lr: 0.001000, loss: 2.376284, loss_shrink_maps: 1.348319, loss_threshold_maps: 0.751474, loss_binary_maps: 0.262278, avg_reader_cost: 2.05953 s, avg_batch_cost: 2.30200 s, avg_samples: 12.5, ips: 5.43006 samples/s, eta: 0:49:10
[2024/07/27 22:08:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:08:47] ppocr INFO: epoch: [83/200], global_step: 249, lr: 0.001000, loss: 2.376284, loss_shrink_maps: 1.348010, loss_threshold_maps: 0.750550, loss_binary_maps: 0.262278, avg_reader_cost: 2.05671 s, avg_batch_cost: 2.29445 s, avg_samples: 12.5, ips: 5.44792 samples/s, eta: 0:48:42
[2024/07/27 22:08:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:08:55] ppocr INFO: epoch: [84/200], global_step: 250, lr: 0.001000, loss: 2.366037, loss_shrink_maps: 1.348010, loss_threshold_maps: 0.749952, loss_binary_maps: 0.261978, avg_reader_cost: 0.51147 s, avg_batch_cost: 0.69139 s, avg_samples: 4.8, ips: 6.94250 samples/s, eta: 0:48:32
[2024/07/27 22:08:56] ppocr INFO: epoch: [84/200], global_step: 252, lr: 0.001000, loss: 2.343424, loss_shrink_maps: 1.327846, loss_threshold_maps: 0.746466, loss_binary_maps: 0.256837, avg_reader_cost: 1.47380 s, avg_batch_cost: 1.61823 s, avg_samples: 7.7, ips: 4.75828 samples/s, eta: 0:48:15
[2024/07/27 22:08:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:09:06] ppocr INFO: epoch: [85/200], global_step: 255, lr: 0.001000, loss: 2.329880, loss_shrink_maps: 1.322611, loss_threshold_maps: 0.745561, loss_binary_maps: 0.257634, avg_reader_cost: 2.10968 s, avg_batch_cost: 2.46220 s, avg_samples: 12.5, ips: 5.07675 samples/s, eta: 0:47:49
[2024/07/27 22:09:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:09:15] ppocr INFO: epoch: [86/200], global_step: 258, lr: 0.001000, loss: 2.304705, loss_shrink_maps: 1.310108, loss_threshold_maps: 0.743890, loss_binary_maps: 0.252861, avg_reader_cost: 1.99943 s, avg_batch_cost: 2.33484 s, avg_samples: 12.5, ips: 5.35369 samples/s, eta: 0:47:22
[2024/07/27 22:09:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:09:23] ppocr INFO: epoch: [87/200], global_step: 260, lr: 0.001000, loss: 2.280252, loss_shrink_maps: 1.300411, loss_threshold_maps: 0.734825, loss_binary_maps: 0.250523, avg_reader_cost: 1.25871 s, avg_batch_cost: 1.43703 s, avg_samples: 9.6, ips: 6.68045 samples/s, eta: 0:47:03
[2024/07/27 22:09:23] ppocr INFO: epoch: [87/200], global_step: 261, lr: 0.001000, loss: 2.265597, loss_shrink_maps: 1.284865, loss_threshold_maps: 0.726352, loss_binary_maps: 0.244958, avg_reader_cost: 0.76421 s, avg_batch_cost: 0.81884 s, avg_samples: 2.9, ips: 3.54161 samples/s, eta: 0:46:54
[2024/07/27 22:09:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:09:32] ppocr INFO: epoch: [88/200], global_step: 264, lr: 0.001000, loss: 2.287727, loss_shrink_maps: 1.319392, loss_threshold_maps: 0.726352, loss_binary_maps: 0.258351, avg_reader_cost: 2.08918 s, avg_batch_cost: 2.32441 s, avg_samples: 12.5, ips: 5.37770 samples/s, eta: 0:46:27
[2024/07/27 22:09:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:09:41] ppocr INFO: epoch: [89/200], global_step: 267, lr: 0.001000, loss: 2.322932, loss_shrink_maps: 1.329088, loss_threshold_maps: 0.743418, loss_binary_maps: 0.260860, avg_reader_cost: 1.92351 s, avg_batch_cost: 2.23920 s, avg_samples: 12.5, ips: 5.58236 samples/s, eta: 0:45:59
[2024/07/27 22:09:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:09:50] ppocr INFO: epoch: [90/200], global_step: 270, lr: 0.001000, loss: 2.322932, loss_shrink_maps: 1.329088, loss_threshold_maps: 0.734985, loss_binary_maps: 0.260860, avg_reader_cost: 1.99451 s, avg_batch_cost: 2.29539 s, avg_samples: 12.5, ips: 5.44570 samples/s, eta: 0:45:32
[2024/07/27 22:09:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:09:58] ppocr INFO: epoch: [91/200], global_step: 273, lr: 0.001000, loss: 2.298479, loss_shrink_maps: 1.319392, loss_threshold_maps: 0.744611, loss_binary_maps: 0.258522, avg_reader_cost: 1.88123 s, avg_batch_cost: 2.14871 s, avg_samples: 12.5, ips: 5.81743 samples/s, eta: 0:45:03
[2024/07/27 22:09:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:10:07] ppocr INFO: epoch: [92/200], global_step: 276, lr: 0.001000, loss: 2.307649, loss_shrink_maps: 1.319392, loss_threshold_maps: 0.737086, loss_binary_maps: 0.258589, avg_reader_cost: 2.05392 s, avg_batch_cost: 2.29102 s, avg_samples: 12.5, ips: 5.45608 samples/s, eta: 0:44:36
[2024/07/27 22:10:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:10:16] ppocr INFO: epoch: [93/200], global_step: 279, lr: 0.001000, loss: 2.307649, loss_shrink_maps: 1.317192, loss_threshold_maps: 0.742302, loss_binary_maps: 0.258589, avg_reader_cost: 2.03677 s, avg_batch_cost: 2.27488 s, avg_samples: 12.5, ips: 5.49479 samples/s, eta: 0:44:09
[2024/07/27 22:10:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:10:23] ppocr INFO: epoch: [94/200], global_step: 280, lr: 0.001000, loss: 2.296897, loss_shrink_maps: 1.305717, loss_threshold_maps: 0.739750, loss_binary_maps: 0.254575, avg_reader_cost: 0.51654 s, avg_batch_cost: 0.69028 s, avg_samples: 4.8, ips: 6.95367 samples/s, eta: 0:43:59
[2024/07/27 22:10:25] ppocr INFO: epoch: [94/200], global_step: 282, lr: 0.001000, loss: 2.296897, loss_shrink_maps: 1.305717, loss_threshold_maps: 0.739750, loss_binary_maps: 0.254575, avg_reader_cost: 1.47200 s, avg_batch_cost: 1.61811 s, avg_samples: 7.7, ips: 4.75865 samples/s, eta: 0:43:42
[2024/07/27 22:10:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:10:34] ppocr INFO: epoch: [95/200], global_step: 285, lr: 0.001000, loss: 2.295036, loss_shrink_maps: 1.291200, loss_threshold_maps: 0.739750, loss_binary_maps: 0.250812, avg_reader_cost: 2.10601 s, avg_batch_cost: 2.35206 s, avg_samples: 12.5, ips: 5.31449 samples/s, eta: 0:43:16
[2024/07/27 22:10:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:10:43] ppocr INFO: epoch: [96/200], global_step: 288, lr: 0.001000, loss: 2.217992, loss_shrink_maps: 1.242790, loss_threshold_maps: 0.734700, loss_binary_maps: 0.242426, avg_reader_cost: 2.02186 s, avg_batch_cost: 2.27145 s, avg_samples: 12.5, ips: 5.50310 samples/s, eta: 0:42:49
[2024/07/27 22:10:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:10:51] ppocr INFO: epoch: [97/200], global_step: 290, lr: 0.001000, loss: 2.201778, loss_shrink_maps: 1.232859, loss_threshold_maps: 0.730113, loss_binary_maps: 0.241598, avg_reader_cost: 1.26379 s, avg_batch_cost: 1.44707 s, avg_samples: 9.6, ips: 6.63407 samples/s, eta: 0:42:31
[2024/07/27 22:10:51] ppocr INFO: epoch: [97/200], global_step: 291, lr: 0.001000, loss: 2.192500, loss_shrink_maps: 1.223696, loss_threshold_maps: 0.725847, loss_binary_maps: 0.240677, avg_reader_cost: 0.76914 s, avg_batch_cost: 0.82380 s, avg_samples: 2.9, ips: 3.52026 samples/s, eta: 0:42:22
[2024/07/27 22:10:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:11:00] ppocr INFO: epoch: [98/200], global_step: 294, lr: 0.001000, loss: 2.168750, loss_shrink_maps: 1.223696, loss_threshold_maps: 0.723445, loss_binary_maps: 0.240677, avg_reader_cost: 2.09867 s, avg_batch_cost: 2.32945 s, avg_samples: 12.5, ips: 5.36607 samples/s, eta: 0:41:56
[2024/07/27 22:11:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:11:09] ppocr INFO: epoch: [99/200], global_step: 297, lr: 0.001000, loss: 2.182751, loss_shrink_maps: 1.229448, loss_threshold_maps: 0.723445, loss_binary_maps: 0.241570, avg_reader_cost: 2.04250 s, avg_batch_cost: 2.27954 s, avg_samples: 12.5, ips: 5.48357 samples/s, eta: 0:41:30
[2024/07/27 22:11:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:11:18] ppocr INFO: epoch: [100/200], global_step: 300, lr: 0.001000, loss: 2.172809, loss_shrink_maps: 1.219480, loss_threshold_maps: 0.716738, loss_binary_maps: 0.239635, avg_reader_cost: 2.07210 s, avg_batch_cost: 2.31348 s, avg_samples: 12.5, ips: 5.40311 samples/s, eta: 0:41:04

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.60it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.72it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.78it/s]
eval model::   2%|▏         | 9/500 [00:00<00:25, 18.92it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.76it/s]
eval model::   3%|▎         | 13/500 [00:00<00:28, 17.25it/s]
eval model::   3%|▎         | 16/500 [00:00<00:25, 18.89it/s]
eval model::   4%|▍         | 19/500 [00:01<00:25, 18.97it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 18.62it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.04it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 19.76it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.09it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.08it/s]
eval model::   7%|▋         | 34/500 [00:01<00:26, 17.70it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 19.52it/s]
eval model::   8%|▊         | 39/500 [00:02<00:26, 17.26it/s]
eval model::   8%|▊         | 41/500 [00:02<00:26, 17.65it/s]
eval model::   9%|▊         | 43/500 [00:02<00:26, 17.06it/s]
eval model::   9%|▉         | 46/500 [00:02<00:26, 17.39it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 19.90it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 19.41it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.28it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:21, 20.17it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:25, 17.55it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.76it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:21, 19.98it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:21, 20.20it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:22, 18.77it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:21, 19.95it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:21, 19.70it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 20.05it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.42it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 18.81it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:24, 16.95it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:23, 17.50it/s]
eval model::  19%|█▉        | 94/500 [00:05<00:24, 16.77it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:24, 16.74it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:21, 18.65it/s]
eval model::  20%|██        | 101/500 [00:05<00:21, 18.58it/s]
eval model::  21%|██        | 103/500 [00:05<00:21, 18.87it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 19.83it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 21.03it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 20.67it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:17, 22.58it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:15, 24.14it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:16, 22.42it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:16, 22.55it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:16, 22.69it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:16, 22.19it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:16, 22.60it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:17, 20.99it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:17, 20.35it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 20.56it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:16, 21.68it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:16, 20.78it/s]
eval model::  30%|███       | 152/500 [00:07<00:17, 19.39it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 20.65it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.49it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:18, 17.84it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:18, 17.93it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 19.15it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 19.07it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 18.74it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 16.67it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.76it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.08it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:17, 17.70it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 20.87it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.38it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.88it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:13, 21.83it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:13, 21.89it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 22.42it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.69it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:19, 15.27it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:18, 15.55it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:18, 15.86it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.30it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.21it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.73it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 16.72it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:16, 16.38it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:13, 20.03it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:12, 20.87it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 21.79it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 21.42it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 22.13it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 22.25it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:12, 19.99it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:11, 21.29it/s]
eval model::  50%|█████     | 250/500 [00:12<00:12, 19.99it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 20.90it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 22.03it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 22.66it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 20.75it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:11, 21.15it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:11, 20.31it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:11, 20.40it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:11, 20.30it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 18.99it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 19.82it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:10, 21.35it/s]
eval model::  57%|█████▋    | 286/500 [00:14<00:14, 14.76it/s]
eval model::  58%|█████▊    | 289/500 [00:14<00:12, 16.78it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:10, 19.08it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:12, 16.21it/s]
eval model::  60%|█████▉    | 298/500 [00:15<00:11, 18.02it/s]
eval model::  60%|██████    | 301/500 [00:15<00:10, 18.77it/s]
eval model::  61%|██████    | 304/500 [00:15<00:09, 19.69it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:08, 21.83it/s]
eval model::  62%|██████▏   | 310/500 [00:15<00:08, 21.37it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 21.65it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 20.95it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 21.86it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:08, 21.03it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:07, 22.66it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:07, 22.74it/s]
eval model::  66%|██████▌   | 331/500 [00:16<00:07, 22.30it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 22.78it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:06, 23.34it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 17.37it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:08, 18.87it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 20.50it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:06, 21.98it/s]
eval model::  70%|███████   | 352/500 [00:17<00:06, 21.28it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.69it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 21.24it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 19.42it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 17.78it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:07, 18.10it/s]
eval model::  74%|███████▍  | 369/500 [00:18<00:06, 18.93it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 19.13it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 19.49it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:06, 19.37it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:06, 18.43it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:05, 20.37it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:08, 14.11it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 16.62it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 17.82it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 18.10it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:05, 19.28it/s]
eval model::  80%|████████  | 401/500 [00:20<00:05, 18.82it/s]
eval model::  81%|████████  | 404/500 [00:20<00:04, 20.06it/s]
eval model::  81%|████████▏ | 407/500 [00:20<00:04, 20.69it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 21.48it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:03, 22.62it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:03, 22.01it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:03, 20.50it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 17.70it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 16.89it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:04, 17.31it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 19.66it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 20.25it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:02, 22.42it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 24.55it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 22.80it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 21.28it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 19.79it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 19.01it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 19.49it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:02, 20.63it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 20.22it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 18.79it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:02, 17.20it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 19.94it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.92it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.38it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 21.93it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 21.02it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 22.34it/s]
eval model::  97%|█████████▋| 487/500 [00:24<00:00, 19.86it/s]
eval model::  98%|█████████▊| 490/500 [00:24<00:00, 19.79it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 20.20it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 20.33it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 24.00it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.69it/s]
[2024/07/27 22:11:43] ppocr INFO: cur metric, precision: 0.6292935839274141, recall: 0.46750120365912373, hmean: 0.53646408839779, fps: 44.39734099314374
[2024/07/27 22:11:43] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 22:11:43] ppocr INFO: best metric, hmean: 0.53646408839779, precision: 0.6292935839274141, recall: 0.46750120365912373, fps: 44.39734099314374, best_epoch: 100
[2024/07/27 22:11:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:11:52] ppocr INFO: epoch: [101/200], global_step: 303, lr: 0.001000, loss: 2.172809, loss_shrink_maps: 1.219480, loss_threshold_maps: 0.708619, loss_binary_maps: 0.239635, avg_reader_cost: 2.02600 s, avg_batch_cost: 2.32198 s, avg_samples: 12.5, ips: 5.38334 samples/s, eta: 0:40:37
[2024/07/27 22:11:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:12:01] ppocr INFO: epoch: [102/200], global_step: 306, lr: 0.001000, loss: 2.172809, loss_shrink_maps: 1.219480, loss_threshold_maps: 0.708619, loss_binary_maps: 0.239635, avg_reader_cost: 1.91633 s, avg_batch_cost: 2.24814 s, avg_samples: 12.5, ips: 5.56016 samples/s, eta: 0:40:11
[2024/07/27 22:12:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:12:09] ppocr INFO: epoch: [103/200], global_step: 309, lr: 0.001000, loss: 2.205022, loss_shrink_maps: 1.244131, loss_threshold_maps: 0.720319, loss_binary_maps: 0.243798, avg_reader_cost: 1.90660 s, avg_batch_cost: 2.21577 s, avg_samples: 12.5, ips: 5.64139 samples/s, eta: 0:39:44
[2024/07/27 22:12:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:12:16] ppocr INFO: epoch: [104/200], global_step: 310, lr: 0.001000, loss: 2.196520, loss_shrink_maps: 1.244131, loss_threshold_maps: 0.716738, loss_binary_maps: 0.243798, avg_reader_cost: 0.56600 s, avg_batch_cost: 0.65757 s, avg_samples: 4.8, ips: 7.29956 samples/s, eta: 0:39:34
[2024/07/27 22:12:18] ppocr INFO: epoch: [104/200], global_step: 312, lr: 0.001000, loss: 2.196520, loss_shrink_maps: 1.244131, loss_threshold_maps: 0.708619, loss_binary_maps: 0.243798, avg_reader_cost: 1.40663 s, avg_batch_cost: 1.55229 s, avg_samples: 7.7, ips: 4.96040 samples/s, eta: 0:39:17
[2024/07/27 22:12:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:12:27] ppocr INFO: epoch: [105/200], global_step: 315, lr: 0.001000, loss: 2.192318, loss_shrink_maps: 1.238769, loss_threshold_maps: 0.716738, loss_binary_maps: 0.241872, avg_reader_cost: 1.99608 s, avg_batch_cost: 2.32730 s, avg_samples: 12.5, ips: 5.37102 samples/s, eta: 0:38:51
[2024/07/27 22:12:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:12:36] ppocr INFO: epoch: [106/200], global_step: 318, lr: 0.001000, loss: 2.220697, loss_shrink_maps: 1.250815, loss_threshold_maps: 0.712011, loss_binary_maps: 0.243587, avg_reader_cost: 1.93402 s, avg_batch_cost: 2.29378 s, avg_samples: 12.5, ips: 5.44951 samples/s, eta: 0:38:25
[2024/07/27 22:12:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:12:44] ppocr INFO: epoch: [107/200], global_step: 320, lr: 0.001000, loss: 2.175532, loss_shrink_maps: 1.229056, loss_threshold_maps: 0.705954, loss_binary_maps: 0.238388, avg_reader_cost: 1.27896 s, avg_batch_cost: 1.46114 s, avg_samples: 9.6, ips: 6.57022 samples/s, eta: 0:38:07
[2024/07/27 22:12:45] ppocr INFO: epoch: [107/200], global_step: 321, lr: 0.001000, loss: 2.131506, loss_shrink_maps: 1.196719, loss_threshold_maps: 0.701453, loss_binary_maps: 0.232799, avg_reader_cost: 0.77627 s, avg_batch_cost: 0.83113 s, avg_samples: 2.9, ips: 3.48922 samples/s, eta: 0:37:59
[2024/07/27 22:12:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:12:53] ppocr INFO: epoch: [108/200], global_step: 324, lr: 0.001000, loss: 2.131506, loss_shrink_maps: 1.196719, loss_threshold_maps: 0.702912, loss_binary_maps: 0.232799, avg_reader_cost: 1.98673 s, avg_batch_cost: 2.31885 s, avg_samples: 12.5, ips: 5.39061 samples/s, eta: 0:37:34
[2024/07/27 22:12:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:13:02] ppocr INFO: epoch: [109/200], global_step: 327, lr: 0.001000, loss: 2.131506, loss_shrink_maps: 1.196719, loss_threshold_maps: 0.704368, loss_binary_maps: 0.232799, avg_reader_cost: 1.98090 s, avg_batch_cost: 2.31706 s, avg_samples: 12.5, ips: 5.39477 samples/s, eta: 0:37:08
[2024/07/27 22:13:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:13:11] ppocr INFO: epoch: [110/200], global_step: 330, lr: 0.001000, loss: 2.131506, loss_shrink_maps: 1.196719, loss_threshold_maps: 0.704368, loss_binary_maps: 0.232799, avg_reader_cost: 1.97763 s, avg_batch_cost: 2.29848 s, avg_samples: 12.5, ips: 5.43837 samples/s, eta: 0:36:42
[2024/07/27 22:13:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:13:20] ppocr INFO: epoch: [111/200], global_step: 333, lr: 0.001000, loss: 2.158812, loss_shrink_maps: 1.206896, loss_threshold_maps: 0.707410, loss_binary_maps: 0.236259, avg_reader_cost: 1.91322 s, avg_batch_cost: 2.21251 s, avg_samples: 12.5, ips: 5.64970 samples/s, eta: 0:36:16
[2024/07/27 22:13:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:13:29] ppocr INFO: epoch: [112/200], global_step: 336, lr: 0.001000, loss: 2.210091, loss_shrink_maps: 1.235815, loss_threshold_maps: 0.712437, loss_binary_maps: 0.242636, avg_reader_cost: 2.05178 s, avg_batch_cost: 2.28857 s, avg_samples: 12.5, ips: 5.46193 samples/s, eta: 0:35:50
[2024/07/27 22:13:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:13:37] ppocr INFO: epoch: [113/200], global_step: 339, lr: 0.001000, loss: 2.158617, loss_shrink_maps: 1.218481, loss_threshold_maps: 0.709671, loss_binary_maps: 0.238910, avg_reader_cost: 1.93528 s, avg_batch_cost: 2.24355 s, avg_samples: 12.5, ips: 5.57154 samples/s, eta: 0:35:24
[2024/07/27 22:13:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:13:45] ppocr INFO: epoch: [114/200], global_step: 340, lr: 0.001000, loss: 2.210091, loss_shrink_maps: 1.235815, loss_threshold_maps: 0.717644, loss_binary_maps: 0.242636, avg_reader_cost: 0.59371 s, avg_batch_cost: 0.68554 s, avg_samples: 4.8, ips: 7.00173 samples/s, eta: 0:35:15
[2024/07/27 22:13:46] ppocr INFO: epoch: [114/200], global_step: 342, lr: 0.001000, loss: 2.222370, loss_shrink_maps: 1.242715, loss_threshold_maps: 0.724032, loss_binary_maps: 0.242795, avg_reader_cost: 1.46252 s, avg_batch_cost: 1.60837 s, avg_samples: 7.7, ips: 4.78746 samples/s, eta: 0:34:59
[2024/07/27 22:13:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:13:55] ppocr INFO: epoch: [115/200], global_step: 345, lr: 0.001000, loss: 2.231946, loss_shrink_maps: 1.242715, loss_threshold_maps: 0.737449, loss_binary_maps: 0.242795, avg_reader_cost: 1.93988 s, avg_batch_cost: 2.22056 s, avg_samples: 12.5, ips: 5.62920 samples/s, eta: 0:34:33
[2024/07/27 22:13:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:14:03] ppocr INFO: epoch: [116/200], global_step: 348, lr: 0.001000, loss: 2.199857, loss_shrink_maps: 1.230602, loss_threshold_maps: 0.730326, loss_binary_maps: 0.241382, avg_reader_cost: 2.05120 s, avg_batch_cost: 2.28463 s, avg_samples: 12.5, ips: 5.47135 samples/s, eta: 0:34:07
[2024/07/27 22:14:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:14:12] ppocr INFO: epoch: [117/200], global_step: 350, lr: 0.001000, loss: 2.199857, loss_shrink_maps: 1.230602, loss_threshold_maps: 0.730326, loss_binary_maps: 0.241382, avg_reader_cost: 1.20606 s, avg_batch_cost: 1.47673 s, avg_samples: 9.6, ips: 6.50084 samples/s, eta: 0:33:50
[2024/07/27 22:14:12] ppocr INFO: epoch: [117/200], global_step: 351, lr: 0.001000, loss: 2.211081, loss_shrink_maps: 1.236590, loss_threshold_maps: 0.730326, loss_binary_maps: 0.241858, avg_reader_cost: 0.78402 s, avg_batch_cost: 0.83878 s, avg_samples: 2.9, ips: 3.45741 samples/s, eta: 0:33:42
[2024/07/27 22:14:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:14:21] ppocr INFO: epoch: [118/200], global_step: 354, lr: 0.001000, loss: 2.173842, loss_shrink_maps: 1.208823, loss_threshold_maps: 0.709816, loss_binary_maps: 0.237041, avg_reader_cost: 1.96808 s, avg_batch_cost: 2.20382 s, avg_samples: 12.5, ips: 5.67196 samples/s, eta: 0:33:16
[2024/07/27 22:14:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:14:30] ppocr INFO: epoch: [119/200], global_step: 357, lr: 0.001000, loss: 2.150344, loss_shrink_maps: 1.199298, loss_threshold_maps: 0.702440, loss_binary_maps: 0.235386, avg_reader_cost: 2.09110 s, avg_batch_cost: 2.32727 s, avg_samples: 12.5, ips: 5.37110 samples/s, eta: 0:32:51
[2024/07/27 22:14:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:14:39] ppocr INFO: epoch: [120/200], global_step: 360, lr: 0.001000, loss: 2.173842, loss_shrink_maps: 1.204172, loss_threshold_maps: 0.707359, loss_binary_maps: 0.236642, avg_reader_cost: 2.00218 s, avg_batch_cost: 2.33041 s, avg_samples: 12.5, ips: 5.36386 samples/s, eta: 0:32:26

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.31it/s]
eval model::   1%|          | 4/500 [00:00<00:30, 16.18it/s]
eval model::   1%|          | 6/500 [00:00<00:33, 14.90it/s]
eval model::   2%|▏         | 9/500 [00:00<00:26, 18.55it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.08it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 18.74it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.82it/s]
eval model::   4%|▍         | 19/500 [00:01<00:24, 19.32it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 19.03it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.53it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 21.32it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 21.95it/s]
eval model::   7%|▋         | 33/500 [00:01<00:22, 21.21it/s]
eval model::   7%|▋         | 36/500 [00:01<00:22, 20.30it/s]
eval model::   8%|▊         | 39/500 [00:02<00:24, 18.68it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.51it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.93it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 18.01it/s]
eval model::  10%|█         | 50/500 [00:02<00:21, 20.56it/s]
eval model::  11%|█         | 53/500 [00:02<00:20, 22.29it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:17, 25.15it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:19, 22.11it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:25, 17.31it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 19.34it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.88it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.06it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 20.90it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:20, 21.07it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.65it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:17, 23.86it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:17, 24.14it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:19, 20.92it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 19.33it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.59it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:18, 21.93it/s]
eval model::  20%|██        | 102/500 [00:05<00:18, 21.02it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 22.12it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:17, 22.74it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:17, 22.32it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:16, 23.81it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:16, 23.13it/s]
eval model::  24%|██▍       | 120/500 [00:05<00:20, 18.55it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 19.99it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 19.20it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 19.39it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 18.75it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.59it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:18, 20.04it/s]
eval model::  28%|██▊       | 141/500 [00:06<00:18, 19.36it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:18, 19.16it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 20.39it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:18, 18.73it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 17.85it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 21.22it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:15, 22.55it/s]
eval model::  32%|███▏      | 161/500 [00:07<00:17, 18.87it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:17, 19.13it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:16, 20.43it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 20.32it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:17, 19.11it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:18, 17.79it/s]
eval model::  36%|███▌      | 178/500 [00:08<00:15, 20.17it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:18, 17.09it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:16, 19.21it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 19.79it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:14, 21.69it/s]
eval model::  39%|███▉      | 194/500 [00:09<00:12, 25.14it/s]
eval model::  39%|███▉      | 197/500 [00:09<00:13, 23.21it/s]
eval model::  40%|████      | 200/500 [00:09<00:12, 23.39it/s]
eval model::  41%|████      | 203/500 [00:09<00:12, 24.17it/s]
eval model::  41%|████      | 206/500 [00:10<00:16, 17.77it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:16, 17.40it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:16, 17.26it/s]
eval model::  43%|████▎     | 214/500 [00:10<00:15, 19.05it/s]
eval model::  43%|████▎     | 217/500 [00:10<00:17, 15.81it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 17.28it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:17, 15.68it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:14, 18.49it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:12, 22.15it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:10, 25.44it/s]
eval model::  47%|████▋     | 236/500 [00:11<00:10, 25.92it/s]
eval model::  48%|████▊     | 240/500 [00:11<00:09, 27.94it/s]
eval model::  49%|████▊     | 243/500 [00:11<00:10, 23.65it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:10, 24.03it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:11, 21.42it/s]
eval model::  50%|█████     | 252/500 [00:12<00:12, 19.91it/s]
eval model::  51%|█████     | 255/500 [00:12<00:12, 20.27it/s]
eval model::  52%|█████▏    | 258/500 [00:12<00:11, 21.17it/s]
eval model::  52%|█████▏    | 261/500 [00:12<00:12, 19.49it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 18.81it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:12, 18.70it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:11, 19.71it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:12, 18.81it/s]
eval model::  55%|█████▌    | 275/500 [00:13<00:11, 18.82it/s]
eval model::  56%|█████▌    | 278/500 [00:13<00:10, 20.98it/s]
eval model::  56%|█████▌    | 281/500 [00:13<00:11, 19.64it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:11, 19.47it/s]
eval model::  57%|█████▋    | 286/500 [00:14<00:17, 12.07it/s]
eval model::  58%|█████▊    | 289/500 [00:14<00:14, 14.28it/s]
eval model::  58%|█████▊    | 292/500 [00:14<00:12, 16.30it/s]
eval model::  59%|█████▉    | 294/500 [00:14<00:13, 15.40it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:13, 14.77it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:11, 17.65it/s]
eval model::  60%|██████    | 301/500 [00:15<00:11, 16.87it/s]
eval model::  61%|██████    | 304/500 [00:15<00:10, 18.62it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:09, 20.22it/s]
eval model::  62%|██████▏   | 310/500 [00:15<00:09, 19.74it/s]
eval model::  63%|██████▎   | 313/500 [00:15<00:08, 21.48it/s]
eval model::  63%|██████▎   | 316/500 [00:15<00:08, 21.65it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:07, 24.61it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:07, 23.98it/s]
eval model::  65%|██████▌   | 326/500 [00:16<00:06, 25.16it/s]
eval model::  66%|██████▌   | 329/500 [00:16<00:06, 24.44it/s]
eval model::  66%|██████▋   | 332/500 [00:16<00:07, 23.76it/s]
eval model::  67%|██████▋   | 335/500 [00:16<00:07, 22.39it/s]
eval model::  68%|██████▊   | 338/500 [00:16<00:08, 20.13it/s]
eval model::  68%|██████▊   | 341/500 [00:17<00:09, 16.11it/s]
eval model::  69%|██████▉   | 344/500 [00:17<00:08, 18.10it/s]
eval model::  69%|██████▉   | 347/500 [00:17<00:07, 19.71it/s]
eval model::  70%|███████   | 350/500 [00:17<00:07, 21.31it/s]
eval model::  71%|███████   | 353/500 [00:17<00:06, 21.66it/s]
eval model::  71%|███████   | 356/500 [00:17<00:07, 20.14it/s]
eval model::  72%|███████▏  | 359/500 [00:17<00:06, 20.82it/s]
eval model::  72%|███████▏  | 362/500 [00:18<00:06, 21.08it/s]
eval model::  73%|███████▎  | 365/500 [00:18<00:07, 18.93it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:07, 18.58it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:06, 20.45it/s]
eval model::  75%|███████▍  | 373/500 [00:18<00:05, 21.94it/s]
eval model::  75%|███████▌  | 376/500 [00:18<00:05, 22.61it/s]
eval model::  76%|███████▌  | 380/500 [00:18<00:05, 22.73it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:04, 24.11it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:07, 16.03it/s]
eval model::  78%|███████▊  | 389/500 [00:19<00:06, 16.92it/s]
eval model::  78%|███████▊  | 391/500 [00:19<00:06, 17.34it/s]
eval model::  79%|███████▉  | 394/500 [00:19<00:05, 18.96it/s]
eval model::  79%|███████▉  | 397/500 [00:19<00:05, 18.20it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 17.64it/s]
eval model::  80%|████████  | 402/500 [00:20<00:05, 17.78it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 19.12it/s]
eval model::  82%|████████▏ | 408/500 [00:20<00:04, 19.80it/s]
eval model::  82%|████████▏ | 411/500 [00:20<00:04, 21.29it/s]
eval model::  83%|████████▎ | 414/500 [00:20<00:03, 21.89it/s]
eval model::  83%|████████▎ | 417/500 [00:20<00:04, 19.67it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 16.19it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 16.67it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 16.69it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:04, 17.33it/s]
eval model::  86%|████████▌ | 429/500 [00:21<00:03, 19.32it/s]
eval model::  86%|████████▋ | 432/500 [00:21<00:03, 19.98it/s]
eval model::  87%|████████▋ | 435/500 [00:21<00:02, 22.40it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 24.11it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 23.24it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 23.79it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 22.23it/s]
eval model::  90%|█████████ | 451/500 [00:22<00:02, 22.22it/s]
eval model::  91%|█████████ | 454/500 [00:22<00:02, 22.74it/s]
eval model::  91%|█████████▏| 457/500 [00:22<00:01, 21.53it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 21.51it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 18.90it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:02, 17.48it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 20.16it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 21.37it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 21.34it/s]
eval model::  95%|█████████▌| 477/500 [00:23<00:01, 22.66it/s]
eval model::  96%|█████████▌| 480/500 [00:23<00:00, 21.44it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 21.26it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 18.51it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 19.43it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 19.03it/s]
eval model::  99%|█████████▉| 494/500 [00:24<00:00, 19.09it/s]
eval model::  99%|█████████▉| 496/500 [00:24<00:00, 17.45it/s]
eval model:: 100%|█████████▉| 499/500 [00:25<00:00, 19.40it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.95it/s]
[2024/07/27 22:15:04] ppocr INFO: cur metric, precision: 0.6161482461945731, recall: 0.44824265767934524, hmean: 0.5189520624303233, fps: 44.57960630621576
[2024/07/27 22:15:04] ppocr INFO: best metric, hmean: 0.53646408839779, precision: 0.6292935839274141, recall: 0.46750120365912373, fps: 44.39734099314374, best_epoch: 100
[2024/07/27 22:15:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:15:13] ppocr INFO: epoch: [121/200], global_step: 363, lr: 0.001000, loss: 2.134604, loss_shrink_maps: 1.193905, loss_threshold_maps: 0.701676, loss_binary_maps: 0.234044, avg_reader_cost: 2.05242 s, avg_batch_cost: 2.45056 s, avg_samples: 12.5, ips: 5.10088 samples/s, eta: 0:32:02
[2024/07/27 22:15:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:15:22] ppocr INFO: epoch: [122/200], global_step: 366, lr: 0.001000, loss: 2.095627, loss_shrink_maps: 1.186588, loss_threshold_maps: 0.691609, loss_binary_maps: 0.232759, avg_reader_cost: 2.13331 s, avg_batch_cost: 2.40283 s, avg_samples: 12.5, ips: 5.20219 samples/s, eta: 0:31:37
[2024/07/27 22:15:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:15:31] ppocr INFO: epoch: [123/200], global_step: 369, lr: 0.001000, loss: 2.086906, loss_shrink_maps: 1.176191, loss_threshold_maps: 0.691609, loss_binary_maps: 0.231042, avg_reader_cost: 2.05665 s, avg_batch_cost: 2.30199 s, avg_samples: 12.5, ips: 5.43008 samples/s, eta: 0:31:12
[2024/07/27 22:15:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:15:38] ppocr INFO: epoch: [124/200], global_step: 370, lr: 0.001000, loss: 2.095627, loss_shrink_maps: 1.186588, loss_threshold_maps: 0.701676, loss_binary_maps: 0.232759, avg_reader_cost: 0.55933 s, avg_batch_cost: 0.67368 s, avg_samples: 4.8, ips: 7.12504 samples/s, eta: 0:31:03
[2024/07/27 22:15:39] ppocr INFO: epoch: [124/200], global_step: 372, lr: 0.001000, loss: 2.104721, loss_shrink_maps: 1.191354, loss_threshold_maps: 0.715045, loss_binary_maps: 0.233582, avg_reader_cost: 1.43897 s, avg_batch_cost: 1.58497 s, avg_samples: 7.7, ips: 4.85814 samples/s, eta: 0:30:47
[2024/07/27 22:15:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:15:48] ppocr INFO: epoch: [125/200], global_step: 375, lr: 0.001000, loss: 2.099714, loss_shrink_maps: 1.186588, loss_threshold_maps: 0.715045, loss_binary_maps: 0.233241, avg_reader_cost: 2.11499 s, avg_batch_cost: 2.35121 s, avg_samples: 12.5, ips: 5.31641 samples/s, eta: 0:30:22
[2024/07/27 22:15:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:15:57] ppocr INFO: epoch: [126/200], global_step: 378, lr: 0.001000, loss: 2.224692, loss_shrink_maps: 1.224488, loss_threshold_maps: 0.724158, loss_binary_maps: 0.240271, avg_reader_cost: 2.03396 s, avg_batch_cost: 2.27851 s, avg_samples: 12.5, ips: 5.48603 samples/s, eta: 0:29:57
[2024/07/27 22:15:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:16:06] ppocr INFO: epoch: [127/200], global_step: 380, lr: 0.001000, loss: 2.151284, loss_shrink_maps: 1.197394, loss_threshold_maps: 0.724158, loss_binary_maps: 0.235433, avg_reader_cost: 1.29820 s, avg_batch_cost: 1.51545 s, avg_samples: 9.6, ips: 6.33476 samples/s, eta: 0:29:40
[2024/07/27 22:16:06] ppocr INFO: epoch: [127/200], global_step: 381, lr: 0.001000, loss: 2.151284, loss_shrink_maps: 1.197394, loss_threshold_maps: 0.724158, loss_binary_maps: 0.235433, avg_reader_cost: 0.80388 s, avg_batch_cost: 0.85923 s, avg_samples: 2.9, ips: 3.37513 samples/s, eta: 0:29:32
[2024/07/27 22:16:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:16:15] ppocr INFO: epoch: [128/200], global_step: 384, lr: 0.001000, loss: 2.195812, loss_shrink_maps: 1.208332, loss_threshold_maps: 0.739546, loss_binary_maps: 0.236986, avg_reader_cost: 2.08420 s, avg_batch_cost: 2.32751 s, avg_samples: 12.5, ips: 5.37055 samples/s, eta: 0:29:07
[2024/07/27 22:16:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:16:24] ppocr INFO: epoch: [129/200], global_step: 387, lr: 0.001000, loss: 2.142969, loss_shrink_maps: 1.182796, loss_threshold_maps: 0.724852, loss_binary_maps: 0.231942, avg_reader_cost: 2.04837 s, avg_batch_cost: 2.28556 s, avg_samples: 12.5, ips: 5.46912 samples/s, eta: 0:28:42
[2024/07/27 22:16:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:16:33] ppocr INFO: epoch: [130/200], global_step: 390, lr: 0.001000, loss: 2.197293, loss_shrink_maps: 1.221663, loss_threshold_maps: 0.731829, loss_binary_maps: 0.240455, avg_reader_cost: 2.07701 s, avg_batch_cost: 2.32226 s, avg_samples: 12.5, ips: 5.38269 samples/s, eta: 0:28:17
[2024/07/27 22:16:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:16:41] ppocr INFO: epoch: [131/200], global_step: 393, lr: 0.001000, loss: 2.142969, loss_shrink_maps: 1.182796, loss_threshold_maps: 0.721552, loss_binary_maps: 0.231942, avg_reader_cost: 1.98886 s, avg_batch_cost: 2.30223 s, avg_samples: 12.5, ips: 5.42953 samples/s, eta: 0:27:52
[2024/07/27 22:16:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:16:50] ppocr INFO: epoch: [132/200], global_step: 396, lr: 0.001000, loss: 2.142969, loss_shrink_maps: 1.182796, loss_threshold_maps: 0.721552, loss_binary_maps: 0.231942, avg_reader_cost: 1.96920 s, avg_batch_cost: 2.20135 s, avg_samples: 12.5, ips: 5.67834 samples/s, eta: 0:27:27
[2024/07/27 22:16:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:16:59] ppocr INFO: epoch: [133/200], global_step: 399, lr: 0.001000, loss: 2.101796, loss_shrink_maps: 1.156158, loss_threshold_maps: 0.714566, loss_binary_maps: 0.226480, avg_reader_cost: 2.02910 s, avg_batch_cost: 2.28148 s, avg_samples: 12.5, ips: 5.47890 samples/s, eta: 0:27:02
[2024/07/27 22:17:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:17:06] ppocr INFO: epoch: [134/200], global_step: 400, lr: 0.001000, loss: 2.101796, loss_shrink_maps: 1.156158, loss_threshold_maps: 0.711366, loss_binary_maps: 0.226480, avg_reader_cost: 0.50148 s, avg_batch_cost: 0.69385 s, avg_samples: 4.8, ips: 6.91795 samples/s, eta: 0:26:53
[2024/07/27 22:17:08] ppocr INFO: epoch: [134/200], global_step: 402, lr: 0.001000, loss: 2.125088, loss_shrink_maps: 1.182234, loss_threshold_maps: 0.711366, loss_binary_maps: 0.232935, avg_reader_cost: 1.47886 s, avg_batch_cost: 1.62471 s, avg_samples: 7.7, ips: 4.73932 samples/s, eta: 0:26:37
[2024/07/27 22:17:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:17:16] ppocr INFO: epoch: [135/200], global_step: 405, lr: 0.001000, loss: 2.143667, loss_shrink_maps: 1.209676, loss_threshold_maps: 0.712340, loss_binary_maps: 0.236780, avg_reader_cost: 2.03124 s, avg_batch_cost: 2.32807 s, avg_samples: 12.5, ips: 5.36926 samples/s, eta: 0:26:13
[2024/07/27 22:17:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:17:25] ppocr INFO: epoch: [136/200], global_step: 408, lr: 0.001000, loss: 2.138917, loss_shrink_maps: 1.198724, loss_threshold_maps: 0.711015, loss_binary_maps: 0.236036, avg_reader_cost: 2.02462 s, avg_batch_cost: 2.31735 s, avg_samples: 12.5, ips: 5.39409 samples/s, eta: 0:25:48
[2024/07/27 22:17:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:17:34] ppocr INFO: epoch: [137/200], global_step: 410, lr: 0.001000, loss: 2.138917, loss_shrink_maps: 1.205141, loss_threshold_maps: 0.705501, loss_binary_maps: 0.237309, avg_reader_cost: 1.30369 s, avg_batch_cost: 1.49317 s, avg_samples: 9.6, ips: 6.42927 samples/s, eta: 0:25:31
[2024/07/27 22:17:34] ppocr INFO: epoch: [137/200], global_step: 411, lr: 0.001000, loss: 2.128541, loss_shrink_maps: 1.198724, loss_threshold_maps: 0.699788, loss_binary_maps: 0.235911, avg_reader_cost: 0.79290 s, avg_batch_cost: 0.84718 s, avg_samples: 2.9, ips: 3.42312 samples/s, eta: 0:25:23
[2024/07/27 22:17:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:17:43] ppocr INFO: epoch: [138/200], global_step: 414, lr: 0.001000, loss: 2.138917, loss_shrink_maps: 1.205141, loss_threshold_maps: 0.705501, loss_binary_maps: 0.237309, avg_reader_cost: 2.04564 s, avg_batch_cost: 2.28284 s, avg_samples: 12.5, ips: 5.47563 samples/s, eta: 0:24:59
[2024/07/27 22:17:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:17:52] ppocr INFO: epoch: [139/200], global_step: 417, lr: 0.001000, loss: 2.138917, loss_shrink_maps: 1.205141, loss_threshold_maps: 0.699989, loss_binary_maps: 0.237309, avg_reader_cost: 2.05131 s, avg_batch_cost: 2.29623 s, avg_samples: 12.5, ips: 5.44370 samples/s, eta: 0:24:34
[2024/07/27 22:17:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:18:01] ppocr INFO: epoch: [140/200], global_step: 420, lr: 0.001000, loss: 2.128541, loss_shrink_maps: 1.198724, loss_threshold_maps: 0.698762, loss_binary_maps: 0.235911, avg_reader_cost: 1.97901 s, avg_batch_cost: 2.31510 s, avg_samples: 12.5, ips: 5.39934 samples/s, eta: 0:24:09

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:58,  8.57it/s]
eval model::   1%|          | 3/500 [00:00<00:35, 14.13it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.61it/s]
eval model::   2%|▏         | 9/500 [00:00<00:23, 20.52it/s]
eval model::   2%|▏         | 12/500 [00:00<00:25, 19.33it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.99it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.61it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 18.57it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.72it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 21.39it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 21.87it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.20it/s]
eval model::   7%|▋         | 35/500 [00:01<00:25, 18.08it/s]
eval model::   8%|▊         | 38/500 [00:01<00:24, 18.86it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.27it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 18.01it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 19.98it/s]
eval model::  10%|▉         | 48/500 [00:02<00:22, 19.89it/s]
eval model::  10%|█         | 51/500 [00:02<00:21, 21.03it/s]
eval model::  11%|█         | 54/500 [00:02<00:20, 21.45it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:21, 20.48it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:21, 20.61it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:25, 17.01it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 19.49it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 21.90it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 20.05it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:20, 21.05it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:20, 20.34it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.37it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 19.91it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.13it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.74it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.81it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:20, 19.41it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.35it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.02it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 20.93it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:20, 19.14it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:20, 19.30it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:19, 19.36it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:18, 20.47it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:19, 20.01it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.76it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:20, 18.33it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:19, 18.99it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:20, 18.19it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 18.35it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 20.21it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.77it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:19, 18.89it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 19.95it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.84it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.18it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 19.28it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 20.62it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 20.22it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:19, 17.62it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:17, 19.23it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:17, 18.83it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:16, 19.38it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:17, 18.36it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:17, 18.58it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:15, 20.55it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:17, 17.68it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:15, 19.89it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:16, 19.24it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:15, 20.15it/s]
eval model::  39%|███▉      | 194/500 [00:09<00:13, 22.15it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:14, 21.00it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 20.73it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 20.04it/s]
eval model::  41%|████      | 206/500 [00:10<00:19, 15.12it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:19, 15.00it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:19, 14.52it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.75it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.77it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 14.11it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:18, 14.95it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:19, 14.43it/s]
eval model::  45%|████▍     | 223/500 [00:11<00:19, 14.56it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:17, 16.03it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 20.51it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 22.17it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.85it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 23.24it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 20.12it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:11, 22.07it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 21.65it/s]
eval model::  50%|█████     | 251/500 [00:13<00:12, 19.67it/s]
eval model::  51%|█████     | 254/500 [00:13<00:12, 20.29it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.20it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:12, 19.79it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:11, 20.47it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:12, 19.19it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 19.76it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.88it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.94it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 18.79it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.15it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 20.49it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:16, 13.17it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:13, 15.51it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:12, 16.93it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:13, 15.68it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:11, 17.31it/s]
eval model::  60%|██████    | 301/500 [00:15<00:11, 16.59it/s]
eval model::  61%|██████    | 304/500 [00:16<00:10, 18.50it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:09, 20.07it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.19it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 19.67it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:09, 18.53it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:09, 18.70it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:09, 19.72it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 21.21it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:07, 23.03it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:07, 23.30it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:06, 23.95it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:07, 21.56it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 16.13it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:08, 18.25it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 19.80it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:06, 21.70it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 21.88it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 20.90it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 20.42it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:06, 20.00it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 19.15it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 18.76it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 20.08it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 20.75it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:05, 21.51it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 23.31it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:05, 21.82it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:05, 22.50it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 14.51it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 16.21it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 17.76it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 17.85it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:05, 19.51it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 20.69it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 20.59it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 20.94it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 22.20it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 23.22it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 20.94it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 17.54it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 16.84it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 16.49it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.17it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 18.94it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 20.05it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 21.35it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 22.40it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 20.58it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 23.80it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 22.02it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 22.14it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 22.63it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 22.09it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 20.32it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 18.63it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:01, 17.51it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 20.20it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.22it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.99it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 21.04it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 20.42it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 20.79it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 19.23it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 19.84it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 19.65it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 19.70it/s]
eval model::  99%|█████████▉| 497/500 [00:25<00:00, 21.61it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 23.45it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.42it/s]
[2024/07/27 22:18:26] ppocr INFO: cur metric, precision: 0.626733921815889, recall: 0.4785748675974964, hmean: 0.5427245427245426, fps: 45.12310625519069
[2024/07/27 22:18:26] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 22:18:26] ppocr INFO: best metric, hmean: 0.5427245427245426, precision: 0.626733921815889, recall: 0.4785748675974964, fps: 45.12310625519069, best_epoch: 140
[2024/07/27 22:18:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:18:35] ppocr INFO: epoch: [141/200], global_step: 423, lr: 0.001000, loss: 2.091997, loss_shrink_maps: 1.169733, loss_threshold_maps: 0.697332, loss_binary_maps: 0.227208, avg_reader_cost: 1.92406 s, avg_batch_cost: 2.21693 s, avg_samples: 12.5, ips: 5.63843 samples/s, eta: 0:23:44
[2024/07/27 22:18:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:18:43] ppocr INFO: epoch: [142/200], global_step: 426, lr: 0.001000, loss: 2.020028, loss_shrink_maps: 1.132895, loss_threshold_maps: 0.697332, loss_binary_maps: 0.221490, avg_reader_cost: 1.95655 s, avg_batch_cost: 2.28403 s, avg_samples: 12.5, ips: 5.47279 samples/s, eta: 0:23:20
[2024/07/27 22:18:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:18:52] ppocr INFO: epoch: [143/200], global_step: 429, lr: 0.001000, loss: 2.014629, loss_shrink_maps: 1.119787, loss_threshold_maps: 0.695922, loss_binary_maps: 0.219113, avg_reader_cost: 2.06504 s, avg_batch_cost: 2.30856 s, avg_samples: 12.5, ips: 5.41463 samples/s, eta: 0:22:55
[2024/07/27 22:18:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:19:00] ppocr INFO: epoch: [144/200], global_step: 430, lr: 0.001000, loss: 2.014629, loss_shrink_maps: 1.119787, loss_threshold_maps: 0.698625, loss_binary_maps: 0.219113, avg_reader_cost: 0.61506 s, avg_batch_cost: 0.72519 s, avg_samples: 4.8, ips: 6.61891 samples/s, eta: 0:22:47
[2024/07/27 22:19:01] ppocr INFO: epoch: [144/200], global_step: 432, lr: 0.001000, loss: 2.014629, loss_shrink_maps: 1.119951, loss_threshold_maps: 0.698963, loss_binary_maps: 0.219344, avg_reader_cost: 1.54169 s, avg_batch_cost: 1.68763 s, avg_samples: 7.7, ips: 4.56260 samples/s, eta: 0:22:31
[2024/07/27 22:19:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:19:10] ppocr INFO: epoch: [145/200], global_step: 435, lr: 0.001000, loss: 2.014629, loss_shrink_maps: 1.108208, loss_threshold_maps: 0.698625, loss_binary_maps: 0.218034, avg_reader_cost: 1.97033 s, avg_batch_cost: 2.25726 s, avg_samples: 12.5, ips: 5.53768 samples/s, eta: 0:22:06
[2024/07/27 22:19:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:19:19] ppocr INFO: epoch: [146/200], global_step: 438, lr: 0.001000, loss: 2.014629, loss_shrink_maps: 1.119951, loss_threshold_maps: 0.693791, loss_binary_maps: 0.219689, avg_reader_cost: 2.05332 s, avg_batch_cost: 2.29158 s, avg_samples: 12.5, ips: 5.45475 samples/s, eta: 0:21:42
[2024/07/27 22:19:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:19:27] ppocr INFO: epoch: [147/200], global_step: 440, lr: 0.001000, loss: 2.014629, loss_shrink_maps: 1.119951, loss_threshold_maps: 0.691908, loss_binary_maps: 0.219689, avg_reader_cost: 1.25057 s, avg_batch_cost: 1.46316 s, avg_samples: 9.6, ips: 6.56114 samples/s, eta: 0:21:25
[2024/07/27 22:19:28] ppocr INFO: epoch: [147/200], global_step: 441, lr: 0.001000, loss: 2.009224, loss_shrink_maps: 1.106200, loss_threshold_maps: 0.691908, loss_binary_maps: 0.218034, avg_reader_cost: 0.77734 s, avg_batch_cost: 0.83220 s, avg_samples: 2.9, ips: 3.48475 samples/s, eta: 0:21:17
[2024/07/27 22:19:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:19:37] ppocr INFO: epoch: [148/200], global_step: 444, lr: 0.001000, loss: 2.020643, loss_shrink_maps: 1.121112, loss_threshold_maps: 0.698496, loss_binary_maps: 0.219689, avg_reader_cost: 2.05334 s, avg_batch_cost: 2.30310 s, avg_samples: 12.5, ips: 5.42747 samples/s, eta: 0:20:53
[2024/07/27 22:19:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:19:46] ppocr INFO: epoch: [149/200], global_step: 447, lr: 0.001000, loss: 2.019398, loss_shrink_maps: 1.109533, loss_threshold_maps: 0.686176, loss_binary_maps: 0.218123, avg_reader_cost: 1.96977 s, avg_batch_cost: 2.32016 s, avg_samples: 12.5, ips: 5.38755 samples/s, eta: 0:20:28
[2024/07/27 22:19:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:19:55] ppocr INFO: epoch: [150/200], global_step: 450, lr: 0.001000, loss: 1.990251, loss_shrink_maps: 1.106200, loss_threshold_maps: 0.681204, loss_binary_maps: 0.217778, avg_reader_cost: 1.99860 s, avg_batch_cost: 2.35627 s, avg_samples: 12.5, ips: 5.30499 samples/s, eta: 0:20:04
[2024/07/27 22:19:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:20:03] ppocr INFO: epoch: [151/200], global_step: 453, lr: 0.001000, loss: 2.037508, loss_shrink_maps: 1.124197, loss_threshold_maps: 0.691908, loss_binary_maps: 0.220995, avg_reader_cost: 2.08931 s, avg_batch_cost: 2.32716 s, avg_samples: 12.5, ips: 5.37136 samples/s, eta: 0:19:40
[2024/07/27 22:20:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:20:12] ppocr INFO: epoch: [152/200], global_step: 456, lr: 0.001000, loss: 2.037508, loss_shrink_maps: 1.122864, loss_threshold_maps: 0.688345, loss_binary_maps: 0.220524, avg_reader_cost: 1.92636 s, avg_batch_cost: 2.24863 s, avg_samples: 12.5, ips: 5.55894 samples/s, eta: 0:19:15
[2024/07/27 22:20:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:20:21] ppocr INFO: epoch: [153/200], global_step: 459, lr: 0.001000, loss: 2.037508, loss_shrink_maps: 1.122864, loss_threshold_maps: 0.675927, loss_binary_maps: 0.221016, avg_reader_cost: 1.95283 s, avg_batch_cost: 2.26011 s, avg_samples: 12.5, ips: 5.53070 samples/s, eta: 0:18:51
[2024/07/27 22:20:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:20:28] ppocr INFO: epoch: [154/200], global_step: 460, lr: 0.001000, loss: 2.049054, loss_shrink_maps: 1.136366, loss_threshold_maps: 0.686794, loss_binary_maps: 0.223396, avg_reader_cost: 0.56766 s, avg_batch_cost: 0.67116 s, avg_samples: 4.8, ips: 7.15179 samples/s, eta: 0:18:42
[2024/07/27 22:20:30] ppocr INFO: epoch: [154/200], global_step: 462, lr: 0.001000, loss: 2.037508, loss_shrink_maps: 1.122864, loss_threshold_maps: 0.671995, loss_binary_maps: 0.221016, avg_reader_cost: 1.43373 s, avg_batch_cost: 1.57994 s, avg_samples: 7.7, ips: 4.87359 samples/s, eta: 0:18:26
[2024/07/27 22:20:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:20:38] ppocr INFO: epoch: [155/200], global_step: 465, lr: 0.001000, loss: 2.042624, loss_shrink_maps: 1.136366, loss_threshold_maps: 0.678756, loss_binary_maps: 0.223396, avg_reader_cost: 1.93189 s, avg_batch_cost: 2.23612 s, avg_samples: 12.5, ips: 5.59003 samples/s, eta: 0:18:01
[2024/07/27 22:20:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:20:47] ppocr INFO: epoch: [156/200], global_step: 468, lr: 0.001000, loss: 2.004381, loss_shrink_maps: 1.092220, loss_threshold_maps: 0.687916, loss_binary_maps: 0.215938, avg_reader_cost: 2.00737 s, avg_batch_cost: 2.32849 s, avg_samples: 12.5, ips: 5.36829 samples/s, eta: 0:17:37
[2024/07/27 22:20:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:20:55] ppocr INFO: epoch: [157/200], global_step: 470, lr: 0.001000, loss: 1.998597, loss_shrink_maps: 1.075380, loss_threshold_maps: 0.679151, loss_binary_maps: 0.212161, avg_reader_cost: 1.24648 s, avg_batch_cost: 1.42936 s, avg_samples: 9.6, ips: 6.71632 samples/s, eta: 0:17:21
[2024/07/27 22:20:56] ppocr INFO: epoch: [157/200], global_step: 471, lr: 0.001000, loss: 1.957718, loss_shrink_maps: 1.057285, loss_threshold_maps: 0.676322, loss_binary_maps: 0.208341, avg_reader_cost: 0.76021 s, avg_batch_cost: 0.81500 s, avg_samples: 2.9, ips: 3.55827 samples/s, eta: 0:17:13
[2024/07/27 22:20:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:21:05] ppocr INFO: epoch: [158/200], global_step: 474, lr: 0.001000, loss: 1.892291, loss_shrink_maps: 1.019810, loss_threshold_maps: 0.671712, loss_binary_maps: 0.200997, avg_reader_cost: 2.05023 s, avg_batch_cost: 2.30658 s, avg_samples: 12.5, ips: 5.41927 samples/s, eta: 0:16:48
[2024/07/27 22:21:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:21:14] ppocr INFO: epoch: [159/200], global_step: 477, lr: 0.001000, loss: 1.998597, loss_shrink_maps: 1.075380, loss_threshold_maps: 0.679151, loss_binary_maps: 0.212161, avg_reader_cost: 2.08102 s, avg_batch_cost: 2.34034 s, avg_samples: 12.5, ips: 5.34109 samples/s, eta: 0:16:24
[2024/07/27 22:21:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:21:23] ppocr INFO: epoch: [160/200], global_step: 480, lr: 0.001000, loss: 1.938916, loss_shrink_maps: 1.057683, loss_threshold_maps: 0.674261, loss_binary_maps: 0.208804, avg_reader_cost: 2.00280 s, avg_batch_cost: 2.23572 s, avg_samples: 12.5, ips: 5.59104 samples/s, eta: 0:16:00

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:56,  8.91it/s]
eval model::   1%|          | 4/500 [00:00<00:31, 15.98it/s]
eval model::   1%|          | 6/500 [00:00<00:30, 16.20it/s]
eval model::   2%|▏         | 9/500 [00:00<00:25, 18.92it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.59it/s]
eval model::   3%|▎         | 13/500 [00:00<00:25, 18.76it/s]
eval model::   3%|▎         | 15/500 [00:00<00:26, 18.40it/s]
eval model::   4%|▎         | 18/500 [00:00<00:22, 21.68it/s]
eval model::   4%|▍         | 21/500 [00:01<00:23, 20.55it/s]
eval model::   5%|▍         | 24/500 [00:01<00:21, 22.21it/s]
eval model::   5%|▌         | 27/500 [00:01<00:21, 22.09it/s]
eval model::   6%|▌         | 30/500 [00:01<00:20, 22.42it/s]
eval model::   7%|▋         | 33/500 [00:01<00:22, 21.22it/s]
eval model::   7%|▋         | 36/500 [00:01<00:23, 19.74it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 17.87it/s]
eval model::   8%|▊         | 42/500 [00:02<00:24, 18.78it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.64it/s]
eval model::  10%|▉         | 48/500 [00:02<00:22, 19.84it/s]
eval model::  10%|█         | 51/500 [00:02<00:21, 21.00it/s]
eval model::  11%|█         | 54/500 [00:02<00:20, 21.60it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:20, 21.45it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:21, 20.66it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.62it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.69it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 21.28it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 20.67it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:20, 21.18it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:19, 21.59it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.92it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.21it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:19, 20.61it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 17.99it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.23it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:21, 19.09it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:19, 20.35it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.59it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.38it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:16, 23.03it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:16, 23.17it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:16, 23.57it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:16, 23.53it/s]
eval model::  24%|██▍       | 121/500 [00:05<00:17, 21.28it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 20.31it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.41it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:18, 19.76it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:17, 20.56it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:18, 19.83it/s]
eval model::  28%|██▊       | 139/500 [00:06<00:18, 19.29it/s]
eval model::  28%|██▊       | 142/500 [00:06<00:17, 20.27it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 20.91it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 18.24it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 17.79it/s]
eval model::  31%|███       | 154/500 [00:07<00:17, 20.10it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:15, 21.70it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:20, 16.69it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 16.92it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:19, 17.22it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:18, 18.29it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 18.29it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:20, 15.86it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:20, 16.17it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.37it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:18, 17.57it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:16, 19.67it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 19.63it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:14, 20.74it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:14, 21.30it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:14, 21.16it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:13, 22.04it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 22.30it/s]
eval model::  41%|████      | 205/500 [00:10<00:19, 14.82it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:18, 15.59it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:18, 15.54it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:17, 16.79it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:17, 15.94it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.71it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 18.04it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:17, 15.58it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:15, 18.24it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:13, 20.08it/s]
eval model::  46%|████▋     | 232/500 [00:11<00:12, 22.33it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:11, 22.15it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 23.31it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:10, 23.59it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:12, 20.28it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:12, 21.06it/s]
eval model::  50%|█████     | 250/500 [00:12<00:13, 18.89it/s]
eval model::  51%|█████     | 253/500 [00:12<00:12, 19.93it/s]
eval model::  51%|█████     | 256/500 [00:13<00:12, 20.00it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:11, 21.17it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 20.86it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:12, 19.57it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:11, 19.66it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:11, 19.28it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:10, 20.91it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 18.12it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 18.78it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 20.15it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:16, 13.00it/s]
eval model::  58%|█████▊    | 288/500 [00:14<00:13, 15.18it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:11, 17.67it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 17.02it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:12, 16.57it/s]
eval model::  60%|██████    | 300/500 [00:15<00:12, 15.94it/s]
eval model::  60%|██████    | 302/500 [00:15<00:11, 16.61it/s]
eval model::  61%|██████    | 304/500 [00:15<00:11, 17.23it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:10, 18.83it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 19.58it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 20.73it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 21.57it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 22.27it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:09, 19.37it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:08, 19.94it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:08, 19.78it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 20.17it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 20.84it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:07, 20.50it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:10, 15.22it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:09, 17.01it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:08, 17.98it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 19.04it/s]
eval model::  70%|███████   | 352/500 [00:18<00:07, 20.05it/s]
eval model::  71%|███████   | 355/500 [00:18<00:07, 19.81it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 20.68it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 19.23it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.59it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 18.35it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 19.56it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 19.69it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:05, 21.19it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 23.58it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 22.35it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:04, 23.95it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:06, 16.33it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:06, 17.40it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:05, 19.06it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 20.01it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 19.64it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 20.24it/s]
eval model::  81%|████████  | 406/500 [00:20<00:04, 22.14it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:03, 23.32it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:03, 23.83it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:03, 23.30it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 20.28it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:04, 16.42it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 16.39it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.80it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 19.30it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 20.54it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:02, 21.97it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 23.39it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 21.45it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 24.44it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 22.09it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.40it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 21.41it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:02, 21.47it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 21.29it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 20.69it/s]
eval model::  93%|█████████▎| 466/500 [00:23<00:01, 18.90it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 18.84it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 19.73it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.47it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 21.50it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 20.29it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 20.88it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 18.96it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 19.88it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 20.01it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 20.91it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 21.97it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.58it/s]
[2024/07/27 22:21:48] ppocr INFO: cur metric, precision: 0.6211849192100538, recall: 0.4997592681752528, hmean: 0.5538954108858057, fps: 44.66914332049495
[2024/07/27 22:21:48] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 22:21:48] ppocr INFO: best metric, hmean: 0.5538954108858057, precision: 0.6211849192100538, recall: 0.4997592681752528, fps: 44.66914332049495, best_epoch: 160
[2024/07/27 22:21:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:21:57] ppocr INFO: epoch: [161/200], global_step: 483, lr: 0.001000, loss: 2.026045, loss_shrink_maps: 1.096957, loss_threshold_maps: 0.679151, loss_binary_maps: 0.216394, avg_reader_cost: 2.09292 s, avg_batch_cost: 2.36493 s, avg_samples: 12.5, ips: 5.28558 samples/s, eta: 0:15:36
[2024/07/27 22:21:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:22:06] ppocr INFO: epoch: [162/200], global_step: 486, lr: 0.001000, loss: 2.026045, loss_shrink_maps: 1.107234, loss_threshold_maps: 0.676682, loss_binary_maps: 0.216394, avg_reader_cost: 1.96183 s, avg_batch_cost: 2.29362 s, avg_samples: 12.5, ips: 5.44989 samples/s, eta: 0:15:11
[2024/07/27 22:22:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:22:15] ppocr INFO: epoch: [163/200], global_step: 489, lr: 0.001000, loss: 2.068678, loss_shrink_maps: 1.155963, loss_threshold_maps: 0.689893, loss_binary_maps: 0.223927, avg_reader_cost: 2.14688 s, avg_batch_cost: 2.38163 s, avg_samples: 12.5, ips: 5.24851 samples/s, eta: 0:14:47
[2024/07/27 22:22:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:22:23] ppocr INFO: epoch: [164/200], global_step: 490, lr: 0.001000, loss: 2.068678, loss_shrink_maps: 1.155963, loss_threshold_maps: 0.697520, loss_binary_maps: 0.223927, avg_reader_cost: 0.60308 s, avg_batch_cost: 0.70091 s, avg_samples: 4.8, ips: 6.84828 samples/s, eta: 0:14:39
[2024/07/27 22:22:24] ppocr INFO: epoch: [164/200], global_step: 492, lr: 0.001000, loss: 2.094341, loss_shrink_maps: 1.177125, loss_threshold_maps: 0.705811, loss_binary_maps: 0.230152, avg_reader_cost: 1.49290 s, avg_batch_cost: 1.63838 s, avg_samples: 7.7, ips: 4.69975 samples/s, eta: 0:14:23
[2024/07/27 22:22:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:22:33] ppocr INFO: epoch: [165/200], global_step: 495, lr: 0.001000, loss: 2.077588, loss_shrink_maps: 1.176212, loss_threshold_maps: 0.705811, loss_binary_maps: 0.227966, avg_reader_cost: 1.94791 s, avg_batch_cost: 2.27319 s, avg_samples: 12.5, ips: 5.49887 samples/s, eta: 0:13:59
[2024/07/27 22:22:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:22:41] ppocr INFO: epoch: [166/200], global_step: 498, lr: 0.001000, loss: 2.077588, loss_shrink_maps: 1.156793, loss_threshold_maps: 0.709362, loss_binary_maps: 0.223290, avg_reader_cost: 1.90824 s, avg_batch_cost: 2.18329 s, avg_samples: 12.5, ips: 5.72531 samples/s, eta: 0:13:35
[2024/07/27 22:22:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:22:50] ppocr INFO: epoch: [167/200], global_step: 500, lr: 0.001000, loss: 2.099170, loss_shrink_maps: 1.177125, loss_threshold_maps: 0.715999, loss_binary_maps: 0.227966, avg_reader_cost: 1.26484 s, avg_batch_cost: 1.44616 s, avg_samples: 9.6, ips: 6.63828 samples/s, eta: 0:13:18
[2024/07/27 22:22:50] ppocr INFO: epoch: [167/200], global_step: 501, lr: 0.001000, loss: 2.099170, loss_shrink_maps: 1.177125, loss_threshold_maps: 0.715999, loss_binary_maps: 0.227966, avg_reader_cost: 0.76901 s, avg_batch_cost: 0.82368 s, avg_samples: 2.9, ips: 3.52079 samples/s, eta: 0:13:10
[2024/07/27 22:22:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:22:59] ppocr INFO: epoch: [168/200], global_step: 504, lr: 0.001000, loss: 2.132762, loss_shrink_maps: 1.201308, loss_threshold_maps: 0.709362, loss_binary_maps: 0.231321, avg_reader_cost: 1.90536 s, avg_batch_cost: 2.19930 s, avg_samples: 12.5, ips: 5.68363 samples/s, eta: 0:12:46
[2024/07/27 22:22:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:23:08] ppocr INFO: epoch: [169/200], global_step: 507, lr: 0.001000, loss: 2.132762, loss_shrink_maps: 1.201308, loss_threshold_maps: 0.709362, loss_binary_maps: 0.231321, avg_reader_cost: 2.08089 s, avg_batch_cost: 2.32197 s, avg_samples: 12.5, ips: 5.38335 samples/s, eta: 0:12:22
[2024/07/27 22:23:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:23:16] ppocr INFO: epoch: [170/200], global_step: 510, lr: 0.001000, loss: 2.122355, loss_shrink_maps: 1.169995, loss_threshold_maps: 0.712078, loss_binary_maps: 0.226268, avg_reader_cost: 1.95128 s, avg_batch_cost: 2.27062 s, avg_samples: 12.5, ips: 5.50510 samples/s, eta: 0:11:58
[2024/07/27 22:23:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:23:25] ppocr INFO: epoch: [171/200], global_step: 513, lr: 0.001000, loss: 2.059783, loss_shrink_maps: 1.112161, loss_threshold_maps: 0.712078, loss_binary_maps: 0.217531, avg_reader_cost: 2.08331 s, avg_batch_cost: 2.31989 s, avg_samples: 12.5, ips: 5.38818 samples/s, eta: 0:11:34
[2024/07/27 22:23:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:23:34] ppocr INFO: epoch: [172/200], global_step: 516, lr: 0.001000, loss: 2.059783, loss_shrink_maps: 1.109411, loss_threshold_maps: 0.703526, loss_binary_maps: 0.217531, avg_reader_cost: 1.98614 s, avg_batch_cost: 2.31844 s, avg_samples: 12.5, ips: 5.39155 samples/s, eta: 0:11:10
[2024/07/27 22:23:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:23:43] ppocr INFO: epoch: [173/200], global_step: 519, lr: 0.001000, loss: 2.093375, loss_shrink_maps: 1.167740, loss_threshold_maps: 0.703526, loss_binary_maps: 0.228204, avg_reader_cost: 2.07345 s, avg_batch_cost: 2.30818 s, avg_samples: 12.5, ips: 5.41552 samples/s, eta: 0:10:46
[2024/07/27 22:23:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:23:50] ppocr INFO: epoch: [174/200], global_step: 520, lr: 0.001000, loss: 2.093375, loss_shrink_maps: 1.167740, loss_threshold_maps: 0.711872, loss_binary_maps: 0.228204, avg_reader_cost: 0.57906 s, avg_batch_cost: 0.68262 s, avg_samples: 4.8, ips: 7.03172 samples/s, eta: 0:10:37
[2024/07/27 22:23:52] ppocr INFO: epoch: [174/200], global_step: 522, lr: 0.001000, loss: 2.064622, loss_shrink_maps: 1.123928, loss_threshold_maps: 0.719744, loss_binary_maps: 0.220662, avg_reader_cost: 1.45651 s, avg_batch_cost: 1.60231 s, avg_samples: 7.7, ips: 4.80555 samples/s, eta: 0:10:21
[2024/07/27 22:23:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:24:01] ppocr INFO: epoch: [175/200], global_step: 525, lr: 0.001000, loss: 2.025248, loss_shrink_maps: 1.120012, loss_threshold_maps: 0.704031, loss_binary_maps: 0.220306, avg_reader_cost: 1.92213 s, avg_batch_cost: 2.25320 s, avg_samples: 12.5, ips: 5.54766 samples/s, eta: 0:09:57
[2024/07/27 22:24:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:24:10] ppocr INFO: epoch: [176/200], global_step: 528, lr: 0.001000, loss: 2.025248, loss_shrink_maps: 1.120012, loss_threshold_maps: 0.717097, loss_binary_maps: 0.220306, avg_reader_cost: 1.99408 s, avg_batch_cost: 2.32725 s, avg_samples: 12.5, ips: 5.37116 samples/s, eta: 0:09:33
[2024/07/27 22:24:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:24:18] ppocr INFO: epoch: [177/200], global_step: 530, lr: 0.001000, loss: 2.063033, loss_shrink_maps: 1.134035, loss_threshold_maps: 0.720370, loss_binary_maps: 0.223289, avg_reader_cost: 1.16989 s, avg_batch_cost: 1.41263 s, avg_samples: 9.6, ips: 6.79582 samples/s, eta: 0:09:17
[2024/07/27 22:24:18] ppocr INFO: epoch: [177/200], global_step: 531, lr: 0.001000, loss: 2.041609, loss_shrink_maps: 1.134035, loss_threshold_maps: 0.717097, loss_binary_maps: 0.223289, avg_reader_cost: 0.75170 s, avg_batch_cost: 0.80645 s, avg_samples: 2.9, ips: 3.59603 samples/s, eta: 0:09:09
[2024/07/27 22:24:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:24:27] ppocr INFO: epoch: [178/200], global_step: 534, lr: 0.001000, loss: 2.077015, loss_shrink_maps: 1.138540, loss_threshold_maps: 0.711412, loss_binary_maps: 0.223819, avg_reader_cost: 1.95086 s, avg_batch_cost: 2.27684 s, avg_samples: 12.5, ips: 5.49007 samples/s, eta: 0:08:45
[2024/07/27 22:24:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:24:35] ppocr INFO: epoch: [179/200], global_step: 537, lr: 0.001000, loss: 2.043035, loss_shrink_maps: 1.134035, loss_threshold_maps: 0.697942, loss_binary_maps: 0.223289, avg_reader_cost: 1.89266 s, avg_batch_cost: 2.14173 s, avg_samples: 12.5, ips: 5.83642 samples/s, eta: 0:08:21
[2024/07/27 22:24:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:24:44] ppocr INFO: epoch: [180/200], global_step: 540, lr: 0.001000, loss: 2.026674, loss_shrink_maps: 1.129997, loss_threshold_maps: 0.694686, loss_binary_maps: 0.222091, avg_reader_cost: 1.96314 s, avg_batch_cost: 2.27863 s, avg_samples: 12.5, ips: 5.48576 samples/s, eta: 0:07:57

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:36, 13.73it/s]
eval model::   1%|          | 5/500 [00:00<00:24, 20.25it/s]
eval model::   2%|▏         | 8/500 [00:00<00:24, 20.21it/s]
eval model::   2%|▏         | 11/500 [00:00<00:25, 18.98it/s]
eval model::   3%|▎         | 13/500 [00:00<00:25, 18.99it/s]
eval model::   3%|▎         | 16/500 [00:00<00:23, 20.78it/s]
eval model::   4%|▍         | 19/500 [00:00<00:21, 22.09it/s]
eval model::   4%|▍         | 22/500 [00:01<00:23, 20.57it/s]
eval model::   5%|▌         | 25/500 [00:01<00:22, 21.49it/s]
eval model::   6%|▌         | 28/500 [00:01<00:22, 20.95it/s]
eval model::   6%|▌         | 31/500 [00:01<00:24, 19.15it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 19.40it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 21.42it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.10it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.45it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.47it/s]
eval model::   9%|▉         | 47/500 [00:02<00:28, 16.09it/s]
eval model::  10%|█         | 50/500 [00:02<00:25, 17.93it/s]
eval model::  11%|█         | 53/500 [00:02<00:23, 19.17it/s]
eval model::  11%|█         | 56/500 [00:02<00:21, 20.87it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:21, 20.15it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:25, 17.37it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:26, 16.68it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:21, 20.56it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:19, 21.66it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:19, 21.44it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:20, 20.32it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:18, 22.24it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:18, 23.16it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:19, 21.71it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:20, 19.86it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:22, 17.74it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:23, 17.56it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:20, 19.29it/s]
eval model::  20%|██        | 100/500 [00:05<00:20, 19.26it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 20.06it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.65it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 20.99it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 20.66it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:17, 21.52it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:17, 22.07it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:17, 21.87it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:17, 21.79it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:16, 22.59it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:17, 21.04it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:18, 19.74it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:17, 20.86it/s]
eval model::  28%|██▊       | 139/500 [00:06<00:16, 22.47it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:16, 21.58it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 22.03it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:16, 21.14it/s]
eval model::  30%|███       | 151/500 [00:07<00:17, 19.42it/s]
eval model::  31%|███       | 155/500 [00:07<00:15, 22.30it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:15, 22.40it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:19, 17.81it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.31it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 18.92it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 18.71it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:18, 18.15it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:19, 16.36it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:19, 16.93it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.80it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:17, 18.53it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:15, 20.83it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:15, 19.65it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:14, 21.11it/s]
eval model::  39%|███▉      | 194/500 [00:09<00:13, 22.55it/s]
eval model::  39%|███▉      | 197/500 [00:09<00:15, 19.14it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 20.13it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 20.17it/s]
eval model::  41%|████      | 206/500 [00:10<00:22, 13.33it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:21, 13.52it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:20, 14.48it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:18, 15.77it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.82it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.70it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 16.80it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 14.76it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:15, 17.80it/s]
eval model::  45%|████▌     | 227/500 [00:11<00:15, 17.47it/s]
eval model::  46%|████▌     | 231/500 [00:11<00:12, 21.62it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 21.41it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 22.42it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 23.09it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 20.31it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:12, 20.98it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:12, 19.76it/s]
eval model::  50%|█████     | 252/500 [00:13<00:13, 18.11it/s]
eval model::  51%|█████     | 255/500 [00:13<00:12, 19.97it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 20.90it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 22.31it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:11, 20.33it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:12, 19.36it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:11, 20.33it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 19.43it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.62it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 17.90it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:12, 17.97it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 19.18it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:16, 13.08it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:15, 14.19it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:12, 17.12it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:11, 17.96it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:13, 15.25it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:11, 17.67it/s]
eval model::  60%|██████    | 302/500 [00:15<00:10, 18.24it/s]
eval model::  61%|██████    | 305/500 [00:15<00:10, 18.93it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 19.58it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:08, 21.16it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:08, 21.23it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:08, 20.35it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:08, 21.09it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:09, 18.60it/s]
eval model::  65%|██████▌   | 326/500 [00:16<00:09, 18.99it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 19.80it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:08, 20.02it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 20.78it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:08, 18.59it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:10, 15.52it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:09, 16.75it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 18.62it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.95it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 22.59it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.95it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 21.31it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 18.76it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 17.81it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.93it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 19.06it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 18.73it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 18.79it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 18.85it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 22.56it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 21.86it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:04, 23.75it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:07, 14.95it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:06, 16.66it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:05, 17.76it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 20.17it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 19.70it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 20.04it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 20.36it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 21.08it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:04, 21.83it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:04, 20.50it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 17.61it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:05, 15.42it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:04, 17.58it/s]
eval model::  85%|████████▌ | 425/500 [00:22<00:04, 17.22it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 21.43it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:02, 22.95it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:02, 23.20it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 23.60it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 21.37it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 22.33it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 21.67it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 19.44it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 21.05it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:02, 20.11it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:02, 19.60it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:02, 17.30it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 17.73it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:02, 16.81it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 18.27it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 20.34it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 19.01it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 20.64it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 20.58it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 19.94it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 20.21it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 21.09it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 21.26it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 21.35it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 24.55it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.43it/s]
[2024/07/27 22:25:10] ppocr INFO: cur metric, precision: 0.5274551214361141, recall: 0.4809821858449687, hmean: 0.5031478217073785, fps: 45.19470696923418
[2024/07/27 22:25:10] ppocr INFO: best metric, hmean: 0.5538954108858057, precision: 0.6211849192100538, recall: 0.4997592681752528, fps: 44.66914332049495, best_epoch: 160
[2024/07/27 22:25:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:25:18] ppocr INFO: epoch: [181/200], global_step: 543, lr: 0.001000, loss: 2.008845, loss_shrink_maps: 1.114730, loss_threshold_maps: 0.690093, loss_binary_maps: 0.219198, avg_reader_cost: 2.09595 s, avg_batch_cost: 2.34531 s, avg_samples: 12.5, ips: 5.32979 samples/s, eta: 0:07:33
[2024/07/27 22:25:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:25:27] ppocr INFO: epoch: [182/200], global_step: 546, lr: 0.001000, loss: 2.020774, loss_shrink_maps: 1.132464, loss_threshold_maps: 0.698757, loss_binary_maps: 0.222621, avg_reader_cost: 2.09485 s, avg_batch_cost: 2.32606 s, avg_samples: 12.5, ips: 5.37389 samples/s, eta: 0:07:09
[2024/07/27 22:25:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:25:36] ppocr INFO: epoch: [183/200], global_step: 549, lr: 0.001000, loss: 2.043035, loss_shrink_maps: 1.132464, loss_threshold_maps: 0.698757, loss_binary_maps: 0.222719, avg_reader_cost: 2.06089 s, avg_batch_cost: 2.35818 s, avg_samples: 12.5, ips: 5.30070 samples/s, eta: 0:06:45
[2024/07/27 22:25:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:25:44] ppocr INFO: epoch: [184/200], global_step: 550, lr: 0.001000, loss: 2.020774, loss_shrink_maps: 1.130535, loss_threshold_maps: 0.695501, loss_binary_maps: 0.222534, avg_reader_cost: 0.59239 s, avg_batch_cost: 0.69066 s, avg_samples: 4.8, ips: 6.94992 samples/s, eta: 0:06:37
[2024/07/27 22:25:45] ppocr INFO: epoch: [184/200], global_step: 552, lr: 0.001000, loss: 2.019041, loss_shrink_maps: 1.126233, loss_threshold_maps: 0.698757, loss_binary_maps: 0.221347, avg_reader_cost: 1.47251 s, avg_batch_cost: 1.61833 s, avg_samples: 7.7, ips: 4.75800 samples/s, eta: 0:06:21
[2024/07/27 22:25:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:25:54] ppocr INFO: epoch: [185/200], global_step: 555, lr: 0.001000, loss: 2.030306, loss_shrink_maps: 1.130535, loss_threshold_maps: 0.704585, loss_binary_maps: 0.222534, avg_reader_cost: 1.94302 s, avg_batch_cost: 2.27417 s, avg_samples: 12.5, ips: 5.49651 samples/s, eta: 0:05:57
[2024/07/27 22:25:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:26:03] ppocr INFO: epoch: [186/200], global_step: 558, lr: 0.001000, loss: 2.004916, loss_shrink_maps: 1.119634, loss_threshold_maps: 0.704585, loss_binary_maps: 0.219458, avg_reader_cost: 2.04005 s, avg_batch_cost: 2.27776 s, avg_samples: 12.5, ips: 5.48785 samples/s, eta: 0:05:33
[2024/07/27 22:26:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:26:11] ppocr INFO: epoch: [187/200], global_step: 560, lr: 0.001000, loss: 1.984022, loss_shrink_maps: 1.087133, loss_threshold_maps: 0.680735, loss_binary_maps: 0.213094, avg_reader_cost: 1.30853 s, avg_batch_cost: 1.48869 s, avg_samples: 9.6, ips: 6.44863 samples/s, eta: 0:05:17
[2024/07/27 22:26:11] ppocr INFO: epoch: [187/200], global_step: 561, lr: 0.001000, loss: 1.938969, loss_shrink_maps: 1.050831, loss_threshold_maps: 0.680735, loss_binary_maps: 0.206616, avg_reader_cost: 0.78994 s, avg_batch_cost: 0.84485 s, avg_samples: 2.9, ips: 3.43258 samples/s, eta: 0:05:10
[2024/07/27 22:26:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:26:20] ppocr INFO: epoch: [188/200], global_step: 564, lr: 0.001000, loss: 1.978387, loss_shrink_maps: 1.107205, loss_threshold_maps: 0.680735, loss_binary_maps: 0.217414, avg_reader_cost: 2.07852 s, avg_batch_cost: 2.30984 s, avg_samples: 12.5, ips: 5.41164 samples/s, eta: 0:04:46
[2024/07/27 22:26:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:26:29] ppocr INFO: epoch: [189/200], global_step: 567, lr: 0.001000, loss: 1.899540, loss_shrink_maps: 1.045873, loss_threshold_maps: 0.670293, loss_binary_maps: 0.207009, avg_reader_cost: 2.10936 s, avg_batch_cost: 2.37833 s, avg_samples: 12.5, ips: 5.25578 samples/s, eta: 0:04:22
[2024/07/27 22:26:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:26:38] ppocr INFO: epoch: [190/200], global_step: 570, lr: 0.001000, loss: 1.840078, loss_shrink_maps: 0.987809, loss_threshold_maps: 0.662224, loss_binary_maps: 0.195373, avg_reader_cost: 1.95736 s, avg_batch_cost: 2.19507 s, avg_samples: 12.5, ips: 5.69457 samples/s, eta: 0:03:58
[2024/07/27 22:26:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:26:47] ppocr INFO: epoch: [191/200], global_step: 573, lr: 0.001000, loss: 1.840078, loss_shrink_maps: 0.989315, loss_threshold_maps: 0.656896, loss_binary_maps: 0.195621, avg_reader_cost: 1.96730 s, avg_batch_cost: 2.29948 s, avg_samples: 12.5, ips: 5.43601 samples/s, eta: 0:03:34
[2024/07/27 22:26:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:26:56] ppocr INFO: epoch: [192/200], global_step: 576, lr: 0.001000, loss: 1.847817, loss_shrink_maps: 0.998742, loss_threshold_maps: 0.654539, loss_binary_maps: 0.196654, avg_reader_cost: 1.95646 s, avg_batch_cost: 2.27081 s, avg_samples: 12.5, ips: 5.50466 samples/s, eta: 0:03:10
[2024/07/27 22:26:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:27:04] ppocr INFO: epoch: [193/200], global_step: 579, lr: 0.001000, loss: 1.853898, loss_shrink_maps: 1.014466, loss_threshold_maps: 0.648581, loss_binary_maps: 0.199154, avg_reader_cost: 2.04867 s, avg_batch_cost: 2.29260 s, avg_samples: 12.5, ips: 5.45232 samples/s, eta: 0:02:46
[2024/07/27 22:27:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:27:12] ppocr INFO: epoch: [194/200], global_step: 580, lr: 0.001000, loss: 1.853898, loss_shrink_maps: 1.014466, loss_threshold_maps: 0.642842, loss_binary_maps: 0.199148, avg_reader_cost: 0.58383 s, avg_batch_cost: 0.70260 s, avg_samples: 4.8, ips: 6.83173 samples/s, eta: 0:02:38
[2024/07/27 22:27:13] ppocr INFO: epoch: [194/200], global_step: 582, lr: 0.001000, loss: 1.853898, loss_shrink_maps: 1.011716, loss_threshold_maps: 0.648581, loss_binary_maps: 0.198874, avg_reader_cost: 1.49720 s, avg_batch_cost: 1.64370 s, avg_samples: 7.7, ips: 4.68454 samples/s, eta: 0:02:22
[2024/07/27 22:27:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:27:22] ppocr INFO: epoch: [195/200], global_step: 585, lr: 0.001000, loss: 1.853898, loss_shrink_maps: 1.011716, loss_threshold_maps: 0.637831, loss_binary_maps: 0.198874, avg_reader_cost: 1.98771 s, avg_batch_cost: 2.28213 s, avg_samples: 12.5, ips: 5.47733 samples/s, eta: 0:01:59
[2024/07/27 22:27:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:27:31] ppocr INFO: epoch: [196/200], global_step: 588, lr: 0.001000, loss: 1.874290, loss_shrink_maps: 1.021738, loss_threshold_maps: 0.642842, loss_binary_maps: 0.201374, avg_reader_cost: 1.98950 s, avg_batch_cost: 2.22595 s, avg_samples: 12.5, ips: 5.61558 samples/s, eta: 0:01:35
[2024/07/27 22:27:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:27:39] ppocr INFO: epoch: [197/200], global_step: 590, lr: 0.001000, loss: 1.871492, loss_shrink_maps: 1.011716, loss_threshold_maps: 0.637831, loss_binary_maps: 0.198874, avg_reader_cost: 1.22666 s, avg_batch_cost: 1.49422 s, avg_samples: 9.6, ips: 6.42477 samples/s, eta: 0:01:19
[2024/07/27 22:27:39] ppocr INFO: epoch: [197/200], global_step: 591, lr: 0.001000, loss: 1.890849, loss_shrink_maps: 1.011716, loss_threshold_maps: 0.637831, loss_binary_maps: 0.199103, avg_reader_cost: 0.79278 s, avg_batch_cost: 0.84755 s, avg_samples: 2.9, ips: 3.42162 samples/s, eta: 0:01:11
[2024/07/27 22:27:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:27:48] ppocr INFO: epoch: [198/200], global_step: 594, lr: 0.001000, loss: 1.903474, loss_shrink_maps: 1.045835, loss_threshold_maps: 0.643664, loss_binary_maps: 0.206490, avg_reader_cost: 2.04018 s, avg_batch_cost: 2.27059 s, avg_samples: 12.5, ips: 5.50518 samples/s, eta: 0:00:47
[2024/07/27 22:27:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:27:58] ppocr INFO: epoch: [199/200], global_step: 597, lr: 0.001000, loss: 1.896935, loss_shrink_maps: 1.039654, loss_threshold_maps: 0.641583, loss_binary_maps: 0.204891, avg_reader_cost: 2.16844 s, avg_batch_cost: 2.43775 s, avg_samples: 12.5, ips: 5.12768 samples/s, eta: 0:00:23
[2024/07/27 22:27:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:28:07] ppocr INFO: epoch: [200/200], global_step: 600, lr: 0.001000, loss: 1.898665, loss_shrink_maps: 1.039654, loss_threshold_maps: 0.654584, loss_binary_maps: 0.204891, avg_reader_cost: 2.02174 s, avg_batch_cost: 2.26389 s, avg_samples: 12.5, ips: 5.52146 samples/s, eta: 0:00:00

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:38, 12.99it/s]
eval model::   1%|          | 5/500 [00:00<00:26, 18.82it/s]
eval model::   2%|▏         | 8/500 [00:00<00:24, 19.79it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.71it/s]
eval model::   3%|▎         | 13/500 [00:00<00:26, 18.65it/s]
eval model::   3%|▎         | 16/500 [00:00<00:24, 19.58it/s]
eval model::   4%|▍         | 19/500 [00:00<00:24, 19.92it/s]
eval model::   4%|▍         | 22/500 [00:01<00:22, 21.06it/s]
eval model::   5%|▌         | 25/500 [00:01<00:24, 19.57it/s]
eval model::   6%|▌         | 28/500 [00:01<00:23, 20.25it/s]
eval model::   6%|▌         | 31/500 [00:01<00:23, 20.20it/s]
eval model::   7%|▋         | 34/500 [00:01<00:23, 20.02it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 21.56it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.28it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 17.99it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.27it/s]
eval model::   9%|▉         | 47/500 [00:02<00:24, 18.78it/s]
eval model::  10%|█         | 50/500 [00:02<00:21, 20.89it/s]
eval model::  11%|█         | 53/500 [00:02<00:23, 19.14it/s]
eval model::  11%|█         | 56/500 [00:02<00:22, 19.72it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:24, 18.26it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.65it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.71it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.10it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.25it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 20.86it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:21, 20.23it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:21, 20.09it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 22.05it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.64it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 20.27it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.56it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:22, 18.46it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:22, 18.23it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:20, 20.11it/s]
eval model::  20%|██        | 100/500 [00:05<00:19, 20.85it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 22.02it/s]
eval model::  21%|██        | 106/500 [00:05<00:16, 23.36it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 21.49it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:19, 19.97it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:19, 20.08it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:18, 20.33it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:20, 18.87it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.12it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:19, 19.00it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:20, 18.34it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:18, 19.83it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:17, 20.76it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.17it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.58it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 18.72it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:20, 17.34it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 18.68it/s]
eval model::  30%|███       | 152/500 [00:07<00:20, 16.84it/s]
eval model::  31%|███       | 155/500 [00:07<00:18, 18.77it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:18, 18.97it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:21, 16.17it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.46it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 19.02it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:17, 18.68it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:18, 17.67it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:23, 14.08it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:22, 14.39it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:19, 16.46it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.34it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 17.69it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 19.16it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:15, 20.31it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:14, 21.43it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:16, 18.10it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:15, 19.18it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 21.09it/s]
eval model::  41%|████      | 205/500 [00:11<00:22, 13.33it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:20, 14.42it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 14.42it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.89it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.80it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.50it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:18, 15.17it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:19, 14.54it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:15, 17.47it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.90it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:13, 20.77it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:11, 22.95it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:11, 22.21it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 22.87it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 21.98it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:12, 20.44it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:12, 21.06it/s]
eval model::  50%|█████     | 250/500 [00:13<00:12, 19.73it/s]
eval model::  51%|█████     | 253/500 [00:13<00:12, 20.42it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 21.34it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 22.65it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:10, 22.35it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:11, 19.84it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:11, 19.40it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 19.20it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 17.61it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:13, 16.89it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.10it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.56it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:10, 21.17it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:17, 12.42it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:14, 14.59it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:13, 15.30it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.14it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:13, 15.12it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:11, 17.64it/s]
eval model::  60%|██████    | 301/500 [00:16<00:12, 15.95it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.52it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.36it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.39it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 21.89it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 20.78it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 21.71it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.95it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:07, 22.75it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 20.97it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 21.00it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 22.86it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:08, 19.46it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 15.17it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:09, 15.97it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 17.88it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:07, 19.78it/s]
eval model::  70%|███████   | 351/500 [00:18<00:06, 21.30it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 22.91it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:06, 22.10it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:06, 22.12it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:06, 20.98it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 18.13it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 20.19it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 19.53it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:05, 21.26it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 22.50it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:05, 21.18it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 22.86it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 15.82it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 18.01it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 19.27it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 20.65it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:04, 21.94it/s]
eval model::  80%|████████  | 402/500 [00:21<00:04, 19.81it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 21.69it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 22.80it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 24.41it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:04, 19.91it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 19.36it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:04, 16.32it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 16.28it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.29it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:04, 17.32it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 19.33it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 20.41it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:03, 21.26it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 23.17it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 21.18it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 23.25it/s]
eval model::  90%|████████▉ | 449/500 [00:23<00:02, 21.90it/s]
eval model::  90%|█████████ | 452/500 [00:23<00:02, 23.51it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:01, 22.89it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:01, 21.86it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 21.28it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:01, 18.34it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 17.10it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 17.38it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 19.58it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.32it/s]
eval model::  95%|█████████▌| 476/500 [00:24<00:01, 19.21it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:00, 21.51it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 20.65it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 21.22it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 20.61it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 20.67it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 21.29it/s]
eval model::  99%|█████████▉| 497/500 [00:25<00:00, 22.26it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 22.37it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.19it/s]
[2024/07/27 22:28:33] ppocr INFO: cur metric, precision: 0.64, recall: 0.523832450649976, hmean: 0.5761186126555468, fps: 44.45321715372161
[2024/07/27 22:28:33] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 22:28:33] ppocr INFO: best metric, hmean: 0.5761186126555468, precision: 0.64, recall: 0.523832450649976, fps: 44.45321715372161, best_epoch: 200
[2024/07/27 22:28:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:28:33] ppocr INFO: best metric, hmean: 0.5761186126555468, precision: 0.64, recall: 0.523832450649976, fps: 44.45321715372161, best_epoch: 200
I0727 22:28:35.282444 33267 tcp_store.cc:289] receive shutdown event and so quit from MasterDaemon run loop
[2024/07/27 22:42:58] ppocr INFO: Architecture : 
[2024/07/27 22:42:58] ppocr INFO:     Backbone : 
[2024/07/27 22:42:58] ppocr INFO:         model_name : large
[2024/07/27 22:42:58] ppocr INFO:         name : MobileNetV3
[2024/07/27 22:42:58] ppocr INFO:         scale : 0.5
[2024/07/27 22:42:58] ppocr INFO:     Head : 
[2024/07/27 22:42:58] ppocr INFO:         k : 50
[2024/07/27 22:42:58] ppocr INFO:         name : DBHead
[2024/07/27 22:42:58] ppocr INFO:     Neck : 
[2024/07/27 22:42:58] ppocr INFO:         name : DBFPN
[2024/07/27 22:42:58] ppocr INFO:         out_channels : 256
[2024/07/27 22:42:58] ppocr INFO:     Transform : None
[2024/07/27 22:42:58] ppocr INFO:     algorithm : DB
[2024/07/27 22:42:58] ppocr INFO:     model_type : det
[2024/07/27 22:42:58] ppocr INFO: Eval : 
[2024/07/27 22:42:58] ppocr INFO:     dataset : 
[2024/07/27 22:42:58] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/07/27 22:42:58] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
[2024/07/27 22:42:58] ppocr INFO:         name : SimpleDataSet
[2024/07/27 22:42:58] ppocr INFO:         transforms : 
[2024/07/27 22:42:58] ppocr INFO:             DecodeImage : 
[2024/07/27 22:42:58] ppocr INFO:                 channel_first : False
[2024/07/27 22:42:58] ppocr INFO:                 img_mode : BGR
[2024/07/27 22:42:58] ppocr INFO:             DetLabelEncode : None
[2024/07/27 22:42:58] ppocr INFO:             DetResizeForTest : 
[2024/07/27 22:42:58] ppocr INFO:                 image_shape : [736, 1280]
[2024/07/27 22:42:58] ppocr INFO:             NormalizeImage : 
[2024/07/27 22:42:58] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/07/27 22:42:58] ppocr INFO:                 order : hwc
[2024/07/27 22:42:58] ppocr INFO:                 scale : 1./255.
[2024/07/27 22:42:58] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/07/27 22:42:58] ppocr INFO:             ToCHWImage : None
[2024/07/27 22:42:58] ppocr INFO:             KeepKeys : 
[2024/07/27 22:42:58] ppocr INFO:                 keep_keys : ['image', 'shape', 'polys', 'ignore_tags']
[2024/07/27 22:42:58] ppocr INFO:     loader : 
[2024/07/27 22:42:58] ppocr INFO:         batch_size_per_card : 1
[2024/07/27 22:42:58] ppocr INFO:         drop_last : False
[2024/07/27 22:42:58] ppocr INFO:         num_workers : 0
[2024/07/27 22:42:58] ppocr INFO:         shuffle : False
[2024/07/27 22:42:58] ppocr INFO:         use_shared_memory : True
[2024/07/27 22:42:58] ppocr INFO: Global : 
[2024/07/27 22:42:58] ppocr INFO:     cal_metric_during_train : False
[2024/07/27 22:42:58] ppocr INFO:     checkpoints : None
[2024/07/27 22:42:58] ppocr INFO:     distributed : True
[2024/07/27 22:42:58] ppocr INFO:     epoch_num : 200
[2024/07/27 22:42:58] ppocr INFO:     eval_batch_step : [0, 60]
[2024/07/27 22:42:58] ppocr INFO:     infer_img : doc/imgs_en/img_10.jpg
[2024/07/27 22:42:58] ppocr INFO:     log_smooth_window : 20
[2024/07/27 22:42:58] ppocr INFO:     pretrained_model : ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/07/27 22:42:58] ppocr INFO:     print_batch_step : 10
[2024/07/27 22:42:58] ppocr INFO:     save_epoch_step : 1200
[2024/07/27 22:42:58] ppocr INFO:     save_inference_dir : None
[2024/07/27 22:42:58] ppocr INFO:     save_model_dir : ./output/db_mv3/
[2024/07/27 22:42:58] ppocr INFO:     save_res_path : ./output/det_db/predicts_db.txt
[2024/07/27 22:42:58] ppocr INFO:     use_gpu : True
[2024/07/27 22:42:58] ppocr INFO:     use_visualdl : False
[2024/07/27 22:42:58] ppocr INFO:     use_xpu : False
[2024/07/27 22:42:58] ppocr INFO: Loss : 
[2024/07/27 22:42:58] ppocr INFO:     alpha : 5
[2024/07/27 22:42:58] ppocr INFO:     balance_loss : True
[2024/07/27 22:42:58] ppocr INFO:     beta : 10
[2024/07/27 22:42:58] ppocr INFO:     main_loss_type : DiceLoss
[2024/07/27 22:42:58] ppocr INFO:     name : DBLoss
[2024/07/27 22:42:58] ppocr INFO:     ohem_ratio : 3
[2024/07/27 22:42:58] ppocr INFO: Metric : 
[2024/07/27 22:42:58] ppocr INFO:     main_indicator : hmean
[2024/07/27 22:42:58] ppocr INFO:     name : DetMetric
[2024/07/27 22:42:58] ppocr INFO: Optimizer : 
[2024/07/27 22:42:58] ppocr INFO:     beta1 : 0.9
[2024/07/27 22:42:58] ppocr INFO:     beta2 : 0.999
[2024/07/27 22:42:58] ppocr INFO:     lr : 
[2024/07/27 22:42:58] ppocr INFO:         learning_rate : 0.001
[2024/07/27 22:42:58] ppocr INFO:     name : Adam
[2024/07/27 22:42:58] ppocr INFO:     regularizer : 
[2024/07/27 22:42:58] ppocr INFO:         factor : 0
[2024/07/27 22:42:58] ppocr INFO:         name : L2
[2024/07/27 22:42:58] ppocr INFO: PostProcess : 
[2024/07/27 22:42:58] ppocr INFO:     box_thresh : 0.6
[2024/07/27 22:42:58] ppocr INFO:     max_candidates : 1000
[2024/07/27 22:42:58] ppocr INFO:     name : DBPostProcess
[2024/07/27 22:42:58] ppocr INFO:     thresh : 0.3
[2024/07/27 22:42:58] ppocr INFO:     unclip_ratio : 1.5
[2024/07/27 22:42:58] ppocr INFO: Train : 
[2024/07/27 22:42:58] ppocr INFO:     dataset : 
[2024/07/27 22:42:58] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/07/27 22:42:58] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/07/27 22:42:58] ppocr INFO:         name : SimpleDataSet
[2024/07/27 22:42:58] ppocr INFO:         ratio_list : [1.0]
[2024/07/27 22:42:58] ppocr INFO:         transforms : 
[2024/07/27 22:42:58] ppocr INFO:             DecodeImage : 
[2024/07/27 22:42:58] ppocr INFO:                 channel_first : False
[2024/07/27 22:42:58] ppocr INFO:                 img_mode : BGR
[2024/07/27 22:42:58] ppocr INFO:             DetLabelEncode : None
[2024/07/27 22:42:58] ppocr INFO:             IaaAugment : 
[2024/07/27 22:42:58] ppocr INFO:                 augmenter_args : 
[2024/07/27 22:42:58] ppocr INFO:                     args : 
[2024/07/27 22:42:58] ppocr INFO:                         p : 0.5
[2024/07/27 22:42:58] ppocr INFO:                     type : Fliplr
[2024/07/27 22:42:58] ppocr INFO:                     args : 
[2024/07/27 22:42:58] ppocr INFO:                         rotate : [-10, 10]
[2024/07/27 22:42:58] ppocr INFO:                     type : Affine
[2024/07/27 22:42:58] ppocr INFO:                     args : 
[2024/07/27 22:42:58] ppocr INFO:                         size : [0.5, 3]
[2024/07/27 22:42:58] ppocr INFO:                     type : Resize
[2024/07/27 22:42:58] ppocr INFO:             EastRandomCropData : 
[2024/07/27 22:42:58] ppocr INFO:                 keep_ratio : True
[2024/07/27 22:42:58] ppocr INFO:                 max_tries : 50
[2024/07/27 22:42:58] ppocr INFO:                 size : [640, 640]
[2024/07/27 22:42:58] ppocr INFO:             MakeBorderMap : 
[2024/07/27 22:42:58] ppocr INFO:                 shrink_ratio : 0.4
[2024/07/27 22:42:58] ppocr INFO:                 thresh_max : 0.7
[2024/07/27 22:42:58] ppocr INFO:                 thresh_min : 0.3
[2024/07/27 22:42:58] ppocr INFO:             MakeShrinkMap : 
[2024/07/27 22:42:58] ppocr INFO:                 min_text_size : 8
[2024/07/27 22:42:58] ppocr INFO:                 shrink_ratio : 0.4
[2024/07/27 22:42:58] ppocr INFO:             NormalizeImage : 
[2024/07/27 22:42:58] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/07/27 22:42:58] ppocr INFO:                 order : hwc
[2024/07/27 22:42:58] ppocr INFO:                 scale : 1./255.
[2024/07/27 22:42:58] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/07/27 22:42:58] ppocr INFO:             ToCHWImage : None
[2024/07/27 22:42:58] ppocr INFO:             KeepKeys : 
[2024/07/27 22:42:58] ppocr INFO:                 keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask']
[2024/07/27 22:42:58] ppocr INFO:     loader : 
[2024/07/27 22:42:58] ppocr INFO:         batch_size_per_card : 48
[2024/07/27 22:42:58] ppocr INFO:         drop_last : False
[2024/07/27 22:42:58] ppocr INFO:         num_workers : 8
[2024/07/27 22:42:58] ppocr INFO:         shuffle : True
[2024/07/27 22:42:58] ppocr INFO:         use_shared_memory : True
[2024/07/27 22:42:58] ppocr INFO: profiler_options : None
[2024/07/27 22:42:58] ppocr INFO: train with paddle 2.5.2 and device Place(gpu:0)
======================= Modified FLAGS detected =======================
FLAGS(name='FLAGS_cudnn_batchnorm_spatial_persistent', current_value=True, default_value=False)
FLAGS(name='FLAGS_selected_gpus', current_value='0', default_value='')
=======================================================================
I0727 22:42:58.989707 113003 tcp_utils.cc:181] The server starts to listen on IP_ANY:49759
I0727 22:42:58.989928 113003 tcp_utils.cc:130] Successfully connected to 127.0.0.1:49759
I0727 22:43:02.121116 113003 process_group_nccl.cc:120] ProcessGroupNCCL pg_timeout_ 1800000
[2024/07/27 22:43:02] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/07/27 22:43:02] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
W0727 22:43:02.131742 113003 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 90.2, Driver API Version: 50724.2, Runtime API Version: 50724.2
[2024/07/27 22:43:03] ppocr INFO: load pretrain successful from ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/07/27 22:43:03] ppocr INFO: train dataloader has 3 iters
[2024/07/27 22:43:03] ppocr INFO: valid dataloader has 500 iters
[2024/07/27 22:43:03] ppocr INFO: During the training process, after the 0th iteration, an evaluation is run every 60 iterations
[2024/07/27 22:43:25] ppocr INFO: epoch: [1/200], global_step: 3, lr: 0.001000, loss: 9.214807, loss_shrink_maps: 4.916750, loss_threshold_maps: 3.360262, loss_binary_maps: 0.986084, avg_reader_cost: 5.91051 s, avg_batch_cost: 6.47016 s, avg_samples: 12.5, ips: 1.93195 samples/s, eta: 3:34:35
[2024/07/27 22:43:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:43:35] ppocr INFO: epoch: [2/200], global_step: 6, lr: 0.001000, loss: 8.570575, loss_shrink_maps: 4.882092, loss_threshold_maps: 2.684278, loss_binary_maps: 0.980060, avg_reader_cost: 2.19937 s, avg_batch_cost: 2.45303 s, avg_samples: 12.5, ips: 5.09574 samples/s, eta: 2:27:13
[2024/07/27 22:43:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:43:44] ppocr INFO: epoch: [3/200], global_step: 9, lr: 0.001000, loss: 7.533838, loss_shrink_maps: 4.869375, loss_threshold_maps: 1.728939, loss_binary_maps: 0.976358, avg_reader_cost: 2.21259 s, avg_batch_cost: 2.46653 s, avg_samples: 12.5, ips: 5.06784 samples/s, eta: 2:04:39
[2024/07/27 22:43:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:43:52] ppocr INFO: epoch: [4/200], global_step: 10, lr: 0.001000, loss: 7.373862, loss_shrink_maps: 4.863960, loss_threshold_maps: 1.554715, loss_binary_maps: 0.975603, avg_reader_cost: 0.65914 s, avg_batch_cost: 0.76651 s, avg_samples: 4.8, ips: 6.26217 samples/s, eta: 1:59:32
[2024/07/27 22:43:54] ppocr INFO: epoch: [4/200], global_step: 12, lr: 0.001000, loss: 7.168330, loss_shrink_maps: 4.846024, loss_threshold_maps: 1.328768, loss_binary_maps: 0.973122, avg_reader_cost: 1.62417 s, avg_batch_cost: 1.76965 s, avg_samples: 7.7, ips: 4.35114 samples/s, eta: 1:53:43
[2024/07/27 22:43:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:44:03] ppocr INFO: epoch: [5/200], global_step: 15, lr: 0.001000, loss: 7.008501, loss_shrink_maps: 4.828668, loss_threshold_maps: 1.210941, loss_binary_maps: 0.968892, avg_reader_cost: 2.16547 s, avg_batch_cost: 2.42070 s, avg_samples: 12.5, ips: 5.16380 samples/s, eta: 1:46:15
[2024/07/27 22:44:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:44:13] ppocr INFO: epoch: [6/200], global_step: 18, lr: 0.001000, loss: 6.914212, loss_shrink_maps: 4.804575, loss_threshold_maps: 1.175695, loss_binary_maps: 0.960891, avg_reader_cost: 2.20976 s, avg_batch_cost: 2.44414 s, avg_samples: 12.5, ips: 5.11427 samples/s, eta: 1:41:15
[2024/07/27 22:44:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:44:22] ppocr INFO: epoch: [7/200], global_step: 20, lr: 0.001000, loss: 6.899466, loss_shrink_maps: 4.789934, loss_threshold_maps: 1.159795, loss_binary_maps: 0.957565, avg_reader_cost: 1.42358 s, avg_batch_cost: 1.61871 s, avg_samples: 9.6, ips: 5.93064 samples/s, eta: 1:38:38
[2024/07/27 22:44:22] ppocr INFO: epoch: [7/200], global_step: 21, lr: 0.001000, loss: 6.893832, loss_shrink_maps: 4.782106, loss_threshold_maps: 1.156187, loss_binary_maps: 0.954628, avg_reader_cost: 0.85498 s, avg_batch_cost: 0.90957 s, avg_samples: 2.9, ips: 3.18834 samples/s, eta: 1:37:57
[2024/07/27 22:44:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:44:32] ppocr INFO: epoch: [8/200], global_step: 24, lr: 0.001000, loss: 6.845388, loss_shrink_maps: 4.729246, loss_threshold_maps: 1.149984, loss_binary_maps: 0.944536, avg_reader_cost: 2.24741 s, avg_batch_cost: 2.48039 s, avg_samples: 12.5, ips: 5.03953 samples/s, eta: 1:35:11
[2024/07/27 22:44:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:44:41] ppocr INFO: epoch: [9/200], global_step: 27, lr: 0.001000, loss: 6.694258, loss_shrink_maps: 4.660166, loss_threshold_maps: 1.138932, loss_binary_maps: 0.908647, avg_reader_cost: 2.22664 s, avg_batch_cost: 2.46920 s, avg_samples: 12.5, ips: 5.06236 samples/s, eta: 1:32:54
[2024/07/27 22:44:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:44:51] ppocr INFO: epoch: [10/200], global_step: 30, lr: 0.001000, loss: 6.531146, loss_shrink_maps: 4.529896, loss_threshold_maps: 1.125738, loss_binary_maps: 0.865667, avg_reader_cost: 2.26021 s, avg_batch_cost: 2.48888 s, avg_samples: 12.5, ips: 5.02235 samples/s, eta: 1:31:03
[2024/07/27 22:44:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:45:01] ppocr INFO: epoch: [11/200], global_step: 33, lr: 0.001000, loss: 6.322057, loss_shrink_maps: 4.430686, loss_threshold_maps: 1.048806, loss_binary_maps: 0.843624, avg_reader_cost: 2.21925 s, avg_batch_cost: 2.47654 s, avg_samples: 12.5, ips: 5.04737 samples/s, eta: 1:29:26
[2024/07/27 22:45:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:45:10] ppocr INFO: epoch: [12/200], global_step: 36, lr: 0.001000, loss: 6.145428, loss_shrink_maps: 4.311808, loss_threshold_maps: 1.027968, loss_binary_maps: 0.814468, avg_reader_cost: 2.23134 s, avg_batch_cost: 2.47325 s, avg_samples: 12.5, ips: 5.05409 samples/s, eta: 1:28:00
[2024/07/27 22:45:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:45:20] ppocr INFO: epoch: [13/200], global_step: 39, lr: 0.001000, loss: 6.056415, loss_shrink_maps: 4.239466, loss_threshold_maps: 1.003157, loss_binary_maps: 0.804386, avg_reader_cost: 2.21343 s, avg_batch_cost: 2.53628 s, avg_samples: 12.5, ips: 4.92848 samples/s, eta: 1:26:53
[2024/07/27 22:45:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:45:28] ppocr INFO: epoch: [14/200], global_step: 40, lr: 0.001000, loss: 6.046594, loss_shrink_maps: 4.228755, loss_threshold_maps: 1.001732, loss_binary_maps: 0.799174, avg_reader_cost: 0.66909 s, avg_batch_cost: 0.75137 s, avg_samples: 4.8, ips: 6.38836 samples/s, eta: 1:26:19
[2024/07/27 22:45:29] ppocr INFO: epoch: [14/200], global_step: 42, lr: 0.001000, loss: 5.909098, loss_shrink_maps: 4.161334, loss_threshold_maps: 0.991109, loss_binary_maps: 0.784112, avg_reader_cost: 1.59409 s, avg_batch_cost: 1.73946 s, avg_samples: 7.7, ips: 4.42665 samples/s, eta: 1:25:46
[2024/07/27 22:45:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:45:39] ppocr INFO: epoch: [15/200], global_step: 45, lr: 0.001000, loss: 5.858473, loss_shrink_maps: 4.120656, loss_threshold_maps: 0.956674, loss_binary_maps: 0.777182, avg_reader_cost: 2.23659 s, avg_batch_cost: 2.46398 s, avg_samples: 12.5, ips: 5.07309 samples/s, eta: 1:24:41
[2024/07/27 22:45:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:45:49] ppocr INFO: epoch: [16/200], global_step: 48, lr: 0.001000, loss: 5.770984, loss_shrink_maps: 4.018530, loss_threshold_maps: 0.946972, loss_binary_maps: 0.772211, avg_reader_cost: 2.26151 s, avg_batch_cost: 2.48939 s, avg_samples: 12.5, ips: 5.02131 samples/s, eta: 1:23:44
[2024/07/27 22:45:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:45:57] ppocr INFO: epoch: [17/200], global_step: 50, lr: 0.001000, loss: 5.704740, loss_shrink_maps: 3.992442, loss_threshold_maps: 0.943107, loss_binary_maps: 0.770535, avg_reader_cost: 1.39153 s, avg_batch_cost: 1.56482 s, avg_samples: 9.6, ips: 6.13489 samples/s, eta: 1:22:57
[2024/07/27 22:45:58] ppocr INFO: epoch: [17/200], global_step: 51, lr: 0.001000, loss: 5.664546, loss_shrink_maps: 3.985991, loss_threshold_maps: 0.935127, loss_binary_maps: 0.769916, avg_reader_cost: 0.82804 s, avg_batch_cost: 0.88298 s, avg_samples: 2.9, ips: 3.28432 samples/s, eta: 1:22:46
[2024/07/27 22:45:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:46:07] ppocr INFO: epoch: [18/200], global_step: 54, lr: 0.001000, loss: 5.645924, loss_shrink_maps: 3.967980, loss_threshold_maps: 0.927544, loss_binary_maps: 0.765515, avg_reader_cost: 2.13611 s, avg_batch_cost: 2.41966 s, avg_samples: 12.5, ips: 5.16602 samples/s, eta: 1:21:49
[2024/07/27 22:46:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:46:17] ppocr INFO: epoch: [19/200], global_step: 57, lr: 0.001000, loss: 5.603149, loss_shrink_maps: 3.930323, loss_threshold_maps: 0.923530, loss_binary_maps: 0.756731, avg_reader_cost: 2.24925 s, avg_batch_cost: 2.49127 s, avg_samples: 12.5, ips: 5.01752 samples/s, eta: 1:21:02
[2024/07/27 22:46:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:46:26] ppocr INFO: epoch: [20/200], global_step: 60, lr: 0.001000, loss: 5.505049, loss_shrink_maps: 3.870460, loss_threshold_maps: 0.910711, loss_binary_maps: 0.742010, avg_reader_cost: 2.23889 s, avg_batch_cost: 2.48992 s, avg_samples: 12.5, ips: 5.02025 samples/s, eta: 1:20:18

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:54,  9.13it/s]
eval model::   1%|          | 3/500 [00:00<00:35, 13.92it/s]
eval model::   1%|          | 6/500 [00:00<00:25, 19.36it/s]
eval model::   2%|▏         | 10/500 [00:00<00:20, 23.87it/s]
eval model::   3%|▎         | 13/500 [00:00<00:21, 22.36it/s]
eval model::   3%|▎         | 16/500 [00:00<00:20, 23.88it/s]
eval model::   4%|▍         | 19/500 [00:00<00:19, 24.62it/s]
eval model::   4%|▍         | 22/500 [00:00<00:18, 25.25it/s]
eval model::   5%|▌         | 25/500 [00:01<00:19, 23.96it/s]
eval model::   6%|▌         | 28/500 [00:01<00:19, 24.46it/s]
eval model::   6%|▌         | 31/500 [00:01<00:20, 22.89it/s]
eval model::   7%|▋         | 34/500 [00:01<00:19, 23.83it/s]
eval model::   8%|▊         | 38/500 [00:01<00:17, 25.90it/s]
eval model::   8%|▊         | 41/500 [00:01<00:18, 24.68it/s]
eval model::   9%|▉         | 44/500 [00:01<00:19, 23.56it/s]
eval model::   9%|▉         | 47/500 [00:02<00:20, 21.82it/s]
eval model::  10%|█         | 50/500 [00:02<00:20, 22.39it/s]
eval model::  11%|█         | 53/500 [00:02<00:20, 21.66it/s]
eval model::  11%|█         | 56/500 [00:02<00:20, 21.34it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:21, 20.57it/s]
eval model::  12%|█▏        | 62/500 [00:02<00:23, 18.99it/s]
eval model::  13%|█▎        | 64/500 [00:02<00:22, 18.98it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 19.17it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.50it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 21.82it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:21, 19.79it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:22, 19.07it/s]
eval model::  16%|█▌        | 80/500 [00:03<00:22, 18.55it/s]
eval model::  16%|█▋        | 82/500 [00:03<00:23, 18.09it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:26, 15.60it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:27, 15.31it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:25, 16.27it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:25, 16.06it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:24, 16.88it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:25, 15.87it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:26, 15.51it/s]
eval model::  20%|█▉        | 98/500 [00:04<00:24, 16.18it/s]
eval model::  20%|██        | 100/500 [00:05<00:24, 16.00it/s]
eval model::  20%|██        | 102/500 [00:05<00:24, 16.27it/s]
eval model::  21%|██        | 104/500 [00:05<00:23, 17.05it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:23, 17.06it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:22, 17.71it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:22, 17.62it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:21, 17.89it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:21, 17.75it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:21, 17.56it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:23, 16.08it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:20, 18.73it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:20, 18.60it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 20.06it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 18.45it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:17, 20.34it/s]
eval model::  27%|██▋       | 137/500 [00:06<00:16, 21.72it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:16, 21.54it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:15, 22.60it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 20.75it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:16, 20.70it/s]
eval model::  30%|███       | 152/500 [00:07<00:19, 17.83it/s]
eval model::  31%|███       | 155/500 [00:07<00:18, 18.61it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:18, 18.69it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:17, 18.94it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:17, 18.86it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:16, 19.76it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:16, 19.95it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:16, 20.19it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:17, 18.45it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:17, 18.56it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:17, 18.82it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:20, 15.77it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:20, 15.73it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:19, 16.54it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 19.11it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:15, 19.85it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:14, 20.67it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:18, 16.40it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:16, 18.23it/s]
eval model::  40%|████      | 201/500 [00:10<00:16, 18.23it/s]
eval model::  41%|████      | 204/500 [00:10<00:15, 19.66it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:20, 14.03it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:20, 14.35it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 14.99it/s]
eval model::  43%|████▎     | 214/500 [00:11<00:16, 17.08it/s]
eval model::  43%|████▎     | 216/500 [00:11<00:17, 16.12it/s]
eval model::  44%|████▎     | 218/500 [00:11<00:18, 15.17it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:16, 17.39it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:13, 19.95it/s]
eval model::  45%|████▌     | 227/500 [00:11<00:13, 20.18it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:11, 22.80it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:11, 22.18it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 21.91it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 22.69it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 20.07it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:13, 18.32it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:12, 19.67it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 20.84it/s]
eval model::  51%|█████     | 255/500 [00:13<00:12, 20.27it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 21.85it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 20.49it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 19.52it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:11, 20.11it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:10, 21.00it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:12, 18.72it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:11, 19.35it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:10, 20.69it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:09, 21.84it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:14, 14.50it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:14, 14.93it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:12, 16.84it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:11, 18.55it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:11, 18.22it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:10, 20.09it/s]
eval model::  60%|██████    | 302/500 [00:15<00:11, 17.83it/s]
eval model::  61%|██████    | 305/500 [00:15<00:10, 19.28it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 19.20it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:10, 17.81it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 20.32it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 21.53it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 22.23it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 19.89it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 19.59it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 19.86it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:08, 19.47it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:08, 19.39it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:08, 19.08it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 17.12it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:07, 19.83it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 20.30it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:06, 22.00it/s]
eval model::  70%|███████   | 352/500 [00:18<00:07, 20.80it/s]
eval model::  71%|███████   | 355/500 [00:18<00:07, 19.68it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:07, 20.20it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 18.86it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 17.28it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.31it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 18.31it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 20.02it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:05, 21.36it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 21.54it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:05, 20.79it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:06, 18.79it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:07, 14.93it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 17.36it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:05, 18.23it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 19.73it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 21.91it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 20.00it/s]
eval model::  81%|████████  | 405/500 [00:21<00:05, 17.53it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:05, 17.84it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:05, 18.04it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:04, 19.68it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:04, 19.37it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 19.03it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 17.90it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:05, 14.97it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 16.60it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.54it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 19.29it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 20.97it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:02, 21.87it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 21.21it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 20.47it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:03, 18.01it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 18.30it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 18.11it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:03, 16.41it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 18.82it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:02, 18.72it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:02, 20.40it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:02, 19.05it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 18.11it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 15.52it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:02, 15.86it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 17.98it/s]
eval model::  95%|█████████▍| 473/500 [00:24<00:01, 19.78it/s]
eval model::  95%|█████████▌| 476/500 [00:24<00:01, 21.12it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:01, 20.38it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 20.98it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 20.12it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 20.43it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 20.99it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 20.93it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 23.85it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.24it/s]
[2024/07/27 22:46:52] ppocr INFO: cur metric, precision: 0.24829931972789115, recall: 0.10544053923928744, hmean: 0.1480229807367354, fps: 43.48616482293093
[2024/07/27 22:46:52] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 22:46:52] ppocr INFO: best metric, hmean: 0.1480229807367354, precision: 0.24829931972789115, recall: 0.10544053923928744, fps: 43.48616482293093, best_epoch: 20
[2024/07/27 22:46:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:46:59] ppocr INFO: epoch: [21/200], global_step: 63, lr: 0.001000, loss: 5.505049, loss_shrink_maps: 3.839820, loss_threshold_maps: 0.907937, loss_binary_maps: 0.734553, avg_reader_cost: 1.52471 s, avg_batch_cost: 1.75283 s, avg_samples: 12.5, ips: 7.13133 samples/s, eta: 1:18:32
[2024/07/27 22:47:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:47:06] ppocr INFO: epoch: [22/200], global_step: 66, lr: 0.001000, loss: 5.377532, loss_shrink_maps: 3.783771, loss_threshold_maps: 0.895779, loss_binary_maps: 0.722096, avg_reader_cost: 1.48309 s, avg_batch_cost: 1.71845 s, avg_samples: 12.5, ips: 7.27401 samples/s, eta: 1:16:52
[2024/07/27 22:47:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:47:13] ppocr INFO: epoch: [23/200], global_step: 69, lr: 0.001000, loss: 5.345214, loss_shrink_maps: 3.742182, loss_threshold_maps: 0.890608, loss_binary_maps: 0.715718, avg_reader_cost: 1.55623 s, avg_batch_cost: 1.78387 s, avg_samples: 12.5, ips: 7.00724 samples/s, eta: 1:15:24
[2024/07/27 22:47:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:47:19] ppocr INFO: epoch: [24/200], global_step: 70, lr: 0.001000, loss: 5.337322, loss_shrink_maps: 3.716169, loss_threshold_maps: 0.884578, loss_binary_maps: 0.711641, avg_reader_cost: 0.41462 s, avg_batch_cost: 0.51374 s, avg_samples: 4.8, ips: 9.34331 samples/s, eta: 1:14:50
[2024/07/27 22:47:21] ppocr INFO: epoch: [24/200], global_step: 72, lr: 0.001000, loss: 5.254516, loss_shrink_maps: 3.685966, loss_threshold_maps: 0.889641, loss_binary_maps: 0.685602, avg_reader_cost: 1.11829 s, avg_batch_cost: 1.26359 s, avg_samples: 7.7, ips: 6.09375 samples/s, eta: 1:14:01
[2024/07/27 22:47:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:47:28] ppocr INFO: epoch: [25/200], global_step: 75, lr: 0.001000, loss: 4.940720, loss_shrink_maps: 3.451328, loss_threshold_maps: 0.881492, loss_binary_maps: 0.624661, avg_reader_cost: 1.52731 s, avg_batch_cost: 1.76516 s, avg_samples: 12.5, ips: 7.08153 samples/s, eta: 1:12:43
[2024/07/27 22:47:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:47:35] ppocr INFO: epoch: [26/200], global_step: 78, lr: 0.001000, loss: 4.479806, loss_shrink_maps: 3.078111, loss_threshold_maps: 0.881152, loss_binary_maps: 0.484190, avg_reader_cost: 1.52129 s, avg_batch_cost: 1.74938 s, avg_samples: 12.5, ips: 7.14540 samples/s, eta: 1:11:28
[2024/07/27 22:47:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:47:42] ppocr INFO: epoch: [27/200], global_step: 80, lr: 0.001000, loss: 4.252490, loss_shrink_maps: 2.906988, loss_threshold_maps: 0.875854, loss_binary_maps: 0.455888, avg_reader_cost: 0.99958 s, avg_batch_cost: 1.20560 s, avg_samples: 9.6, ips: 7.96282 samples/s, eta: 1:10:43
[2024/07/27 22:47:42] ppocr INFO: epoch: [27/200], global_step: 81, lr: 0.001000, loss: 4.135272, loss_shrink_maps: 2.848414, loss_threshold_maps: 0.875854, loss_binary_maps: 0.433059, avg_reader_cost: 0.64839 s, avg_batch_cost: 0.70292 s, avg_samples: 2.9, ips: 4.12563 samples/s, eta: 1:10:28
[2024/07/27 22:47:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:47:50] ppocr INFO: epoch: [28/200], global_step: 84, lr: 0.001000, loss: 3.745096, loss_shrink_maps: 2.444026, loss_threshold_maps: 0.875854, loss_binary_maps: 0.393227, avg_reader_cost: 1.52780 s, avg_batch_cost: 1.77324 s, avg_samples: 12.5, ips: 7.04925 samples/s, eta: 1:09:22
[2024/07/27 22:47:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:47:57] ppocr INFO: epoch: [29/200], global_step: 87, lr: 0.001000, loss: 3.616979, loss_shrink_maps: 2.386529, loss_threshold_maps: 0.862485, loss_binary_maps: 0.382117, avg_reader_cost: 1.47401 s, avg_batch_cost: 1.71926 s, avg_samples: 12.5, ips: 7.27055 samples/s, eta: 1:08:17
[2024/07/27 22:47:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:48:04] ppocr INFO: epoch: [30/200], global_step: 90, lr: 0.001000, loss: 3.505298, loss_shrink_maps: 2.245906, loss_threshold_maps: 0.859193, loss_binary_maps: 0.366507, avg_reader_cost: 1.52742 s, avg_batch_cost: 1.76028 s, avg_samples: 12.5, ips: 7.10112 samples/s, eta: 1:07:17
[2024/07/27 22:48:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:48:11] ppocr INFO: epoch: [31/200], global_step: 93, lr: 0.001000, loss: 3.254544, loss_shrink_maps: 2.043609, loss_threshold_maps: 0.856357, loss_binary_maps: 0.357178, avg_reader_cost: 1.53910 s, avg_batch_cost: 1.78167 s, avg_samples: 12.5, ips: 7.01588 samples/s, eta: 1:06:21
[2024/07/27 22:48:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:48:18] ppocr INFO: epoch: [32/200], global_step: 96, lr: 0.001000, loss: 3.195118, loss_shrink_maps: 1.996416, loss_threshold_maps: 0.853605, loss_binary_maps: 0.343086, avg_reader_cost: 1.50221 s, avg_batch_cost: 1.75357 s, avg_samples: 12.5, ips: 7.12831 samples/s, eta: 1:05:25
[2024/07/27 22:48:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:48:25] ppocr INFO: epoch: [33/200], global_step: 99, lr: 0.001000, loss: 3.086817, loss_shrink_maps: 1.926239, loss_threshold_maps: 0.852091, loss_binary_maps: 0.326914, avg_reader_cost: 1.53598 s, avg_batch_cost: 1.78495 s, avg_samples: 12.5, ips: 7.00299 samples/s, eta: 1:04:34
[2024/07/27 22:48:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:48:31] ppocr INFO: epoch: [34/200], global_step: 100, lr: 0.001000, loss: 3.065157, loss_shrink_maps: 1.900544, loss_threshold_maps: 0.852091, loss_binary_maps: 0.334777, avg_reader_cost: 0.43806 s, avg_batch_cost: 0.52071 s, avg_samples: 4.8, ips: 9.21826 samples/s, eta: 1:04:14
[2024/07/27 22:48:32] ppocr INFO: epoch: [34/200], global_step: 102, lr: 0.001000, loss: 3.065157, loss_shrink_maps: 1.900544, loss_threshold_maps: 0.852091, loss_binary_maps: 0.334777, avg_reader_cost: 1.13277 s, avg_batch_cost: 1.27818 s, avg_samples: 7.7, ips: 6.02418 samples/s, eta: 1:03:45
[2024/07/27 22:48:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:48:40] ppocr INFO: epoch: [35/200], global_step: 105, lr: 0.001000, loss: 3.006904, loss_shrink_maps: 1.865479, loss_threshold_maps: 0.850413, loss_binary_maps: 0.326914, avg_reader_cost: 1.59253 s, avg_batch_cost: 1.82038 s, avg_samples: 12.5, ips: 6.86670 samples/s, eta: 1:03:00
[2024/07/27 22:48:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:48:47] ppocr INFO: epoch: [36/200], global_step: 108, lr: 0.001000, loss: 2.906699, loss_shrink_maps: 1.779342, loss_threshold_maps: 0.821863, loss_binary_maps: 0.317935, avg_reader_cost: 1.55139 s, avg_batch_cost: 1.79944 s, avg_samples: 12.5, ips: 6.94661 samples/s, eta: 1:02:14
[2024/07/27 22:48:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:48:54] ppocr INFO: epoch: [37/200], global_step: 110, lr: 0.001000, loss: 2.919498, loss_shrink_maps: 1.795053, loss_threshold_maps: 0.816314, loss_binary_maps: 0.326311, avg_reader_cost: 0.94947 s, avg_batch_cost: 1.12302 s, avg_samples: 9.6, ips: 8.54836 samples/s, eta: 1:01:42
[2024/07/27 22:48:54] ppocr INFO: epoch: [37/200], global_step: 111, lr: 0.001000, loss: 2.906699, loss_shrink_maps: 1.772016, loss_threshold_maps: 0.809464, loss_binary_maps: 0.326867, avg_reader_cost: 0.60726 s, avg_batch_cost: 0.66211 s, avg_samples: 2.9, ips: 4.37995 samples/s, eta: 1:01:30
[2024/07/27 22:48:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:49:02] ppocr INFO: epoch: [38/200], global_step: 114, lr: 0.001000, loss: 2.885542, loss_shrink_maps: 1.732742, loss_threshold_maps: 0.809225, loss_binary_maps: 0.314286, avg_reader_cost: 1.55380 s, avg_batch_cost: 1.80288 s, avg_samples: 12.5, ips: 6.93335 samples/s, eta: 1:00:48
[2024/07/27 22:49:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:49:09] ppocr INFO: epoch: [39/200], global_step: 117, lr: 0.001000, loss: 2.893923, loss_shrink_maps: 1.739032, loss_threshold_maps: 0.809225, loss_binary_maps: 0.318491, avg_reader_cost: 1.50148 s, avg_batch_cost: 1.73438 s, avg_samples: 12.5, ips: 7.20719 samples/s, eta: 1:00:04
[2024/07/27 22:49:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:49:16] ppocr INFO: epoch: [40/200], global_step: 120, lr: 0.001000, loss: 2.862408, loss_shrink_maps: 1.709872, loss_threshold_maps: 0.810934, loss_binary_maps: 0.314286, avg_reader_cost: 1.51645 s, avg_batch_cost: 1.76897 s, avg_samples: 12.5, ips: 7.06624 samples/s, eta: 0:59:22

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:54,  9.23it/s]
eval model::   1%|          | 4/500 [00:00<00:30, 16.28it/s]
eval model::   1%|▏         | 7/500 [00:00<00:23, 20.90it/s]
eval model::   2%|▏         | 10/500 [00:00<00:26, 18.73it/s]
eval model::   3%|▎         | 13/500 [00:00<00:26, 18.54it/s]
eval model::   3%|▎         | 15/500 [00:00<00:25, 18.81it/s]
eval model::   4%|▎         | 18/500 [00:00<00:22, 21.00it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 19.11it/s]
eval model::   5%|▍         | 24/500 [00:01<00:24, 19.80it/s]
eval model::   5%|▌         | 27/500 [00:01<00:23, 20.39it/s]
eval model::   6%|▌         | 30/500 [00:01<00:23, 20.08it/s]
eval model::   7%|▋         | 33/500 [00:01<00:22, 20.62it/s]
eval model::   7%|▋         | 36/500 [00:01<00:23, 19.81it/s]
eval model::   8%|▊         | 39/500 [00:02<00:24, 18.98it/s]
eval model::   8%|▊         | 42/500 [00:02<00:22, 20.34it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.13it/s]
eval model::  10%|▉         | 48/500 [00:02<00:24, 18.55it/s]
eval model::  10%|█         | 51/500 [00:02<00:23, 19.29it/s]
eval model::  11%|█         | 54/500 [00:02<00:22, 19.83it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:22, 20.12it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:21, 20.86it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:23, 18.56it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:19, 22.20it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 22.29it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 19.53it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:20, 21.02it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:21, 20.04it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 20.54it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.57it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:19, 20.78it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:20, 20.30it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:19, 21.17it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:19, 21.21it/s]
eval model::  20%|██        | 100/500 [00:05<00:21, 18.74it/s]
eval model::  21%|██        | 103/500 [00:05<00:20, 19.32it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 20.64it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 21.57it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:19, 20.13it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:17, 21.93it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:17, 22.37it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:19, 19.82it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:17, 21.59it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:17, 21.93it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:18, 20.32it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:16, 22.56it/s]
eval model::  27%|██▋       | 137/500 [00:06<00:16, 21.82it/s]
eval model::  28%|██▊       | 140/500 [00:06<00:16, 21.40it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:16, 21.40it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:16, 21.12it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:17, 19.86it/s]
eval model::  30%|███       | 152/500 [00:07<00:18, 19.05it/s]
eval model::  31%|███       | 156/500 [00:07<00:15, 22.11it/s]
eval model::  32%|███▏      | 159/500 [00:07<00:16, 20.27it/s]
eval model::  32%|███▏      | 162/500 [00:07<00:17, 19.22it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:16, 20.04it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:16, 20.28it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:15, 21.58it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:14, 21.75it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:14, 22.48it/s]
eval model::  36%|███▌      | 180/500 [00:08<00:15, 21.01it/s]
eval model::  37%|███▋      | 183/500 [00:08<00:14, 21.83it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 22.31it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.80it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.10it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:15, 19.37it/s]
eval model::  40%|███▉      | 198/500 [00:09<00:14, 20.33it/s]
eval model::  40%|████      | 201/500 [00:09<00:15, 19.54it/s]
eval model::  41%|████      | 204/500 [00:09<00:14, 20.85it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:19, 15.03it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:18, 15.44it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:17, 16.28it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:17, 16.69it/s]
eval model::  43%|████▎     | 215/500 [00:10<00:18, 15.56it/s]
eval model::  43%|████▎     | 217/500 [00:10<00:19, 14.84it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 17.34it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:17, 16.31it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:14, 18.49it/s]
eval model::  45%|████▌     | 227/500 [00:11<00:14, 18.45it/s]
eval model::  46%|████▌     | 231/500 [00:11<00:12, 22.13it/s]
eval model::  47%|████▋     | 234/500 [00:11<00:12, 21.45it/s]
eval model::  47%|████▋     | 237/500 [00:11<00:11, 23.11it/s]
eval model::  48%|████▊     | 240/500 [00:11<00:11, 22.81it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 20.79it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:11, 21.76it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:11, 20.96it/s]
eval model::  50%|█████     | 252/500 [00:12<00:11, 21.41it/s]
eval model::  51%|█████     | 255/500 [00:12<00:11, 21.43it/s]
eval model::  52%|█████▏    | 258/500 [00:12<00:10, 22.17it/s]
eval model::  52%|█████▏    | 261/500 [00:12<00:10, 21.94it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:11, 21.17it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:11, 20.71it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:11, 20.88it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:11, 20.20it/s]
eval model::  55%|█████▌    | 276/500 [00:13<00:11, 19.12it/s]
eval model::  56%|█████▌    | 279/500 [00:13<00:11, 19.97it/s]
eval model::  56%|█████▋    | 282/500 [00:13<00:10, 21.10it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:14, 14.82it/s]
eval model::  58%|█████▊    | 288/500 [00:14<00:12, 17.21it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:11, 18.92it/s]
eval model::  59%|█████▉    | 294/500 [00:14<00:10, 18.78it/s]
eval model::  59%|█████▉    | 297/500 [00:14<00:10, 18.47it/s]
eval model::  60%|██████    | 300/500 [00:15<00:10, 19.14it/s]
eval model::  61%|██████    | 303/500 [00:15<00:09, 19.95it/s]
eval model::  61%|██████    | 306/500 [00:15<00:09, 21.28it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:08, 21.94it/s]
eval model::  62%|██████▏   | 312/500 [00:15<00:08, 20.91it/s]
eval model::  63%|██████▎   | 315/500 [00:15<00:09, 20.36it/s]
eval model::  64%|██████▍   | 319/500 [00:15<00:07, 23.14it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:08, 21.77it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:07, 22.91it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:08, 21.14it/s]
eval model::  66%|██████▌   | 331/500 [00:16<00:07, 21.43it/s]
eval model::  67%|██████▋   | 334/500 [00:16<00:07, 22.09it/s]
eval model::  67%|██████▋   | 337/500 [00:16<00:08, 20.37it/s]
eval model::  68%|██████▊   | 340/500 [00:16<00:08, 18.26it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:08, 19.44it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 20.86it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:06, 22.53it/s]
eval model::  70%|███████   | 352/500 [00:17<00:06, 21.98it/s]
eval model::  71%|███████   | 355/500 [00:17<00:07, 20.12it/s]
eval model::  72%|███████▏  | 358/500 [00:17<00:06, 21.06it/s]
eval model::  72%|███████▏  | 361/500 [00:17<00:07, 18.81it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 19.01it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:06, 20.26it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:06, 21.29it/s]
eval model::  75%|███████▍  | 373/500 [00:18<00:06, 20.95it/s]
eval model::  75%|███████▌  | 376/500 [00:18<00:05, 20.95it/s]
eval model::  76%|███████▌  | 379/500 [00:18<00:05, 22.52it/s]
eval model::  76%|███████▋  | 382/500 [00:18<00:05, 20.97it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 21.62it/s]
eval model::  78%|███████▊  | 388/500 [00:19<00:07, 16.00it/s]
eval model::  78%|███████▊  | 391/500 [00:19<00:05, 18.25it/s]
eval model::  79%|███████▉  | 394/500 [00:19<00:05, 19.54it/s]
eval model::  79%|███████▉  | 397/500 [00:19<00:05, 20.18it/s]
eval model::  80%|████████  | 400/500 [00:19<00:04, 20.89it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 20.81it/s]
eval model::  81%|████████  | 406/500 [00:20<00:04, 21.81it/s]
eval model::  82%|████████▏ | 409/500 [00:20<00:04, 22.43it/s]
eval model::  82%|████████▏ | 412/500 [00:20<00:03, 22.26it/s]
eval model::  83%|████████▎ | 415/500 [00:20<00:04, 20.27it/s]
eval model::  84%|████████▎ | 418/500 [00:20<00:04, 20.13it/s]
eval model::  84%|████████▍ | 421/500 [00:20<00:04, 18.15it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:03, 20.03it/s]
eval model::  85%|████████▌ | 427/500 [00:21<00:03, 21.46it/s]
eval model::  86%|████████▌ | 430/500 [00:21<00:03, 21.67it/s]
eval model::  87%|████████▋ | 433/500 [00:21<00:03, 21.84it/s]
eval model::  87%|████████▋ | 436/500 [00:21<00:02, 21.69it/s]
eval model::  88%|████████▊ | 439/500 [00:21<00:02, 20.81it/s]
eval model::  88%|████████▊ | 442/500 [00:21<00:02, 19.50it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 20.20it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 19.62it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 19.52it/s]
eval model::  90%|█████████ | 452/500 [00:22<00:02, 19.56it/s]
eval model::  91%|█████████ | 454/500 [00:22<00:02, 18.64it/s]
eval model::  91%|█████████ | 456/500 [00:22<00:02, 18.08it/s]
eval model::  92%|█████████▏| 459/500 [00:22<00:02, 19.34it/s]
eval model::  92%|█████████▏| 461/500 [00:22<00:02, 18.54it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:02, 16.25it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:02, 15.50it/s]
eval model::  94%|█████████▍| 469/500 [00:23<00:01, 20.49it/s]
eval model::  94%|█████████▍| 472/500 [00:23<00:01, 20.24it/s]
eval model::  95%|█████████▌| 475/500 [00:23<00:01, 20.88it/s]
eval model::  96%|█████████▌| 478/500 [00:23<00:01, 19.62it/s]
eval model::  96%|█████████▌| 481/500 [00:23<00:00, 19.32it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 19.62it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 17.61it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 18.68it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 19.41it/s]
eval model::  99%|█████████▉| 494/500 [00:24<00:00, 19.53it/s]
eval model::  99%|█████████▉| 497/500 [00:24<00:00, 20.43it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 19.76it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 20.06it/s]
[2024/07/27 22:49:41] ppocr INFO: cur metric, precision: 0.5393634840871022, recall: 0.3100625902744343, hmean: 0.3937633751146439, fps: 44.630790084791506
[2024/07/27 22:49:41] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 22:49:41] ppocr INFO: best metric, hmean: 0.3937633751146439, precision: 0.5393634840871022, recall: 0.3100625902744343, fps: 44.630790084791506, best_epoch: 40
[2024/07/27 22:49:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:49:48] ppocr INFO: epoch: [41/200], global_step: 123, lr: 0.001000, loss: 2.862408, loss_shrink_maps: 1.709872, loss_threshold_maps: 0.813570, loss_binary_maps: 0.314286, avg_reader_cost: 1.62067 s, avg_batch_cost: 1.85751 s, avg_samples: 12.5, ips: 6.72945 samples/s, eta: 0:58:46
[2024/07/27 22:49:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:49:55] ppocr INFO: epoch: [42/200], global_step: 126, lr: 0.001000, loss: 2.869300, loss_shrink_maps: 1.729180, loss_threshold_maps: 0.808999, loss_binary_maps: 0.316458, avg_reader_cost: 1.52557 s, avg_batch_cost: 1.75411 s, avg_samples: 12.5, ips: 7.12614 samples/s, eta: 0:58:06
[2024/07/27 22:49:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:50:02] ppocr INFO: epoch: [43/200], global_step: 129, lr: 0.001000, loss: 2.830234, loss_shrink_maps: 1.671033, loss_threshold_maps: 0.810151, loss_binary_maps: 0.308717, avg_reader_cost: 1.52352 s, avg_batch_cost: 1.76403 s, avg_samples: 12.5, ips: 7.08603 samples/s, eta: 0:57:28
[2024/07/27 22:50:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:50:08] ppocr INFO: epoch: [44/200], global_step: 130, lr: 0.001000, loss: 2.839770, loss_shrink_maps: 1.671033, loss_threshold_maps: 0.810151, loss_binary_maps: 0.308717, avg_reader_cost: 0.40375 s, avg_batch_cost: 0.50481 s, avg_samples: 4.8, ips: 9.50862 samples/s, eta: 0:57:13
[2024/07/27 22:50:09] ppocr INFO: epoch: [44/200], global_step: 132, lr: 0.001000, loss: 2.809932, loss_shrink_maps: 1.666877, loss_threshold_maps: 0.813570, loss_binary_maps: 0.305741, avg_reader_cost: 1.10099 s, avg_batch_cost: 1.24700 s, avg_samples: 7.7, ips: 6.17481 samples/s, eta: 0:56:50
[2024/07/27 22:50:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:50:17] ppocr INFO: epoch: [45/200], global_step: 135, lr: 0.001000, loss: 2.809932, loss_shrink_maps: 1.666569, loss_threshold_maps: 0.802419, loss_binary_maps: 0.309405, avg_reader_cost: 1.58990 s, avg_batch_cost: 1.85139 s, avg_samples: 12.5, ips: 6.75169 samples/s, eta: 0:56:17
[2024/07/27 22:50:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:50:25] ppocr INFO: epoch: [46/200], global_step: 138, lr: 0.001000, loss: 2.809932, loss_shrink_maps: 1.666569, loss_threshold_maps: 0.798631, loss_binary_maps: 0.311316, avg_reader_cost: 1.58776 s, avg_batch_cost: 1.87653 s, avg_samples: 12.5, ips: 6.66123 samples/s, eta: 0:55:45
[2024/07/27 22:50:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:50:31] ppocr INFO: epoch: [47/200], global_step: 140, lr: 0.001000, loss: 2.768803, loss_shrink_maps: 1.657803, loss_threshold_maps: 0.795032, loss_binary_maps: 0.310408, avg_reader_cost: 0.96872 s, avg_batch_cost: 1.14691 s, avg_samples: 9.6, ips: 8.37032 samples/s, eta: 0:55:21
[2024/07/27 22:50:32] ppocr INFO: epoch: [47/200], global_step: 141, lr: 0.001000, loss: 2.732636, loss_shrink_maps: 1.634427, loss_threshold_maps: 0.795032, loss_binary_maps: 0.307809, avg_reader_cost: 0.61918 s, avg_batch_cost: 0.67399 s, avg_samples: 2.9, ips: 4.30271 samples/s, eta: 0:55:12
[2024/07/27 22:50:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:50:39] ppocr INFO: epoch: [48/200], global_step: 144, lr: 0.001000, loss: 2.732636, loss_shrink_maps: 1.621741, loss_threshold_maps: 0.798631, loss_binary_maps: 0.307968, avg_reader_cost: 1.52144 s, avg_batch_cost: 1.75838 s, avg_samples: 12.5, ips: 7.10881 samples/s, eta: 0:54:37
[2024/07/27 22:50:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:50:46] ppocr INFO: epoch: [49/200], global_step: 147, lr: 0.001000, loss: 2.732636, loss_shrink_maps: 1.621741, loss_threshold_maps: 0.800555, loss_binary_maps: 0.307968, avg_reader_cost: 1.51935 s, avg_batch_cost: 1.74741 s, avg_samples: 12.5, ips: 7.15342 samples/s, eta: 0:54:03
[2024/07/27 22:50:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:50:53] ppocr INFO: epoch: [50/200], global_step: 150, lr: 0.001000, loss: 2.715534, loss_shrink_maps: 1.605607, loss_threshold_maps: 0.800555, loss_binary_maps: 0.303728, avg_reader_cost: 1.50337 s, avg_batch_cost: 1.74390 s, avg_samples: 12.5, ips: 7.16782 samples/s, eta: 0:53:30
[2024/07/27 22:50:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:51:01] ppocr INFO: epoch: [51/200], global_step: 153, lr: 0.001000, loss: 2.715534, loss_shrink_maps: 1.609823, loss_threshold_maps: 0.800555, loss_binary_maps: 0.299896, avg_reader_cost: 1.53812 s, avg_batch_cost: 1.80544 s, avg_samples: 12.5, ips: 6.92353 samples/s, eta: 0:52:58
[2024/07/27 22:51:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:51:08] ppocr INFO: epoch: [52/200], global_step: 156, lr: 0.001000, loss: 2.705464, loss_shrink_maps: 1.598361, loss_threshold_maps: 0.795059, loss_binary_maps: 0.299510, avg_reader_cost: 1.53632 s, avg_batch_cost: 1.79338 s, avg_samples: 12.5, ips: 6.97007 samples/s, eta: 0:52:27
[2024/07/27 22:51:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:51:15] ppocr INFO: epoch: [53/200], global_step: 159, lr: 0.001000, loss: 2.715534, loss_shrink_maps: 1.609823, loss_threshold_maps: 0.795059, loss_binary_maps: 0.299896, avg_reader_cost: 1.61396 s, avg_batch_cost: 1.84249 s, avg_samples: 12.5, ips: 6.78429 samples/s, eta: 0:51:58
[2024/07/27 22:51:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:51:21] ppocr INFO: epoch: [54/200], global_step: 160, lr: 0.001000, loss: 2.731385, loss_shrink_maps: 1.627869, loss_threshold_maps: 0.800207, loss_binary_maps: 0.303681, avg_reader_cost: 0.43579 s, avg_batch_cost: 0.51777 s, avg_samples: 4.8, ips: 9.27048 samples/s, eta: 0:51:46
[2024/07/27 22:51:22] ppocr INFO: epoch: [54/200], global_step: 162, lr: 0.001000, loss: 2.762898, loss_shrink_maps: 1.652004, loss_threshold_maps: 0.791260, loss_binary_maps: 0.308412, avg_reader_cost: 1.12716 s, avg_batch_cost: 1.27283 s, avg_samples: 7.7, ips: 6.04951 samples/s, eta: 0:51:28
[2024/07/27 22:51:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:51:30] ppocr INFO: epoch: [55/200], global_step: 165, lr: 0.001000, loss: 2.760427, loss_shrink_maps: 1.661858, loss_threshold_maps: 0.791260, loss_binary_maps: 0.308412, avg_reader_cost: 1.66984 s, avg_batch_cost: 1.91678 s, avg_samples: 12.5, ips: 6.52135 samples/s, eta: 0:51:02
[2024/07/27 22:51:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:51:37] ppocr INFO: epoch: [56/200], global_step: 168, lr: 0.001000, loss: 2.781625, loss_shrink_maps: 1.661858, loss_threshold_maps: 0.791260, loss_binary_maps: 0.308412, avg_reader_cost: 1.57910 s, avg_batch_cost: 1.80785 s, avg_samples: 12.5, ips: 6.91430 samples/s, eta: 0:50:33
[2024/07/27 22:51:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:51:44] ppocr INFO: epoch: [57/200], global_step: 170, lr: 0.001000, loss: 2.800550, loss_shrink_maps: 1.682599, loss_threshold_maps: 0.796997, loss_binary_maps: 0.311142, avg_reader_cost: 0.91268 s, avg_batch_cost: 1.09156 s, avg_samples: 9.6, ips: 8.79477 samples/s, eta: 0:50:11
[2024/07/27 22:51:45] ppocr INFO: epoch: [57/200], global_step: 171, lr: 0.001000, loss: 2.819092, loss_shrink_maps: 1.699608, loss_threshold_maps: 0.800025, loss_binary_maps: 0.314334, avg_reader_cost: 0.59134 s, avg_batch_cost: 0.64630 s, avg_samples: 2.9, ips: 4.48706 samples/s, eta: 0:50:02
[2024/07/27 22:51:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:51:52] ppocr INFO: epoch: [58/200], global_step: 174, lr: 0.001000, loss: 2.819092, loss_shrink_maps: 1.699608, loss_threshold_maps: 0.800025, loss_binary_maps: 0.314334, avg_reader_cost: 1.51062 s, avg_batch_cost: 1.75703 s, avg_samples: 12.5, ips: 7.11427 samples/s, eta: 0:49:33
[2024/07/27 22:51:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:51:59] ppocr INFO: epoch: [59/200], global_step: 177, lr: 0.001000, loss: 2.807053, loss_shrink_maps: 1.682599, loss_threshold_maps: 0.800688, loss_binary_maps: 0.317258, avg_reader_cost: 1.59035 s, avg_batch_cost: 1.81844 s, avg_samples: 12.5, ips: 6.87403 samples/s, eta: 0:49:05
[2024/07/27 22:52:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:52:06] ppocr INFO: epoch: [60/200], global_step: 180, lr: 0.001000, loss: 2.781625, loss_shrink_maps: 1.645051, loss_threshold_maps: 0.797596, loss_binary_maps: 0.312184, avg_reader_cost: 1.57216 s, avg_batch_cost: 1.87187 s, avg_samples: 12.5, ips: 6.67782 samples/s, eta: 0:48:39

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.25it/s]
eval model::   1%|          | 4/500 [00:00<00:25, 19.37it/s]
eval model::   1%|          | 6/500 [00:00<00:25, 19.58it/s]
eval model::   2%|▏         | 9/500 [00:00<00:22, 21.82it/s]
eval model::   2%|▏         | 12/500 [00:00<00:22, 22.10it/s]
eval model::   3%|▎         | 15/500 [00:00<00:22, 21.39it/s]
eval model::   4%|▎         | 18/500 [00:00<00:21, 22.14it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 18.62it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.04it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 20.79it/s]
eval model::   6%|▌         | 30/500 [00:01<00:20, 22.53it/s]
eval model::   7%|▋         | 33/500 [00:01<00:21, 22.06it/s]
eval model::   7%|▋         | 36/500 [00:01<00:23, 19.51it/s]
eval model::   8%|▊         | 39/500 [00:01<00:24, 18.78it/s]
eval model::   8%|▊         | 42/500 [00:02<00:23, 19.23it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.76it/s]
eval model::  10%|▉         | 48/500 [00:02<00:22, 20.41it/s]
eval model::  10%|█         | 51/500 [00:02<00:22, 20.31it/s]
eval model::  11%|█         | 54/500 [00:02<00:22, 19.71it/s]
eval model::  11%|█         | 56/500 [00:02<00:24, 18.24it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:22, 19.20it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:24, 17.60it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:22, 19.02it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:20, 21.14it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 20.62it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 19.75it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.23it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:20, 20.10it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:23, 17.84it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:21, 19.05it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:23, 17.43it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:23, 17.59it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:23, 17.78it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:21, 19.23it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:18, 21.28it/s]
eval model::  20%|██        | 100/500 [00:05<00:20, 19.14it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 20.97it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 20.53it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:19, 19.61it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:19, 20.15it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:19, 20.08it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:17, 21.32it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:17, 21.20it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.11it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.78it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 18.98it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:20, 17.94it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:19, 19.06it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:18, 19.22it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:17, 21.14it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:15, 22.47it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:15, 22.62it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:16, 21.18it/s]
eval model::  30%|███       | 151/500 [00:07<00:18, 19.30it/s]
eval model::  31%|███       | 154/500 [00:07<00:16, 20.96it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:15, 21.92it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:16, 20.05it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:16, 20.40it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:15, 20.94it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 18.63it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 18.63it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:17, 19.04it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:17, 18.96it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:15, 20.37it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:17, 17.81it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 18.25it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:17, 18.10it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 19.68it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:15, 19.58it/s]
eval model::  39%|███▉      | 194/500 [00:09<00:15, 19.47it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:17, 16.94it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:16, 18.45it/s]
eval model::  40%|████      | 201/500 [00:10<00:17, 17.09it/s]
eval model::  41%|████      | 204/500 [00:10<00:14, 19.92it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:22, 12.79it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:21, 13.54it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.40it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.75it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.28it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 18.19it/s]
eval model::  45%|████▍     | 223/500 [00:11<00:15, 18.41it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:14, 18.55it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:13, 20.43it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 21.85it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 21.97it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 22.77it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:12, 21.19it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:13, 19.66it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:12, 19.84it/s]
eval model::  50%|█████     | 250/500 [00:12<00:13, 18.33it/s]
eval model::  51%|█████     | 253/500 [00:13<00:12, 20.41it/s]
eval model::  51%|█████     | 256/500 [00:13<00:12, 19.83it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:12, 19.75it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:12, 19.04it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 18.31it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:12, 19.19it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:10, 21.11it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:10, 21.23it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:10, 22.20it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:10, 21.18it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 20.76it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:13, 16.32it/s]
eval model::  58%|█████▊    | 288/500 [00:14<00:11, 18.42it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:10, 20.40it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:11, 18.14it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:10, 19.03it/s]
eval model::  60%|██████    | 300/500 [00:15<00:09, 20.02it/s]
eval model::  61%|██████    | 303/500 [00:15<00:09, 20.67it/s]
eval model::  61%|██████    | 306/500 [00:15<00:09, 20.29it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:08, 22.15it/s]
eval model::  62%|██████▏   | 312/500 [00:15<00:07, 23.69it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 22.04it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 20.53it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 20.41it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 20.32it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:08, 20.36it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:09, 18.84it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:09, 17.03it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:09, 17.68it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:09, 17.63it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 17.22it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:08, 18.94it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 20.49it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:07, 20.36it/s]
eval model::  70%|███████   | 352/500 [00:18<00:07, 20.91it/s]
eval model::  71%|███████   | 355/500 [00:18<00:07, 19.40it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 20.33it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:06, 20.34it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 19.24it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:06, 20.06it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.49it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 19.56it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 18.95it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:06, 19.52it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:06, 18.27it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:05, 20.28it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:07, 15.23it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 17.68it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:05, 18.81it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 17.73it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:05, 20.00it/s]
eval model::  80%|████████  | 401/500 [00:20<00:05, 19.02it/s]
eval model::  81%|████████  | 404/500 [00:20<00:04, 20.08it/s]
eval model::  81%|████████▏ | 407/500 [00:20<00:04, 21.49it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 22.36it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:03, 22.25it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:03, 21.61it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 19.25it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 17.55it/s]
eval model::  85%|████████▌ | 425/500 [00:21<00:03, 19.35it/s]
eval model::  86%|████████▌ | 429/500 [00:21<00:03, 22.68it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 21.56it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 18.72it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:03, 18.14it/s]
eval model::  88%|████████▊ | 440/500 [00:22<00:03, 16.71it/s]
eval model::  89%|████████▊ | 443/500 [00:22<00:03, 18.61it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:03, 18.15it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 17.79it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 18.10it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 18.91it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:02, 20.97it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:01, 21.64it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:02, 18.73it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 18.56it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:02, 15.93it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 18.48it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 19.37it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 19.55it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 21.71it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 20.65it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 19.45it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 19.29it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 20.90it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 21.10it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 20.82it/s]
eval model:: 100%|█████████▉| 499/500 [00:25<00:00, 21.98it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.48it/s]
[2024/07/27 22:52:32] ppocr INFO: cur metric, precision: 0.4856674856674857, recall: 0.2855079441502167, hmean: 0.35961188599151006, fps: 43.48894500728973
[2024/07/27 22:52:32] ppocr INFO: best metric, hmean: 0.3937633751146439, precision: 0.5393634840871022, recall: 0.3100625902744343, fps: 44.630790084791506, best_epoch: 40
[2024/07/27 22:52:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:52:39] ppocr INFO: epoch: [61/200], global_step: 183, lr: 0.001000, loss: 2.761134, loss_shrink_maps: 1.634458, loss_threshold_maps: 0.800688, loss_binary_maps: 0.310566, avg_reader_cost: 1.58068 s, avg_batch_cost: 1.80799 s, avg_samples: 12.5, ips: 6.91377 samples/s, eta: 0:48:12
[2024/07/27 22:52:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:52:46] ppocr INFO: epoch: [62/200], global_step: 186, lr: 0.001000, loss: 2.714184, loss_shrink_maps: 1.619299, loss_threshold_maps: 0.791485, loss_binary_maps: 0.303286, avg_reader_cost: 1.55529 s, avg_batch_cost: 1.78367 s, avg_samples: 12.5, ips: 7.00802 samples/s, eta: 0:47:45
[2024/07/27 22:52:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:52:54] ppocr INFO: epoch: [63/200], global_step: 189, lr: 0.001000, loss: 2.730275, loss_shrink_maps: 1.628531, loss_threshold_maps: 0.799263, loss_binary_maps: 0.310433, avg_reader_cost: 1.53843 s, avg_batch_cost: 1.77103 s, avg_samples: 12.5, ips: 7.05805 samples/s, eta: 0:47:17
[2024/07/27 22:52:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:53:00] ppocr INFO: epoch: [64/200], global_step: 190, lr: 0.001000, loss: 2.714184, loss_shrink_maps: 1.619299, loss_threshold_maps: 0.791485, loss_binary_maps: 0.309302, avg_reader_cost: 0.44466 s, avg_batch_cost: 0.54140 s, avg_samples: 4.8, ips: 8.86592 samples/s, eta: 0:47:07
[2024/07/27 22:53:01] ppocr INFO: epoch: [64/200], global_step: 192, lr: 0.001000, loss: 2.710151, loss_shrink_maps: 1.610044, loss_threshold_maps: 0.787534, loss_binary_maps: 0.303536, avg_reader_cost: 1.17561 s, avg_batch_cost: 1.32213 s, avg_samples: 7.7, ips: 5.82391 samples/s, eta: 0:46:52
[2024/07/27 22:53:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:53:09] ppocr INFO: epoch: [65/200], global_step: 195, lr: 0.001000, loss: 2.688580, loss_shrink_maps: 1.610044, loss_threshold_maps: 0.784868, loss_binary_maps: 0.303536, avg_reader_cost: 1.62918 s, avg_batch_cost: 1.85743 s, avg_samples: 12.5, ips: 6.72973 samples/s, eta: 0:46:27
[2024/07/27 22:53:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:53:16] ppocr INFO: epoch: [66/200], global_step: 198, lr: 0.001000, loss: 2.672901, loss_shrink_maps: 1.591697, loss_threshold_maps: 0.787440, loss_binary_maps: 0.301946, avg_reader_cost: 1.49447 s, avg_batch_cost: 1.72595 s, avg_samples: 12.5, ips: 7.24241 samples/s, eta: 0:46:00
[2024/07/27 22:53:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:53:23] ppocr INFO: epoch: [67/200], global_step: 200, lr: 0.001000, loss: 2.664440, loss_shrink_maps: 1.578102, loss_threshold_maps: 0.784868, loss_binary_maps: 0.297335, avg_reader_cost: 1.05292 s, avg_batch_cost: 1.23388 s, avg_samples: 9.6, ips: 7.78034 samples/s, eta: 0:45:43
[2024/07/27 22:53:24] ppocr INFO: epoch: [67/200], global_step: 201, lr: 0.001000, loss: 2.672901, loss_shrink_maps: 1.591697, loss_threshold_maps: 0.787440, loss_binary_maps: 0.301946, avg_reader_cost: 0.66316 s, avg_batch_cost: 0.71743 s, avg_samples: 2.9, ips: 4.04223 samples/s, eta: 0:45:37
[2024/07/27 22:53:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:53:31] ppocr INFO: epoch: [68/200], global_step: 204, lr: 0.001000, loss: 2.669932, loss_shrink_maps: 1.578102, loss_threshold_maps: 0.784868, loss_binary_maps: 0.301946, avg_reader_cost: 1.56376 s, avg_batch_cost: 1.85209 s, avg_samples: 12.5, ips: 6.74912 samples/s, eta: 0:45:12
[2024/07/27 22:53:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:53:38] ppocr INFO: epoch: [69/200], global_step: 207, lr: 0.001000, loss: 2.669932, loss_shrink_maps: 1.578102, loss_threshold_maps: 0.783410, loss_binary_maps: 0.301946, avg_reader_cost: 1.50533 s, avg_batch_cost: 1.73707 s, avg_samples: 12.5, ips: 7.19604 samples/s, eta: 0:44:46
[2024/07/27 22:53:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:53:46] ppocr INFO: epoch: [70/200], global_step: 210, lr: 0.001000, loss: 2.641843, loss_shrink_maps: 1.557222, loss_threshold_maps: 0.785152, loss_binary_maps: 0.293117, avg_reader_cost: 1.56459 s, avg_batch_cost: 1.81472 s, avg_samples: 12.5, ips: 6.88810 samples/s, eta: 0:44:21
[2024/07/27 22:53:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:53:53] ppocr INFO: epoch: [71/200], global_step: 213, lr: 0.001000, loss: 2.580212, loss_shrink_maps: 1.527909, loss_threshold_maps: 0.775341, loss_binary_maps: 0.286117, avg_reader_cost: 1.52201 s, avg_batch_cost: 1.75511 s, avg_samples: 12.5, ips: 7.12205 samples/s, eta: 0:43:55
[2024/07/27 22:53:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:54:00] ppocr INFO: epoch: [72/200], global_step: 216, lr: 0.001000, loss: 2.527924, loss_shrink_maps: 1.483813, loss_threshold_maps: 0.774889, loss_binary_maps: 0.286094, avg_reader_cost: 1.52074 s, avg_batch_cost: 1.74992 s, avg_samples: 12.5, ips: 7.14319 samples/s, eta: 0:43:29
[2024/07/27 22:54:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:54:07] ppocr INFO: epoch: [73/200], global_step: 219, lr: 0.001000, loss: 2.538662, loss_shrink_maps: 1.483813, loss_threshold_maps: 0.778675, loss_binary_maps: 0.286094, avg_reader_cost: 1.52922 s, avg_batch_cost: 1.75886 s, avg_samples: 12.5, ips: 7.10690 samples/s, eta: 0:43:04
[2024/07/27 22:54:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:54:13] ppocr INFO: epoch: [74/200], global_step: 220, lr: 0.001000, loss: 2.538662, loss_shrink_maps: 1.483813, loss_threshold_maps: 0.778675, loss_binary_maps: 0.286094, avg_reader_cost: 0.42019 s, avg_batch_cost: 0.50402 s, avg_samples: 4.8, ips: 9.52340 samples/s, eta: 0:42:54
[2024/07/27 22:54:14] ppocr INFO: epoch: [74/200], global_step: 222, lr: 0.001000, loss: 2.528662, loss_shrink_maps: 1.483128, loss_threshold_maps: 0.775469, loss_binary_maps: 0.286094, avg_reader_cost: 1.09905 s, avg_batch_cost: 1.24455 s, avg_samples: 7.7, ips: 6.18697 samples/s, eta: 0:42:39
[2024/07/27 22:54:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:54:22] ppocr INFO: epoch: [75/200], global_step: 225, lr: 0.001000, loss: 2.511218, loss_shrink_maps: 1.454382, loss_threshold_maps: 0.775469, loss_binary_maps: 0.284236, avg_reader_cost: 1.52484 s, avg_batch_cost: 1.75410 s, avg_samples: 12.5, ips: 7.12616 samples/s, eta: 0:42:14
[2024/07/27 22:54:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:54:29] ppocr INFO: epoch: [76/200], global_step: 228, lr: 0.001000, loss: 2.489786, loss_shrink_maps: 1.436770, loss_threshold_maps: 0.770664, loss_binary_maps: 0.278647, avg_reader_cost: 1.54290 s, avg_batch_cost: 1.79447 s, avg_samples: 12.5, ips: 6.96584 samples/s, eta: 0:41:50
[2024/07/27 22:54:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:54:35] ppocr INFO: epoch: [77/200], global_step: 230, lr: 0.001000, loss: 2.489786, loss_shrink_maps: 1.436770, loss_threshold_maps: 0.770664, loss_binary_maps: 0.278647, avg_reader_cost: 0.93753 s, avg_batch_cost: 1.11063 s, avg_samples: 9.6, ips: 8.64377 samples/s, eta: 0:41:33
[2024/07/27 22:54:36] ppocr INFO: epoch: [77/200], global_step: 231, lr: 0.001000, loss: 2.489786, loss_shrink_maps: 1.436770, loss_threshold_maps: 0.766299, loss_binary_maps: 0.278647, avg_reader_cost: 0.60096 s, avg_batch_cost: 0.65599 s, avg_samples: 2.9, ips: 4.42082 samples/s, eta: 0:41:26
[2024/07/27 22:54:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:54:43] ppocr INFO: epoch: [78/200], global_step: 234, lr: 0.001000, loss: 2.461014, loss_shrink_maps: 1.422646, loss_threshold_maps: 0.770664, loss_binary_maps: 0.275255, avg_reader_cost: 1.52691 s, avg_batch_cost: 1.77804 s, avg_samples: 12.5, ips: 7.03021 samples/s, eta: 0:41:02
[2024/07/27 22:54:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:54:50] ppocr INFO: epoch: [79/200], global_step: 237, lr: 0.001000, loss: 2.447735, loss_shrink_maps: 1.422646, loss_threshold_maps: 0.763517, loss_binary_maps: 0.275255, avg_reader_cost: 1.53971 s, avg_batch_cost: 1.76882 s, avg_samples: 12.5, ips: 7.06686 samples/s, eta: 0:40:38
[2024/07/27 22:54:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:54:58] ppocr INFO: epoch: [80/200], global_step: 240, lr: 0.001000, loss: 2.426032, loss_shrink_maps: 1.415533, loss_threshold_maps: 0.755503, loss_binary_maps: 0.274593, avg_reader_cost: 1.53347 s, avg_batch_cost: 1.80495 s, avg_samples: 12.5, ips: 6.92542 samples/s, eta: 0:40:14

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:37, 13.21it/s]
eval model::   1%|          | 5/500 [00:00<00:26, 18.62it/s]
eval model::   1%|▏         | 7/500 [00:00<00:26, 18.90it/s]
eval model::   2%|▏         | 10/500 [00:00<00:24, 19.94it/s]
eval model::   3%|▎         | 13/500 [00:00<00:25, 19.42it/s]
eval model::   3%|▎         | 15/500 [00:00<00:27, 17.80it/s]
eval model::   4%|▎         | 18/500 [00:00<00:23, 20.22it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.74it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.36it/s]
eval model::   5%|▌         | 27/500 [00:01<00:23, 19.80it/s]
eval model::   6%|▌         | 30/500 [00:01<00:23, 20.34it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 19.46it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 18.68it/s]
eval model::   8%|▊         | 38/500 [00:01<00:25, 18.18it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.68it/s]
eval model::   9%|▉         | 44/500 [00:02<00:21, 20.94it/s]
eval model::   9%|▉         | 47/500 [00:02<00:21, 20.88it/s]
eval model::  10%|█         | 51/500 [00:02<00:19, 23.44it/s]
eval model::  11%|█         | 54/500 [00:02<00:19, 22.66it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:20, 21.81it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:22, 19.30it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:27, 16.09it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:26, 16.44it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:23, 18.53it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:23, 18.48it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 19.56it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:21, 19.65it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:22, 18.48it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:20, 20.75it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:20, 20.62it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:20, 20.15it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:21, 19.38it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:21, 19.47it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:19, 20.50it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:18, 21.53it/s]
eval model::  20%|██        | 100/500 [00:05<00:18, 21.89it/s]
eval model::  21%|██        | 104/500 [00:05<00:17, 22.75it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:16, 23.33it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 21.54it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:16, 23.08it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:17, 22.16it/s]
eval model::  24%|██▍       | 119/500 [00:05<00:18, 20.90it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:19, 19.59it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:20, 18.44it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:19, 19.48it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 18.59it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 18.09it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 20.26it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:16, 21.95it/s]
eval model::  28%|██▊       | 141/500 [00:06<00:15, 23.09it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.62it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:17, 20.26it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 21.62it/s]
eval model::  31%|███       | 153/500 [00:07<00:16, 20.57it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 21.38it/s]
eval model::  32%|███▏      | 159/500 [00:07<00:16, 20.77it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:17, 19.75it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:16, 20.71it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:15, 21.00it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:15, 21.35it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:15, 20.52it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:14, 21.77it/s]
eval model::  36%|███▌      | 180/500 [00:08<00:15, 20.31it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:15, 21.11it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 21.08it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.14it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:15, 20.36it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:17, 17.83it/s]
eval model::  39%|███▉      | 197/500 [00:09<00:17, 17.46it/s]
eval model::  40%|████      | 200/500 [00:09<00:15, 19.27it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 20.39it/s]
eval model::  41%|████      | 206/500 [00:10<00:21, 13.87it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:21, 13.84it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:20, 14.19it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:18, 15.35it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.56it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.31it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.23it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 15.35it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:14, 18.35it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:13, 19.59it/s]
eval model::  46%|████▌     | 231/500 [00:11<00:12, 21.62it/s]
eval model::  47%|████▋     | 234/500 [00:11<00:12, 20.95it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 23.02it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:10, 25.14it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:11, 22.50it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:10, 24.78it/s]
eval model::  50%|█████     | 251/500 [00:12<00:10, 22.77it/s]
eval model::  51%|█████     | 254/500 [00:12<00:10, 24.21it/s]
eval model::  51%|█████▏    | 257/500 [00:12<00:09, 25.09it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:08, 26.69it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:11, 20.71it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:11, 20.82it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:10, 22.67it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:11, 19.43it/s]
eval model::  55%|█████▌    | 276/500 [00:13<00:11, 19.39it/s]
eval model::  56%|█████▌    | 279/500 [00:13<00:11, 20.04it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 20.36it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:14, 14.36it/s]
eval model::  58%|█████▊    | 288/500 [00:14<00:12, 16.86it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:10, 19.03it/s]
eval model::  59%|█████▉    | 294/500 [00:14<00:12, 17.15it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:12, 16.42it/s]
eval model::  60%|██████    | 300/500 [00:15<00:12, 16.00it/s]
eval model::  60%|██████    | 302/500 [00:15<00:11, 16.55it/s]
eval model::  61%|██████    | 304/500 [00:15<00:11, 17.12it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:09, 19.34it/s]
eval model::  62%|██████▏   | 310/500 [00:15<00:09, 19.46it/s]
eval model::  63%|██████▎   | 313/500 [00:15<00:09, 19.26it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 19.17it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:09, 19.86it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 21.52it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 21.05it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:08, 21.38it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:08, 20.70it/s]
eval model::  67%|██████▋   | 333/500 [00:16<00:08, 20.39it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:08, 18.92it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:08, 18.91it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:10, 15.41it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:09, 17.36it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 19.69it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:07, 20.79it/s]
eval model::  70%|███████   | 352/500 [00:17<00:07, 19.56it/s]
eval model::  71%|███████   | 355/500 [00:18<00:07, 18.49it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:07, 18.10it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:08, 17.58it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 17.44it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 17.14it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:07, 18.51it/s]
eval model::  74%|███████▍  | 369/500 [00:18<00:07, 18.44it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 19.45it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 19.66it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 20.93it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:05, 20.03it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:05, 20.49it/s]
eval model::  77%|███████▋  | 387/500 [00:19<00:07, 15.12it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 17.74it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 19.56it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 19.56it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 20.61it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 20.64it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 22.67it/s]
eval model::  82%|████████▏ | 408/500 [00:20<00:04, 22.93it/s]
eval model::  82%|████████▏ | 411/500 [00:20<00:04, 21.33it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:04, 20.47it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 18.94it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 17.27it/s]
eval model::  85%|████████▍ | 423/500 [00:21<00:04, 18.69it/s]
eval model::  85%|████████▌ | 425/500 [00:21<00:04, 18.36it/s]
eval model::  86%|████████▌ | 429/500 [00:21<00:03, 22.61it/s]
eval model::  86%|████████▋ | 432/500 [00:21<00:02, 24.33it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 27.66it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 25.55it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 23.23it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 22.61it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 20.76it/s]
eval model::  90%|█████████ | 451/500 [00:22<00:02, 22.10it/s]
eval model::  91%|█████████ | 454/500 [00:22<00:01, 23.40it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 24.04it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 23.65it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 21.55it/s]
eval model::  93%|█████████▎| 466/500 [00:23<00:01, 19.64it/s]
eval model::  94%|█████████▍| 469/500 [00:23<00:01, 21.87it/s]
eval model::  94%|█████████▍| 472/500 [00:23<00:01, 21.29it/s]
eval model::  95%|█████████▌| 475/500 [00:23<00:01, 22.70it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:00, 22.55it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 24.06it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 24.24it/s]
eval model::  97%|█████████▋| 487/500 [00:24<00:00, 21.32it/s]
eval model::  98%|█████████▊| 490/500 [00:24<00:00, 20.50it/s]
eval model::  99%|█████████▊| 493/500 [00:24<00:00, 20.85it/s]
eval model::  99%|█████████▉| 496/500 [00:24<00:00, 20.57it/s]
eval model:: 100%|█████████▉| 499/500 [00:25<00:00, 20.13it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.92it/s]
[2024/07/27 22:55:23] ppocr INFO: cur metric, precision: 0.5894511760513186, recall: 0.39817043813192105, hmean: 0.47528735632183905, fps: 45.44820460204594
[2024/07/27 22:55:23] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 22:55:23] ppocr INFO: best metric, hmean: 0.47528735632183905, precision: 0.5894511760513186, recall: 0.39817043813192105, fps: 45.44820460204594, best_epoch: 80
[2024/07/27 22:55:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:55:30] ppocr INFO: epoch: [81/200], global_step: 243, lr: 0.001000, loss: 2.413708, loss_shrink_maps: 1.393320, loss_threshold_maps: 0.762523, loss_binary_maps: 0.270839, avg_reader_cost: 1.65188 s, avg_batch_cost: 1.93480 s, avg_samples: 12.5, ips: 6.46061 samples/s, eta: 0:39:53
[2024/07/27 22:55:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:55:38] ppocr INFO: epoch: [82/200], global_step: 246, lr: 0.001000, loss: 2.407914, loss_shrink_maps: 1.384549, loss_threshold_maps: 0.738498, loss_binary_maps: 0.268179, avg_reader_cost: 1.52385 s, avg_batch_cost: 1.76864 s, avg_samples: 12.5, ips: 7.06757 samples/s, eta: 0:39:29
[2024/07/27 22:55:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:55:45] ppocr INFO: epoch: [83/200], global_step: 249, lr: 0.001000, loss: 2.401487, loss_shrink_maps: 1.370736, loss_threshold_maps: 0.746446, loss_binary_maps: 0.267107, avg_reader_cost: 1.49902 s, avg_batch_cost: 1.73194 s, avg_samples: 12.5, ips: 7.21734 samples/s, eta: 0:39:05
[2024/07/27 22:55:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:55:51] ppocr INFO: epoch: [84/200], global_step: 250, lr: 0.001000, loss: 2.413314, loss_shrink_maps: 1.384549, loss_threshold_maps: 0.755885, loss_binary_maps: 0.268179, avg_reader_cost: 0.38855 s, avg_batch_cost: 0.55387 s, avg_samples: 4.8, ips: 8.66622 samples/s, eta: 0:38:57
[2024/07/27 22:55:52] ppocr INFO: epoch: [84/200], global_step: 252, lr: 0.001000, loss: 2.380627, loss_shrink_maps: 1.354594, loss_threshold_maps: 0.755885, loss_binary_maps: 0.265598, avg_reader_cost: 1.19909 s, avg_batch_cost: 1.34490 s, avg_samples: 7.7, ips: 5.72535 samples/s, eta: 0:38:44
[2024/07/27 22:55:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:56:00] ppocr INFO: epoch: [85/200], global_step: 255, lr: 0.001000, loss: 2.380627, loss_shrink_maps: 1.365031, loss_threshold_maps: 0.746446, loss_binary_maps: 0.266738, avg_reader_cost: 1.65561 s, avg_batch_cost: 1.88336 s, avg_samples: 12.5, ips: 6.63706 samples/s, eta: 0:38:22
[2024/07/27 22:56:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:56:07] ppocr INFO: epoch: [86/200], global_step: 258, lr: 0.001000, loss: 2.358946, loss_shrink_maps: 1.354594, loss_threshold_maps: 0.752293, loss_binary_maps: 0.265027, avg_reader_cost: 1.53284 s, avg_batch_cost: 1.76277 s, avg_samples: 12.5, ips: 7.09111 samples/s, eta: 0:37:59
[2024/07/27 22:56:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:56:14] ppocr INFO: epoch: [87/200], global_step: 260, lr: 0.001000, loss: 2.358946, loss_shrink_maps: 1.354594, loss_threshold_maps: 0.752293, loss_binary_maps: 0.265027, avg_reader_cost: 0.91586 s, avg_batch_cost: 1.14909 s, avg_samples: 9.6, ips: 8.35442 samples/s, eta: 0:37:43
[2024/07/27 22:56:15] ppocr INFO: epoch: [87/200], global_step: 261, lr: 0.001000, loss: 2.358946, loss_shrink_maps: 1.365031, loss_threshold_maps: 0.752293, loss_binary_maps: 0.266738, avg_reader_cost: 0.62055 s, avg_batch_cost: 0.67539 s, avg_samples: 2.9, ips: 4.29384 samples/s, eta: 0:37:37
[2024/07/27 22:56:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:56:22] ppocr INFO: epoch: [88/200], global_step: 264, lr: 0.001000, loss: 2.344645, loss_shrink_maps: 1.353956, loss_threshold_maps: 0.752293, loss_binary_maps: 0.265027, avg_reader_cost: 1.55293 s, avg_batch_cost: 1.79349 s, avg_samples: 12.5, ips: 6.96967 samples/s, eta: 0:37:14
[2024/07/27 22:56:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:56:30] ppocr INFO: epoch: [89/200], global_step: 267, lr: 0.001000, loss: 2.347100, loss_shrink_maps: 1.353956, loss_threshold_maps: 0.740899, loss_binary_maps: 0.265027, avg_reader_cost: 1.62962 s, avg_batch_cost: 1.89718 s, avg_samples: 12.5, ips: 6.58874 samples/s, eta: 0:36:53
[2024/07/27 22:56:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:56:37] ppocr INFO: epoch: [90/200], global_step: 270, lr: 0.001000, loss: 2.347100, loss_shrink_maps: 1.350423, loss_threshold_maps: 0.742010, loss_binary_maps: 0.264234, avg_reader_cost: 1.54797 s, avg_batch_cost: 1.77743 s, avg_samples: 12.5, ips: 7.03263 samples/s, eta: 0:36:30
[2024/07/27 22:56:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:56:44] ppocr INFO: epoch: [91/200], global_step: 273, lr: 0.001000, loss: 2.384087, loss_shrink_maps: 1.377540, loss_threshold_maps: 0.738346, loss_binary_maps: 0.269044, avg_reader_cost: 1.58510 s, avg_batch_cost: 1.81392 s, avg_samples: 12.5, ips: 6.89114 samples/s, eta: 0:36:08
[2024/07/27 22:56:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:56:52] ppocr INFO: epoch: [92/200], global_step: 276, lr: 0.001000, loss: 2.357289, loss_shrink_maps: 1.325421, loss_threshold_maps: 0.742010, loss_binary_maps: 0.261328, avg_reader_cost: 1.76000 s, avg_batch_cost: 1.98832 s, avg_samples: 12.5, ips: 6.28671 samples/s, eta: 0:35:48
[2024/07/27 22:56:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:57:00] ppocr INFO: epoch: [93/200], global_step: 279, lr: 0.001000, loss: 2.375874, loss_shrink_maps: 1.357206, loss_threshold_maps: 0.742010, loss_binary_maps: 0.268044, avg_reader_cost: 1.54059 s, avg_batch_cost: 1.77125 s, avg_samples: 12.5, ips: 7.05717 samples/s, eta: 0:35:26
[2024/07/27 22:57:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:57:06] ppocr INFO: epoch: [94/200], global_step: 280, lr: 0.001000, loss: 2.357289, loss_shrink_maps: 1.325421, loss_threshold_maps: 0.742010, loss_binary_maps: 0.261328, avg_reader_cost: 0.40670 s, avg_batch_cost: 0.51694 s, avg_samples: 4.8, ips: 9.28549 samples/s, eta: 0:35:18
[2024/07/27 22:57:07] ppocr INFO: epoch: [94/200], global_step: 282, lr: 0.001000, loss: 2.303834, loss_shrink_maps: 1.307159, loss_threshold_maps: 0.737994, loss_binary_maps: 0.257220, avg_reader_cost: 1.12606 s, avg_batch_cost: 1.27107 s, avg_samples: 7.7, ips: 6.05788 samples/s, eta: 0:35:04
[2024/07/27 22:57:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:57:14] ppocr INFO: epoch: [95/200], global_step: 285, lr: 0.001000, loss: 2.311686, loss_shrink_maps: 1.303561, loss_threshold_maps: 0.737994, loss_binary_maps: 0.256632, avg_reader_cost: 1.54104 s, avg_batch_cost: 1.78521 s, avg_samples: 12.5, ips: 7.00199 samples/s, eta: 0:34:42
[2024/07/27 22:57:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:57:22] ppocr INFO: epoch: [96/200], global_step: 288, lr: 0.001000, loss: 2.311686, loss_shrink_maps: 1.303561, loss_threshold_maps: 0.737994, loss_binary_maps: 0.256632, avg_reader_cost: 1.56378 s, avg_batch_cost: 1.83825 s, avg_samples: 12.5, ips: 6.79993 samples/s, eta: 0:34:20
[2024/07/27 22:57:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:57:29] ppocr INFO: epoch: [97/200], global_step: 290, lr: 0.001000, loss: 2.234694, loss_shrink_maps: 1.275660, loss_threshold_maps: 0.732614, loss_binary_maps: 0.251646, avg_reader_cost: 0.92867 s, avg_batch_cost: 1.10644 s, avg_samples: 9.6, ips: 8.67646 samples/s, eta: 0:34:05
[2024/07/27 22:57:29] ppocr INFO: epoch: [97/200], global_step: 291, lr: 0.001000, loss: 2.234694, loss_shrink_maps: 1.273227, loss_threshold_maps: 0.732614, loss_binary_maps: 0.249761, avg_reader_cost: 0.59887 s, avg_batch_cost: 0.65388 s, avg_samples: 2.9, ips: 4.43509 samples/s, eta: 0:33:58
[2024/07/27 22:57:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:57:36] ppocr INFO: epoch: [98/200], global_step: 294, lr: 0.001000, loss: 2.229774, loss_shrink_maps: 1.275477, loss_threshold_maps: 0.739655, loss_binary_maps: 0.250321, avg_reader_cost: 1.55785 s, avg_batch_cost: 1.81880 s, avg_samples: 12.5, ips: 6.87266 samples/s, eta: 0:33:37
[2024/07/27 22:57:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:57:44] ppocr INFO: epoch: [99/200], global_step: 297, lr: 0.001000, loss: 2.229774, loss_shrink_maps: 1.275477, loss_threshold_maps: 0.739655, loss_binary_maps: 0.250321, avg_reader_cost: 1.51688 s, avg_batch_cost: 1.75537 s, avg_samples: 12.5, ips: 7.12102 samples/s, eta: 0:33:15
[2024/07/27 22:57:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:57:51] ppocr INFO: epoch: [100/200], global_step: 300, lr: 0.001000, loss: 2.217878, loss_shrink_maps: 1.250502, loss_threshold_maps: 0.739655, loss_binary_maps: 0.245927, avg_reader_cost: 1.63054 s, avg_batch_cost: 1.88173 s, avg_samples: 12.5, ips: 6.64281 samples/s, eta: 0:32:54

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:58,  8.52it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 17.02it/s]
eval model::   1%|▏         | 7/500 [00:00<00:26, 18.85it/s]
eval model::   2%|▏         | 9/500 [00:00<00:30, 15.90it/s]
eval model::   2%|▏         | 11/500 [00:00<00:33, 14.55it/s]
eval model::   3%|▎         | 13/500 [00:00<00:30, 15.72it/s]
eval model::   3%|▎         | 15/500 [00:00<00:29, 16.46it/s]
eval model::   4%|▎         | 18/500 [00:01<00:25, 19.10it/s]
eval model::   4%|▍         | 20/500 [00:01<00:27, 17.41it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.37it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 21.12it/s]
eval model::   6%|▌         | 29/500 [00:01<00:20, 23.03it/s]
eval model::   6%|▋         | 32/500 [00:01<00:20, 22.73it/s]
eval model::   7%|▋         | 35/500 [00:01<00:21, 21.97it/s]
eval model::   8%|▊         | 38/500 [00:01<00:21, 21.01it/s]
eval model::   8%|▊         | 41/500 [00:02<00:22, 20.26it/s]
eval model::   9%|▉         | 44/500 [00:02<00:24, 18.53it/s]
eval model::   9%|▉         | 46/500 [00:02<00:26, 17.08it/s]
eval model::  10%|█         | 50/500 [00:02<00:22, 19.61it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 19.25it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 21.12it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 19.03it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:24, 17.78it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:25, 17.08it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 19.68it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 21.38it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 22.28it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 20.88it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 20.57it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:20, 20.94it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 20.41it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.17it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.49it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.50it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:23, 17.56it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:22, 17.91it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:20, 19.22it/s]
eval model::  20%|██        | 100/500 [00:05<00:21, 18.65it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 20.91it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.53it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 20.68it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:20, 18.94it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:18, 20.58it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:17, 21.41it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:20, 18.78it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 20.33it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.58it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:21, 16.95it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 17.24it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:19, 18.59it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.48it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.92it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.32it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:16, 20.95it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 21.30it/s]
eval model::  31%|███       | 153/500 [00:07<00:16, 21.08it/s]
eval model::  31%|███       | 156/500 [00:07<00:15, 22.74it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:15, 21.45it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:20, 16.40it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:18, 17.64it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 18.06it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:18, 18.28it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 18.42it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:18, 17.36it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:17, 18.87it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:18, 17.78it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 19.75it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 21.16it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.13it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:15, 19.75it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:17, 17.45it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:17, 17.42it/s]
eval model::  40%|████      | 200/500 [00:10<00:15, 19.10it/s]
eval model::  41%|████      | 203/500 [00:10<00:15, 19.53it/s]
eval model::  41%|████      | 206/500 [00:10<00:21, 13.47it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:21, 13.71it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 14.24it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.62it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.31it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.32it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.51it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 15.37it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:15, 17.93it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:14, 19.40it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 21.18it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 21.01it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 21.80it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 23.01it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 20.55it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:11, 22.42it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:11, 21.85it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 19.14it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.13it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:10, 22.28it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 20.08it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 18.17it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:12, 18.45it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 19.29it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 20.25it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 20.32it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 21.40it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:09, 22.02it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:09, 22.87it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:12, 16.44it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:11, 18.86it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:10, 19.44it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:11, 17.02it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:10, 18.79it/s]
eval model::  60%|██████    | 302/500 [00:15<00:11, 17.73it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 18.61it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:10, 19.02it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 19.61it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:09, 20.06it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:08, 21.31it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:07, 22.59it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:08, 20.82it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:07, 22.08it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 21.14it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:07, 22.78it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 22.78it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:07, 21.43it/s]
eval model::  68%|██████▊   | 341/500 [00:17<00:09, 16.99it/s]
eval model::  69%|██████▉   | 344/500 [00:17<00:08, 19.41it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:06, 22.29it/s]
eval model::  70%|███████   | 351/500 [00:18<00:06, 21.69it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.90it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 21.79it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:06, 21.75it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:06, 20.50it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:06, 19.33it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.85it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 20.45it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:06, 20.63it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 21.35it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 22.04it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 20.75it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:07, 14.22it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:06, 16.29it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 16.86it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:06, 17.09it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 17.63it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 18.00it/s]
eval model::  81%|████████  | 404/500 [00:20<00:04, 20.89it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 22.41it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 24.11it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 25.29it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 22.43it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 18.54it/s]
eval model::  85%|████████▍ | 423/500 [00:21<00:03, 19.34it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:03, 18.93it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 20.33it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 19.86it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 21.31it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 21.25it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 19.78it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 21.06it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 20.79it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 20.79it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 22.03it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:02, 21.68it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:01, 21.27it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:02, 18.18it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:02, 16.90it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 17.70it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 18.77it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 18.68it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 20.99it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 21.26it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 21.84it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 19.36it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 20.08it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 20.84it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 21.41it/s]
eval model:: 100%|█████████▉| 499/500 [00:25<00:00, 23.28it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.54it/s]
[2024/07/27 22:58:17] ppocr INFO: cur metric, precision: 0.5856142584341184, recall: 0.44294655753490614, hmean: 0.5043859649122808, fps: 45.04990682412584
[2024/07/27 22:58:17] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 22:58:17] ppocr INFO: best metric, hmean: 0.5043859649122808, precision: 0.5856142584341184, recall: 0.44294655753490614, fps: 45.04990682412584, best_epoch: 100
[2024/07/27 22:58:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:58:24] ppocr INFO: epoch: [101/200], global_step: 303, lr: 0.001000, loss: 2.185793, loss_shrink_maps: 1.230610, loss_threshold_maps: 0.724084, loss_binary_maps: 0.242696, avg_reader_cost: 1.63002 s, avg_batch_cost: 1.92717 s, avg_samples: 12.5, ips: 6.48620 samples/s, eta: 0:32:34
[2024/07/27 22:58:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:58:32] ppocr INFO: epoch: [102/200], global_step: 306, lr: 0.001000, loss: 2.214250, loss_shrink_maps: 1.243780, loss_threshold_maps: 0.738014, loss_binary_maps: 0.244770, avg_reader_cost: 1.55592 s, avg_batch_cost: 1.78454 s, avg_samples: 12.5, ips: 7.00461 samples/s, eta: 0:32:12
[2024/07/27 22:58:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:58:39] ppocr INFO: epoch: [103/200], global_step: 309, lr: 0.001000, loss: 2.214250, loss_shrink_maps: 1.247642, loss_threshold_maps: 0.733562, loss_binary_maps: 0.245231, avg_reader_cost: 1.49417 s, avg_batch_cost: 1.73004 s, avg_samples: 12.5, ips: 7.22526 samples/s, eta: 0:31:50
[2024/07/27 22:58:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:58:45] ppocr INFO: epoch: [104/200], global_step: 310, lr: 0.001000, loss: 2.214250, loss_shrink_maps: 1.247642, loss_threshold_maps: 0.738014, loss_binary_maps: 0.245231, avg_reader_cost: 0.41476 s, avg_batch_cost: 0.52622 s, avg_samples: 4.8, ips: 9.12167 samples/s, eta: 0:31:43
[2024/07/27 22:58:46] ppocr INFO: epoch: [104/200], global_step: 312, lr: 0.001000, loss: 2.199984, loss_shrink_maps: 1.237943, loss_threshold_maps: 0.735469, loss_binary_maps: 0.243349, avg_reader_cost: 1.14355 s, avg_batch_cost: 1.28896 s, avg_samples: 7.7, ips: 5.97382 samples/s, eta: 0:31:29
[2024/07/27 22:58:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:58:54] ppocr INFO: epoch: [105/200], global_step: 315, lr: 0.001000, loss: 2.175625, loss_shrink_maps: 1.217362, loss_threshold_maps: 0.725882, loss_binary_maps: 0.240560, avg_reader_cost: 1.52159 s, avg_batch_cost: 1.75023 s, avg_samples: 12.5, ips: 7.14191 samples/s, eta: 0:31:08
[2024/07/27 22:58:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:59:01] ppocr INFO: epoch: [106/200], global_step: 318, lr: 0.001000, loss: 2.147609, loss_shrink_maps: 1.197535, loss_threshold_maps: 0.715122, loss_binary_maps: 0.236154, avg_reader_cost: 1.52172 s, avg_batch_cost: 1.75422 s, avg_samples: 12.5, ips: 7.12566 samples/s, eta: 0:30:46
[2024/07/27 22:59:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:59:08] ppocr INFO: epoch: [107/200], global_step: 320, lr: 0.001000, loss: 2.147609, loss_shrink_maps: 1.197535, loss_threshold_maps: 0.719279, loss_binary_maps: 0.236154, avg_reader_cost: 0.95338 s, avg_batch_cost: 1.13005 s, avg_samples: 9.6, ips: 8.49519 samples/s, eta: 0:30:31
[2024/07/27 22:59:08] ppocr INFO: epoch: [107/200], global_step: 321, lr: 0.001000, loss: 2.130305, loss_shrink_maps: 1.184942, loss_threshold_maps: 0.719279, loss_binary_maps: 0.233893, avg_reader_cost: 0.61133 s, avg_batch_cost: 0.66560 s, avg_samples: 2.9, ips: 4.35699 samples/s, eta: 0:30:25
[2024/07/27 22:59:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:59:15] ppocr INFO: epoch: [108/200], global_step: 324, lr: 0.001000, loss: 2.189816, loss_shrink_maps: 1.218327, loss_threshold_maps: 0.730540, loss_binary_maps: 0.239622, avg_reader_cost: 1.51711 s, avg_batch_cost: 1.76710 s, avg_samples: 12.5, ips: 7.07373 samples/s, eta: 0:30:04
[2024/07/27 22:59:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:59:23] ppocr INFO: epoch: [109/200], global_step: 327, lr: 0.001000, loss: 2.157946, loss_shrink_maps: 1.200234, loss_threshold_maps: 0.723937, loss_binary_maps: 0.236510, avg_reader_cost: 1.55515 s, avg_batch_cost: 1.80805 s, avg_samples: 12.5, ips: 6.91352 samples/s, eta: 0:29:43
[2024/07/27 22:59:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:59:30] ppocr INFO: epoch: [110/200], global_step: 330, lr: 0.001000, loss: 2.197896, loss_shrink_maps: 1.224333, loss_threshold_maps: 0.722624, loss_binary_maps: 0.239998, avg_reader_cost: 1.53404 s, avg_batch_cost: 1.78330 s, avg_samples: 12.5, ips: 7.00947 samples/s, eta: 0:29:22
[2024/07/27 22:59:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:59:37] ppocr INFO: epoch: [111/200], global_step: 333, lr: 0.001000, loss: 2.197896, loss_shrink_maps: 1.224333, loss_threshold_maps: 0.722624, loss_binary_maps: 0.239998, avg_reader_cost: 1.53841 s, avg_batch_cost: 1.76621 s, avg_samples: 12.5, ips: 7.07731 samples/s, eta: 0:29:01
[2024/07/27 22:59:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:59:45] ppocr INFO: epoch: [112/200], global_step: 336, lr: 0.001000, loss: 2.221738, loss_shrink_maps: 1.242462, loss_threshold_maps: 0.732319, loss_binary_maps: 0.245337, avg_reader_cost: 1.53690 s, avg_batch_cost: 1.76895 s, avg_samples: 12.5, ips: 7.06634 samples/s, eta: 0:28:40
[2024/07/27 22:59:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:59:52] ppocr INFO: epoch: [113/200], global_step: 339, lr: 0.001000, loss: 2.225064, loss_shrink_maps: 1.249877, loss_threshold_maps: 0.733288, loss_binary_maps: 0.247279, avg_reader_cost: 1.52740 s, avg_batch_cost: 1.76416 s, avg_samples: 12.5, ips: 7.08552 samples/s, eta: 0:28:19
[2024/07/27 22:59:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 22:59:58] ppocr INFO: epoch: [114/200], global_step: 340, lr: 0.001000, loss: 2.221738, loss_shrink_maps: 1.247350, loss_threshold_maps: 0.732319, loss_binary_maps: 0.246126, avg_reader_cost: 0.42210 s, avg_batch_cost: 0.50710 s, avg_samples: 4.8, ips: 9.46566 samples/s, eta: 0:28:11
[2024/07/27 22:59:59] ppocr INFO: epoch: [114/200], global_step: 342, lr: 0.001000, loss: 2.218864, loss_shrink_maps: 1.247350, loss_threshold_maps: 0.731368, loss_binary_maps: 0.246126, avg_reader_cost: 1.10577 s, avg_batch_cost: 1.25164 s, avg_samples: 7.7, ips: 6.15191 samples/s, eta: 0:27:58
[2024/07/27 23:00:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:00:07] ppocr INFO: epoch: [115/200], global_step: 345, lr: 0.001000, loss: 2.242110, loss_shrink_maps: 1.258454, loss_threshold_maps: 0.732136, loss_binary_maps: 0.248720, avg_reader_cost: 1.52538 s, avg_batch_cost: 1.75324 s, avg_samples: 12.5, ips: 7.12965 samples/s, eta: 0:27:37
[2024/07/27 23:00:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:00:14] ppocr INFO: epoch: [116/200], global_step: 348, lr: 0.001000, loss: 2.242110, loss_shrink_maps: 1.258454, loss_threshold_maps: 0.732136, loss_binary_maps: 0.248720, avg_reader_cost: 1.49899 s, avg_batch_cost: 1.73589 s, avg_samples: 12.5, ips: 7.20094 samples/s, eta: 0:27:15
[2024/07/27 23:00:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:00:20] ppocr INFO: epoch: [117/200], global_step: 350, lr: 0.001000, loss: 2.269278, loss_shrink_maps: 1.277204, loss_threshold_maps: 0.736985, loss_binary_maps: 0.252209, avg_reader_cost: 0.92103 s, avg_batch_cost: 1.10182 s, avg_samples: 9.6, ips: 8.71286 samples/s, eta: 0:27:01
[2024/07/27 23:00:21] ppocr INFO: epoch: [117/200], global_step: 351, lr: 0.001000, loss: 2.294173, loss_shrink_maps: 1.302214, loss_threshold_maps: 0.740341, loss_binary_maps: 0.256901, avg_reader_cost: 0.59673 s, avg_batch_cost: 0.65141 s, avg_samples: 2.9, ips: 4.45191 samples/s, eta: 0:26:55
[2024/07/27 23:00:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:00:28] ppocr INFO: epoch: [118/200], global_step: 354, lr: 0.001000, loss: 2.269278, loss_shrink_maps: 1.277204, loss_threshold_maps: 0.732534, loss_binary_maps: 0.252209, avg_reader_cost: 1.56508 s, avg_batch_cost: 1.80991 s, avg_samples: 12.5, ips: 6.90643 samples/s, eta: 0:26:34
[2024/07/27 23:00:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:00:36] ppocr INFO: epoch: [119/200], global_step: 357, lr: 0.001000, loss: 2.236965, loss_shrink_maps: 1.263318, loss_threshold_maps: 0.730353, loss_binary_maps: 0.249615, avg_reader_cost: 1.59956 s, avg_batch_cost: 1.82829 s, avg_samples: 12.5, ips: 6.83701 samples/s, eta: 0:26:14
[2024/07/27 23:00:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:00:43] ppocr INFO: epoch: [120/200], global_step: 360, lr: 0.001000, loss: 2.178655, loss_shrink_maps: 1.223585, loss_threshold_maps: 0.726326, loss_binary_maps: 0.241688, avg_reader_cost: 1.50602 s, avg_batch_cost: 1.74843 s, avg_samples: 12.5, ips: 7.14929 samples/s, eta: 0:25:53

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.39it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.38it/s]
eval model::   1%|▏         | 7/500 [00:00<00:24, 20.02it/s]
eval model::   2%|▏         | 10/500 [00:00<00:22, 21.53it/s]
eval model::   3%|▎         | 13/500 [00:00<00:23, 20.56it/s]
eval model::   3%|▎         | 16/500 [00:00<00:21, 22.25it/s]
eval model::   4%|▍         | 19/500 [00:00<00:22, 21.46it/s]
eval model::   4%|▍         | 22/500 [00:01<00:21, 22.38it/s]
eval model::   5%|▌         | 25/500 [00:01<00:21, 22.48it/s]
eval model::   6%|▌         | 28/500 [00:01<00:20, 23.03it/s]
eval model::   6%|▌         | 31/500 [00:01<00:24, 19.50it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 18.96it/s]
eval model::   8%|▊         | 38/500 [00:01<00:21, 21.34it/s]
eval model::   8%|▊         | 41/500 [00:01<00:20, 21.88it/s]
eval model::   9%|▉         | 44/500 [00:02<00:21, 21.66it/s]
eval model::   9%|▉         | 47/500 [00:02<00:21, 20.76it/s]
eval model::  10%|█         | 50/500 [00:02<00:22, 19.84it/s]
eval model::  11%|█         | 53/500 [00:02<00:20, 21.54it/s]
eval model::  11%|█         | 56/500 [00:02<00:22, 19.91it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:23, 19.05it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:25, 16.98it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:25, 16.92it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 19.20it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.83it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:18, 22.54it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:19, 21.85it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:19, 21.62it/s]
eval model::  16%|█▌        | 81/500 [00:03<00:19, 21.96it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 20.35it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 19.94it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 17.91it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.35it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:20, 19.91it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:19, 20.67it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.53it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 20.23it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:19, 20.50it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 21.38it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 21.38it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:18, 20.50it/s]
eval model::  24%|██▍       | 120/500 [00:05<00:19, 19.11it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.13it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 19.39it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.78it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 16.84it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:19, 18.28it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:18, 19.68it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.45it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.44it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.81it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.76it/s]
eval model::  31%|███       | 153/500 [00:07<00:19, 17.98it/s]
eval model::  31%|███       | 156/500 [00:07<00:17, 20.18it/s]
eval model::  32%|███▏      | 159/500 [00:07<00:17, 19.87it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:18, 17.90it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:17, 19.37it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:16, 20.28it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:16, 19.78it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:17, 18.35it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:16, 19.82it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:18, 17.29it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 18.77it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:14, 21.63it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:13, 22.62it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:12, 24.26it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:13, 22.14it/s]
eval model::  40%|███▉      | 199/500 [00:09<00:13, 21.97it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 22.34it/s]
eval model::  41%|████      | 205/500 [00:10<00:23, 12.75it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:20, 14.05it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:19, 15.00it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:17, 16.85it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.57it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.53it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 17.85it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:16, 16.48it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:13, 20.38it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:11, 23.78it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:10, 25.21it/s]
eval model::  47%|████▋     | 236/500 [00:11<00:11, 23.60it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:09, 26.15it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 21.28it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:11, 21.81it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:12, 20.05it/s]
eval model::  50%|█████     | 252/500 [00:12<00:12, 19.80it/s]
eval model::  51%|█████     | 255/500 [00:12<00:12, 20.26it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 20.55it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 20.80it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 18.49it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:12, 18.74it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:11, 20.20it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:11, 20.10it/s]
eval model::  55%|█████▌    | 275/500 [00:13<00:11, 19.23it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 20.47it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.59it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:09, 21.86it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:14, 14.86it/s]
eval model::  58%|█████▊    | 290/500 [00:14<00:12, 16.71it/s]
eval model::  59%|█████▊    | 293/500 [00:14<00:11, 17.66it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:12, 15.85it/s]
eval model::  60%|█████▉    | 298/500 [00:15<00:10, 18.62it/s]
eval model::  60%|██████    | 301/500 [00:15<00:11, 17.45it/s]
eval model::  61%|██████    | 304/500 [00:15<00:10, 18.53it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:09, 20.06it/s]
eval model::  62%|██████▏   | 310/500 [00:15<00:08, 21.58it/s]
eval model::  63%|██████▎   | 313/500 [00:15<00:08, 22.59it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 22.92it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:07, 22.97it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:07, 22.46it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:07, 23.76it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:07, 23.02it/s]
eval model::  66%|██████▌   | 331/500 [00:16<00:07, 23.41it/s]
eval model::  67%|██████▋   | 334/500 [00:16<00:07, 21.87it/s]
eval model::  67%|██████▋   | 337/500 [00:16<00:07, 21.55it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 16.84it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:08, 17.94it/s]
eval model::  69%|██████▉   | 347/500 [00:17<00:07, 21.02it/s]
eval model::  70%|███████   | 350/500 [00:17<00:06, 22.44it/s]
eval model::  71%|███████   | 353/500 [00:17<00:07, 20.95it/s]
eval model::  71%|███████   | 356/500 [00:17<00:07, 19.99it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:07, 19.94it/s]
eval model::  72%|███████▏  | 362/500 [00:18<00:07, 19.05it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 19.20it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:07, 18.75it/s]
eval model::  74%|███████▍  | 369/500 [00:18<00:06, 19.81it/s]
eval model::  74%|███████▍  | 372/500 [00:18<00:06, 20.17it/s]
eval model::  75%|███████▌  | 375/500 [00:18<00:06, 19.24it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 21.67it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 20.88it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 21.41it/s]
eval model::  78%|███████▊  | 388/500 [00:19<00:07, 14.61it/s]
eval model::  78%|███████▊  | 391/500 [00:19<00:06, 16.59it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:06, 17.54it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 19.06it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 19.10it/s]
eval model::  81%|████████  | 404/500 [00:20<00:04, 21.34it/s]
eval model::  81%|████████▏ | 407/500 [00:20<00:04, 22.15it/s]
eval model::  82%|████████▏ | 410/500 [00:20<00:03, 22.95it/s]
eval model::  83%|████████▎ | 413/500 [00:20<00:03, 23.06it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:04, 20.26it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 19.10it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:04, 16.16it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 17.31it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:04, 17.55it/s]
eval model::  86%|████████▌ | 430/500 [00:21<00:03, 20.41it/s]
eval model::  87%|████████▋ | 434/500 [00:21<00:02, 23.14it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 25.55it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 23.93it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 24.27it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 22.43it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 19.60it/s]
eval model::  91%|█████████ | 453/500 [00:22<00:02, 21.64it/s]
eval model::  91%|█████████ | 456/500 [00:22<00:01, 23.28it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:01, 23.15it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:01, 19.43it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:02, 17.11it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 19.44it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 21.56it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 19.75it/s]
eval model::  95%|█████████▌| 477/500 [00:23<00:01, 21.29it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 21.66it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 22.73it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 20.82it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 20.01it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 19.44it/s]
eval model::  99%|█████████▉| 495/500 [00:24<00:00, 20.97it/s]
eval model:: 100%|█████████▉| 498/500 [00:24<00:00, 21.69it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.98it/s]
[2024/07/27 23:01:08] ppocr INFO: cur metric, precision: 0.5815423514538559, recall: 0.44294655753490614, hmean: 0.5028696365127083, fps: 45.57850161614825
[2024/07/27 23:01:08] ppocr INFO: best metric, hmean: 0.5043859649122808, precision: 0.5856142584341184, recall: 0.44294655753490614, fps: 45.04990682412584, best_epoch: 100
[2024/07/27 23:01:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:01:16] ppocr INFO: epoch: [121/200], global_step: 363, lr: 0.001000, loss: 2.213460, loss_shrink_maps: 1.236552, loss_threshold_maps: 0.730914, loss_binary_maps: 0.243339, avg_reader_cost: 1.70998 s, avg_batch_cost: 2.04573 s, avg_samples: 12.5, ips: 6.11029 samples/s, eta: 0:25:35
[2024/07/27 23:01:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:01:23] ppocr INFO: epoch: [122/200], global_step: 366, lr: 0.001000, loss: 2.152224, loss_shrink_maps: 1.190393, loss_threshold_maps: 0.730914, loss_binary_maps: 0.234547, avg_reader_cost: 1.58023 s, avg_batch_cost: 1.85969 s, avg_samples: 12.5, ips: 6.72155 samples/s, eta: 0:25:15
[2024/07/27 23:01:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:01:31] ppocr INFO: epoch: [123/200], global_step: 369, lr: 0.001000, loss: 2.168348, loss_shrink_maps: 1.201017, loss_threshold_maps: 0.727720, loss_binary_maps: 0.236881, avg_reader_cost: 1.60513 s, avg_batch_cost: 1.91723 s, avg_samples: 12.5, ips: 6.51983 samples/s, eta: 0:24:55
[2024/07/27 23:01:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:01:37] ppocr INFO: epoch: [124/200], global_step: 370, lr: 0.001000, loss: 2.168348, loss_shrink_maps: 1.201017, loss_threshold_maps: 0.723115, loss_binary_maps: 0.236881, avg_reader_cost: 0.41731 s, avg_batch_cost: 0.54049 s, avg_samples: 4.8, ips: 8.88086 samples/s, eta: 0:24:48
[2024/07/27 23:01:39] ppocr INFO: epoch: [124/200], global_step: 372, lr: 0.001000, loss: 2.152224, loss_shrink_maps: 1.190393, loss_threshold_maps: 0.727720, loss_binary_maps: 0.234547, avg_reader_cost: 1.17227 s, avg_batch_cost: 1.31821 s, avg_samples: 7.7, ips: 5.84125 samples/s, eta: 0:24:35
[2024/07/27 23:01:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:01:47] ppocr INFO: epoch: [125/200], global_step: 375, lr: 0.001000, loss: 2.152224, loss_shrink_maps: 1.190393, loss_threshold_maps: 0.728498, loss_binary_maps: 0.234547, avg_reader_cost: 1.59900 s, avg_batch_cost: 1.87353 s, avg_samples: 12.5, ips: 6.67191 samples/s, eta: 0:24:15
[2024/07/27 23:01:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:01:54] ppocr INFO: epoch: [126/200], global_step: 378, lr: 0.001000, loss: 2.176328, loss_shrink_maps: 1.216590, loss_threshold_maps: 0.732689, loss_binary_maps: 0.239838, avg_reader_cost: 1.69288 s, avg_batch_cost: 1.95990 s, avg_samples: 12.5, ips: 6.37786 samples/s, eta: 0:23:56
[2024/07/27 23:01:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:02:02] ppocr INFO: epoch: [127/200], global_step: 380, lr: 0.001000, loss: 2.168875, loss_shrink_maps: 1.201586, loss_threshold_maps: 0.732446, loss_binary_maps: 0.237319, avg_reader_cost: 0.95560 s, avg_batch_cost: 1.18922 s, avg_samples: 9.6, ips: 8.07249 samples/s, eta: 0:23:42
[2024/07/27 23:02:02] ppocr INFO: epoch: [127/200], global_step: 381, lr: 0.001000, loss: 2.160896, loss_shrink_maps: 1.190962, loss_threshold_maps: 0.728498, loss_binary_maps: 0.234984, avg_reader_cost: 0.64018 s, avg_batch_cost: 0.69531 s, avg_samples: 2.9, ips: 4.17082 samples/s, eta: 0:23:36
[2024/07/27 23:02:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:02:09] ppocr INFO: epoch: [128/200], global_step: 384, lr: 0.001000, loss: 2.086622, loss_shrink_maps: 1.133772, loss_threshold_maps: 0.710940, loss_binary_maps: 0.223760, avg_reader_cost: 1.53329 s, avg_batch_cost: 1.76612 s, avg_samples: 12.5, ips: 7.07764 samples/s, eta: 0:23:16
[2024/07/27 23:02:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:02:17] ppocr INFO: epoch: [129/200], global_step: 387, lr: 0.001000, loss: 2.096550, loss_shrink_maps: 1.150425, loss_threshold_maps: 0.716556, loss_binary_maps: 0.227402, avg_reader_cost: 1.58849 s, avg_batch_cost: 1.81618 s, avg_samples: 12.5, ips: 6.88259 samples/s, eta: 0:22:56
[2024/07/27 23:02:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:02:24] ppocr INFO: epoch: [130/200], global_step: 390, lr: 0.001000, loss: 2.067268, loss_shrink_maps: 1.123976, loss_threshold_maps: 0.716392, loss_binary_maps: 0.221751, avg_reader_cost: 1.50634 s, avg_batch_cost: 1.74037 s, avg_samples: 12.5, ips: 7.18238 samples/s, eta: 0:22:35
[2024/07/27 23:02:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:02:31] ppocr INFO: epoch: [131/200], global_step: 393, lr: 0.001000, loss: 2.080692, loss_shrink_maps: 1.140625, loss_threshold_maps: 0.710406, loss_binary_maps: 0.224914, avg_reader_cost: 1.51455 s, avg_batch_cost: 1.75457 s, avg_samples: 12.5, ips: 7.12426 samples/s, eta: 0:22:15
[2024/07/27 23:02:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:02:39] ppocr INFO: epoch: [132/200], global_step: 396, lr: 0.001000, loss: 2.080692, loss_shrink_maps: 1.140625, loss_threshold_maps: 0.707744, loss_binary_maps: 0.224914, avg_reader_cost: 1.52774 s, avg_batch_cost: 1.76182 s, avg_samples: 12.5, ips: 7.09495 samples/s, eta: 0:21:55
[2024/07/27 23:02:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:02:46] ppocr INFO: epoch: [133/200], global_step: 399, lr: 0.001000, loss: 2.080692, loss_shrink_maps: 1.140625, loss_threshold_maps: 0.707744, loss_binary_maps: 0.224914, avg_reader_cost: 1.51926 s, avg_batch_cost: 1.77549 s, avg_samples: 12.5, ips: 7.04031 samples/s, eta: 0:21:35
[2024/07/27 23:02:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:02:52] ppocr INFO: epoch: [134/200], global_step: 400, lr: 0.001000, loss: 2.089626, loss_shrink_maps: 1.168801, loss_threshold_maps: 0.707744, loss_binary_maps: 0.231537, avg_reader_cost: 0.42619 s, avg_batch_cost: 0.50859 s, avg_samples: 4.8, ips: 9.43792 samples/s, eta: 0:21:27
[2024/07/27 23:02:54] ppocr INFO: epoch: [134/200], global_step: 402, lr: 0.001000, loss: 2.127810, loss_shrink_maps: 1.200623, loss_threshold_maps: 0.710406, loss_binary_maps: 0.237930, avg_reader_cost: 1.10935 s, avg_batch_cost: 1.25430 s, avg_samples: 7.7, ips: 6.13887 samples/s, eta: 0:21:14
[2024/07/27 23:02:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:03:01] ppocr INFO: epoch: [135/200], global_step: 405, lr: 0.001000, loss: 2.169436, loss_shrink_maps: 1.214572, loss_threshold_maps: 0.707890, loss_binary_maps: 0.239936, avg_reader_cost: 1.54027 s, avg_batch_cost: 1.77018 s, avg_samples: 12.5, ips: 7.06143 samples/s, eta: 0:20:54
[2024/07/27 23:03:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:03:08] ppocr INFO: epoch: [136/200], global_step: 408, lr: 0.001000, loss: 2.098882, loss_shrink_maps: 1.176453, loss_threshold_maps: 0.703069, loss_binary_maps: 0.233009, avg_reader_cost: 1.56243 s, avg_batch_cost: 1.81447 s, avg_samples: 12.5, ips: 6.88907 samples/s, eta: 0:20:34
[2024/07/27 23:03:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:03:15] ppocr INFO: epoch: [137/200], global_step: 410, lr: 0.001000, loss: 2.169436, loss_shrink_maps: 1.206882, loss_threshold_maps: 0.707890, loss_binary_maps: 0.238847, avg_reader_cost: 0.91552 s, avg_batch_cost: 1.09026 s, avg_samples: 9.6, ips: 8.80527 samples/s, eta: 0:20:21
[2024/07/27 23:03:15] ppocr INFO: epoch: [137/200], global_step: 411, lr: 0.001000, loss: 2.197238, loss_shrink_maps: 1.206882, loss_threshold_maps: 0.712019, loss_binary_maps: 0.238847, avg_reader_cost: 0.59078 s, avg_batch_cost: 0.64654 s, avg_samples: 2.9, ips: 4.48544 samples/s, eta: 0:20:14
[2024/07/27 23:03:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:03:23] ppocr INFO: epoch: [138/200], global_step: 414, lr: 0.001000, loss: 2.177958, loss_shrink_maps: 1.206882, loss_threshold_maps: 0.709918, loss_binary_maps: 0.238847, avg_reader_cost: 1.55297 s, avg_batch_cost: 1.78316 s, avg_samples: 12.5, ips: 7.01003 samples/s, eta: 0:19:54
[2024/07/27 23:03:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:03:31] ppocr INFO: epoch: [139/200], global_step: 417, lr: 0.001000, loss: 2.127338, loss_shrink_maps: 1.182712, loss_threshold_maps: 0.700869, loss_binary_maps: 0.233925, avg_reader_cost: 1.63359 s, avg_batch_cost: 1.86354 s, avg_samples: 12.5, ips: 6.70765 samples/s, eta: 0:19:35
[2024/07/27 23:03:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:03:38] ppocr INFO: epoch: [140/200], global_step: 420, lr: 0.001000, loss: 2.102644, loss_shrink_maps: 1.150294, loss_threshold_maps: 0.696212, loss_binary_maps: 0.227707, avg_reader_cost: 1.54799 s, avg_batch_cost: 1.81040 s, avg_samples: 12.5, ips: 6.90456 samples/s, eta: 0:19:15

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:07,  7.44it/s]
eval model::   1%|          | 4/500 [00:00<00:32, 15.28it/s]
eval model::   1%|          | 6/500 [00:00<00:30, 16.39it/s]
eval model::   2%|▏         | 8/500 [00:00<00:30, 16.06it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.12it/s]
eval model::   3%|▎         | 13/500 [00:00<00:30, 16.19it/s]
eval model::   3%|▎         | 15/500 [00:00<00:29, 16.35it/s]
eval model::   4%|▎         | 18/500 [00:01<00:25, 18.67it/s]
eval model::   4%|▍         | 20/500 [00:01<00:27, 17.18it/s]
eval model::   4%|▍         | 22/500 [00:01<00:27, 17.56it/s]
eval model::   5%|▌         | 25/500 [00:01<00:24, 19.18it/s]
eval model::   5%|▌         | 27/500 [00:01<00:24, 19.10it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 21.53it/s]
eval model::   7%|▋         | 33/500 [00:01<00:26, 17.46it/s]
eval model::   7%|▋         | 35/500 [00:02<00:26, 17.26it/s]
eval model::   8%|▊         | 38/500 [00:02<00:24, 18.53it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.63it/s]
eval model::   9%|▊         | 43/500 [00:02<00:24, 18.54it/s]
eval model::   9%|▉         | 46/500 [00:02<00:25, 17.52it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 19.80it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 20.22it/s]
eval model::  11%|█         | 55/500 [00:03<00:21, 20.69it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:21, 20.29it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:23, 18.77it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:24, 17.86it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 18.98it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.18it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 20.17it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:20, 20.31it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 20.43it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 21.94it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 20.59it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.24it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:21, 19.17it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 19.09it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:19, 20.39it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 21.01it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.29it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.36it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 20.72it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 20.39it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:19, 20.23it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:19, 19.71it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:20, 18.23it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:19, 19.59it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 19.50it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:18, 20.20it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:17, 21.49it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:16, 21.59it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:17, 20.60it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.18it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 21.18it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:16, 21.46it/s]
eval model::  30%|███       | 151/500 [00:07<00:17, 20.39it/s]
eval model::  31%|███       | 155/500 [00:07<00:14, 23.06it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 21.90it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:17, 19.09it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:17, 18.95it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.44it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 20.16it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:16, 20.44it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:16, 19.53it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:15, 20.93it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:17, 18.32it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:15, 19.94it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 19.43it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:15, 19.80it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:13, 21.99it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:15, 19.80it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 20.12it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 20.75it/s]
eval model::  41%|████      | 205/500 [00:10<00:20, 14.10it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:19, 15.06it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:18, 15.42it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.69it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.17it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.53it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:18, 15.25it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:17, 15.51it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:15, 17.90it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:15, 17.28it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 20.60it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 21.97it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 21.18it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 22.55it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 21.32it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:11, 23.03it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:10, 23.71it/s]
eval model::  50%|█████     | 252/500 [00:13<00:10, 22.59it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 20.81it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 21.35it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 20.64it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 19.02it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:12, 19.28it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:12, 19.01it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.74it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.47it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.84it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.80it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 21.28it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:13, 15.88it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:11, 18.32it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:11, 18.61it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:12, 16.83it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:11, 18.01it/s]
eval model::  60%|██████    | 301/500 [00:15<00:12, 16.20it/s]
eval model::  61%|██████    | 304/500 [00:15<00:11, 17.66it/s]
eval model::  61%|██████    | 306/500 [00:15<00:10, 18.06it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:10, 18.35it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 19.46it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 19.02it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 18.81it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:09, 19.70it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 21.01it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 19.89it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:08, 21.29it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 20.50it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 21.27it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 22.69it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 20.49it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:09, 17.07it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:08, 18.20it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:07, 19.59it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 21.19it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 23.12it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:06, 22.15it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:06, 22.14it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:06, 20.35it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:07, 18.40it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:07, 18.60it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 19.79it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 21.00it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:06, 20.31it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:05, 20.46it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:05, 22.60it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:08, 13.39it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:07, 14.96it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:06, 15.81it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 16.58it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:06, 15.99it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:05, 18.08it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 17.35it/s]
eval model::  81%|████████  | 403/500 [00:21<00:05, 19.27it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 21.01it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 22.55it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:03, 23.28it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:03, 23.11it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 19.82it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:04, 16.62it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 17.17it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:03, 18.63it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 20.64it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 21.28it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 22.29it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 21.65it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 21.19it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 22.82it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 22.15it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 22.98it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 21.68it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 22.57it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 22.45it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 19.45it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 18.37it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 19.71it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 20.69it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 21.49it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 20.46it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 20.71it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 19.86it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 20.57it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 20.92it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 22.64it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 21.85it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 23.80it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.54it/s]
[2024/07/27 23:04:04] ppocr INFO: cur metric, precision: 0.6235216819973719, recall: 0.45690900337024554, hmean: 0.5273687135315366, fps: 43.184167661199886
[2024/07/27 23:04:04] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 23:04:04] ppocr INFO: best metric, hmean: 0.5273687135315366, precision: 0.6235216819973719, recall: 0.45690900337024554, fps: 43.184167661199886, best_epoch: 140
[2024/07/27 23:04:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:04:11] ppocr INFO: epoch: [141/200], global_step: 423, lr: 0.001000, loss: 2.076569, loss_shrink_maps: 1.139908, loss_threshold_maps: 0.694255, loss_binary_maps: 0.225985, avg_reader_cost: 1.52826 s, avg_batch_cost: 1.75656 s, avg_samples: 12.5, ips: 7.11617 samples/s, eta: 0:18:55
[2024/07/27 23:04:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:04:18] ppocr INFO: epoch: [142/200], global_step: 426, lr: 0.001000, loss: 2.116404, loss_shrink_maps: 1.159950, loss_threshold_maps: 0.705348, loss_binary_maps: 0.229507, avg_reader_cost: 1.55965 s, avg_batch_cost: 1.82088 s, avg_samples: 12.5, ips: 6.86482 samples/s, eta: 0:18:35
[2024/07/27 23:04:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:04:25] ppocr INFO: epoch: [143/200], global_step: 429, lr: 0.001000, loss: 2.111426, loss_shrink_maps: 1.149564, loss_threshold_maps: 0.695294, loss_binary_maps: 0.227452, avg_reader_cost: 1.53023 s, avg_batch_cost: 1.76587 s, avg_samples: 12.5, ips: 7.07866 samples/s, eta: 0:18:16
[2024/07/27 23:04:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:04:32] ppocr INFO: epoch: [144/200], global_step: 430, lr: 0.001000, loss: 2.111426, loss_shrink_maps: 1.149564, loss_threshold_maps: 0.695294, loss_binary_maps: 0.227452, avg_reader_cost: 0.45434 s, avg_batch_cost: 0.53725 s, avg_samples: 4.8, ips: 8.93432 samples/s, eta: 0:18:09
[2024/07/27 23:04:33] ppocr INFO: epoch: [144/200], global_step: 432, lr: 0.001000, loss: 2.111426, loss_shrink_maps: 1.149564, loss_threshold_maps: 0.695294, loss_binary_maps: 0.227452, avg_reader_cost: 1.16589 s, avg_batch_cost: 1.31119 s, avg_samples: 7.7, ips: 5.87253 samples/s, eta: 0:17:56
[2024/07/27 23:04:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:04:41] ppocr INFO: epoch: [145/200], global_step: 435, lr: 0.001000, loss: 2.120217, loss_shrink_maps: 1.162835, loss_threshold_maps: 0.702988, loss_binary_maps: 0.230165, avg_reader_cost: 1.60424 s, avg_batch_cost: 1.85870 s, avg_samples: 12.5, ips: 6.72512 samples/s, eta: 0:17:37
[2024/07/27 23:04:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:04:48] ppocr INFO: epoch: [146/200], global_step: 438, lr: 0.001000, loss: 2.120217, loss_shrink_maps: 1.165676, loss_threshold_maps: 0.702988, loss_binary_maps: 0.230361, avg_reader_cost: 1.54840 s, avg_batch_cost: 1.77635 s, avg_samples: 12.5, ips: 7.03692 samples/s, eta: 0:17:17
[2024/07/27 23:04:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:04:55] ppocr INFO: epoch: [147/200], global_step: 440, lr: 0.001000, loss: 2.120217, loss_shrink_maps: 1.165676, loss_threshold_maps: 0.702920, loss_binary_maps: 0.230361, avg_reader_cost: 0.91030 s, avg_batch_cost: 1.08727 s, avg_samples: 9.6, ips: 8.82947 samples/s, eta: 0:17:03
[2024/07/27 23:04:55] ppocr INFO: epoch: [147/200], global_step: 441, lr: 0.001000, loss: 2.125292, loss_shrink_maps: 1.176672, loss_threshold_maps: 0.702920, loss_binary_maps: 0.232231, avg_reader_cost: 0.58978 s, avg_batch_cost: 0.64473 s, avg_samples: 2.9, ips: 4.49801 samples/s, eta: 0:16:57
[2024/07/27 23:04:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:05:03] ppocr INFO: epoch: [148/200], global_step: 444, lr: 0.001000, loss: 2.125292, loss_shrink_maps: 1.176672, loss_threshold_maps: 0.706232, loss_binary_maps: 0.232231, avg_reader_cost: 1.60725 s, avg_batch_cost: 1.83510 s, avg_samples: 12.5, ips: 6.81161 samples/s, eta: 0:16:37
[2024/07/27 23:05:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:05:10] ppocr INFO: epoch: [149/200], global_step: 447, lr: 0.001000, loss: 2.120217, loss_shrink_maps: 1.165676, loss_threshold_maps: 0.706232, loss_binary_maps: 0.230361, avg_reader_cost: 1.51991 s, avg_batch_cost: 1.79839 s, avg_samples: 12.5, ips: 6.95065 samples/s, eta: 0:16:18
[2024/07/27 23:05:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:05:18] ppocr INFO: epoch: [150/200], global_step: 450, lr: 0.001000, loss: 2.099400, loss_shrink_maps: 1.164243, loss_threshold_maps: 0.694440, loss_binary_maps: 0.230361, avg_reader_cost: 1.55149 s, avg_batch_cost: 1.81126 s, avg_samples: 12.5, ips: 6.90129 samples/s, eta: 0:15:58
[2024/07/27 23:05:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:05:25] ppocr INFO: epoch: [151/200], global_step: 453, lr: 0.001000, loss: 2.081555, loss_shrink_maps: 1.159127, loss_threshold_maps: 0.694440, loss_binary_maps: 0.229965, avg_reader_cost: 1.55123 s, avg_batch_cost: 1.81836 s, avg_samples: 12.5, ips: 6.87432 samples/s, eta: 0:15:39
[2024/07/27 23:05:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:05:33] ppocr INFO: epoch: [152/200], global_step: 456, lr: 0.001000, loss: 2.079210, loss_shrink_maps: 1.141551, loss_threshold_maps: 0.700328, loss_binary_maps: 0.226050, avg_reader_cost: 1.53525 s, avg_batch_cost: 1.77076 s, avg_samples: 12.5, ips: 7.05911 samples/s, eta: 0:15:19
[2024/07/27 23:05:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:05:40] ppocr INFO: epoch: [153/200], global_step: 459, lr: 0.001000, loss: 2.079210, loss_shrink_maps: 1.141551, loss_threshold_maps: 0.703724, loss_binary_maps: 0.226050, avg_reader_cost: 1.56226 s, avg_batch_cost: 1.79081 s, avg_samples: 12.5, ips: 6.98009 samples/s, eta: 0:15:00
[2024/07/27 23:05:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:05:46] ppocr INFO: epoch: [154/200], global_step: 460, lr: 0.001000, loss: 2.079210, loss_shrink_maps: 1.141551, loss_threshold_maps: 0.714844, loss_binary_maps: 0.226050, avg_reader_cost: 0.42766 s, avg_batch_cost: 0.52092 s, avg_samples: 4.8, ips: 9.21452 samples/s, eta: 0:14:53
[2024/07/27 23:05:48] ppocr INFO: epoch: [154/200], global_step: 462, lr: 0.001000, loss: 2.069539, loss_shrink_maps: 1.126239, loss_threshold_maps: 0.706014, loss_binary_maps: 0.223105, avg_reader_cost: 1.13294 s, avg_batch_cost: 1.27843 s, avg_samples: 7.7, ips: 6.02302 samples/s, eta: 0:14:40
[2024/07/27 23:05:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:05:55] ppocr INFO: epoch: [155/200], global_step: 465, lr: 0.001000, loss: 2.079210, loss_shrink_maps: 1.150874, loss_threshold_maps: 0.696841, loss_binary_maps: 0.226874, avg_reader_cost: 1.52026 s, avg_batch_cost: 1.76413 s, avg_samples: 12.5, ips: 7.08564 samples/s, eta: 0:14:20
[2024/07/27 23:05:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:06:03] ppocr INFO: epoch: [156/200], global_step: 468, lr: 0.001000, loss: 2.070642, loss_shrink_maps: 1.135562, loss_threshold_maps: 0.696841, loss_binary_maps: 0.223929, avg_reader_cost: 1.58963 s, avg_batch_cost: 1.82674 s, avg_samples: 12.5, ips: 6.84279 samples/s, eta: 0:14:01
[2024/07/27 23:06:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:06:10] ppocr INFO: epoch: [157/200], global_step: 470, lr: 0.001000, loss: 2.062628, loss_shrink_maps: 1.123772, loss_threshold_maps: 0.700937, loss_binary_maps: 0.222851, avg_reader_cost: 0.96214 s, avg_batch_cost: 1.15260 s, avg_samples: 9.6, ips: 8.32897 samples/s, eta: 0:13:48
[2024/07/27 23:06:10] ppocr INFO: epoch: [157/200], global_step: 471, lr: 0.001000, loss: 2.062628, loss_shrink_maps: 1.123772, loss_threshold_maps: 0.700937, loss_binary_maps: 0.222851, avg_reader_cost: 0.62194 s, avg_batch_cost: 0.67697 s, avg_samples: 2.9, ips: 4.28380 samples/s, eta: 0:13:42
[2024/07/27 23:06:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:06:18] ppocr INFO: epoch: [158/200], global_step: 474, lr: 0.001000, loss: 2.052772, loss_shrink_maps: 1.135562, loss_threshold_maps: 0.696841, loss_binary_maps: 0.223929, avg_reader_cost: 1.58952 s, avg_batch_cost: 1.81813 s, avg_samples: 12.5, ips: 6.87521 samples/s, eta: 0:13:22
[2024/07/27 23:06:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:06:25] ppocr INFO: epoch: [159/200], global_step: 477, lr: 0.001000, loss: 2.052772, loss_shrink_maps: 1.135562, loss_threshold_maps: 0.693281, loss_binary_maps: 0.223929, avg_reader_cost: 1.56108 s, avg_batch_cost: 1.78961 s, avg_samples: 12.5, ips: 6.98476 samples/s, eta: 0:13:03
[2024/07/27 23:06:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:06:33] ppocr INFO: epoch: [160/200], global_step: 480, lr: 0.001000, loss: 2.019316, loss_shrink_maps: 1.106678, loss_threshold_maps: 0.691907, loss_binary_maps: 0.219128, avg_reader_cost: 1.50864 s, avg_batch_cost: 1.75720 s, avg_samples: 12.5, ips: 7.11359 samples/s, eta: 0:12:43

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:35, 13.99it/s]
eval model::   1%|          | 5/500 [00:00<00:27, 17.97it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.89it/s]
eval model::   2%|▏         | 10/500 [00:00<00:26, 18.42it/s]
eval model::   2%|▏         | 12/500 [00:00<00:27, 17.43it/s]
eval model::   3%|▎         | 14/500 [00:00<00:28, 16.85it/s]
eval model::   3%|▎         | 17/500 [00:00<00:26, 18.17it/s]
eval model::   4%|▍         | 19/500 [00:01<00:26, 18.20it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 18.53it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.17it/s]
eval model::   5%|▌         | 27/500 [00:01<00:23, 20.25it/s]
eval model::   6%|▌         | 30/500 [00:01<00:25, 18.76it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 20.00it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 19.18it/s]
eval model::   8%|▊         | 38/500 [00:02<00:24, 18.64it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.11it/s]
eval model::   9%|▊         | 43/500 [00:02<00:24, 18.88it/s]
eval model::   9%|▉         | 46/500 [00:02<00:22, 19.89it/s]
eval model::  10%|▉         | 49/500 [00:02<00:20, 21.59it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 20.64it/s]
eval model::  11%|█         | 55/500 [00:02<00:23, 18.71it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:25, 17.70it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:26, 16.94it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:30, 14.42it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:33, 13.16it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:27, 15.62it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:23, 17.98it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 19.66it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:22, 19.24it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 20.29it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:20, 20.25it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:21, 19.50it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:21, 19.45it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:22, 17.99it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:26, 15.34it/s]
eval model::  18%|█▊        | 92/500 [00:05<00:25, 15.85it/s]
eval model::  19%|█▉        | 94/500 [00:05<00:25, 15.92it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:22, 17.79it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:22, 17.94it/s]
eval model::  20%|██        | 101/500 [00:05<00:21, 18.21it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 20.91it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:16, 23.97it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:17, 22.43it/s]
eval model::  23%|██▎       | 114/500 [00:06<00:18, 20.89it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:18, 20.77it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:16, 22.96it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:17, 22.09it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:16, 22.43it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:17, 20.87it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:16, 22.21it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:15, 23.08it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:17, 20.85it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.29it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 21.85it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:17, 19.59it/s]
eval model::  30%|███       | 151/500 [00:07<00:20, 17.14it/s]
eval model::  31%|███       | 155/500 [00:08<00:17, 20.05it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:15, 21.69it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:17, 19.56it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:18, 18.59it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:16, 19.69it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:16, 19.59it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:18, 18.01it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.31it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.81it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.43it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 17.77it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 19.73it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:15, 20.37it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:14, 21.19it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:15, 19.71it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 21.35it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 22.79it/s]
eval model::  41%|████      | 205/500 [00:10<00:20, 14.39it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:18, 15.39it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 15.66it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.32it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.62it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.84it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:18, 15.00it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:18, 14.69it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:16, 17.18it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.98it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:14, 19.16it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:13, 20.16it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:13, 20.37it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:14, 18.20it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:14, 18.40it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:15, 16.33it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:13, 18.58it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:13, 19.21it/s]
eval model::  50%|█████     | 250/500 [00:13<00:13, 19.14it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 21.33it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 21.78it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 23.46it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:10, 22.86it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:11, 21.23it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:10, 21.35it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:10, 22.37it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:10, 21.94it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 19.72it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 19.21it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:10, 20.21it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:15, 14.17it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:13, 15.94it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 17.13it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.66it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:12, 15.74it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:11, 17.44it/s]
eval model::  60%|██████    | 301/500 [00:16<00:12, 16.08it/s]
eval model::  61%|██████    | 304/500 [00:16<00:10, 17.99it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 18.36it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:10, 18.73it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 19.73it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:09, 19.81it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:09, 19.78it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 21.71it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 21.43it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:07, 22.38it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 21.38it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 19.02it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:08, 20.62it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:08, 19.12it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:09, 16.79it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:08, 19.04it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 20.86it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:06, 22.95it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 22.92it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 22.25it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 21.88it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 19.27it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:06, 19.50it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 18.51it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 19.32it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 18.95it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 20.72it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 22.20it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:05, 21.41it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:04, 24.64it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:06, 16.73it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:05, 18.33it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:05, 18.54it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 19.90it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 19.81it/s]
eval model::  81%|████████  | 403/500 [00:21<00:04, 20.17it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 20.41it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 20.26it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:04, 21.08it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:04, 18.98it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 18.08it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 17.38it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:05, 15.42it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 15.11it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.31it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 18.06it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 18.03it/s]
eval model::  87%|████████▋ | 434/500 [00:22<00:03, 20.40it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:02, 21.33it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:03, 19.37it/s]
eval model::  89%|████████▊ | 443/500 [00:23<00:02, 21.08it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 22.35it/s]
eval model::  90%|████████▉ | 449/500 [00:23<00:02, 22.08it/s]
eval model::  90%|█████████ | 452/500 [00:23<00:02, 23.86it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:01, 23.77it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:01, 23.32it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 19.50it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 17.23it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:02, 16.30it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 17.70it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 18.62it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 18.39it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.86it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 21.18it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 22.79it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 20.82it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 22.44it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 21.69it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 21.83it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 23.39it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.20it/s]
[2024/07/27 23:06:59] ppocr INFO: cur metric, precision: 0.6209476309226932, recall: 0.4795377948964853, hmean: 0.541157294213529, fps: 43.06713264514292
[2024/07/27 23:06:59] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 23:06:59] ppocr INFO: best metric, hmean: 0.541157294213529, precision: 0.6209476309226932, recall: 0.4795377948964853, fps: 43.06713264514292, best_epoch: 160
[2024/07/27 23:06:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:07:06] ppocr INFO: epoch: [161/200], global_step: 483, lr: 0.001000, loss: 2.030277, loss_shrink_maps: 1.116061, loss_threshold_maps: 0.693239, loss_binary_maps: 0.219373, avg_reader_cost: 1.53273 s, avg_batch_cost: 1.76126 s, avg_samples: 12.5, ips: 7.09720 samples/s, eta: 0:12:24
[2024/07/27 23:07:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:07:13] ppocr INFO: epoch: [162/200], global_step: 486, lr: 0.001000, loss: 2.015708, loss_shrink_maps: 1.088892, loss_threshold_maps: 0.699955, loss_binary_maps: 0.216192, avg_reader_cost: 1.53602 s, avg_batch_cost: 1.76495 s, avg_samples: 12.5, ips: 7.08237 samples/s, eta: 0:12:05
[2024/07/27 23:07:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:07:21] ppocr INFO: epoch: [163/200], global_step: 489, lr: 0.001000, loss: 2.082448, loss_shrink_maps: 1.164285, loss_threshold_maps: 0.695617, loss_binary_maps: 0.230191, avg_reader_cost: 1.51454 s, avg_batch_cost: 1.74586 s, avg_samples: 12.5, ips: 7.15981 samples/s, eta: 0:11:45
[2024/07/27 23:07:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:07:27] ppocr INFO: epoch: [164/200], global_step: 490, lr: 0.001000, loss: 2.082448, loss_shrink_maps: 1.164285, loss_threshold_maps: 0.695617, loss_binary_maps: 0.230191, avg_reader_cost: 0.49301 s, avg_batch_cost: 0.57578 s, avg_samples: 4.8, ips: 8.33647 samples/s, eta: 0:11:39
[2024/07/27 23:07:29] ppocr INFO: epoch: [164/200], global_step: 492, lr: 0.001000, loss: 2.077806, loss_shrink_maps: 1.150864, loss_threshold_maps: 0.699921, loss_binary_maps: 0.228432, avg_reader_cost: 1.24271 s, avg_batch_cost: 1.38854 s, avg_samples: 7.7, ips: 5.54538 samples/s, eta: 0:11:26
[2024/07/27 23:07:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:07:36] ppocr INFO: epoch: [165/200], global_step: 495, lr: 0.001000, loss: 2.088863, loss_shrink_maps: 1.178305, loss_threshold_maps: 0.704076, loss_binary_maps: 0.233030, avg_reader_cost: 1.53940 s, avg_batch_cost: 1.77474 s, avg_samples: 12.5, ips: 7.04329 samples/s, eta: 0:11:07
[2024/07/27 23:07:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:07:44] ppocr INFO: epoch: [166/200], global_step: 498, lr: 0.001000, loss: 2.088863, loss_shrink_maps: 1.178305, loss_threshold_maps: 0.708893, loss_binary_maps: 0.233030, avg_reader_cost: 1.55748 s, avg_batch_cost: 1.78713 s, avg_samples: 12.5, ips: 6.99446 samples/s, eta: 0:10:48
[2024/07/27 23:07:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:07:51] ppocr INFO: epoch: [167/200], global_step: 500, lr: 0.001000, loss: 2.089289, loss_shrink_maps: 1.178305, loss_threshold_maps: 0.713383, loss_binary_maps: 0.233030, avg_reader_cost: 0.94304 s, avg_batch_cost: 1.21969 s, avg_samples: 9.6, ips: 7.87086 samples/s, eta: 0:10:35
[2024/07/27 23:07:52] ppocr INFO: epoch: [167/200], global_step: 501, lr: 0.001000, loss: 2.089289, loss_shrink_maps: 1.162200, loss_threshold_maps: 0.713383, loss_binary_maps: 0.230363, avg_reader_cost: 0.65550 s, avg_batch_cost: 0.71033 s, avg_samples: 2.9, ips: 4.08259 samples/s, eta: 0:10:29
[2024/07/27 23:07:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:07:59] ppocr INFO: epoch: [168/200], global_step: 504, lr: 0.001000, loss: 2.084648, loss_shrink_maps: 1.150864, loss_threshold_maps: 0.704076, loss_binary_maps: 0.228432, avg_reader_cost: 1.51149 s, avg_batch_cost: 1.74998 s, avg_samples: 12.5, ips: 7.14294 samples/s, eta: 0:10:09
[2024/07/27 23:08:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:08:06] ppocr INFO: epoch: [169/200], global_step: 507, lr: 0.001000, loss: 2.077806, loss_shrink_maps: 1.138263, loss_threshold_maps: 0.719916, loss_binary_maps: 0.225929, avg_reader_cost: 1.55574 s, avg_batch_cost: 1.80623 s, avg_samples: 12.5, ips: 6.92051 samples/s, eta: 0:09:50
[2024/07/27 23:08:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:08:14] ppocr INFO: epoch: [170/200], global_step: 510, lr: 0.001000, loss: 2.084648, loss_shrink_maps: 1.138263, loss_threshold_maps: 0.724181, loss_binary_maps: 0.225929, avg_reader_cost: 1.54360 s, avg_batch_cost: 1.78494 s, avg_samples: 12.5, ips: 7.00302 samples/s, eta: 0:09:31
[2024/07/27 23:08:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:08:21] ppocr INFO: epoch: [171/200], global_step: 513, lr: 0.001000, loss: 1.980146, loss_shrink_maps: 1.068848, loss_threshold_maps: 0.718118, loss_binary_maps: 0.211059, avg_reader_cost: 1.54792 s, avg_batch_cost: 1.80027 s, avg_samples: 12.5, ips: 6.94342 samples/s, eta: 0:09:11
[2024/07/27 23:08:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:08:29] ppocr INFO: epoch: [172/200], global_step: 516, lr: 0.001000, loss: 2.026310, loss_shrink_maps: 1.084986, loss_threshold_maps: 0.718118, loss_binary_maps: 0.215327, avg_reader_cost: 1.55493 s, avg_batch_cost: 1.78252 s, avg_samples: 12.5, ips: 7.01254 samples/s, eta: 0:08:52
[2024/07/27 23:08:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:08:36] ppocr INFO: epoch: [173/200], global_step: 519, lr: 0.001000, loss: 2.002848, loss_shrink_maps: 1.084986, loss_threshold_maps: 0.697548, loss_binary_maps: 0.215327, avg_reader_cost: 1.52313 s, avg_batch_cost: 1.78278 s, avg_samples: 12.5, ips: 7.01153 samples/s, eta: 0:08:33
[2024/07/27 23:08:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:08:42] ppocr INFO: epoch: [174/200], global_step: 520, lr: 0.001000, loss: 2.002848, loss_shrink_maps: 1.084986, loss_threshold_maps: 0.690937, loss_binary_maps: 0.215327, avg_reader_cost: 0.41620 s, avg_batch_cost: 0.50765 s, avg_samples: 4.8, ips: 9.45536 samples/s, eta: 0:08:26
[2024/07/27 23:08:44] ppocr INFO: epoch: [174/200], global_step: 522, lr: 0.001000, loss: 2.002848, loss_shrink_maps: 1.084986, loss_threshold_maps: 0.690937, loss_binary_maps: 0.215327, avg_reader_cost: 1.10672 s, avg_batch_cost: 1.25281 s, avg_samples: 7.7, ips: 6.14620 samples/s, eta: 0:08:14
[2024/07/27 23:08:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:08:51] ppocr INFO: epoch: [175/200], global_step: 525, lr: 0.001000, loss: 2.002848, loss_shrink_maps: 1.084986, loss_threshold_maps: 0.690937, loss_binary_maps: 0.215327, avg_reader_cost: 1.51593 s, avg_batch_cost: 1.74470 s, avg_samples: 12.5, ips: 7.16457 samples/s, eta: 0:07:55
[2024/07/27 23:08:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:08:58] ppocr INFO: epoch: [176/200], global_step: 528, lr: 0.001000, loss: 1.985734, loss_shrink_maps: 1.084986, loss_threshold_maps: 0.675032, loss_binary_maps: 0.215327, avg_reader_cost: 1.51594 s, avg_batch_cost: 1.74797 s, avg_samples: 12.5, ips: 7.15117 samples/s, eta: 0:07:35
[2024/07/27 23:09:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:09:06] ppocr INFO: epoch: [177/200], global_step: 530, lr: 0.001000, loss: 1.971131, loss_shrink_maps: 1.068848, loss_threshold_maps: 0.675032, loss_binary_maps: 0.211059, avg_reader_cost: 0.93692 s, avg_batch_cost: 1.13560 s, avg_samples: 9.6, ips: 8.45371 samples/s, eta: 0:07:22
[2024/07/27 23:09:06] ppocr INFO: epoch: [177/200], global_step: 531, lr: 0.001000, loss: 1.985734, loss_shrink_maps: 1.084986, loss_threshold_maps: 0.681511, loss_binary_maps: 0.215327, avg_reader_cost: 0.61386 s, avg_batch_cost: 0.66850 s, avg_samples: 2.9, ips: 4.33806 samples/s, eta: 0:07:16
[2024/07/27 23:09:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:09:14] ppocr INFO: epoch: [178/200], global_step: 534, lr: 0.001000, loss: 2.010606, loss_shrink_maps: 1.101970, loss_threshold_maps: 0.691136, loss_binary_maps: 0.218362, avg_reader_cost: 1.55916 s, avg_batch_cost: 1.78906 s, avg_samples: 12.5, ips: 6.98690 samples/s, eta: 0:06:57
[2024/07/27 23:09:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:09:21] ppocr INFO: epoch: [179/200], global_step: 537, lr: 0.001000, loss: 2.010606, loss_shrink_maps: 1.115043, loss_threshold_maps: 0.691154, loss_binary_maps: 0.221028, avg_reader_cost: 1.50758 s, avg_batch_cost: 1.74273 s, avg_samples: 12.5, ips: 7.17264 samples/s, eta: 0:06:38
[2024/07/27 23:09:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:09:28] ppocr INFO: epoch: [180/200], global_step: 540, lr: 0.001000, loss: 2.040858, loss_shrink_maps: 1.122413, loss_threshold_maps: 0.700011, loss_binary_maps: 0.223115, avg_reader_cost: 1.52262 s, avg_batch_cost: 1.75982 s, avg_samples: 12.5, ips: 7.10301 samples/s, eta: 0:06:19

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:58,  8.54it/s]
eval model::   1%|          | 4/500 [00:00<00:30, 16.43it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.13it/s]
eval model::   2%|▏         | 9/500 [00:00<00:25, 18.96it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 18.06it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 19.08it/s]
eval model::   3%|▎         | 17/500 [00:00<00:22, 21.37it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.50it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 21.63it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 21.33it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 21.36it/s]
eval model::   6%|▋         | 32/500 [00:01<00:22, 20.92it/s]
eval model::   7%|▋         | 35/500 [00:01<00:23, 19.61it/s]
eval model::   8%|▊         | 38/500 [00:01<00:22, 20.66it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.77it/s]
eval model::   9%|▉         | 44/500 [00:02<00:23, 19.35it/s]
eval model::   9%|▉         | 46/500 [00:02<00:23, 19.30it/s]
eval model::  10%|▉         | 49/500 [00:02<00:21, 21.22it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 20.39it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 19.92it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:23, 18.76it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:25, 17.48it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.50it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 18.07it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.15it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 21.43it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 21.02it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:19, 21.68it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:21, 19.91it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 20.57it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.73it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:23, 17.39it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:23, 17.16it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:23, 17.56it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:20, 19.64it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 22.13it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.90it/s]
eval model::  21%|██        | 106/500 [00:05<00:16, 23.30it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:16, 23.36it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:17, 21.62it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:17, 21.61it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:18, 21.01it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:20, 18.77it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.76it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.27it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 19.20it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 19.36it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 20.04it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:17, 20.21it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.45it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:19, 18.17it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 19.68it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 18.11it/s]
eval model::  30%|███       | 151/500 [00:07<00:20, 17.44it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 20.41it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:16, 20.20it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:18, 17.89it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:17, 19.41it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:16, 19.99it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:15, 21.56it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:15, 21.58it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:18, 17.74it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.32it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:19, 16.49it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:17, 18.29it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:16, 18.38it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:15, 19.55it/s]
eval model::  39%|███▉      | 194/500 [00:09<00:14, 21.51it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:14, 20.42it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 20.08it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 20.76it/s]
eval model::  41%|████      | 206/500 [00:10<00:20, 14.61it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:19, 14.85it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:19, 15.25it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.02it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 14.16it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.28it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 16.58it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:17, 15.72it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:14, 18.97it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:12, 21.13it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:11, 22.74it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 21.79it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 22.27it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 22.33it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:12, 20.61it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:11, 21.99it/s]
eval model::  50%|█████     | 250/500 [00:12<00:12, 20.20it/s]
eval model::  51%|█████     | 253/500 [00:13<00:12, 20.49it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 20.85it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:12, 19.66it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 20.38it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:12, 18.50it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:12, 18.75it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:11, 20.89it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 19.74it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:11, 19.43it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 19.50it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 21.24it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:14, 14.91it/s]
eval model::  58%|█████▊    | 288/500 [00:14<00:12, 17.19it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:10, 20.14it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:12, 16.51it/s]
eval model::  60%|█████▉    | 298/500 [00:15<00:11, 18.36it/s]
eval model::  60%|██████    | 301/500 [00:15<00:11, 17.66it/s]
eval model::  61%|██████    | 304/500 [00:15<00:10, 18.96it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:09, 20.48it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.99it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 21.61it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 20.82it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 21.80it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:08, 20.38it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:08, 21.59it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:08, 19.72it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 19.49it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:08, 20.36it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:08, 18.50it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:08, 18.66it/s]
eval model::  68%|██████▊   | 341/500 [00:17<00:09, 16.15it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:07, 20.35it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:07, 21.53it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 19.31it/s]
eval model::  71%|███████   | 354/500 [00:18<00:07, 20.55it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:07, 18.11it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:07, 18.72it/s]
eval model::  72%|███████▏  | 362/500 [00:18<00:07, 18.09it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 17.77it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.54it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:07, 18.04it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 19.28it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 19.14it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 21.62it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 23.49it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 22.54it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 22.76it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:07, 14.74it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:07, 15.62it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 17.49it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 19.15it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:05, 19.03it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 19.73it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 21.42it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 22.86it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 23.05it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 22.73it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 19.81it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 16.04it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 16.46it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.63it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:04, 17.75it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 18.87it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 18.86it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 20.62it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 22.04it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 20.60it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 22.65it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 22.49it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 22.01it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 21.25it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:02, 21.41it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:02, 19.86it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:02, 17.03it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 17.41it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:02, 16.41it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 18.83it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 20.76it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 20.91it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:00, 22.47it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 22.09it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 23.57it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 21.55it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 22.78it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 20.50it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 19.82it/s]
eval model:: 100%|█████████▉| 499/500 [00:25<00:00, 20.72it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.44it/s]
[2024/07/27 23:09:54] ppocr INFO: cur metric, precision: 0.631233595800525, recall: 0.4631680308136736, hmean: 0.5342960288808665, fps: 44.052735179340345
[2024/07/27 23:09:54] ppocr INFO: best metric, hmean: 0.541157294213529, precision: 0.6209476309226932, recall: 0.4795377948964853, fps: 43.06713264514292, best_epoch: 160
[2024/07/27 23:09:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:10:01] ppocr INFO: epoch: [181/200], global_step: 543, lr: 0.001000, loss: 2.060701, loss_shrink_maps: 1.144028, loss_threshold_maps: 0.700011, loss_binary_maps: 0.226812, avg_reader_cost: 1.61978 s, avg_batch_cost: 1.88149 s, avg_samples: 12.5, ips: 6.64366 samples/s, eta: 0:06:00
[2024/07/27 23:10:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:10:10] ppocr INFO: epoch: [182/200], global_step: 546, lr: 0.001000, loss: 2.105980, loss_shrink_maps: 1.159122, loss_threshold_maps: 0.703757, loss_binary_maps: 0.229680, avg_reader_cost: 1.76060 s, avg_batch_cost: 1.98873 s, avg_samples: 12.5, ips: 6.28541 samples/s, eta: 0:05:41
[2024/07/27 23:10:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:10:17] ppocr INFO: epoch: [183/200], global_step: 549, lr: 0.001000, loss: 2.105980, loss_shrink_maps: 1.159122, loss_threshold_maps: 0.701947, loss_binary_maps: 0.229680, avg_reader_cost: 1.53881 s, avg_batch_cost: 1.77107 s, avg_samples: 12.5, ips: 7.05790 samples/s, eta: 0:05:22
[2024/07/27 23:10:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:10:23] ppocr INFO: epoch: [184/200], global_step: 550, lr: 0.001000, loss: 2.105980, loss_shrink_maps: 1.159122, loss_threshold_maps: 0.700871, loss_binary_maps: 0.229680, avg_reader_cost: 0.47212 s, avg_batch_cost: 0.55503 s, avg_samples: 4.8, ips: 8.64814 samples/s, eta: 0:05:15
[2024/07/27 23:10:25] ppocr INFO: epoch: [184/200], global_step: 552, lr: 0.001000, loss: 2.094718, loss_shrink_maps: 1.144028, loss_threshold_maps: 0.697024, loss_binary_maps: 0.226812, avg_reader_cost: 1.20185 s, avg_batch_cost: 1.34822 s, avg_samples: 7.7, ips: 5.71122 samples/s, eta: 0:05:03
[2024/07/27 23:10:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:10:32] ppocr INFO: epoch: [185/200], global_step: 555, lr: 0.001000, loss: 2.094718, loss_shrink_maps: 1.146756, loss_threshold_maps: 0.695385, loss_binary_maps: 0.227475, avg_reader_cost: 1.52994 s, avg_batch_cost: 1.77593 s, avg_samples: 12.5, ips: 7.03858 samples/s, eta: 0:04:44
[2024/07/27 23:10:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:10:40] ppocr INFO: epoch: [186/200], global_step: 558, lr: 0.001000, loss: 2.041999, loss_shrink_maps: 1.134851, loss_threshold_maps: 0.686480, loss_binary_maps: 0.224880, avg_reader_cost: 1.48691 s, avg_batch_cost: 1.73906 s, avg_samples: 12.5, ips: 7.18780 samples/s, eta: 0:04:25
[2024/07/27 23:10:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:10:47] ppocr INFO: epoch: [187/200], global_step: 560, lr: 0.001000, loss: 2.041999, loss_shrink_maps: 1.134851, loss_threshold_maps: 0.684250, loss_binary_maps: 0.224870, avg_reader_cost: 0.95499 s, avg_batch_cost: 1.13456 s, avg_samples: 9.6, ips: 8.46145 samples/s, eta: 0:04:12
[2024/07/27 23:10:47] ppocr INFO: epoch: [187/200], global_step: 561, lr: 0.001000, loss: 2.037408, loss_shrink_maps: 1.118221, loss_threshold_maps: 0.676694, loss_binary_maps: 0.220766, avg_reader_cost: 0.61301 s, avg_batch_cost: 0.66773 s, avg_samples: 2.9, ips: 4.34304 samples/s, eta: 0:04:06
[2024/07/27 23:10:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:10:55] ppocr INFO: epoch: [188/200], global_step: 564, lr: 0.001000, loss: 2.025835, loss_shrink_maps: 1.094532, loss_threshold_maps: 0.666873, loss_binary_maps: 0.216250, avg_reader_cost: 1.52412 s, avg_batch_cost: 1.75209 s, avg_samples: 12.5, ips: 7.13434 samples/s, eta: 0:03:47
[2024/07/27 23:10:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:11:02] ppocr INFO: epoch: [189/200], global_step: 567, lr: 0.001000, loss: 2.025835, loss_shrink_maps: 1.108146, loss_threshold_maps: 0.670617, loss_binary_maps: 0.220369, avg_reader_cost: 1.49374 s, avg_batch_cost: 1.72451 s, avg_samples: 12.5, ips: 7.24845 samples/s, eta: 0:03:28
[2024/07/27 23:11:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:11:10] ppocr INFO: epoch: [190/200], global_step: 570, lr: 0.001000, loss: 2.025835, loss_shrink_maps: 1.098069, loss_threshold_maps: 0.675949, loss_binary_maps: 0.217134, avg_reader_cost: 1.55210 s, avg_batch_cost: 1.80174 s, avg_samples: 12.5, ips: 6.93775 samples/s, eta: 0:03:09
[2024/07/27 23:11:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:11:17] ppocr INFO: epoch: [191/200], global_step: 573, lr: 0.001000, loss: 2.010429, loss_shrink_maps: 1.089128, loss_threshold_maps: 0.675949, loss_binary_maps: 0.215853, avg_reader_cost: 1.56327 s, avg_batch_cost: 1.81619 s, avg_samples: 12.5, ips: 6.88254 samples/s, eta: 0:02:50
[2024/07/27 23:11:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:11:25] ppocr INFO: epoch: [192/200], global_step: 576, lr: 0.001000, loss: 1.922496, loss_shrink_maps: 1.045724, loss_threshold_maps: 0.677107, loss_binary_maps: 0.207013, avg_reader_cost: 1.57581 s, avg_batch_cost: 1.80454 s, avg_samples: 12.5, ips: 6.92699 samples/s, eta: 0:02:31
[2024/07/27 23:11:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:11:33] ppocr INFO: epoch: [193/200], global_step: 579, lr: 0.001000, loss: 1.948941, loss_shrink_maps: 1.049438, loss_threshold_maps: 0.681881, loss_binary_maps: 0.207093, avg_reader_cost: 1.59984 s, avg_batch_cost: 1.83015 s, avg_samples: 12.5, ips: 6.83003 samples/s, eta: 0:02:12
[2024/07/27 23:11:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:11:39] ppocr INFO: epoch: [194/200], global_step: 580, lr: 0.001000, loss: 1.922496, loss_shrink_maps: 1.034084, loss_threshold_maps: 0.678076, loss_binary_maps: 0.203306, avg_reader_cost: 0.40172 s, avg_batch_cost: 0.52188 s, avg_samples: 4.8, ips: 9.19754 samples/s, eta: 0:02:06
[2024/07/27 23:11:40] ppocr INFO: epoch: [194/200], global_step: 582, lr: 0.001000, loss: 1.975362, loss_shrink_maps: 1.057069, loss_threshold_maps: 0.686071, loss_binary_maps: 0.209770, avg_reader_cost: 1.13540 s, avg_batch_cost: 1.28105 s, avg_samples: 7.7, ips: 6.01067 samples/s, eta: 0:01:53
[2024/07/27 23:11:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:11:48] ppocr INFO: epoch: [195/200], global_step: 585, lr: 0.001000, loss: 1.975362, loss_shrink_maps: 1.069529, loss_threshold_maps: 0.681881, loss_binary_maps: 0.213768, avg_reader_cost: 1.51876 s, avg_batch_cost: 1.77673 s, avg_samples: 12.5, ips: 7.03541 samples/s, eta: 0:01:34
[2024/07/27 23:11:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:11:55] ppocr INFO: epoch: [196/200], global_step: 588, lr: 0.001000, loss: 1.980742, loss_shrink_maps: 1.069529, loss_threshold_maps: 0.688508, loss_binary_maps: 0.213768, avg_reader_cost: 1.54117 s, avg_batch_cost: 1.79298 s, avg_samples: 12.5, ips: 6.97163 samples/s, eta: 0:01:15
[2024/07/27 23:11:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:12:02] ppocr INFO: epoch: [197/200], global_step: 590, lr: 0.001000, loss: 1.984796, loss_shrink_maps: 1.072904, loss_threshold_maps: 0.684661, loss_binary_maps: 0.214244, avg_reader_cost: 0.91438 s, avg_batch_cost: 1.08761 s, avg_samples: 9.6, ips: 8.82668 samples/s, eta: 0:01:02
[2024/07/27 23:12:02] ppocr INFO: epoch: [197/200], global_step: 591, lr: 0.001000, loss: 2.002328, loss_shrink_maps: 1.105892, loss_threshold_maps: 0.688508, loss_binary_maps: 0.220123, avg_reader_cost: 0.58948 s, avg_batch_cost: 0.64420 s, avg_samples: 2.9, ips: 4.50170 samples/s, eta: 0:00:56
[2024/07/27 23:12:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:12:10] ppocr INFO: epoch: [198/200], global_step: 594, lr: 0.001000, loss: 2.002328, loss_shrink_maps: 1.105892, loss_threshold_maps: 0.687102, loss_binary_maps: 0.220123, avg_reader_cost: 1.54211 s, avg_batch_cost: 1.77142 s, avg_samples: 12.5, ips: 7.05647 samples/s, eta: 0:00:37
[2024/07/27 23:12:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:12:18] ppocr INFO: epoch: [199/200], global_step: 597, lr: 0.001000, loss: 2.002328, loss_shrink_maps: 1.105892, loss_threshold_maps: 0.681492, loss_binary_maps: 0.220123, avg_reader_cost: 1.52315 s, avg_batch_cost: 1.78840 s, avg_samples: 12.5, ips: 6.98951 samples/s, eta: 0:00:18
[2024/07/27 23:12:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:12:25] ppocr INFO: epoch: [200/200], global_step: 600, lr: 0.001000, loss: 1.996198, loss_shrink_maps: 1.079593, loss_threshold_maps: 0.684349, loss_binary_maps: 0.214244, avg_reader_cost: 1.50024 s, avg_batch_cost: 1.73364 s, avg_samples: 12.5, ips: 7.21027 samples/s, eta: 0:00:00

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:54,  9.15it/s]
eval model::   1%|          | 4/500 [00:00<00:25, 19.56it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.12it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.01it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.83it/s]
eval model::   3%|▎         | 13/500 [00:00<00:26, 18.29it/s]
eval model::   3%|▎         | 16/500 [00:00<00:24, 19.57it/s]
eval model::   4%|▍         | 19/500 [00:01<00:23, 20.27it/s]
eval model::   4%|▍         | 22/500 [00:01<00:23, 20.56it/s]
eval model::   5%|▌         | 25/500 [00:01<00:23, 19.82it/s]
eval model::   6%|▌         | 28/500 [00:01<00:22, 21.05it/s]
eval model::   6%|▌         | 31/500 [00:01<00:24, 19.31it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 19.02it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 20.02it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.33it/s]
eval model::   9%|▊         | 43/500 [00:02<00:23, 19.39it/s]
eval model::   9%|▉         | 46/500 [00:02<00:23, 19.53it/s]
eval model::  10%|▉         | 49/500 [00:02<00:21, 20.51it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 20.66it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 20.18it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 18.96it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:22, 19.13it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:27, 15.83it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:28, 15.04it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:24, 17.45it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:21, 19.71it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 19.88it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.68it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:21, 19.91it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.58it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.21it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.01it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:23, 17.15it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:22, 18.33it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:20, 19.67it/s]
eval model::  20%|██        | 100/500 [00:05<00:19, 20.76it/s]
eval model::  21%|██        | 103/500 [00:05<00:17, 22.07it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 20.70it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 20.70it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 20.71it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:19, 19.97it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:17, 21.79it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 20.49it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 20.12it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.46it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:18, 19.54it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:17, 20.75it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:18, 20.18it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.34it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.70it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.50it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 18.41it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 17.93it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 21.26it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:15, 22.60it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:18, 18.30it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:18, 18.43it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 18.69it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:17, 19.20it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 18.69it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:18, 17.71it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:16, 19.12it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:18, 17.11it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 19.42it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 21.30it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.45it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.26it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:14, 20.97it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.34it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.09it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 21.54it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:18, 16.05it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:17, 16.47it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:18, 15.62it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.08it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.66it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.23it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.33it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 15.25it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:16, 16.26it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:17, 15.91it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:14, 18.57it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:13, 20.59it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:13, 19.00it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:13, 20.11it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 22.36it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:12, 20.69it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:11, 22.45it/s]
eval model::  50%|█████     | 250/500 [00:12<00:11, 21.65it/s]
eval model::  51%|█████     | 254/500 [00:13<00:10, 24.31it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 22.46it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 23.27it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:11, 21.48it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:11, 20.21it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:11, 20.84it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:11, 20.24it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.24it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 20.55it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 21.67it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 20.76it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:16, 13.23it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:13, 15.54it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 16.23it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.20it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:13, 15.45it/s]
eval model::  60%|██████    | 300/500 [00:15<00:11, 16.90it/s]
eval model::  61%|██████    | 303/500 [00:15<00:10, 18.75it/s]
eval model::  61%|██████    | 306/500 [00:15<00:09, 20.04it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 20.92it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:08, 21.14it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 21.11it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 21.58it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 22.25it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 21.17it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:07, 22.19it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 19.81it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:08, 20.57it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 20.74it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:08, 18.92it/s]
eval model::  68%|██████▊   | 341/500 [00:17<00:09, 16.17it/s]
eval model::  69%|██████▉   | 344/500 [00:17<00:08, 18.68it/s]
eval model::  69%|██████▉   | 347/500 [00:17<00:07, 19.35it/s]
eval model::  70%|███████   | 350/500 [00:18<00:07, 21.24it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 22.75it/s]
eval model::  71%|███████   | 356/500 [00:18<00:06, 21.12it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:06, 21.37it/s]
eval model::  72%|███████▏  | 362/500 [00:18<00:07, 18.87it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.60it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:07, 18.33it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.38it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:05, 21.46it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 21.71it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 20.83it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 20.22it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 21.64it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:08, 13.88it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:07, 15.48it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:06, 16.59it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 18.30it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 18.02it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 19.57it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 20.57it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 22.71it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:03, 23.22it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:03, 22.19it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 19.84it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:04, 17.12it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 17.39it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:04, 18.13it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 19.88it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 19.89it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:03, 21.19it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 20.35it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 21.58it/s]
eval model::  89%|████████▉ | 446/500 [00:22<00:02, 23.85it/s]
eval model::  90%|████████▉ | 449/500 [00:23<00:02, 22.34it/s]
eval model::  90%|█████████ | 452/500 [00:23<00:02, 23.29it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:01, 23.07it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:01, 23.63it/s]
eval model::  92%|█████████▏| 461/500 [00:23<00:01, 22.30it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 18.26it/s]
eval model::  93%|█████████▎| 466/500 [00:23<00:01, 17.18it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 17.55it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 19.30it/s]
eval model::  95%|█████████▍| 473/500 [00:24<00:01, 19.35it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 19.44it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 21.54it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 22.83it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 22.60it/s]
eval model::  97%|█████████▋| 487/500 [00:24<00:00, 23.40it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 23.80it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 22.50it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 20.30it/s]
eval model:: 100%|█████████▉| 499/500 [00:25<00:00, 22.50it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.61it/s]
[2024/07/27 23:12:51] ppocr INFO: cur metric, precision: 0.6872100728959576, recall: 0.4992778045257583, hmean: 0.5783602900167318, fps: 43.8015493763807
[2024/07/27 23:12:51] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 23:12:51] ppocr INFO: best metric, hmean: 0.5783602900167318, precision: 0.6872100728959576, recall: 0.4992778045257583, fps: 43.8015493763807, best_epoch: 200
[2024/07/27 23:12:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:12:51] ppocr INFO: best metric, hmean: 0.5783602900167318, precision: 0.6872100728959576, recall: 0.4992778045257583, fps: 43.8015493763807, best_epoch: 200
I0727 23:12:53.432174 113207 tcp_store.cc:289] receive shutdown event and so quit from MasterDaemon run loop
[2024/07/27 23:25:12] ppocr INFO: Architecture : 
[2024/07/27 23:25:12] ppocr INFO:     Backbone : 
[2024/07/27 23:25:12] ppocr INFO:         model_name : large
[2024/07/27 23:25:12] ppocr INFO:         name : MobileNetV3
[2024/07/27 23:25:12] ppocr INFO:         scale : 0.5
[2024/07/27 23:25:12] ppocr INFO:     Head : 
[2024/07/27 23:25:12] ppocr INFO:         k : 50
[2024/07/27 23:25:12] ppocr INFO:         name : DBHead
[2024/07/27 23:25:12] ppocr INFO:     Neck : 
[2024/07/27 23:25:12] ppocr INFO:         name : DBFPN
[2024/07/27 23:25:12] ppocr INFO:         out_channels : 256
[2024/07/27 23:25:12] ppocr INFO:     Transform : None
[2024/07/27 23:25:12] ppocr INFO:     algorithm : DB
[2024/07/27 23:25:12] ppocr INFO:     model_type : det
[2024/07/27 23:25:12] ppocr INFO: Eval : 
[2024/07/27 23:25:12] ppocr INFO:     dataset : 
[2024/07/27 23:25:12] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/07/27 23:25:12] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
[2024/07/27 23:25:12] ppocr INFO:         name : SimpleDataSet
[2024/07/27 23:25:12] ppocr INFO:         transforms : 
[2024/07/27 23:25:12] ppocr INFO:             DecodeImage : 
[2024/07/27 23:25:12] ppocr INFO:                 channel_first : False
[2024/07/27 23:25:12] ppocr INFO:                 img_mode : BGR
[2024/07/27 23:25:12] ppocr INFO:             DetLabelEncode : None
[2024/07/27 23:25:12] ppocr INFO:             DetResizeForTest : 
[2024/07/27 23:25:12] ppocr INFO:                 image_shape : [736, 1280]
[2024/07/27 23:25:12] ppocr INFO:             NormalizeImage : 
[2024/07/27 23:25:12] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/07/27 23:25:12] ppocr INFO:                 order : hwc
[2024/07/27 23:25:12] ppocr INFO:                 scale : 1./255.
[2024/07/27 23:25:12] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/07/27 23:25:12] ppocr INFO:             ToCHWImage : None
[2024/07/27 23:25:12] ppocr INFO:             KeepKeys : 
[2024/07/27 23:25:12] ppocr INFO:                 keep_keys : ['image', 'shape', 'polys', 'ignore_tags']
[2024/07/27 23:25:12] ppocr INFO:     loader : 
[2024/07/27 23:25:12] ppocr INFO:         batch_size_per_card : 1
[2024/07/27 23:25:12] ppocr INFO:         drop_last : False
[2024/07/27 23:25:12] ppocr INFO:         num_workers : 0
[2024/07/27 23:25:12] ppocr INFO:         shuffle : False
[2024/07/27 23:25:12] ppocr INFO:         use_shared_memory : True
[2024/07/27 23:25:12] ppocr INFO: Global : 
[2024/07/27 23:25:12] ppocr INFO:     cal_metric_during_train : False
[2024/07/27 23:25:12] ppocr INFO:     checkpoints : None
[2024/07/27 23:25:12] ppocr INFO:     distributed : True
[2024/07/27 23:25:12] ppocr INFO:     epoch_num : 1500
[2024/07/27 23:25:12] ppocr INFO:     eval_batch_step : [0, 60]
[2024/07/27 23:25:12] ppocr INFO:     infer_img : doc/imgs_en/img_10.jpg
[2024/07/27 23:25:12] ppocr INFO:     log_smooth_window : 20
[2024/07/27 23:25:12] ppocr INFO:     pretrained_model : ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/07/27 23:25:12] ppocr INFO:     print_batch_step : 10
[2024/07/27 23:25:12] ppocr INFO:     save_epoch_step : 1200
[2024/07/27 23:25:12] ppocr INFO:     save_inference_dir : None
[2024/07/27 23:25:12] ppocr INFO:     save_model_dir : ./output/db_mv3/
[2024/07/27 23:25:12] ppocr INFO:     save_res_path : ./output/det_db/predicts_db.txt
[2024/07/27 23:25:12] ppocr INFO:     use_gpu : True
[2024/07/27 23:25:12] ppocr INFO:     use_visualdl : False
[2024/07/27 23:25:12] ppocr INFO:     use_xpu : False
[2024/07/27 23:25:12] ppocr INFO: Loss : 
[2024/07/27 23:25:12] ppocr INFO:     alpha : 5
[2024/07/27 23:25:12] ppocr INFO:     balance_loss : True
[2024/07/27 23:25:12] ppocr INFO:     beta : 10
[2024/07/27 23:25:12] ppocr INFO:     main_loss_type : DiceLoss
[2024/07/27 23:25:12] ppocr INFO:     name : DBLoss
[2024/07/27 23:25:12] ppocr INFO:     ohem_ratio : 3
[2024/07/27 23:25:12] ppocr INFO: Metric : 
[2024/07/27 23:25:12] ppocr INFO:     main_indicator : hmean
[2024/07/27 23:25:12] ppocr INFO:     name : DetMetric
[2024/07/27 23:25:12] ppocr INFO: Optimizer : 
[2024/07/27 23:25:12] ppocr INFO:     beta1 : 0.9
[2024/07/27 23:25:12] ppocr INFO:     beta2 : 0.999
[2024/07/27 23:25:12] ppocr INFO:     lr : 
[2024/07/27 23:25:12] ppocr INFO:         learning_rate : 0.001
[2024/07/27 23:25:12] ppocr INFO:     name : Adam
[2024/07/27 23:25:12] ppocr INFO:     regularizer : 
[2024/07/27 23:25:12] ppocr INFO:         factor : 0
[2024/07/27 23:25:12] ppocr INFO:         name : L2
[2024/07/27 23:25:12] ppocr INFO: PostProcess : 
[2024/07/27 23:25:12] ppocr INFO:     box_thresh : 0.6
[2024/07/27 23:25:12] ppocr INFO:     max_candidates : 1000
[2024/07/27 23:25:12] ppocr INFO:     name : DBPostProcess
[2024/07/27 23:25:12] ppocr INFO:     thresh : 0.3
[2024/07/27 23:25:12] ppocr INFO:     unclip_ratio : 1.5
[2024/07/27 23:25:12] ppocr INFO: Train : 
[2024/07/27 23:25:12] ppocr INFO:     dataset : 
[2024/07/27 23:25:12] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/07/27 23:25:12] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/07/27 23:25:12] ppocr INFO:         name : SimpleDataSet
[2024/07/27 23:25:12] ppocr INFO:         ratio_list : [1.0]
[2024/07/27 23:25:12] ppocr INFO:         transforms : 
[2024/07/27 23:25:12] ppocr INFO:             DecodeImage : 
[2024/07/27 23:25:12] ppocr INFO:                 channel_first : False
[2024/07/27 23:25:12] ppocr INFO:                 img_mode : BGR
[2024/07/27 23:25:12] ppocr INFO:             DetLabelEncode : None
[2024/07/27 23:25:12] ppocr INFO:             IaaAugment : 
[2024/07/27 23:25:12] ppocr INFO:                 augmenter_args : 
[2024/07/27 23:25:12] ppocr INFO:                     args : 
[2024/07/27 23:25:12] ppocr INFO:                         p : 0.5
[2024/07/27 23:25:12] ppocr INFO:                     type : Fliplr
[2024/07/27 23:25:12] ppocr INFO:                     args : 
[2024/07/27 23:25:12] ppocr INFO:                         rotate : [-10, 10]
[2024/07/27 23:25:12] ppocr INFO:                     type : Affine
[2024/07/27 23:25:12] ppocr INFO:                     args : 
[2024/07/27 23:25:12] ppocr INFO:                         size : [0.5, 3]
[2024/07/27 23:25:12] ppocr INFO:                     type : Resize
[2024/07/27 23:25:12] ppocr INFO:             EastRandomCropData : 
[2024/07/27 23:25:12] ppocr INFO:                 keep_ratio : True
[2024/07/27 23:25:12] ppocr INFO:                 max_tries : 50
[2024/07/27 23:25:12] ppocr INFO:                 size : [640, 640]
[2024/07/27 23:25:12] ppocr INFO:             MakeBorderMap : 
[2024/07/27 23:25:12] ppocr INFO:                 shrink_ratio : 0.4
[2024/07/27 23:25:12] ppocr INFO:                 thresh_max : 0.7
[2024/07/27 23:25:12] ppocr INFO:                 thresh_min : 0.3
[2024/07/27 23:25:12] ppocr INFO:             MakeShrinkMap : 
[2024/07/27 23:25:12] ppocr INFO:                 min_text_size : 8
[2024/07/27 23:25:12] ppocr INFO:                 shrink_ratio : 0.4
[2024/07/27 23:25:12] ppocr INFO:             NormalizeImage : 
[2024/07/27 23:25:12] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/07/27 23:25:12] ppocr INFO:                 order : hwc
[2024/07/27 23:25:12] ppocr INFO:                 scale : 1./255.
[2024/07/27 23:25:12] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/07/27 23:25:12] ppocr INFO:             ToCHWImage : None
[2024/07/27 23:25:12] ppocr INFO:             KeepKeys : 
[2024/07/27 23:25:12] ppocr INFO:                 keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask']
[2024/07/27 23:25:12] ppocr INFO:     loader : 
[2024/07/27 23:25:12] ppocr INFO:         batch_size_per_card : 48
[2024/07/27 23:25:12] ppocr INFO:         drop_last : False
[2024/07/27 23:25:12] ppocr INFO:         num_workers : 8
[2024/07/27 23:25:12] ppocr INFO:         shuffle : True
[2024/07/27 23:25:12] ppocr INFO:         use_shared_memory : True
[2024/07/27 23:25:12] ppocr INFO: profiler_options : None
[2024/07/27 23:25:12] ppocr INFO: train with paddle 2.5.2 and device Place(gpu:0)
======================= Modified FLAGS detected =======================
FLAGS(name='FLAGS_selected_gpus', current_value='0', default_value='')
FLAGS(name='FLAGS_cudnn_batchnorm_spatial_persistent', current_value=True, default_value=False)
=======================================================================
I0727 23:25:12.009635 192372 tcp_utils.cc:181] The server starts to listen on IP_ANY:57742
I0727 23:25:12.009845 192372 tcp_utils.cc:130] Successfully connected to 127.0.0.1:57742
I0727 23:25:12.276495 192372 process_group_nccl.cc:120] ProcessGroupNCCL pg_timeout_ 1800000
[2024/07/27 23:25:12] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/07/27 23:25:12] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
W0727 23:25:12.297878 192372 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 90.2, Driver API Version: 50724.2, Runtime API Version: 50724.2
[2024/07/27 23:25:13] ppocr INFO: load pretrain successful from ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/07/27 23:25:13] ppocr INFO: train dataloader has 3 iters
[2024/07/27 23:25:13] ppocr INFO: valid dataloader has 500 iters
[2024/07/27 23:25:13] ppocr INFO: During the training process, after the 0th iteration, an evaluation is run every 60 iterations
[2024/07/27 23:25:36] ppocr INFO: epoch: [1/1500], global_step: 3, lr: 0.001000, loss: 9.375711, loss_shrink_maps: 4.915188, loss_threshold_maps: 3.474780, loss_binary_maps: 0.985743, avg_reader_cost: 5.86177 s, avg_batch_cost: 6.51959 s, avg_samples: 12.5, ips: 1.91730 samples/s, eta: 1 day, 3:08:48
[2024/07/27 23:25:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:25:45] ppocr INFO: epoch: [2/1500], global_step: 6, lr: 0.001000, loss: 8.349568, loss_shrink_maps: 4.871605, loss_threshold_maps: 2.502100, loss_binary_maps: 0.978699, avg_reader_cost: 2.23706 s, avg_batch_cost: 2.46490 s, avg_samples: 12.5, ips: 5.07120 samples/s, eta: 18:41:33
[2024/07/27 23:25:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:25:55] ppocr INFO: epoch: [3/1500], global_step: 9, lr: 0.001000, loss: 7.682193, loss_shrink_maps: 4.862057, loss_threshold_maps: 1.842980, loss_binary_maps: 0.977156, avg_reader_cost: 2.24538 s, avg_batch_cost: 2.47261 s, avg_samples: 12.5, ips: 5.05538 samples/s, eta: 15:52:50
[2024/07/27 23:25:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:26:03] ppocr INFO: epoch: [4/1500], global_step: 10, lr: 0.001000, loss: 7.487565, loss_shrink_maps: 4.860974, loss_threshold_maps: 1.649941, loss_binary_maps: 0.976730, avg_reader_cost: 0.64250 s, avg_batch_cost: 0.73961 s, avg_samples: 4.8, ips: 6.48990 samples/s, eta: 15:12:43
[2024/07/27 23:26:04] ppocr INFO: epoch: [4/1500], global_step: 12, lr: 0.001000, loss: 7.192430, loss_shrink_maps: 4.858564, loss_threshold_maps: 1.371744, loss_binary_maps: 0.976224, avg_reader_cost: 1.57013 s, avg_batch_cost: 1.71551 s, avg_samples: 7.7, ips: 4.48847 samples/s, eta: 14:27:11
[2024/07/27 23:26:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:26:14] ppocr INFO: epoch: [5/1500], global_step: 15, lr: 0.001000, loss: 7.051856, loss_shrink_maps: 4.825602, loss_threshold_maps: 1.235765, loss_binary_maps: 0.967080, avg_reader_cost: 2.26830 s, avg_batch_cost: 2.51290 s, avg_samples: 12.5, ips: 4.97433 samples/s, eta: 13:38:31
[2024/07/27 23:26:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:26:23] ppocr INFO: epoch: [6/1500], global_step: 18, lr: 0.001000, loss: 6.932699, loss_shrink_maps: 4.799246, loss_threshold_maps: 1.161819, loss_binary_maps: 0.962392, avg_reader_cost: 2.18515 s, avg_batch_cost: 2.43845 s, avg_samples: 12.5, ips: 5.12622 samples/s, eta: 13:02:50
[2024/07/27 23:26:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:26:32] ppocr INFO: epoch: [7/1500], global_step: 20, lr: 0.001000, loss: 6.904118, loss_shrink_maps: 4.785454, loss_threshold_maps: 1.161083, loss_binary_maps: 0.957916, avg_reader_cost: 1.39656 s, avg_batch_cost: 1.58187 s, avg_samples: 9.6, ips: 6.06877 samples/s, eta: 12:43:17
[2024/07/27 23:26:33] ppocr INFO: epoch: [7/1500], global_step: 21, lr: 0.001000, loss: 6.884484, loss_shrink_maps: 4.767570, loss_threshold_maps: 1.158091, loss_binary_maps: 0.955695, avg_reader_cost: 0.83681 s, avg_batch_cost: 0.89153 s, avg_samples: 2.9, ips: 3.25285 samples/s, eta: 12:38:28
[2024/07/27 23:26:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:26:42] ppocr INFO: epoch: [8/1500], global_step: 24, lr: 0.001000, loss: 6.811518, loss_shrink_maps: 4.715513, loss_threshold_maps: 1.153006, loss_binary_maps: 0.946022, avg_reader_cost: 2.22658 s, avg_batch_cost: 2.46713 s, avg_samples: 12.5, ips: 5.06662 samples/s, eta: 12:19:54
[2024/07/27 23:26:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:26:52] ppocr INFO: epoch: [9/1500], global_step: 27, lr: 0.001000, loss: 6.742378, loss_shrink_maps: 4.671161, loss_threshold_maps: 1.129713, loss_binary_maps: 0.930989, avg_reader_cost: 2.10302 s, avg_batch_cost: 2.39705 s, avg_samples: 12.5, ips: 5.21475 samples/s, eta: 12:03:26
[2024/07/27 23:26:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:27:01] ppocr INFO: epoch: [10/1500], global_step: 30, lr: 0.001000, loss: 6.512224, loss_shrink_maps: 4.568359, loss_threshold_maps: 1.094460, loss_binary_maps: 0.871143, avg_reader_cost: 2.25572 s, avg_batch_cost: 2.52676 s, avg_samples: 12.5, ips: 4.94705 samples/s, eta: 11:53:24
[2024/07/27 23:27:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:27:11] ppocr INFO: epoch: [11/1500], global_step: 33, lr: 0.001000, loss: 6.387847, loss_shrink_maps: 4.492524, loss_threshold_maps: 1.067466, loss_binary_maps: 0.822320, avg_reader_cost: 2.16144 s, avg_batch_cost: 2.44110 s, avg_samples: 12.5, ips: 5.12064 samples/s, eta: 11:43:11
[2024/07/27 23:27:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:27:20] ppocr INFO: epoch: [12/1500], global_step: 36, lr: 0.001000, loss: 6.017320, loss_shrink_maps: 4.261142, loss_threshold_maps: 1.047703, loss_binary_maps: 0.730894, avg_reader_cost: 2.23210 s, avg_batch_cost: 2.49150 s, avg_samples: 12.5, ips: 5.01705 samples/s, eta: 11:35:39
[2024/07/27 23:27:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:27:30] ppocr INFO: epoch: [13/1500], global_step: 39, lr: 0.001000, loss: 5.809624, loss_shrink_maps: 4.084210, loss_threshold_maps: 1.015606, loss_binary_maps: 0.704834, avg_reader_cost: 2.20560 s, avg_batch_cost: 2.46133 s, avg_samples: 12.5, ips: 5.07855 samples/s, eta: 11:28:37
[2024/07/27 23:27:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:27:38] ppocr INFO: epoch: [14/1500], global_step: 40, lr: 0.001000, loss: 5.717099, loss_shrink_maps: 4.012658, loss_threshold_maps: 1.010852, loss_binary_maps: 0.694131, avg_reader_cost: 0.64115 s, avg_batch_cost: 0.74817 s, avg_samples: 4.8, ips: 6.41562 samples/s, eta: 11:25:10
[2024/07/27 23:27:39] ppocr INFO: epoch: [14/1500], global_step: 42, lr: 0.001000, loss: 5.635980, loss_shrink_maps: 3.958893, loss_threshold_maps: 0.987196, loss_binary_maps: 0.668112, avg_reader_cost: 1.58756 s, avg_batch_cost: 1.73334 s, avg_samples: 7.7, ips: 4.44229 samples/s, eta: 11:22:54
[2024/07/27 23:27:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:27:49] ppocr INFO: epoch: [15/1500], global_step: 45, lr: 0.001000, loss: 5.374820, loss_shrink_maps: 3.758679, loss_threshold_maps: 0.974061, loss_binary_maps: 0.633635, avg_reader_cost: 2.21792 s, avg_batch_cost: 2.45682 s, avg_samples: 12.5, ips: 5.08787 samples/s, eta: 11:17:29
[2024/07/27 23:27:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:27:58] ppocr INFO: epoch: [16/1500], global_step: 48, lr: 0.001000, loss: 4.915116, loss_shrink_maps: 3.431638, loss_threshold_maps: 0.967177, loss_binary_maps: 0.532846, avg_reader_cost: 2.25993 s, avg_batch_cost: 2.48755 s, avg_samples: 12.5, ips: 5.02502 samples/s, eta: 11:13:10
[2024/07/27 23:27:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:28:08] ppocr INFO: epoch: [17/1500], global_step: 50, lr: 0.001000, loss: 4.716512, loss_shrink_maps: 3.259915, loss_threshold_maps: 0.960539, loss_binary_maps: 0.489610, avg_reader_cost: 1.43263 s, avg_batch_cost: 1.62292 s, avg_samples: 9.6, ips: 5.91528 samples/s, eta: 11:10:01
[2024/07/27 23:28:08] ppocr INFO: epoch: [17/1500], global_step: 51, lr: 0.001000, loss: 4.620404, loss_shrink_maps: 3.215099, loss_threshold_maps: 0.955661, loss_binary_maps: 0.476025, avg_reader_cost: 0.85680 s, avg_batch_cost: 0.91132 s, avg_samples: 2.9, ips: 3.18219 samples/s, eta: 11:09:59
[2024/07/27 23:28:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:28:18] ppocr INFO: epoch: [18/1500], global_step: 54, lr: 0.001000, loss: 4.412524, loss_shrink_maps: 3.018656, loss_threshold_maps: 0.950579, loss_binary_maps: 0.444982, avg_reader_cost: 2.26470 s, avg_batch_cost: 2.50988 s, avg_samples: 12.5, ips: 4.98032 samples/s, eta: 11:06:47
[2024/07/27 23:28:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:28:27] ppocr INFO: epoch: [19/1500], global_step: 57, lr: 0.001000, loss: 4.219252, loss_shrink_maps: 2.834966, loss_threshold_maps: 0.940522, loss_binary_maps: 0.424838, avg_reader_cost: 2.23139 s, avg_batch_cost: 2.45908 s, avg_samples: 12.5, ips: 5.08321 samples/s, eta: 11:03:12
[2024/07/27 23:28:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:28:37] ppocr INFO: epoch: [20/1500], global_step: 60, lr: 0.001000, loss: 4.151111, loss_shrink_maps: 2.791682, loss_threshold_maps: 0.937163, loss_binary_maps: 0.417132, avg_reader_cost: 2.23098 s, avg_batch_cost: 2.45910 s, avg_samples: 12.5, ips: 5.08317 samples/s, eta: 10:59:57

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:00,  8.21it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 17.89it/s]
eval model::   1%|▏         | 7/500 [00:00<00:25, 19.53it/s]
eval model::   2%|▏         | 9/500 [00:00<00:26, 18.37it/s]
eval model::   2%|▏         | 12/500 [00:00<00:23, 20.40it/s]
eval model::   3%|▎         | 15/500 [00:00<00:22, 21.85it/s]
eval model::   4%|▎         | 18/500 [00:00<00:21, 22.12it/s]
eval model::   4%|▍         | 21/500 [00:01<00:22, 20.83it/s]
eval model::   5%|▌         | 25/500 [00:01<00:19, 24.54it/s]
eval model::   6%|▌         | 28/500 [00:01<00:19, 23.95it/s]
eval model::   6%|▌         | 31/500 [00:01<00:21, 21.86it/s]
eval model::   7%|▋         | 34/500 [00:01<00:21, 21.39it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.75it/s]
eval model::   8%|▊         | 40/500 [00:01<00:22, 20.40it/s]
eval model::   9%|▊         | 43/500 [00:02<00:21, 21.11it/s]
eval model::   9%|▉         | 46/500 [00:02<00:21, 20.81it/s]
eval model::  10%|█         | 50/500 [00:02<00:18, 24.01it/s]
eval model::  11%|█         | 53/500 [00:02<00:17, 25.14it/s]
eval model::  11%|█         | 56/500 [00:02<00:18, 24.54it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:18, 23.77it/s]
eval model::  12%|█▏        | 62/500 [00:02<00:20, 21.21it/s]
eval model::  13%|█▎        | 65/500 [00:02<00:19, 22.24it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:17, 24.57it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:16, 26.67it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:16, 25.84it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:16, 25.97it/s]
eval model::  16%|█▋        | 82/500 [00:03<00:17, 23.82it/s]
eval model::  17%|█▋        | 85/500 [00:03<00:17, 23.09it/s]
eval model::  18%|█▊        | 88/500 [00:03<00:17, 23.06it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:18, 22.27it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:18, 22.53it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:17, 22.49it/s]
eval model::  20%|██        | 100/500 [00:04<00:18, 21.29it/s]
eval model::  21%|██        | 103/500 [00:04<00:17, 22.18it/s]
eval model::  21%|██        | 106/500 [00:04<00:16, 23.27it/s]
eval model::  22%|██▏       | 109/500 [00:04<00:17, 22.03it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 20.65it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:18, 20.80it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:18, 20.36it/s]
eval model::  24%|██▍       | 121/500 [00:05<00:18, 20.54it/s]
eval model::  25%|██▍       | 124/500 [00:05<00:18, 20.47it/s]
eval model::  25%|██▌       | 127/500 [00:05<00:17, 21.32it/s]
eval model::  26%|██▌       | 130/500 [00:05<00:20, 17.93it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 18.20it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:19, 18.56it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:20, 17.97it/s]
eval model::  28%|██▊       | 139/500 [00:06<00:19, 18.96it/s]
eval model::  28%|██▊       | 142/500 [00:06<00:18, 19.81it/s]
eval model::  29%|██▉       | 144/500 [00:06<00:19, 18.02it/s]
eval model::  29%|██▉       | 146/500 [00:06<00:20, 17.34it/s]
eval model::  30%|██▉       | 148/500 [00:06<00:20, 17.49it/s]
eval model::  30%|███       | 151/500 [00:07<00:20, 16.68it/s]
eval model::  31%|███       | 154/500 [00:07<00:19, 18.05it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:16, 20.40it/s]
eval model::  32%|███▏      | 160/500 [00:07<00:18, 18.81it/s]
eval model::  33%|███▎      | 163/500 [00:07<00:17, 19.57it/s]
eval model::  33%|███▎      | 166/500 [00:07<00:17, 19.58it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:19, 17.11it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 18.33it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:17, 18.53it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:18, 17.98it/s]
eval model::  36%|███▌      | 178/500 [00:08<00:18, 17.74it/s]
eval model::  36%|███▌      | 180/500 [00:08<00:19, 16.27it/s]
eval model::  36%|███▋      | 182/500 [00:08<00:18, 17.06it/s]
eval model::  37%|███▋      | 185/500 [00:08<00:16, 19.56it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:14, 22.13it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:15, 19.71it/s]
eval model::  39%|███▉      | 194/500 [00:09<00:14, 20.60it/s]
eval model::  39%|███▉      | 197/500 [00:09<00:19, 15.94it/s]
eval model::  40%|████      | 200/500 [00:09<00:16, 18.08it/s]
eval model::  41%|████      | 203/500 [00:09<00:15, 18.94it/s]
eval model::  41%|████      | 206/500 [00:10<00:23, 12.67it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:21, 13.54it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:20, 14.33it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:17, 16.56it/s]
eval model::  43%|████▎     | 215/500 [00:10<00:18, 15.10it/s]
eval model::  43%|████▎     | 217/500 [00:10<00:19, 14.78it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:14, 19.34it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:13, 20.93it/s]
eval model::  45%|████▌     | 227/500 [00:11<00:12, 22.45it/s]
eval model::  46%|████▌     | 231/500 [00:11<00:10, 26.38it/s]
eval model::  47%|████▋     | 234/500 [00:11<00:10, 24.49it/s]
eval model::  47%|████▋     | 237/500 [00:11<00:10, 24.21it/s]
eval model::  48%|████▊     | 240/500 [00:11<00:10, 24.40it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:11, 21.48it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:10, 23.22it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:11, 22.06it/s]
eval model::  50%|█████     | 252/500 [00:12<00:11, 21.02it/s]
eval model::  51%|█████     | 255/500 [00:12<00:11, 20.70it/s]
eval model::  52%|█████▏    | 258/500 [00:12<00:11, 21.94it/s]
eval model::  52%|█████▏    | 261/500 [00:12<00:10, 22.63it/s]
eval model::  53%|█████▎    | 264/500 [00:12<00:10, 22.55it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:10, 21.81it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:10, 21.63it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:10, 20.95it/s]
eval model::  55%|█████▌    | 276/500 [00:13<00:11, 20.31it/s]
eval model::  56%|█████▌    | 279/500 [00:13<00:11, 19.76it/s]
eval model::  56%|█████▋    | 282/500 [00:13<00:10, 20.78it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:13, 15.64it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:13, 16.24it/s]
eval model::  58%|█████▊    | 290/500 [00:14<00:11, 18.38it/s]
eval model::  59%|█████▊    | 293/500 [00:14<00:10, 19.63it/s]
eval model::  59%|█████▉    | 296/500 [00:14<00:11, 18.35it/s]
eval model::  60%|█████▉    | 299/500 [00:14<00:09, 20.19it/s]
eval model::  60%|██████    | 302/500 [00:14<00:10, 19.70it/s]
eval model::  61%|██████    | 305/500 [00:15<00:09, 20.32it/s]
eval model::  62%|██████▏   | 308/500 [00:15<00:08, 21.82it/s]
eval model::  62%|██████▏   | 311/500 [00:15<00:08, 21.68it/s]
eval model::  63%|██████▎   | 314/500 [00:15<00:08, 22.59it/s]
eval model::  63%|██████▎   | 317/500 [00:15<00:08, 20.59it/s]
eval model::  64%|██████▍   | 320/500 [00:15<00:08, 21.20it/s]
eval model::  65%|██████▍   | 323/500 [00:15<00:08, 20.97it/s]
eval model::  65%|██████▌   | 326/500 [00:16<00:08, 21.74it/s]
eval model::  66%|██████▌   | 329/500 [00:16<00:08, 21.18it/s]
eval model::  66%|██████▋   | 332/500 [00:16<00:08, 20.56it/s]
eval model::  67%|██████▋   | 335/500 [00:16<00:08, 20.27it/s]
eval model::  68%|██████▊   | 338/500 [00:16<00:07, 20.34it/s]
eval model::  68%|██████▊   | 341/500 [00:16<00:08, 18.94it/s]
eval model::  69%|██████▉   | 345/500 [00:16<00:07, 21.65it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:06, 21.83it/s]
eval model::  70%|███████   | 351/500 [00:17<00:07, 20.65it/s]
eval model::  71%|███████   | 354/500 [00:17<00:07, 20.01it/s]
eval model::  71%|███████▏  | 357/500 [00:17<00:07, 18.20it/s]
eval model::  72%|███████▏  | 359/500 [00:17<00:07, 18.47it/s]
eval model::  72%|███████▏  | 361/500 [00:17<00:07, 17.89it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 17.57it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:07, 18.01it/s]
eval model::  74%|███████▍  | 369/500 [00:18<00:06, 19.76it/s]
eval model::  74%|███████▍  | 372/500 [00:18<00:05, 21.81it/s]
eval model::  75%|███████▌  | 375/500 [00:18<00:05, 22.43it/s]
eval model::  76%|███████▌  | 378/500 [00:18<00:05, 21.11it/s]
eval model::  76%|███████▌  | 381/500 [00:18<00:05, 21.53it/s]
eval model::  77%|███████▋  | 384/500 [00:18<00:05, 21.92it/s]
eval model::  77%|███████▋  | 387/500 [00:19<00:05, 18.91it/s]
eval model::  78%|███████▊  | 390/500 [00:19<00:05, 20.55it/s]
eval model::  79%|███████▊  | 393/500 [00:19<00:05, 21.23it/s]
eval model::  79%|███████▉  | 396/500 [00:19<00:04, 20.93it/s]
eval model::  80%|███████▉  | 399/500 [00:19<00:04, 20.34it/s]
eval model::  80%|████████  | 402/500 [00:19<00:04, 20.60it/s]
eval model::  81%|████████  | 405/500 [00:19<00:04, 21.24it/s]
eval model::  82%|████████▏ | 408/500 [00:20<00:04, 20.53it/s]
eval model::  82%|████████▏ | 411/500 [00:20<00:04, 21.48it/s]
eval model::  83%|████████▎ | 414/500 [00:20<00:04, 20.59it/s]
eval model::  83%|████████▎ | 417/500 [00:20<00:04, 19.44it/s]
eval model::  84%|████████▍ | 419/500 [00:20<00:04, 18.28it/s]
eval model::  84%|████████▍ | 421/500 [00:20<00:04, 16.58it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 18.27it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:03, 18.55it/s]
eval model::  86%|████████▌ | 430/500 [00:21<00:03, 21.81it/s]
eval model::  87%|████████▋ | 433/500 [00:21<00:03, 21.29it/s]
eval model::  87%|████████▋ | 436/500 [00:21<00:03, 21.15it/s]
eval model::  88%|████████▊ | 439/500 [00:21<00:02, 20.84it/s]
eval model::  88%|████████▊ | 442/500 [00:21<00:02, 21.01it/s]
eval model::  89%|████████▉ | 445/500 [00:21<00:02, 21.05it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 19.99it/s]
eval model::  90%|█████████ | 451/500 [00:22<00:02, 20.50it/s]
eval model::  91%|█████████ | 454/500 [00:22<00:02, 19.67it/s]
eval model::  91%|█████████▏| 457/500 [00:22<00:02, 20.30it/s]
eval model::  92%|█████████▏| 460/500 [00:22<00:01, 20.90it/s]
eval model::  93%|█████████▎| 463/500 [00:22<00:01, 21.08it/s]
eval model::  93%|█████████▎| 466/500 [00:23<00:01, 20.26it/s]
eval model::  94%|█████████▍| 469/500 [00:23<00:01, 21.21it/s]
eval model::  94%|█████████▍| 472/500 [00:23<00:01, 20.29it/s]
eval model::  95%|█████████▌| 475/500 [00:23<00:01, 21.48it/s]
eval model::  96%|█████████▌| 478/500 [00:23<00:01, 21.10it/s]
eval model::  96%|█████████▌| 481/500 [00:23<00:00, 20.18it/s]
eval model::  97%|█████████▋| 484/500 [00:23<00:00, 20.12it/s]
eval model::  97%|█████████▋| 487/500 [00:24<00:00, 20.49it/s]
eval model::  98%|█████████▊| 490/500 [00:24<00:00, 20.61it/s]
eval model::  99%|█████████▊| 493/500 [00:24<00:00, 20.36it/s]
eval model::  99%|█████████▉| 496/500 [00:24<00:00, 20.34it/s]
eval model:: 100%|█████████▉| 499/500 [00:24<00:00, 22.37it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 20.31it/s]
[2024/07/27 23:29:01] ppocr INFO: cur metric, precision: 0.29863013698630136, recall: 0.15743861338468945, hmean: 0.20617906683480453, fps: 45.08311599737208
[2024/07/27 23:29:01] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 23:29:01] ppocr INFO: best metric, hmean: 0.20617906683480453, precision: 0.29863013698630136, recall: 0.15743861338468945, fps: 45.08311599737208, best_epoch: 20
[2024/07/27 23:29:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:29:09] ppocr INFO: epoch: [21/1500], global_step: 63, lr: 0.001000, loss: 4.080241, loss_shrink_maps: 2.738514, loss_threshold_maps: 0.937163, loss_binary_maps: 0.402558, avg_reader_cost: 1.62885 s, avg_batch_cost: 1.91093 s, avg_samples: 12.5, ips: 6.54131 samples/s, eta: 10:50:32
[2024/07/27 23:29:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:29:16] ppocr INFO: epoch: [22/1500], global_step: 66, lr: 0.001000, loss: 4.034955, loss_shrink_maps: 2.699648, loss_threshold_maps: 0.934345, loss_binary_maps: 0.402558, avg_reader_cost: 1.58337 s, avg_batch_cost: 1.81529 s, avg_samples: 12.5, ips: 6.88595 samples/s, eta: 10:40:52
[2024/07/27 23:29:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:29:23] ppocr INFO: epoch: [23/1500], global_step: 69, lr: 0.001000, loss: 3.981198, loss_shrink_maps: 2.657832, loss_threshold_maps: 0.929097, loss_binary_maps: 0.396769, avg_reader_cost: 1.51271 s, avg_batch_cost: 1.76303 s, avg_samples: 12.5, ips: 7.09007 samples/s, eta: 10:31:27
[2024/07/27 23:29:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:29:29] ppocr INFO: epoch: [24/1500], global_step: 70, lr: 0.001000, loss: 3.910433, loss_shrink_maps: 2.596458, loss_threshold_maps: 0.926523, loss_binary_maps: 0.396769, avg_reader_cost: 0.41085 s, avg_batch_cost: 0.50778 s, avg_samples: 4.8, ips: 9.45286 samples/s, eta: 10:27:39
[2024/07/27 23:29:30] ppocr INFO: epoch: [24/1500], global_step: 72, lr: 0.001000, loss: 3.920726, loss_shrink_maps: 2.596458, loss_threshold_maps: 0.929989, loss_binary_maps: 0.400353, avg_reader_cost: 1.10667 s, avg_batch_cost: 1.25224 s, avg_samples: 7.7, ips: 6.14900 samples/s, eta: 10:22:46
[2024/07/27 23:29:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:29:37] ppocr INFO: epoch: [25/1500], global_step: 75, lr: 0.001000, loss: 3.859083, loss_shrink_maps: 2.502296, loss_threshold_maps: 0.926523, loss_binary_maps: 0.404162, avg_reader_cost: 1.52482 s, avg_batch_cost: 1.75290 s, avg_samples: 12.5, ips: 7.13105 samples/s, eta: 10:14:42
[2024/07/27 23:29:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:29:44] ppocr INFO: epoch: [26/1500], global_step: 78, lr: 0.001000, loss: 3.740852, loss_shrink_maps: 2.419104, loss_threshold_maps: 0.912594, loss_binary_maps: 0.395320, avg_reader_cost: 1.50883 s, avg_batch_cost: 1.75149 s, avg_samples: 12.5, ips: 7.13680 samples/s, eta: 10:07:12
[2024/07/27 23:29:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:29:51] ppocr INFO: epoch: [27/1500], global_step: 80, lr: 0.001000, loss: 3.683215, loss_shrink_maps: 2.370544, loss_threshold_maps: 0.909663, loss_binary_maps: 0.387887, avg_reader_cost: 0.89569 s, avg_batch_cost: 1.07283 s, avg_samples: 9.6, ips: 8.94829 samples/s, eta: 10:01:38
[2024/07/27 23:29:51] ppocr INFO: epoch: [27/1500], global_step: 81, lr: 0.001000, loss: 3.647673, loss_shrink_maps: 2.350705, loss_threshold_maps: 0.906193, loss_binary_maps: 0.381212, avg_reader_cost: 0.58191 s, avg_batch_cost: 0.63697 s, avg_samples: 2.9, ips: 4.55278 samples/s, eta: 9:59:52
[2024/07/27 23:29:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:29:58] ppocr INFO: epoch: [28/1500], global_step: 84, lr: 0.001000, loss: 3.406248, loss_shrink_maps: 2.177213, loss_threshold_maps: 0.886992, loss_binary_maps: 0.358823, avg_reader_cost: 1.53018 s, avg_batch_cost: 1.78292 s, avg_samples: 12.5, ips: 7.01097 samples/s, eta: 9:53:40
[2024/07/27 23:29:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:30:06] ppocr INFO: epoch: [29/1500], global_step: 87, lr: 0.001000, loss: 3.349561, loss_shrink_maps: 2.121737, loss_threshold_maps: 0.883556, loss_binary_maps: 0.348226, avg_reader_cost: 1.66590 s, avg_batch_cost: 1.89398 s, avg_samples: 12.5, ips: 6.59985 samples/s, eta: 9:48:49
[2024/07/27 23:30:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:30:13] ppocr INFO: epoch: [30/1500], global_step: 90, lr: 0.001000, loss: 3.293404, loss_shrink_maps: 2.096559, loss_threshold_maps: 0.883388, loss_binary_maps: 0.350930, avg_reader_cost: 1.50401 s, avg_batch_cost: 1.73596 s, avg_samples: 12.5, ips: 7.20062 samples/s, eta: 9:42:59
[2024/07/27 23:30:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:30:20] ppocr INFO: epoch: [31/1500], global_step: 93, lr: 0.001000, loss: 3.256478, loss_shrink_maps: 2.023408, loss_threshold_maps: 0.866258, loss_binary_maps: 0.350930, avg_reader_cost: 1.53432 s, avg_batch_cost: 1.78011 s, avg_samples: 12.5, ips: 7.02202 samples/s, eta: 9:37:51
[2024/07/27 23:30:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:30:27] ppocr INFO: epoch: [32/1500], global_step: 96, lr: 0.001000, loss: 3.223192, loss_shrink_maps: 2.004760, loss_threshold_maps: 0.852820, loss_binary_maps: 0.342458, avg_reader_cost: 1.51782 s, avg_batch_cost: 1.77830 s, avg_samples: 12.5, ips: 7.02918 samples/s, eta: 9:33:00
[2024/07/27 23:30:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:30:34] ppocr INFO: epoch: [33/1500], global_step: 99, lr: 0.001000, loss: 3.153462, loss_shrink_maps: 1.951061, loss_threshold_maps: 0.845036, loss_binary_maps: 0.334254, avg_reader_cost: 1.49794 s, avg_batch_cost: 1.72771 s, avg_samples: 12.5, ips: 7.23499 samples/s, eta: 9:28:04
[2024/07/27 23:30:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:30:39] ppocr INFO: epoch: [34/1500], global_step: 100, lr: 0.001000, loss: 3.148788, loss_shrink_maps: 1.938410, loss_threshold_maps: 0.841603, loss_binary_maps: 0.334254, avg_reader_cost: 0.41143 s, avg_batch_cost: 0.49608 s, avg_samples: 4.8, ips: 9.67586 samples/s, eta: 9:25:54
[2024/07/27 23:30:41] ppocr INFO: epoch: [34/1500], global_step: 102, lr: 0.001000, loss: 3.025582, loss_shrink_maps: 1.863062, loss_threshold_maps: 0.840630, loss_binary_maps: 0.328599, avg_reader_cost: 1.08355 s, avg_batch_cost: 1.22937 s, avg_samples: 7.7, ips: 6.26339 samples/s, eta: 9:23:23
[2024/07/27 23:30:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:30:48] ppocr INFO: epoch: [35/1500], global_step: 105, lr: 0.001000, loss: 2.993498, loss_shrink_maps: 1.839174, loss_threshold_maps: 0.831249, loss_binary_maps: 0.328271, avg_reader_cost: 1.51914 s, avg_batch_cost: 1.75079 s, avg_samples: 12.5, ips: 7.13965 samples/s, eta: 9:19:07
[2024/07/27 23:30:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:30:55] ppocr INFO: epoch: [36/1500], global_step: 108, lr: 0.001000, loss: 2.994124, loss_shrink_maps: 1.845578, loss_threshold_maps: 0.831249, loss_binary_maps: 0.332773, avg_reader_cost: 1.52896 s, avg_batch_cost: 1.79093 s, avg_samples: 12.5, ips: 6.97961 samples/s, eta: 9:15:22
[2024/07/27 23:30:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:31:02] ppocr INFO: epoch: [37/1500], global_step: 110, lr: 0.001000, loss: 2.994124, loss_shrink_maps: 1.841758, loss_threshold_maps: 0.831249, loss_binary_maps: 0.333325, avg_reader_cost: 0.98818 s, avg_batch_cost: 1.16375 s, avg_samples: 9.6, ips: 8.24919 samples/s, eta: 9:12:45
[2024/07/27 23:31:03] ppocr INFO: epoch: [37/1500], global_step: 111, lr: 0.001000, loss: 2.992690, loss_shrink_maps: 1.835354, loss_threshold_maps: 0.827756, loss_binary_maps: 0.332539, avg_reader_cost: 0.62748 s, avg_batch_cost: 0.68237 s, avg_samples: 2.9, ips: 4.24987 samples/s, eta: 9:12:09
[2024/07/27 23:31:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:31:10] ppocr INFO: epoch: [38/1500], global_step: 114, lr: 0.001000, loss: 2.978850, loss_shrink_maps: 1.825602, loss_threshold_maps: 0.825094, loss_binary_maps: 0.324127, avg_reader_cost: 1.52141 s, avg_batch_cost: 1.75202 s, avg_samples: 12.5, ips: 7.13460 samples/s, eta: 9:08:29
[2024/07/27 23:31:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:31:17] ppocr INFO: epoch: [39/1500], global_step: 117, lr: 0.001000, loss: 2.961697, loss_shrink_maps: 1.811566, loss_threshold_maps: 0.835785, loss_binary_maps: 0.323504, avg_reader_cost: 1.53859 s, avg_batch_cost: 1.76720 s, avg_samples: 12.5, ips: 7.07332 samples/s, eta: 9:05:05
[2024/07/27 23:31:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:31:24] ppocr INFO: epoch: [40/1500], global_step: 120, lr: 0.001000, loss: 2.935220, loss_shrink_maps: 1.793961, loss_threshold_maps: 0.815128, loss_binary_maps: 0.321562, avg_reader_cost: 1.54088 s, avg_batch_cost: 1.80016 s, avg_samples: 12.5, ips: 6.94381 samples/s, eta: 9:02:03

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:06,  7.55it/s]
eval model::   1%|          | 3/500 [00:00<00:35, 13.85it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.81it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.63it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.41it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.44it/s]
eval model::   3%|▎         | 17/500 [00:00<00:25, 18.98it/s]
eval model::   4%|▍         | 19/500 [00:01<00:25, 18.61it/s]
eval model::   4%|▍         | 22/500 [00:01<00:24, 19.12it/s]
eval model::   5%|▍         | 24/500 [00:01<00:24, 19.19it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 20.63it/s]
eval model::   6%|▌         | 30/500 [00:01<00:22, 21.24it/s]
eval model::   7%|▋         | 33/500 [00:01<00:22, 21.22it/s]
eval model::   7%|▋         | 36/500 [00:01<00:22, 20.32it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 18.23it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 17.98it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.83it/s]
eval model::   9%|▉         | 47/500 [00:02<00:24, 18.55it/s]
eval model::  10%|█         | 50/500 [00:02<00:25, 17.87it/s]
eval model::  10%|█         | 52/500 [00:02<00:26, 16.98it/s]
eval model::  11%|█         | 55/500 [00:02<00:24, 18.10it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 18.93it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:24, 17.99it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:24, 17.75it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 19.05it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.47it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 20.50it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:19, 21.41it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:21, 20.09it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:21, 19.63it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 19.90it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.11it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.24it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.47it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:23, 17.19it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:21, 19.08it/s]
eval model::  20%|██        | 100/500 [00:05<00:21, 18.53it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 20.51it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 20.42it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:19, 19.73it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:20, 19.25it/s]
eval model::  23%|██▎       | 114/500 [00:06<00:20, 19.07it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:19, 19.86it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:20, 18.59it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 19.93it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:17, 20.81it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 18.67it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:18, 20.19it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:16, 21.45it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.71it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:19, 18.76it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:19, 18.68it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:18, 18.72it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 18.09it/s]
eval model::  30%|███       | 150/500 [00:07<00:19, 18.33it/s]
eval model::  30%|███       | 152/500 [00:08<00:20, 16.76it/s]
eval model::  31%|███       | 155/500 [00:08<00:18, 18.71it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:17, 19.11it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:18, 17.99it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:17, 18.92it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 19.59it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:19, 17.10it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:18, 17.89it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 16.16it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:18, 17.84it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:17, 17.94it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 19.21it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.81it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.63it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:14, 21.42it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.47it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.73it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.21it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.38it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:20, 14.49it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:19, 14.57it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 14.90it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.74it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.60it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.47it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:18, 15.50it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:17, 16.27it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:14, 18.72it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:14, 18.69it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:12, 21.27it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:13, 20.51it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 21.96it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:10, 24.41it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:11, 22.82it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:10, 24.38it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:11, 22.77it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 21.27it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 20.55it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:12, 19.83it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:12, 18.82it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 18.88it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:12, 19.33it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 19.09it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.33it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:12, 18.69it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 18.48it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:12, 18.46it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:11, 19.16it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:11, 18.43it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:16, 13.18it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:13, 16.21it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:11, 18.59it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:10, 18.79it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:10, 19.16it/s]
eval model::  60%|██████    | 300/500 [00:15<00:11, 18.09it/s]
eval model::  60%|██████    | 302/500 [00:16<00:10, 18.30it/s]
eval model::  61%|██████    | 304/500 [00:16<00:10, 18.05it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:09, 19.54it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:10, 18.93it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:10, 18.63it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:10, 17.81it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:11, 16.40it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:09, 18.87it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.00it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 20.72it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:09, 18.38it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 19.72it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 20.82it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:08, 18.95it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 15.69it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:08, 18.13it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 20.33it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:06, 21.77it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 21.28it/s]
eval model::  71%|███████   | 355/500 [00:18<00:07, 19.43it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 20.91it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:06, 20.09it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:06, 19.83it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:06, 20.44it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:05, 22.03it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:05, 22.13it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 21.51it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:06, 19.90it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 20.41it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:05, 22.19it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:07, 15.45it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 15.78it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 17.92it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 19.11it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:04, 20.25it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 19.58it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 20.44it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.92it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 23.21it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 22.98it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 22.11it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:04, 17.86it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 18.07it/s]
eval model::  85%|████████▌ | 425/500 [00:22<00:03, 18.95it/s]
eval model::  86%|████████▌ | 428/500 [00:22<00:03, 20.94it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:02, 24.12it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 26.11it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 24.01it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 23.30it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 22.40it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 20.80it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 20.85it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 20.37it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:02, 21.07it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 21.08it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 18.09it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.54it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 20.33it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 21.18it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 21.56it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 21.59it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 20.06it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 20.46it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 19.69it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 21.27it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 21.56it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 22.34it/s]
eval model:: 100%|█████████▉| 499/500 [00:25<00:00, 23.79it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.38it/s]
[2024/07/27 23:31:50] ppocr INFO: cur metric, precision: 0.5166023166023166, recall: 0.32209918151179584, hmean: 0.39679715302491103, fps: 46.076865625715506
[2024/07/27 23:31:50] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 23:31:50] ppocr INFO: best metric, hmean: 0.39679715302491103, precision: 0.5166023166023166, recall: 0.32209918151179584, fps: 46.076865625715506, best_epoch: 40
[2024/07/27 23:31:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:31:56] ppocr INFO: epoch: [41/1500], global_step: 123, lr: 0.001000, loss: 2.883159, loss_shrink_maps: 1.735100, loss_threshold_maps: 0.815128, loss_binary_maps: 0.312276, avg_reader_cost: 1.44015 s, avg_batch_cost: 1.67706 s, avg_samples: 12.5, ips: 7.45352 samples/s, eta: 8:58:25
[2024/07/27 23:31:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:32:04] ppocr INFO: epoch: [42/1500], global_step: 126, lr: 0.001000, loss: 2.784557, loss_shrink_maps: 1.669631, loss_threshold_maps: 0.815128, loss_binary_maps: 0.302257, avg_reader_cost: 1.57560 s, avg_batch_cost: 1.81912 s, avg_samples: 12.5, ips: 6.87144 samples/s, eta: 8:55:45
[2024/07/27 23:32:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:32:11] ppocr INFO: epoch: [43/1500], global_step: 129, lr: 0.001000, loss: 2.769441, loss_shrink_maps: 1.649815, loss_threshold_maps: 0.810003, loss_binary_maps: 0.297281, avg_reader_cost: 1.51696 s, avg_batch_cost: 1.75118 s, avg_samples: 12.5, ips: 7.13803 samples/s, eta: 8:52:50
[2024/07/27 23:32:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:32:17] ppocr INFO: epoch: [44/1500], global_step: 130, lr: 0.001000, loss: 2.764102, loss_shrink_maps: 1.642196, loss_threshold_maps: 0.806035, loss_binary_maps: 0.295727, avg_reader_cost: 0.42675 s, avg_batch_cost: 0.52127 s, avg_samples: 4.8, ips: 9.20833 samples/s, eta: 8:51:32
[2024/07/27 23:32:18] ppocr INFO: epoch: [44/1500], global_step: 132, lr: 0.001000, loss: 2.776821, loss_shrink_maps: 1.655495, loss_threshold_maps: 0.810003, loss_binary_maps: 0.297281, avg_reader_cost: 1.13391 s, avg_batch_cost: 1.27915 s, avg_samples: 7.7, ips: 6.01964 samples/s, eta: 8:50:17
[2024/07/27 23:32:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:32:25] ppocr INFO: epoch: [45/1500], global_step: 135, lr: 0.001000, loss: 2.775065, loss_shrink_maps: 1.664439, loss_threshold_maps: 0.804146, loss_binary_maps: 0.304573, avg_reader_cost: 1.55032 s, avg_batch_cost: 1.78582 s, avg_samples: 12.5, ips: 6.99957 samples/s, eta: 8:47:46
[2024/07/27 23:32:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:32:32] ppocr INFO: epoch: [46/1500], global_step: 138, lr: 0.001000, loss: 2.801608, loss_shrink_maps: 1.690853, loss_threshold_maps: 0.805666, loss_binary_maps: 0.306324, avg_reader_cost: 1.52002 s, avg_batch_cost: 1.77090 s, avg_samples: 12.5, ips: 7.05857 samples/s, eta: 8:45:16
[2024/07/27 23:32:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:32:39] ppocr INFO: epoch: [47/1500], global_step: 140, lr: 0.001000, loss: 2.761428, loss_shrink_maps: 1.664439, loss_threshold_maps: 0.804146, loss_binary_maps: 0.299543, avg_reader_cost: 0.92682 s, avg_batch_cost: 1.10657 s, avg_samples: 9.6, ips: 8.67544 samples/s, eta: 8:43:17
[2024/07/27 23:32:39] ppocr INFO: epoch: [47/1500], global_step: 141, lr: 0.001000, loss: 2.792115, loss_shrink_maps: 1.685216, loss_threshold_maps: 0.808615, loss_binary_maps: 0.305752, avg_reader_cost: 0.59882 s, avg_batch_cost: 0.65420 s, avg_samples: 2.9, ips: 4.43292 samples/s, eta: 8:42:49
[2024/07/27 23:32:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:32:47] ppocr INFO: epoch: [48/1500], global_step: 144, lr: 0.001000, loss: 2.741264, loss_shrink_maps: 1.656588, loss_threshold_maps: 0.804146, loss_binary_maps: 0.296682, avg_reader_cost: 1.56473 s, avg_batch_cost: 1.82044 s, avg_samples: 12.5, ips: 6.86649 samples/s, eta: 8:40:45
[2024/07/27 23:32:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:32:54] ppocr INFO: epoch: [49/1500], global_step: 147, lr: 0.001000, loss: 2.741264, loss_shrink_maps: 1.661702, loss_threshold_maps: 0.808402, loss_binary_maps: 0.298334, avg_reader_cost: 1.63377 s, avg_batch_cost: 1.91885 s, avg_samples: 12.5, ips: 6.51431 samples/s, eta: 8:39:14
[2024/07/27 23:32:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:33:02] ppocr INFO: epoch: [50/1500], global_step: 150, lr: 0.001000, loss: 2.706133, loss_shrink_maps: 1.631373, loss_threshold_maps: 0.788792, loss_binary_maps: 0.298334, avg_reader_cost: 1.59973 s, avg_batch_cost: 1.87536 s, avg_samples: 12.5, ips: 6.66537 samples/s, eta: 8:37:34
[2024/07/27 23:33:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:33:09] ppocr INFO: epoch: [51/1500], global_step: 153, lr: 0.001000, loss: 2.686611, loss_shrink_maps: 1.603767, loss_threshold_maps: 0.780494, loss_binary_maps: 0.296459, avg_reader_cost: 1.55318 s, avg_batch_cost: 1.79274 s, avg_samples: 12.5, ips: 6.97258 samples/s, eta: 8:35:34
[2024/07/27 23:33:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:33:16] ppocr INFO: epoch: [52/1500], global_step: 156, lr: 0.001000, loss: 2.670284, loss_shrink_maps: 1.579481, loss_threshold_maps: 0.782544, loss_binary_maps: 0.290991, avg_reader_cost: 1.50835 s, avg_batch_cost: 1.73943 s, avg_samples: 12.5, ips: 7.18628 samples/s, eta: 8:33:22
[2024/07/27 23:33:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:33:24] ppocr INFO: epoch: [53/1500], global_step: 159, lr: 0.001000, loss: 2.629240, loss_shrink_maps: 1.546434, loss_threshold_maps: 0.783303, loss_binary_maps: 0.288077, avg_reader_cost: 1.54704 s, avg_batch_cost: 1.77484 s, avg_samples: 12.5, ips: 7.04291 samples/s, eta: 8:31:25
[2024/07/27 23:33:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:33:29] ppocr INFO: epoch: [54/1500], global_step: 160, lr: 0.001000, loss: 2.670284, loss_shrink_maps: 1.569450, loss_threshold_maps: 0.784121, loss_binary_maps: 0.291360, avg_reader_cost: 0.41779 s, avg_batch_cost: 0.50667 s, avg_samples: 4.8, ips: 9.47367 samples/s, eta: 8:30:23
[2024/07/27 23:33:31] ppocr INFO: epoch: [54/1500], global_step: 162, lr: 0.001000, loss: 2.668161, loss_shrink_maps: 1.572882, loss_threshold_maps: 0.783303, loss_binary_maps: 0.296459, avg_reader_cost: 1.10487 s, avg_batch_cost: 1.25090 s, avg_samples: 7.7, ips: 6.15559 samples/s, eta: 8:29:26
[2024/07/27 23:33:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:33:38] ppocr INFO: epoch: [55/1500], global_step: 165, lr: 0.001000, loss: 2.668161, loss_shrink_maps: 1.572882, loss_threshold_maps: 0.783303, loss_binary_maps: 0.296514, avg_reader_cost: 1.51618 s, avg_batch_cost: 1.75431 s, avg_samples: 12.5, ips: 7.12530 samples/s, eta: 8:27:31
[2024/07/27 23:33:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:33:46] ppocr INFO: epoch: [56/1500], global_step: 168, lr: 0.001000, loss: 2.588552, loss_shrink_maps: 1.513257, loss_threshold_maps: 0.784121, loss_binary_maps: 0.288074, avg_reader_cost: 1.64681 s, avg_batch_cost: 1.92639 s, avg_samples: 12.5, ips: 6.48880 samples/s, eta: 8:26:23
[2024/07/27 23:33:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:33:52] ppocr INFO: epoch: [57/1500], global_step: 170, lr: 0.001000, loss: 2.623957, loss_shrink_maps: 1.537436, loss_threshold_maps: 0.784121, loss_binary_maps: 0.291877, avg_reader_cost: 0.95033 s, avg_batch_cost: 1.12382 s, avg_samples: 9.6, ips: 8.54230 samples/s, eta: 8:24:58
[2024/07/27 23:33:53] ppocr INFO: epoch: [57/1500], global_step: 171, lr: 0.001000, loss: 2.623957, loss_shrink_maps: 1.537436, loss_threshold_maps: 0.785261, loss_binary_maps: 0.291877, avg_reader_cost: 0.60776 s, avg_batch_cost: 0.66238 s, avg_samples: 2.9, ips: 4.37815 samples/s, eta: 8:24:41
[2024/07/27 23:33:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:34:00] ppocr INFO: epoch: [58/1500], global_step: 174, lr: 0.001000, loss: 2.679314, loss_shrink_maps: 1.575697, loss_threshold_maps: 0.786489, loss_binary_maps: 0.299474, avg_reader_cost: 1.52836 s, avg_batch_cost: 1.75780 s, avg_samples: 12.5, ips: 7.11117 samples/s, eta: 8:22:56
[2024/07/27 23:34:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:34:07] ppocr INFO: epoch: [59/1500], global_step: 177, lr: 0.001000, loss: 2.662877, loss_shrink_maps: 1.560451, loss_threshold_maps: 0.780708, loss_binary_maps: 0.297437, avg_reader_cost: 1.54185 s, avg_batch_cost: 1.80270 s, avg_samples: 12.5, ips: 6.93404 samples/s, eta: 8:21:24
[2024/07/27 23:34:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:34:14] ppocr INFO: epoch: [60/1500], global_step: 180, lr: 0.001000, loss: 2.662877, loss_shrink_maps: 1.573298, loss_threshold_maps: 0.778581, loss_binary_maps: 0.297710, avg_reader_cost: 1.52940 s, avg_batch_cost: 1.78184 s, avg_samples: 12.5, ips: 7.01524 samples/s, eta: 8:19:50

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.35it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.43it/s]
eval model::   1%|▏         | 7/500 [00:00<00:24, 20.40it/s]
eval model::   2%|▏         | 9/500 [00:00<00:24, 19.85it/s]
eval model::   2%|▏         | 11/500 [00:00<00:25, 19.20it/s]
eval model::   3%|▎         | 14/500 [00:00<00:23, 20.43it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.98it/s]
eval model::   4%|▍         | 19/500 [00:01<00:26, 18.14it/s]
eval model::   4%|▍         | 21/500 [00:01<00:27, 17.17it/s]
eval model::   5%|▍         | 23/500 [00:01<00:27, 17.58it/s]
eval model::   5%|▌         | 26/500 [00:01<00:26, 18.15it/s]
eval model::   6%|▌         | 28/500 [00:01<00:25, 18.25it/s]
eval model::   6%|▌         | 31/500 [00:01<00:24, 19.01it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.06it/s]
eval model::   7%|▋         | 36/500 [00:02<00:28, 16.42it/s]
eval model::   8%|▊         | 38/500 [00:02<00:28, 16.40it/s]
eval model::   8%|▊         | 40/500 [00:02<00:27, 16.63it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.08it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.10it/s]
eval model::  10%|▉         | 48/500 [00:02<00:22, 20.32it/s]
eval model::  10%|█         | 51/500 [00:02<00:20, 21.74it/s]
eval model::  11%|█         | 54/500 [00:02<00:19, 22.86it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:20, 21.22it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:20, 21.09it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:24, 17.75it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:19, 21.92it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 21.40it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 20.60it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.68it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:20, 20.99it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 20.46it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.53it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 18.91it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:21, 18.76it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:20, 20.22it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:20, 19.48it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.29it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.83it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.07it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:19, 19.75it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:20, 19.12it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:19, 19.66it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:19, 20.13it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:20, 18.63it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:19, 19.33it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:20, 18.75it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 20.07it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 18.73it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:19, 18.78it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:19, 18.48it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.39it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.25it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.46it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:17, 19.71it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 20.62it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 19.00it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 20.31it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 21.06it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:18, 18.54it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:18, 18.25it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 19.04it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 18.76it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 19.00it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:17, 18.88it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:20, 15.79it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:17, 18.30it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:20, 15.52it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:17, 18.45it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 20.31it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.46it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:15, 19.38it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.54it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.85it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 22.19it/s]
eval model::  41%|████      | 204/500 [00:10<00:12, 23.59it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:17, 16.76it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:16, 17.13it/s]
eval model::  42%|████▏     | 212/500 [00:10<00:15, 19.10it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:17, 16.75it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.54it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:17, 16.15it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:16, 16.52it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:14, 18.78it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:14, 18.89it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:12, 21.63it/s]
eval model::  46%|████▋     | 232/500 [00:11<00:11, 23.75it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:11, 23.77it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 23.77it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:10, 24.50it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:11, 23.17it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:11, 22.70it/s]
eval model::  50%|█████     | 250/500 [00:12<00:11, 21.16it/s]
eval model::  51%|█████     | 253/500 [00:12<00:11, 22.39it/s]
eval model::  51%|█████     | 256/500 [00:13<00:10, 22.36it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 22.65it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 20.45it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:12, 19.19it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:11, 20.35it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:11, 20.53it/s]
eval model::  55%|█████▍    | 274/500 [00:13<00:10, 20.62it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:10, 20.64it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:10, 20.24it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:11, 19.44it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:15, 13.66it/s]
eval model::  58%|█████▊    | 288/500 [00:14<00:12, 16.34it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:11, 18.11it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:11, 17.59it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:12, 16.39it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:11, 18.04it/s]
eval model::  60%|██████    | 301/500 [00:15<00:12, 15.85it/s]
eval model::  61%|██████    | 304/500 [00:15<00:12, 16.02it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:10, 17.90it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 19.26it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 20.71it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 20.57it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:07, 22.79it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:08, 21.36it/s]
eval model::  65%|██████▌   | 326/500 [00:16<00:07, 21.81it/s]
eval model::  66%|██████▌   | 329/500 [00:16<00:07, 22.13it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:07, 22.36it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 23.26it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:07, 22.76it/s]
eval model::  68%|██████▊   | 341/500 [00:17<00:08, 17.84it/s]
eval model::  69%|██████▉   | 344/500 [00:17<00:08, 19.29it/s]
eval model::  69%|██████▉   | 347/500 [00:17<00:07, 19.82it/s]
eval model::  70%|███████   | 350/500 [00:17<00:06, 21.91it/s]
eval model::  71%|███████   | 353/500 [00:17<00:06, 23.47it/s]
eval model::  71%|███████   | 356/500 [00:18<00:06, 20.92it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:06, 21.85it/s]
eval model::  72%|███████▏  | 362/500 [00:18<00:06, 20.32it/s]
eval model::  73%|███████▎  | 365/500 [00:18<00:06, 19.34it/s]
eval model::  74%|███████▎  | 368/500 [00:18<00:07, 18.35it/s]
eval model::  74%|███████▍  | 371/500 [00:18<00:07, 18.19it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 19.36it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:06, 20.24it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:05, 20.55it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:05, 22.16it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:07, 15.83it/s]
eval model::  78%|███████▊  | 389/500 [00:19<00:06, 18.02it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:05, 18.48it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 18.93it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:04, 21.01it/s]
eval model::  80%|████████  | 401/500 [00:20<00:04, 20.69it/s]
eval model::  81%|████████  | 404/500 [00:20<00:04, 21.97it/s]
eval model::  81%|████████▏ | 407/500 [00:20<00:04, 20.26it/s]
eval model::  82%|████████▏ | 410/500 [00:20<00:04, 20.86it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:04, 19.73it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:04, 18.56it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 18.03it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 16.99it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 16.94it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 16.73it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:04, 17.21it/s]
eval model::  86%|████████▌ | 428/500 [00:22<00:04, 16.19it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 17.46it/s]
eval model::  87%|████████▋ | 434/500 [00:22<00:03, 19.04it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:03, 19.15it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:03, 19.97it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:03, 19.55it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 19.46it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 20.54it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 19.64it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 21.98it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:01, 22.85it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:01, 24.27it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:01, 21.58it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:01, 19.32it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 21.32it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 21.64it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 21.74it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 21.34it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 20.40it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 20.58it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 17.73it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 18.85it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 19.02it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 20.81it/s]
eval model::  99%|█████████▉| 497/500 [00:25<00:00, 22.92it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.68it/s]
[2024/07/27 23:34:40] ppocr INFO: cur metric, precision: 0.5042904290429043, recall: 0.3678382282137699, hmean: 0.4253897550111359, fps: 43.79320023378904
[2024/07/27 23:34:40] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 23:34:40] ppocr INFO: best metric, hmean: 0.4253897550111359, precision: 0.5042904290429043, recall: 0.3678382282137699, fps: 43.79320023378904, best_epoch: 60
[2024/07/27 23:34:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:34:47] ppocr INFO: epoch: [61/1500], global_step: 183, lr: 0.001000, loss: 2.633545, loss_shrink_maps: 1.534923, loss_threshold_maps: 0.782445, loss_binary_maps: 0.293202, avg_reader_cost: 1.55837 s, avg_batch_cost: 1.78621 s, avg_samples: 12.5, ips: 6.99805 samples/s, eta: 8:18:19
[2024/07/27 23:34:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:34:54] ppocr INFO: epoch: [62/1500], global_step: 186, lr: 0.001000, loss: 2.493396, loss_shrink_maps: 1.443069, loss_threshold_maps: 0.777017, loss_binary_maps: 0.277909, avg_reader_cost: 1.55452 s, avg_batch_cost: 1.82102 s, avg_samples: 12.5, ips: 6.86430 samples/s, eta: 8:16:59
[2024/07/27 23:34:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:35:01] ppocr INFO: epoch: [63/1500], global_step: 189, lr: 0.001000, loss: 2.560382, loss_shrink_maps: 1.481440, loss_threshold_maps: 0.777017, loss_binary_maps: 0.284606, avg_reader_cost: 1.62224 s, avg_batch_cost: 1.85034 s, avg_samples: 12.5, ips: 6.75552 samples/s, eta: 8:15:47
[2024/07/27 23:35:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:35:07] ppocr INFO: epoch: [64/1500], global_step: 190, lr: 0.001000, loss: 2.507715, loss_shrink_maps: 1.440216, loss_threshold_maps: 0.777865, loss_binary_maps: 0.277909, avg_reader_cost: 0.39923 s, avg_batch_cost: 0.50380 s, avg_samples: 4.8, ips: 9.52765 samples/s, eta: 8:14:58
[2024/07/27 23:35:09] ppocr INFO: epoch: [64/1500], global_step: 192, lr: 0.001000, loss: 2.482508, loss_shrink_maps: 1.428812, loss_threshold_maps: 0.773859, loss_binary_maps: 0.276773, avg_reader_cost: 1.09867 s, avg_batch_cost: 1.24447 s, avg_samples: 7.7, ips: 6.18737 samples/s, eta: 8:14:14
[2024/07/27 23:35:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:35:16] ppocr INFO: epoch: [65/1500], global_step: 195, lr: 0.001000, loss: 2.482508, loss_shrink_maps: 1.428812, loss_threshold_maps: 0.773859, loss_binary_maps: 0.276424, avg_reader_cost: 1.51552 s, avg_batch_cost: 1.74586 s, avg_samples: 12.5, ips: 7.15978 samples/s, eta: 8:12:43
[2024/07/27 23:35:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:35:23] ppocr INFO: epoch: [66/1500], global_step: 198, lr: 0.001000, loss: 2.444984, loss_shrink_maps: 1.416468, loss_threshold_maps: 0.773859, loss_binary_maps: 0.273036, avg_reader_cost: 1.49745 s, avg_batch_cost: 1.73271 s, avg_samples: 12.5, ips: 7.21415 samples/s, eta: 8:11:11
[2024/07/27 23:35:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:35:29] ppocr INFO: epoch: [67/1500], global_step: 200, lr: 0.001000, loss: 2.444984, loss_shrink_maps: 1.416468, loss_threshold_maps: 0.762099, loss_binary_maps: 0.273036, avg_reader_cost: 0.91740 s, avg_batch_cost: 1.10383 s, avg_samples: 9.6, ips: 8.69701 samples/s, eta: 8:10:00
[2024/07/27 23:35:30] ppocr INFO: epoch: [67/1500], global_step: 201, lr: 0.001000, loss: 2.444984, loss_shrink_maps: 1.416468, loss_threshold_maps: 0.759970, loss_binary_maps: 0.273036, avg_reader_cost: 0.59759 s, avg_batch_cost: 0.65250 s, avg_samples: 2.9, ips: 4.44444 samples/s, eta: 8:09:47
[2024/07/27 23:35:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:35:37] ppocr INFO: epoch: [68/1500], global_step: 204, lr: 0.001000, loss: 2.444984, loss_shrink_maps: 1.416468, loss_threshold_maps: 0.759970, loss_binary_maps: 0.273036, avg_reader_cost: 1.50078 s, avg_batch_cost: 1.73062 s, avg_samples: 12.5, ips: 7.22284 samples/s, eta: 8:08:19
[2024/07/27 23:35:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:35:44] ppocr INFO: epoch: [69/1500], global_step: 207, lr: 0.001000, loss: 2.403663, loss_shrink_maps: 1.380546, loss_threshold_maps: 0.759970, loss_binary_maps: 0.265484, avg_reader_cost: 1.51407 s, avg_batch_cost: 1.76251 s, avg_samples: 12.5, ips: 7.09214 samples/s, eta: 8:06:59
[2024/07/27 23:35:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:35:51] ppocr INFO: epoch: [70/1500], global_step: 210, lr: 0.001000, loss: 2.436618, loss_shrink_maps: 1.408582, loss_threshold_maps: 0.763456, loss_binary_maps: 0.272804, avg_reader_cost: 1.52374 s, avg_batch_cost: 1.75286 s, avg_samples: 12.5, ips: 7.13120 samples/s, eta: 8:05:40
[2024/07/27 23:35:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:35:59] ppocr INFO: epoch: [71/1500], global_step: 213, lr: 0.001000, loss: 2.436618, loss_shrink_maps: 1.413770, loss_threshold_maps: 0.756995, loss_binary_maps: 0.273377, avg_reader_cost: 1.52920 s, avg_batch_cost: 1.76111 s, avg_samples: 12.5, ips: 7.09779 samples/s, eta: 8:04:24
[2024/07/27 23:35:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:36:06] ppocr INFO: epoch: [72/1500], global_step: 216, lr: 0.001000, loss: 2.487045, loss_shrink_maps: 1.447402, loss_threshold_maps: 0.766760, loss_binary_maps: 0.279472, avg_reader_cost: 1.54122 s, avg_batch_cost: 1.77030 s, avg_samples: 12.5, ips: 7.06095 samples/s, eta: 8:03:11
[2024/07/27 23:36:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:36:13] ppocr INFO: epoch: [73/1500], global_step: 219, lr: 0.001000, loss: 2.471851, loss_shrink_maps: 1.435933, loss_threshold_maps: 0.766760, loss_binary_maps: 0.275358, avg_reader_cost: 1.52994 s, avg_batch_cost: 1.77518 s, avg_samples: 12.5, ips: 7.04156 samples/s, eta: 8:02:01
[2024/07/27 23:36:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:36:19] ppocr INFO: epoch: [74/1500], global_step: 220, lr: 0.001000, loss: 2.484640, loss_shrink_maps: 1.438137, loss_threshold_maps: 0.766760, loss_binary_maps: 0.277044, avg_reader_cost: 0.42160 s, avg_batch_cost: 0.50407 s, avg_samples: 4.8, ips: 9.52250 samples/s, eta: 8:01:21
[2024/07/27 23:36:20] ppocr INFO: epoch: [74/1500], global_step: 222, lr: 0.001000, loss: 2.434213, loss_shrink_maps: 1.404505, loss_threshold_maps: 0.760298, loss_binary_maps: 0.270949, avg_reader_cost: 1.09954 s, avg_batch_cost: 1.24533 s, avg_samples: 7.7, ips: 6.18308 samples/s, eta: 8:00:47
[2024/07/27 23:36:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:36:28] ppocr INFO: epoch: [75/1500], global_step: 225, lr: 0.001000, loss: 2.392224, loss_shrink_maps: 1.379028, loss_threshold_maps: 0.750002, loss_binary_maps: 0.266747, avg_reader_cost: 1.58783 s, avg_batch_cost: 1.82564 s, avg_samples: 12.5, ips: 6.84690 samples/s, eta: 7:59:50
[2024/07/27 23:36:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:36:35] ppocr INFO: epoch: [76/1500], global_step: 228, lr: 0.001000, loss: 2.376469, loss_shrink_maps: 1.366992, loss_threshold_maps: 0.756002, loss_binary_maps: 0.263791, avg_reader_cost: 1.51839 s, avg_batch_cost: 1.75660 s, avg_samples: 12.5, ips: 7.11602 samples/s, eta: 7:58:40
[2024/07/27 23:36:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:36:42] ppocr INFO: epoch: [77/1500], global_step: 230, lr: 0.001000, loss: 2.365944, loss_shrink_maps: 1.343203, loss_threshold_maps: 0.741022, loss_binary_maps: 0.261653, avg_reader_cost: 1.09646 s, avg_batch_cost: 1.31576 s, avg_samples: 9.6, ips: 7.29619 samples/s, eta: 7:58:21
[2024/07/27 23:36:43] ppocr INFO: epoch: [77/1500], global_step: 231, lr: 0.001000, loss: 2.365944, loss_shrink_maps: 1.343203, loss_threshold_maps: 0.741022, loss_binary_maps: 0.261653, avg_reader_cost: 0.70358 s, avg_batch_cost: 0.75845 s, avg_samples: 2.9, ips: 3.82361 samples/s, eta: 7:58:30
[2024/07/27 23:36:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:36:51] ppocr INFO: epoch: [78/1500], global_step: 234, lr: 0.001000, loss: 2.376469, loss_shrink_maps: 1.343203, loss_threshold_maps: 0.762685, loss_binary_maps: 0.261653, avg_reader_cost: 1.58901 s, avg_batch_cost: 1.83955 s, avg_samples: 12.5, ips: 6.79515 samples/s, eta: 7:57:38
[2024/07/27 23:36:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:36:58] ppocr INFO: epoch: [79/1500], global_step: 237, lr: 0.001000, loss: 2.338708, loss_shrink_maps: 1.334671, loss_threshold_maps: 0.754564, loss_binary_maps: 0.259675, avg_reader_cost: 1.67321 s, avg_batch_cost: 1.90207 s, avg_samples: 12.5, ips: 6.57179 samples/s, eta: 7:56:57
[2024/07/27 23:36:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:37:06] ppocr INFO: epoch: [80/1500], global_step: 240, lr: 0.001000, loss: 2.430758, loss_shrink_maps: 1.383901, loss_threshold_maps: 0.773299, loss_binary_maps: 0.269900, avg_reader_cost: 1.56829 s, avg_batch_cost: 1.83636 s, avg_samples: 12.5, ips: 6.80694 samples/s, eta: 7:56:06

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:37, 13.24it/s]
eval model::   1%|          | 5/500 [00:00<00:26, 18.79it/s]
eval model::   1%|▏         | 7/500 [00:00<00:26, 18.85it/s]
eval model::   2%|▏         | 10/500 [00:00<00:24, 20.13it/s]
eval model::   3%|▎         | 13/500 [00:00<00:24, 20.06it/s]
eval model::   3%|▎         | 16/500 [00:00<00:23, 20.38it/s]
eval model::   4%|▍         | 19/500 [00:01<00:26, 18.47it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 18.56it/s]
eval model::   5%|▍         | 24/500 [00:01<00:24, 19.36it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 20.63it/s]
eval model::   6%|▌         | 30/500 [00:01<00:23, 19.83it/s]
eval model::   7%|▋         | 33/500 [00:01<00:22, 20.74it/s]
eval model::   7%|▋         | 36/500 [00:01<00:22, 21.03it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 18.14it/s]
eval model::   8%|▊         | 42/500 [00:02<00:24, 18.49it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.75it/s]
eval model::  10%|▉         | 48/500 [00:02<00:24, 18.61it/s]
eval model::  10%|█         | 51/500 [00:02<00:22, 19.70it/s]
eval model::  11%|█         | 54/500 [00:02<00:20, 21.55it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:20, 21.40it/s]
eval model::  12%|█▏        | 60/500 [00:02<00:19, 22.50it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:22, 19.50it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:20, 21.41it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:18, 23.41it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 22.38it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.72it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:19, 21.89it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.03it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.28it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:22, 18.62it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 17.83it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:23, 17.64it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:21, 19.02it/s]
eval model::  20%|█▉        | 98/500 [00:04<00:19, 20.28it/s]
eval model::  20%|██        | 101/500 [00:05<00:20, 19.82it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 21.03it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 20.97it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 21.05it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 20.76it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:18, 21.33it/s]
eval model::  24%|██▍       | 119/500 [00:05<00:18, 20.25it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.45it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:19, 19.53it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 19.82it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:20, 18.31it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:18, 20.26it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:17, 20.74it/s]
eval model::  28%|██▊       | 139/500 [00:06<00:18, 20.02it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.24it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:18, 18.93it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.73it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.22it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 19.17it/s]
eval model::  31%|███       | 156/500 [00:07<00:17, 19.15it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:18, 18.16it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:19, 17.12it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:18, 18.41it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.26it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 19.60it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 18.44it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:18, 17.64it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:18, 17.57it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.29it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:18, 17.09it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:16, 18.65it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 19.50it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:15, 20.35it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:14, 20.82it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:14, 21.06it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:13, 22.60it/s]
eval model::  40%|████      | 202/500 [00:10<00:12, 22.94it/s]
eval model::  41%|████      | 205/500 [00:10<00:18, 15.92it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:17, 16.80it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:17, 16.35it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:15, 17.99it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:16, 17.64it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:17, 16.45it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:14, 18.87it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:15, 18.12it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:13, 20.45it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:11, 24.38it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:11, 23.93it/s]
eval model::  47%|████▋     | 236/500 [00:11<00:11, 22.37it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:10, 24.08it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 20.86it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:11, 22.96it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:12, 20.66it/s]
eval model::  50%|█████     | 252/500 [00:12<00:12, 20.53it/s]
eval model::  51%|█████     | 255/500 [00:12<00:12, 20.10it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:12, 19.47it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:12, 18.88it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:12, 18.88it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:13, 18.00it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:12, 19.19it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:11, 20.18it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:10, 20.67it/s]
eval model::  55%|█████▌    | 276/500 [00:13<00:11, 19.77it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 19.32it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 21.35it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:14, 15.06it/s]
eval model::  58%|█████▊    | 289/500 [00:14<00:11, 18.57it/s]
eval model::  59%|█████▊    | 293/500 [00:14<00:09, 21.68it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:10, 19.28it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:09, 21.04it/s]
eval model::  60%|██████    | 302/500 [00:15<00:09, 20.25it/s]
eval model::  61%|██████    | 305/500 [00:15<00:09, 21.13it/s]
eval model::  62%|██████▏   | 308/500 [00:15<00:08, 21.37it/s]
eval model::  62%|██████▏   | 311/500 [00:15<00:08, 22.18it/s]
eval model::  63%|██████▎   | 314/500 [00:15<00:08, 21.52it/s]
eval model::  63%|██████▎   | 317/500 [00:15<00:08, 20.77it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:09, 19.36it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:09, 18.82it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:08, 19.66it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:08, 19.41it/s]
eval model::  66%|██████▌   | 331/500 [00:16<00:08, 20.14it/s]
eval model::  67%|██████▋   | 334/500 [00:16<00:07, 21.56it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:08, 19.46it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 16.72it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:08, 18.19it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 20.47it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:07, 21.11it/s]
eval model::  70%|███████   | 352/500 [00:17<00:07, 19.67it/s]
eval model::  71%|███████   | 355/500 [00:17<00:07, 19.56it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:07, 19.06it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 19.28it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.47it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:07, 18.73it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:06, 20.17it/s]
eval model::  75%|███████▍  | 373/500 [00:18<00:06, 19.00it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 18.50it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:06, 20.27it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:06, 17.92it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:05, 19.61it/s]
eval model::  77%|███████▋  | 387/500 [00:19<00:07, 14.81it/s]
eval model::  78%|███████▊  | 390/500 [00:19<00:06, 16.40it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 17.57it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 17.77it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:05, 20.17it/s]
eval model::  80%|████████  | 401/500 [00:20<00:05, 19.62it/s]
eval model::  81%|████████  | 404/500 [00:20<00:04, 20.66it/s]
eval model::  81%|████████▏ | 407/500 [00:20<00:04, 21.88it/s]
eval model::  82%|████████▏ | 410/500 [00:20<00:04, 22.00it/s]
eval model::  83%|████████▎ | 413/500 [00:20<00:03, 23.06it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:03, 21.29it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 19.86it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 18.05it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 17.58it/s]
eval model::  85%|████████▌ | 427/500 [00:21<00:03, 20.22it/s]
eval model::  86%|████████▌ | 431/500 [00:21<00:02, 23.39it/s]
eval model::  87%|████████▋ | 434/500 [00:21<00:02, 23.21it/s]
eval model::  87%|████████▋ | 437/500 [00:22<00:02, 22.38it/s]
eval model::  88%|████████▊ | 440/500 [00:22<00:03, 17.96it/s]
eval model::  89%|████████▊ | 443/500 [00:22<00:02, 20.25it/s]
eval model::  89%|████████▉ | 446/500 [00:22<00:02, 21.65it/s]
eval model::  90%|████████▉ | 449/500 [00:22<00:02, 20.34it/s]
eval model::  90%|█████████ | 452/500 [00:22<00:02, 22.47it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:02, 21.61it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:01, 21.44it/s]
eval model::  92%|█████████▏| 461/500 [00:23<00:01, 21.07it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 19.50it/s]
eval model::  93%|█████████▎| 467/500 [00:23<00:01, 18.50it/s]
eval model::  94%|█████████▍| 470/500 [00:23<00:01, 19.56it/s]
eval model::  95%|█████████▍| 473/500 [00:23<00:01, 18.04it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 18.44it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 19.22it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 21.07it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 19.97it/s]
eval model::  97%|█████████▋| 487/500 [00:24<00:00, 17.72it/s]
eval model::  98%|█████████▊| 490/500 [00:24<00:00, 18.81it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 18.61it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 20.99it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 22.40it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.77it/s]
[2024/07/27 23:37:31] ppocr INFO: cur metric, precision: 0.6025903203817314, recall: 0.4256138661531054, hmean: 0.49887133182844245, fps: 45.47610497136633
[2024/07/27 23:37:31] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 23:37:31] ppocr INFO: best metric, hmean: 0.49887133182844245, precision: 0.6025903203817314, recall: 0.4256138661531054, fps: 45.47610497136633, best_epoch: 80
[2024/07/27 23:37:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:37:38] ppocr INFO: epoch: [81/1500], global_step: 243, lr: 0.001000, loss: 2.450931, loss_shrink_maps: 1.394744, loss_threshold_maps: 0.773299, loss_binary_maps: 0.271241, avg_reader_cost: 1.56201 s, avg_batch_cost: 1.83608 s, avg_samples: 12.5, ips: 6.80798 samples/s, eta: 7:55:15
[2024/07/27 23:37:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:37:46] ppocr INFO: epoch: [82/1500], global_step: 246, lr: 0.001000, loss: 2.473295, loss_shrink_maps: 1.410013, loss_threshold_maps: 0.778276, loss_binary_maps: 0.273380, avg_reader_cost: 1.56562 s, avg_batch_cost: 1.81666 s, avg_samples: 12.5, ips: 6.88076 samples/s, eta: 7:54:21
[2024/07/27 23:37:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:37:53] ppocr INFO: epoch: [83/1500], global_step: 249, lr: 0.001000, loss: 2.474975, loss_shrink_maps: 1.420564, loss_threshold_maps: 0.771397, loss_binary_maps: 0.275856, avg_reader_cost: 1.51465 s, avg_batch_cost: 1.76289 s, avg_samples: 12.5, ips: 7.09062 samples/s, eta: 7:53:20
[2024/07/27 23:37:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:37:59] ppocr INFO: epoch: [84/1500], global_step: 250, lr: 0.001000, loss: 2.471704, loss_shrink_maps: 1.410013, loss_threshold_maps: 0.764074, loss_binary_maps: 0.273380, avg_reader_cost: 0.42903 s, avg_batch_cost: 0.51183 s, avg_samples: 4.8, ips: 9.37812 samples/s, eta: 7:52:46
[2024/07/27 23:38:00] ppocr INFO: epoch: [84/1500], global_step: 252, lr: 0.001000, loss: 2.418985, loss_shrink_maps: 1.388735, loss_threshold_maps: 0.760388, loss_binary_maps: 0.270539, avg_reader_cost: 1.11458 s, avg_batch_cost: 1.26017 s, avg_samples: 7.7, ips: 6.11028 samples/s, eta: 7:52:20
[2024/07/27 23:38:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:38:07] ppocr INFO: epoch: [85/1500], global_step: 255, lr: 0.001000, loss: 2.424484, loss_shrink_maps: 1.394565, loss_threshold_maps: 0.760850, loss_binary_maps: 0.271047, avg_reader_cost: 1.54455 s, avg_batch_cost: 1.80239 s, avg_samples: 12.5, ips: 6.93522 samples/s, eta: 7:51:27
[2024/07/27 23:38:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:38:15] ppocr INFO: epoch: [86/1500], global_step: 258, lr: 0.001000, loss: 2.404061, loss_shrink_maps: 1.363493, loss_threshold_maps: 0.753860, loss_binary_maps: 0.265935, avg_reader_cost: 1.51833 s, avg_batch_cost: 1.75846 s, avg_samples: 12.5, ips: 7.10849 samples/s, eta: 7:50:28
[2024/07/27 23:38:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:38:21] ppocr INFO: epoch: [87/1500], global_step: 260, lr: 0.001000, loss: 2.360428, loss_shrink_maps: 1.340032, loss_threshold_maps: 0.746452, loss_binary_maps: 0.259612, avg_reader_cost: 0.91733 s, avg_batch_cost: 1.10750 s, avg_samples: 9.6, ips: 8.66817 samples/s, eta: 7:49:38
[2024/07/27 23:38:22] ppocr INFO: epoch: [87/1500], global_step: 261, lr: 0.001000, loss: 2.360428, loss_shrink_maps: 1.340032, loss_threshold_maps: 0.746452, loss_binary_maps: 0.259612, avg_reader_cost: 0.59966 s, avg_batch_cost: 0.65461 s, avg_samples: 2.9, ips: 4.43015 samples/s, eta: 7:49:30
[2024/07/27 23:38:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:38:29] ppocr INFO: epoch: [88/1500], global_step: 264, lr: 0.001000, loss: 2.345607, loss_shrink_maps: 1.333139, loss_threshold_maps: 0.745279, loss_binary_maps: 0.258223, avg_reader_cost: 1.54245 s, avg_batch_cost: 1.79098 s, avg_samples: 12.5, ips: 6.97941 samples/s, eta: 7:48:37
[2024/07/27 23:38:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:38:37] ppocr INFO: epoch: [89/1500], global_step: 267, lr: 0.001000, loss: 2.271658, loss_shrink_maps: 1.314958, loss_threshold_maps: 0.742581, loss_binary_maps: 0.256435, avg_reader_cost: 1.61916 s, avg_batch_cost: 1.85028 s, avg_samples: 12.5, ips: 6.75573 samples/s, eta: 7:47:55
[2024/07/27 23:38:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:38:44] ppocr INFO: epoch: [90/1500], global_step: 270, lr: 0.001000, loss: 2.258403, loss_shrink_maps: 1.295921, loss_threshold_maps: 0.725529, loss_binary_maps: 0.253276, avg_reader_cost: 1.50475 s, avg_batch_cost: 1.75936 s, avg_samples: 12.5, ips: 7.10484 samples/s, eta: 7:46:59
[2024/07/27 23:38:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:38:51] ppocr INFO: epoch: [91/1500], global_step: 273, lr: 0.001000, loss: 2.262432, loss_shrink_maps: 1.297075, loss_threshold_maps: 0.725529, loss_binary_maps: 0.252806, avg_reader_cost: 1.50852 s, avg_batch_cost: 1.76572 s, avg_samples: 12.5, ips: 7.07925 samples/s, eta: 7:46:05
[2024/07/27 23:38:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:38:59] ppocr INFO: epoch: [92/1500], global_step: 276, lr: 0.001000, loss: 2.262432, loss_shrink_maps: 1.297075, loss_threshold_maps: 0.725529, loss_binary_maps: 0.252806, avg_reader_cost: 1.63683 s, avg_batch_cost: 1.95603 s, avg_samples: 12.5, ips: 6.39051 samples/s, eta: 7:45:40
[2024/07/27 23:39:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:39:06] ppocr INFO: epoch: [93/1500], global_step: 279, lr: 0.001000, loss: 2.250143, loss_shrink_maps: 1.284062, loss_threshold_maps: 0.722341, loss_binary_maps: 0.249610, avg_reader_cost: 1.52972 s, avg_batch_cost: 1.81600 s, avg_samples: 12.5, ips: 6.88327 samples/s, eta: 7:44:55
[2024/07/27 23:39:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:39:12] ppocr INFO: epoch: [94/1500], global_step: 280, lr: 0.001000, loss: 2.270944, loss_shrink_maps: 1.286430, loss_threshold_maps: 0.725529, loss_binary_maps: 0.249817, avg_reader_cost: 0.40684 s, avg_batch_cost: 0.50478 s, avg_samples: 4.8, ips: 9.50905 samples/s, eta: 7:44:25
[2024/07/27 23:39:14] ppocr INFO: epoch: [94/1500], global_step: 282, lr: 0.001000, loss: 2.281600, loss_shrink_maps: 1.293334, loss_threshold_maps: 0.733596, loss_binary_maps: 0.252944, avg_reader_cost: 1.10156 s, avg_batch_cost: 1.24821 s, avg_samples: 7.7, ips: 6.16884 samples/s, eta: 7:44:01
[2024/07/27 23:39:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:39:21] ppocr INFO: epoch: [95/1500], global_step: 285, lr: 0.001000, loss: 2.281600, loss_shrink_maps: 1.293334, loss_threshold_maps: 0.737424, loss_binary_maps: 0.252944, avg_reader_cost: 1.51541 s, avg_batch_cost: 1.74489 s, avg_samples: 12.5, ips: 7.16377 samples/s, eta: 7:43:06
[2024/07/27 23:39:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:39:28] ppocr INFO: epoch: [96/1500], global_step: 288, lr: 0.001000, loss: 2.297625, loss_shrink_maps: 1.302846, loss_threshold_maps: 0.737424, loss_binary_maps: 0.256125, avg_reader_cost: 1.50920 s, avg_batch_cost: 1.73790 s, avg_samples: 12.5, ips: 7.19258 samples/s, eta: 7:42:12
[2024/07/27 23:39:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:39:35] ppocr INFO: epoch: [97/1500], global_step: 290, lr: 0.001000, loss: 2.313153, loss_shrink_maps: 1.314552, loss_threshold_maps: 0.740601, loss_binary_maps: 0.258328, avg_reader_cost: 0.93153 s, avg_batch_cost: 1.10731 s, avg_samples: 9.6, ips: 8.66965 samples/s, eta: 7:41:28
[2024/07/27 23:39:35] ppocr INFO: epoch: [97/1500], global_step: 291, lr: 0.001000, loss: 2.297625, loss_shrink_maps: 1.302846, loss_threshold_maps: 0.737424, loss_binary_maps: 0.256125, avg_reader_cost: 0.59933 s, avg_batch_cost: 0.65443 s, avg_samples: 2.9, ips: 4.43136 samples/s, eta: 7:41:21
[2024/07/27 23:39:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:39:42] ppocr INFO: epoch: [98/1500], global_step: 294, lr: 0.001000, loss: 2.313153, loss_shrink_maps: 1.329103, loss_threshold_maps: 0.732865, loss_binary_maps: 0.260704, avg_reader_cost: 1.49369 s, avg_batch_cost: 1.73095 s, avg_samples: 12.5, ips: 7.22146 samples/s, eta: 7:40:27
[2024/07/27 23:39:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:39:49] ppocr INFO: epoch: [99/1500], global_step: 297, lr: 0.001000, loss: 2.305367, loss_shrink_maps: 1.310790, loss_threshold_maps: 0.727011, loss_binary_maps: 0.258501, avg_reader_cost: 1.50086 s, avg_batch_cost: 1.77244 s, avg_samples: 12.5, ips: 7.05244 samples/s, eta: 7:39:39
[2024/07/27 23:39:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:39:57] ppocr INFO: epoch: [100/1500], global_step: 300, lr: 0.001000, loss: 2.278742, loss_shrink_maps: 1.286905, loss_threshold_maps: 0.727011, loss_binary_maps: 0.251951, avg_reader_cost: 1.49925 s, avg_batch_cost: 1.74988 s, avg_samples: 12.5, ips: 7.14333 samples/s, eta: 7:38:49

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:04,  7.73it/s]
eval model::   1%|          | 3/500 [00:00<00:35, 14.05it/s]
eval model::   1%|          | 6/500 [00:00<00:30, 16.21it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.30it/s]
eval model::   2%|▏         | 10/500 [00:00<00:27, 17.65it/s]
eval model::   2%|▏         | 12/500 [00:00<00:28, 17.32it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.63it/s]
eval model::   3%|▎         | 17/500 [00:00<00:26, 18.45it/s]
eval model::   4%|▍         | 19/500 [00:01<00:26, 18.02it/s]
eval model::   4%|▍         | 22/500 [00:01<00:26, 18.04it/s]
eval model::   5%|▍         | 24/500 [00:01<00:25, 18.41it/s]
eval model::   5%|▌         | 26/500 [00:01<00:27, 17.10it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 19.79it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.02it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.48it/s]
eval model::   7%|▋         | 37/500 [00:02<00:22, 20.58it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.04it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 18.28it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.05it/s]
eval model::   9%|▉         | 47/500 [00:02<00:23, 18.94it/s]
eval model::  10%|█         | 50/500 [00:02<00:21, 20.83it/s]
eval model::  11%|█         | 53/500 [00:02<00:20, 21.53it/s]
eval model::  11%|█         | 56/500 [00:02<00:21, 20.55it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:20, 21.42it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:24, 17.77it/s]
eval model::  13%|█▎        | 65/500 [00:03<00:22, 18.92it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:20, 21.32it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:19, 21.48it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:21, 19.37it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:22, 18.87it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:19, 21.12it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:19, 21.55it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:20, 20.17it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:23, 17.21it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:22, 18.46it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:20, 19.30it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:19, 20.35it/s]
eval model::  20%|██        | 101/500 [00:05<00:20, 19.74it/s]
eval model::  21%|██        | 104/500 [00:05<00:19, 19.99it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:19, 20.17it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:19, 20.05it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 21.30it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:20, 18.85it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:19, 20.00it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.45it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:19, 19.53it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 19.69it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:18, 19.81it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:18, 20.07it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:17, 20.34it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:17, 20.62it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:16, 21.04it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:16, 21.43it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:17, 20.37it/s]
eval model::  30%|███       | 152/500 [00:07<00:18, 18.54it/s]
eval model::  31%|███       | 155/500 [00:07<00:17, 19.91it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:17, 20.06it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:18, 18.29it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:18, 18.31it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.18it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 20.08it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 19.12it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:16, 19.25it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:15, 20.95it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:19, 16.49it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:16, 19.73it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:14, 21.01it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:14, 21.32it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:13, 23.12it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:14, 20.57it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 21.42it/s]
eval model::  40%|████      | 202/500 [00:10<00:15, 19.84it/s]
eval model::  41%|████      | 205/500 [00:10<00:19, 15.07it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:17, 16.91it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:16, 17.18it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:15, 18.39it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:17, 15.84it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.43it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 17.34it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:15, 17.61it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:13, 20.48it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:12, 22.00it/s]
eval model::  46%|████▋     | 232/500 [00:11<00:10, 24.61it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:11, 22.99it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:10, 23.95it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:10, 24.05it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:11, 22.77it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:11, 22.24it/s]
eval model::  50%|█████     | 250/500 [00:12<00:12, 20.00it/s]
eval model::  51%|█████     | 253/500 [00:12<00:11, 21.97it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 21.00it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:11, 21.07it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 21.12it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:12, 19.45it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:11, 20.50it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:11, 20.36it/s]
eval model::  55%|█████▍    | 274/500 [00:13<00:11, 19.40it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 18.55it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 19.54it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 21.01it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:14, 15.11it/s]
eval model::  58%|█████▊    | 289/500 [00:14<00:11, 18.57it/s]
eval model::  58%|█████▊    | 292/500 [00:14<00:10, 19.97it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:11, 17.14it/s]
eval model::  60%|█████▉    | 298/500 [00:15<00:10, 19.51it/s]
eval model::  60%|██████    | 301/500 [00:15<00:10, 18.67it/s]
eval model::  61%|██████    | 304/500 [00:15<00:10, 19.58it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:09, 19.95it/s]
eval model::  62%|██████▏   | 310/500 [00:15<00:08, 21.28it/s]
eval model::  63%|██████▎   | 313/500 [00:15<00:08, 21.74it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 21.09it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:07, 22.72it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:08, 22.05it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:07, 22.34it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:08, 20.01it/s]
eval model::  66%|██████▌   | 331/500 [00:16<00:07, 21.24it/s]
eval model::  67%|██████▋   | 334/500 [00:16<00:07, 22.99it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:08, 19.87it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 16.48it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:08, 18.16it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 20.16it/s]
eval model::  70%|███████   | 350/500 [00:17<00:06, 22.53it/s]
eval model::  71%|███████   | 353/500 [00:17<00:06, 22.42it/s]
eval model::  71%|███████   | 356/500 [00:18<00:06, 21.52it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:06, 21.71it/s]
eval model::  72%|███████▏  | 362/500 [00:18<00:06, 20.59it/s]
eval model::  73%|███████▎  | 365/500 [00:18<00:06, 19.49it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:06, 19.37it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:06, 21.38it/s]
eval model::  75%|███████▍  | 373/500 [00:18<00:06, 19.96it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:06, 20.19it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 20.47it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 19.85it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 20.40it/s]
eval model::  78%|███████▊  | 388/500 [00:19<00:07, 15.75it/s]
eval model::  78%|███████▊  | 391/500 [00:19<00:06, 17.47it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:05, 19.63it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 20.51it/s]
eval model::  80%|████████  | 400/500 [00:20<00:04, 21.45it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 22.65it/s]
eval model::  81%|████████  | 406/500 [00:20<00:04, 22.61it/s]
eval model::  82%|████████▏ | 409/500 [00:20<00:03, 22.85it/s]
eval model::  82%|████████▏ | 412/500 [00:20<00:03, 22.03it/s]
eval model::  83%|████████▎ | 415/500 [00:20<00:03, 22.95it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 20.47it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:04, 17.97it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:03, 19.70it/s]
eval model::  85%|████████▌ | 427/500 [00:21<00:03, 20.76it/s]
eval model::  86%|████████▌ | 430/500 [00:21<00:03, 20.88it/s]
eval model::  87%|████████▋ | 433/500 [00:21<00:03, 20.39it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:03, 20.42it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:03, 19.89it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 20.54it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 20.47it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 19.95it/s]
eval model::  90%|█████████ | 451/500 [00:22<00:02, 20.34it/s]
eval model::  91%|█████████ | 454/500 [00:22<00:02, 19.79it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:02, 20.27it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:02, 20.00it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 19.31it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:01, 18.24it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 20.86it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 21.73it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 18.39it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 19.91it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:01, 19.65it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 18.82it/s]
eval model::  97%|█████████▋| 485/500 [00:24<00:00, 19.00it/s]
eval model::  98%|█████████▊| 488/500 [00:24<00:00, 20.18it/s]
eval model::  98%|█████████▊| 491/500 [00:24<00:00, 19.64it/s]
eval model::  99%|█████████▉| 494/500 [00:24<00:00, 19.89it/s]
eval model::  99%|█████████▉| 497/500 [00:25<00:00, 20.90it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 20.91it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.83it/s]
[2024/07/27 23:40:22] ppocr INFO: cur metric, precision: 0.6433308769344142, recall: 0.4203177660086663, hmean: 0.5084449621432731, fps: 45.83175235810987
[2024/07/27 23:40:22] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 23:40:22] ppocr INFO: best metric, hmean: 0.5084449621432731, precision: 0.6433308769344142, recall: 0.4203177660086663, fps: 45.83175235810987, best_epoch: 100
[2024/07/27 23:40:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:40:29] ppocr INFO: epoch: [101/1500], global_step: 303, lr: 0.001000, loss: 2.289311, loss_shrink_maps: 1.309103, loss_threshold_maps: 0.731390, loss_binary_maps: 0.256603, avg_reader_cost: 1.57900 s, avg_batch_cost: 1.83677 s, avg_samples: 12.5, ips: 6.80541 samples/s, eta: 7:38:11
[2024/07/27 23:40:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:40:37] ppocr INFO: epoch: [102/1500], global_step: 306, lr: 0.001000, loss: 2.245837, loss_shrink_maps: 1.263778, loss_threshold_maps: 0.724061, loss_binary_maps: 0.246900, avg_reader_cost: 1.57118 s, avg_batch_cost: 1.82921 s, avg_samples: 12.5, ips: 6.83353 samples/s, eta: 7:37:33
[2024/07/27 23:40:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:40:44] ppocr INFO: epoch: [103/1500], global_step: 309, lr: 0.001000, loss: 2.185150, loss_shrink_maps: 1.229291, loss_threshold_maps: 0.717026, loss_binary_maps: 0.240870, avg_reader_cost: 1.49258 s, avg_batch_cost: 1.72702 s, avg_samples: 12.5, ips: 7.23791 samples/s, eta: 7:36:41
[2024/07/27 23:40:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:40:50] ppocr INFO: epoch: [104/1500], global_step: 310, lr: 0.001000, loss: 2.191876, loss_shrink_maps: 1.229291, loss_threshold_maps: 0.721876, loss_binary_maps: 0.240870, avg_reader_cost: 0.41832 s, avg_batch_cost: 0.50084 s, avg_samples: 4.8, ips: 9.58385 samples/s, eta: 7:36:14
[2024/07/27 23:40:51] ppocr INFO: epoch: [104/1500], global_step: 312, lr: 0.001000, loss: 2.210989, loss_shrink_maps: 1.239950, loss_threshold_maps: 0.723314, loss_binary_maps: 0.242095, avg_reader_cost: 1.09300 s, avg_batch_cost: 1.23906 s, avg_samples: 7.7, ips: 6.21441 samples/s, eta: 7:35:52
[2024/07/27 23:40:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:40:59] ppocr INFO: epoch: [105/1500], global_step: 315, lr: 0.001000, loss: 2.206601, loss_shrink_maps: 1.232135, loss_threshold_maps: 0.726139, loss_binary_maps: 0.241913, avg_reader_cost: 1.54680 s, avg_batch_cost: 1.77639 s, avg_samples: 12.5, ips: 7.03675 samples/s, eta: 7:35:08
[2024/07/27 23:40:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:41:06] ppocr INFO: epoch: [106/1500], global_step: 318, lr: 0.001000, loss: 2.195047, loss_shrink_maps: 1.232135, loss_threshold_maps: 0.726139, loss_binary_maps: 0.240688, avg_reader_cost: 1.53802 s, avg_batch_cost: 1.80053 s, avg_samples: 12.5, ips: 6.94241 samples/s, eta: 7:34:27
[2024/07/27 23:41:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:41:13] ppocr INFO: epoch: [107/1500], global_step: 320, lr: 0.001000, loss: 2.195047, loss_shrink_maps: 1.228196, loss_threshold_maps: 0.730097, loss_binary_maps: 0.240002, avg_reader_cost: 0.94487 s, avg_batch_cost: 1.11835 s, avg_samples: 9.6, ips: 8.58410 samples/s, eta: 7:33:50
[2024/07/27 23:41:13] ppocr INFO: epoch: [107/1500], global_step: 321, lr: 0.001000, loss: 2.195047, loss_shrink_maps: 1.228196, loss_threshold_maps: 0.730206, loss_binary_maps: 0.240002, avg_reader_cost: 0.60480 s, avg_batch_cost: 0.65989 s, avg_samples: 2.9, ips: 4.39469 samples/s, eta: 7:33:45
[2024/07/27 23:41:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:41:21] ppocr INFO: epoch: [108/1500], global_step: 324, lr: 0.001000, loss: 2.179350, loss_shrink_maps: 1.219583, loss_threshold_maps: 0.726139, loss_binary_maps: 0.238265, avg_reader_cost: 1.49794 s, avg_batch_cost: 1.76444 s, avg_samples: 12.5, ips: 7.08439 samples/s, eta: 7:33:01
[2024/07/27 23:41:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:41:28] ppocr INFO: epoch: [109/1500], global_step: 327, lr: 0.001000, loss: 2.179350, loss_shrink_maps: 1.219583, loss_threshold_maps: 0.725392, loss_binary_maps: 0.238265, avg_reader_cost: 1.51925 s, avg_batch_cost: 1.77213 s, avg_samples: 12.5, ips: 7.05365 samples/s, eta: 7:32:18
[2024/07/27 23:41:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:41:36] ppocr INFO: epoch: [110/1500], global_step: 330, lr: 0.001000, loss: 2.175030, loss_shrink_maps: 1.228051, loss_threshold_maps: 0.721963, loss_binary_maps: 0.239154, avg_reader_cost: 1.73879 s, avg_batch_cost: 1.96714 s, avg_samples: 12.5, ips: 6.35440 samples/s, eta: 7:32:01
[2024/07/27 23:41:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:41:43] ppocr INFO: epoch: [111/1500], global_step: 333, lr: 0.001000, loss: 2.147031, loss_shrink_maps: 1.219449, loss_threshold_maps: 0.718753, loss_binary_maps: 0.238265, avg_reader_cost: 1.52522 s, avg_batch_cost: 1.77442 s, avg_samples: 12.5, ips: 7.04457 samples/s, eta: 7:31:19
[2024/07/27 23:41:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:41:50] ppocr INFO: epoch: [112/1500], global_step: 336, lr: 0.001000, loss: 2.147031, loss_shrink_maps: 1.219449, loss_threshold_maps: 0.716373, loss_binary_maps: 0.239154, avg_reader_cost: 1.53078 s, avg_batch_cost: 1.75951 s, avg_samples: 12.5, ips: 7.10426 samples/s, eta: 7:30:36
[2024/07/27 23:41:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:41:58] ppocr INFO: epoch: [113/1500], global_step: 339, lr: 0.001000, loss: 2.177678, loss_shrink_maps: 1.227917, loss_threshold_maps: 0.710225, loss_binary_maps: 0.240596, avg_reader_cost: 1.52368 s, avg_batch_cost: 1.75225 s, avg_samples: 12.5, ips: 7.13367 samples/s, eta: 7:29:52
[2024/07/27 23:41:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:42:04] ppocr INFO: epoch: [114/1500], global_step: 340, lr: 0.001000, loss: 2.219978, loss_shrink_maps: 1.251387, loss_threshold_maps: 0.716373, loss_binary_maps: 0.244948, avg_reader_cost: 0.42486 s, avg_batch_cost: 0.51690 s, avg_samples: 4.8, ips: 9.28606 samples/s, eta: 7:29:30
[2024/07/27 23:42:05] ppocr INFO: epoch: [114/1500], global_step: 342, lr: 0.001000, loss: 2.219978, loss_shrink_maps: 1.251387, loss_threshold_maps: 0.717984, loss_binary_maps: 0.244948, avg_reader_cost: 1.12531 s, avg_batch_cost: 1.27131 s, avg_samples: 7.7, ips: 6.05672 samples/s, eta: 7:29:14
[2024/07/27 23:42:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:42:13] ppocr INFO: epoch: [115/1500], global_step: 345, lr: 0.001000, loss: 2.177678, loss_shrink_maps: 1.227917, loss_threshold_maps: 0.712982, loss_binary_maps: 0.240596, avg_reader_cost: 1.54280 s, avg_batch_cost: 1.81464 s, avg_samples: 12.5, ips: 6.88843 samples/s, eta: 7:28:39
[2024/07/27 23:42:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:42:20] ppocr INFO: epoch: [116/1500], global_step: 348, lr: 0.001000, loss: 2.258319, loss_shrink_maps: 1.290975, loss_threshold_maps: 0.718888, loss_binary_maps: 0.253692, avg_reader_cost: 1.53453 s, avg_batch_cost: 1.76513 s, avg_samples: 12.5, ips: 7.08164 samples/s, eta: 7:27:58
[2024/07/27 23:42:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:42:27] ppocr INFO: epoch: [117/1500], global_step: 350, lr: 0.001000, loss: 2.276583, loss_shrink_maps: 1.309475, loss_threshold_maps: 0.722866, loss_binary_maps: 0.257341, avg_reader_cost: 0.96844 s, avg_batch_cost: 1.14231 s, avg_samples: 9.6, ips: 8.40400 samples/s, eta: 7:27:27
[2024/07/27 23:42:27] ppocr INFO: epoch: [117/1500], global_step: 351, lr: 0.001000, loss: 2.276583, loss_shrink_maps: 1.309475, loss_threshold_maps: 0.722866, loss_binary_maps: 0.257341, avg_reader_cost: 0.61692 s, avg_batch_cost: 0.67185 s, avg_samples: 2.9, ips: 4.31644 samples/s, eta: 7:27:23
[2024/07/27 23:42:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:42:35] ppocr INFO: epoch: [118/1500], global_step: 354, lr: 0.001000, loss: 2.212352, loss_shrink_maps: 1.257312, loss_threshold_maps: 0.720380, loss_binary_maps: 0.246734, avg_reader_cost: 1.80327 s, avg_batch_cost: 2.03780 s, avg_samples: 12.5, ips: 6.13406 samples/s, eta: 7:27:15
[2024/07/27 23:42:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:42:43] ppocr INFO: epoch: [119/1500], global_step: 357, lr: 0.001000, loss: 2.212352, loss_shrink_maps: 1.257312, loss_threshold_maps: 0.715498, loss_binary_maps: 0.246734, avg_reader_cost: 1.56686 s, avg_batch_cost: 1.79488 s, avg_samples: 12.5, ips: 6.96424 samples/s, eta: 7:26:39
[2024/07/27 23:42:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:42:50] ppocr INFO: epoch: [120/1500], global_step: 360, lr: 0.001000, loss: 2.195211, loss_shrink_maps: 1.236896, loss_threshold_maps: 0.710050, loss_binary_maps: 0.243644, avg_reader_cost: 1.51267 s, avg_batch_cost: 1.78159 s, avg_samples: 12.5, ips: 7.01622 samples/s, eta: 7:26:01

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:50,  9.97it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 16.91it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.70it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 18.11it/s]
eval model::   2%|▏         | 11/500 [00:00<00:23, 20.42it/s]
eval model::   3%|▎         | 14/500 [00:00<00:24, 19.88it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.28it/s]
eval model::   4%|▍         | 20/500 [00:01<00:27, 17.21it/s]
eval model::   5%|▍         | 23/500 [00:01<00:25, 19.07it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.28it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.27it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.42it/s]
eval model::   7%|▋         | 35/500 [00:01<00:23, 20.02it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 20.06it/s]
eval model::   8%|▊         | 41/500 [00:02<00:24, 19.09it/s]
eval model::   9%|▊         | 43/500 [00:02<00:24, 18.41it/s]
eval model::   9%|▉         | 46/500 [00:02<00:24, 18.55it/s]
eval model::  10%|█         | 50/500 [00:02<00:20, 21.60it/s]
eval model::  11%|█         | 53/500 [00:02<00:19, 22.48it/s]
eval model::  11%|█         | 56/500 [00:02<00:20, 21.36it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:20, 21.69it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:25, 17.00it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:25, 17.19it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:23, 18.59it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:21, 20.15it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 22.13it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:18, 23.49it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:18, 22.49it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.83it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.79it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 20.32it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.47it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:20, 19.88it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:18, 21.29it/s]
eval model::  20%|██        | 100/500 [00:05<00:20, 19.38it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 21.26it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.66it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.14it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 20.88it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:18, 21.34it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:17, 21.93it/s]
eval model::  24%|██▍       | 121/500 [00:05<00:17, 21.89it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 20.57it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:17, 21.60it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 18.55it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 18.73it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:17, 21.05it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:17, 20.22it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.57it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.14it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 17.85it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 18.97it/s]
eval model::  30%|███       | 152/500 [00:07<00:19, 17.84it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 21.37it/s]
eval model::  32%|███▏      | 159/500 [00:07<00:16, 21.13it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:19, 17.35it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:18, 18.37it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 18.37it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 20.33it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:17, 18.46it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:18, 17.27it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:18, 17.62it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.68it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:21, 14.60it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 17.64it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 18.66it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:16, 18.49it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:16, 18.60it/s]
eval model::  39%|███▉      | 194/500 [00:09<00:14, 20.78it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:15, 19.78it/s]
eval model::  40%|████      | 200/500 [00:10<00:13, 21.72it/s]
eval model::  41%|████      | 203/500 [00:10<00:13, 21.42it/s]
eval model::  41%|████      | 206/500 [00:10<00:22, 13.20it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:21, 13.31it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:20, 14.04it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.87it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.94it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.31it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.00it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 14.79it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:16, 16.84it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:14, 18.94it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 22.00it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 21.49it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:10, 24.09it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 19.89it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:12, 20.76it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 22.18it/s]
eval model::  50%|█████     | 251/500 [00:12<00:11, 22.44it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.90it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 22.19it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 22.18it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 21.63it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:12, 18.64it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:12, 18.29it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:11, 19.44it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 19.44it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.18it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.96it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:09, 21.94it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:09, 22.70it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:16, 13.27it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:13, 15.32it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:12, 16.96it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:12, 16.30it/s]
eval model::  60%|██████    | 300/500 [00:15<00:11, 18.07it/s]
eval model::  60%|██████    | 302/500 [00:15<00:10, 18.32it/s]
eval model::  61%|██████    | 305/500 [00:15<00:09, 19.73it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 21.25it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:08, 22.22it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:08, 22.66it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:08, 21.97it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:08, 20.94it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:08, 19.70it/s]
eval model::  65%|██████▌   | 326/500 [00:16<00:08, 19.41it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 19.80it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:08, 20.44it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 21.35it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:08, 18.44it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:10, 14.73it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:09, 16.87it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:08, 18.90it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.44it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 21.86it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.75it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 21.39it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 19.44it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.75it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 18.97it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.32it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 20.01it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:06, 19.91it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 21.02it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 19.92it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 19.51it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:08, 13.80it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 16.57it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 18.45it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 19.12it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 20.58it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 21.09it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 21.76it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.09it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 20.38it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:04, 20.49it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 17.81it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 17.03it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:05, 15.59it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 17.69it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:03, 19.50it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 20.10it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 21.73it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 21.50it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 21.52it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 20.01it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 21.70it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 19.67it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.05it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 19.50it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:02, 20.73it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 20.10it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 19.20it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 17.50it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 19.54it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.40it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.62it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 20.91it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 20.29it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 19.37it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 18.23it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 19.39it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 19.50it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 20.40it/s]
eval model::  99%|█████████▉| 497/500 [00:25<00:00, 21.74it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.44it/s]
[2024/07/27 23:43:16] ppocr INFO: cur metric, precision: 0.6432825943084051, recall: 0.4679826673086182, hmean: 0.5418060200668896, fps: 45.18622528921397
[2024/07/27 23:43:16] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 23:43:16] ppocr INFO: best metric, hmean: 0.5418060200668896, precision: 0.6432825943084051, recall: 0.4679826673086182, fps: 45.18622528921397, best_epoch: 120
[2024/07/27 23:43:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:43:23] ppocr INFO: epoch: [121/1500], global_step: 363, lr: 0.001000, loss: 2.200762, loss_shrink_maps: 1.243786, loss_threshold_maps: 0.710954, loss_binary_maps: 0.244542, avg_reader_cost: 1.54369 s, avg_batch_cost: 1.80039 s, avg_samples: 12.5, ips: 6.94294 samples/s, eta: 7:25:26
[2024/07/27 23:43:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:43:31] ppocr INFO: epoch: [122/1500], global_step: 366, lr: 0.001000, loss: 2.187952, loss_shrink_maps: 1.224304, loss_threshold_maps: 0.700027, loss_binary_maps: 0.240850, avg_reader_cost: 1.73625 s, avg_batch_cost: 1.97554 s, avg_samples: 12.5, ips: 6.32739 samples/s, eta: 7:25:11
[2024/07/27 23:43:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:43:39] ppocr INFO: epoch: [123/1500], global_step: 369, lr: 0.001000, loss: 2.150370, loss_shrink_maps: 1.200460, loss_threshold_maps: 0.705192, loss_binary_maps: 0.235632, avg_reader_cost: 1.54518 s, avg_batch_cost: 1.77458 s, avg_samples: 12.5, ips: 7.04394 samples/s, eta: 7:24:33
[2024/07/27 23:43:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:43:44] ppocr INFO: epoch: [124/1500], global_step: 370, lr: 0.001000, loss: 2.150370, loss_shrink_maps: 1.200460, loss_threshold_maps: 0.705192, loss_binary_maps: 0.235632, avg_reader_cost: 0.43961 s, avg_batch_cost: 0.52211 s, avg_samples: 4.8, ips: 9.19352 samples/s, eta: 7:24:13
[2024/07/27 23:43:46] ppocr INFO: epoch: [124/1500], global_step: 372, lr: 0.001000, loss: 2.150370, loss_shrink_maps: 1.200460, loss_threshold_maps: 0.705192, loss_binary_maps: 0.235632, avg_reader_cost: 1.13556 s, avg_batch_cost: 1.28118 s, avg_samples: 7.7, ips: 6.01008 samples/s, eta: 7:23:59
[2024/07/27 23:43:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:43:53] ppocr INFO: epoch: [125/1500], global_step: 375, lr: 0.001000, loss: 2.104001, loss_shrink_maps: 1.151974, loss_threshold_maps: 0.699047, loss_binary_maps: 0.227237, avg_reader_cost: 1.50420 s, avg_batch_cost: 1.75267 s, avg_samples: 12.5, ips: 7.13198 samples/s, eta: 7:23:19
[2024/07/27 23:43:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:44:00] ppocr INFO: epoch: [126/1500], global_step: 378, lr: 0.001000, loss: 2.127295, loss_shrink_maps: 1.181484, loss_threshold_maps: 0.699047, loss_binary_maps: 0.232697, avg_reader_cost: 1.50258 s, avg_batch_cost: 1.73841 s, avg_samples: 12.5, ips: 7.19049 samples/s, eta: 7:22:39
[2024/07/27 23:44:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:44:07] ppocr INFO: epoch: [127/1500], global_step: 380, lr: 0.001000, loss: 2.087334, loss_shrink_maps: 1.151974, loss_threshold_maps: 0.700721, loss_binary_maps: 0.227237, avg_reader_cost: 0.94105 s, avg_batch_cost: 1.11466 s, avg_samples: 9.6, ips: 8.61248 samples/s, eta: 7:22:07
[2024/07/27 23:44:08] ppocr INFO: epoch: [127/1500], global_step: 381, lr: 0.001000, loss: 2.055261, loss_shrink_maps: 1.141891, loss_threshold_maps: 0.689910, loss_binary_maps: 0.225001, avg_reader_cost: 0.60292 s, avg_batch_cost: 0.65795 s, avg_samples: 2.9, ips: 4.40764 samples/s, eta: 7:22:02
[2024/07/27 23:44:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:44:16] ppocr INFO: epoch: [128/1500], global_step: 384, lr: 0.001000, loss: 2.143697, loss_shrink_maps: 1.181484, loss_threshold_maps: 0.717168, loss_binary_maps: 0.232697, avg_reader_cost: 1.63080 s, avg_batch_cost: 1.87814 s, avg_samples: 12.5, ips: 6.65553 samples/s, eta: 7:21:37
[2024/07/27 23:44:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:44:24] ppocr INFO: epoch: [129/1500], global_step: 387, lr: 0.001000, loss: 2.087334, loss_shrink_maps: 1.151974, loss_threshold_maps: 0.711403, loss_binary_maps: 0.227237, avg_reader_cost: 1.70338 s, avg_batch_cost: 1.93202 s, avg_samples: 12.5, ips: 6.46992 samples/s, eta: 7:21:18
[2024/07/27 23:44:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:44:31] ppocr INFO: epoch: [130/1500], global_step: 390, lr: 0.001000, loss: 2.117614, loss_shrink_maps: 1.191429, loss_threshold_maps: 0.702596, loss_binary_maps: 0.234849, avg_reader_cost: 1.49562 s, avg_batch_cost: 1.73222 s, avg_samples: 12.5, ips: 7.21618 samples/s, eta: 7:20:37
[2024/07/27 23:44:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:44:38] ppocr INFO: epoch: [131/1500], global_step: 393, lr: 0.001000, loss: 2.037670, loss_shrink_maps: 1.141219, loss_threshold_maps: 0.719700, loss_binary_maps: 0.224851, avg_reader_cost: 1.50417 s, avg_batch_cost: 1.73535 s, avg_samples: 12.5, ips: 7.20316 samples/s, eta: 7:19:58
[2024/07/27 23:44:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:44:46] ppocr INFO: epoch: [132/1500], global_step: 396, lr: 0.001000, loss: 2.082449, loss_shrink_maps: 1.157526, loss_threshold_maps: 0.711837, loss_binary_maps: 0.228328, avg_reader_cost: 1.54839 s, avg_batch_cost: 1.78239 s, avg_samples: 12.5, ips: 7.01307 samples/s, eta: 7:19:23
[2024/07/27 23:44:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:44:53] ppocr INFO: epoch: [133/1500], global_step: 399, lr: 0.001000, loss: 2.140510, loss_shrink_maps: 1.206403, loss_threshold_maps: 0.711837, loss_binary_maps: 0.237763, avg_reader_cost: 1.50757 s, avg_batch_cost: 1.74929 s, avg_samples: 12.5, ips: 7.14575 samples/s, eta: 7:18:46
[2024/07/27 23:44:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:44:59] ppocr INFO: epoch: [134/1500], global_step: 400, lr: 0.001000, loss: 2.082449, loss_shrink_maps: 1.157526, loss_threshold_maps: 0.707184, loss_binary_maps: 0.228328, avg_reader_cost: 0.40194 s, avg_batch_cost: 0.50520 s, avg_samples: 4.8, ips: 9.50124 samples/s, eta: 7:18:25
[2024/07/27 23:45:00] ppocr INFO: epoch: [134/1500], global_step: 402, lr: 0.001000, loss: 2.082449, loss_shrink_maps: 1.157526, loss_threshold_maps: 0.707184, loss_binary_maps: 0.228328, avg_reader_cost: 1.10241 s, avg_batch_cost: 1.24917 s, avg_samples: 7.7, ips: 6.16409 samples/s, eta: 7:18:09
[2024/07/27 23:45:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:45:07] ppocr INFO: epoch: [135/1500], global_step: 405, lr: 0.001000, loss: 2.066156, loss_shrink_maps: 1.149538, loss_threshold_maps: 0.700651, loss_binary_maps: 0.224583, avg_reader_cost: 1.48227 s, avg_batch_cost: 1.71449 s, avg_samples: 12.5, ips: 7.29081 samples/s, eta: 7:17:29
[2024/07/27 23:45:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:45:15] ppocr INFO: epoch: [136/1500], global_step: 408, lr: 0.001000, loss: 2.066156, loss_shrink_maps: 1.139768, loss_threshold_maps: 0.695294, loss_binary_maps: 0.222923, avg_reader_cost: 1.55079 s, avg_batch_cost: 1.80052 s, avg_samples: 12.5, ips: 6.94243 samples/s, eta: 7:16:57
[2024/07/27 23:45:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:45:21] ppocr INFO: epoch: [137/1500], global_step: 410, lr: 0.001000, loss: 2.066156, loss_shrink_maps: 1.139768, loss_threshold_maps: 0.692952, loss_binary_maps: 0.222923, avg_reader_cost: 0.91993 s, avg_batch_cost: 1.10070 s, avg_samples: 9.6, ips: 8.72170 samples/s, eta: 7:16:26
[2024/07/27 23:45:22] ppocr INFO: epoch: [137/1500], global_step: 411, lr: 0.001000, loss: 2.066156, loss_shrink_maps: 1.139768, loss_threshold_maps: 0.692952, loss_binary_maps: 0.222923, avg_reader_cost: 0.59582 s, avg_batch_cost: 0.65055 s, avg_samples: 2.9, ips: 4.45774 samples/s, eta: 7:16:21
[2024/07/27 23:45:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:45:29] ppocr INFO: epoch: [138/1500], global_step: 414, lr: 0.001000, loss: 2.035359, loss_shrink_maps: 1.109319, loss_threshold_maps: 0.684148, loss_binary_maps: 0.219003, avg_reader_cost: 1.56664 s, avg_batch_cost: 1.80136 s, avg_samples: 12.5, ips: 6.93920 samples/s, eta: 7:15:50
[2024/07/27 23:45:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:45:37] ppocr INFO: epoch: [139/1500], global_step: 417, lr: 0.001000, loss: 1.986979, loss_shrink_maps: 1.086796, loss_threshold_maps: 0.685317, loss_binary_maps: 0.214120, avg_reader_cost: 1.51554 s, avg_batch_cost: 1.76869 s, avg_samples: 12.5, ips: 7.06738 samples/s, eta: 7:15:16
[2024/07/27 23:45:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:45:44] ppocr INFO: epoch: [140/1500], global_step: 420, lr: 0.001000, loss: 1.975574, loss_shrink_maps: 1.075849, loss_threshold_maps: 0.685811, loss_binary_maps: 0.212468, avg_reader_cost: 1.49792 s, avg_batch_cost: 1.72727 s, avg_samples: 12.5, ips: 7.23686 samples/s, eta: 7:14:38

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:00,  8.22it/s]
eval model::   1%|          | 3/500 [00:00<00:34, 14.31it/s]
eval model::   1%|          | 5/500 [00:00<00:30, 16.50it/s]
eval model::   1%|▏         | 7/500 [00:00<00:30, 16.38it/s]
eval model::   2%|▏         | 9/500 [00:00<00:29, 16.44it/s]
eval model::   2%|▏         | 12/500 [00:00<00:25, 18.88it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.20it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.53it/s]
eval model::   4%|▍         | 19/500 [00:01<00:25, 18.66it/s]
eval model::   4%|▍         | 21/500 [00:01<00:28, 16.84it/s]
eval model::   5%|▍         | 24/500 [00:01<00:24, 19.59it/s]
eval model::   5%|▌         | 27/500 [00:01<00:21, 21.77it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 21.60it/s]
eval model::   7%|▋         | 33/500 [00:01<00:22, 21.00it/s]
eval model::   7%|▋         | 36/500 [00:01<00:22, 20.19it/s]
eval model::   8%|▊         | 39/500 [00:02<00:24, 19.10it/s]
eval model::   8%|▊         | 42/500 [00:02<00:23, 19.50it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.41it/s]
eval model::  10%|▉         | 48/500 [00:02<00:21, 20.73it/s]
eval model::  10%|█         | 51/500 [00:02<00:20, 22.26it/s]
eval model::  11%|█         | 54/500 [00:02<00:20, 22.07it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:21, 20.14it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:22, 19.96it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.84it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:25, 17.20it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:22, 18.91it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.14it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:19, 22.01it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:19, 21.17it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 21.93it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.86it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.69it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.65it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:23, 17.48it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:21, 19.13it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:19, 21.02it/s]
eval model::  20%|██        | 100/500 [00:05<00:20, 19.40it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 20.88it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 19.95it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 21.59it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 20.81it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:19, 20.16it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:17, 21.76it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:17, 21.94it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:17, 21.31it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:16, 22.27it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:18, 19.71it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:17, 20.85it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:17, 20.34it/s]
eval model::  28%|██▊       | 139/500 [00:06<00:17, 20.92it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:16, 21.97it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 20.90it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 18.29it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 17.61it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 20.96it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:15, 21.70it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:17, 19.53it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:16, 20.17it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:16, 20.70it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:15, 20.90it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:16, 19.31it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:20, 15.74it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:19, 16.21it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:18, 16.92it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 19.65it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 20.12it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.04it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.25it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:15, 20.30it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.28it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.74it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.28it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:16, 18.20it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:15, 18.39it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:16, 17.44it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:16, 17.74it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.38it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.45it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 18.59it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:14, 18.56it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:13, 20.56it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:12, 22.19it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:11, 23.95it/s]
eval model::  47%|████▋     | 236/500 [00:11<00:11, 23.54it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:10, 25.09it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:11, 22.09it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:11, 21.93it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 22.84it/s]
eval model::  50%|█████     | 251/500 [00:12<00:11, 22.03it/s]
eval model::  51%|█████     | 254/500 [00:12<00:11, 21.83it/s]
eval model::  51%|█████▏    | 257/500 [00:12<00:11, 20.94it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:11, 21.63it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:11, 21.41it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:10, 21.44it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:11, 19.57it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:11, 20.24it/s]
eval model::  55%|█████▌    | 275/500 [00:13<00:11, 19.00it/s]
eval model::  56%|█████▌    | 278/500 [00:13<00:10, 20.72it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.92it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 20.76it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:15, 14.09it/s]
eval model::  58%|█████▊    | 290/500 [00:14<00:12, 16.28it/s]
eval model::  59%|█████▊    | 293/500 [00:14<00:11, 17.52it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:12, 16.71it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:10, 19.16it/s]
eval model::  60%|██████    | 302/500 [00:15<00:10, 19.14it/s]
eval model::  61%|██████    | 305/500 [00:15<00:10, 19.40it/s]
eval model::  62%|██████▏   | 308/500 [00:15<00:09, 20.06it/s]
eval model::  62%|██████▏   | 311/500 [00:15<00:08, 21.04it/s]
eval model::  63%|██████▎   | 314/500 [00:15<00:09, 20.30it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:08, 21.16it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:08, 21.13it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:08, 20.03it/s]
eval model::  65%|██████▌   | 326/500 [00:16<00:08, 20.37it/s]
eval model::  66%|██████▌   | 329/500 [00:16<00:08, 19.71it/s]
eval model::  66%|██████▋   | 332/500 [00:16<00:08, 20.63it/s]
eval model::  67%|██████▋   | 335/500 [00:16<00:07, 21.79it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:07, 20.71it/s]
eval model::  68%|██████▊   | 341/500 [00:17<00:09, 17.33it/s]
eval model::  69%|██████▉   | 344/500 [00:17<00:08, 18.93it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:06, 22.51it/s]
eval model::  70%|███████   | 351/500 [00:17<00:06, 22.25it/s]
eval model::  71%|███████   | 354/500 [00:17<00:06, 22.60it/s]
eval model::  71%|███████▏  | 357/500 [00:17<00:06, 21.75it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:06, 22.21it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:06, 22.02it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:06, 19.42it/s]
eval model::  74%|███████▍  | 369/500 [00:18<00:06, 20.72it/s]
eval model::  74%|███████▍  | 372/500 [00:18<00:06, 19.62it/s]
eval model::  75%|███████▌  | 375/500 [00:18<00:06, 20.62it/s]
eval model::  76%|███████▌  | 378/500 [00:18<00:05, 20.46it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:06, 19.15it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:06, 18.89it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:08, 13.14it/s]
eval model::  78%|███████▊  | 389/500 [00:19<00:06, 15.88it/s]
eval model::  78%|███████▊  | 392/500 [00:19<00:06, 17.50it/s]
eval model::  79%|███████▉  | 395/500 [00:19<00:05, 19.42it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 22.23it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 22.31it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 22.62it/s]
eval model::  82%|████████▏ | 408/500 [00:20<00:04, 22.49it/s]
eval model::  82%|████████▏ | 411/500 [00:20<00:04, 22.21it/s]
eval model::  83%|████████▎ | 414/500 [00:20<00:03, 21.88it/s]
eval model::  83%|████████▎ | 417/500 [00:20<00:03, 20.96it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 17.30it/s]
eval model::  85%|████████▍ | 423/500 [00:21<00:04, 18.85it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:03, 18.86it/s]
eval model::  86%|████████▌ | 429/500 [00:21<00:03, 20.78it/s]
eval model::  86%|████████▋ | 432/500 [00:21<00:03, 19.88it/s]
eval model::  87%|████████▋ | 435/500 [00:21<00:03, 20.37it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 21.88it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:03, 19.21it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 19.49it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 19.22it/s]
eval model::  90%|█████████ | 451/500 [00:22<00:02, 20.29it/s]
eval model::  91%|█████████ | 454/500 [00:22<00:02, 18.95it/s]
eval model::  91%|█████████ | 456/500 [00:22<00:02, 18.92it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:02, 19.44it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:02, 18.91it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:01, 17.83it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 19.75it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 19.99it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 19.44it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 19.70it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 20.79it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 20.14it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 16.67it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 18.36it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 19.15it/s]
eval model::  99%|█████████▉| 495/500 [00:24<00:00, 20.70it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 22.16it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.85it/s]
[2024/07/27 23:46:09] ppocr INFO: cur metric, precision: 0.7136498516320475, recall: 0.4631680308136736, hmean: 0.5617518248175183, fps: 45.67912762631318
[2024/07/27 23:46:09] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 23:46:09] ppocr INFO: best metric, hmean: 0.5617518248175183, precision: 0.7136498516320475, recall: 0.4631680308136736, fps: 45.67912762631318, best_epoch: 140
[2024/07/27 23:46:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:46:17] ppocr INFO: epoch: [141/1500], global_step: 423, lr: 0.001000, loss: 1.949074, loss_shrink_maps: 1.070845, loss_threshold_maps: 0.676432, loss_binary_maps: 0.210051, avg_reader_cost: 1.58101 s, avg_batch_cost: 1.81992 s, avg_samples: 12.5, ips: 6.86843 samples/s, eta: 7:14:09
[2024/07/27 23:46:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:46:24] ppocr INFO: epoch: [142/1500], global_step: 426, lr: 0.001000, loss: 1.977204, loss_shrink_maps: 1.078130, loss_threshold_maps: 0.685811, loss_binary_maps: 0.212468, avg_reader_cost: 1.46673 s, avg_batch_cost: 1.70860 s, avg_samples: 12.5, ips: 7.31595 samples/s, eta: 7:13:30
[2024/07/27 23:46:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:46:31] ppocr INFO: epoch: [143/1500], global_step: 429, lr: 0.001000, loss: 1.961635, loss_shrink_maps: 1.073094, loss_threshold_maps: 0.681505, loss_binary_maps: 0.209769, avg_reader_cost: 1.54340 s, avg_batch_cost: 1.80739 s, avg_samples: 12.5, ips: 6.91604 samples/s, eta: 7:13:01
[2024/07/27 23:46:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:46:37] ppocr INFO: epoch: [144/1500], global_step: 430, lr: 0.001000, loss: 1.974834, loss_shrink_maps: 1.073094, loss_threshold_maps: 0.691056, loss_binary_maps: 0.209769, avg_reader_cost: 0.40773 s, avg_batch_cost: 0.50025 s, avg_samples: 4.8, ips: 9.59517 samples/s, eta: 7:12:41
[2024/07/27 23:46:39] ppocr INFO: epoch: [144/1500], global_step: 432, lr: 0.001000, loss: 1.961635, loss_shrink_maps: 1.068969, loss_threshold_maps: 0.691056, loss_binary_maps: 0.209328, avg_reader_cost: 1.09234 s, avg_batch_cost: 1.23750 s, avg_samples: 7.7, ips: 6.22221 samples/s, eta: 7:12:25
[2024/07/27 23:46:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:46:46] ppocr INFO: epoch: [145/1500], global_step: 435, lr: 0.001000, loss: 1.981626, loss_shrink_maps: 1.075376, loss_threshold_maps: 0.691056, loss_binary_maps: 0.210176, avg_reader_cost: 1.57586 s, avg_batch_cost: 1.80479 s, avg_samples: 12.5, ips: 6.92603 samples/s, eta: 7:11:56
[2024/07/27 23:46:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:46:54] ppocr INFO: epoch: [146/1500], global_step: 438, lr: 0.001000, loss: 1.984950, loss_shrink_maps: 1.081067, loss_threshold_maps: 0.684711, loss_binary_maps: 0.211336, avg_reader_cost: 1.54943 s, avg_batch_cost: 1.77958 s, avg_samples: 12.5, ips: 7.02412 samples/s, eta: 7:11:24
[2024/07/27 23:46:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:47:01] ppocr INFO: epoch: [147/1500], global_step: 440, lr: 0.001000, loss: 2.012243, loss_shrink_maps: 1.107649, loss_threshold_maps: 0.698560, loss_binary_maps: 0.217707, avg_reader_cost: 0.94236 s, avg_batch_cost: 1.11604 s, avg_samples: 9.6, ips: 8.60188 samples/s, eta: 7:10:57
[2024/07/27 23:47:01] ppocr INFO: epoch: [147/1500], global_step: 441, lr: 0.001000, loss: 2.069197, loss_shrink_maps: 1.151056, loss_threshold_maps: 0.704053, loss_binary_maps: 0.225466, avg_reader_cost: 0.60362 s, avg_batch_cost: 0.65844 s, avg_samples: 2.9, ips: 4.40437 samples/s, eta: 7:10:53
[2024/07/27 23:47:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:47:09] ppocr INFO: epoch: [148/1500], global_step: 444, lr: 0.001000, loss: 2.012243, loss_shrink_maps: 1.123560, loss_threshold_maps: 0.707328, loss_binary_maps: 0.220579, avg_reader_cost: 1.54616 s, avg_batch_cost: 1.82129 s, avg_samples: 12.5, ips: 6.86326 samples/s, eta: 7:10:25
[2024/07/27 23:47:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:47:16] ppocr INFO: epoch: [149/1500], global_step: 447, lr: 0.001000, loss: 2.012243, loss_shrink_maps: 1.123560, loss_threshold_maps: 0.707328, loss_binary_maps: 0.220579, avg_reader_cost: 1.55267 s, avg_batch_cost: 1.79608 s, avg_samples: 12.5, ips: 6.95958 samples/s, eta: 7:09:56
[2024/07/27 23:47:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:47:24] ppocr INFO: epoch: [150/1500], global_step: 450, lr: 0.001000, loss: 2.092118, loss_shrink_maps: 1.160945, loss_threshold_maps: 0.708800, loss_binary_maps: 0.229324, avg_reader_cost: 1.52568 s, avg_batch_cost: 1.75431 s, avg_samples: 12.5, ips: 7.12532 samples/s, eta: 7:09:23
[2024/07/27 23:47:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:47:31] ppocr INFO: epoch: [151/1500], global_step: 453, lr: 0.001000, loss: 2.122918, loss_shrink_maps: 1.186805, loss_threshold_maps: 0.708140, loss_binary_maps: 0.235248, avg_reader_cost: 1.55988 s, avg_batch_cost: 1.78812 s, avg_samples: 12.5, ips: 6.99060 samples/s, eta: 7:08:53
[2024/07/27 23:47:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:47:38] ppocr INFO: epoch: [152/1500], global_step: 456, lr: 0.001000, loss: 2.094515, loss_shrink_maps: 1.160945, loss_threshold_maps: 0.708140, loss_binary_maps: 0.229324, avg_reader_cost: 1.53235 s, avg_batch_cost: 1.78866 s, avg_samples: 12.5, ips: 6.98849 samples/s, eta: 7:08:23
[2024/07/27 23:47:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:47:46] ppocr INFO: epoch: [153/1500], global_step: 459, lr: 0.001000, loss: 2.045575, loss_shrink_maps: 1.136683, loss_threshold_maps: 0.697151, loss_binary_maps: 0.223723, avg_reader_cost: 1.53388 s, avg_batch_cost: 1.78203 s, avg_samples: 12.5, ips: 7.01446 samples/s, eta: 7:07:53
[2024/07/27 23:47:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:47:52] ppocr INFO: epoch: [154/1500], global_step: 460, lr: 0.001000, loss: 1.999756, loss_shrink_maps: 1.120553, loss_threshold_maps: 0.688079, loss_binary_maps: 0.220667, avg_reader_cost: 0.41192 s, avg_batch_cost: 0.53018 s, avg_samples: 4.8, ips: 9.05346 samples/s, eta: 7:07:38
[2024/07/27 23:47:53] ppocr INFO: epoch: [154/1500], global_step: 462, lr: 0.001000, loss: 2.045575, loss_shrink_maps: 1.136683, loss_threshold_maps: 0.688079, loss_binary_maps: 0.223723, avg_reader_cost: 1.15149 s, avg_batch_cost: 1.29712 s, avg_samples: 7.7, ips: 5.93624 samples/s, eta: 7:07:27
[2024/07/27 23:47:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:48:01] ppocr INFO: epoch: [155/1500], global_step: 465, lr: 0.001000, loss: 2.094515, loss_shrink_maps: 1.168359, loss_threshold_maps: 0.696643, loss_binary_maps: 0.230707, avg_reader_cost: 1.58610 s, avg_batch_cost: 1.88135 s, avg_samples: 12.5, ips: 6.64416 samples/s, eta: 7:07:06
[2024/07/27 23:48:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:48:08] ppocr INFO: epoch: [156/1500], global_step: 468, lr: 0.001000, loss: 2.127028, loss_shrink_maps: 1.195218, loss_threshold_maps: 0.701211, loss_binary_maps: 0.236680, avg_reader_cost: 1.49987 s, avg_batch_cost: 1.72817 s, avg_samples: 12.5, ips: 7.23307 samples/s, eta: 7:06:32
[2024/07/27 23:48:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:48:15] ppocr INFO: epoch: [157/1500], global_step: 470, lr: 0.001000, loss: 2.116817, loss_shrink_maps: 1.189145, loss_threshold_maps: 0.698402, loss_binary_maps: 0.235032, avg_reader_cost: 0.90598 s, avg_batch_cost: 1.09583 s, avg_samples: 9.6, ips: 8.76047 samples/s, eta: 7:06:04
[2024/07/27 23:48:15] ppocr INFO: epoch: [157/1500], global_step: 471, lr: 0.001000, loss: 2.076497, loss_shrink_maps: 1.151424, loss_threshold_maps: 0.694342, loss_binary_maps: 0.226617, avg_reader_cost: 0.59370 s, avg_batch_cost: 0.64833 s, avg_samples: 2.9, ips: 4.47305 samples/s, eta: 7:05:59
[2024/07/27 23:48:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:48:23] ppocr INFO: epoch: [158/1500], global_step: 474, lr: 0.001000, loss: 2.061648, loss_shrink_maps: 1.151424, loss_threshold_maps: 0.692413, loss_binary_maps: 0.226528, avg_reader_cost: 1.58997 s, avg_batch_cost: 1.87246 s, avg_samples: 12.5, ips: 6.67570 samples/s, eta: 7:05:38
[2024/07/27 23:48:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:48:30] ppocr INFO: epoch: [159/1500], global_step: 477, lr: 0.001000, loss: 2.061648, loss_shrink_maps: 1.137980, loss_threshold_maps: 0.698402, loss_binary_maps: 0.223596, avg_reader_cost: 1.49385 s, avg_batch_cost: 1.73734 s, avg_samples: 12.5, ips: 7.19491 samples/s, eta: 7:05:05
[2024/07/27 23:48:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:48:38] ppocr INFO: epoch: [160/1500], global_step: 480, lr: 0.001000, loss: 2.094162, loss_shrink_maps: 1.170390, loss_threshold_maps: 0.701292, loss_binary_maps: 0.231066, avg_reader_cost: 1.64534 s, avg_batch_cost: 1.93316 s, avg_samples: 12.5, ips: 6.46610 samples/s, eta: 7:04:48

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:55,  9.07it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.90it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.53it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.37it/s]
eval model::   2%|▏         | 10/500 [00:00<00:29, 16.78it/s]
eval model::   3%|▎         | 13/500 [00:00<00:25, 18.79it/s]
eval model::   3%|▎         | 15/500 [00:00<00:27, 17.81it/s]
eval model::   4%|▎         | 18/500 [00:00<00:24, 20.01it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 18.74it/s]
eval model::   5%|▍         | 24/500 [00:01<00:25, 18.54it/s]
eval model::   5%|▌         | 26/500 [00:01<00:25, 18.84it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 21.30it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 19.74it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 18.65it/s]
eval model::   7%|▋         | 37/500 [00:02<00:25, 18.08it/s]
eval model::   8%|▊         | 39/500 [00:02<00:29, 15.78it/s]
eval model::   8%|▊         | 42/500 [00:02<00:29, 15.74it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 18.17it/s]
eval model::   9%|▉         | 47/500 [00:02<00:24, 18.14it/s]
eval model::  10%|█         | 50/500 [00:02<00:22, 20.21it/s]
eval model::  11%|█         | 53/500 [00:02<00:23, 18.90it/s]
eval model::  11%|█         | 56/500 [00:03<00:22, 19.43it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:22, 19.27it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.65it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.43it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 19.38it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 21.28it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 21.08it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:18, 22.77it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.35it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.74it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 21.12it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.53it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.07it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.09it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.52it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.76it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.68it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 19.96it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:19, 20.62it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 19.86it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:17, 21.49it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:17, 21.30it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.55it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 19.98it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 18.20it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 19.30it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 19.18it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:19, 19.20it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:18, 19.79it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.28it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:19, 18.59it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:18, 19.61it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 19.86it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 18.34it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 17.68it/s]
eval model::  31%|███       | 154/500 [00:08<00:17, 19.90it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:17, 19.98it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:20, 16.22it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.34it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 18.65it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:17, 18.53it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:18, 17.72it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:17, 18.18it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:21, 14.95it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:18, 16.96it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:21, 14.86it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:18, 16.74it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.79it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:15, 20.21it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:15, 20.12it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 20.53it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.23it/s]
eval model::  40%|████      | 201/500 [00:10<00:15, 19.91it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.03it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:19, 15.18it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:18, 15.98it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:17, 16.23it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:16, 16.94it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.82it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.56it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:18, 14.96it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:21, 13.05it/s]
eval model::  45%|████▍     | 223/500 [00:12<00:19, 14.33it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.16it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:14, 18.48it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:13, 19.85it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:14, 18.58it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:14, 18.57it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:12, 20.37it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:12, 19.82it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:13, 19.31it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:14, 17.45it/s]
eval model::  50%|█████     | 252/500 [00:13<00:13, 18.93it/s]
eval model::  51%|█████     | 254/500 [00:13<00:12, 18.95it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:12, 19.37it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:11, 20.51it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:11, 20.79it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:13, 17.65it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:12, 18.07it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:11, 19.86it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:10, 20.65it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 19.70it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:10, 20.72it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:09, 22.83it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:14, 14.42it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 15.10it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 17.25it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.23it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:13, 15.45it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:11, 16.90it/s]
eval model::  60%|██████    | 301/500 [00:16<00:13, 15.30it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 16.25it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 17.94it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 19.19it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 20.47it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 18.52it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:09, 20.04it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.16it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 21.72it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 20.79it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 20.36it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 21.46it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 19.75it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 14.67it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 17.19it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 18.68it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 19.68it/s]
eval model::  70%|███████   | 352/500 [00:18<00:07, 20.88it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 19.77it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 19.14it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.53it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.09it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:06, 19.59it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.85it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:06, 20.24it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 20.72it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 21.26it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 22.72it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:07, 14.44it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:06, 17.45it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:05, 19.08it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 19.26it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:04, 20.59it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 19.57it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 21.14it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 22.25it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 23.05it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 22.73it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 20.39it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:04, 16.09it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 15.50it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.46it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.36it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 19.32it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 20.12it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:02, 22.89it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 23.43it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 22.33it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 22.10it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 21.30it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 20.31it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 19.47it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:02, 19.00it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:02, 19.73it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 19.75it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 18.54it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:01, 17.96it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 19.69it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 21.66it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 20.30it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 21.71it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 21.72it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 22.16it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 21.20it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 23.02it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 22.21it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 22.76it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 24.68it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.03it/s]
[2024/07/27 23:49:05] ppocr INFO: cur metric, precision: 0.6331930246542393, recall: 0.5069812229176697, hmean: 0.5631016042780749, fps: 44.85164591471328
[2024/07/27 23:49:05] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/27 23:49:05] ppocr INFO: best metric, hmean: 0.5631016042780749, precision: 0.6331930246542393, recall: 0.5069812229176697, fps: 44.85164591471328, best_epoch: 160
[2024/07/27 23:49:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:49:11] ppocr INFO: epoch: [161/1500], global_step: 483, lr: 0.001000, loss: 2.041810, loss_shrink_maps: 1.120030, loss_threshold_maps: 0.692413, loss_binary_maps: 0.220275, avg_reader_cost: 1.43582 s, avg_batch_cost: 1.67100 s, avg_samples: 12.5, ips: 7.48053 samples/s, eta: 7:04:10
[2024/07/27 23:49:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:49:19] ppocr INFO: epoch: [162/1500], global_step: 486, lr: 0.001000, loss: 2.012747, loss_shrink_maps: 1.104158, loss_threshold_maps: 0.676631, loss_binary_maps: 0.218636, avg_reader_cost: 1.51513 s, avg_batch_cost: 1.74969 s, avg_samples: 12.5, ips: 7.14413 samples/s, eta: 7:03:38
[2024/07/27 23:49:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:49:26] ppocr INFO: epoch: [163/1500], global_step: 489, lr: 0.001000, loss: 2.007298, loss_shrink_maps: 1.102692, loss_threshold_maps: 0.676565, loss_binary_maps: 0.217724, avg_reader_cost: 1.53565 s, avg_batch_cost: 1.76420 s, avg_samples: 12.5, ips: 7.08537 samples/s, eta: 7:03:08
[2024/07/27 23:49:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:49:32] ppocr INFO: epoch: [164/1500], global_step: 490, lr: 0.001000, loss: 2.007298, loss_shrink_maps: 1.104158, loss_threshold_maps: 0.676565, loss_binary_maps: 0.218636, avg_reader_cost: 0.42717 s, avg_batch_cost: 0.50973 s, avg_samples: 4.8, ips: 9.41673 samples/s, eta: 7:02:52
[2024/07/27 23:49:33] ppocr INFO: epoch: [164/1500], global_step: 492, lr: 0.001000, loss: 2.012747, loss_shrink_maps: 1.104158, loss_threshold_maps: 0.680691, loss_binary_maps: 0.218636, avg_reader_cost: 1.11071 s, avg_batch_cost: 1.25645 s, avg_samples: 7.7, ips: 6.12838 samples/s, eta: 7:02:38
[2024/07/27 23:49:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:49:41] ppocr INFO: epoch: [165/1500], global_step: 495, lr: 0.001000, loss: 2.039208, loss_shrink_maps: 1.121711, loss_threshold_maps: 0.681392, loss_binary_maps: 0.221763, avg_reader_cost: 1.58363 s, avg_batch_cost: 1.85801 s, avg_samples: 12.5, ips: 6.72763 samples/s, eta: 7:02:16
[2024/07/27 23:49:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:49:48] ppocr INFO: epoch: [166/1500], global_step: 498, lr: 0.001000, loss: 2.030554, loss_shrink_maps: 1.125770, loss_threshold_maps: 0.680736, loss_binary_maps: 0.222078, avg_reader_cost: 1.53228 s, avg_batch_cost: 1.76004 s, avg_samples: 12.5, ips: 7.10210 samples/s, eta: 7:01:46
[2024/07/27 23:49:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:49:55] ppocr INFO: epoch: [167/1500], global_step: 500, lr: 0.001000, loss: 2.012276, loss_shrink_maps: 1.116617, loss_threshold_maps: 0.680669, loss_binary_maps: 0.220656, avg_reader_cost: 0.91631 s, avg_batch_cost: 1.09228 s, avg_samples: 9.6, ips: 8.78894 samples/s, eta: 7:01:20
[2024/07/27 23:49:56] ppocr INFO: epoch: [167/1500], global_step: 501, lr: 0.001000, loss: 2.030554, loss_shrink_maps: 1.137456, loss_threshold_maps: 0.681370, loss_binary_maps: 0.224195, avg_reader_cost: 0.59167 s, avg_batch_cost: 0.64679 s, avg_samples: 2.9, ips: 4.48365 samples/s, eta: 7:01:15
[2024/07/27 23:49:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:50:04] ppocr INFO: epoch: [168/1500], global_step: 504, lr: 0.001000, loss: 2.047999, loss_shrink_maps: 1.149449, loss_threshold_maps: 0.690792, loss_binary_maps: 0.227212, avg_reader_cost: 1.63840 s, avg_batch_cost: 1.92163 s, avg_samples: 12.5, ips: 6.50490 samples/s, eta: 7:00:58
[2024/07/27 23:50:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:50:11] ppocr INFO: epoch: [169/1500], global_step: 507, lr: 0.001000, loss: 2.076146, loss_shrink_maps: 1.160236, loss_threshold_maps: 0.696608, loss_binary_maps: 0.229369, avg_reader_cost: 1.60112 s, avg_batch_cost: 1.83104 s, avg_samples: 12.5, ips: 6.82671 samples/s, eta: 7:00:34
[2024/07/27 23:50:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:50:19] ppocr INFO: epoch: [170/1500], global_step: 510, lr: 0.001000, loss: 2.036999, loss_shrink_maps: 1.131685, loss_threshold_maps: 0.692763, loss_binary_maps: 0.222926, avg_reader_cost: 1.57981 s, avg_batch_cost: 1.82244 s, avg_samples: 12.5, ips: 6.85895 samples/s, eta: 7:00:09
[2024/07/27 23:50:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:50:26] ppocr INFO: epoch: [171/1500], global_step: 513, lr: 0.001000, loss: 2.001013, loss_shrink_maps: 1.098705, loss_threshold_maps: 0.689921, loss_binary_maps: 0.217471, avg_reader_cost: 1.53778 s, avg_batch_cost: 1.78672 s, avg_samples: 12.5, ips: 6.99604 samples/s, eta: 6:59:41
[2024/07/27 23:50:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:50:34] ppocr INFO: epoch: [172/1500], global_step: 516, lr: 0.001000, loss: 2.001013, loss_shrink_maps: 1.095372, loss_threshold_maps: 0.691709, loss_binary_maps: 0.216600, avg_reader_cost: 1.64183 s, avg_batch_cost: 1.94721 s, avg_samples: 12.5, ips: 6.41944 samples/s, eta: 6:59:27
[2024/07/27 23:50:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:50:42] ppocr INFO: epoch: [173/1500], global_step: 519, lr: 0.001000, loss: 1.995640, loss_shrink_maps: 1.095372, loss_threshold_maps: 0.691709, loss_binary_maps: 0.216600, avg_reader_cost: 1.51639 s, avg_batch_cost: 1.74520 s, avg_samples: 12.5, ips: 7.16249 samples/s, eta: 6:58:56
[2024/07/27 23:50:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:50:48] ppocr INFO: epoch: [174/1500], global_step: 520, lr: 0.001000, loss: 1.994522, loss_shrink_maps: 1.095372, loss_threshold_maps: 0.689921, loss_binary_maps: 0.216600, avg_reader_cost: 0.37924 s, avg_batch_cost: 0.50916 s, avg_samples: 4.8, ips: 9.42724 samples/s, eta: 6:58:40
[2024/07/27 23:50:49] ppocr INFO: epoch: [174/1500], global_step: 522, lr: 0.001000, loss: 1.987276, loss_shrink_maps: 1.092127, loss_threshold_maps: 0.677239, loss_binary_maps: 0.215896, avg_reader_cost: 1.10981 s, avg_batch_cost: 1.25560 s, avg_samples: 7.7, ips: 6.13253 samples/s, eta: 6:58:27
[2024/07/27 23:50:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:50:57] ppocr INFO: epoch: [175/1500], global_step: 525, lr: 0.001000, loss: 1.987276, loss_shrink_maps: 1.092127, loss_threshold_maps: 0.664425, loss_binary_maps: 0.215896, avg_reader_cost: 1.62004 s, avg_batch_cost: 1.89999 s, avg_samples: 12.5, ips: 6.57899 samples/s, eta: 6:58:09
[2024/07/27 23:50:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:51:04] ppocr INFO: epoch: [176/1500], global_step: 528, lr: 0.001000, loss: 1.970208, loss_shrink_maps: 1.090610, loss_threshold_maps: 0.671497, loss_binary_maps: 0.215512, avg_reader_cost: 1.48288 s, avg_batch_cost: 1.74077 s, avg_samples: 12.5, ips: 7.18074 samples/s, eta: 6:57:38
[2024/07/27 23:51:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:51:12] ppocr INFO: epoch: [177/1500], global_step: 530, lr: 0.001000, loss: 1.968479, loss_shrink_maps: 1.092127, loss_threshold_maps: 0.671497, loss_binary_maps: 0.215512, avg_reader_cost: 0.99900 s, avg_batch_cost: 1.17232 s, avg_samples: 9.6, ips: 8.18888 samples/s, eta: 6:57:19
[2024/07/27 23:51:12] ppocr INFO: epoch: [177/1500], global_step: 531, lr: 0.001000, loss: 1.945938, loss_shrink_maps: 1.081574, loss_threshold_maps: 0.671497, loss_binary_maps: 0.213702, avg_reader_cost: 0.63197 s, avg_batch_cost: 0.68731 s, avg_samples: 2.9, ips: 4.21932 samples/s, eta: 6:57:17
[2024/07/27 23:51:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:51:20] ppocr INFO: epoch: [178/1500], global_step: 534, lr: 0.001000, loss: 1.945938, loss_shrink_maps: 1.081574, loss_threshold_maps: 0.671497, loss_binary_maps: 0.213702, avg_reader_cost: 1.56714 s, avg_batch_cost: 1.84310 s, avg_samples: 12.5, ips: 6.78205 samples/s, eta: 6:56:54
[2024/07/27 23:51:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:51:28] ppocr INFO: epoch: [179/1500], global_step: 537, lr: 0.001000, loss: 1.938330, loss_shrink_maps: 1.066443, loss_threshold_maps: 0.664663, loss_binary_maps: 0.211001, avg_reader_cost: 1.61215 s, avg_batch_cost: 1.84087 s, avg_samples: 12.5, ips: 6.79028 samples/s, eta: 6:56:32
[2024/07/27 23:51:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:51:35] ppocr INFO: epoch: [180/1500], global_step: 540, lr: 0.001000, loss: 1.945938, loss_shrink_maps: 1.081574, loss_threshold_maps: 0.677134, loss_binary_maps: 0.213702, avg_reader_cost: 1.63509 s, avg_batch_cost: 1.89334 s, avg_samples: 12.5, ips: 6.60208 samples/s, eta: 6:56:13

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:00,  8.30it/s]
eval model::   1%|          | 3/500 [00:00<00:36, 13.76it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.44it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.64it/s]
eval model::   2%|▏         | 10/500 [00:00<00:26, 18.28it/s]
eval model::   2%|▏         | 12/500 [00:00<00:27, 17.57it/s]
eval model::   3%|▎         | 15/500 [00:00<00:26, 18.56it/s]
eval model::   4%|▎         | 18/500 [00:00<00:23, 20.51it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.63it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.26it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 21.38it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 21.42it/s]
eval model::   7%|▋         | 33/500 [00:01<00:21, 21.52it/s]
eval model::   7%|▋         | 36/500 [00:01<00:23, 19.39it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.42it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 17.74it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.41it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 18.16it/s]
eval model::   9%|▉         | 47/500 [00:02<00:27, 16.77it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.16it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 20.50it/s]
eval model::  11%|█         | 56/500 [00:02<00:20, 21.90it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:20, 21.20it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:26, 16.62it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:25, 17.24it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 19.38it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 20.77it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 20.32it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:20, 20.84it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:20, 20.73it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 20.54it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.00it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 18.75it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.42it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:23, 17.72it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:21, 18.83it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:18, 21.27it/s]
eval model::  20%|██        | 101/500 [00:05<00:20, 19.63it/s]
eval model::  21%|██        | 104/500 [00:05<00:19, 20.68it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:19, 20.16it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 21.95it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 20.49it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:17, 21.59it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 21.18it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:17, 21.65it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:19, 19.32it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:17, 20.92it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:18, 20.50it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:17, 20.39it/s]
eval model::  27%|██▋       | 137/500 [00:06<00:17, 20.33it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:17, 20.30it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:16, 21.77it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 20.56it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:18, 19.15it/s]
eval model::  30%|███       | 151/500 [00:07<00:20, 17.22it/s]
eval model::  31%|███       | 154/500 [00:07<00:17, 19.57it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:17, 19.93it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:19, 17.07it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:17, 18.81it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.10it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 20.56it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:15, 21.27it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:19, 16.68it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:17, 18.36it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.25it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:19, 16.36it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:18, 16.83it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:18, 16.77it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:17, 17.22it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:15, 19.91it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:15, 19.78it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:15, 20.00it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 21.33it/s]
eval model::  41%|████      | 205/500 [00:10<00:21, 13.70it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:19, 14.74it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:19, 14.75it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 15.16it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 15.98it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.30it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.31it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.17it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 14.75it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.40it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:13, 19.67it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:13, 20.14it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:14, 18.46it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:14, 17.88it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:13, 19.29it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:14, 17.84it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:13, 19.31it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 20.30it/s]
eval model::  50%|█████     | 251/500 [00:13<00:13, 18.49it/s]
eval model::  51%|█████     | 254/500 [00:13<00:12, 19.47it/s]
eval model::  51%|█████     | 256/500 [00:13<00:12, 19.54it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:11, 21.03it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:10, 21.87it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:11, 19.73it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:11, 20.38it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:12, 18.75it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:11, 20.24it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 18.82it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 19.45it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:10, 20.47it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:14, 14.36it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:12, 16.24it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.95it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:12, 16.38it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:14, 14.11it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:12, 16.11it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 16.40it/s]
eval model::  61%|██████    | 303/500 [00:16<00:10, 18.85it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 18.00it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:11, 17.12it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:11, 16.72it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:11, 16.07it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:10, 17.58it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:09, 19.73it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 21.32it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 21.06it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 22.11it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:07, 21.65it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 22.62it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 21.98it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:08, 19.71it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:09, 16.73it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 18.66it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 17.69it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 19.09it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 20.88it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 19.01it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:07, 18.60it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:07, 17.86it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.72it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 18.35it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 19.89it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 19.71it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 19.44it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 21.45it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:05, 21.42it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 22.03it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 15.13it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 17.59it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 19.21it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 19.99it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 19.38it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 19.51it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 20.81it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 19.01it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 18.65it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:04, 18.88it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:04, 18.49it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 17.91it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 17.18it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.38it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 15.07it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 14.52it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:05, 14.61it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:03, 17.97it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:03, 17.58it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 21.41it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 22.09it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 20.10it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 23.29it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 22.20it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 22.01it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 22.11it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 23.92it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 21.99it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 19.15it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 18.18it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.18it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.42it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 19.71it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:00, 22.55it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 23.63it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 22.83it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 20.36it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 21.03it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 20.31it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 20.26it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 22.19it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.98it/s]
[2024/07/27 23:52:02] ppocr INFO: cur metric, precision: 0.6092560046865847, recall: 0.5007221954742417, hmean: 0.5496828752642705, fps: 44.18784063205819
[2024/07/27 23:52:02] ppocr INFO: best metric, hmean: 0.5631016042780749, precision: 0.6331930246542393, recall: 0.5069812229176697, fps: 44.85164591471328, best_epoch: 160
[2024/07/27 23:52:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:52:09] ppocr INFO: epoch: [181/1500], global_step: 543, lr: 0.001000, loss: 1.960871, loss_shrink_maps: 1.078732, loss_threshold_maps: 0.684330, loss_binary_maps: 0.212784, avg_reader_cost: 1.54389 s, avg_batch_cost: 1.77544 s, avg_samples: 12.5, ips: 7.04052 samples/s, eta: 6:55:46
[2024/07/27 23:52:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:52:16] ppocr INFO: epoch: [182/1500], global_step: 546, lr: 0.001000, loss: 1.954436, loss_shrink_maps: 1.075013, loss_threshold_maps: 0.679596, loss_binary_maps: 0.212494, avg_reader_cost: 1.53630 s, avg_batch_cost: 1.77278 s, avg_samples: 12.5, ips: 7.05109 samples/s, eta: 6:55:18
[2024/07/27 23:52:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:52:23] ppocr INFO: epoch: [183/1500], global_step: 549, lr: 0.001000, loss: 1.990467, loss_shrink_maps: 1.089560, loss_threshold_maps: 0.684330, loss_binary_maps: 0.215502, avg_reader_cost: 1.48864 s, avg_batch_cost: 1.72869 s, avg_samples: 12.5, ips: 7.23090 samples/s, eta: 6:54:47
[2024/07/27 23:52:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:52:29] ppocr INFO: epoch: [184/1500], global_step: 550, lr: 0.001000, loss: 1.990467, loss_shrink_maps: 1.089560, loss_threshold_maps: 0.677642, loss_binary_maps: 0.215502, avg_reader_cost: 0.40792 s, avg_batch_cost: 0.49885 s, avg_samples: 4.8, ips: 9.62218 samples/s, eta: 6:54:32
[2024/07/27 23:52:31] ppocr INFO: epoch: [184/1500], global_step: 552, lr: 0.001000, loss: 1.990467, loss_shrink_maps: 1.089560, loss_threshold_maps: 0.674628, loss_binary_maps: 0.215502, avg_reader_cost: 1.08906 s, avg_batch_cost: 1.23512 s, avg_samples: 7.7, ips: 6.23420 samples/s, eta: 6:54:17
[2024/07/27 23:52:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:52:38] ppocr INFO: epoch: [185/1500], global_step: 555, lr: 0.001000, loss: 1.974481, loss_shrink_maps: 1.086622, loss_threshold_maps: 0.674628, loss_binary_maps: 0.215212, avg_reader_cost: 1.52355 s, avg_batch_cost: 1.75730 s, avg_samples: 12.5, ips: 7.11317 samples/s, eta: 6:53:49
[2024/07/27 23:52:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:52:46] ppocr INFO: epoch: [186/1500], global_step: 558, lr: 0.001000, loss: 2.002833, loss_shrink_maps: 1.093280, loss_threshold_maps: 0.683837, loss_binary_maps: 0.216557, avg_reader_cost: 1.63638 s, avg_batch_cost: 1.86510 s, avg_samples: 12.5, ips: 6.70204 samples/s, eta: 6:53:29
[2024/07/27 23:52:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:52:53] ppocr INFO: epoch: [187/1500], global_step: 560, lr: 0.001000, loss: 1.974481, loss_shrink_maps: 1.086622, loss_threshold_maps: 0.679848, loss_binary_maps: 0.215212, avg_reader_cost: 0.93064 s, avg_batch_cost: 1.10423 s, avg_samples: 9.6, ips: 8.69387 samples/s, eta: 6:53:05
[2024/07/27 23:52:53] ppocr INFO: epoch: [187/1500], global_step: 561, lr: 0.001000, loss: 1.974481, loss_shrink_maps: 1.086622, loss_threshold_maps: 0.679487, loss_binary_maps: 0.215212, avg_reader_cost: 0.59774 s, avg_batch_cost: 0.65272 s, avg_samples: 2.9, ips: 4.44295 samples/s, eta: 6:53:01
[2024/07/27 23:52:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:53:01] ppocr INFO: epoch: [188/1500], global_step: 564, lr: 0.001000, loss: 1.974481, loss_shrink_maps: 1.086622, loss_threshold_maps: 0.679144, loss_binary_maps: 0.215212, avg_reader_cost: 1.55620 s, avg_batch_cost: 1.79668 s, avg_samples: 12.5, ips: 6.95729 samples/s, eta: 6:52:35
[2024/07/27 23:53:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:53:09] ppocr INFO: epoch: [189/1500], global_step: 567, lr: 0.001000, loss: 1.956400, loss_shrink_maps: 1.062959, loss_threshold_maps: 0.675001, loss_binary_maps: 0.210366, avg_reader_cost: 1.53314 s, avg_batch_cost: 1.77355 s, avg_samples: 12.5, ips: 7.04802 samples/s, eta: 6:52:09
[2024/07/27 23:53:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:53:16] ppocr INFO: epoch: [190/1500], global_step: 570, lr: 0.001000, loss: 1.956400, loss_shrink_maps: 1.062959, loss_threshold_maps: 0.679487, loss_binary_maps: 0.210366, avg_reader_cost: 1.55010 s, avg_batch_cost: 1.77866 s, avg_samples: 12.5, ips: 7.02777 samples/s, eta: 6:51:42
[2024/07/27 23:53:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:53:24] ppocr INFO: epoch: [191/1500], global_step: 573, lr: 0.001000, loss: 1.971182, loss_shrink_maps: 1.082446, loss_threshold_maps: 0.682497, loss_binary_maps: 0.214142, avg_reader_cost: 1.49876 s, avg_batch_cost: 1.73178 s, avg_samples: 12.5, ips: 7.21799 samples/s, eta: 6:51:13
[2024/07/27 23:53:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:53:31] ppocr INFO: epoch: [192/1500], global_step: 576, lr: 0.001000, loss: 1.985849, loss_shrink_maps: 1.086241, loss_threshold_maps: 0.682497, loss_binary_maps: 0.214142, avg_reader_cost: 1.55560 s, avg_batch_cost: 1.78313 s, avg_samples: 12.5, ips: 7.01015 samples/s, eta: 6:50:47
[2024/07/27 23:53:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:53:38] ppocr INFO: epoch: [193/1500], global_step: 579, lr: 0.001000, loss: 1.968158, loss_shrink_maps: 1.069174, loss_threshold_maps: 0.682392, loss_binary_maps: 0.211002, avg_reader_cost: 1.57030 s, avg_batch_cost: 1.81834 s, avg_samples: 12.5, ips: 6.87439 samples/s, eta: 6:50:24
[2024/07/27 23:53:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:53:44] ppocr INFO: epoch: [194/1500], global_step: 580, lr: 0.001000, loss: 1.968158, loss_shrink_maps: 1.069174, loss_threshold_maps: 0.682392, loss_binary_maps: 0.211002, avg_reader_cost: 0.41319 s, avg_batch_cost: 0.49621 s, avg_samples: 4.8, ips: 9.67339 samples/s, eta: 6:50:09
[2024/07/27 23:53:46] ppocr INFO: epoch: [194/1500], global_step: 582, lr: 0.001000, loss: 1.982334, loss_shrink_maps: 1.075195, loss_threshold_maps: 0.688344, loss_binary_maps: 0.211988, avg_reader_cost: 1.08340 s, avg_batch_cost: 1.22909 s, avg_samples: 7.7, ips: 6.26480 samples/s, eta: 6:49:54
[2024/07/27 23:53:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:53:54] ppocr INFO: epoch: [195/1500], global_step: 585, lr: 0.001000, loss: 1.987521, loss_shrink_maps: 1.092089, loss_threshold_maps: 0.688373, loss_binary_maps: 0.215835, avg_reader_cost: 1.72788 s, avg_batch_cost: 1.95693 s, avg_samples: 12.5, ips: 6.38755 samples/s, eta: 6:49:40
[2024/07/27 23:53:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:54:02] ppocr INFO: epoch: [196/1500], global_step: 588, lr: 0.001000, loss: 1.978816, loss_shrink_maps: 1.080674, loss_threshold_maps: 0.688373, loss_binary_maps: 0.214374, avg_reader_cost: 1.68687 s, avg_batch_cost: 2.02857 s, avg_samples: 12.5, ips: 6.16197 samples/s, eta: 6:49:31
[2024/07/27 23:54:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:54:09] ppocr INFO: epoch: [197/1500], global_step: 590, lr: 0.001000, loss: 1.978151, loss_shrink_maps: 1.075195, loss_threshold_maps: 0.688373, loss_binary_maps: 0.211988, avg_reader_cost: 0.90202 s, avg_batch_cost: 1.12069 s, avg_samples: 9.6, ips: 8.56614 samples/s, eta: 6:49:10
[2024/07/27 23:54:10] ppocr INFO: epoch: [197/1500], global_step: 591, lr: 0.001000, loss: 1.972226, loss_shrink_maps: 1.071824, loss_threshold_maps: 0.678840, loss_binary_maps: 0.210759, avg_reader_cost: 0.60613 s, avg_batch_cost: 0.66082 s, avg_samples: 2.9, ips: 4.38847 samples/s, eta: 6:49:06
[2024/07/27 23:54:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:54:17] ppocr INFO: epoch: [198/1500], global_step: 594, lr: 0.001000, loss: 1.967867, loss_shrink_maps: 1.074194, loss_threshold_maps: 0.675060, loss_binary_maps: 0.211745, avg_reader_cost: 1.56680 s, avg_batch_cost: 1.81887 s, avg_samples: 12.5, ips: 6.87240 samples/s, eta: 6:48:42
[2024/07/27 23:54:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:54:25] ppocr INFO: epoch: [199/1500], global_step: 597, lr: 0.001000, loss: 1.967867, loss_shrink_maps: 1.076419, loss_threshold_maps: 0.668593, loss_binary_maps: 0.212763, avg_reader_cost: 1.54389 s, avg_batch_cost: 1.77216 s, avg_samples: 12.5, ips: 7.05355 samples/s, eta: 6:48:16
[2024/07/27 23:54:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:54:32] ppocr INFO: epoch: [200/1500], global_step: 600, lr: 0.001000, loss: 1.978151, loss_shrink_maps: 1.079647, loss_threshold_maps: 0.688150, loss_binary_maps: 0.214523, avg_reader_cost: 1.50021 s, avg_batch_cost: 1.72975 s, avg_samples: 12.5, ips: 7.22648 samples/s, eta: 6:47:48

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:38, 13.07it/s]
eval model::   1%|          | 5/500 [00:00<00:24, 20.18it/s]
eval model::   2%|▏         | 8/500 [00:00<00:26, 18.66it/s]
eval model::   2%|▏         | 11/500 [00:00<00:24, 19.68it/s]
eval model::   3%|▎         | 14/500 [00:00<00:24, 19.62it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.53it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 19.03it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 20.82it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 18.97it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.11it/s]
eval model::   6%|▋         | 32/500 [00:01<00:25, 18.47it/s]
eval model::   7%|▋         | 34/500 [00:01<00:26, 17.77it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 19.98it/s]
eval model::   8%|▊         | 40/500 [00:02<00:27, 16.80it/s]
eval model::   8%|▊         | 42/500 [00:02<00:28, 16.20it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.56it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 17.93it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 18.75it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 20.58it/s]
eval model::  11%|█         | 56/500 [00:02<00:21, 20.57it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:20, 21.61it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:22, 19.05it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:23, 18.22it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:21, 20.37it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 22.35it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 22.21it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:18, 22.73it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.77it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.23it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.50it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 19.83it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.23it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.21it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.22it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.19it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.69it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.59it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.54it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 21.40it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:18, 20.71it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:17, 21.84it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:17, 21.70it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 20.85it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:17, 21.13it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:18, 19.51it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:17, 20.56it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:16, 22.18it/s]
eval model::  28%|██▊       | 139/500 [00:06<00:17, 21.19it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.49it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.40it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.98it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 20.69it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 20.29it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:14, 23.74it/s]
eval model::  32%|███▏      | 160/500 [00:07<00:17, 19.71it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:16, 20.54it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:15, 21.17it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 20.64it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 18.90it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:21, 15.24it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:18, 17.06it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:19, 16.29it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:17, 18.23it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.22it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.66it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.46it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:14, 21.58it/s]
eval model::  40%|███▉      | 198/500 [00:09<00:14, 21.49it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.09it/s]
eval model::  41%|████      | 205/500 [00:10<00:20, 14.62it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:18, 15.52it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:18, 15.45it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:16, 16.92it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.72it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.88it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:18, 15.04it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:19, 14.67it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:15, 17.81it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:15, 17.80it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:13, 20.66it/s]
eval model::  46%|████▋     | 232/500 [00:11<00:11, 22.82it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 21.36it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 22.09it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 22.63it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:13, 18.87it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:12, 20.13it/s]
eval model::  50%|█████     | 250/500 [00:12<00:12, 19.96it/s]
eval model::  51%|█████     | 253/500 [00:12<00:11, 21.89it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 21.85it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 22.74it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:09, 24.23it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:10, 22.32it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:10, 21.70it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:10, 21.79it/s]
eval model::  55%|█████▍    | 274/500 [00:13<00:10, 20.79it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 19.61it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:10, 20.63it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:10, 20.84it/s]
eval model::  57%|█████▋    | 286/500 [00:14<00:17, 12.48it/s]
eval model::  58%|█████▊    | 289/500 [00:14<00:14, 14.61it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 16.65it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:13, 14.72it/s]
eval model::  60%|█████▉    | 298/500 [00:15<00:12, 16.70it/s]
eval model::  60%|██████    | 300/500 [00:15<00:12, 16.44it/s]
eval model::  61%|██████    | 303/500 [00:15<00:10, 18.29it/s]
eval model::  61%|██████    | 306/500 [00:15<00:10, 18.29it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 19.35it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 20.26it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 19.97it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 21.98it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 22.36it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 20.96it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:07, 22.61it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:07, 22.20it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:08, 20.12it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:08, 20.41it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:09, 16.85it/s]
eval model::  68%|██████▊   | 341/500 [00:17<00:11, 13.41it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:10, 14.56it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:08, 17.42it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 19.36it/s]
eval model::  70%|███████   | 352/500 [00:18<00:07, 21.12it/s]
eval model::  71%|███████   | 356/500 [00:18<00:06, 22.03it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:06, 21.78it/s]
eval model::  72%|███████▏  | 362/500 [00:18<00:06, 20.03it/s]
eval model::  73%|███████▎  | 365/500 [00:18<00:07, 18.92it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 18.52it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:07, 18.56it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:07, 17.43it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 18.56it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 20.93it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:05, 21.47it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:04, 24.10it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:07, 15.89it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:06, 17.97it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:05, 18.28it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 20.05it/s]
eval model::  80%|████████  | 400/500 [00:20<00:04, 20.84it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 21.53it/s]
eval model::  81%|████████  | 406/500 [00:20<00:04, 21.96it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:03, 23.22it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:03, 23.24it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:03, 21.57it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:03, 20.27it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 17.71it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 16.57it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.99it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 19.42it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 20.65it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 23.85it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 23.62it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 22.44it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 21.64it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 20.87it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 20.78it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 20.34it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 21.56it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 21.33it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 19.82it/s]
eval model::  93%|█████████▎| 466/500 [00:23<00:01, 18.01it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 19.60it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 18.99it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 18.51it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:00, 22.45it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 23.03it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 22.87it/s]
eval model::  97%|█████████▋| 487/500 [00:24<00:00, 21.07it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 22.15it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 21.37it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 21.23it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 24.27it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.66it/s]
[2024/07/27 23:54:58] ppocr INFO: cur metric, precision: 0.6244131455399061, recall: 0.5122773230621088, hmean: 0.5628140703517588, fps: 45.7029364917752
[2024/07/27 23:54:58] ppocr INFO: best metric, hmean: 0.5631016042780749, precision: 0.6331930246542393, recall: 0.5069812229176697, fps: 44.85164591471328, best_epoch: 160
[2024/07/27 23:54:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:55:05] ppocr INFO: epoch: [201/1500], global_step: 603, lr: 0.001000, loss: 1.958547, loss_shrink_maps: 1.065553, loss_threshold_maps: 0.678438, loss_binary_maps: 0.210176, avg_reader_cost: 1.75875 s, avg_batch_cost: 2.00448 s, avg_samples: 12.5, ips: 6.23602 samples/s, eta: 6:47:37
[2024/07/27 23:55:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:55:13] ppocr INFO: epoch: [202/1500], global_step: 606, lr: 0.001000, loss: 1.952297, loss_shrink_maps: 1.058300, loss_threshold_maps: 0.679608, loss_binary_maps: 0.209011, avg_reader_cost: 1.51738 s, avg_batch_cost: 1.76644 s, avg_samples: 12.5, ips: 7.07638 samples/s, eta: 6:47:10
[2024/07/27 23:55:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:55:20] ppocr INFO: epoch: [203/1500], global_step: 609, lr: 0.001000, loss: 1.931891, loss_shrink_maps: 1.046044, loss_threshold_maps: 0.678541, loss_binary_maps: 0.207306, avg_reader_cost: 1.50349 s, avg_batch_cost: 1.73955 s, avg_samples: 12.5, ips: 7.18577 samples/s, eta: 6:46:42
[2024/07/27 23:55:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:55:27] ppocr INFO: epoch: [204/1500], global_step: 610, lr: 0.001000, loss: 1.931891, loss_shrink_maps: 1.046044, loss_threshold_maps: 0.678541, loss_binary_maps: 0.207306, avg_reader_cost: 0.41091 s, avg_batch_cost: 0.54298 s, avg_samples: 4.8, ips: 8.84010 samples/s, eta: 6:46:31
[2024/07/27 23:55:28] ppocr INFO: epoch: [204/1500], global_step: 612, lr: 0.001000, loss: 1.931891, loss_shrink_maps: 1.046044, loss_threshold_maps: 0.678541, loss_binary_maps: 0.207670, avg_reader_cost: 1.17733 s, avg_batch_cost: 1.32340 s, avg_samples: 7.7, ips: 5.81833 samples/s, eta: 6:46:23
[2024/07/27 23:55:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:55:36] ppocr INFO: epoch: [205/1500], global_step: 615, lr: 0.001000, loss: 1.945765, loss_shrink_maps: 1.051362, loss_threshold_maps: 0.681711, loss_binary_maps: 0.208908, avg_reader_cost: 1.53896 s, avg_batch_cost: 1.79860 s, avg_samples: 12.5, ips: 6.94984 samples/s, eta: 6:45:59
[2024/07/27 23:55:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:55:43] ppocr INFO: epoch: [206/1500], global_step: 618, lr: 0.001000, loss: 1.931891, loss_shrink_maps: 1.046044, loss_threshold_maps: 0.678541, loss_binary_maps: 0.207670, avg_reader_cost: 1.53145 s, avg_batch_cost: 1.77742 s, avg_samples: 12.5, ips: 7.03266 samples/s, eta: 6:45:33
[2024/07/27 23:55:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:55:50] ppocr INFO: epoch: [207/1500], global_step: 620, lr: 0.001000, loss: 1.931891, loss_shrink_maps: 1.046044, loss_threshold_maps: 0.678541, loss_binary_maps: 0.207670, avg_reader_cost: 0.92690 s, avg_batch_cost: 1.09992 s, avg_samples: 9.6, ips: 8.72790 samples/s, eta: 6:45:11
[2024/07/27 23:55:51] ppocr INFO: epoch: [207/1500], global_step: 621, lr: 0.001000, loss: 1.945765, loss_shrink_maps: 1.052403, loss_threshold_maps: 0.679608, loss_binary_maps: 0.208908, avg_reader_cost: 0.59552 s, avg_batch_cost: 0.65024 s, avg_samples: 2.9, ips: 4.45987 samples/s, eta: 6:45:06
[2024/07/27 23:55:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:55:59] ppocr INFO: epoch: [208/1500], global_step: 624, lr: 0.001000, loss: 1.945765, loss_shrink_maps: 1.052403, loss_threshold_maps: 0.678541, loss_binary_maps: 0.208908, avg_reader_cost: 1.69747 s, avg_batch_cost: 1.95922 s, avg_samples: 12.5, ips: 6.38010 samples/s, eta: 6:44:52
[2024/07/27 23:56:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:56:06] ppocr INFO: epoch: [209/1500], global_step: 627, lr: 0.001000, loss: 2.005749, loss_shrink_maps: 1.102871, loss_threshold_maps: 0.684232, loss_binary_maps: 0.218686, avg_reader_cost: 1.51293 s, avg_batch_cost: 1.74306 s, avg_samples: 12.5, ips: 7.17129 samples/s, eta: 6:44:25
[2024/07/27 23:56:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:56:14] ppocr INFO: epoch: [210/1500], global_step: 630, lr: 0.001000, loss: 2.005749, loss_shrink_maps: 1.102871, loss_threshold_maps: 0.684232, loss_binary_maps: 0.218686, avg_reader_cost: 1.52463 s, avg_batch_cost: 1.75434 s, avg_samples: 12.5, ips: 7.12518 samples/s, eta: 6:43:59
[2024/07/27 23:56:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:56:21] ppocr INFO: epoch: [211/1500], global_step: 633, lr: 0.001000, loss: 2.025602, loss_shrink_maps: 1.111232, loss_threshold_maps: 0.686868, loss_binary_maps: 0.219671, avg_reader_cost: 1.55638 s, avg_batch_cost: 1.80198 s, avg_samples: 12.5, ips: 6.93680 samples/s, eta: 6:43:35
[2024/07/27 23:56:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:56:29] ppocr INFO: epoch: [212/1500], global_step: 636, lr: 0.001000, loss: 2.012444, loss_shrink_maps: 1.102871, loss_threshold_maps: 0.686868, loss_binary_maps: 0.218686, avg_reader_cost: 1.62918 s, avg_batch_cost: 1.87257 s, avg_samples: 12.5, ips: 6.67531 samples/s, eta: 6:43:16
[2024/07/27 23:56:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:56:37] ppocr INFO: epoch: [213/1500], global_step: 639, lr: 0.001000, loss: 1.981729, loss_shrink_maps: 1.076080, loss_threshold_maps: 0.676039, loss_binary_maps: 0.214030, avg_reader_cost: 1.56984 s, avg_batch_cost: 1.79760 s, avg_samples: 12.5, ips: 6.95371 samples/s, eta: 6:42:52
[2024/07/27 23:56:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:56:43] ppocr INFO: epoch: [214/1500], global_step: 640, lr: 0.001000, loss: 1.971024, loss_shrink_maps: 1.064190, loss_threshold_maps: 0.672115, loss_binary_maps: 0.211495, avg_reader_cost: 0.42102 s, avg_batch_cost: 0.50380 s, avg_samples: 4.8, ips: 9.52758 samples/s, eta: 6:42:39
[2024/07/27 23:56:44] ppocr INFO: epoch: [214/1500], global_step: 642, lr: 0.001000, loss: 1.971024, loss_shrink_maps: 1.064190, loss_threshold_maps: 0.669929, loss_binary_maps: 0.211495, avg_reader_cost: 1.10019 s, avg_batch_cost: 1.24612 s, avg_samples: 7.7, ips: 6.17919 samples/s, eta: 6:42:26
[2024/07/27 23:56:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:56:52] ppocr INFO: epoch: [215/1500], global_step: 645, lr: 0.001000, loss: 1.971024, loss_shrink_maps: 1.074164, loss_threshold_maps: 0.665282, loss_binary_maps: 0.213369, avg_reader_cost: 1.58522 s, avg_batch_cost: 1.81285 s, avg_samples: 12.5, ips: 6.89522 samples/s, eta: 6:42:03
[2024/07/27 23:56:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:57:00] ppocr INFO: epoch: [216/1500], global_step: 648, lr: 0.001000, loss: 1.936592, loss_shrink_maps: 1.044825, loss_threshold_maps: 0.663157, loss_binary_maps: 0.206869, avg_reader_cost: 1.54929 s, avg_batch_cost: 1.83751 s, avg_samples: 12.5, ips: 6.80270 samples/s, eta: 6:41:42
[2024/07/27 23:57:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:57:07] ppocr INFO: epoch: [217/1500], global_step: 650, lr: 0.001000, loss: 1.908999, loss_shrink_maps: 1.041266, loss_threshold_maps: 0.663157, loss_binary_maps: 0.206073, avg_reader_cost: 0.97098 s, avg_batch_cost: 1.18351 s, avg_samples: 9.6, ips: 8.11144 samples/s, eta: 6:41:26
[2024/07/27 23:57:07] ppocr INFO: epoch: [217/1500], global_step: 651, lr: 0.001000, loss: 1.908999, loss_shrink_maps: 1.035606, loss_threshold_maps: 0.663157, loss_binary_maps: 0.204677, avg_reader_cost: 0.63783 s, avg_batch_cost: 0.69207 s, avg_samples: 2.9, ips: 4.19031 samples/s, eta: 6:41:23
[2024/07/27 23:57:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:57:15] ppocr INFO: epoch: [218/1500], global_step: 654, lr: 0.001000, loss: 1.928618, loss_shrink_maps: 1.044825, loss_threshold_maps: 0.663157, loss_binary_maps: 0.206869, avg_reader_cost: 1.53121 s, avg_batch_cost: 1.75981 s, avg_samples: 12.5, ips: 7.10303 samples/s, eta: 6:40:58
[2024/07/27 23:57:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:57:23] ppocr INFO: epoch: [219/1500], global_step: 657, lr: 0.001000, loss: 1.914478, loss_shrink_maps: 1.044825, loss_threshold_maps: 0.663157, loss_binary_maps: 0.206869, avg_reader_cost: 1.56295 s, avg_batch_cost: 1.86778 s, avg_samples: 12.5, ips: 6.69244 samples/s, eta: 6:40:38
[2024/07/27 23:57:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:57:30] ppocr INFO: epoch: [220/1500], global_step: 660, lr: 0.001000, loss: 1.965129, loss_shrink_maps: 1.083242, loss_threshold_maps: 0.675103, loss_binary_maps: 0.214573, avg_reader_cost: 1.53234 s, avg_batch_cost: 1.79772 s, avg_samples: 12.5, ips: 6.95325 samples/s, eta: 6:40:15

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:52,  9.47it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.80it/s]
eval model::   1%|▏         | 7/500 [00:00<00:24, 20.47it/s]
eval model::   2%|▏         | 10/500 [00:00<00:25, 19.38it/s]
eval model::   2%|▏         | 12/500 [00:00<00:28, 17.34it/s]
eval model::   3%|▎         | 15/500 [00:00<00:26, 18.08it/s]
eval model::   4%|▍         | 19/500 [00:00<00:24, 20.00it/s]
eval model::   4%|▍         | 22/500 [00:01<00:22, 20.98it/s]
eval model::   5%|▌         | 25/500 [00:01<00:21, 21.71it/s]
eval model::   6%|▌         | 28/500 [00:01<00:20, 22.67it/s]
eval model::   6%|▌         | 31/500 [00:01<00:23, 19.84it/s]
eval model::   7%|▋         | 34/500 [00:01<00:23, 19.86it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 21.07it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.86it/s]
eval model::   8%|▊         | 42/500 [00:02<00:24, 18.95it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.37it/s]
eval model::  10%|▉         | 48/500 [00:02<00:25, 17.82it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.22it/s]
eval model::  11%|█         | 53/500 [00:02<00:23, 19.15it/s]
eval model::  11%|█         | 56/500 [00:02<00:21, 20.81it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:21, 20.53it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:24, 18.19it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:26, 16.32it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:23, 18.54it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 20.68it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 21.27it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:18, 22.81it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:18, 23.09it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.05it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.08it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:19, 20.64it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.22it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.35it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:20, 19.47it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:19, 21.03it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.66it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 22.02it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:16, 23.15it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:16, 23.25it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:16, 23.98it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:17, 21.97it/s]
eval model::  24%|██▍       | 120/500 [00:05<00:19, 19.58it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:17, 21.03it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:17, 21.67it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:17, 21.01it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:16, 22.11it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:16, 22.68it/s]
eval model::  28%|██▊       | 139/500 [00:06<00:16, 21.29it/s]
eval model::  28%|██▊       | 142/500 [00:06<00:16, 21.27it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.62it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 18.35it/s]
eval model::  30%|███       | 151/500 [00:07<00:18, 18.46it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 21.48it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:15, 21.46it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:19, 17.00it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.35it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 19.03it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:18, 17.85it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:20, 16.40it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:23, 13.78it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:19, 16.20it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:21, 15.13it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:18, 17.43it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.40it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 19.51it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:15, 20.28it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:14, 20.62it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.34it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.85it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.07it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:20, 14.45it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:19, 15.13it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:18, 15.80it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:17, 16.24it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.52it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.24it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 16.66it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:17, 15.90it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:14, 18.99it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:14, 18.95it/s]
eval model::  46%|████▌     | 231/500 [00:11<00:13, 20.13it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:13, 19.54it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 21.03it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:13, 19.64it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:13, 18.64it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:12, 20.59it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:12, 19.57it/s]
eval model::  50%|█████     | 252/500 [00:13<00:13, 18.32it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 20.66it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:11, 21.31it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 20.41it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:11, 20.19it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:11, 20.78it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:10, 21.18it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:11, 19.55it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:13, 17.11it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 19.38it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 21.02it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:17, 12.26it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.77it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:11, 17.52it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:12, 16.06it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:10, 18.95it/s]
eval model::  60%|██████    | 302/500 [00:15<00:10, 19.07it/s]
eval model::  61%|██████    | 305/500 [00:15<00:09, 20.29it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:08, 22.31it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:08, 23.05it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:08, 22.53it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:08, 21.73it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:08, 20.36it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:09, 19.16it/s]
eval model::  65%|██████▌   | 326/500 [00:16<00:08, 19.86it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 19.83it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:08, 19.41it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 20.98it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:08, 19.34it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:10, 15.21it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:08, 18.04it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 20.54it/s]
eval model::  70%|███████   | 350/500 [00:18<00:06, 22.80it/s]
eval model::  71%|███████   | 354/500 [00:18<00:05, 24.65it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:05, 24.15it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:06, 22.09it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:06, 21.35it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:07, 18.34it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 19.62it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 19.30it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 20.01it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 21.07it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:06, 19.69it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:05, 21.74it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 14.78it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 16.95it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 17.93it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 18.70it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 20.22it/s]
eval model::  80%|████████  | 402/500 [00:20<00:05, 19.46it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 21.30it/s]
eval model::  82%|████████▏ | 408/500 [00:20<00:04, 22.74it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 24.06it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 24.72it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 21.38it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 17.81it/s]
eval model::  85%|████████▍ | 423/500 [00:21<00:03, 19.30it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:04, 18.49it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 21.69it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 21.54it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 22.28it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 22.91it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 22.10it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 22.68it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 20.30it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 20.52it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 21.45it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:02, 20.09it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 21.15it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 19.90it/s]
eval model::  93%|█████████▎| 466/500 [00:23<00:01, 19.02it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 18.38it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.92it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.46it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 21.46it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 22.19it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 22.76it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 20.57it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 21.90it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 21.13it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 21.37it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 22.45it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.71it/s]
[2024/07/27 23:57:56] ppocr INFO: cur metric, precision: 0.5990648743424898, recall: 0.49350024073182475, hmean: 0.5411826821541711, fps: 46.257031849573245
[2024/07/27 23:57:56] ppocr INFO: best metric, hmean: 0.5631016042780749, precision: 0.6331930246542393, recall: 0.5069812229176697, fps: 44.85164591471328, best_epoch: 160
[2024/07/27 23:57:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:58:03] ppocr INFO: epoch: [221/1500], global_step: 663, lr: 0.001000, loss: 1.965129, loss_shrink_maps: 1.083242, loss_threshold_maps: 0.678481, loss_binary_maps: 0.214573, avg_reader_cost: 1.64394 s, avg_batch_cost: 1.92655 s, avg_samples: 12.5, ips: 6.48829 samples/s, eta: 6:39:59
[2024/07/27 23:58:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:58:11] ppocr INFO: epoch: [222/1500], global_step: 666, lr: 0.001000, loss: 1.955548, loss_shrink_maps: 1.065690, loss_threshold_maps: 0.678481, loss_binary_maps: 0.211132, avg_reader_cost: 1.53624 s, avg_batch_cost: 1.76447 s, avg_samples: 12.5, ips: 7.08428 samples/s, eta: 6:39:34
[2024/07/27 23:58:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:58:18] ppocr INFO: epoch: [223/1500], global_step: 669, lr: 0.001000, loss: 1.928618, loss_shrink_maps: 1.053649, loss_threshold_maps: 0.678481, loss_binary_maps: 0.208929, avg_reader_cost: 1.51394 s, avg_batch_cost: 1.74361 s, avg_samples: 12.5, ips: 7.16903 samples/s, eta: 6:39:08
[2024/07/27 23:58:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:58:24] ppocr INFO: epoch: [224/1500], global_step: 670, lr: 0.001000, loss: 1.955548, loss_shrink_maps: 1.060485, loss_threshold_maps: 0.683850, loss_binary_maps: 0.209912, avg_reader_cost: 0.41512 s, avg_batch_cost: 0.50434 s, avg_samples: 4.8, ips: 9.51744 samples/s, eta: 6:38:54
[2024/07/27 23:58:26] ppocr INFO: epoch: [224/1500], global_step: 672, lr: 0.001000, loss: 1.943865, loss_shrink_maps: 1.060485, loss_threshold_maps: 0.663960, loss_binary_maps: 0.209912, avg_reader_cost: 1.09963 s, avg_batch_cost: 1.24533 s, avg_samples: 7.7, ips: 6.18310 samples/s, eta: 6:38:42
[2024/07/27 23:58:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:58:33] ppocr INFO: epoch: [225/1500], global_step: 675, lr: 0.001000, loss: 1.937782, loss_shrink_maps: 1.060485, loss_threshold_maps: 0.659131, loss_binary_maps: 0.209912, avg_reader_cost: 1.51363 s, avg_batch_cost: 1.74843 s, avg_samples: 12.5, ips: 7.14927 samples/s, eta: 6:38:16
[2024/07/27 23:58:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:58:41] ppocr INFO: epoch: [226/1500], global_step: 678, lr: 0.001000, loss: 1.937782, loss_shrink_maps: 1.060485, loss_threshold_maps: 0.659131, loss_binary_maps: 0.209912, avg_reader_cost: 1.52364 s, avg_batch_cost: 1.77462 s, avg_samples: 12.5, ips: 7.04377 samples/s, eta: 6:37:51
[2024/07/27 23:58:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:58:48] ppocr INFO: epoch: [227/1500], global_step: 680, lr: 0.001000, loss: 1.937782, loss_shrink_maps: 1.050684, loss_threshold_maps: 0.659131, loss_binary_maps: 0.208214, avg_reader_cost: 0.92222 s, avg_batch_cost: 1.09661 s, avg_samples: 9.6, ips: 8.75427 samples/s, eta: 6:37:30
[2024/07/27 23:58:48] ppocr INFO: epoch: [227/1500], global_step: 681, lr: 0.001000, loss: 1.937782, loss_shrink_maps: 1.050684, loss_threshold_maps: 0.652108, loss_binary_maps: 0.208214, avg_reader_cost: 0.59437 s, avg_batch_cost: 0.64928 s, avg_samples: 2.9, ips: 4.46652 samples/s, eta: 6:37:25
[2024/07/27 23:58:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:58:56] ppocr INFO: epoch: [228/1500], global_step: 684, lr: 0.001000, loss: 1.847172, loss_shrink_maps: 1.010317, loss_threshold_maps: 0.650323, loss_binary_maps: 0.200288, avg_reader_cost: 1.51587 s, avg_batch_cost: 1.76991 s, avg_samples: 12.5, ips: 7.06252 samples/s, eta: 6:37:01
[2024/07/27 23:58:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:59:04] ppocr INFO: epoch: [229/1500], global_step: 687, lr: 0.001000, loss: 1.915916, loss_shrink_maps: 1.049010, loss_threshold_maps: 0.652108, loss_binary_maps: 0.207847, avg_reader_cost: 1.57138 s, avg_batch_cost: 1.80000 s, avg_samples: 12.5, ips: 6.94445 samples/s, eta: 6:36:38
[2024/07/27 23:59:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:59:11] ppocr INFO: epoch: [230/1500], global_step: 690, lr: 0.001000, loss: 1.915916, loss_shrink_maps: 1.044336, loss_threshold_maps: 0.653428, loss_binary_maps: 0.206772, avg_reader_cost: 1.54494 s, avg_batch_cost: 1.80365 s, avg_samples: 12.5, ips: 6.93039 samples/s, eta: 6:36:16
[2024/07/27 23:59:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:59:19] ppocr INFO: epoch: [231/1500], global_step: 693, lr: 0.001000, loss: 1.876064, loss_shrink_maps: 1.013966, loss_threshold_maps: 0.665792, loss_binary_maps: 0.200484, avg_reader_cost: 1.53779 s, avg_batch_cost: 1.77274 s, avg_samples: 12.5, ips: 7.05124 samples/s, eta: 6:35:51
[2024/07/27 23:59:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:59:26] ppocr INFO: epoch: [232/1500], global_step: 696, lr: 0.001000, loss: 1.815720, loss_shrink_maps: 0.943777, loss_threshold_maps: 0.653266, loss_binary_maps: 0.186580, avg_reader_cost: 1.53779 s, avg_batch_cost: 1.77134 s, avg_samples: 12.5, ips: 7.05681 samples/s, eta: 6:35:27
[2024/07/27 23:59:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:59:34] ppocr INFO: epoch: [233/1500], global_step: 699, lr: 0.001000, loss: 1.853877, loss_shrink_maps: 0.998295, loss_threshold_maps: 0.661105, loss_binary_maps: 0.197383, avg_reader_cost: 1.49522 s, avg_batch_cost: 1.73051 s, avg_samples: 12.5, ips: 7.22332 samples/s, eta: 6:35:01
[2024/07/27 23:59:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:59:40] ppocr INFO: epoch: [234/1500], global_step: 700, lr: 0.001000, loss: 1.835105, loss_shrink_maps: 0.977694, loss_threshold_maps: 0.657238, loss_binary_maps: 0.193419, avg_reader_cost: 0.40982 s, avg_batch_cost: 0.50681 s, avg_samples: 4.8, ips: 9.47105 samples/s, eta: 6:34:48
[2024/07/27 23:59:41] ppocr INFO: epoch: [234/1500], global_step: 702, lr: 0.001000, loss: 1.853877, loss_shrink_maps: 0.998295, loss_threshold_maps: 0.661105, loss_binary_maps: 0.197383, avg_reader_cost: 1.10464 s, avg_batch_cost: 1.25039 s, avg_samples: 7.7, ips: 6.15810 samples/s, eta: 6:34:36
[2024/07/27 23:59:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:59:49] ppocr INFO: epoch: [235/1500], global_step: 705, lr: 0.001000, loss: 1.840976, loss_shrink_maps: 0.986208, loss_threshold_maps: 0.659810, loss_binary_maps: 0.195000, avg_reader_cost: 1.58766 s, avg_batch_cost: 1.81685 s, avg_samples: 12.5, ips: 6.88005 samples/s, eta: 6:34:14
[2024/07/27 23:59:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/27 23:59:56] ppocr INFO: epoch: [236/1500], global_step: 708, lr: 0.001000, loss: 1.813416, loss_shrink_maps: 0.962930, loss_threshold_maps: 0.657158, loss_binary_maps: 0.190320, avg_reader_cost: 1.52386 s, avg_batch_cost: 1.76193 s, avg_samples: 12.5, ips: 7.09450 samples/s, eta: 6:33:50
[2024/07/27 23:59:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:00:04] ppocr INFO: epoch: [237/1500], global_step: 710, lr: 0.001000, loss: 1.797323, loss_shrink_maps: 0.957191, loss_threshold_maps: 0.644491, loss_binary_maps: 0.189817, avg_reader_cost: 1.07206 s, avg_batch_cost: 1.24575 s, avg_samples: 9.6, ips: 7.70617 samples/s, eta: 6:33:38
[2024/07/28 00:00:05] ppocr INFO: epoch: [237/1500], global_step: 711, lr: 0.001000, loss: 1.803004, loss_shrink_maps: 0.959592, loss_threshold_maps: 0.652772, loss_binary_maps: 0.190269, avg_reader_cost: 0.66907 s, avg_batch_cost: 0.72351 s, avg_samples: 2.9, ips: 4.00821 samples/s, eta: 6:33:37
[2024/07/28 00:00:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:00:12] ppocr INFO: epoch: [238/1500], global_step: 714, lr: 0.001000, loss: 1.797323, loss_shrink_maps: 0.959592, loss_threshold_maps: 0.644491, loss_binary_maps: 0.190269, avg_reader_cost: 1.51875 s, avg_batch_cost: 1.74721 s, avg_samples: 12.5, ips: 7.15427 samples/s, eta: 6:33:11
[2024/07/28 00:00:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:00:20] ppocr INFO: epoch: [239/1500], global_step: 717, lr: 0.001000, loss: 1.798698, loss_shrink_maps: 0.959592, loss_threshold_maps: 0.652772, loss_binary_maps: 0.190269, avg_reader_cost: 1.56034 s, avg_batch_cost: 1.78880 s, avg_samples: 12.5, ips: 6.98794 samples/s, eta: 6:32:48
[2024/07/28 00:00:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:00:28] ppocr INFO: epoch: [240/1500], global_step: 720, lr: 0.001000, loss: 1.784104, loss_shrink_maps: 0.951852, loss_threshold_maps: 0.644102, loss_binary_maps: 0.188988, avg_reader_cost: 1.50066 s, avg_batch_cost: 1.75778 s, avg_samples: 12.5, ips: 7.11124 samples/s, eta: 6:32:24

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:38, 12.93it/s]
eval model::   1%|          | 5/500 [00:00<00:26, 18.71it/s]
eval model::   1%|▏         | 7/500 [00:00<00:25, 19.14it/s]
eval model::   2%|▏         | 9/500 [00:00<00:25, 18.95it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.47it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 19.22it/s]
eval model::   4%|▎         | 18/500 [00:00<00:21, 21.98it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 18.76it/s]
eval model::   5%|▌         | 25/500 [00:01<00:21, 21.90it/s]
eval model::   6%|▌         | 28/500 [00:01<00:21, 21.64it/s]
eval model::   6%|▌         | 31/500 [00:01<00:23, 19.71it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.30it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 19.44it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.24it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 18.03it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.22it/s]
eval model::  10%|▉         | 48/500 [00:02<00:21, 20.62it/s]
eval model::  10%|█         | 51/500 [00:02<00:21, 20.60it/s]
eval model::  11%|█         | 54/500 [00:02<00:22, 19.84it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:22, 20.08it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:22, 19.61it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:26, 16.62it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:26, 16.44it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:23, 18.80it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:22, 18.90it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 20.64it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 21.23it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 20.47it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 21.94it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 20.24it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:23, 17.41it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:23, 17.70it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.01it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:21, 19.02it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:21, 18.90it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:21, 18.65it/s]
eval model::  20%|██        | 101/500 [00:05<00:21, 18.37it/s]
eval model::  21%|██        | 104/500 [00:05<00:20, 19.02it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.26it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 21.30it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:17, 22.26it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:16, 23.36it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:16, 22.83it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.09it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:19, 19.63it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:19, 18.89it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:20, 17.74it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.88it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 20.11it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:16, 21.39it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:16, 21.51it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.26it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.28it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.08it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 18.47it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 20.76it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 20.47it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:18, 18.14it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:18, 18.15it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 18.92it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:15, 20.93it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:17, 18.66it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:21, 15.11it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:20, 16.01it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:21, 15.08it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:19, 16.35it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.65it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:16, 18.54it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:15, 19.73it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 21.11it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.65it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.80it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 21.58it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:20, 14.55it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:19, 15.17it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 15.17it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.61it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.46it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.71it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:19, 14.12it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:21, 13.14it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:17, 15.81it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.17it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:14, 18.90it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 21.16it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 20.73it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 21.91it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:12, 20.92it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:13, 19.01it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:12, 20.58it/s]
eval model::  50%|█████     | 250/500 [00:13<00:12, 19.61it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 21.12it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 21.97it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 23.42it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:10, 23.50it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:12, 19.17it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:11, 19.82it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:11, 20.72it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:11, 19.91it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 19.14it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 19.11it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 21.24it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:14, 15.09it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:13, 15.82it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:10, 19.72it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:11, 18.10it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:11, 17.07it/s]
eval model::  60%|██████    | 300/500 [00:15<00:11, 16.91it/s]
eval model::  61%|██████    | 303/500 [00:16<00:10, 18.44it/s]
eval model::  61%|██████    | 306/500 [00:16<00:09, 19.52it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 19.87it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 20.55it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 20.58it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 20.71it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 20.45it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 20.68it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 22.24it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 20.65it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 21.80it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 22.76it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:08, 18.37it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 15.13it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:09, 16.80it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 17.34it/s]
eval model::  70%|███████   | 350/500 [00:18<00:08, 18.24it/s]
eval model::  71%|███████   | 353/500 [00:18<00:07, 19.64it/s]
eval model::  71%|███████   | 356/500 [00:18<00:07, 19.03it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:06, 20.24it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:06, 20.37it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:06, 20.52it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:06, 20.31it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 20.04it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 19.49it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:06, 20.47it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:05, 21.21it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:05, 23.13it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:08, 14.12it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 16.28it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 17.22it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 18.45it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:04, 20.51it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 19.37it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 21.13it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 22.34it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:03, 22.82it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:03, 22.50it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:04, 20.21it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 20.20it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 16.01it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.92it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.17it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 18.10it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 17.72it/s]
eval model::  87%|████████▋ | 434/500 [00:22<00:03, 20.07it/s]
eval model::  87%|████████▋ | 437/500 [00:22<00:02, 21.86it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:02, 20.15it/s]
eval model::  89%|████████▊ | 443/500 [00:23<00:02, 21.87it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 21.62it/s]
eval model::  90%|████████▉ | 449/500 [00:23<00:02, 19.90it/s]
eval model::  90%|█████████ | 452/500 [00:23<00:02, 21.83it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:02, 22.04it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:01, 22.17it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 21.98it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:01, 18.33it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 17.03it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 18.60it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 18.99it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 18.85it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 20.97it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 20.68it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 22.00it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 20.42it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 21.53it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 20.44it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 21.39it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 22.28it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.28it/s]
[2024/07/28 00:00:54] ppocr INFO: cur metric, precision: 0.6414565826330533, recall: 0.5512758786711603, hmean: 0.5929570170895909, fps: 44.78792511087625
[2024/07/28 00:00:54] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/28 00:00:54] ppocr INFO: best metric, hmean: 0.5929570170895909, precision: 0.6414565826330533, recall: 0.5512758786711603, fps: 44.78792511087625, best_epoch: 240
[2024/07/28 00:00:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:01:01] ppocr INFO: epoch: [241/1500], global_step: 723, lr: 0.001000, loss: 1.784104, loss_shrink_maps: 0.951852, loss_threshold_maps: 0.644102, loss_binary_maps: 0.188988, avg_reader_cost: 1.55072 s, avg_batch_cost: 1.84335 s, avg_samples: 12.5, ips: 6.78115 samples/s, eta: 6:32:04
[2024/07/28 00:01:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:01:08] ppocr INFO: epoch: [242/1500], global_step: 726, lr: 0.001000, loss: 1.772211, loss_shrink_maps: 0.948284, loss_threshold_maps: 0.641719, loss_binary_maps: 0.187887, avg_reader_cost: 1.49310 s, avg_batch_cost: 1.72724 s, avg_samples: 12.5, ips: 7.23697 samples/s, eta: 6:31:38
[2024/07/28 00:01:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:01:16] ppocr INFO: epoch: [243/1500], global_step: 729, lr: 0.001000, loss: 1.813181, loss_shrink_maps: 0.959519, loss_threshold_maps: 0.647060, loss_binary_maps: 0.190645, avg_reader_cost: 1.49833 s, avg_batch_cost: 1.73537 s, avg_samples: 12.5, ips: 7.20309 samples/s, eta: 6:31:12
[2024/07/28 00:01:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:01:22] ppocr INFO: epoch: [244/1500], global_step: 730, lr: 0.001000, loss: 1.841196, loss_shrink_maps: 0.987987, loss_threshold_maps: 0.650143, loss_binary_maps: 0.195648, avg_reader_cost: 0.43857 s, avg_batch_cost: 0.55638 s, avg_samples: 4.8, ips: 8.62726 samples/s, eta: 6:31:03
[2024/07/28 00:01:24] ppocr INFO: epoch: [244/1500], global_step: 732, lr: 0.001000, loss: 1.847384, loss_shrink_maps: 0.990791, loss_threshold_maps: 0.650143, loss_binary_maps: 0.195889, avg_reader_cost: 1.20422 s, avg_batch_cost: 1.34918 s, avg_samples: 7.7, ips: 5.70716 samples/s, eta: 6:30:56
[2024/07/28 00:01:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:01:31] ppocr INFO: epoch: [245/1500], global_step: 735, lr: 0.001000, loss: 1.868314, loss_shrink_maps: 1.013077, loss_threshold_maps: 0.655977, loss_binary_maps: 0.199961, avg_reader_cost: 1.49581 s, avg_batch_cost: 1.75048 s, avg_samples: 12.5, ips: 7.14092 samples/s, eta: 6:30:31
[2024/07/28 00:01:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:01:40] ppocr INFO: epoch: [246/1500], global_step: 738, lr: 0.001000, loss: 1.849946, loss_shrink_maps: 1.000967, loss_threshold_maps: 0.653850, loss_binary_maps: 0.197480, avg_reader_cost: 1.63330 s, avg_batch_cost: 1.95986 s, avg_samples: 12.5, ips: 6.37802 samples/s, eta: 6:30:17
[2024/07/28 00:01:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:01:47] ppocr INFO: epoch: [247/1500], global_step: 740, lr: 0.001000, loss: 1.868314, loss_shrink_maps: 1.013077, loss_threshold_maps: 0.653850, loss_binary_maps: 0.199961, avg_reader_cost: 0.95782 s, avg_batch_cost: 1.13165 s, avg_samples: 9.6, ips: 8.48318 samples/s, eta: 6:29:59
[2024/07/28 00:01:47] ppocr INFO: epoch: [247/1500], global_step: 741, lr: 0.001000, loss: 1.885513, loss_shrink_maps: 1.025466, loss_threshold_maps: 0.655977, loss_binary_maps: 0.202593, avg_reader_cost: 0.61153 s, avg_batch_cost: 0.66637 s, avg_samples: 2.9, ips: 4.35196 samples/s, eta: 6:29:55
[2024/07/28 00:01:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:01:55] ppocr INFO: epoch: [248/1500], global_step: 744, lr: 0.001000, loss: 1.868598, loss_shrink_maps: 1.018184, loss_threshold_maps: 0.652264, loss_binary_maps: 0.201208, avg_reader_cost: 1.50633 s, avg_batch_cost: 1.73442 s, avg_samples: 12.5, ips: 7.20702 samples/s, eta: 6:29:29
[2024/07/28 00:01:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:02:02] ppocr INFO: epoch: [249/1500], global_step: 747, lr: 0.001000, loss: 1.868598, loss_shrink_maps: 1.018184, loss_threshold_maps: 0.647506, loss_binary_maps: 0.201208, avg_reader_cost: 1.49978 s, avg_batch_cost: 1.73700 s, avg_samples: 12.5, ips: 7.19630 samples/s, eta: 6:29:04
[2024/07/28 00:02:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:02:11] ppocr INFO: epoch: [250/1500], global_step: 750, lr: 0.001000, loss: 1.825942, loss_shrink_maps: 0.987744, loss_threshold_maps: 0.641039, loss_binary_maps: 0.195467, avg_reader_cost: 1.73185 s, avg_batch_cost: 1.97257 s, avg_samples: 12.5, ips: 6.33693 samples/s, eta: 6:28:51
[2024/07/28 00:02:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:02:19] ppocr INFO: epoch: [251/1500], global_step: 753, lr: 0.001000, loss: 1.803528, loss_shrink_maps: 0.961742, loss_threshold_maps: 0.622845, loss_binary_maps: 0.190209, avg_reader_cost: 1.62251 s, avg_batch_cost: 1.85830 s, avg_samples: 12.5, ips: 6.72658 samples/s, eta: 6:28:32
[2024/07/28 00:02:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:02:26] ppocr INFO: epoch: [252/1500], global_step: 756, lr: 0.001000, loss: 1.792442, loss_shrink_maps: 0.971797, loss_threshold_maps: 0.618936, loss_binary_maps: 0.193008, avg_reader_cost: 1.51234 s, avg_batch_cost: 1.74667 s, avg_samples: 12.5, ips: 7.15647 samples/s, eta: 6:28:07
[2024/07/28 00:02:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:02:34] ppocr INFO: epoch: [253/1500], global_step: 759, lr: 0.001000, loss: 1.792442, loss_shrink_maps: 0.961742, loss_threshold_maps: 0.622225, loss_binary_maps: 0.190209, avg_reader_cost: 1.53818 s, avg_batch_cost: 1.77038 s, avg_samples: 12.5, ips: 7.06061 samples/s, eta: 6:27:44
[2024/07/28 00:02:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:02:40] ppocr INFO: epoch: [254/1500], global_step: 760, lr: 0.001000, loss: 1.792442, loss_shrink_maps: 0.961742, loss_threshold_maps: 0.622225, loss_binary_maps: 0.190685, avg_reader_cost: 0.43149 s, avg_batch_cost: 0.51487 s, avg_samples: 4.8, ips: 9.32268 samples/s, eta: 6:27:32
[2024/07/28 00:02:42] ppocr INFO: epoch: [254/1500], global_step: 762, lr: 0.001000, loss: 1.792442, loss_shrink_maps: 0.961742, loss_threshold_maps: 0.622225, loss_binary_maps: 0.190685, avg_reader_cost: 1.12132 s, avg_batch_cost: 1.26731 s, avg_samples: 7.7, ips: 6.07588 samples/s, eta: 6:27:21
[2024/07/28 00:02:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:02:49] ppocr INFO: epoch: [255/1500], global_step: 765, lr: 0.001000, loss: 1.781576, loss_shrink_maps: 0.953593, loss_threshold_maps: 0.623635, loss_binary_maps: 0.188736, avg_reader_cost: 1.50441 s, avg_batch_cost: 1.75992 s, avg_samples: 12.5, ips: 7.10261 samples/s, eta: 6:26:57
[2024/07/28 00:02:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:02:57] ppocr INFO: epoch: [256/1500], global_step: 768, lr: 0.001000, loss: 1.769766, loss_shrink_maps: 0.950090, loss_threshold_maps: 0.622944, loss_binary_maps: 0.187904, avg_reader_cost: 1.64758 s, avg_batch_cost: 1.87630 s, avg_samples: 12.5, ips: 6.66203 samples/s, eta: 6:26:39
[2024/07/28 00:02:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:03:04] ppocr INFO: epoch: [257/1500], global_step: 770, lr: 0.001000, loss: 1.769766, loss_shrink_maps: 0.950090, loss_threshold_maps: 0.623635, loss_binary_maps: 0.187904, avg_reader_cost: 0.90101 s, avg_batch_cost: 1.09046 s, avg_samples: 9.6, ips: 8.80359 samples/s, eta: 6:26:19
[2024/07/28 00:03:05] ppocr INFO: epoch: [257/1500], global_step: 771, lr: 0.001000, loss: 1.778686, loss_shrink_maps: 0.953593, loss_threshold_maps: 0.627518, loss_binary_maps: 0.188736, avg_reader_cost: 0.59092 s, avg_batch_cost: 0.64568 s, avg_samples: 2.9, ips: 4.49140 samples/s, eta: 6:26:14
[2024/07/28 00:03:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:03:12] ppocr INFO: epoch: [258/1500], global_step: 774, lr: 0.001000, loss: 1.778686, loss_shrink_maps: 0.949875, loss_threshold_maps: 0.627586, loss_binary_maps: 0.188342, avg_reader_cost: 1.52490 s, avg_batch_cost: 1.77401 s, avg_samples: 12.5, ips: 7.04618 samples/s, eta: 6:25:51
[2024/07/28 00:03:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:03:20] ppocr INFO: epoch: [259/1500], global_step: 777, lr: 0.001000, loss: 1.770768, loss_shrink_maps: 0.943155, loss_threshold_maps: 0.627586, loss_binary_maps: 0.186393, avg_reader_cost: 1.51260 s, avg_batch_cost: 1.75048 s, avg_samples: 12.5, ips: 7.14091 samples/s, eta: 6:25:27
[2024/07/28 00:03:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:03:28] ppocr INFO: epoch: [260/1500], global_step: 780, lr: 0.001000, loss: 1.779805, loss_shrink_maps: 0.949300, loss_threshold_maps: 0.627586, loss_binary_maps: 0.187436, avg_reader_cost: 1.55198 s, avg_batch_cost: 1.78005 s, avg_samples: 12.5, ips: 7.02226 samples/s, eta: 6:25:05

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:54,  9.24it/s]
eval model::   1%|          | 4/500 [00:00<00:30, 16.09it/s]
eval model::   1%|          | 6/500 [00:00<00:29, 16.63it/s]
eval model::   2%|▏         | 9/500 [00:00<00:26, 18.54it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.07it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.59it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.41it/s]
eval model::   4%|▍         | 20/500 [00:01<00:26, 18.42it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 20.91it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.42it/s]
eval model::   6%|▌         | 29/500 [00:01<00:24, 18.86it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.22it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.25it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 19.82it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.54it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.74it/s]
eval model::   9%|▉         | 44/500 [00:02<00:26, 17.41it/s]
eval model::   9%|▉         | 46/500 [00:02<00:25, 17.47it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 20.39it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 20.54it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 21.13it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:20, 21.05it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:25, 17.23it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.63it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.88it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.33it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.23it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:21, 19.91it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 20.39it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.35it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.84it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 19.75it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.59it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:21, 18.87it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:20, 19.36it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:19, 20.43it/s]
eval model::  20%|██        | 101/500 [00:05<00:19, 20.37it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 21.22it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.72it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 22.53it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:16, 23.03it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:18, 21.21it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 21.72it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.97it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:19, 18.81it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 19.86it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:18, 19.58it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:19, 19.25it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:17, 20.46it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.32it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.09it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:18, 19.24it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:19, 18.42it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:20, 17.43it/s]
eval model::  30%|███       | 151/500 [00:07<00:20, 17.02it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 21.13it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.03it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:18, 18.58it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:17, 19.70it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 19.17it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 19.97it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:17, 18.64it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:20, 15.49it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:18, 17.39it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:22, 14.48it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:19, 16.48it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:17, 18.22it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:16, 19.34it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:15, 20.18it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 20.00it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:15, 19.41it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 19.98it/s]
eval model::  41%|████      | 204/500 [00:10<00:14, 20.84it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:23, 12.72it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:22, 13.05it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 13.90it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.85it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.21it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.84it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.44it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:18, 14.75it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:15, 17.83it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:15, 17.83it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 19.67it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 20.67it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 20.72it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:12, 21.18it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:13, 19.49it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 21.16it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.96it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 20.97it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.70it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.68it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 23.39it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 22.79it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:12, 18.35it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.92it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.69it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.21it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:12, 18.47it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:13, 16.81it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:12, 17.32it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:18, 11.51it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:17, 12.41it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:13, 15.08it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:13, 15.86it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:13, 15.65it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:15, 13.22it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:13, 14.49it/s]
eval model::  60%|██████    | 300/500 [00:16<00:13, 14.50it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 16.94it/s]
eval model::  61%|██████    | 305/500 [00:16<00:11, 17.06it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:10, 19.19it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 19.30it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 20.62it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 20.12it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 21.48it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.75it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 20.70it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 20.23it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 19.55it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:08, 20.11it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 18.48it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:08, 18.70it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:11, 14.38it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 16.55it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 18.17it/s]
eval model::  70%|███████   | 350/500 [00:18<00:07, 20.20it/s]
eval model::  71%|███████   | 353/500 [00:19<00:06, 22.00it/s]
eval model::  71%|███████   | 356/500 [00:19<00:06, 21.83it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:06, 22.73it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:06, 20.34it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 19.19it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:07, 18.44it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:07, 18.19it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 16.21it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 18.51it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 22.32it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 21.77it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:04, 23.14it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:07, 14.52it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:06, 16.23it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 17.30it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 18.98it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 19.80it/s]
eval model::  81%|████████  | 403/500 [00:21<00:04, 20.68it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 19.85it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 20.58it/s]
eval model::  82%|████████▏ | 412/500 [00:22<00:04, 21.59it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:04, 21.23it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 19.18it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:04, 16.85it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 16.31it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.72it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.71it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:03, 18.05it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:03, 18.08it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 20.32it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:02, 21.62it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:03, 17.62it/s]
eval model::  89%|████████▊ | 443/500 [00:23<00:02, 20.06it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 20.75it/s]
eval model::  90%|████████▉ | 449/500 [00:24<00:02, 20.88it/s]
eval model::  90%|█████████ | 452/500 [00:24<00:02, 22.46it/s]
eval model::  91%|█████████ | 455/500 [00:24<00:01, 22.78it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:01, 23.93it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 24.31it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:01, 19.44it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 17.10it/s]
eval model::  94%|█████████▍| 470/500 [00:25<00:01, 19.55it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 20.18it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 20.33it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 22.47it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 23.37it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 22.37it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 22.94it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 22.22it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 22.51it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 24.46it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.96it/s]
[2024/07/28 00:03:54] ppocr INFO: cur metric, precision: 0.6282633371169126, recall: 0.5329802599903707, hmean: 0.576712685595207, fps: 44.6025386214144
[2024/07/28 00:03:54] ppocr INFO: best metric, hmean: 0.5929570170895909, precision: 0.6414565826330533, recall: 0.5512758786711603, fps: 44.78792511087625, best_epoch: 240
[2024/07/28 00:03:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:04:01] ppocr INFO: epoch: [261/1500], global_step: 783, lr: 0.001000, loss: 1.769654, loss_shrink_maps: 0.932984, loss_threshold_maps: 0.627586, loss_binary_maps: 0.184060, avg_reader_cost: 1.57710 s, avg_batch_cost: 1.80652 s, avg_samples: 12.5, ips: 6.91937 samples/s, eta: 6:24:43
[2024/07/28 00:04:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:04:09] ppocr INFO: epoch: [262/1500], global_step: 786, lr: 0.001000, loss: 1.742635, loss_shrink_maps: 0.919281, loss_threshold_maps: 0.632756, loss_binary_maps: 0.182090, avg_reader_cost: 1.55572 s, avg_batch_cost: 1.78525 s, avg_samples: 12.5, ips: 7.00184 samples/s, eta: 6:24:21
[2024/07/28 00:04:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:04:17] ppocr INFO: epoch: [263/1500], global_step: 789, lr: 0.001000, loss: 1.773933, loss_shrink_maps: 0.935734, loss_threshold_maps: 0.632756, loss_binary_maps: 0.185084, avg_reader_cost: 1.54335 s, avg_batch_cost: 1.81639 s, avg_samples: 12.5, ips: 6.88180 samples/s, eta: 6:24:00
[2024/07/28 00:04:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:04:23] ppocr INFO: epoch: [264/1500], global_step: 790, lr: 0.001000, loss: 1.798483, loss_shrink_maps: 0.949300, loss_threshold_maps: 0.640929, loss_binary_maps: 0.187436, avg_reader_cost: 0.40135 s, avg_batch_cost: 0.51777 s, avg_samples: 4.8, ips: 9.27057 samples/s, eta: 6:23:49
[2024/07/28 00:04:24] ppocr INFO: epoch: [264/1500], global_step: 792, lr: 0.001000, loss: 1.804037, loss_shrink_maps: 0.989607, loss_threshold_maps: 0.640929, loss_binary_maps: 0.196213, avg_reader_cost: 1.12717 s, avg_batch_cost: 1.27336 s, avg_samples: 7.7, ips: 6.04700 samples/s, eta: 6:23:38
[2024/07/28 00:04:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:04:32] ppocr INFO: epoch: [265/1500], global_step: 795, lr: 0.001000, loss: 1.849017, loss_shrink_maps: 1.023250, loss_threshold_maps: 0.646418, loss_binary_maps: 0.202906, avg_reader_cost: 1.57105 s, avg_batch_cost: 1.79929 s, avg_samples: 12.5, ips: 6.94718 samples/s, eta: 6:23:17
[2024/07/28 00:04:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:04:40] ppocr INFO: epoch: [266/1500], global_step: 798, lr: 0.001000, loss: 1.863707, loss_shrink_maps: 1.025448, loss_threshold_maps: 0.653907, loss_binary_maps: 0.203678, avg_reader_cost: 1.60520 s, avg_batch_cost: 1.83430 s, avg_samples: 12.5, ips: 6.81459 samples/s, eta: 6:22:57
[2024/07/28 00:04:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:04:47] ppocr INFO: epoch: [267/1500], global_step: 800, lr: 0.001000, loss: 1.863707, loss_shrink_maps: 1.025448, loss_threshold_maps: 0.652725, loss_binary_maps: 0.203678, avg_reader_cost: 0.97285 s, avg_batch_cost: 1.16467 s, avg_samples: 9.6, ips: 8.24270 samples/s, eta: 6:22:41
[2024/07/28 00:04:48] ppocr INFO: epoch: [267/1500], global_step: 801, lr: 0.001000, loss: 1.849017, loss_shrink_maps: 1.015840, loss_threshold_maps: 0.650772, loss_binary_maps: 0.201748, avg_reader_cost: 0.62817 s, avg_batch_cost: 0.68285 s, avg_samples: 2.9, ips: 4.24693 samples/s, eta: 6:22:38
[2024/07/28 00:04:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:04:55] ppocr INFO: epoch: [268/1500], global_step: 804, lr: 0.001000, loss: 1.849017, loss_shrink_maps: 1.015840, loss_threshold_maps: 0.649016, loss_binary_maps: 0.201748, avg_reader_cost: 1.59257 s, avg_batch_cost: 1.82186 s, avg_samples: 12.5, ips: 6.86110 samples/s, eta: 6:22:17
[2024/07/28 00:04:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:05:03] ppocr INFO: epoch: [269/1500], global_step: 807, lr: 0.001000, loss: 1.825997, loss_shrink_maps: 0.978724, loss_threshold_maps: 0.648036, loss_binary_maps: 0.194124, avg_reader_cost: 1.62448 s, avg_batch_cost: 1.85207 s, avg_samples: 12.5, ips: 6.74922 samples/s, eta: 6:21:58
[2024/07/28 00:05:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:05:11] ppocr INFO: epoch: [270/1500], global_step: 810, lr: 0.001000, loss: 1.779125, loss_shrink_maps: 0.945040, loss_threshold_maps: 0.640147, loss_binary_maps: 0.187321, avg_reader_cost: 1.56522 s, avg_batch_cost: 1.79421 s, avg_samples: 12.5, ips: 6.96684 samples/s, eta: 6:21:36
[2024/07/28 00:05:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:05:19] ppocr INFO: epoch: [271/1500], global_step: 813, lr: 0.001000, loss: 1.744180, loss_shrink_maps: 0.943015, loss_threshold_maps: 0.629587, loss_binary_maps: 0.186207, avg_reader_cost: 1.52333 s, avg_batch_cost: 1.75187 s, avg_samples: 12.5, ips: 7.13525 samples/s, eta: 6:21:13
[2024/07/28 00:05:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:05:27] ppocr INFO: epoch: [272/1500], global_step: 816, lr: 0.001000, loss: 1.754815, loss_shrink_maps: 0.945040, loss_threshold_maps: 0.640263, loss_binary_maps: 0.187321, avg_reader_cost: 1.56326 s, avg_batch_cost: 1.86403 s, avg_samples: 12.5, ips: 6.70589 samples/s, eta: 6:20:54
[2024/07/28 00:05:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:05:34] ppocr INFO: epoch: [273/1500], global_step: 819, lr: 0.001000, loss: 1.754270, loss_shrink_maps: 0.943943, loss_threshold_maps: 0.629587, loss_binary_maps: 0.186398, avg_reader_cost: 1.54753 s, avg_batch_cost: 1.79714 s, avg_samples: 12.5, ips: 6.95549 samples/s, eta: 6:20:33
[2024/07/28 00:05:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:05:41] ppocr INFO: epoch: [274/1500], global_step: 820, lr: 0.001000, loss: 1.761802, loss_shrink_maps: 0.945040, loss_threshold_maps: 0.629587, loss_binary_maps: 0.187321, avg_reader_cost: 0.46101 s, avg_batch_cost: 0.57630 s, avg_samples: 4.8, ips: 8.32894 samples/s, eta: 6:20:25
[2024/07/28 00:05:43] ppocr INFO: epoch: [274/1500], global_step: 822, lr: 0.001000, loss: 1.763474, loss_shrink_maps: 0.946795, loss_threshold_maps: 0.629587, loss_binary_maps: 0.188313, avg_reader_cost: 1.24307 s, avg_batch_cost: 1.38806 s, avg_samples: 7.7, ips: 5.54732 samples/s, eta: 6:20:19
[2024/07/28 00:05:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:05:51] ppocr INFO: epoch: [275/1500], global_step: 825, lr: 0.001000, loss: 1.780017, loss_shrink_maps: 0.953388, loss_threshold_maps: 0.640263, loss_binary_maps: 0.190207, avg_reader_cost: 1.58178 s, avg_batch_cost: 1.84845 s, avg_samples: 12.5, ips: 6.76243 samples/s, eta: 6:20:00
[2024/07/28 00:05:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:05:58] ppocr INFO: epoch: [276/1500], global_step: 828, lr: 0.001000, loss: 1.779472, loss_shrink_maps: 0.953388, loss_threshold_maps: 0.641722, loss_binary_maps: 0.190207, avg_reader_cost: 1.50621 s, avg_batch_cost: 1.76659 s, avg_samples: 12.5, ips: 7.07576 samples/s, eta: 6:19:37
[2024/07/28 00:06:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:06:06] ppocr INFO: epoch: [277/1500], global_step: 830, lr: 0.001000, loss: 1.837664, loss_shrink_maps: 1.008350, loss_threshold_maps: 0.647516, loss_binary_maps: 0.199345, avg_reader_cost: 0.93017 s, avg_batch_cost: 1.13013 s, avg_samples: 9.6, ips: 8.49457 samples/s, eta: 6:19:19
[2024/07/28 00:06:06] ppocr INFO: epoch: [277/1500], global_step: 831, lr: 0.001000, loss: 1.837664, loss_shrink_maps: 1.008350, loss_threshold_maps: 0.647516, loss_binary_maps: 0.199345, avg_reader_cost: 0.61065 s, avg_batch_cost: 0.66578 s, avg_samples: 2.9, ips: 4.35579 samples/s, eta: 6:19:15
[2024/07/28 00:06:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:06:14] ppocr INFO: epoch: [278/1500], global_step: 834, lr: 0.001000, loss: 1.846667, loss_shrink_maps: 1.011954, loss_threshold_maps: 0.647516, loss_binary_maps: 0.200868, avg_reader_cost: 1.55000 s, avg_batch_cost: 1.77903 s, avg_samples: 12.5, ips: 7.02631 samples/s, eta: 6:18:53
[2024/07/28 00:06:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:06:21] ppocr INFO: epoch: [279/1500], global_step: 837, lr: 0.001000, loss: 1.823623, loss_shrink_maps: 1.011665, loss_threshold_maps: 0.634668, loss_binary_maps: 0.198949, avg_reader_cost: 1.48699 s, avg_batch_cost: 1.72982 s, avg_samples: 12.5, ips: 7.22618 samples/s, eta: 6:18:29
[2024/07/28 00:06:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:06:29] ppocr INFO: epoch: [280/1500], global_step: 840, lr: 0.001000, loss: 1.838177, loss_shrink_maps: 1.011665, loss_threshold_maps: 0.634668, loss_binary_maps: 0.198949, avg_reader_cost: 1.65340 s, avg_batch_cost: 1.93097 s, avg_samples: 12.5, ips: 6.47341 samples/s, eta: 6:18:13

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:01,  8.16it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.73it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.92it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.60it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.42it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 19.11it/s]
eval model::   3%|▎         | 17/500 [00:00<00:22, 21.74it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.09it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.49it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.42it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 20.50it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 19.54it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 19.17it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 21.73it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.22it/s]
eval model::   9%|▊         | 43/500 [00:02<00:23, 19.78it/s]
eval model::   9%|▉         | 46/500 [00:02<00:23, 19.43it/s]
eval model::  10%|▉         | 49/500 [00:02<00:21, 20.88it/s]
eval model::  10%|█         | 52/500 [00:02<00:20, 21.40it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 20.73it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:22, 19.45it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.60it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.98it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:21, 19.82it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:18, 22.74it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:19, 21.92it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:19, 22.15it/s]
eval model::  16%|█▌        | 80/500 [00:03<00:17, 23.51it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:19, 21.62it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:21, 19.54it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 17.88it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:24, 16.98it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:23, 17.45it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:21, 19.12it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.32it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.15it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.62it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.74it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 21.24it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:17, 22.29it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:18, 20.36it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:17, 21.16it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 20.44it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 18.29it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 20.27it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 18.90it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:17, 20.50it/s]
eval model::  28%|██▊       | 139/500 [00:06<00:17, 20.28it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:16, 21.44it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.18it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:20, 17.05it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 18.54it/s]
eval model::  30%|███       | 152/500 [00:07<00:19, 17.87it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 20.43it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:15, 21.48it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:19, 17.54it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.63it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.11it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 19.04it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 18.50it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:20, 16.19it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.36it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.69it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.76it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:16, 19.11it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 19.93it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:14, 21.49it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:13, 23.19it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:14, 20.49it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 20.61it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 20.72it/s]
eval model::  41%|████      | 205/500 [00:10<00:24, 11.97it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:21, 13.72it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 14.06it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 15.95it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:22, 12.74it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.13it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.54it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 14.92it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:15, 17.90it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:15, 18.19it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 20.64it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.84it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.15it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.05it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:13, 18.91it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:12, 20.25it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.61it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 20.79it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 22.19it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 22.17it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 23.35it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 23.41it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:11, 20.83it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:11, 20.54it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:10, 21.23it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 20.36it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 20.15it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 21.06it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 21.47it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:16, 12.92it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.46it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:11, 17.37it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:12, 16.58it/s]
eval model::  60%|██████    | 300/500 [00:15<00:11, 16.94it/s]
eval model::  61%|██████    | 303/500 [00:15<00:10, 18.47it/s]
eval model::  61%|██████    | 306/500 [00:16<00:09, 19.83it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 20.34it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:08, 21.11it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 20.97it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 22.38it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 21.49it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 21.39it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:07, 22.11it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 20.39it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:08, 19.89it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:08, 19.50it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:09, 17.69it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:11, 14.35it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:10, 15.41it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 17.62it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:07, 19.75it/s]
eval model::  70%|███████   | 351/500 [00:18<00:06, 21.40it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 22.88it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 22.28it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 17.91it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.59it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 18.47it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.10it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 20.26it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 21.88it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:05, 22.70it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:04, 23.99it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:07, 14.34it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 16.36it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 17.77it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 19.54it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 21.65it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 21.52it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 21.86it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:03, 23.10it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 20.90it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:04, 18.32it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:04, 17.47it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 17.52it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.35it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 14.81it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 13.68it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:04, 16.30it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 18.61it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 19.96it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:03, 21.17it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 22.68it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 21.20it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 24.20it/s]
eval model::  90%|████████▉ | 449/500 [00:23<00:02, 20.53it/s]
eval model::  90%|█████████ | 452/500 [00:23<00:02, 21.51it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:02, 22.42it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:02, 20.98it/s]
eval model::  92%|█████████▏| 461/500 [00:23<00:01, 20.61it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 17.70it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:02, 16.82it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 17.20it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 20.13it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 19.75it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 21.58it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 22.78it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 21.34it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 20.92it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 20.86it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 20.31it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 19.68it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 23.06it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.34it/s]
[2024/07/28 00:06:55] ppocr INFO: cur metric, precision: 0.6382113821138211, recall: 0.529128550794415, hmean: 0.5785733087654645, fps: 45.3557077708031
[2024/07/28 00:06:55] ppocr INFO: best metric, hmean: 0.5929570170895909, precision: 0.6414565826330533, recall: 0.5512758786711603, fps: 44.78792511087625, best_epoch: 240
[2024/07/28 00:06:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:07:03] ppocr INFO: epoch: [281/1500], global_step: 843, lr: 0.001000, loss: 1.846667, loss_shrink_maps: 1.013008, loss_threshold_maps: 0.641551, loss_binary_maps: 0.199727, avg_reader_cost: 1.62137 s, avg_batch_cost: 1.90659 s, avg_samples: 12.5, ips: 6.55621 samples/s, eta: 6:17:57
[2024/07/28 00:07:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:07:11] ppocr INFO: epoch: [282/1500], global_step: 846, lr: 0.001000, loss: 1.838177, loss_shrink_maps: 1.009537, loss_threshold_maps: 0.641551, loss_binary_maps: 0.198949, avg_reader_cost: 1.58589 s, avg_batch_cost: 1.83289 s, avg_samples: 12.5, ips: 6.81985 samples/s, eta: 6:17:37
[2024/07/28 00:07:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:07:18] ppocr INFO: epoch: [283/1500], global_step: 849, lr: 0.001000, loss: 1.846667, loss_shrink_maps: 1.013008, loss_threshold_maps: 0.651154, loss_binary_maps: 0.199727, avg_reader_cost: 1.54391 s, avg_batch_cost: 1.77237 s, avg_samples: 12.5, ips: 7.05270 samples/s, eta: 6:17:15
[2024/07/28 00:07:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:07:25] ppocr INFO: epoch: [284/1500], global_step: 850, lr: 0.001000, loss: 1.857326, loss_shrink_maps: 1.018942, loss_threshold_maps: 0.653200, loss_binary_maps: 0.201652, avg_reader_cost: 0.40212 s, avg_batch_cost: 0.49701 s, avg_samples: 4.8, ips: 9.65766 samples/s, eta: 6:17:03
[2024/07/28 00:07:26] ppocr INFO: epoch: [284/1500], global_step: 852, lr: 0.001000, loss: 1.846444, loss_shrink_maps: 1.013008, loss_threshold_maps: 0.651154, loss_binary_maps: 0.199727, avg_reader_cost: 1.08611 s, avg_batch_cost: 1.23189 s, avg_samples: 7.7, ips: 6.25057 samples/s, eta: 6:16:50
[2024/07/28 00:07:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:07:34] ppocr INFO: epoch: [285/1500], global_step: 855, lr: 0.001000, loss: 1.872227, loss_shrink_maps: 1.013008, loss_threshold_maps: 0.657643, loss_binary_maps: 0.199727, avg_reader_cost: 1.53368 s, avg_batch_cost: 1.76228 s, avg_samples: 12.5, ips: 7.09309 samples/s, eta: 6:16:28
[2024/07/28 00:07:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:07:41] ppocr INFO: epoch: [286/1500], global_step: 858, lr: 0.001000, loss: 1.900864, loss_shrink_maps: 1.022336, loss_threshold_maps: 0.661093, loss_binary_maps: 0.203551, avg_reader_cost: 1.51097 s, avg_batch_cost: 1.76109 s, avg_samples: 12.5, ips: 7.09788 samples/s, eta: 6:16:05
[2024/07/28 00:07:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:07:48] ppocr INFO: epoch: [287/1500], global_step: 860, lr: 0.001000, loss: 1.903134, loss_shrink_maps: 1.022336, loss_threshold_maps: 0.661093, loss_binary_maps: 0.203551, avg_reader_cost: 0.90602 s, avg_batch_cost: 1.08196 s, avg_samples: 9.6, ips: 8.87282 samples/s, eta: 6:15:46
[2024/07/28 00:07:49] ppocr INFO: epoch: [287/1500], global_step: 861, lr: 0.001000, loss: 1.892308, loss_shrink_maps: 1.022336, loss_threshold_maps: 0.658866, loss_binary_maps: 0.203551, avg_reader_cost: 0.58645 s, avg_batch_cost: 0.64136 s, avg_samples: 2.9, ips: 4.52166 samples/s, eta: 6:15:41
[2024/07/28 00:07:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:07:57] ppocr INFO: epoch: [288/1500], global_step: 864, lr: 0.001000, loss: 1.892308, loss_shrink_maps: 1.019243, loss_threshold_maps: 0.659136, loss_binary_maps: 0.202513, avg_reader_cost: 1.59879 s, avg_batch_cost: 1.91380 s, avg_samples: 12.5, ips: 6.53150 samples/s, eta: 6:15:24
[2024/07/28 00:07:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:08:05] ppocr INFO: epoch: [289/1500], global_step: 867, lr: 0.001000, loss: 1.859307, loss_shrink_maps: 1.010354, loss_threshold_maps: 0.651154, loss_binary_maps: 0.200445, avg_reader_cost: 1.68659 s, avg_batch_cost: 1.94869 s, avg_samples: 12.5, ips: 6.41456 samples/s, eta: 6:15:09
[2024/07/28 00:08:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:08:13] ppocr INFO: epoch: [290/1500], global_step: 870, lr: 0.001000, loss: 1.826267, loss_shrink_maps: 0.987241, loss_threshold_maps: 0.645531, loss_binary_maps: 0.195932, avg_reader_cost: 1.52971 s, avg_batch_cost: 1.76330 s, avg_samples: 12.5, ips: 7.08896 samples/s, eta: 6:14:47
[2024/07/28 00:08:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:08:21] ppocr INFO: epoch: [291/1500], global_step: 873, lr: 0.001000, loss: 1.818420, loss_shrink_maps: 0.976331, loss_threshold_maps: 0.640354, loss_binary_maps: 0.193942, avg_reader_cost: 1.54750 s, avg_batch_cost: 1.77912 s, avg_samples: 12.5, ips: 7.02596 samples/s, eta: 6:14:25
[2024/07/28 00:08:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:08:29] ppocr INFO: epoch: [292/1500], global_step: 876, lr: 0.001000, loss: 1.785052, loss_shrink_maps: 0.965596, loss_threshold_maps: 0.636858, loss_binary_maps: 0.191883, avg_reader_cost: 1.61602 s, avg_batch_cost: 1.84543 s, avg_samples: 12.5, ips: 6.77348 samples/s, eta: 6:14:06
[2024/07/28 00:08:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:08:37] ppocr INFO: epoch: [293/1500], global_step: 879, lr: 0.001000, loss: 1.726962, loss_shrink_maps: 0.913913, loss_threshold_maps: 0.630083, loss_binary_maps: 0.180991, avg_reader_cost: 1.57504 s, avg_batch_cost: 1.81867 s, avg_samples: 12.5, ips: 6.87316 samples/s, eta: 6:13:46
[2024/07/28 00:08:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:08:43] ppocr INFO: epoch: [294/1500], global_step: 880, lr: 0.001000, loss: 1.716616, loss_shrink_maps: 0.905383, loss_threshold_maps: 0.622253, loss_binary_maps: 0.179806, avg_reader_cost: 0.40291 s, avg_batch_cost: 0.51947 s, avg_samples: 4.8, ips: 9.24021 samples/s, eta: 6:13:35
[2024/07/28 00:08:44] ppocr INFO: epoch: [294/1500], global_step: 882, lr: 0.001000, loss: 1.716616, loss_shrink_maps: 0.905383, loss_threshold_maps: 0.622253, loss_binary_maps: 0.179806, avg_reader_cost: 1.13040 s, avg_batch_cost: 1.27634 s, avg_samples: 7.7, ips: 6.03289 samples/s, eta: 6:13:25
[2024/07/28 00:08:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:08:52] ppocr INFO: epoch: [295/1500], global_step: 885, lr: 0.001000, loss: 1.696951, loss_shrink_maps: 0.896378, loss_threshold_maps: 0.625490, loss_binary_maps: 0.178268, avg_reader_cost: 1.51027 s, avg_batch_cost: 1.73904 s, avg_samples: 12.5, ips: 7.18789 samples/s, eta: 6:13:01
[2024/07/28 00:08:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:09:00] ppocr INFO: epoch: [296/1500], global_step: 888, lr: 0.001000, loss: 1.724864, loss_shrink_maps: 0.910557, loss_threshold_maps: 0.630972, loss_binary_maps: 0.180448, avg_reader_cost: 1.53884 s, avg_batch_cost: 1.76739 s, avg_samples: 12.5, ips: 7.07256 samples/s, eta: 6:12:39
[2024/07/28 00:09:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:09:07] ppocr INFO: epoch: [297/1500], global_step: 890, lr: 0.001000, loss: 1.715084, loss_shrink_maps: 0.896378, loss_threshold_maps: 0.630972, loss_binary_maps: 0.178268, avg_reader_cost: 1.00272 s, avg_batch_cost: 1.22892 s, avg_samples: 9.6, ips: 7.81174 samples/s, eta: 6:12:26
[2024/07/28 00:09:08] ppocr INFO: epoch: [297/1500], global_step: 891, lr: 0.001000, loss: 1.715084, loss_shrink_maps: 0.896378, loss_threshold_maps: 0.630972, loss_binary_maps: 0.178268, avg_reader_cost: 0.66031 s, avg_batch_cost: 0.71483 s, avg_samples: 2.9, ips: 4.05693 samples/s, eta: 6:12:24
[2024/07/28 00:09:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:09:16] ppocr INFO: epoch: [298/1500], global_step: 894, lr: 0.001000, loss: 1.679664, loss_shrink_maps: 0.886335, loss_threshold_maps: 0.619646, loss_binary_maps: 0.175894, avg_reader_cost: 1.53212 s, avg_batch_cost: 1.79234 s, avg_samples: 12.5, ips: 6.97412 samples/s, eta: 6:12:03
[2024/07/28 00:09:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:09:24] ppocr INFO: epoch: [299/1500], global_step: 897, lr: 0.001000, loss: 1.689550, loss_shrink_maps: 0.886335, loss_threshold_maps: 0.618226, loss_binary_maps: 0.175894, avg_reader_cost: 1.50088 s, avg_batch_cost: 1.73780 s, avg_samples: 12.5, ips: 7.19301 samples/s, eta: 6:11:39
[2024/07/28 00:09:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:09:31] ppocr INFO: epoch: [300/1500], global_step: 900, lr: 0.001000, loss: 1.684705, loss_shrink_maps: 0.886335, loss_threshold_maps: 0.618226, loss_binary_maps: 0.175894, avg_reader_cost: 1.53572 s, avg_batch_cost: 1.79819 s, avg_samples: 12.5, ips: 6.95145 samples/s, eta: 6:11:18

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:37, 13.34it/s]
eval model::   1%|          | 5/500 [00:00<00:24, 20.16it/s]
eval model::   2%|▏         | 8/500 [00:00<00:25, 19.23it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.72it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.37it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.81it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.83it/s]
eval model::   5%|▍         | 23/500 [00:01<00:20, 22.80it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 21.34it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 22.02it/s]
eval model::   6%|▋         | 32/500 [00:01<00:21, 21.98it/s]
eval model::   7%|▋         | 35/500 [00:01<00:22, 20.23it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.42it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.55it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.91it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.30it/s]
eval model::   9%|▉         | 47/500 [00:02<00:24, 18.75it/s]
eval model::  10%|█         | 50/500 [00:02<00:21, 20.59it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 20.87it/s]
eval model::  11%|█         | 56/500 [00:02<00:21, 21.09it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:21, 20.26it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:26, 16.39it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:26, 16.18it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:21, 20.57it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:19, 21.62it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:20, 21.06it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:20, 20.84it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:17, 24.15it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.52it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:19, 20.69it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.12it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.20it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:20, 19.73it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:19, 20.67it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.00it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 20.25it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:19, 19.78it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 20.53it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:17, 22.02it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:18, 20.84it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.78it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.21it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 18.97it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 20.15it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.86it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 20.05it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:17, 20.43it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.22it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 19.80it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.35it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.78it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 18.66it/s]
eval model::  31%|███       | 156/500 [00:07<00:17, 19.90it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:18, 18.93it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:21, 15.44it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:21, 15.61it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:20, 16.09it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:20, 15.94it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:18, 18.06it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:20, 15.75it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:24, 13.34it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:22, 14.53it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 17.93it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:21, 15.17it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:15, 19.86it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:15, 19.59it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:15, 20.32it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:14, 21.69it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.66it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 20.53it/s]
eval model::  41%|████      | 203/500 [00:10<00:13, 21.44it/s]
eval model::  41%|████      | 206/500 [00:11<00:25, 11.34it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:23, 12.19it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:24, 11.86it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:21, 13.44it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:23, 11.99it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:26, 10.88it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:23, 12.15it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:23, 11.94it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:18, 14.74it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:18, 15.01it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:14, 19.21it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:13, 20.27it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 20.75it/s]
eval model::  48%|████▊     | 239/500 [00:13<00:11, 22.45it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:12, 19.87it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.55it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 20.77it/s]
eval model::  50%|█████     | 251/500 [00:13<00:12, 19.55it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 20.80it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 22.16it/s]
eval model::  52%|█████▏    | 260/500 [00:14<00:11, 21.47it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:11, 20.61it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:11, 19.61it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.93it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.96it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.39it/s]
eval model::  56%|█████▌    | 278/500 [00:15<00:11, 18.91it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 21.31it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:15, 13.84it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:13, 15.73it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:11, 18.16it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:11, 17.72it/s]
eval model::  59%|█████▉    | 297/500 [00:16<00:12, 15.71it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 16.18it/s]
eval model::  61%|██████    | 303/500 [00:16<00:10, 18.46it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 19.28it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 19.51it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 20.81it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:08, 21.48it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:07, 23.05it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 20.73it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 20.00it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 21.31it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 19.61it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 21.59it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:07, 23.24it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:08, 18.68it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 14.85it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 17.42it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 18.15it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 18.78it/s]
eval model::  71%|███████   | 354/500 [00:19<00:07, 20.38it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 18.85it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:06, 20.39it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:06, 20.77it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.80it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 19.86it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 17.91it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 19.48it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 22.97it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 23.17it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:07, 15.75it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 17.55it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:05, 18.55it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:05, 18.83it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:04, 20.57it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 19.38it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 20.77it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 22.27it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:03, 22.66it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:03, 23.24it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:03, 23.34it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:03, 22.47it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 17.39it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 17.11it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.74it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 18.89it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:03, 17.75it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 21.03it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 22.58it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 21.08it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 24.06it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 20.75it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.56it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 21.27it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 21.60it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 20.27it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 16.93it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.19it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 16.76it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 19.28it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 20.23it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 20.78it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:00, 22.75it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 21.30it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 23.24it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 22.87it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 22.35it/s]
eval model::  99%|█████████▉| 494/500 [00:26<00:00, 21.25it/s]
eval model::  99%|█████████▉| 497/500 [00:26<00:00, 21.40it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 23.09it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.02it/s]
[2024/07/28 00:09:58] ppocr INFO: cur metric, precision: 0.6733181299885975, recall: 0.568608570052961, hmean: 0.6165492038632212, fps: 45.792003513915795
[2024/07/28 00:09:58] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/28 00:09:58] ppocr INFO: best metric, hmean: 0.6165492038632212, precision: 0.6733181299885975, recall: 0.568608570052961, fps: 45.792003513915795, best_epoch: 300
[2024/07/28 00:09:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:10:05] ppocr INFO: epoch: [301/1500], global_step: 903, lr: 0.001000, loss: 1.710967, loss_shrink_maps: 0.903807, loss_threshold_maps: 0.618226, loss_binary_maps: 0.179562, avg_reader_cost: 1.71315 s, avg_batch_cost: 1.97479 s, avg_samples: 12.5, ips: 6.32977 samples/s, eta: 6:11:04
[2024/07/28 00:10:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:10:13] ppocr INFO: epoch: [302/1500], global_step: 906, lr: 0.001000, loss: 1.702414, loss_shrink_maps: 0.891611, loss_threshold_maps: 0.623733, loss_binary_maps: 0.177357, avg_reader_cost: 1.55142 s, avg_batch_cost: 1.80658 s, avg_samples: 12.5, ips: 6.91916 samples/s, eta: 6:10:44
[2024/07/28 00:10:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:10:21] ppocr INFO: epoch: [303/1500], global_step: 909, lr: 0.001000, loss: 1.681748, loss_shrink_maps: 0.886421, loss_threshold_maps: 0.613128, loss_binary_maps: 0.176220, avg_reader_cost: 1.56260 s, avg_batch_cost: 1.82267 s, avg_samples: 12.5, ips: 6.85808 samples/s, eta: 6:10:24
[2024/07/28 00:10:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:10:28] ppocr INFO: epoch: [304/1500], global_step: 910, lr: 0.001000, loss: 1.694732, loss_shrink_maps: 0.891611, loss_threshold_maps: 0.616132, loss_binary_maps: 0.177357, avg_reader_cost: 0.38989 s, avg_batch_cost: 0.55912 s, avg_samples: 4.8, ips: 8.58491 samples/s, eta: 6:10:15
[2024/07/28 00:10:29] ppocr INFO: epoch: [304/1500], global_step: 912, lr: 0.001000, loss: 1.694732, loss_shrink_maps: 0.900516, loss_threshold_maps: 0.616132, loss_binary_maps: 0.179090, avg_reader_cost: 1.20955 s, avg_batch_cost: 1.35524 s, avg_samples: 7.7, ips: 5.68166 samples/s, eta: 6:10:08
[2024/07/28 00:10:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:10:37] ppocr INFO: epoch: [305/1500], global_step: 915, lr: 0.001000, loss: 1.694732, loss_shrink_maps: 0.900516, loss_threshold_maps: 0.618588, loss_binary_maps: 0.179090, avg_reader_cost: 1.63394 s, avg_batch_cost: 1.87864 s, avg_samples: 12.5, ips: 6.65375 samples/s, eta: 6:09:50
[2024/07/28 00:10:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:10:45] ppocr INFO: epoch: [306/1500], global_step: 918, lr: 0.001000, loss: 1.694732, loss_shrink_maps: 0.900516, loss_threshold_maps: 0.617656, loss_binary_maps: 0.179090, avg_reader_cost: 1.51458 s, avg_batch_cost: 1.75072 s, avg_samples: 12.5, ips: 7.13992 samples/s, eta: 6:09:27
[2024/07/28 00:10:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:10:52] ppocr INFO: epoch: [307/1500], global_step: 920, lr: 0.001000, loss: 1.696456, loss_shrink_maps: 0.900516, loss_threshold_maps: 0.620874, loss_binary_maps: 0.179090, avg_reader_cost: 0.95419 s, avg_batch_cost: 1.14435 s, avg_samples: 9.6, ips: 8.38903 samples/s, eta: 6:09:11
[2024/07/28 00:10:53] ppocr INFO: epoch: [307/1500], global_step: 921, lr: 0.001000, loss: 1.684161, loss_shrink_maps: 0.897070, loss_threshold_maps: 0.617656, loss_binary_maps: 0.178110, avg_reader_cost: 0.61815 s, avg_batch_cost: 0.67331 s, avg_samples: 2.9, ips: 4.30710 samples/s, eta: 6:09:07
[2024/07/28 00:10:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:11:01] ppocr INFO: epoch: [308/1500], global_step: 924, lr: 0.001000, loss: 1.674015, loss_shrink_maps: 0.891712, loss_threshold_maps: 0.617656, loss_binary_maps: 0.176942, avg_reader_cost: 1.52231 s, avg_batch_cost: 1.75098 s, avg_samples: 12.5, ips: 7.13887 samples/s, eta: 6:08:45
[2024/07/28 00:11:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:11:08] ppocr INFO: epoch: [309/1500], global_step: 927, lr: 0.001000, loss: 1.698528, loss_shrink_maps: 0.922975, loss_threshold_maps: 0.617858, loss_binary_maps: 0.182575, avg_reader_cost: 1.52980 s, avg_batch_cost: 1.77004 s, avg_samples: 12.5, ips: 7.06199 samples/s, eta: 6:08:23
[2024/07/28 00:11:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:11:16] ppocr INFO: epoch: [310/1500], global_step: 930, lr: 0.001000, loss: 1.741045, loss_shrink_maps: 0.952409, loss_threshold_maps: 0.619417, loss_binary_maps: 0.188109, avg_reader_cost: 1.53883 s, avg_batch_cost: 1.77563 s, avg_samples: 12.5, ips: 7.03977 samples/s, eta: 6:08:01
[2024/07/28 00:11:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:11:24] ppocr INFO: epoch: [311/1500], global_step: 933, lr: 0.001000, loss: 1.741045, loss_shrink_maps: 0.952409, loss_threshold_maps: 0.619417, loss_binary_maps: 0.188109, avg_reader_cost: 1.53292 s, avg_batch_cost: 1.77100 s, avg_samples: 12.5, ips: 7.05816 samples/s, eta: 6:07:39
[2024/07/28 00:11:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:11:32] ppocr INFO: epoch: [312/1500], global_step: 936, lr: 0.001000, loss: 1.794587, loss_shrink_maps: 0.980402, loss_threshold_maps: 0.619417, loss_binary_maps: 0.194768, avg_reader_cost: 1.54636 s, avg_batch_cost: 1.83205 s, avg_samples: 12.5, ips: 6.82297 samples/s, eta: 6:07:20
[2024/07/28 00:11:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:11:40] ppocr INFO: epoch: [313/1500], global_step: 939, lr: 0.001000, loss: 1.821812, loss_shrink_maps: 0.990127, loss_threshold_maps: 0.626903, loss_binary_maps: 0.197372, avg_reader_cost: 1.54035 s, avg_batch_cost: 1.77026 s, avg_samples: 12.5, ips: 7.06110 samples/s, eta: 6:06:58
[2024/07/28 00:11:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:11:46] ppocr INFO: epoch: [314/1500], global_step: 940, lr: 0.001000, loss: 1.794587, loss_shrink_maps: 0.980402, loss_threshold_maps: 0.619417, loss_binary_maps: 0.194768, avg_reader_cost: 0.42233 s, avg_batch_cost: 0.51530 s, avg_samples: 4.8, ips: 9.31494 samples/s, eta: 6:06:48
[2024/07/28 00:11:47] ppocr INFO: epoch: [314/1500], global_step: 942, lr: 0.001000, loss: 1.741045, loss_shrink_maps: 0.952409, loss_threshold_maps: 0.614283, loss_binary_maps: 0.188109, avg_reader_cost: 1.12201 s, avg_batch_cost: 1.26781 s, avg_samples: 7.7, ips: 6.07348 samples/s, eta: 6:06:37
[2024/07/28 00:11:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:11:55] ppocr INFO: epoch: [315/1500], global_step: 945, lr: 0.001000, loss: 1.741045, loss_shrink_maps: 0.952409, loss_threshold_maps: 0.618962, loss_binary_maps: 0.188109, avg_reader_cost: 1.51481 s, avg_batch_cost: 1.74993 s, avg_samples: 12.5, ips: 7.14313 samples/s, eta: 6:06:14
[2024/07/28 00:11:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:12:04] ppocr INFO: epoch: [316/1500], global_step: 948, lr: 0.001000, loss: 1.677360, loss_shrink_maps: 0.895268, loss_threshold_maps: 0.614283, loss_binary_maps: 0.177642, avg_reader_cost: 1.74767 s, avg_batch_cost: 2.01234 s, avg_samples: 12.5, ips: 6.21167 samples/s, eta: 6:06:02
[2024/07/28 00:12:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:12:11] ppocr INFO: epoch: [317/1500], global_step: 950, lr: 0.001000, loss: 1.680534, loss_shrink_maps: 0.895920, loss_threshold_maps: 0.614928, loss_binary_maps: 0.177656, avg_reader_cost: 0.93264 s, avg_batch_cost: 1.10753 s, avg_samples: 9.6, ips: 8.66795 samples/s, eta: 6:05:44
[2024/07/28 00:12:11] ppocr INFO: epoch: [317/1500], global_step: 951, lr: 0.001000, loss: 1.764711, loss_shrink_maps: 0.946008, loss_threshold_maps: 0.626448, loss_binary_maps: 0.187390, avg_reader_cost: 0.60002 s, avg_batch_cost: 0.65439 s, avg_samples: 2.9, ips: 4.43163 samples/s, eta: 6:05:40
[2024/07/28 00:12:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:12:19] ppocr INFO: epoch: [318/1500], global_step: 954, lr: 0.001000, loss: 1.680534, loss_shrink_maps: 0.895920, loss_threshold_maps: 0.611266, loss_binary_maps: 0.177656, avg_reader_cost: 1.52979 s, avg_batch_cost: 1.77378 s, avg_samples: 12.5, ips: 7.04710 samples/s, eta: 6:05:18
[2024/07/28 00:12:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:12:27] ppocr INFO: epoch: [319/1500], global_step: 957, lr: 0.001000, loss: 1.764711, loss_shrink_maps: 0.946008, loss_threshold_maps: 0.611892, loss_binary_maps: 0.187390, avg_reader_cost: 1.52390 s, avg_batch_cost: 1.76135 s, avg_samples: 12.5, ips: 7.09684 samples/s, eta: 6:04:56
[2024/07/28 00:12:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:12:35] ppocr INFO: epoch: [320/1500], global_step: 960, lr: 0.001000, loss: 1.704222, loss_shrink_maps: 0.912415, loss_threshold_maps: 0.605600, loss_binary_maps: 0.181487, avg_reader_cost: 1.59677 s, avg_batch_cost: 1.82590 s, avg_samples: 12.5, ips: 6.84596 samples/s, eta: 6:04:37

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:33, 14.69it/s]
eval model::   1%|          | 5/500 [00:00<00:22, 21.68it/s]
eval model::   2%|▏         | 8/500 [00:00<00:26, 18.68it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.27it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 18.91it/s]
eval model::   3%|▎         | 17/500 [00:00<00:22, 21.24it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.87it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 21.84it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 19.95it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 21.46it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 20.03it/s]
eval model::   7%|▋         | 35/500 [00:01<00:25, 18.07it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.53it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.33it/s]
eval model::   9%|▊         | 43/500 [00:02<00:25, 18.07it/s]
eval model::   9%|▉         | 46/500 [00:02<00:27, 16.59it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.23it/s]
eval model::  10%|█         | 51/500 [00:02<00:24, 18.55it/s]
eval model::  11%|█         | 54/500 [00:02<00:23, 19.26it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:22, 19.77it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:25, 17.58it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:31, 14.03it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:29, 14.73it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:24, 17.71it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:21, 20.30it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 21.11it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.41it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:20, 20.78it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.88it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:17, 23.54it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 19.66it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 17.79it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:20, 19.59it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:19, 20.59it/s]
eval model::  20%|██        | 100/500 [00:05<00:19, 20.52it/s]
eval model::  21%|██        | 103/500 [00:05<00:17, 22.58it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.79it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:16, 23.64it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:17, 22.01it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:18, 21.35it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:17, 21.45it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 20.79it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 20.25it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.14it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 19.36it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 18.38it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:19, 18.77it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.63it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:19, 18.74it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 20.41it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 20.46it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:18, 19.23it/s]
eval model::  30%|███       | 151/500 [00:07<00:21, 16.20it/s]
eval model::  31%|███       | 154/500 [00:07<00:18, 18.78it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:17, 19.96it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:19, 17.24it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:17, 18.78it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.67it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 20.40it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:18, 17.90it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:21, 15.11it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 15.94it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.49it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:19, 16.22it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:16, 18.60it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:18, 16.78it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:17, 18.06it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.83it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.41it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 21.21it/s]
eval model::  41%|████      | 205/500 [00:10<00:19, 15.49it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:18, 16.22it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:17, 16.47it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:18, 15.92it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.77it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.50it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 12.90it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:19, 14.10it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:19, 14.04it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:16, 16.93it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.58it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.89it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.99it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:13, 19.69it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:12, 21.71it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 20.46it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:11, 21.73it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:10, 23.03it/s]
eval model::  50%|█████     | 251/500 [00:13<00:10, 23.34it/s]
eval model::  51%|█████     | 254/500 [00:13<00:10, 24.33it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 23.62it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 22.81it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:11, 21.31it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:12, 19.09it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 19.70it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:10, 21.19it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:10, 20.59it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 21.59it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:09, 23.52it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:09, 22.85it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:17, 12.15it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:14, 14.64it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:12, 16.26it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:15, 13.32it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:13, 15.33it/s]
eval model::  60%|██████    | 301/500 [00:16<00:15, 13.00it/s]
eval model::  61%|██████    | 304/500 [00:16<00:13, 15.05it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:11, 17.47it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 19.55it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:08, 21.74it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:07, 23.03it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:08, 22.28it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:08, 21.50it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:07, 22.36it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 20.77it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:08, 19.24it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 21.36it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 20.34it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 15.71it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 16.12it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:09, 16.59it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 16.95it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 18.93it/s]
eval model::  71%|███████   | 354/500 [00:18<00:07, 20.19it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:07, 18.73it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:07, 18.41it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:08, 17.24it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 17.68it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:08, 15.48it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:09, 13.74it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:08, 14.79it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:08, 15.53it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 18.71it/s]
eval model::  75%|███████▌  | 377/500 [00:20<00:05, 21.34it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 21.67it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:04, 24.97it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 14.24it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 16.37it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:05, 17.83it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 18.69it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:04, 20.31it/s]
eval model::  80%|████████  | 402/500 [00:21<00:04, 21.05it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 21.51it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.62it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 21.27it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:04, 19.87it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 17.28it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 17.38it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:05, 14.42it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.76it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:04, 17.54it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 19.36it/s]
eval model::  87%|████████▋ | 433/500 [00:23<00:03, 18.43it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:03, 19.73it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 21.03it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 19.60it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 21.57it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 21.59it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.17it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 20.98it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:02, 20.13it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 20.29it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 18.21it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:01, 18.15it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 17.59it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 20.23it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 20.64it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 18.02it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:01, 20.35it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 20.38it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 20.81it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 24.72it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 21.46it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 21.60it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 25.02it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.02it/s]
[2024/07/28 00:13:01] ppocr INFO: cur metric, precision: 0.6528697571743929, recall: 0.5695714973519499, hmean: 0.608382617639496, fps: 45.91489514866459
[2024/07/28 00:13:01] ppocr INFO: best metric, hmean: 0.6165492038632212, precision: 0.6733181299885975, recall: 0.568608570052961, fps: 45.792003513915795, best_epoch: 300
[2024/07/28 00:13:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:13:08] ppocr INFO: epoch: [321/1500], global_step: 963, lr: 0.001000, loss: 1.843419, loss_shrink_maps: 1.000370, loss_threshold_maps: 0.631571, loss_binary_maps: 0.198119, avg_reader_cost: 1.56759 s, avg_batch_cost: 1.84304 s, avg_samples: 12.5, ips: 6.78226 samples/s, eta: 6:04:18
[2024/07/28 00:13:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:13:16] ppocr INFO: epoch: [322/1500], global_step: 966, lr: 0.001000, loss: 1.796238, loss_shrink_maps: 0.985737, loss_threshold_maps: 0.605600, loss_binary_maps: 0.195702, avg_reader_cost: 1.52351 s, avg_batch_cost: 1.75210 s, avg_samples: 12.5, ips: 7.13430 samples/s, eta: 6:03:55
[2024/07/28 00:13:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:13:23] ppocr INFO: epoch: [323/1500], global_step: 969, lr: 0.001000, loss: 1.747766, loss_shrink_maps: 0.980160, loss_threshold_maps: 0.601704, loss_binary_maps: 0.194745, avg_reader_cost: 1.47387 s, avg_batch_cost: 1.71516 s, avg_samples: 12.5, ips: 7.28797 samples/s, eta: 6:03:32
[2024/07/28 00:13:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:13:30] ppocr INFO: epoch: [324/1500], global_step: 970, lr: 0.001000, loss: 1.747766, loss_shrink_maps: 0.980160, loss_threshold_maps: 0.604454, loss_binary_maps: 0.194745, avg_reader_cost: 0.39621 s, avg_batch_cost: 0.56285 s, avg_samples: 4.8, ips: 8.52808 samples/s, eta: 6:03:24
[2024/07/28 00:13:32] ppocr INFO: epoch: [324/1500], global_step: 972, lr: 0.001000, loss: 1.779115, loss_shrink_maps: 0.992880, loss_threshold_maps: 0.604454, loss_binary_maps: 0.196841, avg_reader_cost: 1.21790 s, avg_batch_cost: 1.36316 s, avg_samples: 7.7, ips: 5.64865 samples/s, eta: 6:03:16
[2024/07/28 00:13:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:13:39] ppocr INFO: epoch: [325/1500], global_step: 975, lr: 0.001000, loss: 1.747766, loss_shrink_maps: 0.980160, loss_threshold_maps: 0.604044, loss_binary_maps: 0.194745, avg_reader_cost: 1.54946 s, avg_batch_cost: 1.78113 s, avg_samples: 12.5, ips: 7.01800 samples/s, eta: 6:02:55
[2024/07/28 00:13:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:13:48] ppocr INFO: epoch: [326/1500], global_step: 978, lr: 0.001000, loss: 1.747766, loss_shrink_maps: 0.980160, loss_threshold_maps: 0.603875, loss_binary_maps: 0.194745, avg_reader_cost: 1.74909 s, avg_batch_cost: 2.01774 s, avg_samples: 12.5, ips: 6.19505 samples/s, eta: 6:02:42
[2024/07/28 00:13:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:13:56] ppocr INFO: epoch: [327/1500], global_step: 980, lr: 0.001000, loss: 1.733242, loss_shrink_maps: 0.952223, loss_threshold_maps: 0.601535, loss_binary_maps: 0.188918, avg_reader_cost: 0.99189 s, avg_batch_cost: 1.16681 s, avg_samples: 9.6, ips: 8.22759 samples/s, eta: 6:02:27
[2024/07/28 00:13:56] ppocr INFO: epoch: [327/1500], global_step: 981, lr: 0.001000, loss: 1.699306, loss_shrink_maps: 0.946548, loss_threshold_maps: 0.595257, loss_binary_maps: 0.188858, avg_reader_cost: 0.62900 s, avg_batch_cost: 0.68367 s, avg_samples: 2.9, ips: 4.24178 samples/s, eta: 6:02:23
[2024/07/28 00:13:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:14:04] ppocr INFO: epoch: [328/1500], global_step: 984, lr: 0.001000, loss: 1.617858, loss_shrink_maps: 0.858967, loss_threshold_maps: 0.593619, loss_binary_maps: 0.170642, avg_reader_cost: 1.56952 s, avg_batch_cost: 1.80032 s, avg_samples: 12.5, ips: 6.94320 samples/s, eta: 6:02:03
[2024/07/28 00:14:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:14:12] ppocr INFO: epoch: [329/1500], global_step: 987, lr: 0.001000, loss: 1.617858, loss_shrink_maps: 0.858967, loss_threshold_maps: 0.599626, loss_binary_maps: 0.170642, avg_reader_cost: 1.50231 s, avg_batch_cost: 1.73123 s, avg_samples: 12.5, ips: 7.22032 samples/s, eta: 6:01:40
[2024/07/28 00:14:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:14:20] ppocr INFO: epoch: [330/1500], global_step: 990, lr: 0.001000, loss: 1.617858, loss_shrink_maps: 0.858967, loss_threshold_maps: 0.599626, loss_binary_maps: 0.170642, avg_reader_cost: 1.54143 s, avg_batch_cost: 1.77887 s, avg_samples: 12.5, ips: 7.02695 samples/s, eta: 6:01:19
[2024/07/28 00:14:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:14:27] ppocr INFO: epoch: [331/1500], global_step: 993, lr: 0.001000, loss: 1.647462, loss_shrink_maps: 0.889947, loss_threshold_maps: 0.603568, loss_binary_maps: 0.177590, avg_reader_cost: 1.51456 s, avg_batch_cost: 1.75282 s, avg_samples: 12.5, ips: 7.13137 samples/s, eta: 6:00:57
[2024/07/28 00:14:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:14:35] ppocr INFO: epoch: [332/1500], global_step: 996, lr: 0.001000, loss: 1.641897, loss_shrink_maps: 0.885094, loss_threshold_maps: 0.599626, loss_binary_maps: 0.175894, avg_reader_cost: 1.53668 s, avg_batch_cost: 1.77320 s, avg_samples: 12.5, ips: 7.04939 samples/s, eta: 6:00:36
[2024/07/28 00:14:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:14:43] ppocr INFO: epoch: [333/1500], global_step: 999, lr: 0.001000, loss: 1.700925, loss_shrink_maps: 0.922795, loss_threshold_maps: 0.608902, loss_binary_maps: 0.183419, avg_reader_cost: 1.52966 s, avg_batch_cost: 1.76127 s, avg_samples: 12.5, ips: 7.09714 samples/s, eta: 6:00:14
[2024/07/28 00:14:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:14:49] ppocr INFO: epoch: [334/1500], global_step: 1000, lr: 0.001000, loss: 1.738771, loss_shrink_maps: 0.928993, loss_threshold_maps: 0.608902, loss_binary_maps: 0.184001, avg_reader_cost: 0.43265 s, avg_batch_cost: 0.52788 s, avg_samples: 4.8, ips: 9.09305 samples/s, eta: 6:00:05
[2024/07/28 00:14:51] ppocr INFO: epoch: [334/1500], global_step: 1002, lr: 0.001000, loss: 1.752892, loss_shrink_maps: 0.936253, loss_threshold_maps: 0.631463, loss_binary_maps: 0.185399, avg_reader_cost: 1.14722 s, avg_batch_cost: 1.29311 s, avg_samples: 7.7, ips: 5.95462 samples/s, eta: 5:59:54
[2024/07/28 00:14:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:14:59] ppocr INFO: epoch: [335/1500], global_step: 1005, lr: 0.001000, loss: 1.750008, loss_shrink_maps: 0.935382, loss_threshold_maps: 0.621866, loss_binary_maps: 0.185081, avg_reader_cost: 1.55942 s, avg_batch_cost: 1.78879 s, avg_samples: 12.5, ips: 6.98796 samples/s, eta: 5:59:33
[2024/07/28 00:15:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:15:06] ppocr INFO: epoch: [336/1500], global_step: 1008, lr: 0.001000, loss: 1.750008, loss_shrink_maps: 0.935382, loss_threshold_maps: 0.624105, loss_binary_maps: 0.185081, avg_reader_cost: 1.51010 s, avg_batch_cost: 1.73923 s, avg_samples: 12.5, ips: 7.18708 samples/s, eta: 5:59:11
[2024/07/28 00:15:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:15:14] ppocr INFO: epoch: [337/1500], global_step: 1010, lr: 0.001000, loss: 1.750008, loss_shrink_maps: 0.935382, loss_threshold_maps: 0.624105, loss_binary_maps: 0.185081, avg_reader_cost: 0.97631 s, avg_batch_cost: 1.18018 s, avg_samples: 9.6, ips: 8.13437 samples/s, eta: 5:58:57
[2024/07/28 00:15:14] ppocr INFO: epoch: [337/1500], global_step: 1011, lr: 0.001000, loss: 1.750008, loss_shrink_maps: 0.932479, loss_threshold_maps: 0.624105, loss_binary_maps: 0.184793, avg_reader_cost: 0.63584 s, avg_batch_cost: 0.69066 s, avg_samples: 2.9, ips: 4.19890 samples/s, eta: 5:58:53
[2024/07/28 00:15:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:15:22] ppocr INFO: epoch: [338/1500], global_step: 1014, lr: 0.001000, loss: 1.759424, loss_shrink_maps: 0.934916, loss_threshold_maps: 0.619365, loss_binary_maps: 0.185273, avg_reader_cost: 1.54157 s, avg_batch_cost: 1.76982 s, avg_samples: 12.5, ips: 7.06288 samples/s, eta: 5:58:32
[2024/07/28 00:15:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:15:30] ppocr INFO: epoch: [339/1500], global_step: 1017, lr: 0.001000, loss: 1.759424, loss_shrink_maps: 0.934916, loss_threshold_maps: 0.605237, loss_binary_maps: 0.185273, avg_reader_cost: 1.50063 s, avg_batch_cost: 1.73203 s, avg_samples: 12.5, ips: 7.21695 samples/s, eta: 5:58:09
[2024/07/28 00:15:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:15:38] ppocr INFO: epoch: [340/1500], global_step: 1020, lr: 0.001000, loss: 1.724715, loss_shrink_maps: 0.932479, loss_threshold_maps: 0.603823, loss_binary_maps: 0.184570, avg_reader_cost: 1.66152 s, avg_batch_cost: 1.91747 s, avg_samples: 12.5, ips: 6.51899 samples/s, eta: 5:57:53

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:00,  8.22it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.18it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.79it/s]
eval model::   2%|▏         | 8/500 [00:00<00:29, 16.74it/s]
eval model::   2%|▏         | 10/500 [00:00<00:28, 17.14it/s]
eval model::   3%|▎         | 13/500 [00:00<00:24, 19.51it/s]
eval model::   3%|▎         | 16/500 [00:00<00:22, 21.29it/s]
eval model::   4%|▍         | 19/500 [00:00<00:23, 20.38it/s]
eval model::   4%|▍         | 22/500 [00:01<00:25, 18.48it/s]
eval model::   5%|▌         | 25/500 [00:01<00:25, 18.86it/s]
eval model::   6%|▌         | 28/500 [00:01<00:23, 19.72it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.45it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 19.20it/s]
eval model::   8%|▊         | 38/500 [00:01<00:22, 20.94it/s]
eval model::   8%|▊         | 41/500 [00:02<00:25, 18.28it/s]
eval model::   9%|▊         | 43/500 [00:02<00:27, 16.68it/s]
eval model::   9%|▉         | 45/500 [00:02<00:26, 17.34it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 16.84it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 18.88it/s]
eval model::  10%|█         | 52/500 [00:02<00:24, 18.31it/s]
eval model::  11%|█         | 55/500 [00:02<00:23, 19.33it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:22, 19.39it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:24, 17.78it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:30, 14.23it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 14.64it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:26, 16.50it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:24, 17.55it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:23, 18.37it/s]
eval model::  15%|█▍        | 74/500 [00:04<00:22, 18.62it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:21, 20.12it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 23.21it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 20.63it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.25it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:24, 17.07it/s]
eval model::  18%|█▊        | 91/500 [00:05<00:24, 16.76it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:25, 16.24it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:24, 16.60it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:24, 16.50it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:23, 17.18it/s]
eval model::  20%|██        | 101/500 [00:05<00:23, 16.69it/s]
eval model::  21%|██        | 104/500 [00:05<00:21, 18.80it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:19, 20.45it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 20.91it/s]
eval model::  23%|██▎       | 113/500 [00:06<00:18, 20.81it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:18, 21.16it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 22.41it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:17, 21.51it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:18, 19.75it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 19.79it/s]
eval model::  26%|██▌       | 131/500 [00:07<00:18, 19.44it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:20, 18.32it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:18, 19.20it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.08it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 19.24it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 20.59it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:18, 19.55it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 18.91it/s]
eval model::  30%|███       | 151/500 [00:08<00:17, 19.78it/s]
eval model::  31%|███       | 155/500 [00:08<00:14, 23.59it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:14, 22.93it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:19, 17.57it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:17, 18.89it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 19.15it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 20.42it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:16, 19.87it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 15.86it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.19it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.47it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 18.28it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 19.09it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:14, 21.28it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:13, 23.24it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:14, 21.13it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:13, 21.91it/s]
eval model::  40%|████      | 202/500 [00:10<00:12, 23.08it/s]
eval model::  41%|████      | 205/500 [00:10<00:19, 15.43it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:17, 16.52it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 15.26it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.11it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 14.13it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.43it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:19, 14.29it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:18, 15.04it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:16, 17.20it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:14, 19.07it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:11, 22.47it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 21.86it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 23.08it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:10, 24.35it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 21.19it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:11, 21.60it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:11, 20.96it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 21.57it/s]
eval model::  51%|█████     | 255/500 [00:13<00:12, 20.11it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:13, 17.65it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:13, 17.76it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:12, 18.60it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:15, 15.58it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:14, 16.28it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:13, 16.89it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.19it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:13, 16.51it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 18.63it/s]
eval model::  56%|█████▌    | 280/500 [00:15<00:11, 18.77it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:11, 18.71it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:18, 11.91it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.42it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 17.13it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:13, 15.46it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:14, 13.83it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 16.12it/s]
eval model::  61%|██████    | 303/500 [00:16<00:10, 18.08it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 19.16it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 21.17it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 20.60it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:08, 21.50it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:07, 23.34it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:07, 23.91it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:07, 23.32it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 23.91it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:07, 23.05it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 21.84it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:08, 20.45it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:08, 18.49it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:12, 13.14it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:11, 13.75it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:09, 16.13it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:08, 18.45it/s]
eval model::  70%|███████   | 352/500 [00:19<00:08, 18.17it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 18.41it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 19.21it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 17.73it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.09it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 17.81it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 19.51it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:06, 19.99it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 21.65it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 23.38it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:04, 23.72it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:08, 13.24it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:07, 14.22it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:06, 15.86it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:05, 17.85it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:04, 21.40it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 19.42it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 21.25it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:03, 24.06it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 25.23it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 23.54it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 20.60it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:04, 17.71it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:04, 19.23it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.73it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:03, 18.58it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 18.92it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:02, 22.28it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 21.70it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 20.63it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 19.88it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 19.54it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 19.42it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 21.03it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:02, 21.47it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:02, 19.17it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:02, 18.98it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 14.64it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 15.15it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 18.05it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.00it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 19.02it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 21.15it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 23.01it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 25.35it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 24.90it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 24.99it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 23.41it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 22.64it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 25.66it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.97it/s]
[2024/07/28 00:16:04] ppocr INFO: cur metric, precision: 0.7000582411182295, recall: 0.5787193066923447, hmean: 0.6336320506062204, fps: 44.6370540470635
[2024/07/28 00:16:05] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/28 00:16:05] ppocr INFO: best metric, hmean: 0.6336320506062204, precision: 0.7000582411182295, recall: 0.5787193066923447, fps: 44.6370540470635, best_epoch: 340
[2024/07/28 00:16:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:16:12] ppocr INFO: epoch: [341/1500], global_step: 1023, lr: 0.001000, loss: 1.692542, loss_shrink_maps: 0.910822, loss_threshold_maps: 0.603823, loss_binary_maps: 0.180824, avg_reader_cost: 1.62301 s, avg_batch_cost: 1.90427 s, avg_samples: 12.5, ips: 6.56418 samples/s, eta: 5:57:36
[2024/07/28 00:16:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:16:20] ppocr INFO: epoch: [342/1500], global_step: 1026, lr: 0.001000, loss: 1.684275, loss_shrink_maps: 0.904693, loss_threshold_maps: 0.602964, loss_binary_maps: 0.179544, avg_reader_cost: 1.52702 s, avg_batch_cost: 1.75683 s, avg_samples: 12.5, ips: 7.11507 samples/s, eta: 5:57:15
[2024/07/28 00:16:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:16:28] ppocr INFO: epoch: [343/1500], global_step: 1029, lr: 0.001000, loss: 1.679546, loss_shrink_maps: 0.900760, loss_threshold_maps: 0.602964, loss_binary_maps: 0.178837, avg_reader_cost: 1.54295 s, avg_batch_cost: 1.81070 s, avg_samples: 12.5, ips: 6.90340 samples/s, eta: 5:56:55
[2024/07/28 00:16:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:16:34] ppocr INFO: epoch: [344/1500], global_step: 1030, lr: 0.001000, loss: 1.679546, loss_shrink_maps: 0.900760, loss_threshold_maps: 0.603249, loss_binary_maps: 0.178837, avg_reader_cost: 0.42462 s, avg_batch_cost: 0.50874 s, avg_samples: 4.8, ips: 9.43515 samples/s, eta: 5:56:45
[2024/07/28 00:16:35] ppocr INFO: epoch: [344/1500], global_step: 1032, lr: 0.001000, loss: 1.668892, loss_shrink_maps: 0.885846, loss_threshold_maps: 0.602904, loss_binary_maps: 0.176026, avg_reader_cost: 1.10858 s, avg_batch_cost: 1.25386 s, avg_samples: 7.7, ips: 6.14105 samples/s, eta: 5:56:33
[2024/07/28 00:16:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:16:43] ppocr INFO: epoch: [345/1500], global_step: 1035, lr: 0.001000, loss: 1.681645, loss_shrink_maps: 0.900760, loss_threshold_maps: 0.607220, loss_binary_maps: 0.178837, avg_reader_cost: 1.49542 s, avg_batch_cost: 1.74591 s, avg_samples: 12.5, ips: 7.15959 samples/s, eta: 5:56:11
[2024/07/28 00:16:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:16:52] ppocr INFO: epoch: [346/1500], global_step: 1038, lr: 0.001000, loss: 1.704276, loss_shrink_maps: 0.908984, loss_threshold_maps: 0.617838, loss_binary_maps: 0.180544, avg_reader_cost: 1.68602 s, avg_batch_cost: 1.94290 s, avg_samples: 12.5, ips: 6.43369 samples/s, eta: 5:55:56
[2024/07/28 00:16:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:16:59] ppocr INFO: epoch: [347/1500], global_step: 1040, lr: 0.001000, loss: 1.681645, loss_shrink_maps: 0.900760, loss_threshold_maps: 0.617838, loss_binary_maps: 0.178837, avg_reader_cost: 0.93480 s, avg_batch_cost: 1.12775 s, avg_samples: 9.6, ips: 8.51250 samples/s, eta: 5:55:40
[2024/07/28 00:16:59] ppocr INFO: epoch: [347/1500], global_step: 1041, lr: 0.001000, loss: 1.676466, loss_shrink_maps: 0.900760, loss_threshold_maps: 0.612547, loss_binary_maps: 0.178837, avg_reader_cost: 0.60947 s, avg_batch_cost: 0.66425 s, avg_samples: 2.9, ips: 4.36581 samples/s, eta: 5:55:35
[2024/07/28 00:17:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:17:08] ppocr INFO: epoch: [348/1500], global_step: 1044, lr: 0.001000, loss: 1.680368, loss_shrink_maps: 0.908584, loss_threshold_maps: 0.612547, loss_binary_maps: 0.180533, avg_reader_cost: 1.62040 s, avg_batch_cost: 1.84828 s, avg_samples: 12.5, ips: 6.76304 samples/s, eta: 5:55:17
[2024/07/28 00:17:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:17:15] ppocr INFO: epoch: [349/1500], global_step: 1047, lr: 0.001000, loss: 1.733621, loss_shrink_maps: 0.915460, loss_threshold_maps: 0.624931, loss_binary_maps: 0.182296, avg_reader_cost: 1.55168 s, avg_batch_cost: 1.78320 s, avg_samples: 12.5, ips: 7.00988 samples/s, eta: 5:54:56
[2024/07/28 00:17:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:17:23] ppocr INFO: epoch: [350/1500], global_step: 1050, lr: 0.001000, loss: 1.733621, loss_shrink_maps: 0.915460, loss_threshold_maps: 0.631956, loss_binary_maps: 0.182296, avg_reader_cost: 1.57036 s, avg_batch_cost: 1.79822 s, avg_samples: 12.5, ips: 6.95131 samples/s, eta: 5:54:36
[2024/07/28 00:17:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:17:31] ppocr INFO: epoch: [351/1500], global_step: 1053, lr: 0.001000, loss: 1.733621, loss_shrink_maps: 0.915460, loss_threshold_maps: 0.631956, loss_binary_maps: 0.182296, avg_reader_cost: 1.53946 s, avg_batch_cost: 1.77042 s, avg_samples: 12.5, ips: 7.06047 samples/s, eta: 5:54:15
[2024/07/28 00:17:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:17:39] ppocr INFO: epoch: [352/1500], global_step: 1056, lr: 0.001000, loss: 1.706569, loss_shrink_maps: 0.910599, loss_threshold_maps: 0.615671, loss_binary_maps: 0.180634, avg_reader_cost: 1.51942 s, avg_batch_cost: 1.74846 s, avg_samples: 12.5, ips: 7.14917 samples/s, eta: 5:53:53
[2024/07/28 00:17:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:17:47] ppocr INFO: epoch: [353/1500], global_step: 1059, lr: 0.001000, loss: 1.685021, loss_shrink_maps: 0.903598, loss_threshold_maps: 0.608717, loss_binary_maps: 0.178630, avg_reader_cost: 1.54862 s, avg_batch_cost: 1.77755 s, avg_samples: 12.5, ips: 7.03214 samples/s, eta: 5:53:32
[2024/07/28 00:17:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:17:53] ppocr INFO: epoch: [354/1500], global_step: 1060, lr: 0.001000, loss: 1.664191, loss_shrink_maps: 0.878371, loss_threshold_maps: 0.608717, loss_binary_maps: 0.173661, avg_reader_cost: 0.42402 s, avg_batch_cost: 0.51770 s, avg_samples: 4.8, ips: 9.27179 samples/s, eta: 5:53:23
[2024/07/28 00:17:55] ppocr INFO: epoch: [354/1500], global_step: 1062, lr: 0.001000, loss: 1.634132, loss_shrink_maps: 0.847005, loss_threshold_maps: 0.615315, loss_binary_maps: 0.167807, avg_reader_cost: 1.12700 s, avg_batch_cost: 1.27313 s, avg_samples: 7.7, ips: 6.04809 samples/s, eta: 5:53:12
[2024/07/28 00:17:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:18:03] ppocr INFO: epoch: [355/1500], global_step: 1065, lr: 0.001000, loss: 1.609590, loss_shrink_maps: 0.837176, loss_threshold_maps: 0.605219, loss_binary_maps: 0.165830, avg_reader_cost: 1.69031 s, avg_batch_cost: 1.91794 s, avg_samples: 12.5, ips: 6.51740 samples/s, eta: 5:52:55
[2024/07/28 00:18:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:18:11] ppocr INFO: epoch: [356/1500], global_step: 1068, lr: 0.001000, loss: 1.609590, loss_shrink_maps: 0.837176, loss_threshold_maps: 0.599203, loss_binary_maps: 0.165830, avg_reader_cost: 1.60523 s, avg_batch_cost: 1.83362 s, avg_samples: 12.5, ips: 6.81711 samples/s, eta: 5:52:36
[2024/07/28 00:18:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:18:19] ppocr INFO: epoch: [357/1500], global_step: 1070, lr: 0.001000, loss: 1.632425, loss_shrink_maps: 0.850547, loss_threshold_maps: 0.605558, loss_binary_maps: 0.168968, avg_reader_cost: 0.95368 s, avg_batch_cost: 1.14969 s, avg_samples: 9.6, ips: 8.35010 samples/s, eta: 5:52:21
[2024/07/28 00:18:19] ppocr INFO: epoch: [357/1500], global_step: 1071, lr: 0.001000, loss: 1.632425, loss_shrink_maps: 0.850547, loss_threshold_maps: 0.605558, loss_binary_maps: 0.168968, avg_reader_cost: 0.62036 s, avg_batch_cost: 0.67511 s, avg_samples: 2.9, ips: 4.29559 samples/s, eta: 5:52:17
[2024/07/28 00:18:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:18:27] ppocr INFO: epoch: [358/1500], global_step: 1074, lr: 0.001000, loss: 1.632425, loss_shrink_maps: 0.853765, loss_threshold_maps: 0.609245, loss_binary_maps: 0.168968, avg_reader_cost: 1.51478 s, avg_batch_cost: 1.74317 s, avg_samples: 12.5, ips: 7.17084 samples/s, eta: 5:51:55
[2024/07/28 00:18:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:18:35] ppocr INFO: epoch: [359/1500], global_step: 1077, lr: 0.001000, loss: 1.632425, loss_shrink_maps: 0.861998, loss_threshold_maps: 0.609245, loss_binary_maps: 0.170885, avg_reader_cost: 1.52942 s, avg_batch_cost: 1.77768 s, avg_samples: 12.5, ips: 7.03165 samples/s, eta: 5:51:35
[2024/07/28 00:18:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:18:43] ppocr INFO: epoch: [360/1500], global_step: 1080, lr: 0.001000, loss: 1.656538, loss_shrink_maps: 0.875410, loss_threshold_maps: 0.618816, loss_binary_maps: 0.174340, avg_reader_cost: 1.53077 s, avg_batch_cost: 1.77553 s, avg_samples: 12.5, ips: 7.04014 samples/s, eta: 5:51:14

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.30it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 16.92it/s]
eval model::   1%|▏         | 7/500 [00:00<00:24, 19.84it/s]
eval model::   2%|▏         | 9/500 [00:00<00:26, 18.84it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.32it/s]
eval model::   3%|▎         | 13/500 [00:00<00:26, 18.08it/s]
eval model::   3%|▎         | 15/500 [00:00<00:26, 18.49it/s]
eval model::   4%|▎         | 18/500 [00:00<00:22, 21.52it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.56it/s]
eval model::   5%|▍         | 24/500 [00:01<00:21, 21.92it/s]
eval model::   5%|▌         | 27/500 [00:01<00:21, 22.29it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 22.12it/s]
eval model::   7%|▋         | 33/500 [00:01<00:21, 21.65it/s]
eval model::   7%|▋         | 36/500 [00:01<00:22, 20.54it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 17.87it/s]
eval model::   8%|▊         | 41/500 [00:02<00:25, 18.17it/s]
eval model::   9%|▊         | 43/500 [00:02<00:27, 16.53it/s]
eval model::   9%|▉         | 46/500 [00:02<00:28, 16.21it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.24it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 18.99it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 19.53it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:23, 19.06it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:23, 18.57it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 15.72it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.39it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.31it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 19.78it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 20.27it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:22, 19.27it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 20.75it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.63it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.67it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 20.47it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:21, 19.35it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:20, 19.39it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.61it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.68it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.10it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.79it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:17, 22.64it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:17, 21.97it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:16, 23.27it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:16, 23.38it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:16, 23.62it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 20.82it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 17.82it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 18.86it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:20, 17.84it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:20, 18.04it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:18, 19.63it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:19, 18.92it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.59it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.87it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:17, 20.06it/s]
eval model::  30%|███       | 151/500 [00:07<00:17, 20.00it/s]
eval model::  31%|███       | 155/500 [00:07<00:14, 23.26it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:14, 23.53it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:18, 18.20it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:18, 18.46it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 19.20it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 20.10it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:16, 19.52it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 15.56it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.09it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:19, 16.11it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.55it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.14it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.13it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:14, 21.28it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.35it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 21.68it/s]
eval model::  41%|████      | 204/500 [00:10<00:12, 23.16it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:21, 13.80it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:19, 14.72it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:19, 15.05it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.00it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.40it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.87it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:18, 14.90it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:18, 14.85it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:15, 18.19it/s]
eval model::  45%|████▌     | 227/500 [00:11<00:13, 20.31it/s]
eval model::  46%|████▌     | 231/500 [00:11<00:10, 25.14it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:11, 22.75it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 23.49it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:10, 23.87it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:13, 19.61it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:12, 20.92it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:12, 20.69it/s]
eval model::  51%|█████     | 253/500 [00:12<00:10, 23.36it/s]
eval model::  51%|█████     | 256/500 [00:13<00:10, 23.44it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 23.80it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:10, 23.05it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:11, 20.94it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:10, 21.44it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:10, 21.33it/s]
eval model::  55%|█████▍    | 274/500 [00:13<00:10, 20.56it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 19.09it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 18.76it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 20.51it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:17, 11.97it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:16, 12.78it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:13, 15.40it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 16.19it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.47it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:13, 14.59it/s]
eval model::  60%|██████    | 300/500 [00:15<00:12, 15.77it/s]
eval model::  61%|██████    | 303/500 [00:15<00:10, 17.95it/s]
eval model::  61%|██████    | 306/500 [00:15<00:10, 18.59it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 19.98it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 20.86it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 21.53it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 22.23it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 22.32it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:07, 22.43it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:07, 24.03it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:07, 22.93it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 23.28it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:06, 23.61it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 22.41it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:09, 16.07it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:08, 18.54it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:07, 19.29it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 20.97it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.90it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 22.11it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:06, 21.17it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:06, 19.44it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:07, 17.98it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 19.38it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 20.37it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 21.95it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 22.92it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 21.53it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:07, 14.99it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:07, 15.67it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:06, 17.52it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:05, 18.45it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 19.11it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 19.20it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 20.06it/s]
eval model::  81%|████████  | 406/500 [00:20<00:04, 21.17it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:03, 23.58it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:03, 23.73it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:04, 20.61it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 19.50it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 16.62it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 16.02it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.78it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 18.54it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 17.80it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 21.01it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 22.47it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 21.80it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 24.09it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 24.42it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 22.20it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:01, 23.26it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 22.36it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 21.21it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:02, 17.19it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:02, 16.13it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 16.82it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 19.03it/s]
eval model::  95%|█████████▍| 473/500 [00:24<00:01, 19.13it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 18.98it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 21.00it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 23.04it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 21.82it/s]
eval model::  97%|█████████▋| 487/500 [00:24<00:00, 20.55it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 21.59it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 18.34it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 17.63it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 19.66it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.54it/s]
[2024/07/28 00:19:08] ppocr INFO: cur metric, precision: 0.633856138453218, recall: 0.5642753972075109, hmean: 0.5970453387671931, fps: 46.00852840200949
[2024/07/28 00:19:08] ppocr INFO: best metric, hmean: 0.6336320506062204, precision: 0.7000582411182295, recall: 0.5787193066923447, fps: 44.6370540470635, best_epoch: 340
[2024/07/28 00:19:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:19:17] ppocr INFO: epoch: [361/1500], global_step: 1083, lr: 0.001000, loss: 1.710404, loss_shrink_maps: 0.904476, loss_threshold_maps: 0.622944, loss_binary_maps: 0.179257, avg_reader_cost: 1.89698 s, avg_batch_cost: 2.32545 s, avg_samples: 12.5, ips: 5.37530 samples/s, eta: 5:51:10
[2024/07/28 00:19:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:19:25] ppocr INFO: epoch: [362/1500], global_step: 1086, lr: 0.001000, loss: 1.745214, loss_shrink_maps: 0.919082, loss_threshold_maps: 0.625792, loss_binary_maps: 0.182624, avg_reader_cost: 1.52501 s, avg_batch_cost: 1.77253 s, avg_samples: 12.5, ips: 7.05206 samples/s, eta: 5:50:49
[2024/07/28 00:19:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:19:33] ppocr INFO: epoch: [363/1500], global_step: 1089, lr: 0.001000, loss: 1.779011, loss_shrink_maps: 0.951254, loss_threshold_maps: 0.632712, loss_binary_maps: 0.187664, avg_reader_cost: 1.57643 s, avg_batch_cost: 1.86456 s, avg_samples: 12.5, ips: 6.70398 samples/s, eta: 5:50:31
[2024/07/28 00:19:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:19:39] ppocr INFO: epoch: [364/1500], global_step: 1090, lr: 0.001000, loss: 1.779011, loss_shrink_maps: 0.951254, loss_threshold_maps: 0.632712, loss_binary_maps: 0.187664, avg_reader_cost: 0.40712 s, avg_batch_cost: 0.49692 s, avg_samples: 4.8, ips: 9.65955 samples/s, eta: 5:50:21
[2024/07/28 00:19:41] ppocr INFO: epoch: [364/1500], global_step: 1092, lr: 0.001000, loss: 1.765797, loss_shrink_maps: 0.951254, loss_threshold_maps: 0.630058, loss_binary_maps: 0.187664, avg_reader_cost: 1.08654 s, avg_batch_cost: 1.23349 s, avg_samples: 7.7, ips: 6.24244 samples/s, eta: 5:50:09
[2024/07/28 00:19:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:19:49] ppocr INFO: epoch: [365/1500], global_step: 1095, lr: 0.001000, loss: 1.756851, loss_shrink_maps: 0.935207, loss_threshold_maps: 0.627355, loss_binary_maps: 0.185677, avg_reader_cost: 1.59405 s, avg_batch_cost: 1.88524 s, avg_samples: 12.5, ips: 6.63044 samples/s, eta: 5:49:52
[2024/07/28 00:19:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:19:58] ppocr INFO: epoch: [366/1500], global_step: 1098, lr: 0.001000, loss: 1.756851, loss_shrink_maps: 0.935207, loss_threshold_maps: 0.624129, loss_binary_maps: 0.185677, avg_reader_cost: 1.69200 s, avg_batch_cost: 1.95132 s, avg_samples: 12.5, ips: 6.40592 samples/s, eta: 5:49:36
[2024/07/28 00:19:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:20:05] ppocr INFO: epoch: [367/1500], global_step: 1100, lr: 0.001000, loss: 1.754933, loss_shrink_maps: 0.930912, loss_threshold_maps: 0.624129, loss_binary_maps: 0.185148, avg_reader_cost: 0.95177 s, avg_batch_cost: 1.12534 s, avg_samples: 9.6, ips: 8.53075 samples/s, eta: 5:49:21
[2024/07/28 00:20:06] ppocr INFO: epoch: [367/1500], global_step: 1101, lr: 0.001000, loss: 1.754933, loss_shrink_maps: 0.930912, loss_threshold_maps: 0.625693, loss_binary_maps: 0.185148, avg_reader_cost: 0.60831 s, avg_batch_cost: 0.66309 s, avg_samples: 2.9, ips: 4.37348 samples/s, eta: 5:49:16
[2024/07/28 00:20:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:20:14] ppocr INFO: epoch: [368/1500], global_step: 1104, lr: 0.001000, loss: 1.762037, loss_shrink_maps: 0.933497, loss_threshold_maps: 0.625693, loss_binary_maps: 0.185312, avg_reader_cost: 1.71072 s, avg_batch_cost: 1.94592 s, avg_samples: 12.5, ips: 6.42369 samples/s, eta: 5:49:00
[2024/07/28 00:20:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:20:22] ppocr INFO: epoch: [369/1500], global_step: 1107, lr: 0.001000, loss: 1.762037, loss_shrink_maps: 0.933497, loss_threshold_maps: 0.624200, loss_binary_maps: 0.185312, avg_reader_cost: 1.49238 s, avg_batch_cost: 1.74053 s, avg_samples: 12.5, ips: 7.18171 samples/s, eta: 5:48:39
[2024/07/28 00:20:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:20:30] ppocr INFO: epoch: [370/1500], global_step: 1110, lr: 0.001000, loss: 1.765897, loss_shrink_maps: 0.933497, loss_threshold_maps: 0.624200, loss_binary_maps: 0.185312, avg_reader_cost: 1.53529 s, avg_batch_cost: 1.76387 s, avg_samples: 12.5, ips: 7.08670 samples/s, eta: 5:48:18
[2024/07/28 00:20:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:20:38] ppocr INFO: epoch: [371/1500], global_step: 1113, lr: 0.001000, loss: 1.780667, loss_shrink_maps: 0.958705, loss_threshold_maps: 0.625609, loss_binary_maps: 0.190775, avg_reader_cost: 1.56844 s, avg_batch_cost: 1.81764 s, avg_samples: 12.5, ips: 6.87703 samples/s, eta: 5:47:58
[2024/07/28 00:20:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:20:46] ppocr INFO: epoch: [372/1500], global_step: 1116, lr: 0.001000, loss: 1.798127, loss_shrink_maps: 0.971047, loss_threshold_maps: 0.625609, loss_binary_maps: 0.193183, avg_reader_cost: 1.56933 s, avg_batch_cost: 1.79826 s, avg_samples: 12.5, ips: 6.95116 samples/s, eta: 5:47:38
[2024/07/28 00:20:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:20:54] ppocr INFO: epoch: [373/1500], global_step: 1119, lr: 0.001000, loss: 1.798127, loss_shrink_maps: 0.971047, loss_threshold_maps: 0.626206, loss_binary_maps: 0.193183, avg_reader_cost: 1.55987 s, avg_batch_cost: 1.80348 s, avg_samples: 12.5, ips: 6.93106 samples/s, eta: 5:47:18
[2024/07/28 00:20:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:21:00] ppocr INFO: epoch: [374/1500], global_step: 1120, lr: 0.001000, loss: 1.798127, loss_shrink_maps: 0.971047, loss_threshold_maps: 0.626206, loss_binary_maps: 0.193183, avg_reader_cost: 0.41664 s, avg_batch_cost: 0.52828 s, avg_samples: 4.8, ips: 9.08610 samples/s, eta: 5:47:09
[2024/07/28 00:21:02] ppocr INFO: epoch: [374/1500], global_step: 1122, lr: 0.001000, loss: 1.792867, loss_shrink_maps: 0.963565, loss_threshold_maps: 0.624859, loss_binary_maps: 0.191280, avg_reader_cost: 1.14797 s, avg_batch_cost: 1.29250 s, avg_samples: 7.7, ips: 5.95742 samples/s, eta: 5:46:59
[2024/07/28 00:21:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:21:10] ppocr INFO: epoch: [375/1500], global_step: 1125, lr: 0.001000, loss: 1.713756, loss_shrink_maps: 0.908928, loss_threshold_maps: 0.615428, loss_binary_maps: 0.181186, avg_reader_cost: 1.58314 s, avg_batch_cost: 1.81176 s, avg_samples: 12.5, ips: 6.89937 samples/s, eta: 5:46:39
[2024/07/28 00:21:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:21:18] ppocr INFO: epoch: [376/1500], global_step: 1128, lr: 0.001000, loss: 1.708065, loss_shrink_maps: 0.907282, loss_threshold_maps: 0.611492, loss_binary_maps: 0.181076, avg_reader_cost: 1.53733 s, avg_batch_cost: 1.77377 s, avg_samples: 12.5, ips: 7.04716 samples/s, eta: 5:46:18
[2024/07/28 00:21:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:21:25] ppocr INFO: epoch: [377/1500], global_step: 1130, lr: 0.001000, loss: 1.679189, loss_shrink_maps: 0.885790, loss_threshold_maps: 0.607371, loss_binary_maps: 0.176157, avg_reader_cost: 0.97584 s, avg_batch_cost: 1.18134 s, avg_samples: 9.6, ips: 8.12635 samples/s, eta: 5:46:05
[2024/07/28 00:21:26] ppocr INFO: epoch: [377/1500], global_step: 1131, lr: 0.001000, loss: 1.651794, loss_shrink_maps: 0.868979, loss_threshold_maps: 0.607371, loss_binary_maps: 0.172427, avg_reader_cost: 0.63632 s, avg_batch_cost: 0.69125 s, avg_samples: 2.9, ips: 4.19527 samples/s, eta: 5:46:01
[2024/07/28 00:21:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:21:34] ppocr INFO: epoch: [378/1500], global_step: 1134, lr: 0.001000, loss: 1.638454, loss_shrink_maps: 0.868979, loss_threshold_maps: 0.604869, loss_binary_maps: 0.172427, avg_reader_cost: 1.54373 s, avg_batch_cost: 1.77404 s, avg_samples: 12.5, ips: 7.04608 samples/s, eta: 5:45:40
[2024/07/28 00:21:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:21:42] ppocr INFO: epoch: [379/1500], global_step: 1137, lr: 0.001000, loss: 1.638454, loss_shrink_maps: 0.865217, loss_threshold_maps: 0.607371, loss_binary_maps: 0.171275, avg_reader_cost: 1.57503 s, avg_batch_cost: 1.80300 s, avg_samples: 12.5, ips: 6.93288 samples/s, eta: 5:45:20
[2024/07/28 00:21:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:21:50] ppocr INFO: epoch: [380/1500], global_step: 1140, lr: 0.001000, loss: 1.643500, loss_shrink_maps: 0.881331, loss_threshold_maps: 0.607371, loss_binary_maps: 0.175065, avg_reader_cost: 1.54050 s, avg_batch_cost: 1.81082 s, avg_samples: 12.5, ips: 6.90296 samples/s, eta: 5:45:01

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:06,  7.51it/s]
eval model::   1%|          | 3/500 [00:00<00:37, 13.11it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.86it/s]
eval model::   2%|▏         | 9/500 [00:00<00:23, 20.47it/s]
eval model::   2%|▏         | 12/500 [00:00<00:27, 18.06it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.50it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 21.00it/s]
eval model::   4%|▍         | 20/500 [00:01<00:22, 20.92it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 22.50it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 19.82it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 21.00it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 19.95it/s]
eval model::   7%|▋         | 35/500 [00:01<00:23, 19.54it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.71it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.18it/s]
eval model::   9%|▊         | 43/500 [00:02<00:26, 17.48it/s]
eval model::   9%|▉         | 46/500 [00:02<00:24, 18.49it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 19.72it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 21.30it/s]
eval model::  11%|█         | 55/500 [00:02<00:19, 23.11it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:20, 21.48it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:25, 17.43it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:25, 17.40it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 19.70it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:19, 21.82it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 22.51it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:18, 22.45it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:19, 22.14it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:17, 23.81it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:17, 23.75it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 20.21it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:21, 19.24it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.48it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.49it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:18, 21.85it/s]
eval model::  20%|██        | 102/500 [00:05<00:18, 21.24it/s]
eval model::  21%|██        | 106/500 [00:05<00:16, 24.15it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.72it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:17, 22.29it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:18, 20.45it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:17, 22.10it/s]
eval model::  24%|██▍       | 121/500 [00:05<00:19, 19.85it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 19.10it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 18.92it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 20.81it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:17, 20.86it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:16, 21.74it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:17, 20.32it/s]
eval model::  28%|██▊       | 141/500 [00:06<00:18, 19.24it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.28it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:16, 21.08it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 21.33it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 19.22it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 21.31it/s]
eval model::  32%|███▏      | 159/500 [00:07<00:16, 20.51it/s]
eval model::  32%|███▏      | 162/500 [00:07<00:17, 19.00it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:17, 18.74it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:16, 19.89it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:15, 21.93it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:15, 20.83it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:22, 14.67it/s]
eval model::  36%|███▌      | 178/500 [00:08<00:20, 15.36it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:22, 14.53it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:17, 17.63it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 19.98it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.25it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:13, 22.22it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:13, 23.23it/s]
eval model::  40%|███▉      | 198/500 [00:09<00:12, 23.77it/s]
eval model::  40%|████      | 201/500 [00:09<00:13, 22.43it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.54it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:24, 11.82it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:24, 11.98it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:22, 12.74it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:20, 13.89it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:23, 12.17it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.09it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 15.75it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 15.08it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:14, 19.34it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:11, 23.71it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:10, 24.20it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:10, 24.48it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:10, 24.92it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 20.63it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:11, 21.49it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:11, 22.15it/s]
eval model::  50%|█████     | 252/500 [00:12<00:10, 23.30it/s]
eval model::  51%|█████     | 255/500 [00:12<00:10, 22.83it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:10, 22.96it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 23.10it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:11, 20.30it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:11, 19.66it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:11, 20.59it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:10, 21.13it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 18.01it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 18.44it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 19.93it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:17, 12.58it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:16, 13.18it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.78it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.77it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:14, 14.51it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:11, 16.78it/s]
eval model::  60%|██████    | 301/500 [00:15<00:11, 16.66it/s]
eval model::  61%|██████    | 304/500 [00:15<00:10, 18.40it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:09, 19.55it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:08, 21.49it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:07, 23.94it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:07, 24.11it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:07, 23.69it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:07, 22.39it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:07, 23.45it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:07, 21.98it/s]
eval model::  67%|██████▋   | 333/500 [00:16<00:07, 23.66it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 22.56it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 20.15it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:10, 15.77it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:08, 17.99it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:07, 19.74it/s]
eval model::  70%|███████   | 351/500 [00:17<00:07, 20.48it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 22.27it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:06, 20.56it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:06, 20.35it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:07, 18.34it/s]
eval model::  73%|███████▎  | 365/500 [00:18<00:07, 17.58it/s]
eval model::  74%|███████▎  | 368/500 [00:18<00:06, 19.68it/s]
eval model::  74%|███████▍  | 371/500 [00:18<00:06, 20.04it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 20.16it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 21.06it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:05, 21.63it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:05, 23.22it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:08, 13.84it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:07, 15.77it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 16.68it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:06, 16.56it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 18.68it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 19.57it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 20.46it/s]
eval model::  81%|████████  | 406/500 [00:20<00:04, 20.90it/s]
eval model::  82%|████████▏ | 409/500 [00:20<00:04, 22.11it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:03, 22.76it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:03, 21.97it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:03, 21.02it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:04, 18.75it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:03, 19.62it/s]
eval model::  85%|████████▌ | 427/500 [00:21<00:03, 19.14it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 20.74it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 21.12it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 22.78it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 22.82it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 22.88it/s]
eval model::  89%|████████▉ | 446/500 [00:22<00:02, 25.18it/s]
eval model::  90%|████████▉ | 449/500 [00:22<00:02, 22.90it/s]
eval model::  90%|█████████ | 452/500 [00:22<00:02, 23.64it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:01, 23.86it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:01, 23.20it/s]
eval model::  92%|█████████▏| 461/500 [00:23<00:01, 22.97it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 19.18it/s]
eval model::  93%|█████████▎| 467/500 [00:23<00:01, 17.82it/s]
eval model::  94%|█████████▍| 469/500 [00:23<00:01, 18.14it/s]
eval model::  94%|█████████▍| 472/500 [00:23<00:01, 19.14it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 20.14it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 21.25it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 21.23it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 22.34it/s]
eval model::  97%|█████████▋| 487/500 [00:24<00:00, 21.20it/s]
eval model::  98%|█████████▊| 490/500 [00:24<00:00, 22.14it/s]
eval model::  99%|█████████▊| 493/500 [00:24<00:00, 19.31it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 18.94it/s]
eval model:: 100%|█████████▉| 499/500 [00:25<00:00, 20.88it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.76it/s]
[2024/07/28 00:22:15] ppocr INFO: cur metric, precision: 0.736, recall: 0.575830524795378, hmean: 0.6461372231226364, fps: 45.03702120574545
[2024/07/28 00:22:16] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/28 00:22:16] ppocr INFO: best metric, hmean: 0.6461372231226364, precision: 0.736, recall: 0.575830524795378, fps: 45.03702120574545, best_epoch: 380
[2024/07/28 00:22:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:22:24] ppocr INFO: epoch: [381/1500], global_step: 1143, lr: 0.001000, loss: 1.691133, loss_shrink_maps: 0.887713, loss_threshold_maps: 0.616847, loss_binary_maps: 0.177050, avg_reader_cost: 1.89806 s, avg_batch_cost: 2.31581 s, avg_samples: 12.5, ips: 5.39769 samples/s, eta: 5:44:56
[2024/07/28 00:22:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:22:32] ppocr INFO: epoch: [382/1500], global_step: 1146, lr: 0.001000, loss: 1.739494, loss_shrink_maps: 0.927497, loss_threshold_maps: 0.623302, loss_binary_maps: 0.184823, avg_reader_cost: 1.50357 s, avg_batch_cost: 1.73232 s, avg_samples: 12.5, ips: 7.21576 samples/s, eta: 5:44:34
[2024/07/28 00:22:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:22:40] ppocr INFO: epoch: [383/1500], global_step: 1149, lr: 0.001000, loss: 1.682839, loss_shrink_maps: 0.888798, loss_threshold_maps: 0.626474, loss_binary_maps: 0.178135, avg_reader_cost: 1.53571 s, avg_batch_cost: 1.78110 s, avg_samples: 12.5, ips: 7.01814 samples/s, eta: 5:44:13
[2024/07/28 00:22:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:22:46] ppocr INFO: epoch: [384/1500], global_step: 1150, lr: 0.001000, loss: 1.641330, loss_shrink_maps: 0.884311, loss_threshold_maps: 0.626474, loss_binary_maps: 0.176714, avg_reader_cost: 0.41707 s, avg_batch_cost: 0.50414 s, avg_samples: 4.8, ips: 9.52125 samples/s, eta: 5:44:04
[2024/07/28 00:22:48] ppocr INFO: epoch: [384/1500], global_step: 1152, lr: 0.001000, loss: 1.682839, loss_shrink_maps: 0.888798, loss_threshold_maps: 0.628067, loss_binary_maps: 0.178135, avg_reader_cost: 1.09925 s, avg_batch_cost: 1.24451 s, avg_samples: 7.7, ips: 6.18717 samples/s, eta: 5:43:52
[2024/07/28 00:22:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:22:56] ppocr INFO: epoch: [385/1500], global_step: 1155, lr: 0.001000, loss: 1.682839, loss_shrink_maps: 0.884311, loss_threshold_maps: 0.628067, loss_binary_maps: 0.176714, avg_reader_cost: 1.74250 s, avg_batch_cost: 1.97095 s, avg_samples: 12.5, ips: 6.34212 samples/s, eta: 5:43:37
[2024/07/28 00:22:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:23:04] ppocr INFO: epoch: [386/1500], global_step: 1158, lr: 0.001000, loss: 1.773771, loss_shrink_maps: 0.931430, loss_threshold_maps: 0.628067, loss_binary_maps: 0.186438, avg_reader_cost: 1.53018 s, avg_batch_cost: 1.75950 s, avg_samples: 12.5, ips: 7.10428 samples/s, eta: 5:43:16
[2024/07/28 00:23:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:23:12] ppocr INFO: epoch: [387/1500], global_step: 1160, lr: 0.001000, loss: 1.634851, loss_shrink_maps: 0.865136, loss_threshold_maps: 0.621820, loss_binary_maps: 0.171009, avg_reader_cost: 0.94168 s, avg_batch_cost: 1.13247 s, avg_samples: 9.6, ips: 8.47704 samples/s, eta: 5:43:01
[2024/07/28 00:23:12] ppocr INFO: epoch: [387/1500], global_step: 1161, lr: 0.001000, loss: 1.634851, loss_shrink_maps: 0.865136, loss_threshold_maps: 0.621820, loss_binary_maps: 0.171009, avg_reader_cost: 0.61198 s, avg_batch_cost: 0.66725 s, avg_samples: 2.9, ips: 4.34622 samples/s, eta: 5:42:56
[2024/07/28 00:23:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:23:20] ppocr INFO: epoch: [388/1500], global_step: 1164, lr: 0.001000, loss: 1.634851, loss_shrink_maps: 0.870128, loss_threshold_maps: 0.609463, loss_binary_maps: 0.173088, avg_reader_cost: 1.62588 s, avg_batch_cost: 1.87023 s, avg_samples: 12.5, ips: 6.68368 samples/s, eta: 5:42:38
[2024/07/28 00:23:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:23:28] ppocr INFO: epoch: [389/1500], global_step: 1167, lr: 0.001000, loss: 1.634851, loss_shrink_maps: 0.870128, loss_threshold_maps: 0.608213, loss_binary_maps: 0.173088, avg_reader_cost: 1.49652 s, avg_batch_cost: 1.74347 s, avg_samples: 12.5, ips: 7.16959 samples/s, eta: 5:42:17
[2024/07/28 00:23:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:23:36] ppocr INFO: epoch: [390/1500], global_step: 1170, lr: 0.001000, loss: 1.651499, loss_shrink_maps: 0.870128, loss_threshold_maps: 0.608213, loss_binary_maps: 0.173088, avg_reader_cost: 1.54534 s, avg_batch_cost: 1.77391 s, avg_samples: 12.5, ips: 7.04659 samples/s, eta: 5:41:56
[2024/07/28 00:23:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:23:44] ppocr INFO: epoch: [391/1500], global_step: 1173, lr: 0.001000, loss: 1.630760, loss_shrink_maps: 0.857313, loss_threshold_maps: 0.601540, loss_binary_maps: 0.170869, avg_reader_cost: 1.50625 s, avg_batch_cost: 1.77448 s, avg_samples: 12.5, ips: 7.04431 samples/s, eta: 5:41:35
[2024/07/28 00:23:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:23:53] ppocr INFO: epoch: [392/1500], global_step: 1176, lr: 0.001000, loss: 1.634384, loss_shrink_maps: 0.863500, loss_threshold_maps: 0.597774, loss_binary_maps: 0.171647, avg_reader_cost: 1.65777 s, avg_batch_cost: 1.95675 s, avg_samples: 12.5, ips: 6.38813 samples/s, eta: 5:41:20
[2024/07/28 00:23:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:24:01] ppocr INFO: epoch: [393/1500], global_step: 1179, lr: 0.001000, loss: 1.649745, loss_shrink_maps: 0.873604, loss_threshold_maps: 0.597774, loss_binary_maps: 0.173456, avg_reader_cost: 1.53754 s, avg_batch_cost: 1.80381 s, avg_samples: 12.5, ips: 6.92977 samples/s, eta: 5:41:00
[2024/07/28 00:24:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:24:07] ppocr INFO: epoch: [394/1500], global_step: 1180, lr: 0.001000, loss: 1.676150, loss_shrink_maps: 0.882332, loss_threshold_maps: 0.601540, loss_binary_maps: 0.175152, avg_reader_cost: 0.41875 s, avg_batch_cost: 0.52540 s, avg_samples: 4.8, ips: 9.13593 samples/s, eta: 5:40:52
[2024/07/28 00:24:09] ppocr INFO: epoch: [394/1500], global_step: 1182, lr: 0.001000, loss: 1.636300, loss_shrink_maps: 0.863500, loss_threshold_maps: 0.597774, loss_binary_maps: 0.171647, avg_reader_cost: 1.14207 s, avg_batch_cost: 1.28799 s, avg_samples: 7.7, ips: 5.97830 samples/s, eta: 5:40:41
[2024/07/28 00:24:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:24:17] ppocr INFO: epoch: [395/1500], global_step: 1185, lr: 0.001000, loss: 1.630293, loss_shrink_maps: 0.858364, loss_threshold_maps: 0.597774, loss_binary_maps: 0.170946, avg_reader_cost: 1.56981 s, avg_batch_cost: 1.79862 s, avg_samples: 12.5, ips: 6.94978 samples/s, eta: 5:40:21
[2024/07/28 00:24:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:24:25] ppocr INFO: epoch: [396/1500], global_step: 1188, lr: 0.001000, loss: 1.636300, loss_shrink_maps: 0.863500, loss_threshold_maps: 0.605900, loss_binary_maps: 0.171647, avg_reader_cost: 1.57362 s, avg_batch_cost: 1.85321 s, avg_samples: 12.5, ips: 6.74507 samples/s, eta: 5:40:03
[2024/07/28 00:24:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:24:33] ppocr INFO: epoch: [397/1500], global_step: 1190, lr: 0.001000, loss: 1.651719, loss_shrink_maps: 0.867655, loss_threshold_maps: 0.613621, loss_binary_maps: 0.172590, avg_reader_cost: 0.93739 s, avg_batch_cost: 1.14277 s, avg_samples: 9.6, ips: 8.40068 samples/s, eta: 5:39:48
[2024/07/28 00:24:33] ppocr INFO: epoch: [397/1500], global_step: 1191, lr: 0.001000, loss: 1.674454, loss_shrink_maps: 0.887292, loss_threshold_maps: 0.616625, loss_binary_maps: 0.175843, avg_reader_cost: 0.61708 s, avg_batch_cost: 0.67182 s, avg_samples: 2.9, ips: 4.31661 samples/s, eta: 5:39:43
[2024/07/28 00:24:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:24:41] ppocr INFO: epoch: [398/1500], global_step: 1194, lr: 0.001000, loss: 1.685386, loss_shrink_maps: 0.887292, loss_threshold_maps: 0.619225, loss_binary_maps: 0.175843, avg_reader_cost: 1.54064 s, avg_batch_cost: 1.78456 s, avg_samples: 12.5, ips: 7.00451 samples/s, eta: 5:39:23
[2024/07/28 00:24:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:24:49] ppocr INFO: epoch: [399/1500], global_step: 1197, lr: 0.001000, loss: 1.651719, loss_shrink_maps: 0.859607, loss_threshold_maps: 0.616625, loss_binary_maps: 0.171045, avg_reader_cost: 1.65483 s, avg_batch_cost: 1.88442 s, avg_samples: 12.5, ips: 6.63334 samples/s, eta: 5:39:06
[2024/07/28 00:24:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:24:58] ppocr INFO: epoch: [400/1500], global_step: 1200, lr: 0.001000, loss: 1.651719, loss_shrink_maps: 0.859607, loss_threshold_maps: 0.617574, loss_binary_maps: 0.171045, avg_reader_cost: 1.61289 s, avg_batch_cost: 1.84268 s, avg_samples: 12.5, ips: 6.78359 samples/s, eta: 5:38:47

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:57,  8.66it/s]
eval model::   1%|          | 4/500 [00:00<00:31, 15.85it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.20it/s]
eval model::   2%|▏         | 8/500 [00:00<00:30, 15.97it/s]
eval model::   2%|▏         | 10/500 [00:00<00:29, 16.45it/s]
eval model::   2%|▏         | 12/500 [00:00<00:28, 17.21it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.66it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.85it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.83it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 21.45it/s]
eval model::   5%|▌         | 26/500 [00:01<00:21, 22.56it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 21.36it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.48it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 18.60it/s]
eval model::   8%|▊         | 38/500 [00:02<00:24, 18.79it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.07it/s]
eval model::   8%|▊         | 42/500 [00:02<00:28, 15.96it/s]
eval model::   9%|▉         | 45/500 [00:02<00:26, 17.25it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 17.29it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 19.08it/s]
eval model::  11%|█         | 53/500 [00:02<00:23, 19.34it/s]
eval model::  11%|█         | 55/500 [00:02<00:23, 18.90it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:23, 18.59it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:26, 16.76it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.14it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 14.90it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.37it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:22, 18.92it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.29it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:19, 21.79it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 22.02it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 21.83it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.25it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:19, 21.03it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:20, 19.60it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.93it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 20.17it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.86it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.87it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 22.88it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:17, 22.72it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 21.61it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:16, 23.34it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:18, 21.07it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:20, 18.97it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 20.67it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.77it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 19.06it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 18.63it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:17, 20.35it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.69it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.25it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.53it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.52it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.79it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 19.09it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:15, 21.68it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:21, 15.88it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:20, 16.44it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:18, 17.79it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:19, 17.28it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:18, 17.50it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 15.79it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 16.17it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.12it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.42it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:18, 17.01it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 18.57it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:15, 19.55it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:14, 21.42it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:15, 19.55it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:15, 19.73it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 20.55it/s]
eval model::  41%|████      | 205/500 [00:10<00:18, 15.91it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:17, 16.49it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 15.57it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.41it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.29it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.59it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.04it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:19, 14.37it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.63it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:13, 19.98it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 20.66it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:13, 20.07it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:12, 20.72it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:12, 21.53it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:12, 20.41it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:12, 20.90it/s]
eval model::  50%|█████     | 250/500 [00:13<00:12, 20.65it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 20.86it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 21.51it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:09, 24.33it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:09, 24.47it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:10, 21.62it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 19.77it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 19.94it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.31it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 18.80it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 18.75it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 19.27it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.48it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.94it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:11, 17.46it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:14, 14.22it/s]
eval model::  60%|█████▉    | 298/500 [00:15<00:12, 16.45it/s]
eval model::  60%|██████    | 301/500 [00:16<00:11, 16.69it/s]
eval model::  61%|██████    | 304/500 [00:16<00:10, 18.55it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:09, 19.75it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:08, 21.13it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 21.90it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 20.54it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 21.49it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 19.89it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 20.30it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 19.74it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 19.24it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:08, 20.65it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:07, 20.52it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:11, 14.44it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.60it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 18.86it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.88it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 22.48it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 22.18it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 22.10it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 19.79it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.85it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 18.09it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 19.10it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 18.82it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 20.36it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 21.54it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 20.48it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 22.26it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:08, 13.56it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 15.90it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 17.78it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 19.67it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:04, 21.75it/s]
eval model::  80%|████████  | 401/500 [00:21<00:04, 19.86it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 21.60it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 22.69it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:03, 23.77it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:03, 23.22it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:03, 22.73it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:03, 21.03it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 16.79it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 16.22it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.31it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 19.28it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 20.02it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 23.23it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 24.07it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 21.86it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 23.39it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 22.35it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.83it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 21.69it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:02, 19.55it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:02, 18.89it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 16.60it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.87it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.09it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.60it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.42it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 21.22it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 21.76it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 22.11it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 20.49it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 22.63it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 19.64it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 18.98it/s]
eval model:: 100%|█████████▉| 499/500 [00:25<00:00, 20.93it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.21it/s]
[2024/07/28 00:25:24] ppocr INFO: cur metric, precision: 0.6799345692475464, recall: 0.6003851709195955, hmean: 0.6376885706980312, fps: 44.61766559592031
[2024/07/28 00:25:24] ppocr INFO: best metric, hmean: 0.6461372231226364, precision: 0.736, recall: 0.575830524795378, fps: 45.03702120574545, best_epoch: 380
[2024/07/28 00:25:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:25:31] ppocr INFO: epoch: [401/1500], global_step: 1203, lr: 0.001000, loss: 1.586054, loss_shrink_maps: 0.840839, loss_threshold_maps: 0.602548, loss_binary_maps: 0.166893, avg_reader_cost: 1.62907 s, avg_batch_cost: 1.89648 s, avg_samples: 12.5, ips: 6.59116 samples/s, eta: 5:38:30
[2024/07/28 00:25:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:25:39] ppocr INFO: epoch: [402/1500], global_step: 1206, lr: 0.001000, loss: 1.625817, loss_shrink_maps: 0.864749, loss_threshold_maps: 0.602548, loss_binary_maps: 0.172268, avg_reader_cost: 1.57057 s, avg_batch_cost: 1.80036 s, avg_samples: 12.5, ips: 6.94304 samples/s, eta: 5:38:10
[2024/07/28 00:25:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:25:47] ppocr INFO: epoch: [403/1500], global_step: 1209, lr: 0.001000, loss: 1.585797, loss_shrink_maps: 0.836167, loss_threshold_maps: 0.598699, loss_binary_maps: 0.165994, avg_reader_cost: 1.54481 s, avg_batch_cost: 1.77573 s, avg_samples: 12.5, ips: 7.03936 samples/s, eta: 5:37:50
[2024/07/28 00:25:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:25:54] ppocr INFO: epoch: [404/1500], global_step: 1210, lr: 0.001000, loss: 1.560107, loss_shrink_maps: 0.819685, loss_threshold_maps: 0.593415, loss_binary_maps: 0.163123, avg_reader_cost: 0.46200 s, avg_batch_cost: 0.54421 s, avg_samples: 4.8, ips: 8.82010 samples/s, eta: 5:37:41
[2024/07/28 00:25:55] ppocr INFO: epoch: [404/1500], global_step: 1212, lr: 0.001000, loss: 1.560107, loss_shrink_maps: 0.819685, loss_threshold_maps: 0.588799, loss_binary_maps: 0.163123, avg_reader_cost: 1.17978 s, avg_batch_cost: 1.32555 s, avg_samples: 7.7, ips: 5.80892 samples/s, eta: 5:37:32
[2024/07/28 00:25:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:26:03] ppocr INFO: epoch: [405/1500], global_step: 1215, lr: 0.001000, loss: 1.523618, loss_shrink_maps: 0.801569, loss_threshold_maps: 0.581405, loss_binary_maps: 0.159266, avg_reader_cost: 1.50192 s, avg_batch_cost: 1.73890 s, avg_samples: 12.5, ips: 7.18844 samples/s, eta: 5:37:10
[2024/07/28 00:26:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:26:11] ppocr INFO: epoch: [406/1500], global_step: 1218, lr: 0.001000, loss: 1.557924, loss_shrink_maps: 0.808361, loss_threshold_maps: 0.589488, loss_binary_maps: 0.161128, avg_reader_cost: 1.56512 s, avg_batch_cost: 1.79315 s, avg_samples: 12.5, ips: 6.97098 samples/s, eta: 5:36:50
[2024/07/28 00:26:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:26:18] ppocr INFO: epoch: [407/1500], global_step: 1220, lr: 0.001000, loss: 1.563270, loss_shrink_maps: 0.823067, loss_threshold_maps: 0.589488, loss_binary_maps: 0.163606, avg_reader_cost: 0.93112 s, avg_batch_cost: 1.11360 s, avg_samples: 9.6, ips: 8.62070 samples/s, eta: 5:36:35
[2024/07/28 00:26:19] ppocr INFO: epoch: [407/1500], global_step: 1221, lr: 0.001000, loss: 1.561088, loss_shrink_maps: 0.823067, loss_threshold_maps: 0.587074, loss_binary_maps: 0.163606, avg_reader_cost: 0.60278 s, avg_batch_cost: 0.65729 s, avg_samples: 2.9, ips: 4.41205 samples/s, eta: 5:36:30
[2024/07/28 00:26:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:26:27] ppocr INFO: epoch: [408/1500], global_step: 1224, lr: 0.001000, loss: 1.561088, loss_shrink_maps: 0.811743, loss_threshold_maps: 0.587074, loss_binary_maps: 0.161612, avg_reader_cost: 1.60048 s, avg_batch_cost: 1.85791 s, avg_samples: 12.5, ips: 6.72799 samples/s, eta: 5:36:12
[2024/07/28 00:26:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:26:35] ppocr INFO: epoch: [409/1500], global_step: 1227, lr: 0.001000, loss: 1.561088, loss_shrink_maps: 0.811743, loss_threshold_maps: 0.587733, loss_binary_maps: 0.161612, avg_reader_cost: 1.61384 s, avg_batch_cost: 1.86462 s, avg_samples: 12.5, ips: 6.70377 samples/s, eta: 5:35:54
[2024/07/28 00:26:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:26:43] ppocr INFO: epoch: [410/1500], global_step: 1230, lr: 0.001000, loss: 1.588631, loss_shrink_maps: 0.828657, loss_threshold_maps: 0.590265, loss_binary_maps: 0.164850, avg_reader_cost: 1.51835 s, avg_batch_cost: 1.76275 s, avg_samples: 12.5, ips: 7.09121 samples/s, eta: 5:35:33
[2024/07/28 00:26:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:26:51] ppocr INFO: epoch: [411/1500], global_step: 1233, lr: 0.001000, loss: 1.646921, loss_shrink_maps: 0.860624, loss_threshold_maps: 0.606435, loss_binary_maps: 0.171031, avg_reader_cost: 1.54418 s, avg_batch_cost: 1.77295 s, avg_samples: 12.5, ips: 7.05039 samples/s, eta: 5:35:12
[2024/07/28 00:26:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:27:00] ppocr INFO: epoch: [412/1500], global_step: 1236, lr: 0.001000, loss: 1.666135, loss_shrink_maps: 0.884868, loss_threshold_maps: 0.607167, loss_binary_maps: 0.176125, avg_reader_cost: 1.59284 s, avg_batch_cost: 1.83488 s, avg_samples: 12.5, ips: 6.81245 samples/s, eta: 5:34:54
[2024/07/28 00:27:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:27:07] ppocr INFO: epoch: [413/1500], global_step: 1239, lr: 0.001000, loss: 1.666135, loss_shrink_maps: 0.879983, loss_threshold_maps: 0.622257, loss_binary_maps: 0.175232, avg_reader_cost: 1.53621 s, avg_batch_cost: 1.79071 s, avg_samples: 12.5, ips: 6.98046 samples/s, eta: 5:34:34
[2024/07/28 00:27:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:27:14] ppocr INFO: epoch: [414/1500], global_step: 1240, lr: 0.001000, loss: 1.648944, loss_shrink_maps: 0.855739, loss_threshold_maps: 0.608445, loss_binary_maps: 0.170292, avg_reader_cost: 0.42913 s, avg_batch_cost: 0.51813 s, avg_samples: 4.8, ips: 9.26400 samples/s, eta: 5:34:25
[2024/07/28 00:27:16] ppocr INFO: epoch: [414/1500], global_step: 1242, lr: 0.001000, loss: 1.648944, loss_shrink_maps: 0.855739, loss_threshold_maps: 0.608445, loss_binary_maps: 0.170292, avg_reader_cost: 1.12798 s, avg_batch_cost: 1.27473 s, avg_samples: 7.7, ips: 6.04048 samples/s, eta: 5:34:14
[2024/07/28 00:27:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:27:24] ppocr INFO: epoch: [415/1500], global_step: 1245, lr: 0.001000, loss: 1.648944, loss_shrink_maps: 0.855739, loss_threshold_maps: 0.608445, loss_binary_maps: 0.170292, avg_reader_cost: 1.56862 s, avg_batch_cost: 1.79777 s, avg_samples: 12.5, ips: 6.95306 samples/s, eta: 5:33:54
[2024/07/28 00:27:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:27:32] ppocr INFO: epoch: [416/1500], global_step: 1248, lr: 0.001000, loss: 1.613878, loss_shrink_maps: 0.842662, loss_threshold_maps: 0.596350, loss_binary_maps: 0.167690, avg_reader_cost: 1.60874 s, avg_batch_cost: 1.83877 s, avg_samples: 12.5, ips: 6.79802 samples/s, eta: 5:33:35
[2024/07/28 00:27:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:27:39] ppocr INFO: epoch: [417/1500], global_step: 1250, lr: 0.001000, loss: 1.588322, loss_shrink_maps: 0.822585, loss_threshold_maps: 0.580968, loss_binary_maps: 0.163584, avg_reader_cost: 0.96252 s, avg_batch_cost: 1.13584 s, avg_samples: 9.6, ips: 8.45189 samples/s, eta: 5:33:21
[2024/07/28 00:27:40] ppocr INFO: epoch: [417/1500], global_step: 1251, lr: 0.001000, loss: 1.588322, loss_shrink_maps: 0.822585, loss_threshold_maps: 0.580968, loss_binary_maps: 0.163584, avg_reader_cost: 0.61356 s, avg_batch_cost: 0.66854 s, avg_samples: 2.9, ips: 4.33779 samples/s, eta: 5:33:16
[2024/07/28 00:27:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:27:48] ppocr INFO: epoch: [418/1500], global_step: 1254, lr: 0.001000, loss: 1.564924, loss_shrink_maps: 0.816973, loss_threshold_maps: 0.573940, loss_binary_maps: 0.162247, avg_reader_cost: 1.51741 s, avg_batch_cost: 1.75588 s, avg_samples: 12.5, ips: 7.11894 samples/s, eta: 5:32:55
[2024/07/28 00:27:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:27:56] ppocr INFO: epoch: [419/1500], global_step: 1257, lr: 0.001000, loss: 1.564924, loss_shrink_maps: 0.816973, loss_threshold_maps: 0.580616, loss_binary_maps: 0.162247, avg_reader_cost: 1.51926 s, avg_batch_cost: 1.75628 s, avg_samples: 12.5, ips: 7.11732 samples/s, eta: 5:32:34
[2024/07/28 00:27:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:28:04] ppocr INFO: epoch: [420/1500], global_step: 1260, lr: 0.001000, loss: 1.564924, loss_shrink_maps: 0.816973, loss_threshold_maps: 0.580616, loss_binary_maps: 0.162247, avg_reader_cost: 1.52942 s, avg_batch_cost: 1.76665 s, avg_samples: 12.5, ips: 7.07555 samples/s, eta: 5:32:14

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.63it/s]
eval model::   1%|          | 3/500 [00:00<00:34, 14.26it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.70it/s]
eval model::   2%|▏         | 8/500 [00:00<00:29, 16.93it/s]
eval model::   2%|▏         | 10/500 [00:00<00:28, 17.14it/s]
eval model::   2%|▏         | 12/500 [00:00<00:28, 17.24it/s]
eval model::   3%|▎         | 14/500 [00:00<00:29, 16.76it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.57it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.85it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 22.16it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 20.70it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 21.88it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 19.60it/s]
eval model::   7%|▋         | 35/500 [00:01<00:23, 19.46it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.69it/s]
eval model::   8%|▊         | 41/500 [00:02<00:24, 18.40it/s]
eval model::   9%|▊         | 43/500 [00:02<00:26, 17.26it/s]
eval model::   9%|▉         | 46/500 [00:02<00:27, 16.61it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 19.04it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 20.27it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.66it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:22, 19.98it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.36it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.42it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:25, 17.30it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:23, 18.61it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 19.64it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:21, 20.08it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 20.15it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.13it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 21.92it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 20.08it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.33it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 19.21it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:19, 20.51it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.92it/s]
eval model::  20%|██        | 102/500 [00:05<00:17, 22.25it/s]
eval model::  21%|██        | 106/500 [00:05<00:16, 24.60it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:15, 24.75it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:17, 22.16it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:17, 21.80it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 21.74it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:17, 21.44it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:18, 19.75it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 20.48it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 18.77it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:18, 19.27it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:18, 19.24it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.58it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.19it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.27it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 17.79it/s]
eval model::  30%|███       | 151/500 [00:07<00:20, 17.07it/s]
eval model::  31%|███       | 154/500 [00:07<00:17, 19.59it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:15, 21.76it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:19, 17.34it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.67it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:19, 17.58it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:18, 18.42it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 18.71it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 17.01it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:17, 18.91it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:18, 16.92it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 19.12it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 20.85it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.00it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.68it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:14, 21.14it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.86it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.60it/s]
eval model::  41%|████      | 204/500 [00:10<00:14, 20.97it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:20, 14.29it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:20, 14.49it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 14.43it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.26it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.79it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.87it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.46it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 15.16it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:14, 18.68it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.99it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 23.17it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:11, 22.44it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.25it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:13, 18.80it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:12, 20.06it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:12, 20.85it/s]
eval model::  50%|█████     | 251/500 [00:13<00:12, 20.09it/s]
eval model::  51%|█████     | 254/500 [00:13<00:12, 19.13it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:12, 19.36it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:12, 19.40it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:12, 18.79it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:12, 18.25it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:14, 16.66it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:13, 17.20it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:12, 18.79it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.58it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:13, 17.28it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.32it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 21.67it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:09, 22.87it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:18, 11.46it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:14, 14.11it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:13, 15.47it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:14, 13.77it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:12, 15.98it/s]
eval model::  60%|██████    | 302/500 [00:16<00:12, 15.98it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.43it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.15it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 19.19it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 20.40it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:09, 20.12it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 21.54it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.57it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 21.04it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 21.19it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:07, 21.69it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:06, 24.40it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:07, 21.72it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:09, 16.50it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:08, 18.85it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:07, 20.29it/s]
eval model::  70%|███████   | 351/500 [00:18<00:06, 23.06it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 24.16it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:06, 20.95it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:06, 21.01it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:06, 20.30it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 16.93it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 18.63it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 18.97it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 20.55it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 22.31it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:05, 20.28it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 20.99it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:08, 13.12it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:07, 15.32it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 15.93it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 17.85it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 19.31it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 18.76it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 19.61it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 20.99it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 22.03it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:04, 21.17it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:03, 21.23it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:03, 20.25it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 17.66it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 16.39it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.09it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 18.48it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 18.12it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:02, 22.26it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 23.98it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 24.53it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 25.04it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 22.77it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.42it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 22.32it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 22.09it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 22.34it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 18.36it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:01, 18.42it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 18.41it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 21.15it/s]
eval model::  95%|█████████▍| 473/500 [00:24<00:01, 20.07it/s]
eval model::  95%|█████████▌| 476/500 [00:24<00:01, 20.49it/s]
eval model::  96%|█████████▌| 479/500 [00:24<00:00, 21.13it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 21.03it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 21.85it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 21.66it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 19.39it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 19.08it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 18.72it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 22.39it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.25it/s]
[2024/07/28 00:28:30] ppocr INFO: cur metric, precision: 0.6857971014492754, recall: 0.5695714973519499, hmean: 0.6223040504997369, fps: 45.463526348182015
[2024/07/28 00:28:30] ppocr INFO: best metric, hmean: 0.6461372231226364, precision: 0.736, recall: 0.575830524795378, fps: 45.03702120574545, best_epoch: 380
[2024/07/28 00:28:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:28:37] ppocr INFO: epoch: [421/1500], global_step: 1263, lr: 0.001000, loss: 1.612776, loss_shrink_maps: 0.830799, loss_threshold_maps: 0.586668, loss_binary_maps: 0.164796, avg_reader_cost: 1.48787 s, avg_batch_cost: 1.76017 s, avg_samples: 12.5, ips: 7.10157 samples/s, eta: 5:31:53
[2024/07/28 00:28:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:28:45] ppocr INFO: epoch: [422/1500], global_step: 1266, lr: 0.001000, loss: 1.592406, loss_shrink_maps: 0.824495, loss_threshold_maps: 0.581626, loss_binary_maps: 0.163849, avg_reader_cost: 1.51048 s, avg_batch_cost: 1.73919 s, avg_samples: 12.5, ips: 7.18726 samples/s, eta: 5:31:32
[2024/07/28 00:28:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:28:53] ppocr INFO: epoch: [423/1500], global_step: 1269, lr: 0.001000, loss: 1.594537, loss_shrink_maps: 0.827667, loss_threshold_maps: 0.588009, loss_binary_maps: 0.164050, avg_reader_cost: 1.54414 s, avg_batch_cost: 1.81822 s, avg_samples: 12.5, ips: 6.87487 samples/s, eta: 5:31:13
[2024/07/28 00:28:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:28:59] ppocr INFO: epoch: [424/1500], global_step: 1270, lr: 0.001000, loss: 1.616949, loss_shrink_maps: 0.853198, loss_threshold_maps: 0.591786, loss_binary_maps: 0.169040, avg_reader_cost: 0.42000 s, avg_batch_cost: 0.50404 s, avg_samples: 4.8, ips: 9.52297 samples/s, eta: 5:31:04
[2024/07/28 00:29:01] ppocr INFO: epoch: [424/1500], global_step: 1272, lr: 0.001000, loss: 1.570212, loss_shrink_maps: 0.827667, loss_threshold_maps: 0.581626, loss_binary_maps: 0.164050, avg_reader_cost: 1.09910 s, avg_batch_cost: 1.24462 s, avg_samples: 7.7, ips: 6.18663 samples/s, eta: 5:30:52
[2024/07/28 00:29:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:29:09] ppocr INFO: epoch: [425/1500], global_step: 1275, lr: 0.001000, loss: 1.636066, loss_shrink_maps: 0.858825, loss_threshold_maps: 0.594370, loss_binary_maps: 0.170831, avg_reader_cost: 1.51429 s, avg_batch_cost: 1.74761 s, avg_samples: 12.5, ips: 7.15262 samples/s, eta: 5:30:31
[2024/07/28 00:29:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:29:17] ppocr INFO: epoch: [426/1500], global_step: 1278, lr: 0.001000, loss: 1.636066, loss_shrink_maps: 0.858825, loss_threshold_maps: 0.594370, loss_binary_maps: 0.170831, avg_reader_cost: 1.51755 s, avg_batch_cost: 1.76515 s, avg_samples: 12.5, ips: 7.08154 samples/s, eta: 5:30:11
[2024/07/28 00:29:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:29:25] ppocr INFO: epoch: [427/1500], global_step: 1280, lr: 0.001000, loss: 1.596579, loss_shrink_maps: 0.842280, loss_threshold_maps: 0.591426, loss_binary_maps: 0.167785, avg_reader_cost: 0.94765 s, avg_batch_cost: 1.12219 s, avg_samples: 9.6, ips: 8.55469 samples/s, eta: 5:29:56
[2024/07/28 00:29:25] ppocr INFO: epoch: [427/1500], global_step: 1281, lr: 0.001000, loss: 1.636066, loss_shrink_maps: 0.858825, loss_threshold_maps: 0.593511, loss_binary_maps: 0.170831, avg_reader_cost: 0.60732 s, avg_batch_cost: 0.66228 s, avg_samples: 2.9, ips: 4.37882 samples/s, eta: 5:29:51
[2024/07/28 00:29:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:29:33] ppocr INFO: epoch: [428/1500], global_step: 1284, lr: 0.001000, loss: 1.656328, loss_shrink_maps: 0.895036, loss_threshold_maps: 0.591252, loss_binary_maps: 0.177772, avg_reader_cost: 1.58737 s, avg_batch_cost: 1.81654 s, avg_samples: 12.5, ips: 6.88121 samples/s, eta: 5:29:31
[2024/07/28 00:29:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:29:42] ppocr INFO: epoch: [429/1500], global_step: 1287, lr: 0.001000, loss: 1.656328, loss_shrink_maps: 0.895036, loss_threshold_maps: 0.591252, loss_binary_maps: 0.177772, avg_reader_cost: 1.61699 s, avg_batch_cost: 1.88724 s, avg_samples: 12.5, ips: 6.62344 samples/s, eta: 5:29:14
[2024/07/28 00:29:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:29:50] ppocr INFO: epoch: [430/1500], global_step: 1290, lr: 0.001000, loss: 1.590491, loss_shrink_maps: 0.842865, loss_threshold_maps: 0.579189, loss_binary_maps: 0.168335, avg_reader_cost: 1.54700 s, avg_batch_cost: 1.77576 s, avg_samples: 12.5, ips: 7.03924 samples/s, eta: 5:28:54
[2024/07/28 00:29:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:29:58] ppocr INFO: epoch: [431/1500], global_step: 1293, lr: 0.001000, loss: 1.590491, loss_shrink_maps: 0.853076, loss_threshold_maps: 0.580879, loss_binary_maps: 0.170533, avg_reader_cost: 1.51695 s, avg_batch_cost: 1.74744 s, avg_samples: 12.5, ips: 7.15331 samples/s, eta: 5:28:33
[2024/07/28 00:29:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:30:06] ppocr INFO: epoch: [432/1500], global_step: 1296, lr: 0.001000, loss: 1.611322, loss_shrink_maps: 0.871331, loss_threshold_maps: 0.583471, loss_binary_maps: 0.173273, avg_reader_cost: 1.61644 s, avg_batch_cost: 1.85009 s, avg_samples: 12.5, ips: 6.75644 samples/s, eta: 5:28:15
[2024/07/28 00:30:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:30:14] ppocr INFO: epoch: [433/1500], global_step: 1299, lr: 0.001000, loss: 1.645379, loss_shrink_maps: 0.885074, loss_threshold_maps: 0.580879, loss_binary_maps: 0.175372, avg_reader_cost: 1.52212 s, avg_batch_cost: 1.75040 s, avg_samples: 12.5, ips: 7.14122 samples/s, eta: 5:27:54
[2024/07/28 00:30:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:30:20] ppocr INFO: epoch: [434/1500], global_step: 1300, lr: 0.001000, loss: 1.662823, loss_shrink_maps: 0.890357, loss_threshold_maps: 0.585198, loss_binary_maps: 0.177179, avg_reader_cost: 0.41720 s, avg_batch_cost: 0.52371 s, avg_samples: 4.8, ips: 9.16546 samples/s, eta: 5:27:46
[2024/07/28 00:30:22] ppocr INFO: epoch: [434/1500], global_step: 1302, lr: 0.001000, loss: 1.611322, loss_shrink_maps: 0.871331, loss_threshold_maps: 0.580879, loss_binary_maps: 0.173273, avg_reader_cost: 1.13882 s, avg_batch_cost: 1.28465 s, avg_samples: 7.7, ips: 5.99383 samples/s, eta: 5:27:35
[2024/07/28 00:30:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:30:30] ppocr INFO: epoch: [435/1500], global_step: 1305, lr: 0.001000, loss: 1.590491, loss_shrink_maps: 0.847172, loss_threshold_maps: 0.580879, loss_binary_maps: 0.168611, avg_reader_cost: 1.49295 s, avg_batch_cost: 1.74514 s, avg_samples: 12.5, ips: 7.16273 samples/s, eta: 5:27:14
[2024/07/28 00:30:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:30:38] ppocr INFO: epoch: [436/1500], global_step: 1308, lr: 0.001000, loss: 1.617934, loss_shrink_maps: 0.861233, loss_threshold_maps: 0.588210, loss_binary_maps: 0.170758, avg_reader_cost: 1.49988 s, avg_batch_cost: 1.73525 s, avg_samples: 12.5, ips: 7.20359 samples/s, eta: 5:26:53
[2024/07/28 00:30:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:30:46] ppocr INFO: epoch: [437/1500], global_step: 1310, lr: 0.001000, loss: 1.636921, loss_shrink_maps: 0.872822, loss_threshold_maps: 0.593926, loss_binary_maps: 0.172980, avg_reader_cost: 0.92347 s, avg_batch_cost: 1.16787 s, avg_samples: 9.6, ips: 8.22008 samples/s, eta: 5:26:39
[2024/07/28 00:30:46] ppocr INFO: epoch: [437/1500], global_step: 1311, lr: 0.001000, loss: 1.623551, loss_shrink_maps: 0.861233, loss_threshold_maps: 0.593926, loss_binary_maps: 0.170758, avg_reader_cost: 0.62952 s, avg_batch_cost: 0.68430 s, avg_samples: 2.9, ips: 4.23793 samples/s, eta: 5:26:35
[2024/07/28 00:30:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:30:54] ppocr INFO: epoch: [438/1500], global_step: 1314, lr: 0.001000, loss: 1.601754, loss_shrink_maps: 0.841862, loss_threshold_maps: 0.590051, loss_binary_maps: 0.167175, avg_reader_cost: 1.56291 s, avg_batch_cost: 1.81149 s, avg_samples: 12.5, ips: 6.90040 samples/s, eta: 5:26:15
[2024/07/28 00:30:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:31:02] ppocr INFO: epoch: [439/1500], global_step: 1317, lr: 0.001000, loss: 1.553858, loss_shrink_maps: 0.829338, loss_threshold_maps: 0.582963, loss_binary_maps: 0.164732, avg_reader_cost: 1.52214 s, avg_batch_cost: 1.77013 s, avg_samples: 12.5, ips: 7.06161 samples/s, eta: 5:25:55
[2024/07/28 00:31:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:31:11] ppocr INFO: epoch: [440/1500], global_step: 1320, lr: 0.001000, loss: 1.549259, loss_shrink_maps: 0.827956, loss_threshold_maps: 0.580023, loss_binary_maps: 0.164107, avg_reader_cost: 1.65513 s, avg_batch_cost: 1.94365 s, avg_samples: 12.5, ips: 6.43119 samples/s, eta: 5:25:39

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:50,  9.94it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.69it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.10it/s]
eval model::   2%|▏         | 8/500 [00:00<00:29, 16.61it/s]
eval model::   2%|▏         | 11/500 [00:00<00:29, 16.47it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.46it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.60it/s]
eval model::   4%|▍         | 19/500 [00:01<00:24, 19.48it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.24it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.13it/s]
eval model::   5%|▌         | 27/500 [00:01<00:23, 20.54it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 21.63it/s]
eval model::   7%|▋         | 33/500 [00:01<00:21, 21.35it/s]
eval model::   7%|▋         | 36/500 [00:01<00:23, 19.82it/s]
eval model::   8%|▊         | 39/500 [00:02<00:27, 16.60it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.70it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 18.17it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 17.00it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.15it/s]
eval model::  11%|█         | 53/500 [00:02<00:23, 19.06it/s]
eval model::  11%|█         | 56/500 [00:02<00:22, 19.62it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:22, 19.52it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 16.03it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.37it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 19.16it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:21, 20.15it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 20.15it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:19, 21.74it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:18, 22.47it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.71it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.63it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 19.89it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:23, 17.25it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:24, 16.81it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:23, 17.03it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:22, 17.74it/s]
eval model::  20%|██        | 100/500 [00:05<00:22, 17.65it/s]
eval model::  21%|██        | 103/500 [00:05<00:20, 19.51it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.11it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.61it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:17, 22.20it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:19, 19.63it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:18, 20.66it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:19, 19.45it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 20.30it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.57it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:18, 20.24it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:18, 20.33it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:17, 20.46it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:16, 21.26it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:16, 21.16it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 21.11it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 19.50it/s]
eval model::  30%|███       | 151/500 [00:07<00:18, 18.53it/s]
eval model::  31%|███       | 155/500 [00:07<00:15, 21.90it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:14, 23.38it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:18, 18.69it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:18, 18.59it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 19.56it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 20.06it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:17, 19.11it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:21, 15.24it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:18, 17.74it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:20, 15.46it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:18, 17.22it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.19it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:16, 19.36it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 20.75it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 21.45it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.33it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 21.26it/s]
eval model::  41%|████      | 204/500 [00:10<00:12, 23.08it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:20, 14.47it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:19, 15.27it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:18, 15.64it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.06it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.82it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.32it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 17.07it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:17, 15.91it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:14, 19.38it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 22.45it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 22.92it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:13, 19.90it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:12, 21.11it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 20.40it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:11, 21.47it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:10, 23.29it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 21.47it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.86it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:12, 19.19it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:12, 19.33it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:12, 19.68it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:12, 18.09it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:12, 18.34it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:12, 17.77it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:14, 16.16it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:14, 15.21it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:13, 16.98it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:11, 18.97it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 19.83it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:18, 11.80it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:13, 15.41it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:13, 15.49it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:14, 13.91it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:12, 16.34it/s]
eval model::  60%|██████    | 301/500 [00:16<00:13, 14.97it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.93it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 20.13it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 20.37it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:09, 19.32it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:09, 19.83it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:08, 21.37it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:08, 20.83it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:07, 22.29it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 20.81it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:07, 21.18it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:08, 20.53it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:08, 18.48it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:11, 13.64it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.00it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 18.43it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.46it/s]
eval model::  70%|███████   | 352/500 [00:18<00:07, 20.44it/s]
eval model::  71%|███████   | 355/500 [00:18<00:07, 19.74it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 19.96it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.70it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.73it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.35it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 18.85it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 18.67it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 18.53it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 17.99it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 22.38it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 19.92it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:05, 20.73it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:09, 11.41it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:08, 13.42it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 15.85it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 18.35it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 18.15it/s]
eval model::  81%|████████  | 403/500 [00:21<00:05, 18.91it/s]
eval model::  81%|████████  | 406/500 [00:21<00:05, 16.92it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 18.70it/s]
eval model::  82%|████████▏ | 412/500 [00:22<00:04, 20.77it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:04, 20.37it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 17.16it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.46it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:04, 16.43it/s]
eval model::  85%|████████▌ | 425/500 [00:22<00:04, 15.56it/s]
eval model::  86%|████████▌ | 428/500 [00:23<00:03, 18.32it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:03, 19.26it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 21.22it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:02, 22.44it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:03, 19.98it/s]
eval model::  89%|████████▊ | 443/500 [00:23<00:02, 21.56it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 22.10it/s]
eval model::  90%|████████▉ | 449/500 [00:24<00:02, 19.63it/s]
eval model::  90%|█████████ | 452/500 [00:24<00:02, 20.60it/s]
eval model::  91%|█████████ | 455/500 [00:24<00:02, 21.86it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:01, 21.67it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 21.83it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 16.78it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:02, 16.33it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 16.97it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.31it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.71it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:01, 21.83it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 21.23it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 22.02it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 21.03it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 19.86it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 17.55it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 18.14it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 21.23it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.79it/s]
[2024/07/28 00:31:37] ppocr INFO: cur metric, precision: 0.6873223422399091, recall: 0.582089552238806, hmean: 0.6303441084462983, fps: 44.14698926198636
[2024/07/28 00:31:37] ppocr INFO: best metric, hmean: 0.6461372231226364, precision: 0.736, recall: 0.575830524795378, fps: 45.03702120574545, best_epoch: 380
[2024/07/28 00:31:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:31:45] ppocr INFO: epoch: [441/1500], global_step: 1323, lr: 0.001000, loss: 1.563924, loss_shrink_maps: 0.841897, loss_threshold_maps: 0.589290, loss_binary_maps: 0.166510, avg_reader_cost: 1.55972 s, avg_batch_cost: 1.80824 s, avg_samples: 12.5, ips: 6.91280 samples/s, eta: 5:25:20
[2024/07/28 00:31:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:31:53] ppocr INFO: epoch: [442/1500], global_step: 1326, lr: 0.001000, loss: 1.563924, loss_shrink_maps: 0.848012, loss_threshold_maps: 0.589290, loss_binary_maps: 0.167914, avg_reader_cost: 1.54320 s, avg_batch_cost: 1.79496 s, avg_samples: 12.5, ips: 6.96393 samples/s, eta: 5:25:00
[2024/07/28 00:31:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:32:01] ppocr INFO: epoch: [443/1500], global_step: 1329, lr: 0.001000, loss: 1.582372, loss_shrink_maps: 0.846443, loss_threshold_maps: 0.583438, loss_binary_maps: 0.167914, avg_reader_cost: 1.52255 s, avg_batch_cost: 1.77706 s, avg_samples: 12.5, ips: 7.03409 samples/s, eta: 5:24:40
[2024/07/28 00:32:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:32:07] ppocr INFO: epoch: [444/1500], global_step: 1330, lr: 0.001000, loss: 1.564357, loss_shrink_maps: 0.841689, loss_threshold_maps: 0.583438, loss_binary_maps: 0.166770, avg_reader_cost: 0.37305 s, avg_batch_cost: 0.51492 s, avg_samples: 4.8, ips: 9.32180 samples/s, eta: 5:24:32
[2024/07/28 00:32:09] ppocr INFO: epoch: [444/1500], global_step: 1332, lr: 0.001000, loss: 1.582372, loss_shrink_maps: 0.846443, loss_threshold_maps: 0.583438, loss_binary_maps: 0.167914, avg_reader_cost: 1.12110 s, avg_batch_cost: 1.26687 s, avg_samples: 7.7, ips: 6.07796 samples/s, eta: 5:24:20
[2024/07/28 00:32:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:32:17] ppocr INFO: epoch: [445/1500], global_step: 1335, lr: 0.001000, loss: 1.591897, loss_shrink_maps: 0.852337, loss_threshold_maps: 0.580788, loss_binary_maps: 0.169291, avg_reader_cost: 1.61218 s, avg_batch_cost: 1.86151 s, avg_samples: 12.5, ips: 6.71498 samples/s, eta: 5:24:02
[2024/07/28 00:32:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:32:25] ppocr INFO: epoch: [446/1500], global_step: 1338, lr: 0.001000, loss: 1.607916, loss_shrink_maps: 0.855122, loss_threshold_maps: 0.585136, loss_binary_maps: 0.170355, avg_reader_cost: 1.52687 s, avg_batch_cost: 1.75551 s, avg_samples: 12.5, ips: 7.12046 samples/s, eta: 5:23:42
[2024/07/28 00:32:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:32:32] ppocr INFO: epoch: [447/1500], global_step: 1340, lr: 0.001000, loss: 1.607916, loss_shrink_maps: 0.855122, loss_threshold_maps: 0.587614, loss_binary_maps: 0.170355, avg_reader_cost: 0.92399 s, avg_batch_cost: 1.09970 s, avg_samples: 9.6, ips: 8.72963 samples/s, eta: 5:23:27
[2024/07/28 00:32:33] ppocr INFO: epoch: [447/1500], global_step: 1341, lr: 0.001000, loss: 1.607916, loss_shrink_maps: 0.855122, loss_threshold_maps: 0.587614, loss_binary_maps: 0.170355, avg_reader_cost: 0.59553 s, avg_batch_cost: 0.65033 s, avg_samples: 2.9, ips: 4.45931 samples/s, eta: 5:23:21
[2024/07/28 00:32:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:32:41] ppocr INFO: epoch: [448/1500], global_step: 1344, lr: 0.001000, loss: 1.607916, loss_shrink_maps: 0.855122, loss_threshold_maps: 0.585136, loss_binary_maps: 0.170355, avg_reader_cost: 1.48903 s, avg_batch_cost: 1.71855 s, avg_samples: 12.5, ips: 7.27357 samples/s, eta: 5:23:00
[2024/07/28 00:32:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:32:49] ppocr INFO: epoch: [449/1500], global_step: 1347, lr: 0.001000, loss: 1.605290, loss_shrink_maps: 0.850369, loss_threshold_maps: 0.591534, loss_binary_maps: 0.168694, avg_reader_cost: 1.52313 s, avg_batch_cost: 1.75541 s, avg_samples: 12.5, ips: 7.12084 samples/s, eta: 5:22:39
[2024/07/28 00:32:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:32:57] ppocr INFO: epoch: [450/1500], global_step: 1350, lr: 0.001000, loss: 1.643077, loss_shrink_maps: 0.865667, loss_threshold_maps: 0.602776, loss_binary_maps: 0.172347, avg_reader_cost: 1.55357 s, avg_batch_cost: 1.80303 s, avg_samples: 12.5, ips: 6.93279 samples/s, eta: 5:22:20
[2024/07/28 00:32:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:33:05] ppocr INFO: epoch: [451/1500], global_step: 1353, lr: 0.001000, loss: 1.617802, loss_shrink_maps: 0.855054, loss_threshold_maps: 0.602776, loss_binary_maps: 0.170119, avg_reader_cost: 1.52653 s, avg_batch_cost: 1.76715 s, avg_samples: 12.5, ips: 7.07354 samples/s, eta: 5:22:00
[2024/07/28 00:33:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:33:13] ppocr INFO: epoch: [452/1500], global_step: 1356, lr: 0.001000, loss: 1.625391, loss_shrink_maps: 0.855054, loss_threshold_maps: 0.602776, loss_binary_maps: 0.170119, avg_reader_cost: 1.54943 s, avg_batch_cost: 1.77878 s, avg_samples: 12.5, ips: 7.02727 samples/s, eta: 5:21:40
[2024/07/28 00:33:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:33:21] ppocr INFO: epoch: [453/1500], global_step: 1359, lr: 0.001000, loss: 1.598877, loss_shrink_maps: 0.842453, loss_threshold_maps: 0.601460, loss_binary_maps: 0.167753, avg_reader_cost: 1.51481 s, avg_batch_cost: 1.74474 s, avg_samples: 12.5, ips: 7.16438 samples/s, eta: 5:21:19
[2024/07/28 00:33:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:33:27] ppocr INFO: epoch: [454/1500], global_step: 1360, lr: 0.001000, loss: 1.598877, loss_shrink_maps: 0.842453, loss_threshold_maps: 0.591139, loss_binary_maps: 0.167753, avg_reader_cost: 0.40501 s, avg_batch_cost: 0.50192 s, avg_samples: 4.8, ips: 9.56321 samples/s, eta: 5:21:11
[2024/07/28 00:33:29] ppocr INFO: epoch: [454/1500], global_step: 1362, lr: 0.001000, loss: 1.579771, loss_shrink_maps: 0.830377, loss_threshold_maps: 0.575808, loss_binary_maps: 0.164879, avg_reader_cost: 1.09547 s, avg_batch_cost: 1.24155 s, avg_samples: 7.7, ips: 6.20192 samples/s, eta: 5:20:59
[2024/07/28 00:33:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:33:37] ppocr INFO: epoch: [455/1500], global_step: 1365, lr: 0.001000, loss: 1.591234, loss_shrink_maps: 0.842453, loss_threshold_maps: 0.575808, loss_binary_maps: 0.167753, avg_reader_cost: 1.51996 s, avg_batch_cost: 1.76399 s, avg_samples: 12.5, ips: 7.08622 samples/s, eta: 5:20:39
[2024/07/28 00:33:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:33:45] ppocr INFO: epoch: [456/1500], global_step: 1368, lr: 0.001000, loss: 1.591234, loss_shrink_maps: 0.841384, loss_threshold_maps: 0.568673, loss_binary_maps: 0.167753, avg_reader_cost: 1.51205 s, avg_batch_cost: 1.74650 s, avg_samples: 12.5, ips: 7.15717 samples/s, eta: 5:20:18
[2024/07/28 00:33:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:33:52] ppocr INFO: epoch: [457/1500], global_step: 1370, lr: 0.001000, loss: 1.591234, loss_shrink_maps: 0.841384, loss_threshold_maps: 0.570098, loss_binary_maps: 0.167769, avg_reader_cost: 0.88375 s, avg_batch_cost: 1.08187 s, avg_samples: 9.6, ips: 8.87354 samples/s, eta: 5:20:02
[2024/07/28 00:33:53] ppocr INFO: epoch: [457/1500], global_step: 1371, lr: 0.001000, loss: 1.585334, loss_shrink_maps: 0.831399, loss_threshold_maps: 0.570634, loss_binary_maps: 0.165527, avg_reader_cost: 0.58672 s, avg_batch_cost: 0.64163 s, avg_samples: 2.9, ips: 4.51972 samples/s, eta: 5:19:57
[2024/07/28 00:33:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:34:01] ppocr INFO: epoch: [458/1500], global_step: 1374, lr: 0.001000, loss: 1.553081, loss_shrink_maps: 0.818516, loss_threshold_maps: 0.569931, loss_binary_maps: 0.162796, avg_reader_cost: 1.57581 s, avg_batch_cost: 1.80433 s, avg_samples: 12.5, ips: 6.92779 samples/s, eta: 5:19:38
[2024/07/28 00:34:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:34:09] ppocr INFO: epoch: [459/1500], global_step: 1377, lr: 0.001000, loss: 1.544060, loss_shrink_maps: 0.807290, loss_threshold_maps: 0.575105, loss_binary_maps: 0.160634, avg_reader_cost: 1.51012 s, avg_batch_cost: 1.74859 s, avg_samples: 12.5, ips: 7.14862 samples/s, eta: 5:19:17
[2024/07/28 00:34:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:34:17] ppocr INFO: epoch: [460/1500], global_step: 1380, lr: 0.001000, loss: 1.525183, loss_shrink_maps: 0.790052, loss_threshold_maps: 0.575105, loss_binary_maps: 0.156443, avg_reader_cost: 1.48429 s, avg_batch_cost: 1.71558 s, avg_samples: 12.5, ips: 7.28618 samples/s, eta: 5:18:56

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:37, 13.22it/s]
eval model::   1%|          | 6/500 [00:00<00:24, 20.00it/s]
eval model::   2%|▏         | 8/500 [00:00<00:25, 19.54it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.23it/s]
eval model::   3%|▎         | 13/500 [00:00<00:26, 18.46it/s]
eval model::   3%|▎         | 16/500 [00:00<00:24, 19.94it/s]
eval model::   4%|▍         | 19/500 [00:00<00:22, 21.06it/s]
eval model::   4%|▍         | 22/500 [00:01<00:25, 18.85it/s]
eval model::   5%|▌         | 25/500 [00:01<00:26, 18.13it/s]
eval model::   5%|▌         | 27/500 [00:01<00:25, 18.38it/s]
eval model::   6%|▌         | 29/500 [00:01<00:25, 18.41it/s]
eval model::   6%|▌         | 31/500 [00:01<00:29, 16.01it/s]
eval model::   7%|▋         | 33/500 [00:01<00:27, 16.89it/s]
eval model::   7%|▋         | 35/500 [00:01<00:28, 16.54it/s]
eval model::   8%|▊         | 38/500 [00:02<00:27, 16.56it/s]
eval model::   8%|▊         | 40/500 [00:02<00:30, 15.07it/s]
eval model::   8%|▊         | 42/500 [00:02<00:29, 15.38it/s]
eval model::   9%|▉         | 45/500 [00:02<00:26, 17.12it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 17.61it/s]
eval model::  10%|█         | 50/500 [00:02<00:22, 20.17it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 20.87it/s]
eval model::  11%|█         | 56/500 [00:03<00:21, 20.99it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:22, 19.25it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.47it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.85it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:21, 20.44it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:18, 22.67it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:17, 23.91it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:18, 23.18it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.49it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:21, 19.04it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:22, 18.61it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.25it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.56it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:21, 18.75it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.80it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:21, 18.60it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 22.19it/s]
eval model::  20%|██        | 102/500 [00:05<00:18, 21.16it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 20.40it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:20, 19.52it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 20.75it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:17, 22.56it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:18, 20.91it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 20.53it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.33it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 19.67it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 20.97it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 18.14it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:18, 19.78it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:19, 18.66it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:19, 18.23it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:19, 18.54it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:19, 18.05it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:20, 17.19it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.71it/s]
eval model::  31%|███       | 153/500 [00:08<00:18, 18.57it/s]
eval model::  31%|███       | 156/500 [00:08<00:16, 20.71it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:18, 18.66it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:19, 17.29it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.22it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 19.00it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:17, 19.14it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:16, 19.43it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:16, 19.53it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:16, 19.18it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:15, 20.75it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:17, 18.59it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:15, 20.73it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:16, 19.05it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:15, 19.84it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.98it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.82it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.58it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 21.37it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:21, 13.67it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:20, 14.07it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:21, 13.20it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:20, 14.32it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.84it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.79it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.24it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:19, 14.38it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.93it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 22.07it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 22.45it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 20.69it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.48it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 19.94it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.39it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 20.35it/s]
eval model::  50%|█████     | 251/500 [00:13<00:14, 17.43it/s]
eval model::  51%|█████     | 254/500 [00:13<00:12, 18.99it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:12, 19.85it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:11, 20.75it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:11, 19.82it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:13, 17.59it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:13, 17.47it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 17.75it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:13, 16.86it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 17.21it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 19.08it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:10, 20.66it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:15, 13.42it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:13, 16.14it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:11, 17.95it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:14, 14.11it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:11, 17.65it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 17.04it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 17.74it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 19.53it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 19.45it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:10, 17.81it/s]
eval model::  63%|██████▎   | 317/500 [00:17<00:09, 19.56it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:08, 20.79it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:08, 20.58it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:07, 22.71it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:07, 22.03it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:07, 22.46it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 22.54it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 20.25it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:09, 15.90it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 19.21it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:07, 20.74it/s]
eval model::  70%|███████   | 351/500 [00:18<00:06, 21.79it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 23.06it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:07, 19.59it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:06, 20.12it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:06, 20.37it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 16.93it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 18.68it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:07, 18.27it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 18.89it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 20.78it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:06, 18.59it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 20.92it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:08, 13.22it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:06, 16.50it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:05, 18.43it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:04, 21.61it/s]
eval model::  80%|████████  | 401/500 [00:21<00:04, 20.19it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 20.87it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 20.84it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 19.90it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:04, 18.06it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 18.88it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 17.76it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:04, 16.40it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:04, 18.21it/s]
eval model::  85%|████████▌ | 425/500 [00:22<00:04, 15.07it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 19.43it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 20.05it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 21.16it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 22.29it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:03, 18.77it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 21.03it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 22.27it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 20.48it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 22.59it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 24.20it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 22.06it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 16.88it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 16.41it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:02, 16.52it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 16.00it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 18.16it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 18.35it/s]
eval model::  95%|█████████▌| 475/500 [00:25<00:01, 16.78it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:01, 18.57it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:01, 18.84it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 21.02it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 20.19it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 21.84it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 18.54it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 19.77it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 21.70it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.92it/s]
[2024/07/28 00:34:43] ppocr INFO: cur metric, precision: 0.7324185248713551, recall: 0.6167549350024073, hmean: 0.6696288552012545, fps: 45.42158780325782
[2024/07/28 00:34:44] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/28 00:34:44] ppocr INFO: best metric, hmean: 0.6696288552012545, precision: 0.7324185248713551, recall: 0.6167549350024073, fps: 45.42158780325782, best_epoch: 460
[2024/07/28 00:34:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:34:51] ppocr INFO: epoch: [461/1500], global_step: 1383, lr: 0.001000, loss: 1.510768, loss_shrink_maps: 0.776703, loss_threshold_maps: 0.571644, loss_binary_maps: 0.153831, avg_reader_cost: 1.55656 s, avg_batch_cost: 1.81990 s, avg_samples: 12.5, ips: 6.86850 samples/s, eta: 5:18:37
[2024/07/28 00:34:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:35:00] ppocr INFO: epoch: [462/1500], global_step: 1386, lr: 0.001000, loss: 1.525183, loss_shrink_maps: 0.791554, loss_threshold_maps: 0.586632, loss_binary_maps: 0.157212, avg_reader_cost: 1.74745 s, avg_batch_cost: 1.97592 s, avg_samples: 12.5, ips: 6.32617 samples/s, eta: 5:18:22
[2024/07/28 00:35:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:35:08] ppocr INFO: epoch: [463/1500], global_step: 1389, lr: 0.001000, loss: 1.503236, loss_shrink_maps: 0.772331, loss_threshold_maps: 0.584444, loss_binary_maps: 0.153446, avg_reader_cost: 1.55477 s, avg_batch_cost: 1.83016 s, avg_samples: 12.5, ips: 6.83001 samples/s, eta: 5:18:03
[2024/07/28 00:35:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:35:14] ppocr INFO: epoch: [464/1500], global_step: 1390, lr: 0.001000, loss: 1.503236, loss_shrink_maps: 0.772331, loss_threshold_maps: 0.584444, loss_binary_maps: 0.153446, avg_reader_cost: 0.39907 s, avg_batch_cost: 0.51792 s, avg_samples: 4.8, ips: 9.26783 samples/s, eta: 5:17:55
[2024/07/28 00:35:16] ppocr INFO: epoch: [464/1500], global_step: 1392, lr: 0.001000, loss: 1.525183, loss_shrink_maps: 0.776802, loss_threshold_maps: 0.584444, loss_binary_maps: 0.154440, avg_reader_cost: 1.12721 s, avg_batch_cost: 1.27310 s, avg_samples: 7.7, ips: 6.04821 samples/s, eta: 5:17:44
[2024/07/28 00:35:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:35:24] ppocr INFO: epoch: [465/1500], global_step: 1395, lr: 0.001000, loss: 1.541671, loss_shrink_maps: 0.791554, loss_threshold_maps: 0.586632, loss_binary_maps: 0.157212, avg_reader_cost: 1.58558 s, avg_batch_cost: 1.81435 s, avg_samples: 12.5, ips: 6.88952 samples/s, eta: 5:17:25
[2024/07/28 00:35:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:35:32] ppocr INFO: epoch: [466/1500], global_step: 1398, lr: 0.001000, loss: 1.554489, loss_shrink_maps: 0.807871, loss_threshold_maps: 0.584444, loss_binary_maps: 0.159993, avg_reader_cost: 1.52315 s, avg_batch_cost: 1.76391 s, avg_samples: 12.5, ips: 7.08652 samples/s, eta: 5:17:05
[2024/07/28 00:35:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:35:40] ppocr INFO: epoch: [467/1500], global_step: 1400, lr: 0.001000, loss: 1.560642, loss_shrink_maps: 0.822145, loss_threshold_maps: 0.584444, loss_binary_maps: 0.162929, avg_reader_cost: 0.91342 s, avg_batch_cost: 1.09039 s, avg_samples: 9.6, ips: 8.80421 samples/s, eta: 5:16:49
[2024/07/28 00:35:40] ppocr INFO: epoch: [467/1500], global_step: 1401, lr: 0.001000, loss: 1.560642, loss_shrink_maps: 0.822145, loss_threshold_maps: 0.582987, loss_binary_maps: 0.162929, avg_reader_cost: 0.59099 s, avg_batch_cost: 0.64584 s, avg_samples: 2.9, ips: 4.49024 samples/s, eta: 5:16:44
[2024/07/28 00:35:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:35:49] ppocr INFO: epoch: [468/1500], global_step: 1404, lr: 0.001000, loss: 1.594982, loss_shrink_maps: 0.850261, loss_threshold_maps: 0.591373, loss_binary_maps: 0.168478, avg_reader_cost: 1.57189 s, avg_batch_cost: 1.80076 s, avg_samples: 12.5, ips: 6.94149 samples/s, eta: 5:16:25
[2024/07/28 00:35:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:35:57] ppocr INFO: epoch: [469/1500], global_step: 1407, lr: 0.001000, loss: 1.555097, loss_shrink_maps: 0.830400, loss_threshold_maps: 0.579202, loss_binary_maps: 0.164848, avg_reader_cost: 1.54747 s, avg_batch_cost: 1.80198 s, avg_samples: 12.5, ips: 6.93681 samples/s, eta: 5:16:05
[2024/07/28 00:35:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:36:05] ppocr INFO: epoch: [470/1500], global_step: 1410, lr: 0.001000, loss: 1.622727, loss_shrink_maps: 0.870038, loss_threshold_maps: 0.576619, loss_binary_maps: 0.171781, avg_reader_cost: 1.54349 s, avg_batch_cost: 1.77386 s, avg_samples: 12.5, ips: 7.04677 samples/s, eta: 5:15:46
[2024/07/28 00:36:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:36:13] ppocr INFO: epoch: [471/1500], global_step: 1413, lr: 0.001000, loss: 1.531518, loss_shrink_maps: 0.803959, loss_threshold_maps: 0.576619, loss_binary_maps: 0.159125, avg_reader_cost: 1.55825 s, avg_batch_cost: 1.81205 s, avg_samples: 12.5, ips: 6.89826 samples/s, eta: 5:15:27
[2024/07/28 00:36:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:36:21] ppocr INFO: epoch: [472/1500], global_step: 1416, lr: 0.001000, loss: 1.531518, loss_shrink_maps: 0.803959, loss_threshold_maps: 0.576619, loss_binary_maps: 0.159125, avg_reader_cost: 1.52595 s, avg_batch_cost: 1.77591 s, avg_samples: 12.5, ips: 7.03864 samples/s, eta: 5:15:07
[2024/07/28 00:36:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:36:30] ppocr INFO: epoch: [473/1500], global_step: 1419, lr: 0.001000, loss: 1.531518, loss_shrink_maps: 0.803959, loss_threshold_maps: 0.579202, loss_binary_maps: 0.158982, avg_reader_cost: 1.60678 s, avg_batch_cost: 1.91977 s, avg_samples: 12.5, ips: 6.51120 samples/s, eta: 5:14:50
[2024/07/28 00:36:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:36:36] ppocr INFO: epoch: [474/1500], global_step: 1420, lr: 0.001000, loss: 1.573379, loss_shrink_maps: 0.833546, loss_threshold_maps: 0.579112, loss_binary_maps: 0.165746, avg_reader_cost: 0.42139 s, avg_batch_cost: 0.51842 s, avg_samples: 4.8, ips: 9.25895 samples/s, eta: 5:14:42
[2024/07/28 00:36:38] ppocr INFO: epoch: [474/1500], global_step: 1422, lr: 0.001000, loss: 1.609995, loss_shrink_maps: 0.849856, loss_threshold_maps: 0.585156, loss_binary_maps: 0.169451, avg_reader_cost: 1.12853 s, avg_batch_cost: 1.27402 s, avg_samples: 7.7, ips: 6.04384 samples/s, eta: 5:14:31
[2024/07/28 00:36:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:36:46] ppocr INFO: epoch: [475/1500], global_step: 1425, lr: 0.001000, loss: 1.609995, loss_shrink_maps: 0.849856, loss_threshold_maps: 0.576811, loss_binary_maps: 0.169451, avg_reader_cost: 1.59336 s, avg_batch_cost: 1.82501 s, avg_samples: 12.5, ips: 6.84927 samples/s, eta: 5:14:12
[2024/07/28 00:36:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:36:54] ppocr INFO: epoch: [476/1500], global_step: 1428, lr: 0.001000, loss: 1.562101, loss_shrink_maps: 0.814447, loss_threshold_maps: 0.576310, loss_binary_maps: 0.161770, avg_reader_cost: 1.53751 s, avg_batch_cost: 1.78614 s, avg_samples: 12.5, ips: 6.99832 samples/s, eta: 5:13:52
[2024/07/28 00:36:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:37:02] ppocr INFO: epoch: [477/1500], global_step: 1430, lr: 0.001000, loss: 1.520171, loss_shrink_maps: 0.784635, loss_threshold_maps: 0.576811, loss_binary_maps: 0.155678, avg_reader_cost: 0.93000 s, avg_batch_cost: 1.12001 s, avg_samples: 9.6, ips: 8.57132 samples/s, eta: 5:13:38
[2024/07/28 00:37:02] ppocr INFO: epoch: [477/1500], global_step: 1431, lr: 0.001000, loss: 1.523601, loss_shrink_maps: 0.790190, loss_threshold_maps: 0.581328, loss_binary_maps: 0.156573, avg_reader_cost: 0.60543 s, avg_batch_cost: 0.66044 s, avg_samples: 2.9, ips: 4.39101 samples/s, eta: 5:13:33
[2024/07/28 00:37:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:37:11] ppocr INFO: epoch: [478/1500], global_step: 1434, lr: 0.001000, loss: 1.523093, loss_shrink_maps: 0.790190, loss_threshold_maps: 0.576811, loss_binary_maps: 0.156573, avg_reader_cost: 1.60126 s, avg_batch_cost: 1.83171 s, avg_samples: 12.5, ips: 6.82421 samples/s, eta: 5:13:14
[2024/07/28 00:37:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:37:19] ppocr INFO: epoch: [479/1500], global_step: 1437, lr: 0.001000, loss: 1.496398, loss_shrink_maps: 0.784635, loss_threshold_maps: 0.566657, loss_binary_maps: 0.155678, avg_reader_cost: 1.54865 s, avg_batch_cost: 1.81266 s, avg_samples: 12.5, ips: 6.89595 samples/s, eta: 5:12:55
[2024/07/28 00:37:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:37:27] ppocr INFO: epoch: [480/1500], global_step: 1440, lr: 0.001000, loss: 1.491736, loss_shrink_maps: 0.778265, loss_threshold_maps: 0.563023, loss_binary_maps: 0.154394, avg_reader_cost: 1.50683 s, avg_batch_cost: 1.74562 s, avg_samples: 12.5, ips: 7.16077 samples/s, eta: 5:12:35

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:54,  9.08it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.27it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.92it/s]
eval model::   2%|▏         | 8/500 [00:00<00:26, 18.39it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.24it/s]
eval model::   3%|▎         | 13/500 [00:00<00:27, 17.98it/s]
eval model::   3%|▎         | 16/500 [00:00<00:25, 19.09it/s]
eval model::   4%|▍         | 19/500 [00:01<00:25, 18.62it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 18.67it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.67it/s]
eval model::   5%|▌         | 27/500 [00:01<00:24, 19.23it/s]
eval model::   6%|▌         | 30/500 [00:01<00:22, 20.71it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 19.70it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 18.78it/s]
eval model::   8%|▊         | 38/500 [00:02<00:25, 18.37it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.61it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.06it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.37it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 16.81it/s]
eval model::  10%|▉         | 49/500 [00:02<00:27, 16.46it/s]
eval model::  10%|█         | 52/500 [00:02<00:24, 18.07it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 19.43it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 18.69it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 16.14it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 16.11it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.85it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.64it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 20.65it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:20, 20.59it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.83it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:17, 23.38it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.39it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 20.61it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.23it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:23, 17.53it/s]
eval model::  19%|█▉        | 94/500 [00:05<00:23, 17.00it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:24, 16.79it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:21, 18.30it/s]
eval model::  20%|██        | 101/500 [00:05<00:23, 17.09it/s]
eval model::  21%|██        | 104/500 [00:05<00:20, 19.16it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:19, 20.42it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:19, 19.63it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:20, 19.00it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:19, 19.41it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:18, 20.57it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 20.42it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 18.98it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 18.69it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 18.85it/s]
eval model::  26%|██▌       | 131/500 [00:07<00:22, 16.55it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:23, 15.84it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:20, 17.94it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:19, 18.25it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.11it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 21.42it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:16, 20.90it/s]
eval model::  30%|███       | 151/500 [00:08<00:17, 19.89it/s]
eval model::  31%|███       | 154/500 [00:08<00:16, 21.29it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:15, 22.80it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:20, 16.85it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.60it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 19.26it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 18.96it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:17, 18.42it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:22, 14.50it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:21, 15.37it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.71it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.61it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 18.23it/s]
eval model::  37%|███▋      | 187/500 [00:10<00:16, 19.30it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:15, 19.77it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:14, 21.52it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:15, 19.17it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 20.62it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 21.40it/s]
eval model::  41%|████      | 205/500 [00:11<00:20, 14.44it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:19, 15.33it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 15.28it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.31it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.71it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.40it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:19, 14.47it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:21, 13.26it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:17, 15.88it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:14, 18.41it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:11, 22.61it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:13, 20.06it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 20.29it/s]
eval model::  48%|████▊     | 240/500 [00:13<00:11, 21.67it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:12, 19.92it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:11, 21.37it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:12, 19.89it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 20.90it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 22.11it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 21.66it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 23.35it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:10, 21.68it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:10, 22.13it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 20.72it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:10, 21.57it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:11, 19.66it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 20.06it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 21.10it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.24it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.43it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 17.21it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:13, 14.77it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:11, 17.86it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 17.15it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 18.01it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:10, 18.70it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 20.14it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:08, 21.26it/s]
eval model::  63%|██████▎   | 317/500 [00:17<00:08, 21.37it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:08, 21.79it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:08, 20.47it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:08, 20.51it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 19.32it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:08, 19.98it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 22.46it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:07, 20.20it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:09, 16.60it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 17.04it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 18.84it/s]
eval model::  70%|███████   | 350/500 [00:18<00:07, 21.26it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 22.35it/s]
eval model::  71%|███████   | 356/500 [00:19<00:07, 18.95it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:06, 20.20it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:07, 19.27it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 19.06it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:08, 15.26it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:07, 16.68it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 16.27it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 18.24it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 22.40it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 22.61it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:04, 24.22it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:08, 13.10it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:08, 13.73it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:07, 14.75it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 16.34it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 18.21it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 18.02it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 20.06it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 20.93it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:03, 22.53it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:03, 22.69it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:03, 22.45it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:03, 20.96it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 15.48it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.50it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.36it/s]
eval model::  86%|████████▌ | 428/500 [00:23<00:04, 15.43it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:04, 16.15it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:04, 16.51it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:03, 20.16it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 21.70it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 21.43it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 20.87it/s]
eval model::  90%|████████▉ | 448/500 [00:24<00:02, 20.50it/s]
eval model::  90%|█████████ | 451/500 [00:24<00:02, 21.05it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 20.60it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:02, 21.11it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 20.77it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 17.16it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 16.28it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 17.52it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 18.89it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 19.71it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 22.03it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 21.89it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 22.02it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 18.78it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 20.90it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 17.56it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 18.64it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 22.06it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.76it/s]
[2024/07/28 00:37:53] ppocr INFO: cur metric, precision: 0.6902927580893683, recall: 0.6470871449205585, hmean: 0.6679920477137178, fps: 44.86244954837676
[2024/07/28 00:37:53] ppocr INFO: best metric, hmean: 0.6696288552012545, precision: 0.7324185248713551, recall: 0.6167549350024073, fps: 45.42158780325782, best_epoch: 460
[2024/07/28 00:37:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:38:01] ppocr INFO: epoch: [481/1500], global_step: 1443, lr: 0.001000, loss: 1.488510, loss_shrink_maps: 0.772379, loss_threshold_maps: 0.559784, loss_binary_maps: 0.153071, avg_reader_cost: 1.71051 s, avg_batch_cost: 1.99854 s, avg_samples: 12.5, ips: 6.25457 samples/s, eta: 5:12:20
[2024/07/28 00:38:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:38:09] ppocr INFO: epoch: [482/1500], global_step: 1446, lr: 0.001000, loss: 1.491736, loss_shrink_maps: 0.778265, loss_threshold_maps: 0.563023, loss_binary_maps: 0.154601, avg_reader_cost: 1.51735 s, avg_batch_cost: 1.74710 s, avg_samples: 12.5, ips: 7.15471 samples/s, eta: 5:12:00
[2024/07/28 00:38:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:38:18] ppocr INFO: epoch: [483/1500], global_step: 1449, lr: 0.001000, loss: 1.491736, loss_shrink_maps: 0.772379, loss_threshold_maps: 0.563023, loss_binary_maps: 0.153071, avg_reader_cost: 1.56436 s, avg_batch_cost: 1.85500 s, avg_samples: 12.5, ips: 6.73856 samples/s, eta: 5:11:42
[2024/07/28 00:38:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:38:24] ppocr INFO: epoch: [484/1500], global_step: 1450, lr: 0.001000, loss: 1.491736, loss_shrink_maps: 0.764021, loss_threshold_maps: 0.563023, loss_binary_maps: 0.151816, avg_reader_cost: 0.39279 s, avg_batch_cost: 0.51385 s, avg_samples: 4.8, ips: 9.34132 samples/s, eta: 5:11:33
[2024/07/28 00:38:26] ppocr INFO: epoch: [484/1500], global_step: 1452, lr: 0.001000, loss: 1.491736, loss_shrink_maps: 0.764021, loss_threshold_maps: 0.559784, loss_binary_maps: 0.151816, avg_reader_cost: 1.11921 s, avg_batch_cost: 1.26526 s, avg_samples: 7.7, ips: 6.08569 samples/s, eta: 5:11:22
[2024/07/28 00:38:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:38:34] ppocr INFO: epoch: [485/1500], global_step: 1455, lr: 0.001000, loss: 1.479168, loss_shrink_maps: 0.757179, loss_threshold_maps: 0.559784, loss_binary_maps: 0.150627, avg_reader_cost: 1.52482 s, avg_batch_cost: 1.75321 s, avg_samples: 12.5, ips: 7.12979 samples/s, eta: 5:11:02
[2024/07/28 00:38:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:38:43] ppocr INFO: epoch: [486/1500], global_step: 1458, lr: 0.001000, loss: 1.485117, loss_shrink_maps: 0.757179, loss_threshold_maps: 0.576725, loss_binary_maps: 0.150627, avg_reader_cost: 1.66917 s, avg_batch_cost: 2.02302 s, avg_samples: 12.5, ips: 6.17888 samples/s, eta: 5:10:47
[2024/07/28 00:38:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:38:50] ppocr INFO: epoch: [487/1500], global_step: 1460, lr: 0.001000, loss: 1.485117, loss_shrink_maps: 0.756590, loss_threshold_maps: 0.576725, loss_binary_maps: 0.150023, avg_reader_cost: 0.99837 s, avg_batch_cost: 1.18591 s, avg_samples: 9.6, ips: 8.09508 samples/s, eta: 5:10:34
[2024/07/28 00:38:51] ppocr INFO: epoch: [487/1500], global_step: 1461, lr: 0.001000, loss: 1.485117, loss_shrink_maps: 0.756590, loss_threshold_maps: 0.576725, loss_binary_maps: 0.150023, avg_reader_cost: 0.63850 s, avg_batch_cost: 0.69361 s, avg_samples: 2.9, ips: 4.18100 samples/s, eta: 5:10:30
[2024/07/28 00:38:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:38:59] ppocr INFO: epoch: [488/1500], global_step: 1464, lr: 0.001000, loss: 1.485117, loss_shrink_maps: 0.757247, loss_threshold_maps: 0.569789, loss_binary_maps: 0.150322, avg_reader_cost: 1.53612 s, avg_batch_cost: 1.77633 s, avg_samples: 12.5, ips: 7.03697 samples/s, eta: 5:10:10
[2024/07/28 00:39:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:39:08] ppocr INFO: epoch: [489/1500], global_step: 1467, lr: 0.001000, loss: 1.484186, loss_shrink_maps: 0.752751, loss_threshold_maps: 0.569789, loss_binary_maps: 0.148982, avg_reader_cost: 1.64006 s, avg_batch_cost: 1.87074 s, avg_samples: 12.5, ips: 6.68185 samples/s, eta: 5:09:52
[2024/07/28 00:39:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:39:16] ppocr INFO: epoch: [490/1500], global_step: 1470, lr: 0.001000, loss: 1.540274, loss_shrink_maps: 0.790655, loss_threshold_maps: 0.574174, loss_binary_maps: 0.157083, avg_reader_cost: 1.65038 s, avg_batch_cost: 1.94216 s, avg_samples: 12.5, ips: 6.43612 samples/s, eta: 5:09:36
[2024/07/28 00:39:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:39:24] ppocr INFO: epoch: [491/1500], global_step: 1473, lr: 0.001000, loss: 1.540274, loss_shrink_maps: 0.790655, loss_threshold_maps: 0.581021, loss_binary_maps: 0.157419, avg_reader_cost: 1.49852 s, avg_batch_cost: 1.75099 s, avg_samples: 12.5, ips: 7.13881 samples/s, eta: 5:09:16
[2024/07/28 00:39:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:39:32] ppocr INFO: epoch: [492/1500], global_step: 1476, lr: 0.001000, loss: 1.540274, loss_shrink_maps: 0.787087, loss_threshold_maps: 0.581021, loss_binary_maps: 0.156736, avg_reader_cost: 1.52418 s, avg_batch_cost: 1.76037 s, avg_samples: 12.5, ips: 7.10078 samples/s, eta: 5:08:56
[2024/07/28 00:39:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:39:41] ppocr INFO: epoch: [493/1500], global_step: 1479, lr: 0.001000, loss: 1.549348, loss_shrink_maps: 0.790655, loss_threshold_maps: 0.583368, loss_binary_maps: 0.157419, avg_reader_cost: 1.55060 s, avg_batch_cost: 1.81194 s, avg_samples: 12.5, ips: 6.89868 samples/s, eta: 5:08:37
[2024/07/28 00:39:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:39:48] ppocr INFO: epoch: [494/1500], global_step: 1480, lr: 0.001000, loss: 1.552604, loss_shrink_maps: 0.801348, loss_threshold_maps: 0.586564, loss_binary_maps: 0.159254, avg_reader_cost: 0.41671 s, avg_batch_cost: 0.51729 s, avg_samples: 4.8, ips: 9.27910 samples/s, eta: 5:08:29
[2024/07/28 00:39:49] ppocr INFO: epoch: [494/1500], global_step: 1482, lr: 0.001000, loss: 1.549348, loss_shrink_maps: 0.790655, loss_threshold_maps: 0.583368, loss_binary_maps: 0.157419, avg_reader_cost: 1.12563 s, avg_batch_cost: 1.27066 s, avg_samples: 7.7, ips: 6.05982 samples/s, eta: 5:08:17
[2024/07/28 00:39:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:39:57] ppocr INFO: epoch: [495/1500], global_step: 1485, lr: 0.001000, loss: 1.520809, loss_shrink_maps: 0.766907, loss_threshold_maps: 0.580004, loss_binary_maps: 0.152524, avg_reader_cost: 1.57663 s, avg_batch_cost: 1.83100 s, avg_samples: 12.5, ips: 6.82688 samples/s, eta: 5:07:59
[2024/07/28 00:39:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:40:06] ppocr INFO: epoch: [496/1500], global_step: 1488, lr: 0.001000, loss: 1.528041, loss_shrink_maps: 0.782393, loss_threshold_maps: 0.580004, loss_binary_maps: 0.155630, avg_reader_cost: 1.57133 s, avg_batch_cost: 1.79928 s, avg_samples: 12.5, ips: 6.94720 samples/s, eta: 5:07:40
[2024/07/28 00:40:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:40:13] ppocr INFO: epoch: [497/1500], global_step: 1490, lr: 0.001000, loss: 1.520809, loss_shrink_maps: 0.782393, loss_threshold_maps: 0.576784, loss_binary_maps: 0.155966, avg_reader_cost: 0.93452 s, avg_batch_cost: 1.13558 s, avg_samples: 9.6, ips: 8.45386 samples/s, eta: 5:07:26
[2024/07/28 00:40:14] ppocr INFO: epoch: [497/1500], global_step: 1491, lr: 0.001000, loss: 1.518988, loss_shrink_maps: 0.766907, loss_threshold_maps: 0.572203, loss_binary_maps: 0.152524, avg_reader_cost: 0.61341 s, avg_batch_cost: 0.66839 s, avg_samples: 2.9, ips: 4.33876 samples/s, eta: 5:07:21
[2024/07/28 00:40:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:40:22] ppocr INFO: epoch: [498/1500], global_step: 1494, lr: 0.001000, loss: 1.534422, loss_shrink_maps: 0.785570, loss_threshold_maps: 0.569676, loss_binary_maps: 0.156617, avg_reader_cost: 1.52241 s, avg_batch_cost: 1.75090 s, avg_samples: 12.5, ips: 7.13920 samples/s, eta: 5:07:00
[2024/07/28 00:40:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:40:30] ppocr INFO: epoch: [499/1500], global_step: 1497, lr: 0.001000, loss: 1.533468, loss_shrink_maps: 0.794589, loss_threshold_maps: 0.569676, loss_binary_maps: 0.158741, avg_reader_cost: 1.50827 s, avg_batch_cost: 1.73669 s, avg_samples: 12.5, ips: 7.19762 samples/s, eta: 5:06:40
[2024/07/28 00:40:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:40:38] ppocr INFO: epoch: [500/1500], global_step: 1500, lr: 0.001000, loss: 1.511823, loss_shrink_maps: 0.791566, loss_threshold_maps: 0.568531, loss_binary_maps: 0.157838, avg_reader_cost: 1.64435 s, avg_batch_cost: 1.87355 s, avg_samples: 12.5, ips: 6.67182 samples/s, eta: 5:06:22

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:54,  9.10it/s]
eval model::   1%|          | 4/500 [00:00<00:25, 19.82it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.07it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.17it/s]
eval model::   2%|▏         | 10/500 [00:00<00:28, 17.45it/s]
eval model::   2%|▏         | 12/500 [00:00<00:27, 17.78it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.73it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.87it/s]
eval model::   4%|▍         | 19/500 [00:01<00:24, 19.31it/s]
eval model::   4%|▍         | 21/500 [00:01<00:28, 16.99it/s]
eval model::   5%|▍         | 24/500 [00:01<00:24, 19.67it/s]
eval model::   5%|▌         | 27/500 [00:01<00:24, 19.55it/s]
eval model::   6%|▌         | 31/500 [00:01<00:22, 20.81it/s]
eval model::   7%|▋         | 34/500 [00:01<00:22, 20.64it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.93it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 17.83it/s]
eval model::   8%|▊         | 42/500 [00:02<00:28, 16.30it/s]
eval model::   9%|▉         | 44/500 [00:02<00:26, 16.99it/s]
eval model::   9%|▉         | 46/500 [00:02<00:29, 15.62it/s]
eval model::  10%|▉         | 49/500 [00:02<00:25, 17.63it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 19.06it/s]
eval model::  11%|█         | 54/500 [00:02<00:23, 18.86it/s]
eval model::  11%|█         | 56/500 [00:03<00:24, 18.49it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 17.72it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:24, 18.02it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:29, 14.64it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:28, 15.31it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:23, 18.25it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:23, 18.42it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 20.15it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:20, 20.41it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.32it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.10it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.72it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:19, 21.00it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.51it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:22, 18.45it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:22, 18.23it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:20, 19.40it/s]
eval model::  20%|██        | 100/500 [00:05<00:21, 18.55it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 20.45it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.71it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.29it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:19, 19.67it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:19, 19.75it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 22.32it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:16, 22.60it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:20, 18.33it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 19.88it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 19.16it/s]
eval model::  27%|██▋       | 134/500 [00:07<00:18, 19.96it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:17, 21.07it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 19.92it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:16, 21.54it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:16, 20.93it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:18, 19.48it/s]
eval model::  30%|███       | 152/500 [00:07<00:20, 17.33it/s]
eval model::  31%|███       | 156/500 [00:08<00:16, 20.91it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 20.67it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:20, 16.57it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:19, 17.15it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:19, 17.11it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:20, 16.55it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:19, 16.56it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:21, 15.16it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 15.61it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.50it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:21, 14.78it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:18, 17.34it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:17, 18.07it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:16, 18.62it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:15, 19.71it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:16, 18.00it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:15, 19.06it/s]
eval model::  40%|████      | 201/500 [00:10<00:16, 18.68it/s]
eval model::  41%|████      | 205/500 [00:11<00:20, 14.46it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:18, 15.41it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 14.89it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.71it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.44it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.12it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:19, 14.22it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:20, 13.70it/s]
eval model::  45%|████▍     | 223/500 [00:12<00:18, 14.91it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.29it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:15, 17.76it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:13, 19.32it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:14, 18.82it/s]
eval model::  47%|████▋     | 237/500 [00:13<00:13, 19.50it/s]
eval model::  48%|████▊     | 240/500 [00:13<00:12, 20.57it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:13, 19.58it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:11, 21.81it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:10, 23.00it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 22.27it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 22.03it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:10, 22.49it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:12, 19.22it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:13, 17.51it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:14, 16.47it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:15, 15.36it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:13, 17.08it/s]
eval model::  55%|█████▌    | 275/500 [00:15<00:12, 17.41it/s]
eval model::  55%|█████▌    | 277/500 [00:15<00:12, 17.65it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:12, 17.87it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:11, 19.10it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.50it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:15, 13.34it/s]
eval model::  58%|█████▊    | 291/500 [00:16<00:11, 17.43it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:12, 16.71it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:14, 14.05it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 16.17it/s]
eval model::  60%|██████    | 301/500 [00:16<00:13, 15.26it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.98it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:11, 17.50it/s]
eval model::  62%|██████▏   | 310/500 [00:17<00:10, 18.81it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:10, 18.45it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:10, 17.74it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:09, 19.60it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:09, 18.96it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 19.73it/s]
eval model::  66%|██████▌   | 328/500 [00:18<00:07, 21.97it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:07, 21.92it/s]
eval model::  67%|██████▋   | 335/500 [00:18<00:07, 23.07it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:07, 20.42it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:09, 16.55it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:08, 17.64it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:08, 18.00it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:07, 18.93it/s]
eval model::  70%|███████   | 351/500 [00:19<00:08, 18.46it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 20.06it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 18.91it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:07, 18.99it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:07, 17.75it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:08, 16.67it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:08, 15.31it/s]
eval model::  74%|███████▎  | 368/500 [00:20<00:08, 15.94it/s]
eval model::  74%|███████▍  | 370/500 [00:20<00:07, 16.79it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 16.44it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 18.36it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 22.87it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 21.77it/s]
eval model::  77%|███████▋  | 385/500 [00:21<00:05, 22.82it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:08, 13.54it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:06, 15.59it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 16.29it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 18.53it/s]
eval model::  80%|████████  | 400/500 [00:22<00:05, 18.57it/s]
eval model::  81%|████████  | 403/500 [00:22<00:04, 20.03it/s]
eval model::  81%|████████  | 406/500 [00:22<00:04, 21.22it/s]
eval model::  82%|████████▏ | 409/500 [00:22<00:04, 21.88it/s]
eval model::  82%|████████▏ | 412/500 [00:22<00:03, 22.09it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:04, 20.85it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 16.99it/s]
eval model::  84%|████████▍ | 420/500 [00:23<00:05, 14.29it/s]
eval model::  85%|████████▍ | 423/500 [00:23<00:04, 16.15it/s]
eval model::  85%|████████▌ | 425/500 [00:23<00:05, 14.34it/s]
eval model::  86%|████████▌ | 428/500 [00:23<00:04, 16.79it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:03, 18.40it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:02, 22.03it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 23.83it/s]
eval model::  88%|████████▊ | 441/500 [00:24<00:02, 20.66it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 21.95it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 22.91it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 20.33it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 21.01it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 22.06it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 22.47it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 16.80it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 17.01it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 16.58it/s]
eval model::  94%|█████████▍| 469/500 [00:25<00:01, 18.44it/s]
eval model::  94%|█████████▍| 472/500 [00:25<00:01, 19.79it/s]
eval model::  95%|█████████▌| 475/500 [00:25<00:01, 19.99it/s]
eval model::  96%|█████████▌| 478/500 [00:26<00:01, 20.13it/s]
eval model::  96%|█████████▌| 481/500 [00:26<00:00, 19.94it/s]
eval model::  97%|█████████▋| 484/500 [00:26<00:00, 21.01it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 20.84it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 22.48it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 21.11it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 20.68it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 24.15it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.52it/s]
[2024/07/28 00:41:05] ppocr INFO: cur metric, precision: 0.6375609756097561, recall: 0.6292729898892634, hmean: 0.633389871577417, fps: 44.61247566275464
[2024/07/28 00:41:05] ppocr INFO: best metric, hmean: 0.6696288552012545, precision: 0.7324185248713551, recall: 0.6167549350024073, fps: 45.42158780325782, best_epoch: 460
[2024/07/28 00:41:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:41:13] ppocr INFO: epoch: [501/1500], global_step: 1503, lr: 0.001000, loss: 1.533468, loss_shrink_maps: 0.794589, loss_threshold_maps: 0.569676, loss_binary_maps: 0.158741, avg_reader_cost: 1.57984 s, avg_batch_cost: 1.84414 s, avg_samples: 12.5, ips: 6.77823 samples/s, eta: 5:06:04
[2024/07/28 00:41:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:41:21] ppocr INFO: epoch: [502/1500], global_step: 1506, lr: 0.001000, loss: 1.549227, loss_shrink_maps: 0.804807, loss_threshold_maps: 0.576491, loss_binary_maps: 0.160610, avg_reader_cost: 1.60737 s, avg_batch_cost: 1.84921 s, avg_samples: 12.5, ips: 6.75965 samples/s, eta: 5:05:46
[2024/07/28 00:41:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:41:29] ppocr INFO: epoch: [503/1500], global_step: 1509, lr: 0.001000, loss: 1.546758, loss_shrink_maps: 0.804807, loss_threshold_maps: 0.572611, loss_binary_maps: 0.160610, avg_reader_cost: 1.52649 s, avg_batch_cost: 1.75464 s, avg_samples: 12.5, ips: 7.12398 samples/s, eta: 5:05:26
[2024/07/28 00:41:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:41:36] ppocr INFO: epoch: [504/1500], global_step: 1510, lr: 0.001000, loss: 1.546758, loss_shrink_maps: 0.805096, loss_threshold_maps: 0.574294, loss_binary_maps: 0.160766, avg_reader_cost: 0.44642 s, avg_batch_cost: 0.52957 s, avg_samples: 4.8, ips: 9.06401 samples/s, eta: 5:05:18
[2024/07/28 00:41:37] ppocr INFO: epoch: [504/1500], global_step: 1512, lr: 0.001000, loss: 1.551014, loss_shrink_maps: 0.817074, loss_threshold_maps: 0.583081, loss_binary_maps: 0.162728, avg_reader_cost: 1.15065 s, avg_batch_cost: 1.29688 s, avg_samples: 7.7, ips: 5.93734 samples/s, eta: 5:05:07
[2024/07/28 00:41:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:41:46] ppocr INFO: epoch: [505/1500], global_step: 1515, lr: 0.001000, loss: 1.525113, loss_shrink_maps: 0.793331, loss_threshold_maps: 0.574294, loss_binary_maps: 0.158205, avg_reader_cost: 1.50799 s, avg_batch_cost: 1.76092 s, avg_samples: 12.5, ips: 7.09855 samples/s, eta: 5:04:47
[2024/07/28 00:41:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:41:54] ppocr INFO: epoch: [506/1500], global_step: 1518, lr: 0.001000, loss: 1.523872, loss_shrink_maps: 0.792637, loss_threshold_maps: 0.572945, loss_binary_maps: 0.157658, avg_reader_cost: 1.54017 s, avg_batch_cost: 1.78059 s, avg_samples: 12.5, ips: 7.02014 samples/s, eta: 5:04:28
[2024/07/28 00:41:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:42:01] ppocr INFO: epoch: [507/1500], global_step: 1520, lr: 0.001000, loss: 1.488989, loss_shrink_maps: 0.783955, loss_threshold_maps: 0.569698, loss_binary_maps: 0.157050, avg_reader_cost: 0.91099 s, avg_batch_cost: 1.08748 s, avg_samples: 9.6, ips: 8.82777 samples/s, eta: 5:04:13
[2024/07/28 00:42:02] ppocr INFO: epoch: [507/1500], global_step: 1521, lr: 0.001000, loss: 1.488989, loss_shrink_maps: 0.783955, loss_threshold_maps: 0.563622, loss_binary_maps: 0.157050, avg_reader_cost: 0.58948 s, avg_batch_cost: 0.64427 s, avg_samples: 2.9, ips: 4.50124 samples/s, eta: 5:04:07
[2024/07/28 00:42:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:42:10] ppocr INFO: epoch: [508/1500], global_step: 1524, lr: 0.001000, loss: 1.503924, loss_shrink_maps: 0.800916, loss_threshold_maps: 0.562118, loss_binary_maps: 0.158877, avg_reader_cost: 1.58736 s, avg_batch_cost: 1.82586 s, avg_samples: 12.5, ips: 6.84607 samples/s, eta: 5:03:49
[2024/07/28 00:42:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:42:19] ppocr INFO: epoch: [509/1500], global_step: 1527, lr: 0.001000, loss: 1.503924, loss_shrink_maps: 0.800916, loss_threshold_maps: 0.547759, loss_binary_maps: 0.158877, avg_reader_cost: 1.52635 s, avg_batch_cost: 1.75760 s, avg_samples: 12.5, ips: 7.11198 samples/s, eta: 5:03:29
[2024/07/28 00:42:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:42:27] ppocr INFO: epoch: [510/1500], global_step: 1530, lr: 0.001000, loss: 1.503924, loss_shrink_maps: 0.800916, loss_threshold_maps: 0.547759, loss_binary_maps: 0.158877, avg_reader_cost: 1.57908 s, avg_batch_cost: 1.84108 s, avg_samples: 12.5, ips: 6.78948 samples/s, eta: 5:03:11
[2024/07/28 00:42:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:42:35] ppocr INFO: epoch: [511/1500], global_step: 1533, lr: 0.001000, loss: 1.470134, loss_shrink_maps: 0.783955, loss_threshold_maps: 0.546788, loss_binary_maps: 0.156794, avg_reader_cost: 1.51567 s, avg_batch_cost: 1.76508 s, avg_samples: 12.5, ips: 7.08183 samples/s, eta: 5:02:51
[2024/07/28 00:42:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:42:43] ppocr INFO: epoch: [512/1500], global_step: 1536, lr: 0.001000, loss: 1.470134, loss_shrink_maps: 0.783955, loss_threshold_maps: 0.546788, loss_binary_maps: 0.156794, avg_reader_cost: 1.52864 s, avg_batch_cost: 1.78896 s, avg_samples: 12.5, ips: 6.98732 samples/s, eta: 5:02:31
[2024/07/28 00:42:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:42:52] ppocr INFO: epoch: [513/1500], global_step: 1539, lr: 0.001000, loss: 1.490506, loss_shrink_maps: 0.789249, loss_threshold_maps: 0.547759, loss_binary_maps: 0.156794, avg_reader_cost: 1.55875 s, avg_batch_cost: 1.78816 s, avg_samples: 12.5, ips: 6.99041 samples/s, eta: 5:02:12
[2024/07/28 00:42:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:42:58] ppocr INFO: epoch: [514/1500], global_step: 1540, lr: 0.001000, loss: 1.518953, loss_shrink_maps: 0.811922, loss_threshold_maps: 0.556152, loss_binary_maps: 0.160493, avg_reader_cost: 0.42646 s, avg_batch_cost: 0.51260 s, avg_samples: 4.8, ips: 9.36411 samples/s, eta: 5:02:04
[2024/07/28 00:43:00] ppocr INFO: epoch: [514/1500], global_step: 1542, lr: 0.001000, loss: 1.549581, loss_shrink_maps: 0.828288, loss_threshold_maps: 0.565490, loss_binary_maps: 0.164015, avg_reader_cost: 1.11666 s, avg_batch_cost: 1.26260 s, avg_samples: 7.7, ips: 6.09853 samples/s, eta: 5:01:53
[2024/07/28 00:43:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:43:08] ppocr INFO: epoch: [515/1500], global_step: 1545, lr: 0.001000, loss: 1.513444, loss_shrink_maps: 0.799282, loss_threshold_maps: 0.564017, loss_binary_maps: 0.158832, avg_reader_cost: 1.55653 s, avg_batch_cost: 1.78577 s, avg_samples: 12.5, ips: 6.99978 samples/s, eta: 5:01:33
[2024/07/28 00:43:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:43:16] ppocr INFO: epoch: [516/1500], global_step: 1548, lr: 0.001000, loss: 1.513444, loss_shrink_maps: 0.790501, loss_threshold_maps: 0.570960, loss_binary_maps: 0.156676, avg_reader_cost: 1.52209 s, avg_batch_cost: 1.79234 s, avg_samples: 12.5, ips: 6.97412 samples/s, eta: 5:01:14
[2024/07/28 00:43:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:43:24] ppocr INFO: epoch: [517/1500], global_step: 1550, lr: 0.001000, loss: 1.530067, loss_shrink_maps: 0.802159, loss_threshold_maps: 0.578784, loss_binary_maps: 0.159069, avg_reader_cost: 0.93736 s, avg_batch_cost: 1.14840 s, avg_samples: 9.6, ips: 8.35945 samples/s, eta: 5:01:00
[2024/07/28 00:43:25] ppocr INFO: epoch: [517/1500], global_step: 1551, lr: 0.001000, loss: 1.541907, loss_shrink_maps: 0.809300, loss_threshold_maps: 0.580679, loss_binary_maps: 0.161224, avg_reader_cost: 0.62009 s, avg_batch_cost: 0.67502 s, avg_samples: 2.9, ips: 4.29614 samples/s, eta: 5:00:55
[2024/07/28 00:43:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:43:33] ppocr INFO: epoch: [518/1500], global_step: 1554, lr: 0.001000, loss: 1.549485, loss_shrink_maps: 0.815993, loss_threshold_maps: 0.583150, loss_binary_maps: 0.162519, avg_reader_cost: 1.53120 s, avg_batch_cost: 1.77157 s, avg_samples: 12.5, ips: 7.05589 samples/s, eta: 5:00:36
[2024/07/28 00:43:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:43:41] ppocr INFO: epoch: [519/1500], global_step: 1557, lr: 0.001000, loss: 1.549485, loss_shrink_maps: 0.809300, loss_threshold_maps: 0.583150, loss_binary_maps: 0.161377, avg_reader_cost: 1.54430 s, avg_batch_cost: 1.77327 s, avg_samples: 12.5, ips: 7.04911 samples/s, eta: 5:00:16
[2024/07/28 00:43:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:43:50] ppocr INFO: epoch: [520/1500], global_step: 1560, lr: 0.001000, loss: 1.530067, loss_shrink_maps: 0.798828, loss_threshold_maps: 0.578784, loss_binary_maps: 0.158617, avg_reader_cost: 1.55966 s, avg_batch_cost: 1.83146 s, avg_samples: 12.5, ips: 6.82515 samples/s, eta: 4:59:58

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.35it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.48it/s]
eval model::   1%|▏         | 7/500 [00:00<00:23, 21.07it/s]
eval model::   2%|▏         | 10/500 [00:00<00:25, 19.16it/s]
eval model::   2%|▏         | 12/500 [00:00<00:26, 18.14it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.04it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.63it/s]
eval model::   4%|▍         | 19/500 [00:01<00:25, 18.67it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 18.91it/s]
eval model::   5%|▌         | 25/500 [00:01<00:22, 20.74it/s]
eval model::   6%|▌         | 28/500 [00:01<00:22, 20.82it/s]
eval model::   6%|▌         | 31/500 [00:01<00:23, 20.11it/s]
eval model::   7%|▋         | 34/500 [00:01<00:23, 19.85it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 19.04it/s]
eval model::   8%|▊         | 38/500 [00:01<00:24, 19.05it/s]
eval model::   8%|▊         | 40/500 [00:02<00:27, 17.00it/s]
eval model::   8%|▊         | 42/500 [00:02<00:28, 15.86it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 17.62it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 17.85it/s]
eval model::  10%|█         | 50/500 [00:02<00:22, 19.65it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 18.69it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 20.38it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 19.00it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.31it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.41it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.78it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:22, 19.27it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 20.31it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:20, 20.46it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.57it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.54it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 21.97it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.07it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:24, 16.67it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:23, 17.18it/s]
eval model::  19%|█▉        | 94/500 [00:05<00:23, 17.60it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:22, 18.09it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.49it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.19it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.52it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 21.86it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:19, 20.30it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:20, 19.21it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:18, 20.88it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:17, 21.98it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 18.82it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 19.92it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:20, 17.89it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 17.23it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:18, 19.74it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.90it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.17it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.70it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 19.57it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 21.29it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 20.10it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:15, 22.49it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:19, 17.23it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:18, 18.67it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 19.39it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 20.25it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 18.63it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 15.90it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.67it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.54it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:17, 17.80it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 20.63it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 22.00it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:13, 22.93it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:13, 23.01it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:12, 23.32it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 22.21it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.53it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:21, 13.61it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:20, 14.06it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 14.55it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.15it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:22, 12.91it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:23, 12.28it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.36it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 15.05it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:14, 19.23it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:12, 21.34it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 22.93it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:11, 22.57it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.19it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:13, 19.77it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:11, 21.74it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:10, 23.60it/s]
eval model::  50%|█████     | 251/500 [00:13<00:10, 23.02it/s]
eval model::  51%|█████     | 254/500 [00:13<00:10, 23.42it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 23.81it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 22.20it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:11, 20.37it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:12, 18.74it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.15it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 19.02it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.96it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 18.23it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 18.60it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:12, 17.70it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.26it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.31it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:11, 17.54it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:13, 15.16it/s]
eval model::  60%|█████▉    | 298/500 [00:15<00:11, 17.31it/s]
eval model::  60%|██████    | 301/500 [00:16<00:11, 16.69it/s]
eval model::  61%|██████    | 304/500 [00:16<00:10, 18.30it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:09, 19.81it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.36it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 20.67it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 20.77it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 22.05it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:08, 21.78it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:07, 22.20it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:07, 21.78it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:07, 22.24it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 23.39it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:07, 23.09it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:10, 15.23it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.95it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 19.04it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 21.15it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 22.90it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 23.81it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 22.90it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 19.08it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.71it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 18.56it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 19.44it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 20.50it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:06, 20.06it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 22.13it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 20.35it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:05, 22.20it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:08, 12.62it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:07, 14.33it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:07, 15.01it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 17.69it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:05, 19.52it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 19.26it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 21.39it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 22.15it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 22.76it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 22.32it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 20.29it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.38it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 15.24it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.33it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.32it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:04, 17.56it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 17.32it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 21.11it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 24.36it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 24.42it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 23.56it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 22.23it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 22.60it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 20.91it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 22.18it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 20.66it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 18.00it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 17.49it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.73it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.91it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 20.61it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 21.65it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 20.18it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 20.73it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 18.69it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 20.51it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 18.74it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 19.85it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 21.15it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.20it/s]
[2024/07/28 00:44:16] ppocr INFO: cur metric, precision: 0.7261836851112379, recall: 0.6129032258064516, hmean: 0.6647519582245431, fps: 45.61024509760744
[2024/07/28 00:44:16] ppocr INFO: best metric, hmean: 0.6696288552012545, precision: 0.7324185248713551, recall: 0.6167549350024073, fps: 45.42158780325782, best_epoch: 460
[2024/07/28 00:44:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:44:24] ppocr INFO: epoch: [521/1500], global_step: 1563, lr: 0.001000, loss: 1.501168, loss_shrink_maps: 0.796721, loss_threshold_maps: 0.568927, loss_binary_maps: 0.158166, avg_reader_cost: 1.73373 s, avg_batch_cost: 2.08716 s, avg_samples: 12.5, ips: 5.98901 samples/s, eta: 4:59:44
[2024/07/28 00:44:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:44:32] ppocr INFO: epoch: [522/1500], global_step: 1566, lr: 0.001000, loss: 1.473543, loss_shrink_maps: 0.775840, loss_threshold_maps: 0.561880, loss_binary_maps: 0.154122, avg_reader_cost: 1.51767 s, avg_batch_cost: 1.74697 s, avg_samples: 12.5, ips: 7.15524 samples/s, eta: 4:59:24
[2024/07/28 00:44:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:44:40] ppocr INFO: epoch: [523/1500], global_step: 1569, lr: 0.001000, loss: 1.473543, loss_shrink_maps: 0.775840, loss_threshold_maps: 0.561880, loss_binary_maps: 0.154122, avg_reader_cost: 1.53491 s, avg_batch_cost: 1.76441 s, avg_samples: 12.5, ips: 7.08453 samples/s, eta: 4:59:04
[2024/07/28 00:44:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:44:47] ppocr INFO: epoch: [524/1500], global_step: 1570, lr: 0.001000, loss: 1.470053, loss_shrink_maps: 0.770045, loss_threshold_maps: 0.555542, loss_binary_maps: 0.152863, avg_reader_cost: 0.42963 s, avg_batch_cost: 0.51636 s, avg_samples: 4.8, ips: 9.29592 samples/s, eta: 4:58:56
[2024/07/28 00:44:48] ppocr INFO: epoch: [524/1500], global_step: 1572, lr: 0.001000, loss: 1.470053, loss_shrink_maps: 0.770045, loss_threshold_maps: 0.546262, loss_binary_maps: 0.152863, avg_reader_cost: 1.12382 s, avg_batch_cost: 1.26953 s, avg_samples: 7.7, ips: 6.06522 samples/s, eta: 4:58:45
[2024/07/28 00:44:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:44:57] ppocr INFO: epoch: [525/1500], global_step: 1575, lr: 0.001000, loss: 1.470053, loss_shrink_maps: 0.775840, loss_threshold_maps: 0.547187, loss_binary_maps: 0.154122, avg_reader_cost: 1.56320 s, avg_batch_cost: 1.79144 s, avg_samples: 12.5, ips: 6.97763 samples/s, eta: 4:58:26
[2024/07/28 00:44:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:45:05] ppocr INFO: epoch: [526/1500], global_step: 1578, lr: 0.001000, loss: 1.474398, loss_shrink_maps: 0.770045, loss_threshold_maps: 0.547187, loss_binary_maps: 0.152863, avg_reader_cost: 1.60803 s, avg_batch_cost: 1.86807 s, avg_samples: 12.5, ips: 6.69140 samples/s, eta: 4:58:08
[2024/07/28 00:45:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:45:13] ppocr INFO: epoch: [527/1500], global_step: 1580, lr: 0.001000, loss: 1.470944, loss_shrink_maps: 0.754259, loss_threshold_maps: 0.551886, loss_binary_maps: 0.149787, avg_reader_cost: 0.94013 s, avg_batch_cost: 1.17293 s, avg_samples: 9.6, ips: 8.18462 samples/s, eta: 4:57:55
[2024/07/28 00:45:14] ppocr INFO: epoch: [527/1500], global_step: 1581, lr: 0.001000, loss: 1.470944, loss_shrink_maps: 0.739655, loss_threshold_maps: 0.551886, loss_binary_maps: 0.146667, avg_reader_cost: 0.63214 s, avg_batch_cost: 0.68716 s, avg_samples: 2.9, ips: 4.22025 samples/s, eta: 4:57:50
[2024/07/28 00:45:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:45:22] ppocr INFO: epoch: [528/1500], global_step: 1584, lr: 0.001000, loss: 1.470944, loss_shrink_maps: 0.748529, loss_threshold_maps: 0.544050, loss_binary_maps: 0.148759, avg_reader_cost: 1.52158 s, avg_batch_cost: 1.79449 s, avg_samples: 12.5, ips: 6.96579 samples/s, eta: 4:57:31
[2024/07/28 00:45:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:45:30] ppocr INFO: epoch: [529/1500], global_step: 1587, lr: 0.001000, loss: 1.482073, loss_shrink_maps: 0.768896, loss_threshold_maps: 0.554076, loss_binary_maps: 0.153105, avg_reader_cost: 1.50310 s, avg_batch_cost: 1.75612 s, avg_samples: 12.5, ips: 7.11796 samples/s, eta: 4:57:11
[2024/07/28 00:45:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:45:38] ppocr INFO: epoch: [530/1500], global_step: 1590, lr: 0.001000, loss: 1.482073, loss_shrink_maps: 0.768896, loss_threshold_maps: 0.544050, loss_binary_maps: 0.153105, avg_reader_cost: 1.51303 s, avg_batch_cost: 1.74954 s, avg_samples: 12.5, ips: 7.14473 samples/s, eta: 4:56:51
[2024/07/28 00:45:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:45:47] ppocr INFO: epoch: [531/1500], global_step: 1593, lr: 0.001000, loss: 1.462749, loss_shrink_maps: 0.747936, loss_threshold_maps: 0.544050, loss_binary_maps: 0.148371, avg_reader_cost: 1.60706 s, avg_batch_cost: 1.83588 s, avg_samples: 12.5, ips: 6.80872 samples/s, eta: 4:56:33
[2024/07/28 00:45:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:45:55] ppocr INFO: epoch: [532/1500], global_step: 1596, lr: 0.001000, loss: 1.457856, loss_shrink_maps: 0.747249, loss_threshold_maps: 0.540899, loss_binary_maps: 0.148277, avg_reader_cost: 1.53157 s, avg_batch_cost: 1.77026 s, avg_samples: 12.5, ips: 7.06111 samples/s, eta: 4:56:13
[2024/07/28 00:45:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:46:03] ppocr INFO: epoch: [533/1500], global_step: 1599, lr: 0.001000, loss: 1.478471, loss_shrink_maps: 0.769643, loss_threshold_maps: 0.546240, loss_binary_maps: 0.153049, avg_reader_cost: 1.59873 s, avg_batch_cost: 1.82731 s, avg_samples: 12.5, ips: 6.84064 samples/s, eta: 4:55:55
[2024/07/28 00:46:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:46:10] ppocr INFO: epoch: [534/1500], global_step: 1600, lr: 0.001000, loss: 1.478471, loss_shrink_maps: 0.769643, loss_threshold_maps: 0.546240, loss_binary_maps: 0.153049, avg_reader_cost: 0.42534 s, avg_batch_cost: 0.50813 s, avg_samples: 4.8, ips: 9.44647 samples/s, eta: 4:55:47
[2024/07/28 00:46:12] ppocr INFO: epoch: [534/1500], global_step: 1602, lr: 0.001000, loss: 1.482836, loss_shrink_maps: 0.769050, loss_threshold_maps: 0.551617, loss_binary_maps: 0.152747, avg_reader_cost: 1.10748 s, avg_batch_cost: 1.25340 s, avg_samples: 7.7, ips: 6.14328 samples/s, eta: 4:55:35
[2024/07/28 00:46:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:46:20] ppocr INFO: epoch: [535/1500], global_step: 1605, lr: 0.001000, loss: 1.482836, loss_shrink_maps: 0.763024, loss_threshold_maps: 0.551993, loss_binary_maps: 0.151629, avg_reader_cost: 1.50388 s, avg_batch_cost: 1.76028 s, avg_samples: 12.5, ips: 7.10113 samples/s, eta: 4:55:15
[2024/07/28 00:46:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:46:28] ppocr INFO: epoch: [536/1500], global_step: 1608, lr: 0.001000, loss: 1.466704, loss_shrink_maps: 0.752143, loss_threshold_maps: 0.551993, loss_binary_maps: 0.149542, avg_reader_cost: 1.50704 s, avg_batch_cost: 1.75800 s, avg_samples: 12.5, ips: 7.11036 samples/s, eta: 4:54:55
[2024/07/28 00:46:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:46:35] ppocr INFO: epoch: [537/1500], global_step: 1610, lr: 0.001000, loss: 1.482836, loss_shrink_maps: 0.763024, loss_threshold_maps: 0.559144, loss_binary_maps: 0.151629, avg_reader_cost: 0.94377 s, avg_batch_cost: 1.11781 s, avg_samples: 9.6, ips: 8.58819 samples/s, eta: 4:54:41
[2024/07/28 00:46:36] ppocr INFO: epoch: [537/1500], global_step: 1611, lr: 0.001000, loss: 1.482836, loss_shrink_maps: 0.765995, loss_threshold_maps: 0.559144, loss_binary_maps: 0.152326, avg_reader_cost: 0.60485 s, avg_batch_cost: 0.65982 s, avg_samples: 2.9, ips: 4.39514 samples/s, eta: 4:54:36
[2024/07/28 00:46:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:46:44] ppocr INFO: epoch: [538/1500], global_step: 1614, lr: 0.001000, loss: 1.482836, loss_shrink_maps: 0.769771, loss_threshold_maps: 0.559144, loss_binary_maps: 0.152906, avg_reader_cost: 1.57124 s, avg_batch_cost: 1.79936 s, avg_samples: 12.5, ips: 6.94692 samples/s, eta: 4:54:17
[2024/07/28 00:46:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:46:52] ppocr INFO: epoch: [539/1500], global_step: 1617, lr: 0.001000, loss: 1.471976, loss_shrink_maps: 0.769771, loss_threshold_maps: 0.552145, loss_binary_maps: 0.152906, avg_reader_cost: 1.50837 s, avg_batch_cost: 1.74055 s, avg_samples: 12.5, ips: 7.18166 samples/s, eta: 4:53:57
[2024/07/28 00:46:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:47:01] ppocr INFO: epoch: [540/1500], global_step: 1620, lr: 0.001000, loss: 1.471976, loss_shrink_maps: 0.769771, loss_threshold_maps: 0.555886, loss_binary_maps: 0.152906, avg_reader_cost: 1.49094 s, avg_batch_cost: 1.72612 s, avg_samples: 12.5, ips: 7.24169 samples/s, eta: 4:53:37

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:00,  8.21it/s]
eval model::   1%|          | 4/500 [00:00<00:33, 14.62it/s]
eval model::   1%|          | 6/500 [00:00<00:30, 16.22it/s]
eval model::   2%|▏         | 8/500 [00:00<00:29, 16.43it/s]
eval model::   2%|▏         | 11/500 [00:00<00:33, 14.65it/s]
eval model::   3%|▎         | 14/500 [00:00<00:29, 16.71it/s]
eval model::   3%|▎         | 17/500 [00:01<00:25, 19.03it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.80it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 21.91it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 21.42it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 21.39it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 20.21it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 19.34it/s]
eval model::   8%|▊         | 38/500 [00:02<00:22, 20.20it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.82it/s]
eval model::   9%|▉         | 44/500 [00:02<00:24, 18.72it/s]
eval model::   9%|▉         | 46/500 [00:02<00:25, 17.66it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 19.79it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 19.58it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 20.32it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 18.22it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 15.95it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.88it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.43it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.75it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 21.55it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:19, 22.35it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.32it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:17, 24.00it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:17, 23.45it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:19, 21.23it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:21, 19.34it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:19, 20.41it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:19, 20.59it/s]
eval model::  20%|██        | 100/500 [00:05<00:20, 19.39it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 21.51it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 22.94it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 20.78it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 21.16it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:18, 21.19it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 21.46it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:17, 22.15it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:20, 18.35it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 21.06it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 18.39it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:17, 20.63it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:17, 20.77it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.86it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:15, 22.41it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 19.05it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.78it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 18.42it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 20.81it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:17, 19.39it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:20, 16.80it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:19, 17.25it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 17.77it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 20.25it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:16, 19.34it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:22, 14.37it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:19, 16.76it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:20, 15.27it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:17, 17.92it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:17, 18.25it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:15, 19.65it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 20.20it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.38it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.61it/s]
eval model::  41%|████      | 205/500 [00:10<00:21, 13.48it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:20, 14.06it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 14.18it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.30it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.35it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.14it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:19, 14.33it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:20, 13.65it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:17, 15.83it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:15, 17.81it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 21.90it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 21.40it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 22.14it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 21.79it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:13, 19.45it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:11, 22.44it/s]
eval model::  50%|█████     | 250/500 [00:13<00:12, 20.70it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 21.82it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 21.42it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:11, 21.49it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 20.55it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:11, 20.48it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:10, 21.23it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:12, 18.86it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:12, 17.98it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.87it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 20.33it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 21.39it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:09, 22.70it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:17, 12.23it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:13, 15.86it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.11it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:14, 14.12it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 15.40it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.13it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 18.44it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 19.84it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:08, 21.32it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 21.56it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 21.70it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 20.57it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 19.81it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 21.38it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 19.71it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 21.12it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 21.56it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:08, 20.03it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 14.77it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 18.27it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.48it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 21.90it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 22.16it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 21.80it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 19.25it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.10it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.15it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 18.71it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 19.15it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 20.34it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 23.82it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 22.52it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:04, 23.85it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:08, 12.82it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:07, 14.57it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:06, 16.50it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 18.84it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 19.17it/s]
eval model::  81%|████████  | 403/500 [00:21<00:04, 20.42it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 21.12it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:03, 23.02it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:03, 22.63it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:03, 22.49it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 19.96it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 13.79it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 13.25it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:05, 13.85it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:04, 16.72it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:04, 16.54it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 21.33it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 22.05it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 21.32it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 22.78it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 23.62it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 21.46it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 23.24it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:01, 23.23it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 24.50it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:01, 20.60it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:01, 18.94it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 20.52it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 21.74it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 21.30it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 21.93it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 21.70it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 21.53it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 19.37it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 20.95it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 20.75it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 21.81it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 23.53it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.21it/s]
[2024/07/28 00:47:27] ppocr INFO: cur metric, precision: 0.7039957939011566, recall: 0.6446798266730862, hmean: 0.67303342548379, fps: 47.18240125447044
[2024/07/28 00:47:27] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/28 00:47:27] ppocr INFO: best metric, hmean: 0.67303342548379, precision: 0.7039957939011566, recall: 0.6446798266730862, fps: 47.18240125447044, best_epoch: 540
[2024/07/28 00:47:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:47:35] ppocr INFO: epoch: [541/1500], global_step: 1623, lr: 0.001000, loss: 1.515855, loss_shrink_maps: 0.785415, loss_threshold_maps: 0.571882, loss_binary_maps: 0.156507, avg_reader_cost: 1.72635 s, avg_batch_cost: 2.00643 s, avg_samples: 12.5, ips: 6.22998 samples/s, eta: 4:53:21
[2024/07/28 00:47:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:47:43] ppocr INFO: epoch: [542/1500], global_step: 1626, lr: 0.001000, loss: 1.525031, loss_shrink_maps: 0.793041, loss_threshold_maps: 0.571882, loss_binary_maps: 0.158021, avg_reader_cost: 1.63732 s, avg_batch_cost: 1.86679 s, avg_samples: 12.5, ips: 6.69598 samples/s, eta: 4:53:04
[2024/07/28 00:47:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:47:52] ppocr INFO: epoch: [543/1500], global_step: 1629, lr: 0.001000, loss: 1.497808, loss_shrink_maps: 0.779059, loss_threshold_maps: 0.564090, loss_binary_maps: 0.155372, avg_reader_cost: 1.51560 s, avg_batch_cost: 1.74519 s, avg_samples: 12.5, ips: 7.16253 samples/s, eta: 4:52:44
[2024/07/28 00:47:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:47:58] ppocr INFO: epoch: [544/1500], global_step: 1630, lr: 0.001000, loss: 1.470832, loss_shrink_maps: 0.770229, loss_threshold_maps: 0.559466, loss_binary_maps: 0.153286, avg_reader_cost: 0.43414 s, avg_batch_cost: 0.51889 s, avg_samples: 4.8, ips: 9.25050 samples/s, eta: 4:52:36
[2024/07/28 00:48:00] ppocr INFO: epoch: [544/1500], global_step: 1632, lr: 0.001000, loss: 1.537154, loss_shrink_maps: 0.794106, loss_threshold_maps: 0.575043, loss_binary_maps: 0.158193, avg_reader_cost: 1.12920 s, avg_batch_cost: 1.27520 s, avg_samples: 7.7, ips: 6.03827 samples/s, eta: 4:52:25
[2024/07/28 00:48:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:48:08] ppocr INFO: epoch: [545/1500], global_step: 1635, lr: 0.001000, loss: 1.537154, loss_shrink_maps: 0.794106, loss_threshold_maps: 0.572653, loss_binary_maps: 0.158193, avg_reader_cost: 1.53402 s, avg_batch_cost: 1.77250 s, avg_samples: 12.5, ips: 7.05217 samples/s, eta: 4:52:05
[2024/07/28 00:48:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:48:16] ppocr INFO: epoch: [546/1500], global_step: 1638, lr: 0.001000, loss: 1.498054, loss_shrink_maps: 0.770778, loss_threshold_maps: 0.567251, loss_binary_maps: 0.153729, avg_reader_cost: 1.52604 s, avg_batch_cost: 1.75899 s, avg_samples: 12.5, ips: 7.10636 samples/s, eta: 4:51:45
[2024/07/28 00:48:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:48:24] ppocr INFO: epoch: [547/1500], global_step: 1640, lr: 0.001000, loss: 1.537154, loss_shrink_maps: 0.794106, loss_threshold_maps: 0.567251, loss_binary_maps: 0.158193, avg_reader_cost: 0.90522 s, avg_batch_cost: 1.08202 s, avg_samples: 9.6, ips: 8.87231 samples/s, eta: 4:51:31
[2024/07/28 00:48:24] ppocr INFO: epoch: [547/1500], global_step: 1641, lr: 0.001000, loss: 1.572597, loss_shrink_maps: 0.808178, loss_threshold_maps: 0.568818, loss_binary_maps: 0.160610, avg_reader_cost: 0.58667 s, avg_batch_cost: 0.64137 s, avg_samples: 2.9, ips: 4.52159 samples/s, eta: 4:51:25
[2024/07/28 00:48:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:48:33] ppocr INFO: epoch: [548/1500], global_step: 1644, lr: 0.001000, loss: 1.537154, loss_shrink_maps: 0.794106, loss_threshold_maps: 0.568818, loss_binary_maps: 0.158193, avg_reader_cost: 1.57999 s, avg_batch_cost: 1.84087 s, avg_samples: 12.5, ips: 6.79026 samples/s, eta: 4:51:07
[2024/07/28 00:48:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:48:41] ppocr INFO: epoch: [549/1500], global_step: 1647, lr: 0.001000, loss: 1.558770, loss_shrink_maps: 0.808178, loss_threshold_maps: 0.568818, loss_binary_maps: 0.160610, avg_reader_cost: 1.51019 s, avg_batch_cost: 1.74115 s, avg_samples: 12.5, ips: 7.17916 samples/s, eta: 4:50:47
[2024/07/28 00:48:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:48:50] ppocr INFO: epoch: [550/1500], global_step: 1650, lr: 0.001000, loss: 1.530367, loss_shrink_maps: 0.800463, loss_threshold_maps: 0.565029, loss_binary_maps: 0.159330, avg_reader_cost: 1.52728 s, avg_batch_cost: 1.82010 s, avg_samples: 12.5, ips: 6.86776 samples/s, eta: 4:50:28
[2024/07/28 00:48:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:48:58] ppocr INFO: epoch: [551/1500], global_step: 1653, lr: 0.001000, loss: 1.498114, loss_shrink_maps: 0.784873, loss_threshold_maps: 0.561846, loss_binary_maps: 0.155993, avg_reader_cost: 1.56722 s, avg_batch_cost: 1.79608 s, avg_samples: 12.5, ips: 6.95961 samples/s, eta: 4:50:09
[2024/07/28 00:49:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:49:07] ppocr INFO: epoch: [552/1500], global_step: 1656, lr: 0.001000, loss: 1.491523, loss_shrink_maps: 0.781287, loss_threshold_maps: 0.558868, loss_binary_maps: 0.155206, avg_reader_cost: 1.66176 s, avg_batch_cost: 1.89073 s, avg_samples: 12.5, ips: 6.61120 samples/s, eta: 4:49:52
[2024/07/28 00:49:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:49:15] ppocr INFO: epoch: [553/1500], global_step: 1659, lr: 0.001000, loss: 1.491523, loss_shrink_maps: 0.781287, loss_threshold_maps: 0.558868, loss_binary_maps: 0.155206, avg_reader_cost: 1.56787 s, avg_batch_cost: 1.79715 s, avg_samples: 12.5, ips: 6.95548 samples/s, eta: 4:49:33
[2024/07/28 00:49:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:49:22] ppocr INFO: epoch: [554/1500], global_step: 1660, lr: 0.001000, loss: 1.488370, loss_shrink_maps: 0.773643, loss_threshold_maps: 0.552983, loss_binary_maps: 0.153740, avg_reader_cost: 0.43822 s, avg_batch_cost: 0.52027 s, avg_samples: 4.8, ips: 9.22601 samples/s, eta: 4:49:25
[2024/07/28 00:49:23] ppocr INFO: epoch: [554/1500], global_step: 1662, lr: 0.001000, loss: 1.488370, loss_shrink_maps: 0.773643, loss_threshold_maps: 0.547243, loss_binary_maps: 0.153740, avg_reader_cost: 1.13225 s, avg_batch_cost: 1.27853 s, avg_samples: 7.7, ips: 6.02253 samples/s, eta: 4:49:14
[2024/07/28 00:49:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:49:32] ppocr INFO: epoch: [555/1500], global_step: 1665, lr: 0.001000, loss: 1.477059, loss_shrink_maps: 0.773643, loss_threshold_maps: 0.544527, loss_binary_maps: 0.153740, avg_reader_cost: 1.56405 s, avg_batch_cost: 1.79217 s, avg_samples: 12.5, ips: 6.97477 samples/s, eta: 4:48:55
[2024/07/28 00:49:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:49:40] ppocr INFO: epoch: [556/1500], global_step: 1668, lr: 0.001000, loss: 1.461368, loss_shrink_maps: 0.764073, loss_threshold_maps: 0.548192, loss_binary_maps: 0.152103, avg_reader_cost: 1.50722 s, avg_batch_cost: 1.74445 s, avg_samples: 12.5, ips: 7.16557 samples/s, eta: 4:48:35
[2024/07/28 00:49:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:49:48] ppocr INFO: epoch: [557/1500], global_step: 1670, lr: 0.001000, loss: 1.477059, loss_shrink_maps: 0.773643, loss_threshold_maps: 0.555010, loss_binary_maps: 0.153740, avg_reader_cost: 0.95701 s, avg_batch_cost: 1.16285 s, avg_samples: 9.6, ips: 8.25556 samples/s, eta: 4:48:22
[2024/07/28 00:49:48] ppocr INFO: epoch: [557/1500], global_step: 1671, lr: 0.001000, loss: 1.480212, loss_shrink_maps: 0.782357, loss_threshold_maps: 0.555010, loss_binary_maps: 0.155206, avg_reader_cost: 0.62704 s, avg_batch_cost: 0.68207 s, avg_samples: 2.9, ips: 4.25174 samples/s, eta: 4:48:17
[2024/07/28 00:49:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:49:57] ppocr INFO: epoch: [558/1500], global_step: 1674, lr: 0.001000, loss: 1.503486, loss_shrink_maps: 0.793094, loss_threshold_maps: 0.553558, loss_binary_maps: 0.157842, avg_reader_cost: 1.60753 s, avg_batch_cost: 1.84249 s, avg_samples: 12.5, ips: 6.78431 samples/s, eta: 4:47:59
[2024/07/28 00:49:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:50:05] ppocr INFO: epoch: [559/1500], global_step: 1677, lr: 0.001000, loss: 1.522092, loss_shrink_maps: 0.806200, loss_threshold_maps: 0.562235, loss_binary_maps: 0.160921, avg_reader_cost: 1.52050 s, avg_batch_cost: 1.75289 s, avg_samples: 12.5, ips: 7.13109 samples/s, eta: 4:47:39
[2024/07/28 00:50:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:50:14] ppocr INFO: epoch: [560/1500], global_step: 1680, lr: 0.001000, loss: 1.532104, loss_shrink_maps: 0.806200, loss_threshold_maps: 0.567070, loss_binary_maps: 0.160921, avg_reader_cost: 1.54110 s, avg_batch_cost: 1.78842 s, avg_samples: 12.5, ips: 6.98942 samples/s, eta: 4:47:20

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.67it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.74it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.70it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.15it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.76it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.46it/s]
eval model::   3%|▎         | 16/500 [00:00<00:25, 18.69it/s]
eval model::   4%|▍         | 19/500 [00:01<00:24, 19.92it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.82it/s]
eval model::   5%|▍         | 24/500 [00:01<00:21, 22.32it/s]
eval model::   5%|▌         | 27/500 [00:01<00:20, 22.86it/s]
eval model::   6%|▌         | 30/500 [00:01<00:20, 22.95it/s]
eval model::   7%|▋         | 33/500 [00:01<00:21, 21.79it/s]
eval model::   7%|▋         | 36/500 [00:01<00:22, 20.47it/s]
eval model::   8%|▊         | 39/500 [00:02<00:27, 16.86it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.81it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.85it/s]
eval model::  10%|▉         | 48/500 [00:02<00:26, 17.32it/s]
eval model::  10%|█         | 51/500 [00:02<00:24, 18.27it/s]
eval model::  11%|█         | 54/500 [00:02<00:22, 20.04it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:21, 21.03it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:21, 20.62it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 14.99it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:25, 17.23it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:22, 19.02it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 20.20it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 20.43it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 20.88it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 21.94it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.54it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:19, 20.79it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.45it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 19.00it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:20, 20.17it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.45it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.20it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.04it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 20.86it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:19, 19.93it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:19, 19.96it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:18, 20.69it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 20.23it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 19.60it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 19.35it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 20.54it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.74it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 20.22it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 21.13it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.09it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.93it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.62it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.24it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 19.45it/s]
eval model::  31%|███       | 156/500 [00:07<00:15, 21.55it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:15, 22.03it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:18, 18.52it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:17, 18.71it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:17, 19.49it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:16, 20.28it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:16, 19.60it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:15, 20.30it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:19, 16.20it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:18, 17.20it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.94it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.53it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.21it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:14, 20.58it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:13, 21.86it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 21.15it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.37it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:21, 13.50it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:20, 14.14it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.50it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.27it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 12.91it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:20, 13.82it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:21, 12.89it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:17, 15.66it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:15, 17.40it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 22.10it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:11, 22.25it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 23.42it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:10, 23.76it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:13, 18.66it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:12, 20.35it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:12, 19.93it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 20.53it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.46it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:10, 22.20it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 22.78it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 19.34it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:11, 20.13it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 20.17it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 19.63it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:13, 17.08it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:12, 17.34it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:12, 17.63it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:11, 19.20it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:23,  9.31it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:17, 11.83it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:13, 15.60it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:15, 13.61it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:11, 17.26it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 17.47it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 19.42it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 20.45it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:08, 21.43it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:08, 20.90it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:08, 21.71it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:07, 22.61it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:09, 19.55it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:08, 21.04it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 21.23it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:07, 21.38it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 22.08it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 20.58it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:09, 16.04it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 18.23it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 18.86it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 20.67it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 22.31it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:06, 20.44it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:06, 21.79it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 19.47it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.78it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:07, 18.20it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:07, 18.19it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 19.28it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:06, 19.50it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 21.35it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 19.79it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:05, 22.01it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:08, 13.67it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:06, 15.97it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:06, 17.35it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 19.20it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 19.04it/s]
eval model::  81%|████████  | 403/500 [00:21<00:05, 19.11it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 20.29it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 21.55it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:04, 21.95it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:04, 20.08it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 18.12it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.29it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:04, 15.98it/s]
eval model::  85%|████████▌ | 425/500 [00:22<00:04, 15.21it/s]
eval model::  86%|████████▌ | 428/500 [00:22<00:04, 17.60it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 18.88it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:02, 21.95it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 23.49it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 22.32it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 23.87it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 23.24it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 22.02it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 22.73it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 24.01it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 23.02it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 19.36it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 18.78it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.56it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.20it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.58it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 20.85it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 22.19it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 21.28it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 21.27it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 22.27it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 18.73it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 18.76it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 18.94it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 22.03it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.21it/s]
[2024/07/28 00:50:40] ppocr INFO: cur metric, precision: 0.7055427251732102, recall: 0.588348579682234, hmean: 0.6416382252559727, fps: 44.10649999193438
[2024/07/28 00:50:40] ppocr INFO: best metric, hmean: 0.67303342548379, precision: 0.7039957939011566, recall: 0.6446798266730862, fps: 47.18240125447044, best_epoch: 540
[2024/07/28 00:50:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:50:48] ppocr INFO: epoch: [561/1500], global_step: 1683, lr: 0.001000, loss: 1.522092, loss_shrink_maps: 0.805542, loss_threshold_maps: 0.563463, loss_binary_maps: 0.160331, avg_reader_cost: 1.78106 s, avg_batch_cost: 2.11149 s, avg_samples: 12.5, ips: 5.91998 samples/s, eta: 4:47:06
[2024/07/28 00:50:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:50:56] ppocr INFO: epoch: [562/1500], global_step: 1686, lr: 0.001000, loss: 1.532104, loss_shrink_maps: 0.804571, loss_threshold_maps: 0.567070, loss_binary_maps: 0.159245, avg_reader_cost: 1.57707 s, avg_batch_cost: 1.81753 s, avg_samples: 12.5, ips: 6.87746 samples/s, eta: 4:46:48
[2024/07/28 00:50:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:51:04] ppocr INFO: epoch: [563/1500], global_step: 1689, lr: 0.001000, loss: 1.525178, loss_shrink_maps: 0.795988, loss_threshold_maps: 0.565666, loss_binary_maps: 0.158063, avg_reader_cost: 1.59484 s, avg_batch_cost: 1.82601 s, avg_samples: 12.5, ips: 6.84553 samples/s, eta: 4:46:29
[2024/07/28 00:51:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:51:11] ppocr INFO: epoch: [564/1500], global_step: 1690, lr: 0.001000, loss: 1.532104, loss_shrink_maps: 0.804571, loss_threshold_maps: 0.567171, loss_binary_maps: 0.159245, avg_reader_cost: 0.42095 s, avg_batch_cost: 0.50359 s, avg_samples: 4.8, ips: 9.53160 samples/s, eta: 4:46:21
[2024/07/28 00:51:13] ppocr INFO: epoch: [564/1500], global_step: 1692, lr: 0.001000, loss: 1.532104, loss_shrink_maps: 0.795988, loss_threshold_maps: 0.567171, loss_binary_maps: 0.158063, avg_reader_cost: 1.09838 s, avg_batch_cost: 1.24407 s, avg_samples: 7.7, ips: 6.18937 samples/s, eta: 4:46:09
[2024/07/28 00:51:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:51:21] ppocr INFO: epoch: [565/1500], global_step: 1695, lr: 0.001000, loss: 1.533345, loss_shrink_maps: 0.795988, loss_threshold_maps: 0.584605, loss_binary_maps: 0.158063, avg_reader_cost: 1.58131 s, avg_batch_cost: 1.80965 s, avg_samples: 12.5, ips: 6.90742 samples/s, eta: 4:45:51
[2024/07/28 00:51:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:51:30] ppocr INFO: epoch: [566/1500], global_step: 1698, lr: 0.001000, loss: 1.534520, loss_shrink_maps: 0.790645, loss_threshold_maps: 0.586275, loss_binary_maps: 0.156884, avg_reader_cost: 1.55075 s, avg_batch_cost: 1.83471 s, avg_samples: 12.5, ips: 6.81307 samples/s, eta: 4:45:32
[2024/07/28 00:51:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:51:37] ppocr INFO: epoch: [567/1500], global_step: 1700, lr: 0.001000, loss: 1.500569, loss_shrink_maps: 0.776056, loss_threshold_maps: 0.572288, loss_binary_maps: 0.154428, avg_reader_cost: 0.97519 s, avg_batch_cost: 1.16054 s, avg_samples: 9.6, ips: 8.27203 samples/s, eta: 4:45:19
[2024/07/28 00:51:38] ppocr INFO: epoch: [567/1500], global_step: 1701, lr: 0.001000, loss: 1.514577, loss_shrink_maps: 0.777291, loss_threshold_maps: 0.581668, loss_binary_maps: 0.154903, avg_reader_cost: 0.62600 s, avg_batch_cost: 0.68083 s, avg_samples: 2.9, ips: 4.25948 samples/s, eta: 4:45:14
[2024/07/28 00:51:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:51:46] ppocr INFO: epoch: [568/1500], global_step: 1704, lr: 0.001000, loss: 1.513011, loss_shrink_maps: 0.777291, loss_threshold_maps: 0.580529, loss_binary_maps: 0.154903, avg_reader_cost: 1.53664 s, avg_batch_cost: 1.77256 s, avg_samples: 12.5, ips: 7.05196 samples/s, eta: 4:44:55
[2024/07/28 00:51:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:51:55] ppocr INFO: epoch: [569/1500], global_step: 1707, lr: 0.001000, loss: 1.513011, loss_shrink_maps: 0.783129, loss_threshold_maps: 0.580529, loss_binary_maps: 0.155630, avg_reader_cost: 1.53418 s, avg_batch_cost: 1.77723 s, avg_samples: 12.5, ips: 7.03341 samples/s, eta: 4:44:35
[2024/07/28 00:51:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:52:03] ppocr INFO: epoch: [570/1500], global_step: 1710, lr: 0.001000, loss: 1.513011, loss_shrink_maps: 0.793806, loss_threshold_maps: 0.572129, loss_binary_maps: 0.158223, avg_reader_cost: 1.65588 s, avg_batch_cost: 1.89035 s, avg_samples: 12.5, ips: 6.61252 samples/s, eta: 4:44:18
[2024/07/28 00:52:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:52:12] ppocr INFO: epoch: [571/1500], global_step: 1713, lr: 0.001000, loss: 1.545939, loss_shrink_maps: 0.816326, loss_threshold_maps: 0.577773, loss_binary_maps: 0.162090, avg_reader_cost: 1.58249 s, avg_batch_cost: 1.81059 s, avg_samples: 12.5, ips: 6.90382 samples/s, eta: 4:43:59
[2024/07/28 00:52:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:52:20] ppocr INFO: epoch: [572/1500], global_step: 1716, lr: 0.001000, loss: 1.513011, loss_shrink_maps: 0.793806, loss_threshold_maps: 0.568974, loss_binary_maps: 0.158223, avg_reader_cost: 1.51828 s, avg_batch_cost: 1.75186 s, avg_samples: 12.5, ips: 7.13529 samples/s, eta: 4:43:40
[2024/07/28 00:52:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:52:28] ppocr INFO: epoch: [573/1500], global_step: 1719, lr: 0.001000, loss: 1.500378, loss_shrink_maps: 0.778582, loss_threshold_maps: 0.560957, loss_binary_maps: 0.154592, avg_reader_cost: 1.53265 s, avg_batch_cost: 1.79888 s, avg_samples: 12.5, ips: 6.94877 samples/s, eta: 4:43:21
[2024/07/28 00:52:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:52:35] ppocr INFO: epoch: [574/1500], global_step: 1720, lr: 0.001000, loss: 1.500378, loss_shrink_maps: 0.778582, loss_threshold_maps: 0.560957, loss_binary_maps: 0.154592, avg_reader_cost: 0.42428 s, avg_batch_cost: 0.52003 s, avg_samples: 4.8, ips: 9.23026 samples/s, eta: 4:43:13
[2024/07/28 00:52:37] ppocr INFO: epoch: [574/1500], global_step: 1722, lr: 0.001000, loss: 1.493351, loss_shrink_maps: 0.767984, loss_threshold_maps: 0.554754, loss_binary_maps: 0.152856, avg_reader_cost: 1.13137 s, avg_batch_cost: 1.27701 s, avg_samples: 7.7, ips: 6.02970 samples/s, eta: 4:43:02
[2024/07/28 00:52:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:52:45] ppocr INFO: epoch: [575/1500], global_step: 1725, lr: 0.001000, loss: 1.493351, loss_shrink_maps: 0.767984, loss_threshold_maps: 0.557097, loss_binary_maps: 0.152856, avg_reader_cost: 1.57001 s, avg_batch_cost: 1.79807 s, avg_samples: 12.5, ips: 6.95191 samples/s, eta: 4:42:43
[2024/07/28 00:52:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:52:53] ppocr INFO: epoch: [576/1500], global_step: 1728, lr: 0.001000, loss: 1.493351, loss_shrink_maps: 0.767984, loss_threshold_maps: 0.557097, loss_binary_maps: 0.152856, avg_reader_cost: 1.56754 s, avg_batch_cost: 1.79625 s, avg_samples: 12.5, ips: 6.95893 samples/s, eta: 4:42:24
[2024/07/28 00:52:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:53:01] ppocr INFO: epoch: [577/1500], global_step: 1730, lr: 0.001000, loss: 1.493351, loss_shrink_maps: 0.767984, loss_threshold_maps: 0.557763, loss_binary_maps: 0.152856, avg_reader_cost: 0.92038 s, avg_batch_cost: 1.13368 s, avg_samples: 9.6, ips: 8.46800 samples/s, eta: 4:42:10
[2024/07/28 00:53:02] ppocr INFO: epoch: [577/1500], global_step: 1731, lr: 0.001000, loss: 1.489478, loss_shrink_maps: 0.767984, loss_threshold_maps: 0.553453, loss_binary_maps: 0.152856, avg_reader_cost: 0.61242 s, avg_batch_cost: 0.66764 s, avg_samples: 2.9, ips: 4.34366 samples/s, eta: 4:42:05
[2024/07/28 00:53:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:53:10] ppocr INFO: epoch: [578/1500], global_step: 1734, lr: 0.001000, loss: 1.489478, loss_shrink_maps: 0.767984, loss_threshold_maps: 0.557763, loss_binary_maps: 0.152856, avg_reader_cost: 1.54349 s, avg_batch_cost: 1.79421 s, avg_samples: 12.5, ips: 6.96686 samples/s, eta: 4:41:46
[2024/07/28 00:53:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:53:19] ppocr INFO: epoch: [579/1500], global_step: 1737, lr: 0.001000, loss: 1.514850, loss_shrink_maps: 0.789572, loss_threshold_maps: 0.557763, loss_binary_maps: 0.156935, avg_reader_cost: 1.64688 s, avg_batch_cost: 1.92972 s, avg_samples: 12.5, ips: 6.47764 samples/s, eta: 4:41:29
[2024/07/28 00:53:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:53:27] ppocr INFO: epoch: [580/1500], global_step: 1740, lr: 0.001000, loss: 1.544334, loss_shrink_maps: 0.807585, loss_threshold_maps: 0.560939, loss_binary_maps: 0.160337, avg_reader_cost: 1.50864 s, avg_batch_cost: 1.75183 s, avg_samples: 12.5, ips: 7.13538 samples/s, eta: 4:41:10

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:37, 13.39it/s]
eval model::   1%|          | 5/500 [00:00<00:26, 18.87it/s]
eval model::   1%|▏         | 7/500 [00:00<00:28, 17.36it/s]
eval model::   2%|▏         | 10/500 [00:00<00:26, 18.59it/s]
eval model::   2%|▏         | 12/500 [00:00<00:27, 17.91it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.71it/s]
eval model::   3%|▎         | 17/500 [00:00<00:25, 18.77it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.40it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.46it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 19.82it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 20.98it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 18.85it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.25it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.15it/s]
eval model::   8%|▊         | 40/500 [00:02<00:27, 17.03it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.53it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 18.13it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 17.11it/s]
eval model::  10%|█         | 50/500 [00:02<00:25, 18.00it/s]
eval model::  11%|█         | 53/500 [00:02<00:23, 19.34it/s]
eval model::  11%|█         | 56/500 [00:02<00:21, 20.28it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:22, 19.32it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:30, 14.63it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:30, 14.44it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 18.88it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 20.99it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 20.15it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:20, 20.99it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.11it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.13it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 21.19it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.13it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.37it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.91it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:21, 19.14it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:19, 21.07it/s]
eval model::  20%|██        | 101/500 [00:05<00:20, 19.84it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 21.39it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 23.02it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:16, 23.77it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:15, 24.70it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:17, 22.57it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 22.41it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:16, 22.24it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:21, 17.19it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:20, 18.52it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 18.50it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:20, 17.84it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:18, 19.68it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.77it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:16, 21.30it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.86it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 18.76it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 17.74it/s]
eval model::  31%|███       | 154/500 [00:07<00:17, 19.48it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:15, 21.53it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:18, 17.95it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:17, 19.20it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 19.61it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 19.94it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 18.75it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 15.94it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.33it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.41it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:22, 13.90it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:19, 16.60it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:18, 17.29it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:16, 18.48it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:17, 18.10it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:14, 20.52it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:15, 18.95it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 21.13it/s]
eval model::  41%|████      | 203/500 [00:10<00:13, 21.41it/s]
eval model::  41%|████      | 206/500 [00:10<00:19, 15.34it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:18, 15.44it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 14.94it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.22it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.49it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 14.13it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.32it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:19, 14.10it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.69it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:13, 20.08it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 22.04it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 20.80it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 22.44it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 23.25it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:11, 21.52it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:10, 23.06it/s]
eval model::  50%|█████     | 250/500 [00:13<00:11, 22.65it/s]
eval model::  51%|█████     | 253/500 [00:13<00:10, 23.05it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 21.98it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 23.02it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:10, 21.65it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:11, 20.56it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:11, 20.72it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:11, 19.20it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:12, 17.84it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:13, 16.92it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 18.89it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.12it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 20.42it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:17, 12.45it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.26it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.42it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:13, 14.88it/s]
eval model::  60%|██████    | 300/500 [00:16<00:13, 15.27it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.61it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 18.93it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 20.28it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 20.78it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 20.59it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 22.18it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 21.97it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 20.51it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 22.27it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:07, 21.35it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 22.29it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:06, 23.90it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 20.89it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 14.87it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:09, 17.22it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 18.85it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 21.01it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 22.71it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 22.22it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:06, 20.01it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.32it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.79it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:07, 18.16it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 19.70it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 19.91it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 21.55it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:06, 17.89it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 20.94it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:09, 11.86it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:07, 14.37it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 16.43it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:06, 16.63it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 17.50it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 18.71it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 20.45it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 20.97it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 22.76it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 23.45it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 21.10it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:04, 16.83it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 16.88it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 17.42it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:03, 19.18it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 21.09it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 20.89it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 22.13it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 22.29it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 21.28it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 22.28it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 22.47it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 22.11it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 21.96it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 21.67it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:02, 19.08it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 16.38it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.60it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 16.87it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 19.61it/s]
eval model::  95%|█████████▍| 473/500 [00:24<00:01, 18.91it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 19.88it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:01, 20.99it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 21.15it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 23.19it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 21.62it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 21.30it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 19.89it/s]
eval model::  99%|█████████▉| 497/500 [00:25<00:00, 20.63it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.17it/s]
[2024/07/28 00:53:53] ppocr INFO: cur metric, precision: 0.7438502673796792, recall: 0.6697159364467983, hmean: 0.7048391183177097, fps: 46.08540547152116
[2024/07/28 00:53:53] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/28 00:53:53] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 00:53:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:54:01] ppocr INFO: epoch: [581/1500], global_step: 1743, lr: 0.001000, loss: 1.514850, loss_shrink_maps: 0.789572, loss_threshold_maps: 0.559681, loss_binary_maps: 0.156935, avg_reader_cost: 1.76938 s, avg_batch_cost: 2.10128 s, avg_samples: 12.5, ips: 5.94876 samples/s, eta: 4:40:55
[2024/07/28 00:54:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:54:10] ppocr INFO: epoch: [582/1500], global_step: 1746, lr: 0.001000, loss: 1.472762, loss_shrink_maps: 0.770313, loss_threshold_maps: 0.555116, loss_binary_maps: 0.153426, avg_reader_cost: 1.54159 s, avg_batch_cost: 1.79803 s, avg_samples: 12.5, ips: 6.95203 samples/s, eta: 4:40:37
[2024/07/28 00:54:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:54:18] ppocr INFO: epoch: [583/1500], global_step: 1749, lr: 0.001000, loss: 1.468454, loss_shrink_maps: 0.760191, loss_threshold_maps: 0.555116, loss_binary_maps: 0.150876, avg_reader_cost: 1.53446 s, avg_batch_cost: 1.77002 s, avg_samples: 12.5, ips: 7.06206 samples/s, eta: 4:40:17
[2024/07/28 00:54:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:54:25] ppocr INFO: epoch: [584/1500], global_step: 1750, lr: 0.001000, loss: 1.440799, loss_shrink_maps: 0.735086, loss_threshold_maps: 0.551803, loss_binary_maps: 0.146033, avg_reader_cost: 0.43055 s, avg_batch_cost: 0.51326 s, avg_samples: 4.8, ips: 9.35204 samples/s, eta: 4:40:10
[2024/07/28 00:54:26] ppocr INFO: epoch: [584/1500], global_step: 1752, lr: 0.001000, loss: 1.430452, loss_shrink_maps: 0.731668, loss_threshold_maps: 0.548612, loss_binary_maps: 0.145347, avg_reader_cost: 1.11798 s, avg_batch_cost: 1.26423 s, avg_samples: 7.7, ips: 6.09068 samples/s, eta: 4:39:58
[2024/07/28 00:54:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:54:35] ppocr INFO: epoch: [585/1500], global_step: 1755, lr: 0.001000, loss: 1.440799, loss_shrink_maps: 0.740744, loss_threshold_maps: 0.547148, loss_binary_maps: 0.147182, avg_reader_cost: 1.50825 s, avg_batch_cost: 1.74497 s, avg_samples: 12.5, ips: 7.16344 samples/s, eta: 4:39:38
[2024/07/28 00:54:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:54:43] ppocr INFO: epoch: [586/1500], global_step: 1758, lr: 0.001000, loss: 1.440799, loss_shrink_maps: 0.740744, loss_threshold_maps: 0.549958, loss_binary_maps: 0.147182, avg_reader_cost: 1.51498 s, avg_batch_cost: 1.74423 s, avg_samples: 12.5, ips: 7.16647 samples/s, eta: 4:39:18
[2024/07/28 00:54:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:54:51] ppocr INFO: epoch: [587/1500], global_step: 1760, lr: 0.001000, loss: 1.439852, loss_shrink_maps: 0.748142, loss_threshold_maps: 0.547148, loss_binary_maps: 0.148912, avg_reader_cost: 0.98134 s, avg_batch_cost: 1.15436 s, avg_samples: 9.6, ips: 8.31629 samples/s, eta: 4:39:05
[2024/07/28 00:54:52] ppocr INFO: epoch: [587/1500], global_step: 1761, lr: 0.001000, loss: 1.425199, loss_shrink_maps: 0.744724, loss_threshold_maps: 0.547148, loss_binary_maps: 0.148226, avg_reader_cost: 0.62285 s, avg_batch_cost: 0.67783 s, avg_samples: 2.9, ips: 4.27835 samples/s, eta: 4:39:00
[2024/07/28 00:54:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:55:00] ppocr INFO: epoch: [588/1500], global_step: 1764, lr: 0.001000, loss: 1.468869, loss_shrink_maps: 0.766926, loss_threshold_maps: 0.553028, loss_binary_maps: 0.152672, avg_reader_cost: 1.58476 s, avg_batch_cost: 1.81307 s, avg_samples: 12.5, ips: 6.89438 samples/s, eta: 4:38:41
[2024/07/28 00:55:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:55:09] ppocr INFO: epoch: [589/1500], global_step: 1767, lr: 0.001000, loss: 1.425199, loss_shrink_maps: 0.744724, loss_threshold_maps: 0.547148, loss_binary_maps: 0.148226, avg_reader_cost: 1.58982 s, avg_batch_cost: 1.88129 s, avg_samples: 12.5, ips: 6.64437 samples/s, eta: 4:38:24
[2024/07/28 00:55:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:55:18] ppocr INFO: epoch: [590/1500], global_step: 1770, lr: 0.001000, loss: 1.440305, loss_shrink_maps: 0.755780, loss_threshold_maps: 0.549958, loss_binary_maps: 0.150708, avg_reader_cost: 1.62648 s, avg_batch_cost: 1.87582 s, avg_samples: 12.5, ips: 6.66375 samples/s, eta: 4:38:06
[2024/07/28 00:55:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:55:26] ppocr INFO: epoch: [591/1500], global_step: 1773, lr: 0.001000, loss: 1.418812, loss_shrink_maps: 0.746555, loss_threshold_maps: 0.549958, loss_binary_maps: 0.148648, avg_reader_cost: 1.51754 s, avg_batch_cost: 1.74634 s, avg_samples: 12.5, ips: 7.15784 samples/s, eta: 4:37:47
[2024/07/28 00:55:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:55:34] ppocr INFO: epoch: [592/1500], global_step: 1776, lr: 0.001000, loss: 1.418863, loss_shrink_maps: 0.744047, loss_threshold_maps: 0.538382, loss_binary_maps: 0.148013, avg_reader_cost: 1.52449 s, avg_batch_cost: 1.76308 s, avg_samples: 12.5, ips: 7.08988 samples/s, eta: 4:37:27
[2024/07/28 00:55:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:55:43] ppocr INFO: epoch: [593/1500], global_step: 1779, lr: 0.001000, loss: 1.415306, loss_shrink_maps: 0.712699, loss_threshold_maps: 0.538382, loss_binary_maps: 0.141513, avg_reader_cost: 1.59172 s, avg_batch_cost: 1.84921 s, avg_samples: 12.5, ips: 6.75964 samples/s, eta: 4:37:09
[2024/07/28 00:55:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:55:50] ppocr INFO: epoch: [594/1500], global_step: 1780, lr: 0.001000, loss: 1.415306, loss_shrink_maps: 0.712699, loss_threshold_maps: 0.538382, loss_binary_maps: 0.141513, avg_reader_cost: 0.50245 s, avg_batch_cost: 0.58473 s, avg_samples: 4.8, ips: 8.20896 samples/s, eta: 4:37:02
[2024/07/28 00:55:52] ppocr INFO: epoch: [594/1500], global_step: 1782, lr: 0.001000, loss: 1.427531, loss_shrink_maps: 0.744047, loss_threshold_maps: 0.554784, loss_binary_maps: 0.148013, avg_reader_cost: 1.26122 s, avg_batch_cost: 1.40673 s, avg_samples: 7.7, ips: 5.47367 samples/s, eta: 4:36:53
[2024/07/28 00:55:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:56:00] ppocr INFO: epoch: [595/1500], global_step: 1785, lr: 0.001000, loss: 1.415306, loss_shrink_maps: 0.725267, loss_threshold_maps: 0.526898, loss_binary_maps: 0.144316, avg_reader_cost: 1.53203 s, avg_batch_cost: 1.76144 s, avg_samples: 12.5, ips: 7.09649 samples/s, eta: 4:36:34
[2024/07/28 00:56:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:56:09] ppocr INFO: epoch: [596/1500], global_step: 1788, lr: 0.001000, loss: 1.422162, loss_shrink_maps: 0.737489, loss_threshold_maps: 0.560218, loss_binary_maps: 0.147074, avg_reader_cost: 1.58446 s, avg_batch_cost: 1.83551 s, avg_samples: 12.5, ips: 6.81010 samples/s, eta: 4:36:15
[2024/07/28 00:56:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:56:17] ppocr INFO: epoch: [597/1500], global_step: 1790, lr: 0.001000, loss: 1.422162, loss_shrink_maps: 0.737489, loss_threshold_maps: 0.546982, loss_binary_maps: 0.147074, avg_reader_cost: 0.91347 s, avg_batch_cost: 1.08898 s, avg_samples: 9.6, ips: 8.81559 samples/s, eta: 4:36:01
[2024/07/28 00:56:17] ppocr INFO: epoch: [597/1500], global_step: 1791, lr: 0.001000, loss: 1.427531, loss_shrink_maps: 0.740548, loss_threshold_maps: 0.564111, loss_binary_maps: 0.147746, avg_reader_cost: 0.59019 s, avg_batch_cost: 0.64499 s, avg_samples: 2.9, ips: 4.49618 samples/s, eta: 4:35:55
[2024/07/28 00:56:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:56:26] ppocr INFO: epoch: [598/1500], global_step: 1794, lr: 0.001000, loss: 1.437749, loss_shrink_maps: 0.752136, loss_threshold_maps: 0.546916, loss_binary_maps: 0.149655, avg_reader_cost: 1.56194 s, avg_batch_cost: 1.79159 s, avg_samples: 12.5, ips: 6.97705 samples/s, eta: 4:35:37
[2024/07/28 00:56:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:56:34] ppocr INFO: epoch: [599/1500], global_step: 1797, lr: 0.001000, loss: 1.442896, loss_shrink_maps: 0.750592, loss_threshold_maps: 0.545849, loss_binary_maps: 0.149463, avg_reader_cost: 1.54259 s, avg_batch_cost: 1.77673 s, avg_samples: 12.5, ips: 7.03539 samples/s, eta: 4:35:17
[2024/07/28 00:56:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:56:42] ppocr INFO: epoch: [600/1500], global_step: 1800, lr: 0.001000, loss: 1.442896, loss_shrink_maps: 0.754105, loss_threshold_maps: 0.539680, loss_binary_maps: 0.150404, avg_reader_cost: 1.52434 s, avg_batch_cost: 1.76989 s, avg_samples: 12.5, ips: 7.06260 samples/s, eta: 4:34:58

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:56,  8.79it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.32it/s]
eval model::   1%|          | 6/500 [00:00<00:29, 16.84it/s]
eval model::   2%|▏         | 8/500 [00:00<00:29, 16.71it/s]
eval model::   2%|▏         | 10/500 [00:00<00:28, 17.16it/s]
eval model::   2%|▏         | 12/500 [00:00<00:27, 17.99it/s]
eval model::   3%|▎         | 14/500 [00:00<00:28, 17.22it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.91it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.65it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 21.38it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 20.04it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 21.24it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 20.00it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 19.06it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.81it/s]
eval model::   8%|▊         | 41/500 [00:02<00:25, 17.98it/s]
eval model::   9%|▊         | 43/500 [00:02<00:28, 16.21it/s]
eval model::   9%|▉         | 46/500 [00:02<00:27, 16.54it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.21it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 19.20it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 19.99it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 19.17it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:22, 19.24it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:27, 15.87it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:26, 16.73it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:20, 20.88it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 20.48it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:21, 19.68it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:21, 19.64it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:20, 20.77it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:19, 21.88it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:19, 21.03it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:21, 18.86it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:23, 17.53it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:23, 17.51it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.51it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.97it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.45it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.81it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:16, 23.29it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:16, 23.06it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:18, 20.34it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:18, 20.95it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:19, 19.68it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 19.33it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.25it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 19.05it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 18.43it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:17, 20.97it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:16, 21.41it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.26it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.01it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:17, 19.97it/s]
eval model::  30%|███       | 150/500 [00:07<00:15, 21.93it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 19.56it/s]
eval model::  31%|███       | 156/500 [00:07<00:15, 21.57it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 20.31it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:22, 14.86it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:20, 16.56it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:19, 16.93it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:19, 17.24it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:19, 17.17it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:18, 17.32it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.77it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.15it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.49it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:15, 20.49it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:15, 19.62it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:15, 19.57it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:14, 21.82it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:14, 20.40it/s]
eval model::  40%|████      | 200/500 [00:10<00:13, 22.03it/s]
eval model::  41%|████      | 203/500 [00:10<00:13, 21.46it/s]
eval model::  41%|████      | 206/500 [00:10<00:21, 13.82it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:20, 14.29it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 14.25it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.07it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.31it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 14.05it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.44it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:21, 13.10it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.91it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:14, 18.90it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 21.02it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 20.71it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:12, 21.09it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 21.91it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:13, 19.45it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:12, 21.03it/s]
eval model::  50%|█████     | 250/500 [00:13<00:12, 20.43it/s]
eval model::  51%|█████     | 254/500 [00:13<00:10, 22.58it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 22.60it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 22.40it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:11, 20.84it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:12, 18.51it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.15it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.11it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.11it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 17.40it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:13, 16.94it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:12, 17.81it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:18, 11.59it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:15, 14.02it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.57it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.00it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:14, 14.30it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:11, 17.16it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 17.46it/s]
eval model::  61%|██████    | 304/500 [00:16<00:10, 17.91it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:09, 19.90it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.59it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 19.76it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:09, 19.01it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 20.45it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 19.49it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 21.58it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 20.01it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:09, 18.28it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:08, 20.25it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 18.80it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:08, 18.36it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:11, 13.49it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 16.24it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 18.11it/s]
eval model::  70%|███████   | 350/500 [00:18<00:07, 19.58it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 21.17it/s]
eval model::  71%|███████   | 356/500 [00:19<00:07, 20.46it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:06, 20.40it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:07, 19.48it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 19.06it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 18.06it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 19.04it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 18.40it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:05, 20.87it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 21.12it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:06, 19.75it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 21.83it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:08, 12.61it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:07, 15.16it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 16.14it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:06, 16.77it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 18.20it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 18.42it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 20.30it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.02it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 22.68it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 21.67it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 19.47it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:04, 16.07it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:04, 18.12it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.12it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 16.69it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 18.01it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 20.33it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 21.77it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 21.25it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 21.38it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 21.97it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 20.13it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 20.91it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:02, 20.48it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 22.05it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 18.81it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:01, 18.26it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 19.87it/s]
eval model::  94%|█████████▍| 472/500 [00:25<00:01, 22.07it/s]
eval model::  95%|█████████▌| 475/500 [00:25<00:01, 22.08it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:01, 21.02it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 21.42it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 20.95it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 20.32it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 22.01it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 20.19it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 19.99it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 22.78it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.90it/s]
[2024/07/28 00:57:09] ppocr INFO: cur metric, precision: 0.7193717277486911, recall: 0.6615310544053924, hmean: 0.6892400300978179, fps: 45.01795833047385
[2024/07/28 00:57:09] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 00:57:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:57:16] ppocr INFO: epoch: [601/1500], global_step: 1803, lr: 0.001000, loss: 1.426101, loss_shrink_maps: 0.739003, loss_threshold_maps: 0.533669, loss_binary_maps: 0.147555, avg_reader_cost: 1.67218 s, avg_batch_cost: 2.03044 s, avg_samples: 12.5, ips: 6.15630 samples/s, eta: 4:34:43
[2024/07/28 00:57:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:57:25] ppocr INFO: epoch: [602/1500], global_step: 1806, lr: 0.001000, loss: 1.442896, loss_shrink_maps: 0.754105, loss_threshold_maps: 0.539680, loss_binary_maps: 0.150404, avg_reader_cost: 1.58888 s, avg_batch_cost: 1.84313 s, avg_samples: 12.5, ips: 6.78194 samples/s, eta: 4:34:24
[2024/07/28 00:57:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:57:34] ppocr INFO: epoch: [603/1500], global_step: 1809, lr: 0.001000, loss: 1.442896, loss_shrink_maps: 0.765478, loss_threshold_maps: 0.537087, loss_binary_maps: 0.152503, avg_reader_cost: 1.63248 s, avg_batch_cost: 1.88532 s, avg_samples: 12.5, ips: 6.63016 samples/s, eta: 4:34:07
[2024/07/28 00:57:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:57:41] ppocr INFO: epoch: [604/1500], global_step: 1810, lr: 0.001000, loss: 1.442896, loss_shrink_maps: 0.765478, loss_threshold_maps: 0.537087, loss_binary_maps: 0.152503, avg_reader_cost: 0.39909 s, avg_batch_cost: 0.51824 s, avg_samples: 4.8, ips: 9.26203 samples/s, eta: 4:33:59
[2024/07/28 00:57:42] ppocr INFO: epoch: [604/1500], global_step: 1812, lr: 0.001000, loss: 1.442896, loss_shrink_maps: 0.765478, loss_threshold_maps: 0.535185, loss_binary_maps: 0.152503, avg_reader_cost: 1.12819 s, avg_batch_cost: 1.27394 s, avg_samples: 7.7, ips: 6.04423 samples/s, eta: 4:33:48
[2024/07/28 00:57:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:57:51] ppocr INFO: epoch: [605/1500], global_step: 1815, lr: 0.001000, loss: 1.458083, loss_shrink_maps: 0.768916, loss_threshold_maps: 0.537087, loss_binary_maps: 0.153116, avg_reader_cost: 1.50148 s, avg_batch_cost: 1.76209 s, avg_samples: 12.5, ips: 7.09386 samples/s, eta: 4:33:29
[2024/07/28 00:57:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:57:59] ppocr INFO: epoch: [606/1500], global_step: 1818, lr: 0.001000, loss: 1.434665, loss_shrink_maps: 0.768373, loss_threshold_maps: 0.530106, loss_binary_maps: 0.153033, avg_reader_cost: 1.57657 s, avg_batch_cost: 1.83925 s, avg_samples: 12.5, ips: 6.79623 samples/s, eta: 4:33:10
[2024/07/28 00:58:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:58:07] ppocr INFO: epoch: [607/1500], global_step: 1820, lr: 0.001000, loss: 1.414807, loss_shrink_maps: 0.750273, loss_threshold_maps: 0.530106, loss_binary_maps: 0.148872, avg_reader_cost: 0.93806 s, avg_batch_cost: 1.12025 s, avg_samples: 9.6, ips: 8.56948 samples/s, eta: 4:32:57
[2024/07/28 00:58:08] ppocr INFO: epoch: [607/1500], global_step: 1821, lr: 0.001000, loss: 1.414807, loss_shrink_maps: 0.750273, loss_threshold_maps: 0.532340, loss_binary_maps: 0.148872, avg_reader_cost: 0.60582 s, avg_batch_cost: 0.66060 s, avg_samples: 2.9, ips: 4.38996 samples/s, eta: 4:32:51
[2024/07/28 00:58:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:58:16] ppocr INFO: epoch: [608/1500], global_step: 1824, lr: 0.001000, loss: 1.414807, loss_shrink_maps: 0.750273, loss_threshold_maps: 0.532340, loss_binary_maps: 0.148872, avg_reader_cost: 1.49719 s, avg_batch_cost: 1.73558 s, avg_samples: 12.5, ips: 7.20219 samples/s, eta: 4:32:31
[2024/07/28 00:58:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:58:24] ppocr INFO: epoch: [609/1500], global_step: 1827, lr: 0.001000, loss: 1.414807, loss_shrink_maps: 0.750273, loss_threshold_maps: 0.528184, loss_binary_maps: 0.148872, avg_reader_cost: 1.47474 s, avg_batch_cost: 1.71893 s, avg_samples: 12.5, ips: 7.27195 samples/s, eta: 4:32:11
[2024/07/28 00:58:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:58:33] ppocr INFO: epoch: [610/1500], global_step: 1830, lr: 0.001000, loss: 1.398323, loss_shrink_maps: 0.728711, loss_threshold_maps: 0.528184, loss_binary_maps: 0.144918, avg_reader_cost: 1.52875 s, avg_batch_cost: 1.77810 s, avg_samples: 12.5, ips: 7.02998 samples/s, eta: 4:31:52
[2024/07/28 00:58:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:58:41] ppocr INFO: epoch: [611/1500], global_step: 1833, lr: 0.001000, loss: 1.381503, loss_shrink_maps: 0.713640, loss_threshold_maps: 0.531383, loss_binary_maps: 0.142682, avg_reader_cost: 1.59669 s, avg_batch_cost: 1.82437 s, avg_samples: 12.5, ips: 6.85168 samples/s, eta: 4:31:34
[2024/07/28 00:58:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:58:50] ppocr INFO: epoch: [612/1500], global_step: 1836, lr: 0.001000, loss: 1.412563, loss_shrink_maps: 0.736693, loss_threshold_maps: 0.537388, loss_binary_maps: 0.146324, avg_reader_cost: 1.54905 s, avg_batch_cost: 1.78817 s, avg_samples: 12.5, ips: 6.99040 samples/s, eta: 4:31:15
[2024/07/28 00:58:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:58:58] ppocr INFO: epoch: [613/1500], global_step: 1839, lr: 0.001000, loss: 1.458898, loss_shrink_maps: 0.763421, loss_threshold_maps: 0.539673, loss_binary_maps: 0.151780, avg_reader_cost: 1.50670 s, avg_batch_cost: 1.73742 s, avg_samples: 12.5, ips: 7.19459 samples/s, eta: 4:30:55
[2024/07/28 00:59:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:59:05] ppocr INFO: epoch: [614/1500], global_step: 1840, lr: 0.001000, loss: 1.458898, loss_shrink_maps: 0.763421, loss_threshold_maps: 0.539673, loss_binary_maps: 0.151780, avg_reader_cost: 0.41256 s, avg_batch_cost: 0.53082 s, avg_samples: 4.8, ips: 9.04262 samples/s, eta: 4:30:48
[2024/07/28 00:59:07] ppocr INFO: epoch: [614/1500], global_step: 1842, lr: 0.001000, loss: 1.458898, loss_shrink_maps: 0.763421, loss_threshold_maps: 0.539673, loss_binary_maps: 0.151780, avg_reader_cost: 1.15323 s, avg_batch_cost: 1.29935 s, avg_samples: 7.7, ips: 5.92604 samples/s, eta: 4:30:37
[2024/07/28 00:59:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:59:15] ppocr INFO: epoch: [615/1500], global_step: 1845, lr: 0.001000, loss: 1.451280, loss_shrink_maps: 0.763421, loss_threshold_maps: 0.537388, loss_binary_maps: 0.151780, avg_reader_cost: 1.51661 s, avg_batch_cost: 1.75239 s, avg_samples: 12.5, ips: 7.13313 samples/s, eta: 4:30:17
[2024/07/28 00:59:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:59:24] ppocr INFO: epoch: [616/1500], global_step: 1848, lr: 0.001000, loss: 1.451280, loss_shrink_maps: 0.762680, loss_threshold_maps: 0.541042, loss_binary_maps: 0.151656, avg_reader_cost: 1.55728 s, avg_batch_cost: 1.78622 s, avg_samples: 12.5, ips: 6.99801 samples/s, eta: 4:29:58
[2024/07/28 00:59:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:59:32] ppocr INFO: epoch: [617/1500], global_step: 1850, lr: 0.001000, loss: 1.451280, loss_shrink_maps: 0.762680, loss_threshold_maps: 0.544263, loss_binary_maps: 0.151656, avg_reader_cost: 1.03498 s, avg_batch_cost: 1.29301 s, avg_samples: 9.6, ips: 7.42456 samples/s, eta: 4:29:47
[2024/07/28 00:59:33] ppocr INFO: epoch: [617/1500], global_step: 1851, lr: 0.001000, loss: 1.443414, loss_shrink_maps: 0.763412, loss_threshold_maps: 0.541828, loss_binary_maps: 0.151919, avg_reader_cost: 0.69169 s, avg_batch_cost: 0.74674 s, avg_samples: 2.9, ips: 3.88355 samples/s, eta: 4:29:43
[2024/07/28 00:59:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:59:41] ppocr INFO: epoch: [618/1500], global_step: 1854, lr: 0.001000, loss: 1.451904, loss_shrink_maps: 0.772519, loss_threshold_maps: 0.546576, loss_binary_maps: 0.153978, avg_reader_cost: 1.52032 s, avg_batch_cost: 1.77443 s, avg_samples: 12.5, ips: 7.04452 samples/s, eta: 4:29:24
[2024/07/28 00:59:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 00:59:51] ppocr INFO: epoch: [619/1500], global_step: 1857, lr: 0.001000, loss: 1.451904, loss_shrink_maps: 0.763412, loss_threshold_maps: 0.550367, loss_binary_maps: 0.151919, avg_reader_cost: 1.87327 s, avg_batch_cost: 2.13010 s, avg_samples: 12.5, ips: 5.86827 samples/s, eta: 4:29:10
[2024/07/28 00:59:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:00:00] ppocr INFO: epoch: [620/1500], global_step: 1860, lr: 0.001000, loss: 1.443414, loss_shrink_maps: 0.762883, loss_threshold_maps: 0.558833, loss_binary_maps: 0.151369, avg_reader_cost: 1.48856 s, avg_batch_cost: 1.73684 s, avg_samples: 12.5, ips: 7.19696 samples/s, eta: 4:28:50

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.68it/s]
eval model::   1%|          | 4/500 [00:00<00:25, 19.69it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.29it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 18.17it/s]
eval model::   2%|▏         | 10/500 [00:00<00:26, 18.54it/s]
eval model::   3%|▎         | 13/500 [00:00<00:25, 19.42it/s]
eval model::   3%|▎         | 15/500 [00:00<00:25, 19.15it/s]
eval model::   4%|▎         | 18/500 [00:00<00:24, 19.80it/s]
eval model::   4%|▍         | 20/500 [00:01<00:28, 17.01it/s]
eval model::   5%|▍         | 23/500 [00:01<00:26, 18.34it/s]
eval model::   5%|▌         | 25/500 [00:01<00:27, 17.57it/s]
eval model::   5%|▌         | 27/500 [00:01<00:27, 17.46it/s]
eval model::   6%|▌         | 30/500 [00:01<00:23, 19.77it/s]
eval model::   6%|▋         | 32/500 [00:01<00:25, 18.28it/s]
eval model::   7%|▋         | 34/500 [00:01<00:26, 17.74it/s]
eval model::   8%|▊         | 38/500 [00:02<00:23, 19.56it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.44it/s]
eval model::   8%|▊         | 42/500 [00:02<00:28, 16.10it/s]
eval model::   9%|▉         | 44/500 [00:02<00:26, 16.94it/s]
eval model::   9%|▉         | 46/500 [00:02<00:29, 15.36it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 19.11it/s]
eval model::  10%|█         | 52/500 [00:02<00:24, 18.38it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 21.06it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 19.16it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:25, 17.03it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.56it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:21, 20.27it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 22.06it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:18, 22.90it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:18, 22.91it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:20, 20.73it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.92it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 21.96it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:19, 20.70it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.42it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.03it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:22, 18.38it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:20, 20.05it/s]
eval model::  20%|██        | 101/500 [00:05<00:19, 20.27it/s]
eval model::  21%|██        | 104/500 [00:05<00:17, 22.02it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 22.50it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 22.23it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:15, 24.35it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:17, 21.67it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 20.41it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.42it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 18.02it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 19.38it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 18.81it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:17, 20.33it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.17it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.26it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.67it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 19.13it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 20.90it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 20.15it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:14, 23.04it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:20, 16.21it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.06it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:18, 18.42it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 18.69it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 18.22it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 15.53it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:18, 17.48it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:20, 15.63it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:18, 17.52it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:17, 17.96it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:16, 19.11it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:16, 19.05it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:16, 18.95it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.68it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 20.56it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 19.86it/s]
eval model::  41%|████      | 206/500 [00:10<00:20, 14.62it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:20, 14.45it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 14.78it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.82it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.95it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.21it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.35it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 14.71it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:15, 17.73it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:15, 17.29it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 20.08it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.46it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.05it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.24it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 19.96it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:11, 22.77it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:11, 22.10it/s]
eval model::  50%|█████     | 252/500 [00:13<00:10, 23.07it/s]
eval model::  51%|█████     | 255/500 [00:13<00:10, 24.37it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:10, 23.06it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 22.09it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:10, 22.16it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:10, 22.74it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:10, 22.08it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 20.40it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:11, 19.88it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:10, 21.35it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:09, 22.37it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:14, 14.38it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:14, 14.87it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:11, 18.47it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:11, 17.55it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:13, 15.50it/s]
eval model::  60%|██████    | 300/500 [00:15<00:10, 18.20it/s]
eval model::  61%|██████    | 303/500 [00:15<00:09, 20.05it/s]
eval model::  61%|██████    | 306/500 [00:16<00:09, 20.56it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:08, 21.25it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:08, 21.28it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 20.34it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 20.37it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 20.47it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 20.32it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 21.33it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 21.01it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 22.22it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 23.27it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 21.87it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:09, 17.28it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:07, 19.63it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:07, 21.00it/s]
eval model::  70%|███████   | 351/500 [00:18<00:06, 22.27it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 23.66it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 22.67it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:06, 21.01it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 19.14it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:07, 18.09it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 19.57it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 20.38it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:05, 22.37it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 23.94it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:05, 23.29it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:05, 21.29it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:08, 13.47it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:07, 15.60it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 17.27it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 18.71it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 20.75it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 20.07it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 21.25it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.06it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 22.71it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 22.97it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 20.58it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 17.64it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 17.39it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 17.28it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:03, 18.91it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 21.23it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 22.23it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 23.66it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 23.40it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 23.11it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 23.98it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 20.47it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.06it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 21.07it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 21.89it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:02, 19.69it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:02, 17.32it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:02, 16.83it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.22it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.10it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.72it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 21.36it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 20.95it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 21.47it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 20.94it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 22.25it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 19.85it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 20.06it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 21.04it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.55it/s]
[2024/07/28 01:00:25] ppocr INFO: cur metric, precision: 0.6710227272727273, recall: 0.568608570052961, hmean: 0.615585092520198, fps: 45.37646374197571
[2024/07/28 01:00:25] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:00:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:00:33] ppocr INFO: epoch: [621/1500], global_step: 1863, lr: 0.001000, loss: 1.443414, loss_shrink_maps: 0.762883, loss_threshold_maps: 0.557205, loss_binary_maps: 0.151323, avg_reader_cost: 1.70269 s, avg_batch_cost: 2.05246 s, avg_samples: 12.5, ips: 6.09024 samples/s, eta: 4:28:35
[2024/07/28 01:00:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:00:42] ppocr INFO: epoch: [622/1500], global_step: 1866, lr: 0.001000, loss: 1.471868, loss_shrink_maps: 0.772976, loss_threshold_maps: 0.572629, loss_binary_maps: 0.153932, avg_reader_cost: 1.54056 s, avg_batch_cost: 1.77462 s, avg_samples: 12.5, ips: 7.04375 samples/s, eta: 4:28:16
[2024/07/28 01:00:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:00:50] ppocr INFO: epoch: [623/1500], global_step: 1869, lr: 0.001000, loss: 1.507028, loss_shrink_maps: 0.797016, loss_threshold_maps: 0.572629, loss_binary_maps: 0.158221, avg_reader_cost: 1.52648 s, avg_batch_cost: 1.76738 s, avg_samples: 12.5, ips: 7.07261 samples/s, eta: 4:27:56
[2024/07/28 01:00:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:00:57] ppocr INFO: epoch: [624/1500], global_step: 1870, lr: 0.001000, loss: 1.507028, loss_shrink_maps: 0.797016, loss_threshold_maps: 0.572629, loss_binary_maps: 0.158221, avg_reader_cost: 0.41731 s, avg_batch_cost: 0.50945 s, avg_samples: 4.8, ips: 9.42195 samples/s, eta: 4:27:49
[2024/07/28 01:00:58] ppocr INFO: epoch: [624/1500], global_step: 1872, lr: 0.001000, loss: 1.507028, loss_shrink_maps: 0.789411, loss_threshold_maps: 0.572629, loss_binary_maps: 0.156530, avg_reader_cost: 1.11006 s, avg_batch_cost: 1.25577 s, avg_samples: 7.7, ips: 6.13172 samples/s, eta: 4:27:37
[2024/07/28 01:01:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:01:07] ppocr INFO: epoch: [625/1500], global_step: 1875, lr: 0.001000, loss: 1.503615, loss_shrink_maps: 0.763340, loss_threshold_maps: 0.574121, loss_binary_maps: 0.151323, avg_reader_cost: 1.66551 s, avg_batch_cost: 1.89342 s, avg_samples: 12.5, ips: 6.60183 samples/s, eta: 4:27:20
[2024/07/28 01:01:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:01:16] ppocr INFO: epoch: [626/1500], global_step: 1878, lr: 0.001000, loss: 1.503615, loss_shrink_maps: 0.755980, loss_threshold_maps: 0.569380, loss_binary_maps: 0.150804, avg_reader_cost: 1.56029 s, avg_batch_cost: 1.80250 s, avg_samples: 12.5, ips: 6.93482 samples/s, eta: 4:27:01
[2024/07/28 01:01:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:01:23] ppocr INFO: epoch: [627/1500], global_step: 1880, lr: 0.001000, loss: 1.503615, loss_shrink_maps: 0.755980, loss_threshold_maps: 0.569380, loss_binary_maps: 0.150804, avg_reader_cost: 0.94125 s, avg_batch_cost: 1.11458 s, avg_samples: 9.6, ips: 8.61309 samples/s, eta: 4:26:47
[2024/07/28 01:01:24] ppocr INFO: epoch: [627/1500], global_step: 1881, lr: 0.001000, loss: 1.493848, loss_shrink_maps: 0.743406, loss_threshold_maps: 0.569380, loss_binary_maps: 0.148356, avg_reader_cost: 0.60287 s, avg_batch_cost: 0.65748 s, avg_samples: 2.9, ips: 4.41076 samples/s, eta: 4:26:42
[2024/07/28 01:01:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:01:32] ppocr INFO: epoch: [628/1500], global_step: 1884, lr: 0.001000, loss: 1.488555, loss_shrink_maps: 0.743406, loss_threshold_maps: 0.556962, loss_binary_maps: 0.148356, avg_reader_cost: 1.50906 s, avg_batch_cost: 1.77051 s, avg_samples: 12.5, ips: 7.06010 samples/s, eta: 4:26:22
[2024/07/28 01:01:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:01:41] ppocr INFO: epoch: [629/1500], global_step: 1887, lr: 0.001000, loss: 1.421605, loss_shrink_maps: 0.729602, loss_threshold_maps: 0.531114, loss_binary_maps: 0.144801, avg_reader_cost: 1.54043 s, avg_batch_cost: 1.76852 s, avg_samples: 12.5, ips: 7.06806 samples/s, eta: 4:26:03
[2024/07/28 01:01:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:01:49] ppocr INFO: epoch: [630/1500], global_step: 1890, lr: 0.001000, loss: 1.421605, loss_shrink_maps: 0.729602, loss_threshold_maps: 0.540589, loss_binary_maps: 0.144801, avg_reader_cost: 1.53772 s, avg_batch_cost: 1.78207 s, avg_samples: 12.5, ips: 7.01430 samples/s, eta: 4:25:44
[2024/07/28 01:01:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:01:58] ppocr INFO: epoch: [631/1500], global_step: 1893, lr: 0.001000, loss: 1.371811, loss_shrink_maps: 0.710996, loss_threshold_maps: 0.516201, loss_binary_maps: 0.141101, avg_reader_cost: 1.56466 s, avg_batch_cost: 1.81085 s, avg_samples: 12.5, ips: 6.90285 samples/s, eta: 4:25:26
[2024/07/28 01:02:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:02:07] ppocr INFO: epoch: [632/1500], global_step: 1896, lr: 0.001000, loss: 1.348360, loss_shrink_maps: 0.686744, loss_threshold_maps: 0.512638, loss_binary_maps: 0.136701, avg_reader_cost: 1.59484 s, avg_batch_cost: 1.84466 s, avg_samples: 12.5, ips: 6.77632 samples/s, eta: 4:25:07
[2024/07/28 01:02:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:02:15] ppocr INFO: epoch: [633/1500], global_step: 1899, lr: 0.001000, loss: 1.357762, loss_shrink_maps: 0.693730, loss_threshold_maps: 0.513144, loss_binary_maps: 0.138168, avg_reader_cost: 1.52673 s, avg_batch_cost: 1.75424 s, avg_samples: 12.5, ips: 7.12559 samples/s, eta: 4:24:48
[2024/07/28 01:02:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:02:22] ppocr INFO: epoch: [634/1500], global_step: 1900, lr: 0.001000, loss: 1.368374, loss_shrink_maps: 0.694343, loss_threshold_maps: 0.516201, loss_binary_maps: 0.138561, avg_reader_cost: 0.43091 s, avg_batch_cost: 0.54955 s, avg_samples: 4.8, ips: 8.73446 samples/s, eta: 4:24:41
[2024/07/28 01:02:24] ppocr INFO: epoch: [634/1500], global_step: 1902, lr: 0.001000, loss: 1.357762, loss_shrink_maps: 0.693730, loss_threshold_maps: 0.516201, loss_binary_maps: 0.138168, avg_reader_cost: 1.19061 s, avg_batch_cost: 1.33650 s, avg_samples: 7.7, ips: 5.76130 samples/s, eta: 4:24:30
[2024/07/28 01:02:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:02:32] ppocr INFO: epoch: [635/1500], global_step: 1905, lr: 0.001000, loss: 1.368374, loss_shrink_maps: 0.694054, loss_threshold_maps: 0.516924, loss_binary_maps: 0.138561, avg_reader_cost: 1.55141 s, avg_batch_cost: 1.77938 s, avg_samples: 12.5, ips: 7.02491 samples/s, eta: 4:24:11
[2024/07/28 01:02:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:02:41] ppocr INFO: epoch: [636/1500], global_step: 1908, lr: 0.001000, loss: 1.372529, loss_shrink_maps: 0.698631, loss_threshold_maps: 0.520794, loss_binary_maps: 0.139032, avg_reader_cost: 1.51753 s, avg_batch_cost: 1.74783 s, avg_samples: 12.5, ips: 7.15171 samples/s, eta: 4:23:52
[2024/07/28 01:02:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:02:49] ppocr INFO: epoch: [637/1500], global_step: 1910, lr: 0.001000, loss: 1.375966, loss_shrink_maps: 0.706310, loss_threshold_maps: 0.520794, loss_binary_maps: 0.140514, avg_reader_cost: 0.94246 s, avg_batch_cost: 1.11702 s, avg_samples: 9.6, ips: 8.59427 samples/s, eta: 4:23:38
[2024/07/28 01:02:49] ppocr INFO: epoch: [637/1500], global_step: 1911, lr: 0.001000, loss: 1.372529, loss_shrink_maps: 0.698631, loss_threshold_maps: 0.520794, loss_binary_maps: 0.139032, avg_reader_cost: 0.60460 s, avg_batch_cost: 0.65914 s, avg_samples: 2.9, ips: 4.39967 samples/s, eta: 4:23:33
[2024/07/28 01:02:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:02:58] ppocr INFO: epoch: [638/1500], global_step: 1914, lr: 0.001000, loss: 1.427301, loss_shrink_maps: 0.724297, loss_threshold_maps: 0.545300, loss_binary_maps: 0.143826, avg_reader_cost: 1.57939 s, avg_batch_cost: 1.82182 s, avg_samples: 12.5, ips: 6.86126 samples/s, eta: 4:23:14
[2024/07/28 01:03:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:03:06] ppocr INFO: epoch: [639/1500], global_step: 1917, lr: 0.001000, loss: 1.465831, loss_shrink_maps: 0.756785, loss_threshold_maps: 0.548765, loss_binary_maps: 0.150581, avg_reader_cost: 1.53087 s, avg_batch_cost: 1.76034 s, avg_samples: 12.5, ips: 7.10089 samples/s, eta: 4:22:55
[2024/07/28 01:03:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:03:15] ppocr INFO: epoch: [640/1500], global_step: 1920, lr: 0.001000, loss: 1.434342, loss_shrink_maps: 0.740406, loss_threshold_maps: 0.545903, loss_binary_maps: 0.146870, avg_reader_cost: 1.53636 s, avg_batch_cost: 1.76375 s, avg_samples: 12.5, ips: 7.08717 samples/s, eta: 4:22:36

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:57,  8.69it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.47it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.86it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.73it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 18.08it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.41it/s]
eval model::   3%|▎         | 17/500 [00:00<00:25, 18.94it/s]
eval model::   4%|▍         | 19/500 [00:01<00:25, 18.57it/s]
eval model::   4%|▍         | 21/500 [00:01<00:26, 18.42it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.61it/s]
eval model::   5%|▌         | 27/500 [00:01<00:24, 19.71it/s]
eval model::   6%|▌         | 30/500 [00:01<00:22, 21.00it/s]
eval model::   7%|▋         | 33/500 [00:01<00:22, 20.32it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 19.24it/s]
eval model::   8%|▊         | 38/500 [00:02<00:23, 19.33it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.17it/s]
eval model::   8%|▊         | 42/500 [00:02<00:28, 16.06it/s]
eval model::   9%|▉         | 44/500 [00:02<00:27, 16.55it/s]
eval model::   9%|▉         | 46/500 [00:02<00:31, 14.53it/s]
eval model::  10%|▉         | 49/500 [00:02<00:26, 17.28it/s]
eval model::  10%|█         | 51/500 [00:02<00:25, 17.56it/s]
eval model::  11%|█         | 54/500 [00:02<00:23, 19.29it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:22, 20.08it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:23, 18.87it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:28, 15.13it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:28, 15.43it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:21, 20.12it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 20.77it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:19, 21.44it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:18, 22.53it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:17, 23.49it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:17, 24.07it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:20, 20.35it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:24, 16.76it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:25, 16.06it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:24, 16.38it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:21, 18.50it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:21, 18.60it/s]
eval model::  20%|██        | 100/500 [00:05<00:23, 17.28it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 20.04it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.38it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:16, 23.17it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:17, 22.62it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:18, 20.90it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:17, 21.98it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 20.06it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 20.18it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:19, 19.22it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:20, 17.86it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:23, 15.51it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:24, 15.22it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:22, 16.06it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:20, 18.01it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:20, 17.51it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:18, 19.22it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.06it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 17.87it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:19, 18.36it/s]
eval model::  30%|███       | 151/500 [00:08<00:20, 16.76it/s]
eval model::  31%|███       | 154/500 [00:08<00:17, 19.45it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:17, 19.38it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:18, 18.53it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:28, 11.94it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:25, 13.02it/s]
eval model::  33%|███▎      | 165/500 [00:09<00:23, 14.28it/s]
eval model::  34%|███▎      | 168/500 [00:09<00:21, 15.59it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:20, 16.22it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:22, 14.43it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:21, 14.74it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:19, 16.51it/s]
eval model::  36%|███▌      | 181/500 [00:10<00:21, 14.73it/s]
eval model::  37%|███▋      | 184/500 [00:10<00:18, 17.28it/s]
eval model::  37%|███▋      | 186/500 [00:10<00:17, 17.79it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:15, 19.60it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:14, 21.53it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 20.23it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:15, 19.37it/s]
eval model::  40%|████      | 201/500 [00:11<00:14, 20.29it/s]
eval model::  41%|████      | 204/500 [00:11<00:13, 22.01it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:20, 14.00it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:21, 13.78it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 13.86it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:20, 14.18it/s]
eval model::  43%|████▎     | 215/500 [00:12<00:23, 12.05it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:23, 12.28it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:20, 13.54it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:21, 13.09it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:16, 16.35it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:15, 17.90it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 21.67it/s]
eval model::  47%|████▋     | 234/500 [00:13<00:12, 21.31it/s]
eval model::  47%|████▋     | 237/500 [00:13<00:12, 21.28it/s]
eval model::  48%|████▊     | 240/500 [00:13<00:12, 21.31it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:13, 18.58it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:13, 18.78it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:13, 18.65it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:13, 18.15it/s]
eval model::  50%|█████     | 251/500 [00:14<00:13, 18.53it/s]
eval model::  51%|█████     | 254/500 [00:14<00:12, 19.52it/s]
eval model::  51%|█████▏    | 257/500 [00:14<00:12, 20.09it/s]
eval model::  52%|█████▏    | 260/500 [00:14<00:11, 21.47it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:10, 22.36it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:11, 20.12it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.67it/s]
eval model::  54%|█████▍    | 272/500 [00:15<00:12, 18.58it/s]
eval model::  55%|█████▌    | 275/500 [00:15<00:12, 17.51it/s]
eval model::  56%|█████▌    | 278/500 [00:15<00:11, 18.94it/s]
eval model::  56%|█████▌    | 281/500 [00:15<00:10, 20.34it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:09, 21.88it/s]
eval model::  57%|█████▋    | 287/500 [00:16<00:17, 12.25it/s]
eval model::  58%|█████▊    | 291/500 [00:16<00:13, 15.95it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:13, 14.88it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:15, 13.06it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:13, 15.12it/s]
eval model::  60%|██████    | 301/500 [00:17<00:13, 14.38it/s]
eval model::  61%|██████    | 304/500 [00:17<00:12, 16.12it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:10, 18.04it/s]
eval model::  62%|██████▏   | 309/500 [00:17<00:11, 16.92it/s]
eval model::  62%|██████▏   | 311/500 [00:17<00:10, 17.35it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:10, 17.51it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:11, 15.50it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:10, 18.19it/s]
eval model::  64%|██████▍   | 321/500 [00:18<00:09, 19.68it/s]
eval model::  65%|██████▍   | 324/500 [00:18<00:08, 19.83it/s]
eval model::  65%|██████▌   | 327/500 [00:18<00:08, 21.37it/s]
eval model::  66%|██████▌   | 330/500 [00:18<00:08, 19.38it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:08, 19.73it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:07, 20.83it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:07, 20.16it/s]
eval model::  68%|██████▊   | 342/500 [00:19<00:10, 15.45it/s]
eval model::  69%|██████▉   | 345/500 [00:19<00:08, 18.07it/s]
eval model::  70%|██████▉   | 348/500 [00:19<00:08, 18.52it/s]
eval model::  70%|███████   | 352/500 [00:19<00:06, 21.87it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 21.55it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 22.62it/s]
eval model::  72%|███████▏  | 361/500 [00:20<00:07, 18.69it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:07, 17.07it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:08, 16.72it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:07, 17.76it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:07, 17.79it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:07, 16.94it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:06, 19.54it/s]
eval model::  76%|███████▌  | 379/500 [00:21<00:05, 21.26it/s]
eval model::  76%|███████▋  | 382/500 [00:21<00:06, 19.56it/s]
eval model::  77%|███████▋  | 385/500 [00:21<00:05, 21.09it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:09, 11.61it/s]
eval model::  78%|███████▊  | 391/500 [00:22<00:07, 13.70it/s]
eval model::  79%|███████▉  | 394/500 [00:22<00:06, 15.22it/s]
eval model::  79%|███████▉  | 397/500 [00:22<00:06, 17.02it/s]
eval model::  80%|████████  | 400/500 [00:22<00:06, 16.60it/s]
eval model::  80%|████████  | 402/500 [00:22<00:05, 17.08it/s]
eval model::  81%|████████  | 404/500 [00:22<00:05, 17.39it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 19.31it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:03, 23.14it/s]
eval model::  83%|████████▎ | 414/500 [00:23<00:03, 23.68it/s]
eval model::  83%|████████▎ | 417/500 [00:23<00:03, 22.23it/s]
eval model::  84%|████████▍ | 420/500 [00:23<00:05, 14.16it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:05, 15.13it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.51it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.51it/s]
eval model::  86%|████████▌ | 429/500 [00:24<00:04, 17.66it/s]
eval model::  86%|████████▋ | 432/500 [00:24<00:03, 17.48it/s]
eval model::  87%|████████▋ | 434/500 [00:24<00:03, 18.00it/s]
eval model::  87%|████████▋ | 437/500 [00:24<00:03, 20.66it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:03, 19.61it/s]
eval model::  89%|████████▊ | 443/500 [00:24<00:02, 21.46it/s]
eval model::  89%|████████▉ | 446/500 [00:24<00:02, 20.17it/s]
eval model::  90%|████████▉ | 449/500 [00:25<00:02, 20.39it/s]
eval model::  90%|█████████ | 452/500 [00:25<00:02, 21.77it/s]
eval model::  91%|█████████ | 455/500 [00:25<00:02, 21.21it/s]
eval model::  92%|█████████▏| 458/500 [00:25<00:01, 21.21it/s]
eval model::  92%|█████████▏| 461/500 [00:25<00:01, 21.19it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:01, 18.62it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:01, 17.62it/s]
eval model::  94%|█████████▎| 468/500 [00:26<00:01, 18.04it/s]
eval model::  94%|█████████▍| 471/500 [00:26<00:01, 20.08it/s]
eval model::  95%|█████████▍| 474/500 [00:26<00:01, 19.79it/s]
eval model::  95%|█████████▌| 477/500 [00:26<00:01, 20.15it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:00, 20.66it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 20.73it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 19.29it/s]
eval model::  98%|█████████▊| 489/500 [00:27<00:00, 21.56it/s]
eval model::  98%|█████████▊| 492/500 [00:27<00:00, 20.15it/s]
eval model::  99%|█████████▉| 495/500 [00:27<00:00, 19.66it/s]
eval model:: 100%|█████████▉| 498/500 [00:27<00:00, 21.12it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.15it/s]
[2024/07/28 01:03:42] ppocr INFO: cur metric, precision: 0.6890632663070132, recall: 0.6764564275397208, hmean: 0.6827016520894071, fps: 43.92057256379959
[2024/07/28 01:03:42] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:03:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:03:50] ppocr INFO: epoch: [641/1500], global_step: 1923, lr: 0.001000, loss: 1.455818, loss_shrink_maps: 0.746992, loss_threshold_maps: 0.548765, loss_binary_maps: 0.148594, avg_reader_cost: 1.45477 s, avg_batch_cost: 1.68400 s, avg_samples: 12.5, ips: 7.42281 samples/s, eta: 4:22:16
[2024/07/28 01:03:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:03:58] ppocr INFO: epoch: [642/1500], global_step: 1926, lr: 0.001000, loss: 1.485733, loss_shrink_maps: 0.778196, loss_threshold_maps: 0.545903, loss_binary_maps: 0.154062, avg_reader_cost: 1.52227 s, avg_batch_cost: 1.75186 s, avg_samples: 12.5, ips: 7.13527 samples/s, eta: 4:21:56
[2024/07/28 01:04:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:04:07] ppocr INFO: epoch: [643/1500], global_step: 1929, lr: 0.001000, loss: 1.465855, loss_shrink_maps: 0.761899, loss_threshold_maps: 0.537566, loss_binary_maps: 0.151494, avg_reader_cost: 1.63707 s, avg_batch_cost: 1.88863 s, avg_samples: 12.5, ips: 6.61856 samples/s, eta: 4:21:39
[2024/07/28 01:04:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:04:14] ppocr INFO: epoch: [644/1500], global_step: 1930, lr: 0.001000, loss: 1.434342, loss_shrink_maps: 0.740406, loss_threshold_maps: 0.534412, loss_binary_maps: 0.147236, avg_reader_cost: 0.41482 s, avg_batch_cost: 0.51180 s, avg_samples: 4.8, ips: 9.37866 samples/s, eta: 4:21:31
[2024/07/28 01:04:15] ppocr INFO: epoch: [644/1500], global_step: 1932, lr: 0.001000, loss: 1.434342, loss_shrink_maps: 0.737421, loss_threshold_maps: 0.534412, loss_binary_maps: 0.146436, avg_reader_cost: 1.11477 s, avg_batch_cost: 1.26032 s, avg_samples: 7.7, ips: 6.10958 samples/s, eta: 4:21:19
[2024/07/28 01:04:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:04:24] ppocr INFO: epoch: [645/1500], global_step: 1935, lr: 0.001000, loss: 1.427464, loss_shrink_maps: 0.733479, loss_threshold_maps: 0.536436, loss_binary_maps: 0.146436, avg_reader_cost: 1.50515 s, avg_batch_cost: 1.73676 s, avg_samples: 12.5, ips: 7.19733 samples/s, eta: 4:21:00
[2024/07/28 01:04:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:04:32] ppocr INFO: epoch: [646/1500], global_step: 1938, lr: 0.001000, loss: 1.410127, loss_shrink_maps: 0.727243, loss_threshold_maps: 0.529069, loss_binary_maps: 0.144801, avg_reader_cost: 1.53194 s, avg_batch_cost: 1.78486 s, avg_samples: 12.5, ips: 7.00334 samples/s, eta: 4:20:41
[2024/07/28 01:04:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:04:40] ppocr INFO: epoch: [647/1500], global_step: 1940, lr: 0.001000, loss: 1.393471, loss_shrink_maps: 0.717609, loss_threshold_maps: 0.529069, loss_binary_maps: 0.142419, avg_reader_cost: 0.91745 s, avg_batch_cost: 1.10644 s, avg_samples: 9.6, ips: 8.67645 samples/s, eta: 4:20:27
[2024/07/28 01:04:41] ppocr INFO: epoch: [647/1500], global_step: 1941, lr: 0.001000, loss: 1.380312, loss_shrink_maps: 0.710004, loss_threshold_maps: 0.526070, loss_binary_maps: 0.140958, avg_reader_cost: 0.59878 s, avg_batch_cost: 0.65374 s, avg_samples: 2.9, ips: 4.43600 samples/s, eta: 4:20:22
[2024/07/28 01:04:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:04:49] ppocr INFO: epoch: [648/1500], global_step: 1944, lr: 0.001000, loss: 1.390161, loss_shrink_maps: 0.712214, loss_threshold_maps: 0.532735, loss_binary_maps: 0.141331, avg_reader_cost: 1.57300 s, avg_batch_cost: 1.82497 s, avg_samples: 12.5, ips: 6.84943 samples/s, eta: 4:20:03
[2024/07/28 01:04:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:04:58] ppocr INFO: epoch: [649/1500], global_step: 1947, lr: 0.001000, loss: 1.402430, loss_shrink_maps: 0.719819, loss_threshold_maps: 0.536436, loss_binary_maps: 0.142792, avg_reader_cost: 1.50121 s, avg_batch_cost: 1.72982 s, avg_samples: 12.5, ips: 7.22619 samples/s, eta: 4:19:44
[2024/07/28 01:05:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:05:06] ppocr INFO: epoch: [650/1500], global_step: 1950, lr: 0.001000, loss: 1.404078, loss_shrink_maps: 0.721151, loss_threshold_maps: 0.538359, loss_binary_maps: 0.143808, avg_reader_cost: 1.50763 s, avg_batch_cost: 1.73847 s, avg_samples: 12.5, ips: 7.19023 samples/s, eta: 4:19:24
[2024/07/28 01:05:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:05:15] ppocr INFO: epoch: [651/1500], global_step: 1953, lr: 0.001000, loss: 1.391199, loss_shrink_maps: 0.712214, loss_threshold_maps: 0.534760, loss_binary_maps: 0.141331, avg_reader_cost: 1.54795 s, avg_batch_cost: 1.79112 s, avg_samples: 12.5, ips: 6.97888 samples/s, eta: 4:19:05
[2024/07/28 01:05:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:05:23] ppocr INFO: epoch: [652/1500], global_step: 1956, lr: 0.001000, loss: 1.404078, loss_shrink_maps: 0.721151, loss_threshold_maps: 0.538143, loss_binary_maps: 0.143808, avg_reader_cost: 1.56302 s, avg_batch_cost: 1.85852 s, avg_samples: 12.5, ips: 6.72576 samples/s, eta: 4:18:47
[2024/07/28 01:05:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:05:32] ppocr INFO: epoch: [653/1500], global_step: 1959, lr: 0.001000, loss: 1.404078, loss_shrink_maps: 0.718893, loss_threshold_maps: 0.543930, loss_binary_maps: 0.143111, avg_reader_cost: 1.52449 s, avg_batch_cost: 1.79597 s, avg_samples: 12.5, ips: 6.96002 samples/s, eta: 4:18:29
[2024/07/28 01:05:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:05:39] ppocr INFO: epoch: [654/1500], global_step: 1960, lr: 0.001000, loss: 1.415253, loss_shrink_maps: 0.724748, loss_threshold_maps: 0.547805, loss_binary_maps: 0.144837, avg_reader_cost: 0.41677 s, avg_batch_cost: 0.50157 s, avg_samples: 4.8, ips: 9.56994 samples/s, eta: 4:18:21
[2024/07/28 01:05:40] ppocr INFO: epoch: [654/1500], global_step: 1962, lr: 0.001000, loss: 1.431189, loss_shrink_maps: 0.743513, loss_threshold_maps: 0.547805, loss_binary_maps: 0.148193, avg_reader_cost: 1.09540 s, avg_batch_cost: 1.24221 s, avg_samples: 7.7, ips: 6.19865 samples/s, eta: 4:18:09
[2024/07/28 01:05:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:05:49] ppocr INFO: epoch: [655/1500], global_step: 1965, lr: 0.001000, loss: 1.402373, loss_shrink_maps: 0.718832, loss_threshold_maps: 0.541660, loss_binary_maps: 0.143117, avg_reader_cost: 1.50204 s, avg_batch_cost: 1.73157 s, avg_samples: 12.5, ips: 7.21890 samples/s, eta: 4:17:50
[2024/07/28 01:05:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:05:57] ppocr INFO: epoch: [656/1500], global_step: 1968, lr: 0.001000, loss: 1.385129, loss_shrink_maps: 0.704197, loss_threshold_maps: 0.536054, loss_binary_maps: 0.139984, avg_reader_cost: 1.52320 s, avg_batch_cost: 1.76108 s, avg_samples: 12.5, ips: 7.09792 samples/s, eta: 4:17:30
[2024/07/28 01:05:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:06:05] ppocr INFO: epoch: [657/1500], global_step: 1970, lr: 0.001000, loss: 1.391344, loss_shrink_maps: 0.713993, loss_threshold_maps: 0.536054, loss_binary_maps: 0.141871, avg_reader_cost: 0.95470 s, avg_batch_cost: 1.12819 s, avg_samples: 9.6, ips: 8.50919 samples/s, eta: 4:17:17
[2024/07/28 01:06:06] ppocr INFO: epoch: [657/1500], global_step: 1971, lr: 0.001000, loss: 1.374121, loss_shrink_maps: 0.702223, loss_threshold_maps: 0.530735, loss_binary_maps: 0.139984, avg_reader_cost: 0.60979 s, avg_batch_cost: 0.66472 s, avg_samples: 2.9, ips: 4.36276 samples/s, eta: 4:17:12
[2024/07/28 01:06:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:06:14] ppocr INFO: epoch: [658/1500], global_step: 1974, lr: 0.001000, loss: 1.391344, loss_shrink_maps: 0.713993, loss_threshold_maps: 0.530735, loss_binary_maps: 0.141871, avg_reader_cost: 1.50965 s, avg_batch_cost: 1.74853 s, avg_samples: 12.5, ips: 7.14888 samples/s, eta: 4:16:52
[2024/07/28 01:06:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:06:23] ppocr INFO: epoch: [659/1500], global_step: 1977, lr: 0.001000, loss: 1.391344, loss_shrink_maps: 0.713993, loss_threshold_maps: 0.530735, loss_binary_maps: 0.141871, avg_reader_cost: 1.62646 s, avg_batch_cost: 1.89548 s, avg_samples: 12.5, ips: 6.59462 samples/s, eta: 4:16:35
[2024/07/28 01:06:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:06:32] ppocr INFO: epoch: [660/1500], global_step: 1980, lr: 0.001000, loss: 1.423870, loss_shrink_maps: 0.727884, loss_threshold_maps: 0.518914, loss_binary_maps: 0.144437, avg_reader_cost: 1.54665 s, avg_batch_cost: 1.77644 s, avg_samples: 12.5, ips: 7.03653 samples/s, eta: 4:16:16

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:56,  8.83it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.21it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.14it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 18.06it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.68it/s]
eval model::   3%|▎         | 13/500 [00:00<00:26, 18.70it/s]
eval model::   3%|▎         | 15/500 [00:00<00:25, 18.70it/s]
eval model::   4%|▎         | 18/500 [00:00<00:23, 20.34it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.51it/s]
eval model::   5%|▍         | 24/500 [00:01<00:21, 21.85it/s]
eval model::   5%|▌         | 27/500 [00:01<00:21, 21.81it/s]
eval model::   6%|▌         | 30/500 [00:01<00:19, 23.76it/s]
eval model::   7%|▋         | 33/500 [00:01<00:21, 22.16it/s]
eval model::   7%|▋         | 36/500 [00:01<00:23, 19.86it/s]
eval model::   8%|▊         | 39/500 [00:02<00:26, 17.58it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.63it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.00it/s]
eval model::  10%|▉         | 48/500 [00:02<00:25, 18.04it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.42it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 20.48it/s]
eval model::  11%|█         | 56/500 [00:02<00:21, 20.42it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:22, 19.81it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:25, 17.29it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:26, 16.46it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:20, 20.78it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:18, 23.26it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:18, 23.56it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:17, 24.69it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:16, 25.54it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.91it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:19, 20.99it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:24, 16.98it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:24, 16.42it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:21, 18.87it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.77it/s]
eval model::  20%|██        | 102/500 [00:05<00:21, 18.92it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 20.62it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.40it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 20.87it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 21.01it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:19, 19.79it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:20, 18.87it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:19, 19.83it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.08it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:21, 17.19it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:23, 16.05it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:23, 15.70it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:20, 17.65it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:20, 18.12it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 19.43it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 20.80it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:18, 19.60it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 18.83it/s]
eval model::  30%|███       | 151/500 [00:07<00:20, 17.19it/s]
eval model::  31%|███       | 154/500 [00:07<00:17, 19.28it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:17, 20.05it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:22, 14.96it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:20, 16.31it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 19.38it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:15, 21.01it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:17, 18.30it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:21, 15.29it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:20, 15.76it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:19, 16.28it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.23it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.68it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:14, 21.20it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 20.07it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.37it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.22it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 21.77it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:21, 13.62it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 14.20it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.30it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 14.03it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.28it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.39it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:18, 14.67it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:14, 18.60it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 22.36it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 23.89it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:11, 23.58it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:10, 24.97it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 20.12it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:12, 21.18it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 22.86it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 21.62it/s]
eval model::  51%|█████     | 254/500 [00:13<00:10, 23.32it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 24.20it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:09, 24.28it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 22.51it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:12, 18.35it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 17.83it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 17.72it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:13, 17.19it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:13, 16.81it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 18.80it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:11, 19.62it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:19, 10.95it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:16, 13.01it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:13, 14.96it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:13, 15.52it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:15, 13.56it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 16.07it/s]
eval model::  60%|██████    | 301/500 [00:16<00:12, 15.81it/s]
eval model::  61%|██████    | 304/500 [00:16<00:10, 18.15it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:09, 20.74it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:08, 22.96it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:07, 24.05it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:07, 23.35it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:07, 24.57it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:07, 22.40it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:07, 22.34it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:07, 21.62it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 19.81it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:08, 20.61it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:08, 19.95it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:11, 13.55it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.14it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:07, 19.26it/s]
eval model::  70%|███████   | 350/500 [00:18<00:07, 21.13it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 22.69it/s]
eval model::  71%|███████   | 356/500 [00:18<00:06, 20.92it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:06, 21.29it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:07, 19.55it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 19.27it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:08, 14.86it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:07, 17.02it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 18.72it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 20.96it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:06, 19.53it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 21.30it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:08, 13.54it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:07, 15.52it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:06, 15.62it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 16.48it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 16.80it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 19.89it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 18.83it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 20.14it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 21.22it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 22.28it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:04, 20.71it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 20.73it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:03, 20.40it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 14.00it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 14.59it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.02it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 19.36it/s]
eval model::  87%|████████▋ | 433/500 [00:23<00:03, 21.21it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:02, 23.27it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:02, 23.78it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 26.40it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 25.62it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 22.51it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:01, 23.62it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 24.12it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 24.00it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 18.88it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:01, 17.70it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.87it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.66it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 21.38it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 21.82it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 21.20it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 19.31it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 17.55it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 20.70it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 16.95it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 17.02it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 19.56it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.00it/s]
[2024/07/28 01:06:58] ppocr INFO: cur metric, precision: 0.7127100840336135, recall: 0.6533461723639865, hmean: 0.6817382567194172, fps: 45.30683411941943
[2024/07/28 01:06:58] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:06:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:07:06] ppocr INFO: epoch: [661/1500], global_step: 1983, lr: 0.001000, loss: 1.473654, loss_shrink_maps: 0.764972, loss_threshold_maps: 0.532199, loss_binary_maps: 0.151771, avg_reader_cost: 1.78829 s, avg_batch_cost: 2.15964 s, avg_samples: 12.5, ips: 5.78801 samples/s, eta: 4:16:02
[2024/07/28 01:07:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:07:15] ppocr INFO: epoch: [662/1500], global_step: 1986, lr: 0.001000, loss: 1.509432, loss_shrink_maps: 0.792044, loss_threshold_maps: 0.539634, loss_binary_maps: 0.157456, avg_reader_cost: 1.52992 s, avg_batch_cost: 1.75780 s, avg_samples: 12.5, ips: 7.11117 samples/s, eta: 4:15:42
[2024/07/28 01:07:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:07:23] ppocr INFO: epoch: [663/1500], global_step: 1989, lr: 0.001000, loss: 1.443994, loss_shrink_maps: 0.764151, loss_threshold_maps: 0.539191, loss_binary_maps: 0.152019, avg_reader_cost: 1.52993 s, avg_batch_cost: 1.76391 s, avg_samples: 12.5, ips: 7.08652 samples/s, eta: 4:15:23
[2024/07/28 01:07:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:07:30] ppocr INFO: epoch: [664/1500], global_step: 1990, lr: 0.001000, loss: 1.404331, loss_shrink_maps: 0.710454, loss_threshold_maps: 0.539191, loss_binary_maps: 0.141504, avg_reader_cost: 0.42317 s, avg_batch_cost: 0.51376 s, avg_samples: 4.8, ips: 9.34294 samples/s, eta: 4:15:16
[2024/07/28 01:07:31] ppocr INFO: epoch: [664/1500], global_step: 1992, lr: 0.001000, loss: 1.463866, loss_shrink_maps: 0.754476, loss_threshold_maps: 0.556523, loss_binary_maps: 0.149928, avg_reader_cost: 1.11897 s, avg_batch_cost: 1.26465 s, avg_samples: 7.7, ips: 6.08866 samples/s, eta: 4:15:04
[2024/07/28 01:07:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:07:40] ppocr INFO: epoch: [665/1500], global_step: 1995, lr: 0.001000, loss: 1.473166, loss_shrink_maps: 0.756127, loss_threshold_maps: 0.557548, loss_binary_maps: 0.150351, avg_reader_cost: 1.52475 s, avg_batch_cost: 1.78265 s, avg_samples: 12.5, ips: 7.01201 samples/s, eta: 4:14:45
[2024/07/28 01:07:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:07:48] ppocr INFO: epoch: [666/1500], global_step: 1998, lr: 0.001000, loss: 1.399305, loss_shrink_maps: 0.715993, loss_threshold_maps: 0.543172, loss_binary_maps: 0.142607, avg_reader_cost: 1.55270 s, avg_batch_cost: 1.78332 s, avg_samples: 12.5, ips: 7.00942 samples/s, eta: 4:14:26
[2024/07/28 01:07:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:07:56] ppocr INFO: epoch: [667/1500], global_step: 2000, lr: 0.001000, loss: 1.399305, loss_shrink_maps: 0.715993, loss_threshold_maps: 0.552374, loss_binary_maps: 0.142607, avg_reader_cost: 0.93097 s, avg_batch_cost: 1.15101 s, avg_samples: 9.6, ips: 8.34049 samples/s, eta: 4:14:13
[2024/07/28 01:07:57] ppocr INFO: epoch: [667/1500], global_step: 2001, lr: 0.001000, loss: 1.365364, loss_shrink_maps: 0.688649, loss_threshold_maps: 0.543172, loss_binary_maps: 0.137005, avg_reader_cost: 0.62118 s, avg_batch_cost: 0.67618 s, avg_samples: 2.9, ips: 4.28882 samples/s, eta: 4:14:08
[2024/07/28 01:07:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:08:06] ppocr INFO: epoch: [668/1500], global_step: 2004, lr: 0.001000, loss: 1.351884, loss_shrink_maps: 0.682041, loss_threshold_maps: 0.538814, loss_binary_maps: 0.135901, avg_reader_cost: 1.77021 s, avg_batch_cost: 2.00898 s, avg_samples: 12.5, ips: 6.22205 samples/s, eta: 4:13:52
[2024/07/28 01:08:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:08:15] ppocr INFO: epoch: [669/1500], global_step: 2007, lr: 0.001000, loss: 1.365364, loss_shrink_maps: 0.688649, loss_threshold_maps: 0.538814, loss_binary_maps: 0.137005, avg_reader_cost: 1.50341 s, avg_batch_cost: 1.73189 s, avg_samples: 12.5, ips: 7.21754 samples/s, eta: 4:13:32
[2024/07/28 01:08:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:08:23] ppocr INFO: epoch: [670/1500], global_step: 2010, lr: 0.001000, loss: 1.416919, loss_shrink_maps: 0.733189, loss_threshold_maps: 0.538814, loss_binary_maps: 0.146338, avg_reader_cost: 1.55258 s, avg_batch_cost: 1.78190 s, avg_samples: 12.5, ips: 7.01498 samples/s, eta: 4:13:14
[2024/07/28 01:08:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:08:32] ppocr INFO: epoch: [671/1500], global_step: 2013, lr: 0.001000, loss: 1.335755, loss_shrink_maps: 0.670585, loss_threshold_maps: 0.533254, loss_binary_maps: 0.133415, avg_reader_cost: 1.57468 s, avg_batch_cost: 1.80363 s, avg_samples: 12.5, ips: 6.93046 samples/s, eta: 4:12:55
[2024/07/28 01:08:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:08:40] ppocr INFO: epoch: [672/1500], global_step: 2016, lr: 0.001000, loss: 1.326863, loss_shrink_maps: 0.670249, loss_threshold_maps: 0.528787, loss_binary_maps: 0.133415, avg_reader_cost: 1.53228 s, avg_batch_cost: 1.76214 s, avg_samples: 12.5, ips: 7.09364 samples/s, eta: 4:12:36
[2024/07/28 01:08:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:08:49] ppocr INFO: epoch: [673/1500], global_step: 2019, lr: 0.001000, loss: 1.348995, loss_shrink_maps: 0.685861, loss_threshold_maps: 0.531351, loss_binary_maps: 0.136526, avg_reader_cost: 1.51862 s, avg_batch_cost: 1.78617 s, avg_samples: 12.5, ips: 6.99823 samples/s, eta: 4:12:17
[2024/07/28 01:08:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:08:56] ppocr INFO: epoch: [674/1500], global_step: 2020, lr: 0.001000, loss: 1.310524, loss_shrink_maps: 0.661937, loss_threshold_maps: 0.528787, loss_binary_maps: 0.132034, avg_reader_cost: 0.40077 s, avg_batch_cost: 0.51046 s, avg_samples: 4.8, ips: 9.40324 samples/s, eta: 4:12:10
[2024/07/28 01:08:57] ppocr INFO: epoch: [674/1500], global_step: 2022, lr: 0.001000, loss: 1.342617, loss_shrink_maps: 0.680399, loss_threshold_maps: 0.531351, loss_binary_maps: 0.135442, avg_reader_cost: 1.11243 s, avg_batch_cost: 1.25833 s, avg_samples: 7.7, ips: 6.11920 samples/s, eta: 4:11:58
[2024/07/28 01:08:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:09:06] ppocr INFO: epoch: [675/1500], global_step: 2025, lr: 0.001000, loss: 1.342617, loss_shrink_maps: 0.680399, loss_threshold_maps: 0.529443, loss_binary_maps: 0.135442, avg_reader_cost: 1.51377 s, avg_batch_cost: 1.75416 s, avg_samples: 12.5, ips: 7.12591 samples/s, eta: 4:11:39
[2024/07/28 01:09:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:09:14] ppocr INFO: epoch: [676/1500], global_step: 2028, lr: 0.001000, loss: 1.342617, loss_shrink_maps: 0.680399, loss_threshold_maps: 0.528794, loss_binary_maps: 0.135442, avg_reader_cost: 1.52279 s, avg_batch_cost: 1.77205 s, avg_samples: 12.5, ips: 7.05397 samples/s, eta: 4:11:20
[2024/07/28 01:09:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:09:22] ppocr INFO: epoch: [677/1500], global_step: 2030, lr: 0.001000, loss: 1.349018, loss_shrink_maps: 0.680399, loss_threshold_maps: 0.528794, loss_binary_maps: 0.135442, avg_reader_cost: 0.95648 s, avg_batch_cost: 1.13080 s, avg_samples: 9.6, ips: 8.48954 samples/s, eta: 4:11:06
[2024/07/28 01:09:23] ppocr INFO: epoch: [677/1500], global_step: 2031, lr: 0.001000, loss: 1.349018, loss_shrink_maps: 0.686551, loss_threshold_maps: 0.528794, loss_binary_maps: 0.136688, avg_reader_cost: 0.61150 s, avg_batch_cost: 0.66634 s, avg_samples: 2.9, ips: 4.35214 samples/s, eta: 4:11:01
[2024/07/28 01:09:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:09:32] ppocr INFO: epoch: [678/1500], global_step: 2034, lr: 0.001000, loss: 1.349018, loss_shrink_maps: 0.686551, loss_threshold_maps: 0.527974, loss_binary_maps: 0.136688, avg_reader_cost: 1.54620 s, avg_batch_cost: 1.77873 s, avg_samples: 12.5, ips: 7.02747 samples/s, eta: 4:10:42
[2024/07/28 01:09:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:09:40] ppocr INFO: epoch: [679/1500], global_step: 2037, lr: 0.001000, loss: 1.324067, loss_shrink_maps: 0.680399, loss_threshold_maps: 0.524410, loss_binary_maps: 0.135442, avg_reader_cost: 1.53667 s, avg_batch_cost: 1.76779 s, avg_samples: 12.5, ips: 7.07098 samples/s, eta: 4:10:23
[2024/07/28 01:09:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:09:49] ppocr INFO: epoch: [680/1500], global_step: 2040, lr: 0.001000, loss: 1.349018, loss_shrink_maps: 0.686551, loss_threshold_maps: 0.524410, loss_binary_maps: 0.136688, avg_reader_cost: 1.56562 s, avg_batch_cost: 1.79518 s, avg_samples: 12.5, ips: 6.96309 samples/s, eta: 4:10:04

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:03,  7.90it/s]
eval model::   1%|          | 3/500 [00:00<00:37, 13.31it/s]
eval model::   1%|          | 5/500 [00:00<00:31, 15.51it/s]
eval model::   1%|▏         | 7/500 [00:00<00:31, 15.87it/s]
eval model::   2%|▏         | 9/500 [00:00<00:32, 15.10it/s]
eval model::   2%|▏         | 11/500 [00:00<00:34, 14.35it/s]
eval model::   3%|▎         | 14/500 [00:00<00:32, 14.83it/s]
eval model::   3%|▎         | 17/500 [00:01<00:28, 17.21it/s]
eval model::   4%|▍         | 20/500 [00:01<00:28, 17.11it/s]
eval model::   5%|▍         | 23/500 [00:01<00:25, 18.75it/s]
eval model::   5%|▌         | 25/500 [00:01<00:25, 18.33it/s]
eval model::   6%|▌         | 28/500 [00:01<00:24, 19.25it/s]
eval model::   6%|▌         | 30/500 [00:01<00:24, 19.14it/s]
eval model::   6%|▋         | 32/500 [00:01<00:26, 17.66it/s]
eval model::   7%|▋         | 34/500 [00:02<00:26, 17.68it/s]
eval model::   7%|▋         | 37/500 [00:02<00:24, 19.18it/s]
eval model::   8%|▊         | 39/500 [00:02<00:26, 17.31it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.41it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.21it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 17.09it/s]
eval model::  10%|▉         | 49/500 [00:02<00:26, 17.07it/s]
eval model::  10%|█         | 51/500 [00:02<00:25, 17.53it/s]
eval model::  11%|█         | 54/500 [00:03<00:22, 19.52it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:21, 20.79it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:21, 20.24it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:30, 14.53it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:26, 16.36it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:24, 17.52it/s]
eval model::  14%|█▍        | 71/500 [00:04<00:24, 17.82it/s]
eval model::  15%|█▍        | 73/500 [00:04<00:24, 17.10it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:23, 18.43it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:21, 19.95it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 22.00it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:17, 23.40it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:22, 18.28it/s]
eval model::  18%|█▊        | 91/500 [00:05<00:23, 17.69it/s]
eval model::  19%|█▉        | 94/500 [00:05<00:21, 18.58it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:20, 19.45it/s]
eval model::  20%|██        | 100/500 [00:05<00:22, 17.88it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 19.99it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 19.72it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 21.83it/s]
eval model::  22%|██▏       | 112/500 [00:06<00:17, 22.26it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:19, 19.42it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:18, 21.20it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:19, 19.86it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 19.62it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:19, 19.52it/s]
eval model::  26%|██▌       | 130/500 [00:07<00:20, 17.81it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:21, 17.25it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:18, 19.38it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:19, 18.68it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:19, 18.51it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 20.18it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 20.08it/s]
eval model::  30%|██▉       | 149/500 [00:08<00:17, 19.76it/s]
eval model::  30%|███       | 152/500 [00:08<00:20, 17.21it/s]
eval model::  31%|███       | 155/500 [00:08<00:17, 19.39it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:16, 21.02it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:24, 13.80it/s]
eval model::  33%|███▎      | 163/500 [00:09<00:23, 14.26it/s]
eval model::  33%|███▎      | 166/500 [00:09<00:20, 16.06it/s]
eval model::  34%|███▎      | 168/500 [00:09<00:20, 16.19it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:19, 17.07it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:21, 15.23it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:18, 17.54it/s]
eval model::  36%|███▌      | 180/500 [00:10<00:20, 15.46it/s]
eval model::  37%|███▋      | 183/500 [00:10<00:17, 17.95it/s]
eval model::  37%|███▋      | 186/500 [00:10<00:16, 19.38it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:15, 19.55it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:15, 20.03it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.90it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:13, 21.70it/s]
eval model::  40%|████      | 201/500 [00:11<00:14, 20.42it/s]
eval model::  41%|████      | 204/500 [00:11<00:13, 22.56it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:19, 15.08it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 15.41it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.10it/s]
eval model::  43%|████▎     | 215/500 [00:12<00:21, 13.33it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:22, 12.63it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:18, 14.92it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:19, 14.12it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 16.93it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:14, 18.63it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 20.95it/s]
eval model::  47%|████▋     | 234/500 [00:13<00:13, 19.63it/s]
eval model::  47%|████▋     | 237/500 [00:13<00:12, 20.29it/s]
eval model::  48%|████▊     | 240/500 [00:13<00:13, 19.92it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:14, 18.27it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:13, 19.35it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:12, 19.80it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 20.86it/s]
eval model::  51%|█████     | 255/500 [00:14<00:11, 22.04it/s]
eval model::  52%|█████▏    | 258/500 [00:14<00:10, 23.18it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:09, 23.91it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:09, 24.22it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:10, 23.06it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 19.98it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 19.70it/s]
eval model::  55%|█████▌    | 276/500 [00:15<00:12, 18.41it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:10, 20.10it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 21.64it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:15, 13.67it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:14, 14.59it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:11, 18.65it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:11, 18.13it/s]
eval model::  59%|█████▉    | 297/500 [00:16<00:12, 16.61it/s]
eval model::  60%|██████    | 300/500 [00:16<00:11, 17.42it/s]
eval model::  61%|██████    | 303/500 [00:16<00:10, 19.06it/s]
eval model::  61%|██████    | 306/500 [00:16<00:11, 17.40it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:10, 18.98it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:09, 19.70it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:09, 20.00it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:07, 22.92it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 19.65it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 20.44it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 19.48it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:08, 20.64it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:07, 21.51it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 20.96it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 14.84it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.92it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 19.07it/s]
eval model::  70%|███████   | 350/500 [00:19<00:06, 21.51it/s]
eval model::  71%|███████   | 353/500 [00:19<00:06, 22.05it/s]
eval model::  71%|███████   | 356/500 [00:19<00:07, 19.85it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:06, 21.01it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:07, 18.93it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 17.19it/s]
eval model::  73%|███████▎  | 367/500 [00:20<00:08, 15.73it/s]
eval model::  74%|███████▍  | 370/500 [00:20<00:07, 16.43it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:08, 15.87it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:06, 19.45it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:06, 19.99it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 21.70it/s]
eval model::  77%|███████▋  | 386/500 [00:21<00:07, 14.50it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:06, 16.24it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:06, 16.81it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:05, 18.01it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 19.44it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 18.54it/s]
eval model::  81%|████████  | 403/500 [00:21<00:04, 19.87it/s]
eval model::  81%|████████  | 406/500 [00:22<00:04, 20.63it/s]
eval model::  82%|████████▏ | 409/500 [00:22<00:04, 21.35it/s]
eval model::  82%|████████▏ | 412/500 [00:22<00:03, 22.44it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:04, 20.59it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 18.55it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 13.77it/s]
eval model::  85%|████████▍ | 423/500 [00:23<00:04, 15.52it/s]
eval model::  85%|████████▌ | 425/500 [00:23<00:05, 14.57it/s]
eval model::  86%|████████▌ | 428/500 [00:23<00:04, 17.54it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:03, 18.40it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:02, 22.09it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 22.98it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 22.17it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 22.46it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 22.10it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 20.54it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 22.44it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:02, 21.62it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 21.30it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 16.80it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 16.59it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 18.17it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 20.39it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 19.89it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.45it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 21.22it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 21.62it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 18.63it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 20.16it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 18.03it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 19.47it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 20.43it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.59it/s]
[2024/07/28 01:10:16] ppocr INFO: cur metric, precision: 0.7399127589967285, recall: 0.6533461723639865, hmean: 0.6939401687547941, fps: 44.31572707159106
[2024/07/28 01:10:16] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:10:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:10:24] ppocr INFO: epoch: [681/1500], global_step: 2043, lr: 0.001000, loss: 1.349284, loss_shrink_maps: 0.693702, loss_threshold_maps: 0.524410, loss_binary_maps: 0.138415, avg_reader_cost: 1.58733 s, avg_batch_cost: 1.81513 s, avg_samples: 12.5, ips: 6.88655 samples/s, eta: 4:09:46
[2024/07/28 01:10:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:10:32] ppocr INFO: epoch: [682/1500], global_step: 2046, lr: 0.001000, loss: 1.376060, loss_shrink_maps: 0.702377, loss_threshold_maps: 0.527988, loss_binary_maps: 0.140166, avg_reader_cost: 1.52323 s, avg_batch_cost: 1.75219 s, avg_samples: 12.5, ips: 7.13393 samples/s, eta: 4:09:26
[2024/07/28 01:10:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:10:41] ppocr INFO: epoch: [683/1500], global_step: 2049, lr: 0.001000, loss: 1.382610, loss_shrink_maps: 0.714100, loss_threshold_maps: 0.536874, loss_binary_maps: 0.142246, avg_reader_cost: 1.53697 s, avg_batch_cost: 1.77507 s, avg_samples: 12.5, ips: 7.04199 samples/s, eta: 4:09:08
[2024/07/28 01:10:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:10:48] ppocr INFO: epoch: [684/1500], global_step: 2050, lr: 0.001000, loss: 1.376136, loss_shrink_maps: 0.702377, loss_threshold_maps: 0.533518, loss_binary_maps: 0.140166, avg_reader_cost: 0.42434 s, avg_batch_cost: 0.50634 s, avg_samples: 4.8, ips: 9.47984 samples/s, eta: 4:09:00
[2024/07/28 01:10:49] ppocr INFO: epoch: [684/1500], global_step: 2052, lr: 0.001000, loss: 1.382610, loss_shrink_maps: 0.714100, loss_threshold_maps: 0.538891, loss_binary_maps: 0.142246, avg_reader_cost: 1.10369 s, avg_batch_cost: 1.24894 s, avg_samples: 7.7, ips: 6.16524 samples/s, eta: 4:08:48
[2024/07/28 01:10:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:10:58] ppocr INFO: epoch: [685/1500], global_step: 2055, lr: 0.001000, loss: 1.406958, loss_shrink_maps: 0.723676, loss_threshold_maps: 0.541794, loss_binary_maps: 0.143787, avg_reader_cost: 1.51289 s, avg_batch_cost: 1.74657 s, avg_samples: 12.5, ips: 7.15690 samples/s, eta: 4:08:29
[2024/07/28 01:11:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:11:06] ppocr INFO: epoch: [686/1500], global_step: 2058, lr: 0.001000, loss: 1.406958, loss_shrink_maps: 0.723676, loss_threshold_maps: 0.541794, loss_binary_maps: 0.143787, avg_reader_cost: 1.54442 s, avg_batch_cost: 1.77306 s, avg_samples: 12.5, ips: 7.04996 samples/s, eta: 4:08:10
[2024/07/28 01:11:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:11:14] ppocr INFO: epoch: [687/1500], global_step: 2060, lr: 0.001000, loss: 1.403795, loss_shrink_maps: 0.716697, loss_threshold_maps: 0.541794, loss_binary_maps: 0.142526, avg_reader_cost: 0.93141 s, avg_batch_cost: 1.10468 s, avg_samples: 9.6, ips: 8.69029 samples/s, eta: 4:07:57
[2024/07/28 01:11:15] ppocr INFO: epoch: [687/1500], global_step: 2061, lr: 0.001000, loss: 1.379447, loss_shrink_maps: 0.707053, loss_threshold_maps: 0.538891, loss_binary_maps: 0.140985, avg_reader_cost: 0.59792 s, avg_batch_cost: 0.65280 s, avg_samples: 2.9, ips: 4.44241 samples/s, eta: 4:07:51
[2024/07/28 01:11:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:11:23] ppocr INFO: epoch: [688/1500], global_step: 2064, lr: 0.001000, loss: 1.415254, loss_shrink_maps: 0.721163, loss_threshold_maps: 0.545312, loss_binary_maps: 0.143135, avg_reader_cost: 1.52756 s, avg_batch_cost: 1.75651 s, avg_samples: 12.5, ips: 7.11637 samples/s, eta: 4:07:32
[2024/07/28 01:11:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:11:32] ppocr INFO: epoch: [689/1500], global_step: 2067, lr: 0.001000, loss: 1.391632, loss_shrink_maps: 0.711520, loss_threshold_maps: 0.545094, loss_binary_maps: 0.141717, avg_reader_cost: 1.54057 s, avg_batch_cost: 1.79368 s, avg_samples: 12.5, ips: 6.96891 samples/s, eta: 4:07:13
[2024/07/28 01:11:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:11:40] ppocr INFO: epoch: [690/1500], global_step: 2070, lr: 0.001000, loss: 1.391632, loss_shrink_maps: 0.711520, loss_threshold_maps: 0.545094, loss_binary_maps: 0.141717, avg_reader_cost: 1.50867 s, avg_batch_cost: 1.74049 s, avg_samples: 12.5, ips: 7.18189 samples/s, eta: 4:06:54
[2024/07/28 01:11:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:11:49] ppocr INFO: epoch: [691/1500], global_step: 2073, lr: 0.001000, loss: 1.412932, loss_shrink_maps: 0.716776, loss_threshold_maps: 0.548644, loss_binary_maps: 0.142573, avg_reader_cost: 1.48849 s, avg_batch_cost: 1.73929 s, avg_samples: 12.5, ips: 7.18682 samples/s, eta: 4:06:34
[2024/07/28 01:11:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:11:57] ppocr INFO: epoch: [692/1500], global_step: 2076, lr: 0.001000, loss: 1.370307, loss_shrink_maps: 0.705099, loss_threshold_maps: 0.545094, loss_binary_maps: 0.140348, avg_reader_cost: 1.52460 s, avg_batch_cost: 1.79694 s, avg_samples: 12.5, ips: 6.95626 samples/s, eta: 4:06:16
[2024/07/28 01:11:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:12:06] ppocr INFO: epoch: [693/1500], global_step: 2079, lr: 0.001000, loss: 1.356842, loss_shrink_maps: 0.686051, loss_threshold_maps: 0.545094, loss_binary_maps: 0.135835, avg_reader_cost: 1.61936 s, avg_batch_cost: 1.84757 s, avg_samples: 12.5, ips: 6.76566 samples/s, eta: 4:05:58
[2024/07/28 01:12:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:12:13] ppocr INFO: epoch: [694/1500], global_step: 2080, lr: 0.001000, loss: 1.356842, loss_shrink_maps: 0.686051, loss_threshold_maps: 0.545094, loss_binary_maps: 0.135835, avg_reader_cost: 0.43030 s, avg_batch_cost: 0.52422 s, avg_samples: 4.8, ips: 9.15647 samples/s, eta: 4:05:51
[2024/07/28 01:12:15] ppocr INFO: epoch: [694/1500], global_step: 2082, lr: 0.001000, loss: 1.356842, loss_shrink_maps: 0.686051, loss_threshold_maps: 0.545094, loss_binary_maps: 0.135835, avg_reader_cost: 1.13975 s, avg_batch_cost: 1.28561 s, avg_samples: 7.7, ips: 5.98936 samples/s, eta: 4:05:39
[2024/07/28 01:12:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:12:23] ppocr INFO: epoch: [695/1500], global_step: 2085, lr: 0.001000, loss: 1.388007, loss_shrink_maps: 0.710355, loss_threshold_maps: 0.548447, loss_binary_maps: 0.141013, avg_reader_cost: 1.56565 s, avg_batch_cost: 1.79951 s, avg_samples: 12.5, ips: 6.94633 samples/s, eta: 4:05:21
[2024/07/28 01:12:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:12:32] ppocr INFO: epoch: [696/1500], global_step: 2088, lr: 0.001000, loss: 1.352241, loss_shrink_maps: 0.684241, loss_threshold_maps: 0.544955, loss_binary_maps: 0.136079, avg_reader_cost: 1.56290 s, avg_batch_cost: 1.83011 s, avg_samples: 12.5, ips: 6.83019 samples/s, eta: 4:05:02
[2024/07/28 01:12:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:12:40] ppocr INFO: epoch: [697/1500], global_step: 2090, lr: 0.001000, loss: 1.365147, loss_shrink_maps: 0.684241, loss_threshold_maps: 0.544955, loss_binary_maps: 0.136079, avg_reader_cost: 0.90366 s, avg_batch_cost: 1.08362 s, avg_samples: 9.6, ips: 8.85916 samples/s, eta: 4:04:49
[2024/07/28 01:12:41] ppocr INFO: epoch: [697/1500], global_step: 2091, lr: 0.001000, loss: 1.365147, loss_shrink_maps: 0.684241, loss_threshold_maps: 0.543139, loss_binary_maps: 0.136079, avg_reader_cost: 0.58732 s, avg_batch_cost: 0.64226 s, avg_samples: 2.9, ips: 4.51529 samples/s, eta: 4:04:43
[2024/07/28 01:12:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:12:49] ppocr INFO: epoch: [698/1500], global_step: 2094, lr: 0.001000, loss: 1.394803, loss_shrink_maps: 0.714446, loss_threshold_maps: 0.543139, loss_binary_maps: 0.141968, avg_reader_cost: 1.54136 s, avg_batch_cost: 1.78795 s, avg_samples: 12.5, ips: 6.99124 samples/s, eta: 4:04:24
[2024/07/28 01:12:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:12:58] ppocr INFO: epoch: [699/1500], global_step: 2097, lr: 0.001000, loss: 1.454337, loss_shrink_maps: 0.738233, loss_threshold_maps: 0.555602, loss_binary_maps: 0.146457, avg_reader_cost: 1.59740 s, avg_batch_cost: 1.89459 s, avg_samples: 12.5, ips: 6.59775 samples/s, eta: 4:04:07
[2024/07/28 01:13:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:13:07] ppocr INFO: epoch: [700/1500], global_step: 2100, lr: 0.001000, loss: 1.434881, loss_shrink_maps: 0.739814, loss_threshold_maps: 0.549794, loss_binary_maps: 0.146797, avg_reader_cost: 1.57618 s, avg_batch_cost: 1.83561 s, avg_samples: 12.5, ips: 6.80972 samples/s, eta: 4:03:48

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:55,  8.94it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.87it/s]
eval model::   1%|▏         | 7/500 [00:00<00:22, 22.19it/s]
eval model::   2%|▏         | 10/500 [00:00<00:25, 19.26it/s]
eval model::   2%|▏         | 12/500 [00:00<00:26, 18.44it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.74it/s]
eval model::   3%|▎         | 17/500 [00:00<00:22, 21.06it/s]
eval model::   4%|▍         | 20/500 [00:00<00:21, 22.13it/s]
eval model::   5%|▍         | 23/500 [00:01<00:20, 23.30it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 20.42it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 20.57it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.40it/s]
eval model::   7%|▋         | 35/500 [00:01<00:25, 17.97it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.45it/s]
eval model::   8%|▊         | 41/500 [00:02<00:25, 17.97it/s]
eval model::   9%|▊         | 43/500 [00:02<00:30, 15.16it/s]
eval model::   9%|▉         | 46/500 [00:02<00:30, 14.75it/s]
eval model::  10%|▉         | 49/500 [00:02<00:26, 16.93it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 18.72it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 19.56it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:25, 17.62it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.42it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 15.05it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.53it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 19.91it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.36it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:19, 21.81it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.81it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.18it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.64it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 19.94it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.07it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.92it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.72it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.78it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.43it/s]
eval model::  21%|██        | 105/500 [00:05<00:20, 19.57it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:19, 20.09it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:20, 19.36it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 20.86it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:19, 19.61it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.23it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:19, 19.37it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:23, 15.96it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:22, 16.53it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:20, 17.79it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:21, 17.31it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:20, 17.81it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:17, 20.39it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:17, 20.94it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:16, 22.15it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 21.62it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:17, 19.75it/s]
eval model::  30%|███       | 151/500 [00:07<00:17, 19.59it/s]
eval model::  31%|███       | 155/500 [00:08<00:15, 22.59it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:14, 24.07it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:20, 16.25it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:20, 16.68it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 18.02it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 19.90it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:17, 18.25it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 16.08it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:19, 16.79it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:18, 17.54it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.55it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 19.46it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:15, 19.96it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.51it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.43it/s]
eval model::  40%|████      | 200/500 [00:10<00:15, 19.46it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 20.82it/s]
eval model::  41%|████      | 206/500 [00:11<00:21, 13.53it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:20, 14.05it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 14.41it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.67it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 14.19it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.53it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.00it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:19, 14.42it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:15, 17.61it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:15, 17.69it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 21.85it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:13, 20.12it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 22.17it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 22.53it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:12, 20.27it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:12, 20.02it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:13, 18.42it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 19.98it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.53it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 21.38it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 20.61it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:13, 17.98it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:13, 17.64it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.43it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.74it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.03it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 18.09it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 19.58it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:10, 20.82it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:16, 13.05it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:13, 15.49it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:11, 17.36it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:14, 14.32it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:12, 16.45it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 16.51it/s]
eval model::  61%|██████    | 303/500 [00:16<00:10, 18.77it/s]
eval model::  61%|██████    | 306/500 [00:16<00:09, 20.08it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 21.17it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:08, 21.63it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 21.37it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:07, 23.26it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:07, 23.30it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 21.92it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 22.66it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:07, 21.40it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 21.60it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 20.70it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:07, 20.26it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:11, 13.85it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:09, 16.30it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 18.25it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 19.06it/s]
eval model::  71%|███████   | 354/500 [00:18<00:07, 20.73it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 17.98it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:07, 18.49it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 18.15it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 18.17it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 16.86it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 19.57it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:05, 21.43it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 23.47it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 23.58it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:04, 24.53it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 15.04it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 17.24it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 18.66it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 19.53it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:04, 21.21it/s]
eval model::  80%|████████  | 402/500 [00:21<00:04, 19.61it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 20.35it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.31it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 22.18it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 22.09it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 19.81it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.44it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 15.26it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.82it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.68it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 19.84it/s]
eval model::  87%|████████▋ | 433/500 [00:23<00:03, 20.99it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:02, 22.87it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 24.50it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 23.22it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 24.69it/s]
eval model::  90%|████████▉ | 449/500 [00:23<00:02, 21.44it/s]
eval model::  90%|█████████ | 452/500 [00:23<00:02, 22.55it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:02, 22.46it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:01, 21.98it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 21.04it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 17.63it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 17.25it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 17.47it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 19.92it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 20.37it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:00, 22.33it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 21.79it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 22.03it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 18.94it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 20.95it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 18.27it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 17.69it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 19.74it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.03it/s]
[2024/07/28 01:13:33] ppocr INFO: cur metric, precision: 0.7264202978488693, recall: 0.634087626384208, hmean: 0.6771208226221079, fps: 45.464628264609324
[2024/07/28 01:13:33] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:13:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:13:41] ppocr INFO: epoch: [701/1500], global_step: 2103, lr: 0.001000, loss: 1.462396, loss_shrink_maps: 0.762207, loss_threshold_maps: 0.557303, loss_binary_maps: 0.151689, avg_reader_cost: 1.76150 s, avg_batch_cost: 2.04659 s, avg_samples: 12.5, ips: 6.10773 samples/s, eta: 4:03:33
[2024/07/28 01:13:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:13:50] ppocr INFO: epoch: [702/1500], global_step: 2106, lr: 0.001000, loss: 1.443224, loss_shrink_maps: 0.745183, loss_threshold_maps: 0.555579, loss_binary_maps: 0.148540, avg_reader_cost: 1.51181 s, avg_batch_cost: 1.76091 s, avg_samples: 12.5, ips: 7.09861 samples/s, eta: 4:03:13
[2024/07/28 01:13:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:13:58] ppocr INFO: epoch: [703/1500], global_step: 2109, lr: 0.001000, loss: 1.443224, loss_shrink_maps: 0.745183, loss_threshold_maps: 0.555579, loss_binary_maps: 0.148540, avg_reader_cost: 1.54798 s, avg_batch_cost: 1.78188 s, avg_samples: 12.5, ips: 7.01507 samples/s, eta: 4:02:55
[2024/07/28 01:14:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:14:06] ppocr INFO: epoch: [704/1500], global_step: 2110, lr: 0.001000, loss: 1.443224, loss_shrink_maps: 0.745183, loss_threshold_maps: 0.560668, loss_binary_maps: 0.148540, avg_reader_cost: 0.43432 s, avg_batch_cost: 0.51677 s, avg_samples: 4.8, ips: 9.28850 samples/s, eta: 4:02:48
[2024/07/28 01:14:07] ppocr INFO: epoch: [704/1500], global_step: 2112, lr: 0.001000, loss: 1.463556, loss_shrink_maps: 0.762207, loss_threshold_maps: 0.563141, loss_binary_maps: 0.151689, avg_reader_cost: 1.12548 s, avg_batch_cost: 1.27085 s, avg_samples: 7.7, ips: 6.05891 samples/s, eta: 4:02:36
[2024/07/28 01:14:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:14:16] ppocr INFO: epoch: [705/1500], global_step: 2115, lr: 0.001000, loss: 1.456386, loss_shrink_maps: 0.753502, loss_threshold_maps: 0.559458, loss_binary_maps: 0.148948, avg_reader_cost: 1.59878 s, avg_batch_cost: 1.84855 s, avg_samples: 12.5, ips: 6.76205 samples/s, eta: 4:02:18
[2024/07/28 01:14:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:14:25] ppocr INFO: epoch: [706/1500], global_step: 2118, lr: 0.001000, loss: 1.430206, loss_shrink_maps: 0.732371, loss_threshold_maps: 0.544553, loss_binary_maps: 0.146264, avg_reader_cost: 1.57735 s, avg_batch_cost: 1.80595 s, avg_samples: 12.5, ips: 6.92155 samples/s, eta: 4:01:59
[2024/07/28 01:14:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:14:33] ppocr INFO: epoch: [707/1500], global_step: 2120, lr: 0.001000, loss: 1.406928, loss_shrink_maps: 0.725784, loss_threshold_maps: 0.540157, loss_binary_maps: 0.144902, avg_reader_cost: 0.94626 s, avg_batch_cost: 1.12872 s, avg_samples: 9.6, ips: 8.50520 samples/s, eta: 4:01:46
[2024/07/28 01:14:33] ppocr INFO: epoch: [707/1500], global_step: 2121, lr: 0.001000, loss: 1.402573, loss_shrink_maps: 0.723927, loss_threshold_maps: 0.538166, loss_binary_maps: 0.144396, avg_reader_cost: 0.61013 s, avg_batch_cost: 0.66473 s, avg_samples: 2.9, ips: 4.36269 samples/s, eta: 4:01:41
[2024/07/28 01:14:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:14:42] ppocr INFO: epoch: [708/1500], global_step: 2124, lr: 0.001000, loss: 1.387199, loss_shrink_maps: 0.708842, loss_threshold_maps: 0.535596, loss_binary_maps: 0.140818, avg_reader_cost: 1.55921 s, avg_batch_cost: 1.78964 s, avg_samples: 12.5, ips: 6.98463 samples/s, eta: 4:01:22
[2024/07/28 01:14:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:14:51] ppocr INFO: epoch: [709/1500], global_step: 2127, lr: 0.001000, loss: 1.370435, loss_shrink_maps: 0.688471, loss_threshold_maps: 0.535596, loss_binary_maps: 0.137052, avg_reader_cost: 1.55109 s, avg_batch_cost: 1.78791 s, avg_samples: 12.5, ips: 6.99141 samples/s, eta: 4:01:03
[2024/07/28 01:14:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:14:59] ppocr INFO: epoch: [710/1500], global_step: 2130, lr: 0.001000, loss: 1.387199, loss_shrink_maps: 0.704051, loss_threshold_maps: 0.537473, loss_binary_maps: 0.139692, avg_reader_cost: 1.54244 s, avg_batch_cost: 1.78183 s, avg_samples: 12.5, ips: 7.01526 samples/s, eta: 4:00:44
[2024/07/28 01:15:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:15:08] ppocr INFO: epoch: [711/1500], global_step: 2133, lr: 0.001000, loss: 1.370435, loss_shrink_maps: 0.688471, loss_threshold_maps: 0.537473, loss_binary_maps: 0.137052, avg_reader_cost: 1.57860 s, avg_batch_cost: 1.84981 s, avg_samples: 12.5, ips: 6.75745 samples/s, eta: 4:00:26
[2024/07/28 01:15:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:15:17] ppocr INFO: epoch: [712/1500], global_step: 2136, lr: 0.001000, loss: 1.370435, loss_shrink_maps: 0.688471, loss_threshold_maps: 0.537473, loss_binary_maps: 0.137052, avg_reader_cost: 1.61904 s, avg_batch_cost: 1.87182 s, avg_samples: 12.5, ips: 6.67799 samples/s, eta: 4:00:09
[2024/07/28 01:15:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:15:25] ppocr INFO: epoch: [713/1500], global_step: 2139, lr: 0.001000, loss: 1.391391, loss_shrink_maps: 0.696857, loss_threshold_maps: 0.544919, loss_binary_maps: 0.138334, avg_reader_cost: 1.53900 s, avg_batch_cost: 1.76959 s, avg_samples: 12.5, ips: 7.06377 samples/s, eta: 3:59:50
[2024/07/28 01:15:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:15:33] ppocr INFO: epoch: [714/1500], global_step: 2140, lr: 0.001000, loss: 1.391391, loss_shrink_maps: 0.689714, loss_threshold_maps: 0.544919, loss_binary_maps: 0.137260, avg_reader_cost: 0.39986 s, avg_batch_cost: 0.51083 s, avg_samples: 4.8, ips: 9.39647 samples/s, eta: 3:59:42
[2024/07/28 01:15:34] ppocr INFO: epoch: [714/1500], global_step: 2142, lr: 0.001000, loss: 1.391391, loss_shrink_maps: 0.698764, loss_threshold_maps: 0.551917, loss_binary_maps: 0.138786, avg_reader_cost: 1.11314 s, avg_batch_cost: 1.25882 s, avg_samples: 7.7, ips: 6.11682 samples/s, eta: 3:59:31
[2024/07/28 01:15:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:15:43] ppocr INFO: epoch: [715/1500], global_step: 2145, lr: 0.001000, loss: 1.417416, loss_shrink_maps: 0.731815, loss_threshold_maps: 0.559395, loss_binary_maps: 0.145932, avg_reader_cost: 1.52461 s, avg_batch_cost: 1.76397 s, avg_samples: 12.5, ips: 7.08629 samples/s, eta: 3:59:12
[2024/07/28 01:15:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:15:51] ppocr INFO: epoch: [716/1500], global_step: 2148, lr: 0.001000, loss: 1.417416, loss_shrink_maps: 0.735261, loss_threshold_maps: 0.549698, loss_binary_maps: 0.146663, avg_reader_cost: 1.57172 s, avg_batch_cost: 1.81381 s, avg_samples: 12.5, ips: 6.89157 samples/s, eta: 3:58:53
[2024/07/28 01:15:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:15:59] ppocr INFO: epoch: [717/1500], global_step: 2150, lr: 0.001000, loss: 1.424467, loss_shrink_maps: 0.743372, loss_threshold_maps: 0.548656, loss_binary_maps: 0.148407, avg_reader_cost: 0.92932 s, avg_batch_cost: 1.11931 s, avg_samples: 9.6, ips: 8.57669 samples/s, eta: 3:58:40
[2024/07/28 01:16:00] ppocr INFO: epoch: [717/1500], global_step: 2151, lr: 0.001000, loss: 1.424467, loss_shrink_maps: 0.743372, loss_threshold_maps: 0.548656, loss_binary_maps: 0.148407, avg_reader_cost: 0.60533 s, avg_batch_cost: 0.66009 s, avg_samples: 2.9, ips: 4.39332 samples/s, eta: 3:58:34
[2024/07/28 01:16:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:16:09] ppocr INFO: epoch: [718/1500], global_step: 2154, lr: 0.001000, loss: 1.414016, loss_shrink_maps: 0.743372, loss_threshold_maps: 0.544936, loss_binary_maps: 0.148407, avg_reader_cost: 1.51715 s, avg_batch_cost: 1.77022 s, avg_samples: 12.5, ips: 7.06127 samples/s, eta: 3:58:15
[2024/07/28 01:16:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:16:17] ppocr INFO: epoch: [719/1500], global_step: 2157, lr: 0.001000, loss: 1.414016, loss_shrink_maps: 0.735261, loss_threshold_maps: 0.532091, loss_binary_maps: 0.146663, avg_reader_cost: 1.57984 s, avg_batch_cost: 1.80929 s, avg_samples: 12.5, ips: 6.90879 samples/s, eta: 3:57:57
[2024/07/28 01:16:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:16:26] ppocr INFO: epoch: [720/1500], global_step: 2160, lr: 0.001000, loss: 1.445642, loss_shrink_maps: 0.746758, loss_threshold_maps: 0.546171, loss_binary_maps: 0.149212, avg_reader_cost: 1.53462 s, avg_batch_cost: 1.76295 s, avg_samples: 12.5, ips: 7.09041 samples/s, eta: 3:57:38

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.73it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 17.84it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.59it/s]
eval model::   2%|▏         | 9/500 [00:00<00:24, 19.94it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.03it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.37it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.99it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.25it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 21.37it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 20.18it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 21.15it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 20.00it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 19.27it/s]
eval model::   8%|▊         | 38/500 [00:01<00:24, 19.13it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.25it/s]
eval model::   8%|▊         | 42/500 [00:02<00:28, 16.32it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 18.01it/s]
eval model::   9%|▉         | 47/500 [00:02<00:29, 15.54it/s]
eval model::  10%|█         | 50/500 [00:02<00:26, 16.78it/s]
eval model::  11%|█         | 53/500 [00:02<00:24, 18.38it/s]
eval model::  11%|█         | 56/500 [00:02<00:22, 19.72it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:23, 18.58it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.53it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 14.89it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:25, 17.15it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:22, 19.39it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 20.33it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:21, 20.09it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 20.55it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 21.79it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.70it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 19.76it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.69it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:23, 17.11it/s]
eval model::  19%|█▉        | 94/500 [00:05<00:23, 17.58it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:21, 19.15it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.17it/s]
eval model::  20%|██        | 101/500 [00:05<00:22, 17.98it/s]
eval model::  21%|██        | 104/500 [00:05<00:19, 20.06it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.73it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:16, 22.98it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:17, 22.00it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:18, 20.87it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 21.73it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.69it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:20, 18.68it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:19, 19.58it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 18.57it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:20, 18.18it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:19, 18.38it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:19, 18.56it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 19.21it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 20.52it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:18, 19.51it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 18.26it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 18.20it/s]
eval model::  31%|███       | 154/500 [00:08<00:16, 20.69it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:16, 21.38it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:20, 16.74it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.54it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:18, 17.88it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:17, 18.73it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:17, 18.48it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 16.39it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.61it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:18, 17.03it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 19.31it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 19.97it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.05it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:14, 21.50it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.24it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:15, 20.09it/s]
eval model::  40%|████      | 201/500 [00:10<00:15, 19.60it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 21.65it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:21, 13.77it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:20, 14.53it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 14.45it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.88it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.29it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.47it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.50it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.89it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 17.13it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:14, 19.03it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 21.51it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:13, 19.30it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 21.29it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 22.26it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:13, 19.41it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:12, 20.46it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:12, 20.61it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 21.76it/s]
eval model::  51%|█████     | 255/500 [00:13<00:10, 23.19it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:10, 22.71it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:09, 24.01it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:10, 21.86it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 20.42it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 19.32it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 20.39it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 18.24it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 18.52it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:11, 19.80it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:10, 20.90it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:19, 10.77it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:14, 14.43it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:13, 14.98it/s]
eval model::  59%|█████▉    | 297/500 [00:16<00:15, 13.44it/s]
eval model::  60%|██████    | 300/500 [00:16<00:13, 14.72it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.10it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 17.69it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 19.93it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 19.96it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:09, 19.60it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:09, 18.56it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:09, 19.04it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:09, 18.75it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:09, 19.12it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 19.12it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 19.16it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:08, 19.96it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 18.39it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:09, 17.67it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:11, 13.51it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 15.95it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 17.70it/s]
eval model::  70%|███████   | 350/500 [00:19<00:07, 19.06it/s]
eval model::  71%|███████   | 353/500 [00:19<00:07, 20.56it/s]
eval model::  71%|███████   | 356/500 [00:19<00:08, 17.96it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:07, 18.25it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:08, 16.64it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:08, 16.79it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:08, 16.73it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:06, 18.84it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:06, 19.11it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:05, 21.34it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 22.48it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:05, 20.66it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 22.63it/s]
eval model::  77%|███████▋  | 387/500 [00:21<00:08, 13.76it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:06, 16.97it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:05, 17.75it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 18.87it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 17.77it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 18.00it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 19.44it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:04, 19.63it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:04, 21.44it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 22.63it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 20.44it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.86it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:04, 16.71it/s]
eval model::  85%|████████▌ | 425/500 [00:23<00:04, 15.65it/s]
eval model::  86%|████████▌ | 428/500 [00:23<00:03, 18.15it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:03, 17.99it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 20.41it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:03, 20.87it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:03, 19.28it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 22.52it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 21.28it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 19.99it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 21.31it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 22.11it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 23.70it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:01, 20.51it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:01, 19.19it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 19.95it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 21.92it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 21.33it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:00, 23.89it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 22.39it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 22.27it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 21.70it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 23.60it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 18.30it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 17.91it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 20.77it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.75it/s]
[2024/07/28 01:16:52] ppocr INFO: cur metric, precision: 0.6995255666842383, recall: 0.6389022628791526, hmean: 0.6678409662808253, fps: 44.826133165048375
[2024/07/28 01:16:52] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:16:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:17:01] ppocr INFO: epoch: [721/1500], global_step: 2163, lr: 0.001000, loss: 1.428527, loss_shrink_maps: 0.743952, loss_threshold_maps: 0.542450, loss_binary_maps: 0.148421, avg_reader_cost: 1.76284 s, avg_batch_cost: 2.11111 s, avg_samples: 12.5, ips: 5.92105 samples/s, eta: 3:57:23
[2024/07/28 01:17:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:17:09] ppocr INFO: epoch: [722/1500], global_step: 2166, lr: 0.001000, loss: 1.413342, loss_shrink_maps: 0.735261, loss_threshold_maps: 0.536154, loss_binary_maps: 0.146970, avg_reader_cost: 1.55898 s, avg_batch_cost: 1.80034 s, avg_samples: 12.5, ips: 6.94313 samples/s, eta: 3:57:04
[2024/07/28 01:17:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:17:18] ppocr INFO: epoch: [723/1500], global_step: 2169, lr: 0.001000, loss: 1.388856, loss_shrink_maps: 0.721899, loss_threshold_maps: 0.536407, loss_binary_maps: 0.144240, avg_reader_cost: 1.55451 s, avg_batch_cost: 1.79665 s, avg_samples: 12.5, ips: 6.95741 samples/s, eta: 3:56:46
[2024/07/28 01:17:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:17:25] ppocr INFO: epoch: [724/1500], global_step: 2170, lr: 0.001000, loss: 1.388856, loss_shrink_maps: 0.714886, loss_threshold_maps: 0.536407, loss_binary_maps: 0.142376, avg_reader_cost: 0.41528 s, avg_batch_cost: 0.50682 s, avg_samples: 4.8, ips: 9.47089 samples/s, eta: 3:56:38
[2024/07/28 01:17:26] ppocr INFO: epoch: [724/1500], global_step: 2172, lr: 0.001000, loss: 1.406794, loss_shrink_maps: 0.721899, loss_threshold_maps: 0.544048, loss_binary_maps: 0.144240, avg_reader_cost: 1.10479 s, avg_batch_cost: 1.25038 s, avg_samples: 7.7, ips: 6.15811 samples/s, eta: 3:56:27
[2024/07/28 01:17:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:17:35] ppocr INFO: epoch: [725/1500], global_step: 2175, lr: 0.001000, loss: 1.403288, loss_shrink_maps: 0.714886, loss_threshold_maps: 0.544048, loss_binary_maps: 0.142376, avg_reader_cost: 1.53572 s, avg_batch_cost: 1.76700 s, avg_samples: 12.5, ips: 7.07412 samples/s, eta: 3:56:08
[2024/07/28 01:17:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:17:44] ppocr INFO: epoch: [726/1500], global_step: 2178, lr: 0.001000, loss: 1.367699, loss_shrink_maps: 0.706493, loss_threshold_maps: 0.532981, loss_binary_maps: 0.140599, avg_reader_cost: 1.51532 s, avg_batch_cost: 1.77581 s, avg_samples: 12.5, ips: 7.03905 samples/s, eta: 3:55:49
[2024/07/28 01:17:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:17:52] ppocr INFO: epoch: [727/1500], global_step: 2180, lr: 0.001000, loss: 1.348338, loss_shrink_maps: 0.700941, loss_threshold_maps: 0.516469, loss_binary_maps: 0.139459, avg_reader_cost: 0.93087 s, avg_batch_cost: 1.11625 s, avg_samples: 9.6, ips: 8.60023 samples/s, eta: 3:55:35
[2024/07/28 01:17:52] ppocr INFO: epoch: [727/1500], global_step: 2181, lr: 0.001000, loss: 1.367699, loss_shrink_maps: 0.700941, loss_threshold_maps: 0.532981, loss_binary_maps: 0.139459, avg_reader_cost: 0.60383 s, avg_batch_cost: 0.65865 s, avg_samples: 2.9, ips: 4.40293 samples/s, eta: 3:55:30
[2024/07/28 01:17:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:18:01] ppocr INFO: epoch: [728/1500], global_step: 2184, lr: 0.001000, loss: 1.337649, loss_shrink_maps: 0.674098, loss_threshold_maps: 0.512653, loss_binary_maps: 0.134554, avg_reader_cost: 1.56075 s, avg_batch_cost: 1.79017 s, avg_samples: 12.5, ips: 6.98259 samples/s, eta: 3:55:11
[2024/07/28 01:18:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:18:10] ppocr INFO: epoch: [729/1500], global_step: 2187, lr: 0.001000, loss: 1.348338, loss_shrink_maps: 0.687792, loss_threshold_maps: 0.529164, loss_binary_maps: 0.137039, avg_reader_cost: 1.62353 s, avg_batch_cost: 1.89494 s, avg_samples: 12.5, ips: 6.59651 samples/s, eta: 3:54:54
[2024/07/28 01:18:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:18:19] ppocr INFO: epoch: [730/1500], global_step: 2190, lr: 0.001000, loss: 1.337649, loss_shrink_maps: 0.674098, loss_threshold_maps: 0.529164, loss_binary_maps: 0.134554, avg_reader_cost: 1.53822 s, avg_batch_cost: 1.76644 s, avg_samples: 12.5, ips: 7.07639 samples/s, eta: 3:54:35
[2024/07/28 01:18:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:18:28] ppocr INFO: epoch: [731/1500], global_step: 2193, lr: 0.001000, loss: 1.322624, loss_shrink_maps: 0.670519, loss_threshold_maps: 0.518819, loss_binary_maps: 0.133597, avg_reader_cost: 1.56366 s, avg_batch_cost: 1.79883 s, avg_samples: 12.5, ips: 6.94896 samples/s, eta: 3:54:16
[2024/07/28 01:18:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:18:36] ppocr INFO: epoch: [732/1500], global_step: 2196, lr: 0.001000, loss: 1.322624, loss_shrink_maps: 0.670519, loss_threshold_maps: 0.518819, loss_binary_maps: 0.133597, avg_reader_cost: 1.52949 s, avg_batch_cost: 1.75820 s, avg_samples: 12.5, ips: 7.10956 samples/s, eta: 3:53:57
[2024/07/28 01:18:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:18:45] ppocr INFO: epoch: [733/1500], global_step: 2199, lr: 0.001000, loss: 1.344833, loss_shrink_maps: 0.670519, loss_threshold_maps: 0.531102, loss_binary_maps: 0.133597, avg_reader_cost: 1.52073 s, avg_batch_cost: 1.75839 s, avg_samples: 12.5, ips: 7.10877 samples/s, eta: 3:53:38
[2024/07/28 01:18:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:18:52] ppocr INFO: epoch: [734/1500], global_step: 2200, lr: 0.001000, loss: 1.344833, loss_shrink_maps: 0.670519, loss_threshold_maps: 0.531102, loss_binary_maps: 0.133597, avg_reader_cost: 0.42261 s, avg_batch_cost: 0.50592 s, avg_samples: 4.8, ips: 9.48761 samples/s, eta: 3:53:31
[2024/07/28 01:18:53] ppocr INFO: epoch: [734/1500], global_step: 2202, lr: 0.001000, loss: 1.344959, loss_shrink_maps: 0.678723, loss_threshold_maps: 0.528564, loss_binary_maps: 0.135271, avg_reader_cost: 1.10338 s, avg_batch_cost: 1.24979 s, avg_samples: 7.7, ips: 6.16103 samples/s, eta: 3:53:19
[2024/07/28 01:18:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:19:02] ppocr INFO: epoch: [735/1500], global_step: 2205, lr: 0.001000, loss: 1.307318, loss_shrink_maps: 0.665217, loss_threshold_maps: 0.514137, loss_binary_maps: 0.132745, avg_reader_cost: 1.56778 s, avg_batch_cost: 1.79714 s, avg_samples: 12.5, ips: 6.95549 samples/s, eta: 3:53:01
[2024/07/28 01:19:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:19:11] ppocr INFO: epoch: [736/1500], global_step: 2208, lr: 0.001000, loss: 1.328004, loss_shrink_maps: 0.671610, loss_threshold_maps: 0.512305, loss_binary_maps: 0.132945, avg_reader_cost: 1.53105 s, avg_batch_cost: 1.76556 s, avg_samples: 12.5, ips: 7.07991 samples/s, eta: 3:52:42
[2024/07/28 01:19:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:19:20] ppocr INFO: epoch: [737/1500], global_step: 2210, lr: 0.001000, loss: 1.361203, loss_shrink_maps: 0.685116, loss_threshold_maps: 0.524905, loss_binary_maps: 0.136150, avg_reader_cost: 1.07102 s, avg_batch_cost: 1.25534 s, avg_samples: 9.6, ips: 7.64730 samples/s, eta: 3:52:30
[2024/07/28 01:19:20] ppocr INFO: epoch: [737/1500], global_step: 2211, lr: 0.001000, loss: 1.361203, loss_shrink_maps: 0.692430, loss_threshold_maps: 0.524905, loss_binary_maps: 0.137214, avg_reader_cost: 0.67369 s, avg_batch_cost: 0.72824 s, avg_samples: 2.9, ips: 3.98218 samples/s, eta: 3:52:25
[2024/07/28 01:19:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:19:29] ppocr INFO: epoch: [738/1500], global_step: 2214, lr: 0.001000, loss: 1.381670, loss_shrink_maps: 0.710388, loss_threshold_maps: 0.537188, loss_binary_maps: 0.141106, avg_reader_cost: 1.53390 s, avg_batch_cost: 1.76379 s, avg_samples: 12.5, ips: 7.08702 samples/s, eta: 3:52:06
[2024/07/28 01:19:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:19:38] ppocr INFO: epoch: [739/1500], global_step: 2217, lr: 0.001000, loss: 1.381670, loss_shrink_maps: 0.710388, loss_threshold_maps: 0.537188, loss_binary_maps: 0.141247, avg_reader_cost: 1.53108 s, avg_batch_cost: 1.79928 s, avg_samples: 12.5, ips: 6.94724 samples/s, eta: 3:51:47
[2024/07/28 01:19:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:19:46] ppocr INFO: epoch: [740/1500], global_step: 2220, lr: 0.001000, loss: 1.367188, loss_shrink_maps: 0.708379, loss_threshold_maps: 0.532651, loss_binary_maps: 0.141247, avg_reader_cost: 1.57858 s, avg_batch_cost: 1.83671 s, avg_samples: 12.5, ips: 6.80563 samples/s, eta: 3:51:29

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.67it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.10it/s]
eval model::   1%|▏         | 7/500 [00:00<00:23, 21.09it/s]
eval model::   2%|▏         | 10/500 [00:00<00:23, 21.17it/s]
eval model::   3%|▎         | 13/500 [00:00<00:25, 19.47it/s]
eval model::   3%|▎         | 16/500 [00:00<00:23, 20.45it/s]
eval model::   4%|▍         | 19/500 [00:00<00:22, 21.02it/s]
eval model::   4%|▍         | 22/500 [00:01<00:23, 20.39it/s]
eval model::   5%|▌         | 25/500 [00:01<00:22, 20.88it/s]
eval model::   6%|▌         | 28/500 [00:01<00:21, 21.75it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.51it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 19.08it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.38it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 17.85it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.75it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.05it/s]
eval model::  10%|▉         | 48/500 [00:02<00:23, 19.63it/s]
eval model::  10%|█         | 51/500 [00:02<00:23, 19.35it/s]
eval model::  11%|█         | 54/500 [00:02<00:21, 20.91it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:21, 20.76it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:23, 18.58it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:28, 15.12it/s]
eval model::  13%|█▎        | 65/500 [00:03<00:25, 17.04it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:22, 19.40it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 20.84it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:20, 20.83it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:19, 21.82it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:18, 22.65it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:18, 22.19it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:20, 20.54it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.17it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:24, 16.97it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:23, 16.98it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:20, 19.38it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:21, 18.88it/s]
eval model::  20%|██        | 101/500 [00:05<00:21, 18.91it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 20.91it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 22.28it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 22.56it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:17, 22.24it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:18, 20.24it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:18, 20.69it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.56it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:19, 19.12it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 19.89it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 19.03it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:20, 17.80it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:19, 19.06it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.72it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.90it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 22.07it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:17, 19.98it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 17.81it/s]
eval model::  31%|███       | 154/500 [00:07<00:17, 20.15it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:14, 22.80it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:22, 15.40it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:19, 16.90it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 18.31it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 19.94it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:17, 18.85it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.89it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.32it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:18, 16.97it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.42it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 20.90it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.68it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:14, 21.63it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.21it/s]
eval model::  40%|████      | 201/500 [00:10<00:15, 19.85it/s]
eval model::  41%|████      | 204/500 [00:10<00:14, 19.81it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:21, 13.87it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:19, 14.69it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 15.02it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.68it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.48it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.22it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.44it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:20, 13.40it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.24it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:13, 19.60it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 21.76it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 21.51it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 23.18it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 23.37it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:13, 18.54it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:12, 20.94it/s]
eval model::  50%|█████     | 250/500 [00:13<00:12, 20.65it/s]
eval model::  51%|█████     | 253/500 [00:13<00:10, 22.67it/s]
eval model::  51%|█████     | 256/500 [00:13<00:10, 23.10it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:09, 24.63it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 23.17it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:11, 21.04it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:12, 18.85it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.90it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.69it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 18.60it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:12, 17.26it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:12, 17.97it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:20, 10.52it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:16, 12.83it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:13, 15.95it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:14, 14.20it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:11, 17.52it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 17.43it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 19.43it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:08, 23.36it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:07, 24.17it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 21.90it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:07, 24.27it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:07, 22.71it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:07, 23.65it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:07, 23.18it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 21.05it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 22.30it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:07, 20.60it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:11, 13.58it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:10, 15.34it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 17.15it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:08, 17.64it/s]
eval model::  70%|███████   | 352/500 [00:18<00:07, 19.34it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 20.76it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 22.35it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 18.53it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.09it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 16.97it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.05it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 20.92it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 24.42it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:05, 21.69it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:05, 22.91it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:08, 12.79it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:07, 15.05it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 16.09it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:06, 16.36it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 18.52it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 18.61it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 21.87it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 22.47it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 23.71it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 24.52it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 21.16it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 15.92it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:04, 17.62it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.52it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 18.91it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 18.85it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 20.37it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 22.09it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 21.23it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 23.21it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 24.32it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 23.63it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:01, 24.89it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:01, 25.09it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 24.33it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 19.19it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 19.64it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 20.47it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 21.16it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 22.81it/s]
eval model::  96%|█████████▌| 479/500 [00:24<00:00, 24.47it/s]
eval model::  96%|█████████▋| 482/500 [00:24<00:00, 23.29it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 22.62it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 21.29it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 19.62it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 17.67it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 16.65it/s]
eval model:: 100%|█████████▉| 499/500 [00:25<00:00, 19.09it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.31it/s]
[2024/07/28 01:20:12] ppocr INFO: cur metric, precision: 0.7374364765669114, recall: 0.6287915262397689, hmean: 0.6787941787941788, fps: 46.08851275984581
[2024/07/28 01:20:12] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:20:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:20:21] ppocr INFO: epoch: [741/1500], global_step: 2223, lr: 0.001000, loss: 1.353242, loss_shrink_maps: 0.693905, loss_threshold_maps: 0.523295, loss_binary_maps: 0.138512, avg_reader_cost: 1.91711 s, avg_batch_cost: 2.29030 s, avg_samples: 12.5, ips: 5.45780 samples/s, eta: 3:51:16
[2024/07/28 01:20:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:20:30] ppocr INFO: epoch: [742/1500], global_step: 2226, lr: 0.001000, loss: 1.367188, loss_shrink_maps: 0.708379, loss_threshold_maps: 0.536087, loss_binary_maps: 0.141247, avg_reader_cost: 1.50331 s, avg_batch_cost: 1.73594 s, avg_samples: 12.5, ips: 7.20070 samples/s, eta: 3:50:56
[2024/07/28 01:20:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:20:39] ppocr INFO: epoch: [743/1500], global_step: 2229, lr: 0.001000, loss: 1.372477, loss_shrink_maps: 0.710455, loss_threshold_maps: 0.530506, loss_binary_maps: 0.142106, avg_reader_cost: 1.51452 s, avg_batch_cost: 1.74389 s, avg_samples: 12.5, ips: 7.16790 samples/s, eta: 3:50:37
[2024/07/28 01:20:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:20:46] ppocr INFO: epoch: [744/1500], global_step: 2230, lr: 0.001000, loss: 1.348666, loss_shrink_maps: 0.693905, loss_threshold_maps: 0.523422, loss_binary_maps: 0.138512, avg_reader_cost: 0.44436 s, avg_batch_cost: 0.52683 s, avg_samples: 4.8, ips: 9.11105 samples/s, eta: 3:50:30
[2024/07/28 01:20:47] ppocr INFO: epoch: [744/1500], global_step: 2232, lr: 0.001000, loss: 1.348666, loss_shrink_maps: 0.693905, loss_threshold_maps: 0.523904, loss_binary_maps: 0.138512, avg_reader_cost: 1.14503 s, avg_batch_cost: 1.29080 s, avg_samples: 7.7, ips: 5.96528 samples/s, eta: 3:50:19
[2024/07/28 01:20:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:20:56] ppocr INFO: epoch: [745/1500], global_step: 2235, lr: 0.001000, loss: 1.348666, loss_shrink_maps: 0.693905, loss_threshold_maps: 0.523904, loss_binary_maps: 0.138512, avg_reader_cost: 1.52643 s, avg_batch_cost: 1.77026 s, avg_samples: 12.5, ips: 7.06110 samples/s, eta: 3:50:00
[2024/07/28 01:20:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:21:05] ppocr INFO: epoch: [746/1500], global_step: 2238, lr: 0.001000, loss: 1.356292, loss_shrink_maps: 0.690819, loss_threshold_maps: 0.523904, loss_binary_maps: 0.138084, avg_reader_cost: 1.51325 s, avg_batch_cost: 1.75753 s, avg_samples: 12.5, ips: 7.11224 samples/s, eta: 3:49:41
[2024/07/28 01:21:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:21:13] ppocr INFO: epoch: [747/1500], global_step: 2240, lr: 0.001000, loss: 1.334918, loss_shrink_maps: 0.677264, loss_threshold_maps: 0.517550, loss_binary_maps: 0.135303, avg_reader_cost: 0.91014 s, avg_batch_cost: 1.11553 s, avg_samples: 9.6, ips: 8.60579 samples/s, eta: 3:49:28
[2024/07/28 01:21:13] ppocr INFO: epoch: [747/1500], global_step: 2241, lr: 0.001000, loss: 1.322059, loss_shrink_maps: 0.666146, loss_threshold_maps: 0.516967, loss_binary_maps: 0.132611, avg_reader_cost: 0.60337 s, avg_batch_cost: 0.65853 s, avg_samples: 2.9, ips: 4.40378 samples/s, eta: 3:49:22
[2024/07/28 01:21:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:21:22] ppocr INFO: epoch: [748/1500], global_step: 2244, lr: 0.001000, loss: 1.334918, loss_shrink_maps: 0.677264, loss_threshold_maps: 0.517550, loss_binary_maps: 0.135303, avg_reader_cost: 1.71639 s, avg_batch_cost: 1.94404 s, avg_samples: 12.5, ips: 6.42992 samples/s, eta: 3:49:05
[2024/07/28 01:21:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:21:31] ppocr INFO: epoch: [749/1500], global_step: 2247, lr: 0.001000, loss: 1.349334, loss_shrink_maps: 0.681285, loss_threshold_maps: 0.522067, loss_binary_maps: 0.135703, avg_reader_cost: 1.54776 s, avg_batch_cost: 1.77721 s, avg_samples: 12.5, ips: 7.03350 samples/s, eta: 3:48:46
[2024/07/28 01:21:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:21:40] ppocr INFO: epoch: [750/1500], global_step: 2250, lr: 0.001000, loss: 1.360678, loss_shrink_maps: 0.693834, loss_threshold_maps: 0.522067, loss_binary_maps: 0.138419, avg_reader_cost: 1.51223 s, avg_batch_cost: 1.74311 s, avg_samples: 12.5, ips: 7.17108 samples/s, eta: 3:48:27
[2024/07/28 01:21:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:21:49] ppocr INFO: epoch: [751/1500], global_step: 2253, lr: 0.001000, loss: 1.360678, loss_shrink_maps: 0.693356, loss_threshold_maps: 0.522067, loss_binary_maps: 0.138097, avg_reader_cost: 1.69014 s, avg_batch_cost: 1.91932 s, avg_samples: 12.5, ips: 6.51273 samples/s, eta: 3:48:10
[2024/07/28 01:21:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:21:58] ppocr INFO: epoch: [752/1500], global_step: 2256, lr: 0.001000, loss: 1.374516, loss_shrink_maps: 0.699432, loss_threshold_maps: 0.528758, loss_binary_maps: 0.138950, avg_reader_cost: 1.54242 s, avg_batch_cost: 1.77064 s, avg_samples: 12.5, ips: 7.05959 samples/s, eta: 3:47:51
[2024/07/28 01:22:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:22:07] ppocr INFO: epoch: [753/1500], global_step: 2259, lr: 0.001000, loss: 1.363039, loss_shrink_maps: 0.698798, loss_threshold_maps: 0.528509, loss_binary_maps: 0.138574, avg_reader_cost: 1.52394 s, avg_batch_cost: 1.80107 s, avg_samples: 12.5, ips: 6.94030 samples/s, eta: 3:47:33
[2024/07/28 01:22:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:22:14] ppocr INFO: epoch: [754/1500], global_step: 2260, lr: 0.001000, loss: 1.374516, loss_shrink_maps: 0.699432, loss_threshold_maps: 0.530786, loss_binary_maps: 0.138950, avg_reader_cost: 0.42276 s, avg_batch_cost: 0.51643 s, avg_samples: 4.8, ips: 9.29456 samples/s, eta: 3:47:25
[2024/07/28 01:22:15] ppocr INFO: epoch: [754/1500], global_step: 2262, lr: 0.001000, loss: 1.371892, loss_shrink_maps: 0.698798, loss_threshold_maps: 0.530786, loss_binary_maps: 0.138574, avg_reader_cost: 1.12430 s, avg_batch_cost: 1.27052 s, avg_samples: 7.7, ips: 6.06051 samples/s, eta: 3:47:14
[2024/07/28 01:22:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:22:24] ppocr INFO: epoch: [755/1500], global_step: 2265, lr: 0.001000, loss: 1.393532, loss_shrink_maps: 0.702579, loss_threshold_maps: 0.534927, loss_binary_maps: 0.140025, avg_reader_cost: 1.61297 s, avg_batch_cost: 1.85664 s, avg_samples: 12.5, ips: 6.73260 samples/s, eta: 3:46:56
[2024/07/28 01:22:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:22:33] ppocr INFO: epoch: [756/1500], global_step: 2268, lr: 0.001000, loss: 1.393532, loss_shrink_maps: 0.702579, loss_threshold_maps: 0.530942, loss_binary_maps: 0.140025, avg_reader_cost: 1.54903 s, avg_batch_cost: 1.79529 s, avg_samples: 12.5, ips: 6.96268 samples/s, eta: 3:46:37
[2024/07/28 01:22:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:22:41] ppocr INFO: epoch: [757/1500], global_step: 2270, lr: 0.001000, loss: 1.388848, loss_shrink_maps: 0.702579, loss_threshold_maps: 0.530942, loss_binary_maps: 0.140025, avg_reader_cost: 0.98516 s, avg_batch_cost: 1.15851 s, avg_samples: 9.6, ips: 8.28654 samples/s, eta: 3:46:24
[2024/07/28 01:22:42] ppocr INFO: epoch: [757/1500], global_step: 2271, lr: 0.001000, loss: 1.372606, loss_shrink_maps: 0.702423, loss_threshold_maps: 0.526895, loss_binary_maps: 0.139971, avg_reader_cost: 0.62497 s, avg_batch_cost: 0.67977 s, avg_samples: 2.9, ips: 4.26614 samples/s, eta: 3:46:19
[2024/07/28 01:22:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:22:50] ppocr INFO: epoch: [758/1500], global_step: 2274, lr: 0.001000, loss: 1.338786, loss_shrink_maps: 0.690000, loss_threshold_maps: 0.523738, loss_binary_maps: 0.137124, avg_reader_cost: 1.49916 s, avg_batch_cost: 1.73093 s, avg_samples: 12.5, ips: 7.22153 samples/s, eta: 3:46:00
[2024/07/28 01:22:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:22:59] ppocr INFO: epoch: [759/1500], global_step: 2277, lr: 0.001000, loss: 1.311444, loss_shrink_maps: 0.674221, loss_threshold_maps: 0.520939, loss_binary_maps: 0.134243, avg_reader_cost: 1.50866 s, avg_batch_cost: 1.74667 s, avg_samples: 12.5, ips: 7.15646 samples/s, eta: 3:45:41
[2024/07/28 01:23:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:23:08] ppocr INFO: epoch: [760/1500], global_step: 2280, lr: 0.001000, loss: 1.350977, loss_shrink_maps: 0.689000, loss_threshold_maps: 0.520939, loss_binary_maps: 0.137639, avg_reader_cost: 1.56429 s, avg_batch_cost: 1.81137 s, avg_samples: 12.5, ips: 6.90085 samples/s, eta: 3:45:22

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:02,  7.98it/s]
eval model::   1%|          | 4/500 [00:00<00:31, 15.98it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.11it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.36it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.35it/s]
eval model::   3%|▎         | 14/500 [00:00<00:24, 19.85it/s]
eval model::   4%|▎         | 18/500 [00:00<00:20, 23.08it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.94it/s]
eval model::   5%|▍         | 24/500 [00:01<00:24, 19.20it/s]
eval model::   5%|▌         | 26/500 [00:01<00:27, 17.25it/s]
eval model::   6%|▌         | 28/500 [00:01<00:27, 17.35it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.25it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.26it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 19.86it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.15it/s]
eval model::   8%|▊         | 42/500 [00:02<00:29, 15.68it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 18.00it/s]
eval model::   9%|▉         | 47/500 [00:02<00:28, 15.85it/s]
eval model::  10%|▉         | 49/500 [00:02<00:27, 16.53it/s]
eval model::  10%|█         | 51/500 [00:02<00:27, 16.06it/s]
eval model::  11%|█         | 53/500 [00:03<00:26, 16.90it/s]
eval model::  11%|█         | 56/500 [00:03<00:24, 18.36it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 18.14it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 16.01it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 15.03it/s]
eval model::  13%|█▎        | 65/500 [00:03<00:27, 16.05it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:23, 18.30it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:23, 18.37it/s]
eval model::  15%|█▍        | 73/500 [00:04<00:22, 19.17it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:20, 20.28it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:21, 19.95it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 20.81it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 21.35it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 19.68it/s]
eval model::  18%|█▊        | 91/500 [00:05<00:22, 17.84it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:22, 17.91it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.58it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.72it/s]
eval model::  20%|██        | 102/500 [00:05<00:21, 18.85it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 20.68it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.06it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 21.01it/s]
eval model::  23%|██▎       | 114/500 [00:06<00:17, 21.67it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:18, 20.55it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.80it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.34it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.39it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:20, 18.41it/s]
eval model::  26%|██▌       | 131/500 [00:07<00:20, 18.08it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:20, 17.77it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:19, 19.06it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.43it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.27it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.06it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 19.00it/s]
eval model::  30%|███       | 151/500 [00:08<00:19, 18.01it/s]
eval model::  31%|███       | 154/500 [00:08<00:17, 20.20it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.75it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:21, 15.60it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:21, 15.95it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:21, 15.78it/s]
eval model::  34%|███▎      | 168/500 [00:09<00:19, 17.17it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:18, 17.90it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 15.97it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 15.52it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.13it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.45it/s]
eval model::  37%|███▋      | 183/500 [00:10<00:19, 16.31it/s]
eval model::  37%|███▋      | 186/500 [00:10<00:17, 18.43it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:15, 19.67it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:16, 18.91it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:16, 18.91it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.78it/s]
eval model::  40%|████      | 200/500 [00:10<00:15, 19.87it/s]
eval model::  41%|████      | 203/500 [00:11<00:15, 19.23it/s]
eval model::  41%|████      | 205/500 [00:11<00:21, 13.82it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:19, 15.16it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 14.79it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.80it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 14.02it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:22, 12.81it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:20, 13.61it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:22, 12.57it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:18, 15.27it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:17, 15.92it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:14, 18.87it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 21.53it/s]
eval model::  47%|████▋     | 235/500 [00:13<00:12, 20.63it/s]
eval model::  48%|████▊     | 238/500 [00:13<00:12, 21.83it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:13, 19.63it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:15, 16.93it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:14, 17.77it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:14, 17.38it/s]
eval model::  50%|█████     | 252/500 [00:14<00:13, 18.30it/s]
eval model::  51%|█████     | 255/500 [00:14<00:12, 19.43it/s]
eval model::  52%|█████▏    | 258/500 [00:14<00:11, 20.31it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:11, 21.04it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:11, 21.08it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:10, 21.22it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 20.77it/s]
eval model::  55%|█████▍    | 273/500 [00:15<00:11, 20.23it/s]
eval model::  55%|█████▌    | 276/500 [00:15<00:11, 19.12it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:10, 21.36it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:09, 21.81it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:16, 12.96it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:15, 13.92it/s]
eval model::  58%|█████▊    | 290/500 [00:16<00:12, 16.81it/s]
eval model::  59%|█████▊    | 293/500 [00:16<00:11, 17.80it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:13, 14.99it/s]
eval model::  60%|██████    | 300/500 [00:16<00:11, 17.21it/s]
eval model::  61%|██████    | 303/500 [00:16<00:10, 18.80it/s]
eval model::  61%|██████    | 306/500 [00:16<00:09, 19.45it/s]
eval model::  62%|██████▏   | 309/500 [00:17<00:09, 19.78it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:09, 19.07it/s]
eval model::  63%|██████▎   | 314/500 [00:17<00:10, 18.53it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:10, 17.51it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:09, 19.03it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:10, 17.62it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:09, 17.95it/s]
eval model::  65%|██████▌   | 326/500 [00:18<00:08, 19.90it/s]
eval model::  66%|██████▌   | 329/500 [00:18<00:08, 20.49it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:07, 22.92it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:07, 22.91it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:08, 19.75it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 15.70it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:08, 18.86it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:07, 20.09it/s]
eval model::  70%|███████   | 352/500 [00:19<00:07, 20.65it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 20.81it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 20.58it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.34it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.25it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:07, 18.01it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:06, 19.51it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:06, 18.96it/s]
eval model::  75%|███████▍  | 374/500 [00:20<00:06, 20.14it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 23.32it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:05, 22.38it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:04, 24.73it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:09, 12.12it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:07, 13.79it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:07, 14.50it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:07, 14.58it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:06, 16.80it/s]
eval model::  80%|████████  | 400/500 [00:22<00:06, 15.94it/s]
eval model::  81%|████████  | 403/500 [00:22<00:05, 17.78it/s]
eval model::  81%|████████  | 405/500 [00:22<00:05, 18.13it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:04, 20.59it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:04, 22.18it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 22.84it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:03, 21.46it/s]
eval model::  84%|████████▍ | 420/500 [00:23<00:05, 14.69it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:04, 15.61it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:04, 15.70it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:04, 15.76it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:03, 18.34it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 16.15it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 18.63it/s]
eval model::  87%|████████▋ | 437/500 [00:24<00:03, 19.09it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:03, 18.39it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 21.35it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 22.30it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 19.47it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 19.78it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:02, 20.47it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:01, 21.28it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 17.62it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 17.48it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 16.70it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 16.80it/s]
eval model::  94%|█████████▍| 472/500 [00:25<00:01, 20.60it/s]
eval model::  95%|█████████▌| 475/500 [00:26<00:01, 20.71it/s]
eval model::  96%|█████████▌| 478/500 [00:26<00:00, 22.54it/s]
eval model::  96%|█████████▌| 481/500 [00:26<00:00, 23.32it/s]
eval model::  97%|█████████▋| 484/500 [00:26<00:00, 23.40it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 20.84it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 22.44it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 19.26it/s]
eval model::  99%|█████████▉| 496/500 [00:27<00:00, 19.13it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 22.71it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.42it/s]
[2024/07/28 01:23:35] ppocr INFO: cur metric, precision: 0.6941529235382309, recall: 0.6687530091478093, hmean: 0.6812162824914173, fps: 44.723747021325366
[2024/07/28 01:23:35] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:23:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:23:43] ppocr INFO: epoch: [761/1500], global_step: 2283, lr: 0.001000, loss: 1.350977, loss_shrink_maps: 0.689000, loss_threshold_maps: 0.521428, loss_binary_maps: 0.137639, avg_reader_cost: 1.65761 s, avg_batch_cost: 1.93483 s, avg_samples: 12.5, ips: 6.46052 samples/s, eta: 3:45:05
[2024/07/28 01:23:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:23:51] ppocr INFO: epoch: [762/1500], global_step: 2286, lr: 0.001000, loss: 1.284797, loss_shrink_maps: 0.654264, loss_threshold_maps: 0.506196, loss_binary_maps: 0.130584, avg_reader_cost: 1.54396 s, avg_batch_cost: 1.78885 s, avg_samples: 12.5, ips: 6.98774 samples/s, eta: 3:44:46
[2024/07/28 01:23:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:24:00] ppocr INFO: epoch: [763/1500], global_step: 2289, lr: 0.001000, loss: 1.297123, loss_shrink_maps: 0.661491, loss_threshold_maps: 0.510863, loss_binary_maps: 0.131932, avg_reader_cost: 1.53983 s, avg_batch_cost: 1.76838 s, avg_samples: 12.5, ips: 7.06862 samples/s, eta: 3:44:28
[2024/07/28 01:24:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:24:08] ppocr INFO: epoch: [764/1500], global_step: 2290, lr: 0.001000, loss: 1.304189, loss_shrink_maps: 0.662497, loss_threshold_maps: 0.510863, loss_binary_maps: 0.132215, avg_reader_cost: 0.44322 s, avg_batch_cost: 0.53917 s, avg_samples: 4.8, ips: 8.90258 samples/s, eta: 3:44:21
[2024/07/28 01:24:09] ppocr INFO: epoch: [764/1500], global_step: 2292, lr: 0.001000, loss: 1.307470, loss_shrink_maps: 0.664749, loss_threshold_maps: 0.512504, loss_binary_maps: 0.132658, avg_reader_cost: 1.16973 s, avg_batch_cost: 1.31552 s, avg_samples: 7.7, ips: 5.85319 samples/s, eta: 3:44:10
[2024/07/28 01:24:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:24:18] ppocr INFO: epoch: [765/1500], global_step: 2295, lr: 0.001000, loss: 1.322806, loss_shrink_maps: 0.680157, loss_threshold_maps: 0.516030, loss_binary_maps: 0.135161, avg_reader_cost: 1.54507 s, avg_batch_cost: 1.78831 s, avg_samples: 12.5, ips: 6.98983 samples/s, eta: 3:43:51
[2024/07/28 01:24:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:24:26] ppocr INFO: epoch: [766/1500], global_step: 2298, lr: 0.001000, loss: 1.325932, loss_shrink_maps: 0.688604, loss_threshold_maps: 0.516030, loss_binary_maps: 0.136594, avg_reader_cost: 1.52147 s, avg_batch_cost: 1.74974 s, avg_samples: 12.5, ips: 7.14392 samples/s, eta: 3:43:32
[2024/07/28 01:24:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:24:35] ppocr INFO: epoch: [767/1500], global_step: 2300, lr: 0.001000, loss: 1.314011, loss_shrink_maps: 0.674821, loss_threshold_maps: 0.516030, loss_binary_maps: 0.134360, avg_reader_cost: 0.91683 s, avg_batch_cost: 1.10733 s, avg_samples: 9.6, ips: 8.66951 samples/s, eta: 3:43:19
[2024/07/28 01:24:35] ppocr INFO: epoch: [767/1500], global_step: 2301, lr: 0.001000, loss: 1.314011, loss_shrink_maps: 0.674821, loss_threshold_maps: 0.517672, loss_binary_maps: 0.134360, avg_reader_cost: 0.59938 s, avg_batch_cost: 0.65418 s, avg_samples: 2.9, ips: 4.43301 samples/s, eta: 3:43:13
[2024/07/28 01:24:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:24:44] ppocr INFO: epoch: [768/1500], global_step: 2304, lr: 0.001000, loss: 1.325932, loss_shrink_maps: 0.688604, loss_threshold_maps: 0.522081, loss_binary_maps: 0.136594, avg_reader_cost: 1.53969 s, avg_batch_cost: 1.76826 s, avg_samples: 12.5, ips: 7.06910 samples/s, eta: 3:42:54
[2024/07/28 01:24:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:24:53] ppocr INFO: epoch: [769/1500], global_step: 2307, lr: 0.001000, loss: 1.347787, loss_shrink_maps: 0.696753, loss_threshold_maps: 0.523625, loss_binary_maps: 0.138379, avg_reader_cost: 1.56994 s, avg_batch_cost: 1.79786 s, avg_samples: 12.5, ips: 6.95271 samples/s, eta: 3:42:36
[2024/07/28 01:24:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:25:02] ppocr INFO: epoch: [770/1500], global_step: 2310, lr: 0.001000, loss: 1.332904, loss_shrink_maps: 0.681504, loss_threshold_maps: 0.523625, loss_binary_maps: 0.135160, avg_reader_cost: 1.56184 s, avg_batch_cost: 1.82364 s, avg_samples: 12.5, ips: 6.85441 samples/s, eta: 3:42:17
[2024/07/28 01:25:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:25:10] ppocr INFO: epoch: [771/1500], global_step: 2313, lr: 0.001000, loss: 1.332904, loss_shrink_maps: 0.690110, loss_threshold_maps: 0.522081, loss_binary_maps: 0.137514, avg_reader_cost: 1.51901 s, avg_batch_cost: 1.76282 s, avg_samples: 12.5, ips: 7.09093 samples/s, eta: 3:41:58
[2024/07/28 01:25:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:25:19] ppocr INFO: epoch: [772/1500], global_step: 2316, lr: 0.001000, loss: 1.316813, loss_shrink_maps: 0.673518, loss_threshold_maps: 0.516730, loss_binary_maps: 0.133718, avg_reader_cost: 1.53301 s, avg_batch_cost: 1.77357 s, avg_samples: 12.5, ips: 7.04793 samples/s, eta: 3:41:40
[2024/07/28 01:25:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:25:28] ppocr INFO: epoch: [773/1500], global_step: 2319, lr: 0.001000, loss: 1.314028, loss_shrink_maps: 0.665285, loss_threshold_maps: 0.522500, loss_binary_maps: 0.132351, avg_reader_cost: 1.53649 s, avg_batch_cost: 1.77350 s, avg_samples: 12.5, ips: 7.04819 samples/s, eta: 3:41:21
[2024/07/28 01:25:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:25:35] ppocr INFO: epoch: [774/1500], global_step: 2320, lr: 0.001000, loss: 1.318998, loss_shrink_maps: 0.673518, loss_threshold_maps: 0.519634, loss_binary_maps: 0.133718, avg_reader_cost: 0.45218 s, avg_batch_cost: 0.53499 s, avg_samples: 4.8, ips: 8.97209 samples/s, eta: 3:41:14
[2024/07/28 01:25:37] ppocr INFO: epoch: [774/1500], global_step: 2322, lr: 0.001000, loss: 1.318998, loss_shrink_maps: 0.673518, loss_threshold_maps: 0.514282, loss_binary_maps: 0.133718, avg_reader_cost: 1.16192 s, avg_batch_cost: 1.30825 s, avg_samples: 7.7, ips: 5.88573 samples/s, eta: 3:41:03
[2024/07/28 01:25:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:25:45] ppocr INFO: epoch: [775/1500], global_step: 2325, lr: 0.001000, loss: 1.327712, loss_shrink_maps: 0.684715, loss_threshold_maps: 0.514282, loss_binary_maps: 0.135740, avg_reader_cost: 1.50766 s, avg_batch_cost: 1.77246 s, avg_samples: 12.5, ips: 7.05236 samples/s, eta: 3:40:44
[2024/07/28 01:25:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:25:54] ppocr INFO: epoch: [776/1500], global_step: 2328, lr: 0.001000, loss: 1.327712, loss_shrink_maps: 0.693320, loss_threshold_maps: 0.514282, loss_binary_maps: 0.138094, avg_reader_cost: 1.59233 s, avg_batch_cost: 1.86439 s, avg_samples: 12.5, ips: 6.70461 samples/s, eta: 3:40:26
[2024/07/28 01:25:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:26:03] ppocr INFO: epoch: [777/1500], global_step: 2330, lr: 0.001000, loss: 1.354162, loss_shrink_maps: 0.698783, loss_threshold_maps: 0.519889, loss_binary_maps: 0.139692, avg_reader_cost: 0.94722 s, avg_batch_cost: 1.17063 s, avg_samples: 9.6, ips: 8.20072 samples/s, eta: 3:40:13
[2024/07/28 01:26:03] ppocr INFO: epoch: [777/1500], global_step: 2331, lr: 0.001000, loss: 1.354162, loss_shrink_maps: 0.695153, loss_threshold_maps: 0.519889, loss_binary_maps: 0.138550, avg_reader_cost: 0.63082 s, avg_batch_cost: 0.68613 s, avg_samples: 2.9, ips: 4.22660 samples/s, eta: 3:40:08
[2024/07/28 01:26:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:26:12] ppocr INFO: epoch: [778/1500], global_step: 2334, lr: 0.001000, loss: 1.354162, loss_shrink_maps: 0.695153, loss_threshold_maps: 0.523789, loss_binary_maps: 0.138550, avg_reader_cost: 1.62540 s, avg_batch_cost: 1.89015 s, avg_samples: 12.5, ips: 6.61325 samples/s, eta: 3:39:50
[2024/07/28 01:26:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:26:21] ppocr INFO: epoch: [779/1500], global_step: 2337, lr: 0.001000, loss: 1.373259, loss_shrink_maps: 0.701454, loss_threshold_maps: 0.523789, loss_binary_maps: 0.140056, avg_reader_cost: 1.55031 s, avg_batch_cost: 1.77902 s, avg_samples: 12.5, ips: 7.02634 samples/s, eta: 3:39:32
[2024/07/28 01:26:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:26:30] ppocr INFO: epoch: [780/1500], global_step: 2340, lr: 0.001000, loss: 1.373259, loss_shrink_maps: 0.701454, loss_threshold_maps: 0.526061, loss_binary_maps: 0.140056, avg_reader_cost: 1.58431 s, avg_batch_cost: 1.81885 s, avg_samples: 12.5, ips: 6.87247 samples/s, eta: 3:39:13

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:50,  9.90it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.74it/s]
eval model::   1%|▏         | 7/500 [00:00<00:22, 21.65it/s]
eval model::   2%|▏         | 10/500 [00:00<00:28, 17.48it/s]
eval model::   2%|▏         | 12/500 [00:00<00:28, 16.94it/s]
eval model::   3%|▎         | 14/500 [00:00<00:28, 17.10it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.55it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.91it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 21.73it/s]
eval model::   5%|▌         | 26/500 [00:01<00:21, 22.56it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 21.89it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 18.97it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.01it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 19.63it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.53it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 17.83it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.22it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 16.99it/s]
eval model::  10%|█         | 50/500 [00:02<00:25, 17.80it/s]
eval model::  10%|█         | 52/500 [00:02<00:25, 17.83it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 20.04it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 18.23it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:24, 18.01it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:30, 14.28it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:28, 15.25it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:21, 19.69it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 20.88it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:21, 20.04it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:21, 19.73it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 22.02it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.57it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:19, 21.49it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.38it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:20, 19.45it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:21, 19.14it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.25it/s]
eval model::  20%|██        | 102/500 [00:05<00:21, 18.34it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 20.37it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.14it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 21.26it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:16, 22.79it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:17, 21.89it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 20.37it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.53it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 18.07it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 19.45it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:22, 16.56it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:18, 19.99it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:17, 20.14it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 21.03it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.77it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 18.72it/s]
eval model::  30%|███       | 151/500 [00:07<00:20, 16.77it/s]
eval model::  31%|███       | 154/500 [00:07<00:18, 18.79it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:17, 19.97it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:26, 13.06it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:23, 14.54it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:20, 16.55it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:19, 16.79it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:19, 16.53it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:21, 15.02it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:21, 15.05it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.76it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.55it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 17.66it/s]
eval model::  37%|███▋      | 187/500 [00:10<00:17, 18.38it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:16, 18.40it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:15, 19.55it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:17, 17.78it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:16, 18.56it/s]
eval model::  40%|████      | 201/500 [00:10<00:16, 18.11it/s]
eval model::  41%|████      | 204/500 [00:10<00:14, 20.31it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:22, 13.26it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:22, 13.16it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:23, 12.35it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:22, 12.99it/s]
eval model::  43%|████▎     | 215/500 [00:12<00:25, 11.19it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:28,  9.98it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:22, 12.60it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:23, 11.73it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:18, 14.52it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:18, 14.69it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:15, 17.65it/s]
eval model::  47%|████▋     | 233/500 [00:13<00:14, 18.72it/s]
eval model::  47%|████▋     | 236/500 [00:13<00:14, 17.94it/s]
eval model::  48%|████▊     | 239/500 [00:13<00:13, 19.96it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 18.47it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 19.68it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.18it/s]
eval model::  50%|█████     | 251/500 [00:14<00:13, 18.27it/s]
eval model::  51%|█████     | 254/500 [00:14<00:12, 19.87it/s]
eval model::  51%|█████▏    | 257/500 [00:14<00:12, 20.20it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:10, 22.86it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:10, 23.23it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:10, 21.49it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:10, 21.13it/s]
eval model::  55%|█████▍    | 273/500 [00:15<00:11, 20.22it/s]
eval model::  55%|█████▌    | 276/500 [00:15<00:11, 19.18it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:11, 20.05it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 21.13it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.10it/s]
eval model::  58%|█████▊    | 288/500 [00:16<00:14, 14.25it/s]
eval model::  58%|█████▊    | 291/500 [00:16<00:12, 16.78it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:13, 15.28it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:14, 13.69it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 15.54it/s]
eval model::  60%|██████    | 301/500 [00:16<00:14, 14.10it/s]
eval model::  61%|██████    | 304/500 [00:17<00:12, 15.71it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:11, 17.35it/s]
eval model::  62%|██████▏   | 310/500 [00:17<00:10, 18.81it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:09, 19.55it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:10, 18.39it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:09, 19.58it/s]
eval model::  64%|██████▍   | 322/500 [00:18<00:10, 17.34it/s]
eval model::  65%|██████▌   | 325/500 [00:18<00:09, 18.82it/s]
eval model::  66%|██████▌   | 328/500 [00:18<00:08, 19.74it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:08, 19.05it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:08, 20.30it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 19.91it/s]
eval model::  68%|██████▊   | 340/500 [00:19<00:10, 15.61it/s]
eval model::  69%|██████▊   | 343/500 [00:19<00:08, 17.66it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:07, 20.13it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:06, 22.06it/s]
eval model::  70%|███████   | 352/500 [00:19<00:07, 21.01it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 20.10it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 20.22it/s]
eval model::  72%|███████▏  | 361/500 [00:20<00:08, 16.84it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:08, 16.61it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:08, 14.94it/s]
eval model::  74%|███████▎  | 368/500 [00:20<00:08, 15.82it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:07, 17.68it/s]
eval model::  75%|███████▍  | 374/500 [00:20<00:06, 19.50it/s]
eval model::  75%|███████▌  | 377/500 [00:20<00:05, 20.98it/s]
eval model::  76%|███████▌  | 380/500 [00:21<00:05, 20.02it/s]
eval model::  77%|███████▋  | 383/500 [00:21<00:05, 21.19it/s]
eval model::  77%|███████▋  | 386/500 [00:21<00:09, 12.17it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:07, 14.19it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:07, 15.01it/s]
eval model::  79%|███████▉  | 394/500 [00:22<00:06, 16.11it/s]
eval model::  79%|███████▉  | 396/500 [00:22<00:06, 16.79it/s]
eval model::  80%|███████▉  | 399/500 [00:22<00:05, 18.91it/s]
eval model::  80%|████████  | 402/500 [00:22<00:05, 17.91it/s]
eval model::  81%|████████  | 405/500 [00:22<00:05, 18.81it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:04, 19.86it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:04, 20.54it/s]
eval model::  83%|████████▎ | 414/500 [00:23<00:04, 20.79it/s]
eval model::  83%|████████▎ | 417/500 [00:23<00:04, 20.30it/s]
eval model::  84%|████████▍ | 420/500 [00:23<00:05, 13.71it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:05, 14.07it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 13.17it/s]
eval model::  85%|████████▌ | 426/500 [00:24<00:05, 13.87it/s]
eval model::  86%|████████▌ | 429/500 [00:24<00:04, 16.68it/s]
eval model::  86%|████████▌ | 431/500 [00:24<00:04, 15.33it/s]
eval model::  87%|████████▋ | 434/500 [00:24<00:03, 18.20it/s]
eval model::  88%|████████▊ | 438/500 [00:24<00:02, 21.05it/s]
eval model::  88%|████████▊ | 441/500 [00:24<00:03, 18.27it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 19.13it/s]
eval model::  89%|████████▉ | 447/500 [00:25<00:02, 21.13it/s]
eval model::  90%|█████████ | 450/500 [00:25<00:02, 19.46it/s]
eval model::  91%|█████████ | 453/500 [00:25<00:02, 20.24it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:02, 21.07it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:01, 22.55it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:01, 20.12it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:01, 19.31it/s]
eval model::  94%|█████████▎| 468/500 [00:26<00:01, 20.27it/s]
eval model::  94%|█████████▍| 471/500 [00:26<00:01, 22.04it/s]
eval model::  95%|█████████▍| 474/500 [00:26<00:01, 21.35it/s]
eval model::  95%|█████████▌| 477/500 [00:26<00:01, 22.25it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:00, 22.93it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 22.83it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 20.86it/s]
eval model::  98%|█████████▊| 489/500 [00:27<00:00, 22.30it/s]
eval model::  98%|█████████▊| 492/500 [00:27<00:00, 19.84it/s]
eval model::  99%|█████████▉| 495/500 [00:27<00:00, 19.85it/s]
eval model:: 100%|█████████▉| 499/500 [00:27<00:00, 23.62it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.18it/s]
[2024/07/28 01:26:57] ppocr INFO: cur metric, precision: 0.7010710808179162, recall: 0.693307655272027, hmean: 0.6971677559912853, fps: 44.311767166971606
[2024/07/28 01:26:57] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:26:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:27:05] ppocr INFO: epoch: [781/1500], global_step: 2343, lr: 0.001000, loss: 1.377588, loss_shrink_maps: 0.703354, loss_threshold_maps: 0.526061, loss_binary_maps: 0.140180, avg_reader_cost: 1.48035 s, avg_batch_cost: 1.73888 s, avg_samples: 12.5, ips: 7.18854 samples/s, eta: 3:38:54
[2024/07/28 01:27:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:27:14] ppocr INFO: epoch: [782/1500], global_step: 2346, lr: 0.001000, loss: 1.356306, loss_shrink_maps: 0.699977, loss_threshold_maps: 0.522337, loss_binary_maps: 0.139385, avg_reader_cost: 1.66390 s, avg_batch_cost: 1.92139 s, avg_samples: 12.5, ips: 6.50572 samples/s, eta: 3:38:37
[2024/07/28 01:27:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:27:23] ppocr INFO: epoch: [783/1500], global_step: 2349, lr: 0.001000, loss: 1.377588, loss_shrink_maps: 0.703354, loss_threshold_maps: 0.527584, loss_binary_maps: 0.140180, avg_reader_cost: 1.54228 s, avg_batch_cost: 1.77759 s, avg_samples: 12.5, ips: 7.03198 samples/s, eta: 3:38:18
[2024/07/28 01:27:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:27:31] ppocr INFO: epoch: [784/1500], global_step: 2350, lr: 0.001000, loss: 1.356306, loss_shrink_maps: 0.699977, loss_threshold_maps: 0.527584, loss_binary_maps: 0.139385, avg_reader_cost: 0.44165 s, avg_batch_cost: 0.52567 s, avg_samples: 4.8, ips: 9.13118 samples/s, eta: 3:38:11
[2024/07/28 01:27:32] ppocr INFO: epoch: [784/1500], global_step: 2352, lr: 0.001000, loss: 1.372042, loss_shrink_maps: 0.703354, loss_threshold_maps: 0.527584, loss_binary_maps: 0.140180, avg_reader_cost: 1.14269 s, avg_batch_cost: 1.28880 s, avg_samples: 7.7, ips: 5.97454 samples/s, eta: 3:38:00
[2024/07/28 01:27:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:27:41] ppocr INFO: epoch: [785/1500], global_step: 2355, lr: 0.001000, loss: 1.356236, loss_shrink_maps: 0.699977, loss_threshold_maps: 0.525464, loss_binary_maps: 0.139385, avg_reader_cost: 1.61360 s, avg_batch_cost: 1.85823 s, avg_samples: 12.5, ips: 6.72682 samples/s, eta: 3:37:42
[2024/07/28 01:27:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:27:50] ppocr INFO: epoch: [786/1500], global_step: 2358, lr: 0.001000, loss: 1.356236, loss_shrink_maps: 0.702579, loss_threshold_maps: 0.525464, loss_binary_maps: 0.140111, avg_reader_cost: 1.56643 s, avg_batch_cost: 1.80749 s, avg_samples: 12.5, ips: 6.91569 samples/s, eta: 3:37:23
[2024/07/28 01:27:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:27:58] ppocr INFO: epoch: [787/1500], global_step: 2360, lr: 0.001000, loss: 1.369891, loss_shrink_maps: 0.709748, loss_threshold_maps: 0.525464, loss_binary_maps: 0.141491, avg_reader_cost: 0.95543 s, avg_batch_cost: 1.12854 s, avg_samples: 9.6, ips: 8.50658 samples/s, eta: 3:37:10
[2024/07/28 01:27:59] ppocr INFO: epoch: [787/1500], global_step: 2361, lr: 0.001000, loss: 1.354156, loss_shrink_maps: 0.702579, loss_threshold_maps: 0.525464, loss_binary_maps: 0.140111, avg_reader_cost: 0.60989 s, avg_batch_cost: 0.66491 s, avg_samples: 2.9, ips: 4.36150 samples/s, eta: 3:37:05
[2024/07/28 01:28:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:28:08] ppocr INFO: epoch: [788/1500], global_step: 2364, lr: 0.001000, loss: 1.354156, loss_shrink_maps: 0.702579, loss_threshold_maps: 0.518651, loss_binary_maps: 0.140111, avg_reader_cost: 1.64320 s, avg_batch_cost: 1.88490 s, avg_samples: 12.5, ips: 6.63166 samples/s, eta: 3:36:47
[2024/07/28 01:28:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:28:16] ppocr INFO: epoch: [789/1500], global_step: 2367, lr: 0.001000, loss: 1.333153, loss_shrink_maps: 0.671032, loss_threshold_maps: 0.518651, loss_binary_maps: 0.133464, avg_reader_cost: 1.51788 s, avg_batch_cost: 1.75029 s, avg_samples: 12.5, ips: 7.14166 samples/s, eta: 3:36:28
[2024/07/28 01:28:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:28:25] ppocr INFO: epoch: [790/1500], global_step: 2370, lr: 0.001000, loss: 1.308871, loss_shrink_maps: 0.654896, loss_threshold_maps: 0.522931, loss_binary_maps: 0.130595, avg_reader_cost: 1.56459 s, avg_batch_cost: 1.81643 s, avg_samples: 12.5, ips: 6.88162 samples/s, eta: 3:36:10
[2024/07/28 01:28:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:28:34] ppocr INFO: epoch: [791/1500], global_step: 2373, lr: 0.001000, loss: 1.299752, loss_shrink_maps: 0.648664, loss_threshold_maps: 0.511087, loss_binary_maps: 0.129495, avg_reader_cost: 1.53630 s, avg_batch_cost: 1.78020 s, avg_samples: 12.5, ips: 7.02170 samples/s, eta: 3:35:51
[2024/07/28 01:28:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:28:43] ppocr INFO: epoch: [792/1500], global_step: 2376, lr: 0.001000, loss: 1.333153, loss_shrink_maps: 0.660729, loss_threshold_maps: 0.525571, loss_binary_maps: 0.131423, avg_reader_cost: 1.54301 s, avg_batch_cost: 1.77250 s, avg_samples: 12.5, ips: 7.05220 samples/s, eta: 3:35:32
[2024/07/28 01:28:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:28:51] ppocr INFO: epoch: [793/1500], global_step: 2379, lr: 0.001000, loss: 1.333153, loss_shrink_maps: 0.660729, loss_threshold_maps: 0.527903, loss_binary_maps: 0.131423, avg_reader_cost: 1.53903 s, avg_batch_cost: 1.77036 s, avg_samples: 12.5, ips: 7.06072 samples/s, eta: 3:35:14
[2024/07/28 01:28:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:28:59] ppocr INFO: epoch: [794/1500], global_step: 2380, lr: 0.001000, loss: 1.333153, loss_shrink_maps: 0.660729, loss_threshold_maps: 0.527903, loss_binary_maps: 0.131423, avg_reader_cost: 0.46345 s, avg_batch_cost: 0.54567 s, avg_samples: 4.8, ips: 8.79657 samples/s, eta: 3:35:07
[2024/07/28 01:29:01] ppocr INFO: epoch: [794/1500], global_step: 2382, lr: 0.001000, loss: 1.352754, loss_shrink_maps: 0.673358, loss_threshold_maps: 0.528438, loss_binary_maps: 0.134447, avg_reader_cost: 1.18261 s, avg_batch_cost: 1.32831 s, avg_samples: 7.7, ips: 5.79682 samples/s, eta: 3:34:56
[2024/07/28 01:29:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:29:10] ppocr INFO: epoch: [795/1500], global_step: 2385, lr: 0.001000, loss: 1.371121, loss_shrink_maps: 0.693197, loss_threshold_maps: 0.528438, loss_binary_maps: 0.138249, avg_reader_cost: 1.53997 s, avg_batch_cost: 1.80920 s, avg_samples: 12.5, ips: 6.90913 samples/s, eta: 3:34:37
[2024/07/28 01:29:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:29:18] ppocr INFO: epoch: [796/1500], global_step: 2388, lr: 0.001000, loss: 1.391492, loss_shrink_maps: 0.720763, loss_threshold_maps: 0.534625, loss_binary_maps: 0.143051, avg_reader_cost: 1.55425 s, avg_batch_cost: 1.81288 s, avg_samples: 12.5, ips: 6.89509 samples/s, eta: 3:34:19
[2024/07/28 01:29:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:29:27] ppocr INFO: epoch: [797/1500], global_step: 2390, lr: 0.001000, loss: 1.391492, loss_shrink_maps: 0.720763, loss_threshold_maps: 0.534625, loss_binary_maps: 0.143051, avg_reader_cost: 0.94697 s, avg_batch_cost: 1.14027 s, avg_samples: 9.6, ips: 8.41903 samples/s, eta: 3:34:06
[2024/07/28 01:29:27] ppocr INFO: epoch: [797/1500], global_step: 2391, lr: 0.001000, loss: 1.391492, loss_shrink_maps: 0.720763, loss_threshold_maps: 0.534625, loss_binary_maps: 0.143051, avg_reader_cost: 0.61567 s, avg_batch_cost: 0.67099 s, avg_samples: 2.9, ips: 4.32200 samples/s, eta: 3:34:00
[2024/07/28 01:29:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:29:36] ppocr INFO: epoch: [798/1500], global_step: 2394, lr: 0.001000, loss: 1.391492, loss_shrink_maps: 0.733184, loss_threshold_maps: 0.534625, loss_binary_maps: 0.145195, avg_reader_cost: 1.55239 s, avg_batch_cost: 1.78622 s, avg_samples: 12.5, ips: 6.99803 samples/s, eta: 3:33:42
[2024/07/28 01:29:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:29:45] ppocr INFO: epoch: [799/1500], global_step: 2397, lr: 0.001000, loss: 1.355339, loss_shrink_maps: 0.709740, loss_threshold_maps: 0.505836, loss_binary_maps: 0.140967, avg_reader_cost: 1.54508 s, avg_batch_cost: 1.78387 s, avg_samples: 12.5, ips: 7.00724 samples/s, eta: 3:33:23
[2024/07/28 01:29:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:29:54] ppocr INFO: epoch: [800/1500], global_step: 2400, lr: 0.001000, loss: 1.342052, loss_shrink_maps: 0.688344, loss_threshold_maps: 0.503194, loss_binary_maps: 0.137689, avg_reader_cost: 1.56733 s, avg_batch_cost: 1.83983 s, avg_samples: 12.5, ips: 6.79410 samples/s, eta: 3:33:05

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:33, 14.76it/s]
eval model::   1%|          | 5/500 [00:00<00:24, 20.08it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.71it/s]
eval model::   2%|▏         | 11/500 [00:00<00:25, 19.40it/s]
eval model::   3%|▎         | 14/500 [00:00<00:24, 20.14it/s]
eval model::   3%|▎         | 17/500 [00:00<00:21, 22.06it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.25it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 21.72it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 20.34it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.14it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.43it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 18.66it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 20.11it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.50it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.61it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.03it/s]
eval model::  10%|▉         | 48/500 [00:02<00:23, 19.43it/s]
eval model::  10%|█         | 51/500 [00:02<00:21, 20.99it/s]
eval model::  11%|█         | 54/500 [00:02<00:20, 21.39it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:20, 21.29it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:22, 19.64it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:30, 14.55it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:23, 18.08it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 20.96it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:20, 20.81it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:19, 21.70it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:19, 21.72it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:18, 22.48it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:19, 20.85it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.34it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:21, 18.80it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:21, 19.03it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:20, 19.99it/s]
eval model::  20%|██        | 100/500 [00:05<00:20, 19.05it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 21.83it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 22.86it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:16, 23.29it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:16, 23.35it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:17, 22.11it/s]
eval model::  24%|██▍       | 119/500 [00:05<00:16, 23.16it/s]
eval model::  24%|██▍       | 122/500 [00:05<00:16, 22.76it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:18, 20.23it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:17, 21.38it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:18, 20.45it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:17, 21.24it/s]
eval model::  27%|██▋       | 137/500 [00:06<00:17, 21.16it/s]
eval model::  28%|██▊       | 140/500 [00:06<00:18, 19.49it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 19.99it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 19.95it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:19, 18.04it/s]
eval model::  30%|███       | 151/500 [00:07<00:20, 17.08it/s]
eval model::  31%|███       | 154/500 [00:07<00:17, 19.54it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:15, 22.40it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:22, 15.25it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:21, 15.85it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:18, 18.45it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 20.03it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:16, 19.86it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:19, 16.48it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:17, 18.00it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:20, 15.54it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:17, 18.00it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 20.32it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.63it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.88it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:13, 22.02it/s]
eval model::  40%|███▉      | 198/500 [00:09<00:13, 22.09it/s]
eval model::  40%|████      | 201/500 [00:10<00:15, 19.60it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 21.17it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:20, 14.14it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:20, 14.34it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:20, 13.91it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.49it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.94it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.51it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.32it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:20, 13.87it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:15, 17.80it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:12, 21.38it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.90it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.50it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.29it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:13, 18.43it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:12, 19.94it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:12, 20.41it/s]
eval model::  50%|█████     | 251/500 [00:12<00:12, 20.16it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.68it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 22.47it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 23.28it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:11, 20.97it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:12, 19.40it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:11, 19.65it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:11, 20.28it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.22it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.99it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 21.00it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:09, 21.74it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:20, 10.38it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:16, 12.91it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:14, 14.67it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:15, 13.11it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:12, 15.55it/s]
eval model::  60%|██████    | 302/500 [00:16<00:12, 16.08it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.49it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 19.04it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.68it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 21.08it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:09, 19.37it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 21.58it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:08, 20.28it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 20.12it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 19.58it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:09, 18.17it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:08, 20.29it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:08, 20.01it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:10, 15.31it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.96it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 18.37it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.07it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 21.61it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 22.05it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 22.22it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 18.53it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.93it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 18.07it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 19.52it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 20.13it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:05, 21.38it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 24.07it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 22.44it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:05, 21.77it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:08, 13.38it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:06, 15.63it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:06, 16.74it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:05, 20.37it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 19.69it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 21.58it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 22.60it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:03, 23.57it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:04, 21.01it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:04, 19.52it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 17.10it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:06, 12.32it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 14.16it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:05, 14.75it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:04, 17.63it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 18.11it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 21.53it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 22.72it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 22.96it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 24.81it/s]
eval model::  90%|████████▉ | 449/500 [00:23<00:02, 21.62it/s]
eval model::  90%|█████████ | 452/500 [00:23<00:02, 22.73it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:02, 21.10it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:02, 20.34it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 19.62it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 16.53it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:02, 16.14it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:02, 15.71it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 16.45it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 17.13it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 17.80it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 19.95it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 20.21it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 21.18it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 20.28it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 21.00it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 17.21it/s]
eval model::  99%|█████████▉| 494/500 [00:26<00:00, 17.19it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 16.58it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 19.34it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.01it/s]
[2024/07/28 01:30:20] ppocr INFO: cur metric, precision: 0.7630979498861048, recall: 0.6451612903225806, hmean: 0.6991912340203496, fps: 44.63727256701821
[2024/07/28 01:30:20] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:30:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:30:29] ppocr INFO: epoch: [801/1500], global_step: 2403, lr: 0.001000, loss: 1.342052, loss_shrink_maps: 0.688344, loss_threshold_maps: 0.498335, loss_binary_maps: 0.137689, avg_reader_cost: 1.73859 s, avg_batch_cost: 2.10674 s, avg_samples: 12.5, ips: 5.93334 samples/s, eta: 3:32:49
[2024/07/28 01:30:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:30:37] ppocr INFO: epoch: [802/1500], global_step: 2406, lr: 0.001000, loss: 1.352140, loss_shrink_maps: 0.700414, loss_threshold_maps: 0.502725, loss_binary_maps: 0.140085, avg_reader_cost: 1.52497 s, avg_batch_cost: 1.77295 s, avg_samples: 12.5, ips: 7.05040 samples/s, eta: 3:32:31
[2024/07/28 01:30:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:30:46] ppocr INFO: epoch: [803/1500], global_step: 2409, lr: 0.001000, loss: 1.352856, loss_shrink_maps: 0.705074, loss_threshold_maps: 0.497867, loss_binary_maps: 0.140200, avg_reader_cost: 1.54448 s, avg_batch_cost: 1.77320 s, avg_samples: 12.5, ips: 7.04941 samples/s, eta: 3:32:12
[2024/07/28 01:30:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:30:54] ppocr INFO: epoch: [804/1500], global_step: 2410, lr: 0.001000, loss: 1.356056, loss_shrink_maps: 0.710700, loss_threshold_maps: 0.515726, loss_binary_maps: 0.141742, avg_reader_cost: 0.40861 s, avg_batch_cost: 0.51918 s, avg_samples: 4.8, ips: 9.24542 samples/s, eta: 3:32:05
[2024/07/28 01:30:55] ppocr INFO: epoch: [804/1500], global_step: 2412, lr: 0.001000, loss: 1.367582, loss_shrink_maps: 0.710700, loss_threshold_maps: 0.532336, loss_binary_maps: 0.141742, avg_reader_cost: 1.12941 s, avg_batch_cost: 1.27498 s, avg_samples: 7.7, ips: 6.03929 samples/s, eta: 3:31:53
[2024/07/28 01:30:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:31:04] ppocr INFO: epoch: [805/1500], global_step: 2415, lr: 0.001000, loss: 1.417033, loss_shrink_maps: 0.720722, loss_threshold_maps: 0.535677, loss_binary_maps: 0.143755, avg_reader_cost: 1.54461 s, avg_batch_cost: 1.79897 s, avg_samples: 12.5, ips: 6.94844 samples/s, eta: 3:31:35
[2024/07/28 01:31:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:31:13] ppocr INFO: epoch: [806/1500], global_step: 2418, lr: 0.001000, loss: 1.417033, loss_shrink_maps: 0.720722, loss_threshold_maps: 0.538035, loss_binary_maps: 0.143755, avg_reader_cost: 1.56034 s, avg_batch_cost: 1.78818 s, avg_samples: 12.5, ips: 6.99035 samples/s, eta: 3:31:16
[2024/07/28 01:31:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:31:21] ppocr INFO: epoch: [807/1500], global_step: 2420, lr: 0.001000, loss: 1.398779, loss_shrink_maps: 0.718764, loss_threshold_maps: 0.535677, loss_binary_maps: 0.143688, avg_reader_cost: 0.89952 s, avg_batch_cost: 1.11591 s, avg_samples: 9.6, ips: 8.60283 samples/s, eta: 3:31:03
[2024/07/28 01:31:22] ppocr INFO: epoch: [807/1500], global_step: 2421, lr: 0.001000, loss: 1.367582, loss_shrink_maps: 0.705074, loss_threshold_maps: 0.532336, loss_binary_maps: 0.140328, avg_reader_cost: 0.60346 s, avg_batch_cost: 0.65947 s, avg_samples: 2.9, ips: 4.39750 samples/s, eta: 3:30:57
[2024/07/28 01:31:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:31:31] ppocr INFO: epoch: [808/1500], global_step: 2424, lr: 0.001000, loss: 1.353270, loss_shrink_maps: 0.692840, loss_threshold_maps: 0.524332, loss_binary_maps: 0.137412, avg_reader_cost: 1.53883 s, avg_batch_cost: 1.79155 s, avg_samples: 12.5, ips: 6.97722 samples/s, eta: 3:30:39
[2024/07/28 01:31:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:31:40] ppocr INFO: epoch: [809/1500], global_step: 2427, lr: 0.001000, loss: 1.325925, loss_shrink_maps: 0.657306, loss_threshold_maps: 0.517376, loss_binary_maps: 0.131140, avg_reader_cost: 1.56760 s, avg_batch_cost: 1.79547 s, avg_samples: 12.5, ips: 6.96198 samples/s, eta: 3:30:20
[2024/07/28 01:31:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:31:49] ppocr INFO: epoch: [810/1500], global_step: 2430, lr: 0.001000, loss: 1.320754, loss_shrink_maps: 0.657306, loss_threshold_maps: 0.517157, loss_binary_maps: 0.131140, avg_reader_cost: 1.55666 s, avg_batch_cost: 1.78940 s, avg_samples: 12.5, ips: 6.98558 samples/s, eta: 3:30:02
[2024/07/28 01:31:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:31:57] ppocr INFO: epoch: [811/1500], global_step: 2433, lr: 0.001000, loss: 1.320754, loss_shrink_maps: 0.657306, loss_threshold_maps: 0.517157, loss_binary_maps: 0.131140, avg_reader_cost: 1.54538 s, avg_batch_cost: 1.79000 s, avg_samples: 12.5, ips: 6.98322 samples/s, eta: 3:29:43
[2024/07/28 01:32:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:32:07] ppocr INFO: epoch: [812/1500], global_step: 2436, lr: 0.001000, loss: 1.274835, loss_shrink_maps: 0.635743, loss_threshold_maps: 0.515338, loss_binary_maps: 0.126347, avg_reader_cost: 1.65925 s, avg_batch_cost: 1.88824 s, avg_samples: 12.5, ips: 6.61991 samples/s, eta: 3:29:25
[2024/07/28 01:32:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:32:15] ppocr INFO: epoch: [813/1500], global_step: 2439, lr: 0.001000, loss: 1.309042, loss_shrink_maps: 0.662653, loss_threshold_maps: 0.517157, loss_binary_maps: 0.131743, avg_reader_cost: 1.56821 s, avg_batch_cost: 1.81215 s, avg_samples: 12.5, ips: 6.89789 samples/s, eta: 3:29:07
[2024/07/28 01:32:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:32:23] ppocr INFO: epoch: [814/1500], global_step: 2440, lr: 0.001000, loss: 1.330383, loss_shrink_maps: 0.680726, loss_threshold_maps: 0.519130, loss_binary_maps: 0.135213, avg_reader_cost: 0.44244 s, avg_batch_cost: 0.52496 s, avg_samples: 4.8, ips: 9.14352 samples/s, eta: 3:29:00
[2024/07/28 01:32:24] ppocr INFO: epoch: [814/1500], global_step: 2442, lr: 0.001000, loss: 1.330383, loss_shrink_maps: 0.680726, loss_threshold_maps: 0.519130, loss_binary_maps: 0.135213, avg_reader_cost: 1.14157 s, avg_batch_cost: 1.28775 s, avg_samples: 7.7, ips: 5.97941 samples/s, eta: 3:28:49
[2024/07/28 01:32:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:32:33] ppocr INFO: epoch: [815/1500], global_step: 2445, lr: 0.001000, loss: 1.333720, loss_shrink_maps: 0.676407, loss_threshold_maps: 0.520419, loss_binary_maps: 0.134533, avg_reader_cost: 1.56157 s, avg_batch_cost: 1.80638 s, avg_samples: 12.5, ips: 6.91992 samples/s, eta: 3:28:30
[2024/07/28 01:32:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:32:42] ppocr INFO: epoch: [816/1500], global_step: 2448, lr: 0.001000, loss: 1.296914, loss_shrink_maps: 0.657815, loss_threshold_maps: 0.516788, loss_binary_maps: 0.130852, avg_reader_cost: 1.56874 s, avg_batch_cost: 1.80086 s, avg_samples: 12.5, ips: 6.94114 samples/s, eta: 3:28:12
[2024/07/28 01:32:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:32:50] ppocr INFO: epoch: [817/1500], global_step: 2450, lr: 0.001000, loss: 1.296914, loss_shrink_maps: 0.657815, loss_threshold_maps: 0.518077, loss_binary_maps: 0.130852, avg_reader_cost: 0.92609 s, avg_batch_cost: 1.10993 s, avg_samples: 9.6, ips: 8.64918 samples/s, eta: 3:27:59
[2024/07/28 01:32:51] ppocr INFO: epoch: [817/1500], global_step: 2451, lr: 0.001000, loss: 1.296914, loss_shrink_maps: 0.657815, loss_threshold_maps: 0.518077, loss_binary_maps: 0.130852, avg_reader_cost: 0.60124 s, avg_batch_cost: 0.65608 s, avg_samples: 2.9, ips: 4.42016 samples/s, eta: 3:27:53
[2024/07/28 01:32:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:33:00] ppocr INFO: epoch: [818/1500], global_step: 2454, lr: 0.001000, loss: 1.254657, loss_shrink_maps: 0.635540, loss_threshold_maps: 0.501115, loss_binary_maps: 0.126024, avg_reader_cost: 1.52496 s, avg_batch_cost: 1.75723 s, avg_samples: 12.5, ips: 7.11347 samples/s, eta: 3:27:34
[2024/07/28 01:33:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:33:09] ppocr INFO: epoch: [819/1500], global_step: 2457, lr: 0.001000, loss: 1.280367, loss_shrink_maps: 0.646569, loss_threshold_maps: 0.501547, loss_binary_maps: 0.128676, avg_reader_cost: 1.57327 s, avg_batch_cost: 1.80135 s, avg_samples: 12.5, ips: 6.93925 samples/s, eta: 3:27:16
[2024/07/28 01:33:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:33:17] ppocr INFO: epoch: [820/1500], global_step: 2460, lr: 0.001000, loss: 1.238869, loss_shrink_maps: 0.633158, loss_threshold_maps: 0.491785, loss_binary_maps: 0.126017, avg_reader_cost: 1.51978 s, avg_batch_cost: 1.74966 s, avg_samples: 12.5, ips: 7.14424 samples/s, eta: 3:26:57

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:38, 13.10it/s]
eval model::   1%|          | 5/500 [00:00<00:25, 19.58it/s]
eval model::   2%|▏         | 8/500 [00:00<00:26, 18.48it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.79it/s]
eval model::   3%|▎         | 14/500 [00:00<00:24, 19.53it/s]
eval model::   3%|▎         | 17/500 [00:00<00:22, 21.62it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.40it/s]
eval model::   5%|▍         | 23/500 [00:01<00:24, 19.72it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.04it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.14it/s]
eval model::   6%|▋         | 32/500 [00:01<00:27, 17.31it/s]
eval model::   7%|▋         | 34/500 [00:01<00:28, 16.37it/s]
eval model::   7%|▋         | 36/500 [00:01<00:28, 16.56it/s]
eval model::   8%|▊         | 39/500 [00:02<00:28, 16.17it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.53it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.59it/s]
eval model::   9%|▉         | 47/500 [00:02<00:24, 18.60it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.25it/s]
eval model::  10%|█         | 51/500 [00:02<00:25, 17.84it/s]
eval model::  11%|█         | 53/500 [00:02<00:24, 18.31it/s]
eval model::  11%|█         | 56/500 [00:03<00:22, 19.73it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 19.12it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 16.15it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.67it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 19.38it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 20.96it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 20.88it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:19, 21.58it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:18, 22.51it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:17, 23.37it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 21.71it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 18.85it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:21, 18.75it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.35it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.31it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.91it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.84it/s]
eval model::  21%|██        | 105/500 [00:05<00:20, 19.19it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:19, 20.41it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 20.83it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:17, 22.43it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:17, 21.58it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 20.39it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:17, 21.76it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.64it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:20, 18.43it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 16.87it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:19, 18.88it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.99it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.68it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:18, 19.36it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.86it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.18it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 18.39it/s]
eval model::  31%|███       | 156/500 [00:08<00:17, 19.21it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:18, 18.74it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:21, 15.70it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:20, 16.12it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:19, 16.82it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:18, 17.73it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 18.60it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:18, 17.36it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.59it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.58it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:18, 17.35it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 19.67it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 19.51it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:15, 19.56it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 20.72it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.46it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 21.32it/s]
eval model::  41%|████      | 204/500 [00:10<00:12, 22.78it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:19, 15.31it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:18, 15.97it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:17, 16.24it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.66it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:17, 15.91it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.23it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 17.70it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:17, 15.62it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:14, 18.37it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.96it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 23.47it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:10, 24.17it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:10, 25.60it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:13, 19.60it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:12, 20.18it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 21.08it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 21.43it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 22.31it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.53it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 23.34it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 23.11it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:09, 23.59it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:12, 18.36it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 20.29it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.41it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.85it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.55it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:11, 18.87it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:20, 10.32it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:14, 14.06it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:13, 15.81it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:16, 12.56it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:13, 14.87it/s]
eval model::  60%|██████    | 301/500 [00:16<00:13, 14.34it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.66it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 20.32it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:08, 21.60it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:08, 20.81it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:08, 21.33it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:07, 22.73it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:08, 21.99it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:08, 21.02it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 19.73it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:08, 20.11it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 22.08it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:07, 20.28it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:09, 16.20it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 19.19it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:07, 20.36it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 20.29it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 22.28it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:07, 20.35it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:06, 21.80it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 19.38it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.55it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 18.96it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 19.28it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 21.72it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 22.48it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 20.07it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:06, 18.82it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:10, 11.13it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:08, 13.72it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:07, 14.73it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:06, 16.83it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 17.49it/s]
eval model::  80%|████████  | 400/500 [00:21<00:06, 16.02it/s]
eval model::  81%|████████  | 403/500 [00:21<00:05, 17.96it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 19.46it/s]
eval model::  82%|████████▏ | 409/500 [00:21<00:04, 19.65it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:04, 21.58it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:04, 18.22it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 18.13it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 18.27it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:05, 14.82it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.87it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.66it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 19.49it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 20.29it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 21.07it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 21.05it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 20.80it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 22.79it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 24.54it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 19.68it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 21.64it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 22.26it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 20.72it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 18.86it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:01, 18.33it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 17.33it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 17.85it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 19.37it/s]
eval model::  95%|█████████▌| 475/500 [00:25<00:01, 20.90it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:01, 21.57it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 21.12it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 19.32it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 16.92it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 19.52it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 17.02it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 17.66it/s]
eval model::  99%|█████████▉| 497/500 [00:26<00:00, 17.94it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.96it/s]
[2024/07/28 01:33:44] ppocr INFO: cur metric, precision: 0.7643610785463072, recall: 0.62782859894078, hmean: 0.6893999471319059, fps: 43.292583246357054
[2024/07/28 01:33:44] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:33:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:33:52] ppocr INFO: epoch: [821/1500], global_step: 2463, lr: 0.001000, loss: 1.317818, loss_shrink_maps: 0.671569, loss_threshold_maps: 0.494285, loss_binary_maps: 0.133642, avg_reader_cost: 1.83671 s, avg_batch_cost: 2.17750 s, avg_samples: 12.5, ips: 5.74054 samples/s, eta: 3:26:41
[2024/07/28 01:33:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:34:01] ppocr INFO: epoch: [822/1500], global_step: 2466, lr: 0.001000, loss: 1.272983, loss_shrink_maps: 0.658650, loss_threshold_maps: 0.491785, loss_binary_maps: 0.131085, avg_reader_cost: 1.56457 s, avg_batch_cost: 1.79276 s, avg_samples: 12.5, ips: 6.97247 samples/s, eta: 3:26:23
[2024/07/28 01:34:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:34:10] ppocr INFO: epoch: [823/1500], global_step: 2469, lr: 0.001000, loss: 1.313762, loss_shrink_maps: 0.675768, loss_threshold_maps: 0.494285, loss_binary_maps: 0.134421, avg_reader_cost: 1.53408 s, avg_batch_cost: 1.76281 s, avg_samples: 12.5, ips: 7.09094 samples/s, eta: 3:26:04
[2024/07/28 01:34:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:34:17] ppocr INFO: epoch: [824/1500], global_step: 2470, lr: 0.001000, loss: 1.324260, loss_shrink_maps: 0.677524, loss_threshold_maps: 0.500367, loss_binary_maps: 0.134993, avg_reader_cost: 0.41805 s, avg_batch_cost: 0.50346 s, avg_samples: 4.8, ips: 9.53395 samples/s, eta: 3:25:57
[2024/07/28 01:34:19] ppocr INFO: epoch: [824/1500], global_step: 2472, lr: 0.001000, loss: 1.324260, loss_shrink_maps: 0.675284, loss_threshold_maps: 0.500367, loss_binary_maps: 0.134267, avg_reader_cost: 1.09928 s, avg_batch_cost: 1.24617 s, avg_samples: 7.7, ips: 6.17893 samples/s, eta: 3:25:45
[2024/07/28 01:34:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:34:28] ppocr INFO: epoch: [825/1500], global_step: 2475, lr: 0.001000, loss: 1.324260, loss_shrink_maps: 0.675284, loss_threshold_maps: 0.507457, loss_binary_maps: 0.134267, avg_reader_cost: 1.52122 s, avg_batch_cost: 1.75759 s, avg_samples: 12.5, ips: 7.11203 samples/s, eta: 3:25:26
[2024/07/28 01:34:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:34:36] ppocr INFO: epoch: [826/1500], global_step: 2478, lr: 0.001000, loss: 1.324260, loss_shrink_maps: 0.670492, loss_threshold_maps: 0.515130, loss_binary_maps: 0.133790, avg_reader_cost: 1.56921 s, avg_batch_cost: 1.81871 s, avg_samples: 12.5, ips: 6.87300 samples/s, eta: 3:25:08
[2024/07/28 01:34:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:34:45] ppocr INFO: epoch: [827/1500], global_step: 2480, lr: 0.001000, loss: 1.324260, loss_shrink_maps: 0.665286, loss_threshold_maps: 0.515130, loss_binary_maps: 0.132529, avg_reader_cost: 1.01436 s, avg_batch_cost: 1.18779 s, avg_samples: 9.6, ips: 8.08226 samples/s, eta: 3:24:56
[2024/07/28 01:34:46] ppocr INFO: epoch: [827/1500], global_step: 2481, lr: 0.001000, loss: 1.314424, loss_shrink_maps: 0.665286, loss_threshold_maps: 0.507457, loss_binary_maps: 0.132529, avg_reader_cost: 0.63961 s, avg_batch_cost: 0.69447 s, avg_samples: 2.9, ips: 4.17584 samples/s, eta: 3:24:50
[2024/07/28 01:34:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:34:55] ppocr INFO: epoch: [828/1500], global_step: 2484, lr: 0.001000, loss: 1.325829, loss_shrink_maps: 0.670492, loss_threshold_maps: 0.524094, loss_binary_maps: 0.133790, avg_reader_cost: 1.62681 s, avg_batch_cost: 1.85582 s, avg_samples: 12.5, ips: 6.73557 samples/s, eta: 3:24:32
[2024/07/28 01:34:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:35:04] ppocr INFO: epoch: [829/1500], global_step: 2487, lr: 0.001000, loss: 1.314424, loss_shrink_maps: 0.665286, loss_threshold_maps: 0.524094, loss_binary_maps: 0.132529, avg_reader_cost: 1.50750 s, avg_batch_cost: 1.73794 s, avg_samples: 12.5, ips: 7.19242 samples/s, eta: 3:24:13
[2024/07/28 01:35:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:35:12] ppocr INFO: epoch: [830/1500], global_step: 2490, lr: 0.001000, loss: 1.324228, loss_shrink_maps: 0.674762, loss_threshold_maps: 0.523132, loss_binary_maps: 0.134648, avg_reader_cost: 1.54260 s, avg_batch_cost: 1.79007 s, avg_samples: 12.5, ips: 6.98295 samples/s, eta: 3:23:55
[2024/07/28 01:35:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:35:21] ppocr INFO: epoch: [831/1500], global_step: 2493, lr: 0.001000, loss: 1.340136, loss_shrink_maps: 0.685302, loss_threshold_maps: 0.506248, loss_binary_maps: 0.136676, avg_reader_cost: 1.52240 s, avg_batch_cost: 1.75154 s, avg_samples: 12.5, ips: 7.13658 samples/s, eta: 3:23:36
[2024/07/28 01:35:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:35:31] ppocr INFO: epoch: [832/1500], global_step: 2496, lr: 0.001000, loss: 1.351400, loss_shrink_maps: 0.706166, loss_threshold_maps: 0.513652, loss_binary_maps: 0.140570, avg_reader_cost: 1.54743 s, avg_batch_cost: 1.86753 s, avg_samples: 12.5, ips: 6.69334 samples/s, eta: 3:23:18
[2024/07/28 01:35:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:35:39] ppocr INFO: epoch: [833/1500], global_step: 2499, lr: 0.001000, loss: 1.324228, loss_shrink_maps: 0.686629, loss_threshold_maps: 0.499357, loss_binary_maps: 0.136998, avg_reader_cost: 1.54631 s, avg_batch_cost: 1.80083 s, avg_samples: 12.5, ips: 6.94124 samples/s, eta: 3:22:59
[2024/07/28 01:35:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:35:47] ppocr INFO: epoch: [834/1500], global_step: 2500, lr: 0.001000, loss: 1.337293, loss_shrink_maps: 0.688247, loss_threshold_maps: 0.506248, loss_binary_maps: 0.137283, avg_reader_cost: 0.42424 s, avg_batch_cost: 0.52579 s, avg_samples: 4.8, ips: 9.12911 samples/s, eta: 3:22:53
[2024/07/28 01:35:48] ppocr INFO: epoch: [834/1500], global_step: 2502, lr: 0.001000, loss: 1.337293, loss_shrink_maps: 0.677198, loss_threshold_maps: 0.499357, loss_binary_maps: 0.135102, avg_reader_cost: 1.14258 s, avg_batch_cost: 1.28803 s, avg_samples: 7.7, ips: 5.97811 samples/s, eta: 3:22:41
[2024/07/28 01:35:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:35:57] ppocr INFO: epoch: [835/1500], global_step: 2505, lr: 0.001000, loss: 1.348558, loss_shrink_maps: 0.688247, loss_threshold_maps: 0.499357, loss_binary_maps: 0.137283, avg_reader_cost: 1.54885 s, avg_batch_cost: 1.78201 s, avg_samples: 12.5, ips: 7.01456 samples/s, eta: 3:22:22
[2024/07/28 01:36:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:36:06] ppocr INFO: epoch: [836/1500], global_step: 2508, lr: 0.001000, loss: 1.368041, loss_shrink_maps: 0.710639, loss_threshold_maps: 0.521682, loss_binary_maps: 0.141469, avg_reader_cost: 1.57988 s, avg_batch_cost: 1.80770 s, avg_samples: 12.5, ips: 6.91488 samples/s, eta: 3:22:04
[2024/07/28 01:36:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:36:15] ppocr INFO: epoch: [837/1500], global_step: 2510, lr: 0.001000, loss: 1.348558, loss_shrink_maps: 0.688247, loss_threshold_maps: 0.501613, loss_binary_maps: 0.137283, avg_reader_cost: 0.93154 s, avg_batch_cost: 1.13769 s, avg_samples: 9.6, ips: 8.43818 samples/s, eta: 3:21:51
[2024/07/28 01:36:15] ppocr INFO: epoch: [837/1500], global_step: 2511, lr: 0.001000, loss: 1.348558, loss_shrink_maps: 0.688247, loss_threshold_maps: 0.501613, loss_binary_maps: 0.137283, avg_reader_cost: 0.61456 s, avg_batch_cost: 0.66977 s, avg_samples: 2.9, ips: 4.32986 samples/s, eta: 3:21:46
[2024/07/28 01:36:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:36:24] ppocr INFO: epoch: [838/1500], global_step: 2514, lr: 0.001000, loss: 1.310685, loss_shrink_maps: 0.655271, loss_threshold_maps: 0.526753, loss_binary_maps: 0.130200, avg_reader_cost: 1.49298 s, avg_batch_cost: 1.72435 s, avg_samples: 12.5, ips: 7.24910 samples/s, eta: 3:21:27
[2024/07/28 01:36:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:36:33] ppocr INFO: epoch: [839/1500], global_step: 2517, lr: 0.001000, loss: 1.346354, loss_shrink_maps: 0.678288, loss_threshold_maps: 0.530775, loss_binary_maps: 0.134739, avg_reader_cost: 1.55918 s, avg_batch_cost: 1.78737 s, avg_samples: 12.5, ips: 6.99353 samples/s, eta: 3:21:08
[2024/07/28 01:36:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:36:42] ppocr INFO: epoch: [840/1500], global_step: 2520, lr: 0.001000, loss: 1.366861, loss_shrink_maps: 0.683230, loss_threshold_maps: 0.530775, loss_binary_maps: 0.135668, avg_reader_cost: 1.57453 s, avg_batch_cost: 1.81634 s, avg_samples: 12.5, ips: 6.88199 samples/s, eta: 3:20:50

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:56,  8.89it/s]
eval model::   1%|          | 3/500 [00:00<00:33, 15.05it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.29it/s]
eval model::   2%|▏         | 8/500 [00:00<00:26, 18.73it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.22it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 18.70it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.37it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.73it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 22.63it/s]
eval model::   5%|▌         | 26/500 [00:01<00:20, 22.77it/s]
eval model::   6%|▌         | 29/500 [00:01<00:19, 23.96it/s]
eval model::   6%|▋         | 32/500 [00:01<00:20, 22.30it/s]
eval model::   7%|▋         | 35/500 [00:01<00:21, 21.25it/s]
eval model::   8%|▊         | 38/500 [00:01<00:21, 21.05it/s]
eval model::   8%|▊         | 41/500 [00:02<00:24, 19.06it/s]
eval model::   9%|▊         | 43/500 [00:02<00:26, 17.13it/s]
eval model::   9%|▉         | 46/500 [00:02<00:24, 18.70it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 19.57it/s]
eval model::  10%|█         | 51/500 [00:02<00:23, 19.04it/s]
eval model::  11%|█         | 54/500 [00:02<00:21, 20.34it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:22, 20.09it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:21, 20.01it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.24it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.83it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 19.60it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 20.03it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:21, 19.87it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:19, 21.13it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.29it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:25, 16.20it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:24, 16.72it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:24, 16.82it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:24, 16.78it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:22, 17.97it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:20, 19.36it/s]
eval model::  20%|██        | 100/500 [00:05<00:21, 18.35it/s]
eval model::  20%|██        | 102/500 [00:05<00:21, 18.61it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.73it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.06it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:19, 19.62it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:20, 18.39it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:18, 20.49it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:19, 19.22it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:23, 16.06it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:22, 16.61it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:20, 18.30it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:22, 16.16it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:22, 16.07it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:19, 18.39it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.23it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.61it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.57it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 18.96it/s]
eval model::  30%|███       | 151/500 [00:07<00:18, 18.61it/s]
eval model::  31%|███       | 155/500 [00:08<00:15, 21.71it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:14, 23.50it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:20, 16.61it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:19, 17.15it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:18, 17.67it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:18, 17.75it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:18, 17.84it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 16.28it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:17, 18.08it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:19, 16.48it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:17, 17.88it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.90it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.22it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:14, 20.76it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 21.22it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:13, 21.63it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 21.93it/s]
eval model::  41%|████      | 205/500 [00:10<00:18, 15.77it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:17, 16.43it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:17, 16.15it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:16, 17.20it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.77it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.89it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 16.19it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.60it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 16.75it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:15, 17.20it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 21.67it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:13, 20.15it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 20.56it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 21.95it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 20.01it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:12, 21.03it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:12, 20.72it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 21.93it/s]
eval model::  51%|█████     | 255/500 [00:13<00:12, 20.32it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 20.67it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 20.62it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:11, 21.38it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 20.32it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:12, 18.22it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 19.08it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:13, 16.86it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 17.39it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 19.94it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:09, 22.10it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:17, 12.55it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:12, 16.39it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:11, 18.20it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:13, 15.32it/s]
eval model::  60%|██████    | 300/500 [00:16<00:11, 18.03it/s]
eval model::  61%|██████    | 303/500 [00:16<00:10, 19.35it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 19.16it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 20.05it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:08, 21.01it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 22.06it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:07, 23.49it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 20.99it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 20.19it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 22.23it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 21.09it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 21.43it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 21.78it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 21.06it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 15.58it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 17.62it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 18.21it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 19.61it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 20.73it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 20.92it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 17.69it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.76it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 17.54it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 17.89it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:07, 18.08it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 20.04it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 21.70it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 20.23it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 21.85it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:09, 12.54it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:07, 14.47it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:07, 15.43it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 16.21it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 16.82it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 19.03it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 18.02it/s]
eval model::  81%|████████  | 404/500 [00:21<00:05, 18.84it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 18.68it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 20.86it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:03, 21.82it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:03, 21.71it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:03, 21.59it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 13.93it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 13.78it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:05, 14.73it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 16.37it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 15.82it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 19.29it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 21.24it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 20.11it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 22.13it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 21.65it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 18.55it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 20.74it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 22.14it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 20.77it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 18.72it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:01, 18.63it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:02, 16.35it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 16.59it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.41it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 17.61it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 19.41it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 20.14it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 21.58it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 21.37it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 22.73it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 20.43it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 21.53it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 22.82it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.88it/s]
[2024/07/28 01:37:08] ppocr INFO: cur metric, precision: 0.755056179775281, recall: 0.6470871449205585, hmean: 0.6969147005444646, fps: 44.47852025577219
[2024/07/28 01:37:08] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:37:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:37:17] ppocr INFO: epoch: [841/1500], global_step: 2523, lr: 0.001000, loss: 1.366861, loss_shrink_maps: 0.695516, loss_threshold_maps: 0.530775, loss_binary_maps: 0.138080, avg_reader_cost: 1.61216 s, avg_batch_cost: 1.88366 s, avg_samples: 12.5, ips: 6.63603 samples/s, eta: 3:20:32
[2024/07/28 01:37:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:37:25] ppocr INFO: epoch: [842/1500], global_step: 2526, lr: 0.001000, loss: 1.337947, loss_shrink_maps: 0.675387, loss_threshold_maps: 0.513723, loss_binary_maps: 0.134208, avg_reader_cost: 1.51415 s, avg_batch_cost: 1.74291 s, avg_samples: 12.5, ips: 7.17192 samples/s, eta: 3:20:13
[2024/07/28 01:37:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:37:34] ppocr INFO: epoch: [843/1500], global_step: 2529, lr: 0.001000, loss: 1.337947, loss_shrink_maps: 0.675387, loss_threshold_maps: 0.513723, loss_binary_maps: 0.134208, avg_reader_cost: 1.53275 s, avg_batch_cost: 1.76531 s, avg_samples: 12.5, ips: 7.08090 samples/s, eta: 3:19:54
[2024/07/28 01:37:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:37:42] ppocr INFO: epoch: [844/1500], global_step: 2530, lr: 0.001000, loss: 1.353928, loss_shrink_maps: 0.683230, loss_threshold_maps: 0.523625, loss_binary_maps: 0.135784, avg_reader_cost: 0.41101 s, avg_batch_cost: 0.49405 s, avg_samples: 4.8, ips: 9.71564 samples/s, eta: 3:19:47
[2024/07/28 01:37:43] ppocr INFO: epoch: [844/1500], global_step: 2532, lr: 0.001000, loss: 1.368995, loss_shrink_maps: 0.697981, loss_threshold_maps: 0.530100, loss_binary_maps: 0.139005, avg_reader_cost: 1.07937 s, avg_batch_cost: 1.22524 s, avg_samples: 7.7, ips: 6.28448 samples/s, eta: 3:19:35
[2024/07/28 01:37:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:37:52] ppocr INFO: epoch: [845/1500], global_step: 2535, lr: 0.001000, loss: 1.356063, loss_shrink_maps: 0.685005, loss_threshold_maps: 0.513723, loss_binary_maps: 0.136619, avg_reader_cost: 1.51618 s, avg_batch_cost: 1.74658 s, avg_samples: 12.5, ips: 7.15683 samples/s, eta: 3:19:16
[2024/07/28 01:37:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:38:01] ppocr INFO: epoch: [846/1500], global_step: 2538, lr: 0.001000, loss: 1.356063, loss_shrink_maps: 0.685005, loss_threshold_maps: 0.511718, loss_binary_maps: 0.136619, avg_reader_cost: 1.52222 s, avg_batch_cost: 1.75463 s, avg_samples: 12.5, ips: 7.12400 samples/s, eta: 3:18:58
[2024/07/28 01:38:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:38:09] ppocr INFO: epoch: [847/1500], global_step: 2540, lr: 0.001000, loss: 1.355139, loss_shrink_maps: 0.685005, loss_threshold_maps: 0.513656, loss_binary_maps: 0.136619, avg_reader_cost: 0.95069 s, avg_batch_cost: 1.14627 s, avg_samples: 9.6, ips: 8.37496 samples/s, eta: 3:18:45
[2024/07/28 01:38:10] ppocr INFO: epoch: [847/1500], global_step: 2541, lr: 0.001000, loss: 1.358306, loss_shrink_maps: 0.697981, loss_threshold_maps: 0.515707, loss_binary_maps: 0.139005, avg_reader_cost: 0.61909 s, avg_batch_cost: 0.67398 s, avg_samples: 2.9, ips: 4.30277 samples/s, eta: 3:18:39
[2024/07/28 01:38:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:38:18] ppocr INFO: epoch: [848/1500], global_step: 2544, lr: 0.001000, loss: 1.360404, loss_shrink_maps: 0.697981, loss_threshold_maps: 0.519463, loss_binary_maps: 0.139202, avg_reader_cost: 1.53926 s, avg_batch_cost: 1.79241 s, avg_samples: 12.5, ips: 6.97385 samples/s, eta: 3:18:21
[2024/07/28 01:38:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:38:27] ppocr INFO: epoch: [849/1500], global_step: 2547, lr: 0.001000, loss: 1.358306, loss_shrink_maps: 0.692447, loss_threshold_maps: 0.519463, loss_binary_maps: 0.138182, avg_reader_cost: 1.56150 s, avg_batch_cost: 1.80853 s, avg_samples: 12.5, ips: 6.91168 samples/s, eta: 3:18:02
[2024/07/28 01:38:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:38:37] ppocr INFO: epoch: [850/1500], global_step: 2550, lr: 0.001000, loss: 1.358306, loss_shrink_maps: 0.692447, loss_threshold_maps: 0.519463, loss_binary_maps: 0.138182, avg_reader_cost: 1.60027 s, avg_batch_cost: 1.88434 s, avg_samples: 12.5, ips: 6.63363 samples/s, eta: 3:17:45
[2024/07/28 01:38:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:38:46] ppocr INFO: epoch: [851/1500], global_step: 2553, lr: 0.001000, loss: 1.357101, loss_shrink_maps: 0.689646, loss_threshold_maps: 0.519463, loss_binary_maps: 0.137267, avg_reader_cost: 1.55276 s, avg_batch_cost: 1.79461 s, avg_samples: 12.5, ips: 6.96531 samples/s, eta: 3:17:26
[2024/07/28 01:38:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:38:55] ppocr INFO: epoch: [852/1500], global_step: 2556, lr: 0.001000, loss: 1.357101, loss_shrink_maps: 0.695782, loss_threshold_maps: 0.519463, loss_binary_maps: 0.138490, avg_reader_cost: 1.58439 s, avg_batch_cost: 1.81508 s, avg_samples: 12.5, ips: 6.88675 samples/s, eta: 3:17:08
[2024/07/28 01:38:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:39:04] ppocr INFO: epoch: [853/1500], global_step: 2559, lr: 0.001000, loss: 1.348328, loss_shrink_maps: 0.691144, loss_threshold_maps: 0.514952, loss_binary_maps: 0.137432, avg_reader_cost: 1.58246 s, avg_batch_cost: 1.86984 s, avg_samples: 12.5, ips: 6.68507 samples/s, eta: 3:16:50
[2024/07/28 01:39:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:39:11] ppocr INFO: epoch: [854/1500], global_step: 2560, lr: 0.001000, loss: 1.348328, loss_shrink_maps: 0.691144, loss_threshold_maps: 0.514952, loss_binary_maps: 0.137432, avg_reader_cost: 0.41726 s, avg_batch_cost: 0.49956 s, avg_samples: 4.8, ips: 9.60846 samples/s, eta: 3:16:43
[2024/07/28 01:39:13] ppocr INFO: epoch: [854/1500], global_step: 2562, lr: 0.001000, loss: 1.360988, loss_shrink_maps: 0.693830, loss_threshold_maps: 0.524182, loss_binary_maps: 0.138059, avg_reader_cost: 1.09046 s, avg_batch_cost: 1.23639 s, avg_samples: 7.7, ips: 6.22781 samples/s, eta: 3:16:31
[2024/07/28 01:39:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:39:22] ppocr INFO: epoch: [855/1500], global_step: 2565, lr: 0.001000, loss: 1.348328, loss_shrink_maps: 0.691144, loss_threshold_maps: 0.519962, loss_binary_maps: 0.137432, avg_reader_cost: 1.59443 s, avg_batch_cost: 1.82925 s, avg_samples: 12.5, ips: 6.83342 samples/s, eta: 3:16:13
[2024/07/28 01:39:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:39:31] ppocr INFO: epoch: [856/1500], global_step: 2568, lr: 0.001000, loss: 1.348298, loss_shrink_maps: 0.693830, loss_threshold_maps: 0.516358, loss_binary_maps: 0.138059, avg_reader_cost: 1.55258 s, avg_batch_cost: 1.83203 s, avg_samples: 12.5, ips: 6.82304 samples/s, eta: 3:15:54
[2024/07/28 01:39:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:39:39] ppocr INFO: epoch: [857/1500], global_step: 2570, lr: 0.001000, loss: 1.335016, loss_shrink_maps: 0.678357, loss_threshold_maps: 0.510732, loss_binary_maps: 0.135009, avg_reader_cost: 0.92491 s, avg_batch_cost: 1.11892 s, avg_samples: 9.6, ips: 8.57971 samples/s, eta: 3:15:42
[2024/07/28 01:39:40] ppocr INFO: epoch: [857/1500], global_step: 2571, lr: 0.001000, loss: 1.338347, loss_shrink_maps: 0.686030, loss_threshold_maps: 0.516358, loss_binary_maps: 0.136496, avg_reader_cost: 0.60565 s, avg_batch_cost: 0.66046 s, avg_samples: 2.9, ips: 4.39089 samples/s, eta: 3:15:36
[2024/07/28 01:39:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:39:48] ppocr INFO: epoch: [858/1500], global_step: 2574, lr: 0.001000, loss: 1.318220, loss_shrink_maps: 0.676130, loss_threshold_maps: 0.505955, loss_binary_maps: 0.134519, avg_reader_cost: 1.53744 s, avg_batch_cost: 1.76613 s, avg_samples: 12.5, ips: 7.07764 samples/s, eta: 3:15:17
[2024/07/28 01:39:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:39:58] ppocr INFO: epoch: [859/1500], global_step: 2577, lr: 0.001000, loss: 1.318220, loss_shrink_maps: 0.670048, loss_threshold_maps: 0.505955, loss_binary_maps: 0.133616, avg_reader_cost: 1.52816 s, avg_batch_cost: 1.77948 s, avg_samples: 12.5, ips: 7.02453 samples/s, eta: 3:14:59
[2024/07/28 01:40:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:40:07] ppocr INFO: epoch: [860/1500], global_step: 2580, lr: 0.001000, loss: 1.304200, loss_shrink_maps: 0.657449, loss_threshold_maps: 0.500332, loss_binary_maps: 0.131105, avg_reader_cost: 1.59951 s, avg_batch_cost: 1.85395 s, avg_samples: 12.5, ips: 6.74236 samples/s, eta: 3:14:41

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:40, 12.38it/s]
eval model::   1%|          | 5/500 [00:00<00:28, 17.68it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.61it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.68it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.18it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.52it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 18.84it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 21.22it/s]
eval model::   5%|▌         | 26/500 [00:01<00:25, 18.90it/s]
eval model::   6%|▌         | 29/500 [00:01<00:24, 19.11it/s]
eval model::   6%|▌         | 31/500 [00:01<00:24, 19.15it/s]
eval model::   7%|▋         | 34/500 [00:01<00:23, 19.74it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.67it/s]
eval model::   8%|▊         | 40/500 [00:02<00:29, 15.79it/s]
eval model::   8%|▊         | 42/500 [00:02<00:29, 15.73it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 17.73it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 17.38it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.59it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 21.13it/s]
eval model::  11%|█         | 56/500 [00:02<00:20, 21.36it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:22, 19.24it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:27, 15.94it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:27, 16.05it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:24, 17.65it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:21, 19.90it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 20.51it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:20, 21.09it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.60it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.01it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.89it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:19, 21.10it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.55it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:20, 19.52it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:20, 20.12it/s]
eval model::  20%|██        | 100/500 [00:05<00:20, 19.22it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 21.94it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 22.76it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 22.18it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:16, 22.98it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:17, 21.69it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 21.72it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:17, 21.31it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:20, 18.09it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 20.26it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:20, 18.10it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:20, 18.24it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:18, 20.02it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:17, 20.39it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.81it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 18.81it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.27it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.89it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 19.03it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 20.45it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:18, 18.93it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:24, 13.74it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:23, 14.55it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:19, 17.03it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 18.42it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:18, 17.51it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 15.78it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 16.18it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.16it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.71it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 18.27it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:17, 18.24it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:15, 19.44it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:14, 21.05it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:15, 19.71it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 21.06it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 22.02it/s]
eval model::  41%|████      | 205/500 [00:10<00:20, 14.41it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:19, 15.06it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:18, 15.52it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 15.09it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.67it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 14.17it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.66it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.11it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:21, 13.15it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:17, 15.88it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:16, 16.34it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 19.33it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:13, 20.17it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 20.91it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.69it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 19.50it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:11, 21.51it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.97it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 21.20it/s]
eval model::  51%|█████     | 254/500 [00:13<00:10, 22.99it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.86it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 21.86it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:11, 21.31it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:11, 20.28it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:13, 17.35it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.59it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 18.89it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 20.14it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.31it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:10, 20.69it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:17, 12.52it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.15it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 15.97it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:14, 14.20it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 16.28it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 16.93it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 17.50it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:09, 19.96it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:08, 21.58it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 21.00it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:08, 21.12it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 21.67it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 19.84it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 20.33it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 19.14it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 19.70it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:08, 20.72it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:08, 20.18it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 15.06it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.82it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 19.18it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.40it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 21.42it/s]
eval model::  71%|███████   | 355/500 [00:18<00:07, 20.57it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 21.03it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.62it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.45it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.56it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 19.33it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 18.80it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 20.81it/s]
eval model::  75%|███████▌  | 377/500 [00:20<00:05, 22.72it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 20.96it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:04, 24.80it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 15.74it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 18.24it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 19.43it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 19.37it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 19.98it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 19.55it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 20.50it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.11it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 22.20it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 22.44it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 20.48it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 15.81it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 16.23it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 16.55it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:03, 18.47it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 19.79it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 19.53it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:02, 21.44it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 22.37it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 21.04it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 21.74it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 21.28it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.92it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 22.38it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 21.77it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 20.97it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 18.93it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 17.47it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.94it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.63it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 20.46it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 21.43it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:01, 19.07it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 18.97it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 20.96it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 19.94it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 18.57it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 16.86it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 17.46it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 21.33it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.03it/s]
[2024/07/28 01:40:33] ppocr INFO: cur metric, precision: 0.7364939360529217, recall: 0.6432354357246028, hmean: 0.6867129272680546, fps: 44.881520197478075
[2024/07/28 01:40:33] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:40:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:40:41] ppocr INFO: epoch: [861/1500], global_step: 2583, lr: 0.001000, loss: 1.284791, loss_shrink_maps: 0.646363, loss_threshold_maps: 0.487865, loss_binary_maps: 0.128742, avg_reader_cost: 1.59415 s, avg_batch_cost: 1.87571 s, avg_samples: 12.5, ips: 6.66416 samples/s, eta: 3:14:23
[2024/07/28 01:40:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:40:50] ppocr INFO: epoch: [862/1500], global_step: 2586, lr: 0.001000, loss: 1.298666, loss_shrink_maps: 0.652776, loss_threshold_maps: 0.500332, loss_binary_maps: 0.130411, avg_reader_cost: 1.55584 s, avg_batch_cost: 1.80725 s, avg_samples: 12.5, ips: 6.91659 samples/s, eta: 3:14:04
[2024/07/28 01:40:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:40:59] ppocr INFO: epoch: [863/1500], global_step: 2589, lr: 0.001000, loss: 1.279258, loss_shrink_maps: 0.646363, loss_threshold_maps: 0.491053, loss_binary_maps: 0.128742, avg_reader_cost: 1.65174 s, avg_batch_cost: 1.87970 s, avg_samples: 12.5, ips: 6.65001 samples/s, eta: 3:13:46
[2024/07/28 01:41:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:41:08] ppocr INFO: epoch: [864/1500], global_step: 2590, lr: 0.001000, loss: 1.279258, loss_shrink_maps: 0.646363, loss_threshold_maps: 0.494580, loss_binary_maps: 0.128742, avg_reader_cost: 0.42650 s, avg_batch_cost: 0.57786 s, avg_samples: 4.8, ips: 8.30656 samples/s, eta: 3:13:40
[2024/07/28 01:41:09] ppocr INFO: epoch: [864/1500], global_step: 2592, lr: 0.001000, loss: 1.279258, loss_shrink_maps: 0.646363, loss_threshold_maps: 0.494580, loss_binary_maps: 0.128742, avg_reader_cost: 1.24682 s, avg_batch_cost: 1.39238 s, avg_samples: 7.7, ips: 5.53011 samples/s, eta: 3:13:29
[2024/07/28 01:41:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:41:18] ppocr INFO: epoch: [865/1500], global_step: 2595, lr: 0.001000, loss: 1.282797, loss_shrink_maps: 0.649374, loss_threshold_maps: 0.500591, loss_binary_maps: 0.129758, avg_reader_cost: 1.55919 s, avg_batch_cost: 1.81531 s, avg_samples: 12.5, ips: 6.88587 samples/s, eta: 3:13:11
[2024/07/28 01:41:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:41:27] ppocr INFO: epoch: [866/1500], global_step: 2598, lr: 0.001000, loss: 1.308485, loss_shrink_maps: 0.673008, loss_threshold_maps: 0.512863, loss_binary_maps: 0.133301, avg_reader_cost: 1.54357 s, avg_batch_cost: 1.77134 s, avg_samples: 12.5, ips: 7.05680 samples/s, eta: 3:12:52
[2024/07/28 01:41:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:41:35] ppocr INFO: epoch: [867/1500], global_step: 2600, lr: 0.001000, loss: 1.343094, loss_shrink_maps: 0.673008, loss_threshold_maps: 0.523852, loss_binary_maps: 0.133301, avg_reader_cost: 0.96365 s, avg_batch_cost: 1.13651 s, avg_samples: 9.6, ips: 8.44691 samples/s, eta: 3:12:39
[2024/07/28 01:41:36] ppocr INFO: epoch: [867/1500], global_step: 2601, lr: 0.001000, loss: 1.370275, loss_shrink_maps: 0.685234, loss_threshold_maps: 0.528305, loss_binary_maps: 0.135932, avg_reader_cost: 0.61379 s, avg_batch_cost: 0.66854 s, avg_samples: 2.9, ips: 4.33783 samples/s, eta: 3:12:34
[2024/07/28 01:41:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:41:45] ppocr INFO: epoch: [868/1500], global_step: 2604, lr: 0.001000, loss: 1.335812, loss_shrink_maps: 0.673008, loss_threshold_maps: 0.523852, loss_binary_maps: 0.133301, avg_reader_cost: 1.55739 s, avg_batch_cost: 1.78710 s, avg_samples: 12.5, ips: 6.99456 samples/s, eta: 3:12:15
[2024/07/28 01:41:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:41:54] ppocr INFO: epoch: [869/1500], global_step: 2607, lr: 0.001000, loss: 1.357440, loss_shrink_maps: 0.685234, loss_threshold_maps: 0.528305, loss_binary_maps: 0.135514, avg_reader_cost: 1.55736 s, avg_batch_cost: 1.79289 s, avg_samples: 12.5, ips: 6.97199 samples/s, eta: 3:11:57
[2024/07/28 01:41:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:42:03] ppocr INFO: epoch: [870/1500], global_step: 2610, lr: 0.001000, loss: 1.367462, loss_shrink_maps: 0.701188, loss_threshold_maps: 0.528305, loss_binary_maps: 0.139478, avg_reader_cost: 1.56944 s, avg_batch_cost: 1.81271 s, avg_samples: 12.5, ips: 6.89576 samples/s, eta: 3:11:38
[2024/07/28 01:42:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:42:12] ppocr INFO: epoch: [871/1500], global_step: 2613, lr: 0.001000, loss: 1.367462, loss_shrink_maps: 0.709006, loss_threshold_maps: 0.528305, loss_binary_maps: 0.141401, avg_reader_cost: 1.51397 s, avg_batch_cost: 1.76267 s, avg_samples: 12.5, ips: 7.09152 samples/s, eta: 3:11:20
[2024/07/28 01:42:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:42:21] ppocr INFO: epoch: [872/1500], global_step: 2616, lr: 0.001000, loss: 1.384086, loss_shrink_maps: 0.711432, loss_threshold_maps: 0.528305, loss_binary_maps: 0.142224, avg_reader_cost: 1.51257 s, avg_batch_cost: 1.75135 s, avg_samples: 12.5, ips: 7.13735 samples/s, eta: 3:11:01
[2024/07/28 01:42:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:42:30] ppocr INFO: epoch: [873/1500], global_step: 2619, lr: 0.001000, loss: 1.384086, loss_shrink_maps: 0.711432, loss_threshold_maps: 0.519512, loss_binary_maps: 0.142224, avg_reader_cost: 1.52489 s, avg_batch_cost: 1.80331 s, avg_samples: 12.5, ips: 6.93170 samples/s, eta: 3:10:43
[2024/07/28 01:42:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:42:37] ppocr INFO: epoch: [874/1500], global_step: 2620, lr: 0.001000, loss: 1.357747, loss_shrink_maps: 0.709006, loss_threshold_maps: 0.516685, loss_binary_maps: 0.141401, avg_reader_cost: 0.42500 s, avg_batch_cost: 0.52761 s, avg_samples: 4.8, ips: 9.09768 samples/s, eta: 3:10:36
[2024/07/28 01:42:39] ppocr INFO: epoch: [874/1500], global_step: 2622, lr: 0.001000, loss: 1.322270, loss_shrink_maps: 0.669452, loss_threshold_maps: 0.510468, loss_binary_maps: 0.132485, avg_reader_cost: 1.14654 s, avg_batch_cost: 1.29227 s, avg_samples: 7.7, ips: 5.95851 samples/s, eta: 3:10:24
[2024/07/28 01:42:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:42:48] ppocr INFO: epoch: [875/1500], global_step: 2625, lr: 0.001000, loss: 1.307418, loss_shrink_maps: 0.652734, loss_threshold_maps: 0.509849, loss_binary_maps: 0.129938, avg_reader_cost: 1.54306 s, avg_batch_cost: 1.77313 s, avg_samples: 12.5, ips: 7.04968 samples/s, eta: 3:10:06
[2024/07/28 01:42:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:42:57] ppocr INFO: epoch: [876/1500], global_step: 2628, lr: 0.001000, loss: 1.299998, loss_shrink_maps: 0.652734, loss_threshold_maps: 0.487526, loss_binary_maps: 0.129938, avg_reader_cost: 1.50490 s, avg_batch_cost: 1.75777 s, avg_samples: 12.5, ips: 7.11127 samples/s, eta: 3:09:47
[2024/07/28 01:42:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:43:05] ppocr INFO: epoch: [877/1500], global_step: 2630, lr: 0.001000, loss: 1.255786, loss_shrink_maps: 0.641068, loss_threshold_maps: 0.480872, loss_binary_maps: 0.127565, avg_reader_cost: 0.92416 s, avg_batch_cost: 1.10259 s, avg_samples: 9.6, ips: 8.70679 samples/s, eta: 3:09:34
[2024/07/28 01:43:06] ppocr INFO: epoch: [877/1500], global_step: 2631, lr: 0.001000, loss: 1.255786, loss_shrink_maps: 0.641068, loss_threshold_maps: 0.485509, loss_binary_maps: 0.127565, avg_reader_cost: 0.59689 s, avg_batch_cost: 0.65159 s, avg_samples: 2.9, ips: 4.45067 samples/s, eta: 3:09:28
[2024/07/28 01:43:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:43:15] ppocr INFO: epoch: [878/1500], global_step: 2634, lr: 0.001000, loss: 1.215341, loss_shrink_maps: 0.621636, loss_threshold_maps: 0.478193, loss_binary_maps: 0.123727, avg_reader_cost: 1.52349 s, avg_batch_cost: 1.79803 s, avg_samples: 12.5, ips: 6.95204 samples/s, eta: 3:09:10
[2024/07/28 01:43:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:43:23] ppocr INFO: epoch: [879/1500], global_step: 2637, lr: 0.001000, loss: 1.211913, loss_shrink_maps: 0.621636, loss_threshold_maps: 0.476063, loss_binary_maps: 0.123727, avg_reader_cost: 1.51592 s, avg_batch_cost: 1.74591 s, avg_samples: 12.5, ips: 7.15961 samples/s, eta: 3:08:51
[2024/07/28 01:43:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:43:33] ppocr INFO: epoch: [880/1500], global_step: 2640, lr: 0.001000, loss: 1.202169, loss_shrink_maps: 0.604932, loss_threshold_maps: 0.478985, loss_binary_maps: 0.120490, avg_reader_cost: 1.61346 s, avg_batch_cost: 1.88064 s, avg_samples: 12.5, ips: 6.64666 samples/s, eta: 3:08:33

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:58,  8.55it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 16.68it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.19it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.39it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.53it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 19.18it/s]
eval model::   4%|▎         | 18/500 [00:00<00:20, 22.99it/s]
eval model::   4%|▍         | 21/500 [00:01<00:23, 19.98it/s]
eval model::   5%|▍         | 24/500 [00:01<00:21, 21.82it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 20.70it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 21.77it/s]
eval model::   7%|▋         | 33/500 [00:01<00:24, 18.93it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 18.62it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 17.93it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.79it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.31it/s]
eval model::  10%|▉         | 48/500 [00:02<00:23, 19.02it/s]
eval model::  10%|█         | 51/500 [00:02<00:22, 20.10it/s]
eval model::  11%|█         | 54/500 [00:02<00:21, 21.19it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:20, 22.06it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:21, 20.40it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 14.88it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:23, 18.49it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:21, 19.73it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 19.61it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:20, 20.31it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.28it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 19.97it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:21, 19.56it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:22, 18.71it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:24, 17.04it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.58it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.77it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.38it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.45it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.41it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.23it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 19.54it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 21.19it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:21, 17.96it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:20, 18.41it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.43it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 19.65it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:21, 17.19it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 17.03it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:17, 20.50it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:17, 20.75it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.49it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:16, 21.69it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 19.27it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 17.77it/s]
eval model::  31%|███       | 154/500 [00:07<00:17, 20.16it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:15, 21.51it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:23, 14.64it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:21, 15.97it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:18, 17.87it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 19.12it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:18, 18.09it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:23, 14.03it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:22, 14.51it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:19, 16.39it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:21, 14.87it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 17.79it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 19.12it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:15, 19.83it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:14, 20.95it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:16, 18.57it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 20.10it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 21.26it/s]
eval model::  41%|████      | 205/500 [00:11<00:21, 13.82it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:19, 14.95it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 14.72it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 15.95it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.87it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.81it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:18, 15.21it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:21, 13.18it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:17, 16.12it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:15, 17.80it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:13, 20.10it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:13, 19.34it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 21.40it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 21.91it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:12, 19.94it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:11, 21.31it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:12, 19.60it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 20.84it/s]
eval model::  51%|█████     | 255/500 [00:13<00:10, 22.40it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 21.33it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 21.97it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:11, 21.32it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 20.74it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:12, 18.26it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:13, 17.12it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:13, 16.88it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 17.46it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:12, 18.01it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 20.25it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 11.96it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.22it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:11, 17.38it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:14, 14.23it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:11, 17.51it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 17.09it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 17.43it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 19.20it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 19.65it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 20.97it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 20.40it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 21.71it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.75it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 21.52it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 21.06it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 20.06it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 21.60it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 21.72it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 15.41it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 17.34it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 19.54it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.90it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 21.52it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 21.77it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 21.36it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.54it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.80it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:08, 16.38it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 18.46it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:06, 18.62it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:05, 21.09it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 22.93it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:05, 20.92it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 22.40it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 15.16it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 17.38it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:05, 18.66it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 20.46it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:04, 20.84it/s]
eval model::  80%|████████  | 402/500 [00:21<00:04, 20.32it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 21.32it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 20.76it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 21.69it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 22.41it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:03, 20.82it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 13.59it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 14.50it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 15.05it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.77it/s]
eval model::  86%|████████▌ | 428/500 [00:23<00:04, 16.40it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:04, 17.02it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:04, 15.02it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 17.61it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 20.51it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 19.59it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 20.36it/s]
eval model::  90%|████████▉ | 448/500 [00:24<00:02, 20.31it/s]
eval model::  90%|█████████ | 451/500 [00:24<00:02, 20.29it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 21.75it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 23.35it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 21.62it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 18.79it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.58it/s]
eval model::  93%|█████████▎| 467/500 [00:25<00:01, 16.85it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 20.27it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 20.32it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.68it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:01, 19.98it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 18.26it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 17.41it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 19.07it/s]
eval model::  98%|█████████▊| 491/500 [00:26<00:00, 19.20it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 18.71it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 18.83it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 21.42it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.76it/s]
[2024/07/28 01:43:59] ppocr INFO: cur metric, precision: 0.7382087970323264, recall: 0.6706788637457872, hmean: 0.7028254288597378, fps: 45.28646058667262
[2024/07/28 01:43:59] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:44:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:44:08] ppocr INFO: epoch: [881/1500], global_step: 2643, lr: 0.001000, loss: 1.219072, loss_shrink_maps: 0.627529, loss_threshold_maps: 0.481664, loss_binary_maps: 0.125026, avg_reader_cost: 1.56302 s, avg_batch_cost: 1.80272 s, avg_samples: 12.5, ips: 6.93397 samples/s, eta: 3:08:15
[2024/07/28 01:44:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:44:17] ppocr INFO: epoch: [882/1500], global_step: 2646, lr: 0.001000, loss: 1.228506, loss_shrink_maps: 0.627529, loss_threshold_maps: 0.485425, loss_binary_maps: 0.125026, avg_reader_cost: 1.60469 s, avg_batch_cost: 1.84778 s, avg_samples: 12.5, ips: 6.76489 samples/s, eta: 3:07:57
[2024/07/28 01:44:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:44:26] ppocr INFO: epoch: [883/1500], global_step: 2649, lr: 0.001000, loss: 1.258486, loss_shrink_maps: 0.625132, loss_threshold_maps: 0.495393, loss_binary_maps: 0.124684, avg_reader_cost: 1.53400 s, avg_batch_cost: 1.81083 s, avg_samples: 12.5, ips: 6.90293 samples/s, eta: 3:07:38
[2024/07/28 01:44:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:44:33] ppocr INFO: epoch: [884/1500], global_step: 2650, lr: 0.001000, loss: 1.258486, loss_shrink_maps: 0.625132, loss_threshold_maps: 0.495393, loss_binary_maps: 0.124684, avg_reader_cost: 0.39875 s, avg_batch_cost: 0.52487 s, avg_samples: 4.8, ips: 9.14520 samples/s, eta: 3:07:32
[2024/07/28 01:44:35] ppocr INFO: epoch: [884/1500], global_step: 2652, lr: 0.001000, loss: 1.217559, loss_shrink_maps: 0.609346, loss_threshold_maps: 0.478985, loss_binary_maps: 0.121581, avg_reader_cost: 1.14082 s, avg_batch_cost: 1.28659 s, avg_samples: 7.7, ips: 5.98479 samples/s, eta: 3:07:20
[2024/07/28 01:44:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:44:44] ppocr INFO: epoch: [885/1500], global_step: 2655, lr: 0.001000, loss: 1.284518, loss_shrink_maps: 0.643802, loss_threshold_maps: 0.500002, loss_binary_maps: 0.128550, avg_reader_cost: 1.56230 s, avg_batch_cost: 1.79039 s, avg_samples: 12.5, ips: 6.98173 samples/s, eta: 3:07:01
[2024/07/28 01:44:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:44:53] ppocr INFO: epoch: [886/1500], global_step: 2658, lr: 0.001000, loss: 1.284518, loss_shrink_maps: 0.643802, loss_threshold_maps: 0.500002, loss_binary_maps: 0.128550, avg_reader_cost: 1.56480 s, avg_batch_cost: 1.82822 s, avg_samples: 12.5, ips: 6.83726 samples/s, eta: 3:06:43
[2024/07/28 01:44:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:45:01] ppocr INFO: epoch: [887/1500], global_step: 2660, lr: 0.001000, loss: 1.298308, loss_shrink_maps: 0.652770, loss_threshold_maps: 0.500002, loss_binary_maps: 0.130325, avg_reader_cost: 0.95120 s, avg_batch_cost: 1.17417 s, avg_samples: 9.6, ips: 8.17598 samples/s, eta: 3:06:31
[2024/07/28 01:45:02] ppocr INFO: epoch: [887/1500], global_step: 2661, lr: 0.001000, loss: 1.267617, loss_shrink_maps: 0.650300, loss_threshold_maps: 0.497007, loss_binary_maps: 0.129538, avg_reader_cost: 0.63283 s, avg_batch_cost: 0.68753 s, avg_samples: 2.9, ips: 4.21801 samples/s, eta: 3:06:25
[2024/07/28 01:45:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:45:11] ppocr INFO: epoch: [888/1500], global_step: 2664, lr: 0.001000, loss: 1.241584, loss_shrink_maps: 0.631248, loss_threshold_maps: 0.483082, loss_binary_maps: 0.125672, avg_reader_cost: 1.52072 s, avg_batch_cost: 1.75140 s, avg_samples: 12.5, ips: 7.13714 samples/s, eta: 3:06:06
[2024/07/28 01:45:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:45:20] ppocr INFO: epoch: [889/1500], global_step: 2667, lr: 0.001000, loss: 1.207163, loss_shrink_maps: 0.605096, loss_threshold_maps: 0.468843, loss_binary_maps: 0.120285, avg_reader_cost: 1.56614 s, avg_batch_cost: 1.79455 s, avg_samples: 12.5, ips: 6.96553 samples/s, eta: 3:05:48
[2024/07/28 01:45:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:45:29] ppocr INFO: epoch: [890/1500], global_step: 2670, lr: 0.001000, loss: 1.207656, loss_shrink_maps: 0.605096, loss_threshold_maps: 0.473648, loss_binary_maps: 0.120285, avg_reader_cost: 1.56382 s, avg_batch_cost: 1.83524 s, avg_samples: 12.5, ips: 6.81109 samples/s, eta: 3:05:30
[2024/07/28 01:45:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:45:38] ppocr INFO: epoch: [891/1500], global_step: 2673, lr: 0.001000, loss: 1.277465, loss_shrink_maps: 0.650300, loss_threshold_maps: 0.488549, loss_binary_maps: 0.129538, avg_reader_cost: 1.54559 s, avg_batch_cost: 1.82379 s, avg_samples: 12.5, ips: 6.85386 samples/s, eta: 3:05:12
[2024/07/28 01:45:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:45:47] ppocr INFO: epoch: [892/1500], global_step: 2676, lr: 0.001000, loss: 1.272546, loss_shrink_maps: 0.634553, loss_threshold_maps: 0.488549, loss_binary_maps: 0.126133, avg_reader_cost: 1.51526 s, avg_batch_cost: 1.75790 s, avg_samples: 12.5, ips: 7.11074 samples/s, eta: 3:04:53
[2024/07/28 01:45:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:45:56] ppocr INFO: epoch: [893/1500], global_step: 2679, lr: 0.001000, loss: 1.218798, loss_shrink_maps: 0.613092, loss_threshold_maps: 0.475081, loss_binary_maps: 0.122087, avg_reader_cost: 1.56991 s, avg_batch_cost: 1.82927 s, avg_samples: 12.5, ips: 6.83333 samples/s, eta: 3:04:35
[2024/07/28 01:45:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:46:04] ppocr INFO: epoch: [894/1500], global_step: 2680, lr: 0.001000, loss: 1.199186, loss_shrink_maps: 0.605096, loss_threshold_maps: 0.465466, loss_binary_maps: 0.120277, avg_reader_cost: 0.43726 s, avg_batch_cost: 0.54001 s, avg_samples: 4.8, ips: 8.88871 samples/s, eta: 3:04:28
[2024/07/28 01:46:05] ppocr INFO: epoch: [894/1500], global_step: 2682, lr: 0.001000, loss: 1.268118, loss_shrink_maps: 0.641607, loss_threshold_maps: 0.479115, loss_binary_maps: 0.127989, avg_reader_cost: 1.17104 s, avg_batch_cost: 1.31669 s, avg_samples: 7.7, ips: 5.84800 samples/s, eta: 3:04:17
[2024/07/28 01:46:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:46:14] ppocr INFO: epoch: [895/1500], global_step: 2685, lr: 0.001000, loss: 1.298214, loss_shrink_maps: 0.658718, loss_threshold_maps: 0.505945, loss_binary_maps: 0.131147, avg_reader_cost: 1.60255 s, avg_batch_cost: 1.84333 s, avg_samples: 12.5, ips: 6.78119 samples/s, eta: 3:03:59
[2024/07/28 01:46:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:46:24] ppocr INFO: epoch: [896/1500], global_step: 2688, lr: 0.001000, loss: 1.304093, loss_shrink_maps: 0.667384, loss_threshold_maps: 0.504696, loss_binary_maps: 0.133209, avg_reader_cost: 1.53458 s, avg_batch_cost: 1.77638 s, avg_samples: 12.5, ips: 7.03680 samples/s, eta: 3:03:40
[2024/07/28 01:46:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:46:32] ppocr INFO: epoch: [897/1500], global_step: 2690, lr: 0.001000, loss: 1.302101, loss_shrink_maps: 0.659842, loss_threshold_maps: 0.504696, loss_binary_maps: 0.131987, avg_reader_cost: 0.92647 s, avg_batch_cost: 1.10168 s, avg_samples: 9.6, ips: 8.71396 samples/s, eta: 3:03:27
[2024/07/28 01:46:32] ppocr INFO: epoch: [897/1500], global_step: 2691, lr: 0.001000, loss: 1.298214, loss_shrink_maps: 0.652191, loss_threshold_maps: 0.497306, loss_binary_maps: 0.130185, avg_reader_cost: 0.59636 s, avg_batch_cost: 0.65106 s, avg_samples: 2.9, ips: 4.45425 samples/s, eta: 3:03:21
[2024/07/28 01:46:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:46:42] ppocr INFO: epoch: [898/1500], global_step: 2694, lr: 0.001000, loss: 1.298214, loss_shrink_maps: 0.652191, loss_threshold_maps: 0.512395, loss_binary_maps: 0.130185, avg_reader_cost: 1.56050 s, avg_batch_cost: 1.79874 s, avg_samples: 12.5, ips: 6.94930 samples/s, eta: 3:03:03
[2024/07/28 01:46:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:46:51] ppocr INFO: epoch: [899/1500], global_step: 2697, lr: 0.001000, loss: 1.307877, loss_shrink_maps: 0.667384, loss_threshold_maps: 0.517421, loss_binary_maps: 0.132984, avg_reader_cost: 1.56010 s, avg_batch_cost: 1.80594 s, avg_samples: 12.5, ips: 6.92161 samples/s, eta: 3:02:44
[2024/07/28 01:46:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:47:00] ppocr INFO: epoch: [900/1500], global_step: 2700, lr: 0.001000, loss: 1.310161, loss_shrink_maps: 0.668278, loss_threshold_maps: 0.519288, loss_binary_maps: 0.133049, avg_reader_cost: 1.51595 s, avg_batch_cost: 1.76829 s, avg_samples: 12.5, ips: 7.06896 samples/s, eta: 3:02:26

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.64it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.67it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 19.00it/s]
eval model::   2%|▏         | 8/500 [00:00<00:25, 19.06it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.45it/s]
eval model::   3%|▎         | 14/500 [00:00<00:24, 19.73it/s]
eval model::   3%|▎         | 17/500 [00:00<00:21, 22.32it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.78it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 22.34it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 20.60it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 22.02it/s]
eval model::   6%|▋         | 32/500 [00:01<00:21, 21.33it/s]
eval model::   7%|▋         | 35/500 [00:01<00:23, 19.83it/s]
eval model::   8%|▊         | 38/500 [00:01<00:22, 20.12it/s]
eval model::   8%|▊         | 41/500 [00:02<00:24, 18.40it/s]
eval model::   9%|▊         | 43/500 [00:02<00:28, 16.12it/s]
eval model::   9%|▉         | 46/500 [00:02<00:28, 15.74it/s]
eval model::  10%|▉         | 48/500 [00:02<00:28, 15.99it/s]
eval model::  10%|█         | 50/500 [00:02<00:27, 16.49it/s]
eval model::  11%|█         | 53/500 [00:02<00:24, 18.16it/s]
eval model::  11%|█         | 56/500 [00:02<00:23, 19.17it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 18.17it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 15.68it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 15.01it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 19.22it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 20.59it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:22, 19.18it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:22, 18.52it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:20, 20.31it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 20.71it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 21.02it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.13it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:24, 16.89it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:24, 16.87it/s]
eval model::  19%|█▉        | 94/500 [00:05<00:24, 16.71it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:23, 17.05it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:21, 18.68it/s]
eval model::  20%|██        | 101/500 [00:05<00:22, 17.84it/s]
eval model::  21%|██        | 104/500 [00:05<00:20, 19.03it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.65it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 22.66it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:16, 23.20it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:17, 22.56it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:16, 22.77it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:17, 22.00it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:21, 17.79it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:19, 18.90it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:21, 17.14it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:21, 17.41it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:19, 18.63it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:17, 20.26it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:16, 21.45it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 19.87it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 19.44it/s]
eval model::  30%|███       | 151/500 [00:07<00:18, 19.37it/s]
eval model::  31%|███       | 155/500 [00:08<00:15, 22.50it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.43it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:20, 16.29it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:18, 17.90it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 18.96it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 19.85it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:16, 19.43it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.21it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.58it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:18, 16.85it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.26it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.30it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:15, 19.88it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 20.13it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.19it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 21.57it/s]
eval model::  41%|████      | 204/500 [00:10<00:12, 23.34it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:21, 13.59it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:20, 14.13it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 14.43it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.67it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.33it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.63it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 14.84it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:21, 13.16it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.25it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 20.51it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 22.29it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.94it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.69it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:13, 19.08it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.96it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 22.20it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 21.34it/s]
eval model::  51%|█████     | 254/500 [00:13<00:10, 22.39it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.83it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 23.35it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 23.54it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:11, 20.72it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.67it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 19.16it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.82it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 18.74it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 18.70it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 18.91it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:22,  9.71it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:17, 12.07it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:14, 14.63it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:14, 14.53it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:16, 12.75it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 15.83it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 17.47it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:09, 19.57it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.24it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 20.85it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:08, 21.13it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 21.52it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.41it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:07, 22.26it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 21.40it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:07, 21.15it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 22.70it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:07, 21.68it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:09, 16.26it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:08, 18.44it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 20.84it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:06, 22.08it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 22.54it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.70it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 19.40it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:08, 16.99it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:08, 16.88it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 16.84it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 18.68it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 18.56it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 19.24it/s]
eval model::  75%|███████▌  | 377/500 [00:20<00:05, 20.93it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:06, 18.64it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 20.55it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:08, 13.16it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:07, 15.49it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:06, 17.04it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:05, 18.60it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:04, 20.75it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 17.14it/s]
eval model::  81%|████████  | 404/500 [00:21<00:05, 19.16it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 20.36it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 22.07it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:04, 21.58it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:03, 22.02it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:03, 20.56it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 13.89it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 13.85it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.16it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 17.26it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 18.29it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 20.28it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 22.72it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 21.73it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 22.19it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 21.37it/s]
eval model::  90%|█████████ | 451/500 [00:24<00:02, 20.75it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 22.59it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:01, 24.96it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 22.08it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:01, 19.25it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 17.26it/s]
eval model::  94%|█████████▍| 470/500 [00:25<00:01, 18.70it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 19.96it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 20.47it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:00, 21.01it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 19.93it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 21.56it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 20.72it/s]
eval model::  98%|█████████▊| 491/500 [00:26<00:00, 19.57it/s]
eval model::  99%|█████████▉| 494/500 [00:26<00:00, 18.32it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 18.57it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 22.93it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.89it/s]
[2024/07/28 01:47:26] ppocr INFO: cur metric, precision: 0.722684703433923, recall: 0.6687530091478093, hmean: 0.6946736684171043, fps: 45.70817006252084
[2024/07/28 01:47:26] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:47:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:47:35] ppocr INFO: epoch: [901/1500], global_step: 2703, lr: 0.001000, loss: 1.307877, loss_shrink_maps: 0.659732, loss_threshold_maps: 0.515301, loss_binary_maps: 0.131828, avg_reader_cost: 1.87752 s, avg_batch_cost: 2.26342 s, avg_samples: 12.5, ips: 5.52262 samples/s, eta: 3:02:10
[2024/07/28 01:47:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:47:44] ppocr INFO: epoch: [902/1500], global_step: 2706, lr: 0.001000, loss: 1.307877, loss_shrink_maps: 0.655600, loss_threshold_maps: 0.515301, loss_binary_maps: 0.130918, avg_reader_cost: 1.53072 s, avg_batch_cost: 1.77392 s, avg_samples: 12.5, ips: 7.04656 samples/s, eta: 3:01:52
[2024/07/28 01:47:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:47:54] ppocr INFO: epoch: [903/1500], global_step: 2709, lr: 0.001000, loss: 1.275032, loss_shrink_maps: 0.635526, loss_threshold_maps: 0.520781, loss_binary_maps: 0.126710, avg_reader_cost: 1.71320 s, avg_batch_cost: 1.94089 s, avg_samples: 12.5, ips: 6.44033 samples/s, eta: 3:01:34
[2024/07/28 01:47:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:48:01] ppocr INFO: epoch: [904/1500], global_step: 2710, lr: 0.001000, loss: 1.252762, loss_shrink_maps: 0.632583, loss_threshold_maps: 0.515301, loss_binary_maps: 0.125661, avg_reader_cost: 0.45048 s, avg_batch_cost: 0.53399 s, avg_samples: 4.8, ips: 8.98894 samples/s, eta: 3:01:28
[2024/07/28 01:48:03] ppocr INFO: epoch: [904/1500], global_step: 2712, lr: 0.001000, loss: 1.333040, loss_shrink_maps: 0.664147, loss_threshold_maps: 0.522647, loss_binary_maps: 0.131915, avg_reader_cost: 1.15913 s, avg_batch_cost: 1.30458 s, avg_samples: 7.7, ips: 5.90230 samples/s, eta: 3:01:16
[2024/07/28 01:48:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:48:12] ppocr INFO: epoch: [905/1500], global_step: 2715, lr: 0.001000, loss: 1.242284, loss_shrink_maps: 0.632583, loss_threshold_maps: 0.505561, loss_binary_maps: 0.125661, avg_reader_cost: 1.54726 s, avg_batch_cost: 1.78106 s, avg_samples: 12.5, ips: 7.01829 samples/s, eta: 3:00:58
[2024/07/28 01:48:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:48:21] ppocr INFO: epoch: [906/1500], global_step: 2718, lr: 0.001000, loss: 1.219982, loss_shrink_maps: 0.606660, loss_threshold_maps: 0.493664, loss_binary_maps: 0.120824, avg_reader_cost: 1.64748 s, avg_batch_cost: 1.95747 s, avg_samples: 12.5, ips: 6.38579 samples/s, eta: 3:00:40
[2024/07/28 01:48:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:48:30] ppocr INFO: epoch: [907/1500], global_step: 2720, lr: 0.001000, loss: 1.242284, loss_shrink_maps: 0.632583, loss_threshold_maps: 0.489970, loss_binary_maps: 0.125661, avg_reader_cost: 1.02144 s, avg_batch_cost: 1.19464 s, avg_samples: 9.6, ips: 8.03592 samples/s, eta: 3:00:28
[2024/07/28 01:48:31] ppocr INFO: epoch: [907/1500], global_step: 2721, lr: 0.001000, loss: 1.221887, loss_shrink_maps: 0.615545, loss_threshold_maps: 0.486334, loss_binary_maps: 0.122162, avg_reader_cost: 0.64299 s, avg_batch_cost: 0.69804 s, avg_samples: 2.9, ips: 4.15450 samples/s, eta: 3:00:23
[2024/07/28 01:48:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:48:40] ppocr INFO: epoch: [908/1500], global_step: 2724, lr: 0.001000, loss: 1.222410, loss_shrink_maps: 0.615545, loss_threshold_maps: 0.489970, loss_binary_maps: 0.122162, avg_reader_cost: 1.54229 s, avg_batch_cost: 1.78009 s, avg_samples: 12.5, ips: 7.02212 samples/s, eta: 3:00:04
[2024/07/28 01:48:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:48:49] ppocr INFO: epoch: [909/1500], global_step: 2727, lr: 0.001000, loss: 1.241470, loss_shrink_maps: 0.625519, loss_threshold_maps: 0.486334, loss_binary_maps: 0.124910, avg_reader_cost: 1.51219 s, avg_batch_cost: 1.74026 s, avg_samples: 12.5, ips: 7.18284 samples/s, eta: 2:59:45
[2024/07/28 01:48:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:48:58] ppocr INFO: epoch: [910/1500], global_step: 2730, lr: 0.001000, loss: 1.245347, loss_shrink_maps: 0.625519, loss_threshold_maps: 0.485742, loss_binary_maps: 0.124910, avg_reader_cost: 1.54115 s, avg_batch_cost: 1.81527 s, avg_samples: 12.5, ips: 6.88603 samples/s, eta: 2:59:27
[2024/07/28 01:49:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:49:07] ppocr INFO: epoch: [911/1500], global_step: 2733, lr: 0.001000, loss: 1.220506, loss_shrink_maps: 0.608922, loss_threshold_maps: 0.484331, loss_binary_maps: 0.120954, avg_reader_cost: 1.50694 s, avg_batch_cost: 1.73663 s, avg_samples: 12.5, ips: 7.19785 samples/s, eta: 2:59:08
[2024/07/28 01:49:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:49:16] ppocr INFO: epoch: [912/1500], global_step: 2736, lr: 0.001000, loss: 1.245347, loss_shrink_maps: 0.627961, loss_threshold_maps: 0.488132, loss_binary_maps: 0.125577, avg_reader_cost: 1.59183 s, avg_batch_cost: 1.86045 s, avg_samples: 12.5, ips: 6.71881 samples/s, eta: 2:58:50
[2024/07/28 01:49:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:49:25] ppocr INFO: epoch: [913/1500], global_step: 2739, lr: 0.001000, loss: 1.255790, loss_shrink_maps: 0.627961, loss_threshold_maps: 0.498550, loss_binary_maps: 0.125577, avg_reader_cost: 1.53278 s, avg_batch_cost: 1.76239 s, avg_samples: 12.5, ips: 7.09266 samples/s, eta: 2:58:31
[2024/07/28 01:49:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:49:33] ppocr INFO: epoch: [914/1500], global_step: 2740, lr: 0.001000, loss: 1.232040, loss_shrink_maps: 0.616255, loss_threshold_maps: 0.498550, loss_binary_maps: 0.122706, avg_reader_cost: 0.43161 s, avg_batch_cost: 0.52036 s, avg_samples: 4.8, ips: 9.22445 samples/s, eta: 2:58:25
[2024/07/28 01:49:34] ppocr INFO: epoch: [914/1500], global_step: 2742, lr: 0.001000, loss: 1.284361, loss_shrink_maps: 0.646579, loss_threshold_maps: 0.506051, loss_binary_maps: 0.129209, avg_reader_cost: 1.13202 s, avg_batch_cost: 1.27788 s, avg_samples: 7.7, ips: 6.02561 samples/s, eta: 2:58:13
[2024/07/28 01:49:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:49:43] ppocr INFO: epoch: [915/1500], global_step: 2745, lr: 0.001000, loss: 1.311650, loss_shrink_maps: 0.679809, loss_threshold_maps: 0.512377, loss_binary_maps: 0.135003, avg_reader_cost: 1.53827 s, avg_batch_cost: 1.76802 s, avg_samples: 12.5, ips: 7.07004 samples/s, eta: 2:57:54
[2024/07/28 01:49:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:49:52] ppocr INFO: epoch: [916/1500], global_step: 2748, lr: 0.001000, loss: 1.323784, loss_shrink_maps: 0.684162, loss_threshold_maps: 0.513383, loss_binary_maps: 0.136754, avg_reader_cost: 1.56696 s, avg_batch_cost: 1.82896 s, avg_samples: 12.5, ips: 6.83449 samples/s, eta: 2:57:36
[2024/07/28 01:49:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:50:01] ppocr INFO: epoch: [917/1500], global_step: 2750, lr: 0.001000, loss: 1.275262, loss_shrink_maps: 0.644336, loss_threshold_maps: 0.510544, loss_binary_maps: 0.128216, avg_reader_cost: 0.93219 s, avg_batch_cost: 1.10745 s, avg_samples: 9.6, ips: 8.66856 samples/s, eta: 2:57:23
[2024/07/28 01:50:02] ppocr INFO: epoch: [917/1500], global_step: 2751, lr: 0.001000, loss: 1.326183, loss_shrink_maps: 0.679797, loss_threshold_maps: 0.510544, loss_binary_maps: 0.135181, avg_reader_cost: 0.59927 s, avg_batch_cost: 0.65416 s, avg_samples: 2.9, ips: 4.43317 samples/s, eta: 2:57:17
[2024/07/28 01:50:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:50:10] ppocr INFO: epoch: [918/1500], global_step: 2754, lr: 0.001000, loss: 1.290523, loss_shrink_maps: 0.658770, loss_threshold_maps: 0.510544, loss_binary_maps: 0.131083, avg_reader_cost: 1.51231 s, avg_batch_cost: 1.74160 s, avg_samples: 12.5, ips: 7.17731 samples/s, eta: 2:56:59
[2024/07/28 01:50:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:50:20] ppocr INFO: epoch: [919/1500], global_step: 2757, lr: 0.001000, loss: 1.327885, loss_shrink_maps: 0.674249, loss_threshold_maps: 0.516048, loss_binary_maps: 0.134359, avg_reader_cost: 1.54686 s, avg_batch_cost: 1.79227 s, avg_samples: 12.5, ips: 6.97440 samples/s, eta: 2:56:40
[2024/07/28 01:50:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:50:29] ppocr INFO: epoch: [920/1500], global_step: 2760, lr: 0.001000, loss: 1.327885, loss_shrink_maps: 0.674249, loss_threshold_maps: 0.516048, loss_binary_maps: 0.134359, avg_reader_cost: 1.55827 s, avg_batch_cost: 1.78606 s, avg_samples: 12.5, ips: 6.99865 samples/s, eta: 2:56:22

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:58,  8.46it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 16.76it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.99it/s]
eval model::   2%|▏         | 8/500 [00:00<00:31, 15.47it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.74it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.52it/s]
eval model::   4%|▎         | 18/500 [00:00<00:22, 21.43it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 18.50it/s]
eval model::   5%|▍         | 24/500 [00:01<00:25, 18.61it/s]
eval model::   5%|▌         | 26/500 [00:01<00:25, 18.48it/s]
eval model::   6%|▌         | 29/500 [00:01<00:24, 19.14it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.75it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 18.88it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.30it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.07it/s]
eval model::   8%|▊         | 42/500 [00:02<00:30, 14.85it/s]
eval model::   9%|▉         | 44/500 [00:02<00:28, 15.73it/s]
eval model::   9%|▉         | 46/500 [00:02<00:28, 15.86it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.71it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 19.23it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 20.68it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 18.79it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.63it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.60it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:21, 20.15it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 21.67it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 21.01it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:19, 21.82it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:18, 22.59it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.71it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 21.81it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 19.83it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:21, 18.73it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:25, 16.19it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:23, 16.91it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:20, 19.39it/s]
eval model::  20%|██        | 101/500 [00:05<00:21, 18.64it/s]
eval model::  21%|██        | 104/500 [00:05<00:20, 19.50it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.54it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 22.09it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:16, 22.90it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:16, 22.84it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:18, 20.76it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.69it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:19, 19.27it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:17, 20.77it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 18.46it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:20, 18.28it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:17, 20.39it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:17, 20.87it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.83it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 19.90it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:17, 19.65it/s]
eval model::  30%|███       | 151/500 [00:07<00:18, 18.87it/s]
eval model::  31%|███       | 155/500 [00:08<00:15, 21.89it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:16, 21.13it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:25, 13.53it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:23, 14.17it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:20, 16.50it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:20, 16.42it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:19, 16.75it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:18, 17.39it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:24, 13.36it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:22, 14.45it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:23, 13.35it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:20, 15.39it/s]
eval model::  37%|███▋      | 186/500 [00:10<00:18, 16.99it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:18, 17.20it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:16, 19.14it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:14, 21.41it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:15, 19.97it/s]
eval model::  40%|████      | 200/500 [00:10<00:13, 21.53it/s]
eval model::  41%|████      | 203/500 [00:10<00:13, 21.39it/s]
eval model::  41%|████      | 206/500 [00:11<00:21, 13.55it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:21, 13.87it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:20, 14.21it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.26it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.77it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:23, 12.19it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:19, 14.47it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.74it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 16.71it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:15, 17.26it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 19.79it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:13, 20.23it/s]
eval model::  47%|████▋     | 236/500 [00:13<00:13, 20.27it/s]
eval model::  48%|████▊     | 239/500 [00:13<00:12, 21.58it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 18.81it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:13, 19.39it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:13, 19.20it/s]
eval model::  50%|█████     | 250/500 [00:13<00:13, 19.20it/s]
eval model::  50%|█████     | 252/500 [00:13<00:14, 17.53it/s]
eval model::  51%|█████     | 255/500 [00:14<00:12, 19.79it/s]
eval model::  52%|█████▏    | 258/500 [00:14<00:11, 21.29it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:11, 21.72it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:12, 18.84it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:12, 18.38it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 19.02it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.84it/s]
eval model::  55%|█████▌    | 275/500 [00:15<00:11, 19.30it/s]
eval model::  55%|█████▌    | 277/500 [00:15<00:11, 18.83it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:11, 18.78it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 21.09it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.05it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.45it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:12, 17.20it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:14, 14.32it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:12, 16.17it/s]
eval model::  60%|██████    | 300/500 [00:16<00:13, 15.23it/s]
eval model::  60%|██████    | 302/500 [00:16<00:12, 15.46it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 15.72it/s]
eval model::  61%|██████    | 306/500 [00:17<00:12, 15.99it/s]
eval model::  62%|██████▏   | 309/500 [00:17<00:10, 18.02it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:09, 18.98it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:09, 18.90it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:09, 19.44it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 20.40it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:09, 19.31it/s]
eval model::  65%|██████▌   | 327/500 [00:18<00:08, 20.54it/s]
eval model::  66%|██████▌   | 330/500 [00:18<00:08, 19.80it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:08, 20.39it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:07, 22.17it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:07, 21.66it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:09, 15.81it/s]
eval model::  69%|██████▉   | 345/500 [00:19<00:08, 17.82it/s]
eval model::  70%|██████▉   | 348/500 [00:19<00:08, 18.65it/s]
eval model::  70%|███████   | 351/500 [00:19<00:07, 19.83it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 21.28it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 21.84it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.63it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:07, 17.63it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:08, 16.22it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:07, 17.61it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 18.02it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 22.14it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 23.88it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 21.64it/s]
eval model::  77%|███████▋  | 386/500 [00:21<00:07, 15.67it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:06, 17.31it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:06, 17.90it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:05, 19.55it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:04, 21.48it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 18.46it/s]
eval model::  81%|████████  | 404/500 [00:22<00:04, 19.67it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 20.92it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 22.44it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:04, 21.61it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 20.68it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 19.52it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:05, 14.89it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.65it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.78it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:03, 17.84it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 17.76it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 19.90it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 20.52it/s]
eval model::  88%|████████▊ | 442/500 [00:24<00:02, 20.12it/s]
eval model::  89%|████████▉ | 445/500 [00:24<00:03, 17.91it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 18.01it/s]
eval model::  90%|████████▉ | 449/500 [00:24<00:02, 17.10it/s]
eval model::  90%|█████████ | 452/500 [00:24<00:02, 19.69it/s]
eval model::  91%|█████████ | 455/500 [00:24<00:02, 21.65it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:01, 21.58it/s]
eval model::  92%|█████████▏| 461/500 [00:25<00:01, 20.17it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 17.71it/s]
eval model::  93%|█████████▎| 467/500 [00:25<00:01, 18.65it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 21.39it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 20.16it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:00, 23.02it/s]
eval model::  96%|█████████▌| 481/500 [00:26<00:00, 22.23it/s]
eval model::  97%|█████████▋| 484/500 [00:26<00:00, 21.77it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 20.78it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 21.22it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 18.58it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 19.35it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 23.04it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.53it/s]
[2024/07/28 01:50:56] ppocr INFO: cur metric, precision: 0.6967011324470704, recall: 0.6812710640346654, hmean: 0.6888997078870497, fps: 45.71231374099964
[2024/07/28 01:50:56] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:50:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:51:03] ppocr INFO: epoch: [921/1500], global_step: 2763, lr: 0.001000, loss: 1.306734, loss_shrink_maps: 0.671631, loss_threshold_maps: 0.515282, loss_binary_maps: 0.133881, avg_reader_cost: 1.44793 s, avg_batch_cost: 1.67611 s, avg_samples: 12.5, ips: 7.45776 samples/s, eta: 2:56:03
[2024/07/28 01:51:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:51:12] ppocr INFO: epoch: [922/1500], global_step: 2766, lr: 0.001000, loss: 1.306734, loss_shrink_maps: 0.671631, loss_threshold_maps: 0.515282, loss_binary_maps: 0.133881, avg_reader_cost: 1.56046 s, avg_batch_cost: 1.83197 s, avg_samples: 12.5, ips: 6.82325 samples/s, eta: 2:55:44
[2024/07/28 01:51:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:51:21] ppocr INFO: epoch: [923/1500], global_step: 2769, lr: 0.001000, loss: 1.306734, loss_shrink_maps: 0.668799, loss_threshold_maps: 0.509059, loss_binary_maps: 0.133439, avg_reader_cost: 1.53401 s, avg_batch_cost: 1.77802 s, avg_samples: 12.5, ips: 7.03031 samples/s, eta: 2:55:26
[2024/07/28 01:51:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:51:29] ppocr INFO: epoch: [924/1500], global_step: 2770, lr: 0.001000, loss: 1.306734, loss_shrink_maps: 0.668799, loss_threshold_maps: 0.509059, loss_binary_maps: 0.133439, avg_reader_cost: 0.42505 s, avg_batch_cost: 0.52182 s, avg_samples: 4.8, ips: 9.19857 samples/s, eta: 2:55:19
[2024/07/28 01:51:30] ppocr INFO: epoch: [924/1500], global_step: 2772, lr: 0.001000, loss: 1.290458, loss_shrink_maps: 0.660485, loss_threshold_maps: 0.492151, loss_binary_maps: 0.131839, avg_reader_cost: 1.13472 s, avg_batch_cost: 1.28050 s, avg_samples: 7.7, ips: 6.01328 samples/s, eta: 2:55:07
[2024/07/28 01:51:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:51:40] ppocr INFO: epoch: [925/1500], global_step: 2775, lr: 0.001000, loss: 1.324932, loss_shrink_maps: 0.674692, loss_threshold_maps: 0.511905, loss_binary_maps: 0.134669, avg_reader_cost: 1.59919 s, avg_batch_cost: 1.85443 s, avg_samples: 12.5, ips: 6.74063 samples/s, eta: 2:54:49
[2024/07/28 01:51:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:51:48] ppocr INFO: epoch: [926/1500], global_step: 2778, lr: 0.001000, loss: 1.314458, loss_shrink_maps: 0.674692, loss_threshold_maps: 0.505182, loss_binary_maps: 0.134669, avg_reader_cost: 1.51917 s, avg_batch_cost: 1.74933 s, avg_samples: 12.5, ips: 7.14560 samples/s, eta: 2:54:31
[2024/07/28 01:51:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:51:57] ppocr INFO: epoch: [927/1500], global_step: 2780, lr: 0.001000, loss: 1.314458, loss_shrink_maps: 0.674692, loss_threshold_maps: 0.505182, loss_binary_maps: 0.134669, avg_reader_cost: 0.94260 s, avg_batch_cost: 1.11776 s, avg_samples: 9.6, ips: 8.58860 samples/s, eta: 2:54:18
[2024/07/28 01:51:57] ppocr INFO: epoch: [927/1500], global_step: 2781, lr: 0.001000, loss: 1.296176, loss_shrink_maps: 0.663546, loss_threshold_maps: 0.505182, loss_binary_maps: 0.132628, avg_reader_cost: 0.60433 s, avg_batch_cost: 0.65908 s, avg_samples: 2.9, ips: 4.40008 samples/s, eta: 2:54:12
[2024/07/28 01:52:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:52:07] ppocr INFO: epoch: [928/1500], global_step: 2784, lr: 0.001000, loss: 1.257685, loss_shrink_maps: 0.640139, loss_threshold_maps: 0.495461, loss_binary_maps: 0.127725, avg_reader_cost: 1.46942 s, avg_batch_cost: 1.70669 s, avg_samples: 12.5, ips: 7.32412 samples/s, eta: 2:53:53
[2024/07/28 01:52:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:52:16] ppocr INFO: epoch: [929/1500], global_step: 2787, lr: 0.001000, loss: 1.257685, loss_shrink_maps: 0.640139, loss_threshold_maps: 0.498972, loss_binary_maps: 0.127725, avg_reader_cost: 1.54550 s, avg_batch_cost: 1.79079 s, avg_samples: 12.5, ips: 6.98015 samples/s, eta: 2:53:35
[2024/07/28 01:52:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:52:25] ppocr INFO: epoch: [930/1500], global_step: 2790, lr: 0.001000, loss: 1.222816, loss_shrink_maps: 0.608711, loss_threshold_maps: 0.489045, loss_binary_maps: 0.120966, avg_reader_cost: 1.48447 s, avg_batch_cost: 1.71396 s, avg_samples: 12.5, ips: 7.29307 samples/s, eta: 2:53:16
[2024/07/28 01:52:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:52:34] ppocr INFO: epoch: [931/1500], global_step: 2793, lr: 0.001000, loss: 1.222816, loss_shrink_maps: 0.608711, loss_threshold_maps: 0.498972, loss_binary_maps: 0.120966, avg_reader_cost: 1.62776 s, avg_batch_cost: 1.86150 s, avg_samples: 12.5, ips: 6.71502 samples/s, eta: 2:52:58
[2024/07/28 01:52:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:52:44] ppocr INFO: epoch: [932/1500], global_step: 2796, lr: 0.001000, loss: 1.222816, loss_shrink_maps: 0.614742, loss_threshold_maps: 0.492556, loss_binary_maps: 0.122265, avg_reader_cost: 1.58651 s, avg_batch_cost: 1.86155 s, avg_samples: 12.5, ips: 6.71484 samples/s, eta: 2:52:40
[2024/07/28 01:52:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:52:53] ppocr INFO: epoch: [933/1500], global_step: 2799, lr: 0.001000, loss: 1.231124, loss_shrink_maps: 0.618975, loss_threshold_maps: 0.490042, loss_binary_maps: 0.123384, avg_reader_cost: 1.70305 s, avg_batch_cost: 1.97547 s, avg_samples: 12.5, ips: 6.32762 samples/s, eta: 2:52:23
[2024/07/28 01:52:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:53:01] ppocr INFO: epoch: [934/1500], global_step: 2800, lr: 0.001000, loss: 1.213244, loss_shrink_maps: 0.601092, loss_threshold_maps: 0.482847, loss_binary_maps: 0.119620, avg_reader_cost: 0.44217 s, avg_batch_cost: 0.52605 s, avg_samples: 4.8, ips: 9.12466 samples/s, eta: 2:52:16
[2024/07/28 01:53:03] ppocr INFO: epoch: [934/1500], global_step: 2802, lr: 0.001000, loss: 1.231124, loss_shrink_maps: 0.618975, loss_threshold_maps: 0.490042, loss_binary_maps: 0.123384, avg_reader_cost: 1.14489 s, avg_batch_cost: 1.29215 s, avg_samples: 7.7, ips: 5.95904 samples/s, eta: 2:52:04
[2024/07/28 01:53:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:53:12] ppocr INFO: epoch: [935/1500], global_step: 2805, lr: 0.001000, loss: 1.254550, loss_shrink_maps: 0.624179, loss_threshold_maps: 0.498474, loss_binary_maps: 0.124138, avg_reader_cost: 1.58406 s, avg_batch_cost: 1.81300 s, avg_samples: 12.5, ips: 6.89467 samples/s, eta: 2:51:46
[2024/07/28 01:53:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:53:21] ppocr INFO: epoch: [936/1500], global_step: 2808, lr: 0.001000, loss: 1.290953, loss_shrink_maps: 0.652794, loss_threshold_maps: 0.500319, loss_binary_maps: 0.129869, avg_reader_cost: 1.55354 s, avg_batch_cost: 1.79958 s, avg_samples: 12.5, ips: 6.94606 samples/s, eta: 2:51:28
[2024/07/28 01:53:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:53:30] ppocr INFO: epoch: [937/1500], global_step: 2810, lr: 0.001000, loss: 1.301866, loss_shrink_maps: 0.671115, loss_threshold_maps: 0.515141, loss_binary_maps: 0.133882, avg_reader_cost: 0.96141 s, avg_batch_cost: 1.14809 s, avg_samples: 9.6, ips: 8.36168 samples/s, eta: 2:51:15
[2024/07/28 01:53:30] ppocr INFO: epoch: [937/1500], global_step: 2811, lr: 0.001000, loss: 1.301866, loss_shrink_maps: 0.671115, loss_threshold_maps: 0.514192, loss_binary_maps: 0.133882, avg_reader_cost: 0.61922 s, avg_batch_cost: 0.67390 s, avg_samples: 2.9, ips: 4.30329 samples/s, eta: 2:51:09
[2024/07/28 01:53:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:53:39] ppocr INFO: epoch: [938/1500], global_step: 2814, lr: 0.001000, loss: 1.277455, loss_shrink_maps: 0.643709, loss_threshold_maps: 0.508986, loss_binary_maps: 0.127941, avg_reader_cost: 1.59786 s, avg_batch_cost: 1.83886 s, avg_samples: 12.5, ips: 6.79769 samples/s, eta: 2:50:51
[2024/07/28 01:53:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:53:48] ppocr INFO: epoch: [939/1500], global_step: 2817, lr: 0.001000, loss: 1.236163, loss_shrink_maps: 0.612401, loss_threshold_maps: 0.492942, loss_binary_maps: 0.121750, avg_reader_cost: 1.53506 s, avg_batch_cost: 1.77095 s, avg_samples: 12.5, ips: 7.05837 samples/s, eta: 2:50:33
[2024/07/28 01:53:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:53:57] ppocr INFO: epoch: [940/1500], global_step: 2820, lr: 0.001000, loss: 1.292886, loss_shrink_maps: 0.654999, loss_threshold_maps: 0.508612, loss_binary_maps: 0.130459, avg_reader_cost: 1.52250 s, avg_batch_cost: 1.76385 s, avg_samples: 12.5, ips: 7.08676 samples/s, eta: 2:50:14

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:07,  7.43it/s]
eval model::   1%|          | 4/500 [00:00<00:32, 15.14it/s]
eval model::   1%|          | 6/500 [00:00<00:31, 15.65it/s]
eval model::   2%|▏         | 8/500 [00:00<00:32, 15.26it/s]
eval model::   2%|▏         | 11/500 [00:00<00:29, 16.39it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.39it/s]
eval model::   3%|▎         | 17/500 [00:00<00:25, 19.31it/s]
eval model::   4%|▍         | 19/500 [00:01<00:27, 17.56it/s]
eval model::   4%|▍         | 22/500 [00:01<00:25, 18.88it/s]
eval model::   5%|▍         | 24/500 [00:01<00:27, 17.26it/s]
eval model::   5%|▌         | 26/500 [00:01<00:28, 16.57it/s]
eval model::   6%|▌         | 29/500 [00:01<00:25, 18.41it/s]
eval model::   6%|▌         | 31/500 [00:01<00:27, 17.05it/s]
eval model::   7%|▋         | 34/500 [00:01<00:27, 17.15it/s]
eval model::   7%|▋         | 36/500 [00:02<00:28, 16.49it/s]
eval model::   8%|▊         | 38/500 [00:02<00:28, 16.13it/s]
eval model::   8%|▊         | 40/500 [00:02<00:29, 15.43it/s]
eval model::   8%|▊         | 42/500 [00:02<00:31, 14.49it/s]
eval model::   9%|▉         | 44/500 [00:02<00:29, 15.37it/s]
eval model::   9%|▉         | 46/500 [00:02<00:31, 14.28it/s]
eval model::  10%|▉         | 49/500 [00:02<00:27, 16.66it/s]
eval model::  10%|█         | 52/500 [00:03<00:23, 18.68it/s]
eval model::  11%|█         | 55/500 [00:03<00:21, 20.75it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:22, 19.26it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.61it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 14.59it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:25, 17.00it/s]
eval model::  14%|█▍        | 69/500 [00:04<00:22, 19.32it/s]
eval model::  14%|█▍        | 72/500 [00:04<00:20, 21.05it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:20, 21.04it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.93it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 23.12it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:17, 23.81it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:19, 21.68it/s]
eval model::  18%|█▊        | 90/500 [00:05<00:21, 19.12it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:22, 18.49it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:19, 20.29it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.24it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.87it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.23it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:19, 20.14it/s]
eval model::  22%|██▏       | 111/500 [00:06<00:19, 20.11it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:17, 22.10it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:16, 22.97it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:16, 22.84it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:21, 17.33it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:19, 19.01it/s]
eval model::  26%|██▌       | 131/500 [00:07<00:19, 18.65it/s]
eval model::  27%|██▋       | 134/500 [00:07<00:19, 18.82it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:18, 19.68it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 19.73it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:16, 21.01it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:19, 18.32it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:20, 17.47it/s]
eval model::  30%|███       | 151/500 [00:08<00:20, 17.07it/s]
eval model::  31%|███       | 155/500 [00:08<00:17, 20.25it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.16it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:22, 15.15it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:22, 15.16it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:21, 15.85it/s]
eval model::  34%|███▎      | 168/500 [00:09<00:19, 17.20it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:18, 17.72it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 16.41it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.85it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 17.81it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 15.96it/s]
eval model::  37%|███▋      | 184/500 [00:10<00:17, 17.82it/s]
eval model::  37%|███▋      | 187/500 [00:10<00:16, 19.06it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:15, 20.27it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:13, 21.94it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:14, 20.56it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:13, 22.20it/s]
eval model::  40%|████      | 202/500 [00:10<00:12, 23.17it/s]
eval model::  41%|████      | 205/500 [00:11<00:20, 14.43it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:18, 15.63it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 15.75it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:16, 17.18it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:17, 15.87it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.22it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:16, 16.52it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:18, 15.14it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:15, 18.14it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:14, 18.53it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.34it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 22.65it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:11, 22.68it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 23.02it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 19.20it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.08it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.83it/s]
eval model::  50%|█████     | 251/500 [00:13<00:12, 20.67it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.72it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:10, 23.52it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 23.79it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:11, 20.89it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:10, 21.42it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:12, 18.73it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.34it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.83it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 17.93it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:12, 17.67it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:11, 19.65it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:20, 10.27it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:18, 11.50it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:14, 14.14it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:14, 14.71it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:13, 15.31it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:14, 14.06it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:11, 17.23it/s]
eval model::  60%|██████    | 301/500 [00:16<00:12, 15.77it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.91it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.89it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.65it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:09, 19.50it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 19.40it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 20.44it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 19.62it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 21.65it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:07, 22.12it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:07, 23.15it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:06, 24.63it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 23.23it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 15.80it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 17.30it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 18.58it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.20it/s]
eval model::  70%|███████   | 352/500 [00:19<00:06, 21.32it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 21.06it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 21.44it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 19.71it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.38it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 18.01it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 19.24it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:06, 19.37it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 20.62it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 22.19it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:05, 20.70it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 22.10it/s]
eval model::  77%|███████▋  | 387/500 [00:21<00:08, 13.26it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:07, 14.28it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:07, 14.60it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 15.60it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 16.52it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 19.42it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 17.63it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 20.00it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 21.16it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 22.41it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:03, 21.91it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:03, 21.10it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:03, 20.30it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 14.88it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 13.97it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.60it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 16.19it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 15.54it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 18.10it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:03, 18.94it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:03, 18.59it/s]
eval model::  89%|████████▊ | 443/500 [00:24<00:02, 20.87it/s]
eval model::  89%|████████▉ | 446/500 [00:24<00:02, 21.60it/s]
eval model::  90%|████████▉ | 449/500 [00:24<00:02, 20.51it/s]
eval model::  90%|█████████ | 452/500 [00:24<00:02, 21.71it/s]
eval model::  91%|█████████ | 455/500 [00:24<00:02, 22.18it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:01, 22.37it/s]
eval model::  92%|█████████▏| 461/500 [00:24<00:01, 20.92it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 17.32it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:01, 17.09it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 17.54it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.65it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.88it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.72it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 21.36it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 21.48it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 20.64it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 21.37it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 18.30it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 19.77it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 21.91it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.63it/s]
[2024/07/28 01:54:24] ppocr INFO: cur metric, precision: 0.7254164427727029, recall: 0.6499759268175253, hmean: 0.6856272219400711, fps: 44.36748244379876
[2024/07/28 01:54:24] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:54:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:54:32] ppocr INFO: epoch: [941/1500], global_step: 2823, lr: 0.001000, loss: 1.292886, loss_shrink_maps: 0.654999, loss_threshold_maps: 0.508612, loss_binary_maps: 0.130459, avg_reader_cost: 1.47972 s, avg_batch_cost: 1.71284 s, avg_samples: 12.5, ips: 7.29780 samples/s, eta: 2:49:55
[2024/07/28 01:54:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:54:41] ppocr INFO: epoch: [942/1500], global_step: 2826, lr: 0.001000, loss: 1.276544, loss_shrink_maps: 0.647546, loss_threshold_maps: 0.497301, loss_binary_maps: 0.128735, avg_reader_cost: 1.57573 s, avg_batch_cost: 1.81786 s, avg_samples: 12.5, ips: 6.87623 samples/s, eta: 2:49:37
[2024/07/28 01:54:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:54:50] ppocr INFO: epoch: [943/1500], global_step: 2829, lr: 0.001000, loss: 1.261449, loss_shrink_maps: 0.637255, loss_threshold_maps: 0.488457, loss_binary_maps: 0.127065, avg_reader_cost: 1.58494 s, avg_batch_cost: 1.81422 s, avg_samples: 12.5, ips: 6.89003 samples/s, eta: 2:49:18
[2024/07/28 01:54:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:54:58] ppocr INFO: epoch: [944/1500], global_step: 2830, lr: 0.001000, loss: 1.276544, loss_shrink_maps: 0.647546, loss_threshold_maps: 0.488457, loss_binary_maps: 0.128735, avg_reader_cost: 0.41179 s, avg_batch_cost: 0.52457 s, avg_samples: 4.8, ips: 9.15044 samples/s, eta: 2:49:12
[2024/07/28 01:54:59] ppocr INFO: epoch: [944/1500], global_step: 2832, lr: 0.001000, loss: 1.270631, loss_shrink_maps: 0.637255, loss_threshold_maps: 0.488457, loss_binary_maps: 0.127065, avg_reader_cost: 1.14056 s, avg_batch_cost: 1.28624 s, avg_samples: 7.7, ips: 5.98645 samples/s, eta: 2:49:00
[2024/07/28 01:55:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:55:09] ppocr INFO: epoch: [945/1500], global_step: 2835, lr: 0.001000, loss: 1.237921, loss_shrink_maps: 0.627256, loss_threshold_maps: 0.497357, loss_binary_maps: 0.125378, avg_reader_cost: 1.61883 s, avg_batch_cost: 1.84703 s, avg_samples: 12.5, ips: 6.76762 samples/s, eta: 2:48:42
[2024/07/28 01:55:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:55:18] ppocr INFO: epoch: [946/1500], global_step: 2838, lr: 0.001000, loss: 1.237921, loss_shrink_maps: 0.627256, loss_threshold_maps: 0.497020, loss_binary_maps: 0.125378, avg_reader_cost: 1.66031 s, avg_batch_cost: 2.00550 s, avg_samples: 12.5, ips: 6.23286 samples/s, eta: 2:48:25
[2024/07/28 01:55:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:55:27] ppocr INFO: epoch: [947/1500], global_step: 2840, lr: 0.001000, loss: 1.237921, loss_shrink_maps: 0.627256, loss_threshold_maps: 0.506312, loss_binary_maps: 0.125378, avg_reader_cost: 1.05666 s, avg_batch_cost: 1.23278 s, avg_samples: 9.6, ips: 7.78728 samples/s, eta: 2:48:13
[2024/07/28 01:55:28] ppocr INFO: epoch: [947/1500], global_step: 2841, lr: 0.001000, loss: 1.268462, loss_shrink_maps: 0.635506, loss_threshold_maps: 0.506792, loss_binary_maps: 0.126644, avg_reader_cost: 0.66227 s, avg_batch_cost: 0.71698 s, avg_samples: 2.9, ips: 4.04476 samples/s, eta: 2:48:07
[2024/07/28 01:55:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:55:37] ppocr INFO: epoch: [948/1500], global_step: 2844, lr: 0.001000, loss: 1.268462, loss_shrink_maps: 0.635506, loss_threshold_maps: 0.506792, loss_binary_maps: 0.126644, avg_reader_cost: 1.55183 s, avg_batch_cost: 1.78141 s, avg_samples: 12.5, ips: 7.01691 samples/s, eta: 2:47:49
[2024/07/28 01:55:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:55:46] ppocr INFO: epoch: [949/1500], global_step: 2847, lr: 0.001000, loss: 1.286148, loss_shrink_maps: 0.650028, loss_threshold_maps: 0.509279, loss_binary_maps: 0.129506, avg_reader_cost: 1.53601 s, avg_batch_cost: 1.77523 s, avg_samples: 12.5, ips: 7.04134 samples/s, eta: 2:47:30
[2024/07/28 01:55:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:55:55] ppocr INFO: epoch: [950/1500], global_step: 2850, lr: 0.001000, loss: 1.279020, loss_shrink_maps: 0.637894, loss_threshold_maps: 0.508371, loss_binary_maps: 0.127115, avg_reader_cost: 1.52579 s, avg_batch_cost: 1.76213 s, avg_samples: 12.5, ips: 7.09368 samples/s, eta: 2:47:12
[2024/07/28 01:55:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:56:04] ppocr INFO: epoch: [951/1500], global_step: 2853, lr: 0.001000, loss: 1.274150, loss_shrink_maps: 0.637894, loss_threshold_maps: 0.506454, loss_binary_maps: 0.127115, avg_reader_cost: 1.53795 s, avg_batch_cost: 1.77122 s, avg_samples: 12.5, ips: 7.05727 samples/s, eta: 2:46:53
[2024/07/28 01:56:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:56:14] ppocr INFO: epoch: [952/1500], global_step: 2856, lr: 0.001000, loss: 1.279020, loss_shrink_maps: 0.650028, loss_threshold_maps: 0.508371, loss_binary_maps: 0.129506, avg_reader_cost: 1.66165 s, avg_batch_cost: 1.89053 s, avg_samples: 12.5, ips: 6.61191 samples/s, eta: 2:46:35
[2024/07/28 01:56:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:56:23] ppocr INFO: epoch: [953/1500], global_step: 2859, lr: 0.001000, loss: 1.273222, loss_shrink_maps: 0.641046, loss_threshold_maps: 0.505898, loss_binary_maps: 0.128084, avg_reader_cost: 1.51852 s, avg_batch_cost: 1.76481 s, avg_samples: 12.5, ips: 7.08293 samples/s, eta: 2:46:17
[2024/07/28 01:56:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:56:31] ppocr INFO: epoch: [954/1500], global_step: 2860, lr: 0.001000, loss: 1.273222, loss_shrink_maps: 0.642473, loss_threshold_maps: 0.507334, loss_binary_maps: 0.128084, avg_reader_cost: 0.38194 s, avg_batch_cost: 0.55702 s, avg_samples: 4.8, ips: 8.61724 samples/s, eta: 2:46:10
[2024/07/28 01:56:32] ppocr INFO: epoch: [954/1500], global_step: 2862, lr: 0.001000, loss: 1.261507, loss_shrink_maps: 0.626826, loss_threshold_maps: 0.501068, loss_binary_maps: 0.125198, avg_reader_cost: 1.20507 s, avg_batch_cost: 1.35055 s, avg_samples: 7.7, ips: 5.70136 samples/s, eta: 2:45:59
[2024/07/28 01:56:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:56:41] ppocr INFO: epoch: [955/1500], global_step: 2865, lr: 0.001000, loss: 1.261507, loss_shrink_maps: 0.630079, loss_threshold_maps: 0.501068, loss_binary_maps: 0.125524, avg_reader_cost: 1.48620 s, avg_batch_cost: 1.71662 s, avg_samples: 12.5, ips: 7.28174 samples/s, eta: 2:45:40
[2024/07/28 01:56:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:56:51] ppocr INFO: epoch: [956/1500], global_step: 2868, lr: 0.001000, loss: 1.271223, loss_shrink_maps: 0.646896, loss_threshold_maps: 0.511668, loss_binary_maps: 0.128879, avg_reader_cost: 1.57854 s, avg_batch_cost: 1.82579 s, avg_samples: 12.5, ips: 6.84636 samples/s, eta: 2:45:22
[2024/07/28 01:56:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:56:59] ppocr INFO: epoch: [957/1500], global_step: 2870, lr: 0.001000, loss: 1.271223, loss_shrink_maps: 0.637942, loss_threshold_maps: 0.510955, loss_binary_maps: 0.127022, avg_reader_cost: 0.92808 s, avg_batch_cost: 1.12166 s, avg_samples: 9.6, ips: 8.55876 samples/s, eta: 2:45:09
[2024/07/28 01:57:00] ppocr INFO: epoch: [957/1500], global_step: 2871, lr: 0.001000, loss: 1.273222, loss_shrink_maps: 0.649421, loss_threshold_maps: 0.510955, loss_binary_maps: 0.129555, avg_reader_cost: 0.60655 s, avg_batch_cost: 0.66186 s, avg_samples: 2.9, ips: 4.38156 samples/s, eta: 2:45:03
[2024/07/28 01:57:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:57:09] ppocr INFO: epoch: [958/1500], global_step: 2874, lr: 0.001000, loss: 1.272796, loss_shrink_maps: 0.637942, loss_threshold_maps: 0.506949, loss_binary_maps: 0.127022, avg_reader_cost: 1.52122 s, avg_batch_cost: 1.75135 s, avg_samples: 12.5, ips: 7.13734 samples/s, eta: 2:44:45
[2024/07/28 01:57:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:57:18] ppocr INFO: epoch: [959/1500], global_step: 2877, lr: 0.001000, loss: 1.277918, loss_shrink_maps: 0.647749, loss_threshold_maps: 0.505649, loss_binary_maps: 0.128940, avg_reader_cost: 1.59336 s, avg_batch_cost: 1.82368 s, avg_samples: 12.5, ips: 6.85427 samples/s, eta: 2:44:27
[2024/07/28 01:57:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:57:27] ppocr INFO: epoch: [960/1500], global_step: 2880, lr: 0.001000, loss: 1.275919, loss_shrink_maps: 0.637942, loss_threshold_maps: 0.504896, loss_binary_maps: 0.127022, avg_reader_cost: 1.55705 s, avg_batch_cost: 1.80457 s, avg_samples: 12.5, ips: 6.92687 samples/s, eta: 2:44:08

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.34it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.85it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.88it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.29it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.94it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.50it/s]
eval model::   4%|▎         | 18/500 [00:00<00:22, 21.18it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 18.62it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.15it/s]
eval model::   5%|▌         | 27/500 [00:01<00:23, 20.06it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 21.60it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 20.29it/s]
eval model::   7%|▋         | 36/500 [00:01<00:23, 19.74it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 18.28it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.41it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.34it/s]
eval model::  10%|▉         | 48/500 [00:02<00:25, 17.48it/s]
eval model::  10%|█         | 50/500 [00:02<00:25, 17.90it/s]
eval model::  11%|█         | 53/500 [00:02<00:22, 19.88it/s]
eval model::  11%|█         | 56/500 [00:02<00:20, 21.22it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:23, 19.07it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:28, 15.58it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:27, 15.69it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:23, 18.16it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:22, 19.45it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 20.10it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.68it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:20, 20.41it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.04it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 21.13it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.59it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 17.89it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 17.84it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.88it/s]
eval model::  20%|██        | 100/500 [00:05<00:19, 20.55it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 22.01it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.39it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.01it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:19, 19.69it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:19, 19.67it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:18, 20.74it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:19, 19.51it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.55it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.01it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:20, 18.29it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.70it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.59it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.42it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.86it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.96it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:16, 20.80it/s]
eval model::  30%|███       | 150/500 [00:07<00:15, 22.46it/s]
eval model::  31%|███       | 153/500 [00:07<00:16, 20.78it/s]
eval model::  31%|███       | 156/500 [00:07<00:15, 22.35it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:15, 22.52it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:22, 15.11it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:21, 15.90it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 17.84it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 19.97it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:16, 19.26it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.45it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.95it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:18, 17.31it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 20.83it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.75it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:13, 23.12it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:12, 24.08it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:12, 24.17it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 21.44it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.11it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:21, 13.55it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:21, 13.43it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:21, 13.50it/s]
eval model::  43%|████▎     | 214/500 [00:11<00:17, 15.89it/s]
eval model::  43%|████▎     | 216/500 [00:11<00:19, 14.75it/s]
eval model::  44%|████▎     | 218/500 [00:11<00:20, 13.67it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:19, 14.20it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:16, 16.66it/s]
eval model::  45%|████▌     | 227/500 [00:11<00:14, 19.45it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:10, 25.05it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:10, 25.48it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:09, 26.49it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:09, 26.86it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:11, 21.46it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:11, 22.62it/s]
eval model::  50%|█████     | 250/500 [00:12<00:11, 21.58it/s]
eval model::  51%|█████     | 253/500 [00:12<00:10, 22.86it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 20.42it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 22.23it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 20.65it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:11, 20.39it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:10, 21.55it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:11, 20.12it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:11, 18.88it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:13, 16.04it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:13, 16.53it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:13, 16.55it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:12, 17.08it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:22,  9.69it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:19, 11.08it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:13, 15.31it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:13, 14.92it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:14, 13.61it/s]
eval model::  60%|██████    | 300/500 [00:15<00:12, 16.03it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 16.56it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 18.87it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 20.52it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:08, 21.57it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:08, 21.01it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:08, 22.43it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:07, 23.36it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:08, 20.43it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:08, 20.22it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 19.39it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:09, 18.54it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:08, 19.86it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:09, 17.82it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:11, 13.52it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:10, 15.16it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 17.17it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 17.71it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 19.08it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.23it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 20.47it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.35it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.81it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:08, 16.44it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 18.15it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 18.86it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 20.64it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 22.43it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:05, 21.96it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 23.13it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:08, 13.98it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 16.04it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 16.46it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 17.85it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 18.85it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 16.68it/s]
eval model::  81%|████████  | 405/500 [00:21<00:05, 18.85it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 20.70it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 22.37it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 21.85it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 18.34it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 13.51it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:05, 15.26it/s]
eval model::  85%|████████▌ | 425/500 [00:22<00:05, 13.83it/s]
eval model::  86%|████████▌ | 428/500 [00:22<00:04, 15.49it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 16.08it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 18.27it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:03, 20.30it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:02, 21.54it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 26.45it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 25.08it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 24.41it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:01, 23.13it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:02, 21.38it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 21.07it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 18.85it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 17.16it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.51it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 19.98it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 20.05it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 19.62it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:01, 18.72it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 19.71it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 20.79it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 19.48it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 19.37it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 15.89it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 17.13it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 19.79it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.92it/s]
[2024/07/28 01:57:54] ppocr INFO: cur metric, precision: 0.7571902654867256, recall: 0.6591237361579201, hmean: 0.7047619047619048, fps: 44.329671492959086
[2024/07/28 01:57:54] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 01:57:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:58:03] ppocr INFO: epoch: [961/1500], global_step: 2883, lr: 0.001000, loss: 1.281718, loss_shrink_maps: 0.643205, loss_threshold_maps: 0.504896, loss_binary_maps: 0.127900, avg_reader_cost: 2.00777 s, avg_batch_cost: 2.38727 s, avg_samples: 12.5, ips: 5.23612 samples/s, eta: 2:43:53
[2024/07/28 01:58:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:58:12] ppocr INFO: epoch: [962/1500], global_step: 2886, lr: 0.001000, loss: 1.275214, loss_shrink_maps: 0.641261, loss_threshold_maps: 0.500268, loss_binary_maps: 0.127737, avg_reader_cost: 1.54951 s, avg_batch_cost: 1.79849 s, avg_samples: 12.5, ips: 6.95027 samples/s, eta: 2:43:35
[2024/07/28 01:58:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:58:21] ppocr INFO: epoch: [963/1500], global_step: 2889, lr: 0.001000, loss: 1.251210, loss_shrink_maps: 0.636832, loss_threshold_maps: 0.492788, loss_binary_maps: 0.126717, avg_reader_cost: 1.54615 s, avg_batch_cost: 1.78390 s, avg_samples: 12.5, ips: 7.00711 samples/s, eta: 2:43:16
[2024/07/28 01:58:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:58:29] ppocr INFO: epoch: [964/1500], global_step: 2890, lr: 0.001000, loss: 1.263982, loss_shrink_maps: 0.643205, loss_threshold_maps: 0.498618, loss_binary_maps: 0.127900, avg_reader_cost: 0.43922 s, avg_batch_cost: 0.52141 s, avg_samples: 4.8, ips: 9.20587 samples/s, eta: 2:43:10
[2024/07/28 01:58:31] ppocr INFO: epoch: [964/1500], global_step: 2892, lr: 0.001000, loss: 1.251210, loss_shrink_maps: 0.636832, loss_threshold_maps: 0.493039, loss_binary_maps: 0.126717, avg_reader_cost: 1.13450 s, avg_batch_cost: 1.28060 s, avg_samples: 7.7, ips: 6.01279 samples/s, eta: 2:42:58
[2024/07/28 01:58:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:58:40] ppocr INFO: epoch: [965/1500], global_step: 2895, lr: 0.001000, loss: 1.251210, loss_shrink_maps: 0.636832, loss_threshold_maps: 0.493394, loss_binary_maps: 0.126717, avg_reader_cost: 1.55294 s, avg_batch_cost: 1.78954 s, avg_samples: 12.5, ips: 6.98505 samples/s, eta: 2:42:40
[2024/07/28 01:58:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:58:49] ppocr INFO: epoch: [966/1500], global_step: 2898, lr: 0.001000, loss: 1.251210, loss_shrink_maps: 0.636832, loss_threshold_maps: 0.493322, loss_binary_maps: 0.126674, avg_reader_cost: 1.55025 s, avg_batch_cost: 1.82072 s, avg_samples: 12.5, ips: 6.86542 samples/s, eta: 2:42:21
[2024/07/28 01:58:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:58:58] ppocr INFO: epoch: [967/1500], global_step: 2900, lr: 0.001000, loss: 1.251210, loss_shrink_maps: 0.635591, loss_threshold_maps: 0.493322, loss_binary_maps: 0.126581, avg_reader_cost: 0.93254 s, avg_batch_cost: 1.10887 s, avg_samples: 9.6, ips: 8.65743 samples/s, eta: 2:42:08
[2024/07/28 01:58:58] ppocr INFO: epoch: [967/1500], global_step: 2901, lr: 0.001000, loss: 1.251210, loss_shrink_maps: 0.635591, loss_threshold_maps: 0.493322, loss_binary_maps: 0.126581, avg_reader_cost: 0.60030 s, avg_batch_cost: 0.65527 s, avg_samples: 2.9, ips: 4.42565 samples/s, eta: 2:42:03
[2024/07/28 01:59:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:59:08] ppocr INFO: epoch: [968/1500], global_step: 2904, lr: 0.001000, loss: 1.261522, loss_shrink_maps: 0.635591, loss_threshold_maps: 0.493322, loss_binary_maps: 0.126581, avg_reader_cost: 1.57655 s, avg_batch_cost: 1.83291 s, avg_samples: 12.5, ips: 6.81975 samples/s, eta: 2:41:44
[2024/07/28 01:59:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:59:17] ppocr INFO: epoch: [969/1500], global_step: 2907, lr: 0.001000, loss: 1.249211, loss_shrink_maps: 0.623008, loss_threshold_maps: 0.491018, loss_binary_maps: 0.123978, avg_reader_cost: 1.58298 s, avg_batch_cost: 1.81136 s, avg_samples: 12.5, ips: 6.90090 samples/s, eta: 2:41:26
[2024/07/28 01:59:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:59:26] ppocr INFO: epoch: [970/1500], global_step: 2910, lr: 0.001000, loss: 1.214300, loss_shrink_maps: 0.609493, loss_threshold_maps: 0.488373, loss_binary_maps: 0.121476, avg_reader_cost: 1.62455 s, avg_batch_cost: 1.86396 s, avg_samples: 12.5, ips: 6.70615 samples/s, eta: 2:41:08
[2024/07/28 01:59:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:59:35] ppocr INFO: epoch: [971/1500], global_step: 2913, lr: 0.001000, loss: 1.226004, loss_shrink_maps: 0.617517, loss_threshold_maps: 0.486216, loss_binary_maps: 0.122952, avg_reader_cost: 1.57452 s, avg_batch_cost: 1.80333 s, avg_samples: 12.5, ips: 6.93163 samples/s, eta: 2:40:50
[2024/07/28 01:59:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:59:45] ppocr INFO: epoch: [972/1500], global_step: 2916, lr: 0.001000, loss: 1.277899, loss_shrink_maps: 0.641133, loss_threshold_maps: 0.494807, loss_binary_maps: 0.127595, avg_reader_cost: 1.52770 s, avg_batch_cost: 1.78718 s, avg_samples: 12.5, ips: 6.99426 samples/s, eta: 2:40:31
[2024/07/28 01:59:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 01:59:54] ppocr INFO: epoch: [973/1500], global_step: 2919, lr: 0.001000, loss: 1.257561, loss_shrink_maps: 0.643701, loss_threshold_maps: 0.490495, loss_binary_maps: 0.128308, avg_reader_cost: 1.55242 s, avg_batch_cost: 1.80680 s, avg_samples: 12.5, ips: 6.91832 samples/s, eta: 2:40:13
[2024/07/28 01:59:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:00:02] ppocr INFO: epoch: [974/1500], global_step: 2920, lr: 0.001000, loss: 1.262229, loss_shrink_maps: 0.643701, loss_threshold_maps: 0.494807, loss_binary_maps: 0.128308, avg_reader_cost: 0.44512 s, avg_batch_cost: 0.54449 s, avg_samples: 4.8, ips: 8.81557 samples/s, eta: 2:40:07
[2024/07/28 02:00:03] ppocr INFO: epoch: [974/1500], global_step: 2922, lr: 0.001000, loss: 1.232121, loss_shrink_maps: 0.625991, loss_threshold_maps: 0.490495, loss_binary_maps: 0.124649, avg_reader_cost: 1.18033 s, avg_batch_cost: 1.32613 s, avg_samples: 7.7, ips: 5.80635 samples/s, eta: 2:39:55
[2024/07/28 02:00:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:00:13] ppocr INFO: epoch: [975/1500], global_step: 2925, lr: 0.001000, loss: 1.231281, loss_shrink_maps: 0.618771, loss_threshold_maps: 0.494807, loss_binary_maps: 0.123133, avg_reader_cost: 1.51649 s, avg_batch_cost: 1.74534 s, avg_samples: 12.5, ips: 7.16192 samples/s, eta: 2:39:36
[2024/07/28 02:00:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:00:21] ppocr INFO: epoch: [976/1500], global_step: 2928, lr: 0.001000, loss: 1.235949, loss_shrink_maps: 0.625991, loss_threshold_maps: 0.495416, loss_binary_maps: 0.124649, avg_reader_cost: 1.50775 s, avg_batch_cost: 1.73842 s, avg_samples: 12.5, ips: 7.19044 samples/s, eta: 2:39:18
[2024/07/28 02:00:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:00:30] ppocr INFO: epoch: [977/1500], global_step: 2930, lr: 0.001000, loss: 1.262229, loss_shrink_maps: 0.651612, loss_threshold_maps: 0.507086, loss_binary_maps: 0.129770, avg_reader_cost: 0.97231 s, avg_batch_cost: 1.14543 s, avg_samples: 9.6, ips: 8.38113 samples/s, eta: 2:39:05
[2024/07/28 02:00:31] ppocr INFO: epoch: [977/1500], global_step: 2931, lr: 0.001000, loss: 1.284674, loss_shrink_maps: 0.652698, loss_threshold_maps: 0.513841, loss_binary_maps: 0.129770, avg_reader_cost: 0.61837 s, avg_batch_cost: 0.67324 s, avg_samples: 2.9, ips: 4.30754 samples/s, eta: 2:38:59
[2024/07/28 02:00:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:00:40] ppocr INFO: epoch: [978/1500], global_step: 2934, lr: 0.001000, loss: 1.235949, loss_shrink_maps: 0.624134, loss_threshold_maps: 0.507086, loss_binary_maps: 0.124175, avg_reader_cost: 1.49471 s, avg_batch_cost: 1.72860 s, avg_samples: 12.5, ips: 7.23131 samples/s, eta: 2:38:41
[2024/07/28 02:00:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:00:49] ppocr INFO: epoch: [979/1500], global_step: 2937, lr: 0.001000, loss: 1.250864, loss_shrink_maps: 0.630580, loss_threshold_maps: 0.505805, loss_binary_maps: 0.125706, avg_reader_cost: 1.58038 s, avg_batch_cost: 1.83596 s, avg_samples: 12.5, ips: 6.80843 samples/s, eta: 2:38:22
[2024/07/28 02:00:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:00:58] ppocr INFO: epoch: [980/1500], global_step: 2940, lr: 0.001000, loss: 1.227724, loss_shrink_maps: 0.605952, loss_threshold_maps: 0.491843, loss_binary_maps: 0.120496, avg_reader_cost: 1.52741 s, avg_batch_cost: 1.76647 s, avg_samples: 12.5, ips: 7.07627 samples/s, eta: 2:38:04

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:56,  8.90it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 19.05it/s]
eval model::   1%|▏         | 7/500 [00:00<00:23, 21.19it/s]
eval model::   2%|▏         | 10/500 [00:00<00:23, 20.77it/s]
eval model::   3%|▎         | 13/500 [00:00<00:25, 18.77it/s]
eval model::   3%|▎         | 15/500 [00:00<00:26, 18.03it/s]
eval model::   4%|▎         | 18/500 [00:00<00:23, 20.82it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.39it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.65it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 20.82it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 22.29it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 19.57it/s]
eval model::   7%|▋         | 36/500 [00:01<00:25, 18.28it/s]
eval model::   8%|▊         | 39/500 [00:02<00:26, 17.60it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.24it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 18.17it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 17.72it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 19.30it/s]
eval model::  11%|█         | 53/500 [00:02<00:22, 19.77it/s]
eval model::  11%|█         | 56/500 [00:02<00:22, 19.32it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 17.87it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.61it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.97it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.19it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.32it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 20.86it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:21, 19.93it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.18it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 21.74it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.63it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 20.27it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.15it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.24it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.53it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:21, 18.92it/s]
eval model::  20%|██        | 101/500 [00:05<00:22, 18.13it/s]
eval model::  21%|██        | 104/500 [00:05<00:20, 19.58it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 20.82it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:19, 19.78it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:19, 20.08it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:20, 19.07it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:18, 20.81it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:17, 21.30it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:20, 18.04it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:19, 19.50it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:21, 17.34it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:22, 16.64it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:20, 17.67it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.13it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.50it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.65it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 19.52it/s]
eval model::  30%|███       | 151/500 [00:07<00:20, 17.30it/s]
eval model::  31%|███       | 154/500 [00:08<00:17, 19.26it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 22.04it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:21, 16.14it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:20, 16.68it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 18.90it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 18.92it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:17, 18.50it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 15.92it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.72it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.32it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:20, 15.57it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 17.92it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 18.72it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:15, 20.11it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:14, 21.66it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:14, 20.43it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 21.13it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 20.86it/s]
eval model::  41%|████      | 205/500 [00:11<00:21, 13.65it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:19, 14.65it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 14.59it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.26it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.04it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.95it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 16.69it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.33it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.27it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:14, 18.81it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 20.96it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 20.41it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:12, 21.80it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:12, 20.97it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:14, 17.72it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:13, 19.01it/s]
eval model::  50%|█████     | 250/500 [00:13<00:13, 18.71it/s]
eval model::  51%|█████     | 253/500 [00:13<00:12, 19.63it/s]
eval model::  51%|█████     | 256/500 [00:13<00:12, 18.81it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:11, 21.34it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:11, 20.34it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:12, 19.49it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.06it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:12, 18.34it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:12, 18.41it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.43it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 18.82it/s]
eval model::  56%|█████▌    | 281/500 [00:15<00:10, 20.22it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:10, 20.45it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:17, 12.22it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:13, 15.93it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:13, 15.66it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:15, 13.14it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 15.67it/s]
eval model::  60%|██████    | 301/500 [00:16<00:13, 15.14it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.57it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.84it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 19.79it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:08, 21.04it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:08, 20.85it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:07, 22.84it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 21.80it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:07, 23.57it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:07, 22.06it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:07, 23.38it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:06, 24.25it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:07, 22.06it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:09, 16.60it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 17.06it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 18.87it/s]
eval model::  70%|███████   | 351/500 [00:18<00:06, 21.41it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 21.30it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 20.04it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:06, 21.01it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:06, 19.90it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 16.92it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 19.18it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 18.90it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:05, 21.13it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 22.95it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:05, 20.24it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 22.43it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 15.10it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 17.35it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:05, 18.09it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 17.90it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 18.51it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 16.64it/s]
eval model::  81%|████████  | 405/500 [00:21<00:05, 18.13it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 19.79it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:03, 22.57it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:03, 21.37it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 18.53it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 15.05it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 15.58it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 16.37it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 15.85it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:03, 17.88it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:03, 17.33it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 20.30it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:02, 21.50it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:02, 20.19it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 22.24it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 23.28it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 20.05it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:01, 23.19it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 22.56it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 21.24it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 18.09it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.96it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.49it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.01it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 18.84it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 20.62it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:00, 21.83it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 21.99it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 22.57it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 21.58it/s]
eval model::  98%|█████████▊| 491/500 [00:26<00:00, 21.73it/s]
eval model::  99%|█████████▉| 494/500 [00:26<00:00, 18.28it/s]
eval model::  99%|█████████▉| 497/500 [00:26<00:00, 20.03it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.90it/s]
[2024/07/28 02:01:24] ppocr INFO: cur metric, precision: 0.7534321801208127, recall: 0.6605681271064034, hmean: 0.7039507439712672, fps: 44.18426473663336
[2024/07/28 02:01:24] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 02:01:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:01:33] ppocr INFO: epoch: [981/1500], global_step: 2943, lr: 0.001000, loss: 1.227724, loss_shrink_maps: 0.605952, loss_threshold_maps: 0.482909, loss_binary_maps: 0.120496, avg_reader_cost: 1.66421 s, avg_batch_cost: 1.96747 s, avg_samples: 12.5, ips: 6.35333 samples/s, eta: 2:37:46
[2024/07/28 02:01:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:01:43] ppocr INFO: epoch: [982/1500], global_step: 2946, lr: 0.001000, loss: 1.188825, loss_shrink_maps: 0.590441, loss_threshold_maps: 0.474798, loss_binary_maps: 0.117106, avg_reader_cost: 1.51799 s, avg_batch_cost: 1.74636 s, avg_samples: 12.5, ips: 7.15774 samples/s, eta: 2:37:28
[2024/07/28 02:01:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:01:52] ppocr INFO: epoch: [983/1500], global_step: 2949, lr: 0.001000, loss: 1.188825, loss_shrink_maps: 0.590441, loss_threshold_maps: 0.474798, loss_binary_maps: 0.117106, avg_reader_cost: 1.56970 s, avg_batch_cost: 1.79957 s, avg_samples: 12.5, ips: 6.94611 samples/s, eta: 2:37:09
[2024/07/28 02:01:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:01:59] ppocr INFO: epoch: [984/1500], global_step: 2950, lr: 0.001000, loss: 1.229458, loss_shrink_maps: 0.625962, loss_threshold_maps: 0.483633, loss_binary_maps: 0.124834, avg_reader_cost: 0.42275 s, avg_batch_cost: 0.50495 s, avg_samples: 4.8, ips: 9.50587 samples/s, eta: 2:37:03
[2024/07/28 02:02:01] ppocr INFO: epoch: [984/1500], global_step: 2952, lr: 0.001000, loss: 1.229458, loss_shrink_maps: 0.625216, loss_threshold_maps: 0.483633, loss_binary_maps: 0.124507, avg_reader_cost: 1.10150 s, avg_batch_cost: 1.24760 s, avg_samples: 7.7, ips: 6.17185 samples/s, eta: 2:36:51
[2024/07/28 02:02:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:02:10] ppocr INFO: epoch: [985/1500], global_step: 2955, lr: 0.001000, loss: 1.176024, loss_shrink_maps: 0.590570, loss_threshold_maps: 0.472576, loss_binary_maps: 0.117568, avg_reader_cost: 1.51878 s, avg_batch_cost: 1.77656 s, avg_samples: 12.5, ips: 7.03608 samples/s, eta: 2:36:32
[2024/07/28 02:02:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:02:20] ppocr INFO: epoch: [986/1500], global_step: 2958, lr: 0.001000, loss: 1.160490, loss_shrink_maps: 0.581985, loss_threshold_maps: 0.467693, loss_binary_maps: 0.116037, avg_reader_cost: 1.60043 s, avg_batch_cost: 1.87276 s, avg_samples: 12.5, ips: 6.67464 samples/s, eta: 2:36:14
[2024/07/28 02:02:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:02:28] ppocr INFO: epoch: [987/1500], global_step: 2960, lr: 0.001000, loss: 1.160490, loss_shrink_maps: 0.581985, loss_threshold_maps: 0.467693, loss_binary_maps: 0.116037, avg_reader_cost: 0.96197 s, avg_batch_cost: 1.13711 s, avg_samples: 9.6, ips: 8.44248 samples/s, eta: 2:36:02
[2024/07/28 02:02:29] ppocr INFO: epoch: [987/1500], global_step: 2961, lr: 0.001000, loss: 1.176024, loss_shrink_maps: 0.590895, loss_threshold_maps: 0.468024, loss_binary_maps: 0.117836, avg_reader_cost: 0.61424 s, avg_batch_cost: 0.66890 s, avg_samples: 2.9, ips: 4.33551 samples/s, eta: 2:35:56
[2024/07/28 02:02:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:02:38] ppocr INFO: epoch: [988/1500], global_step: 2964, lr: 0.001000, loss: 1.213846, loss_shrink_maps: 0.605247, loss_threshold_maps: 0.473300, loss_binary_maps: 0.120406, avg_reader_cost: 1.56387 s, avg_batch_cost: 1.79200 s, avg_samples: 12.5, ips: 6.97545 samples/s, eta: 2:35:38
[2024/07/28 02:02:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:02:47] ppocr INFO: epoch: [989/1500], global_step: 2967, lr: 0.001000, loss: 1.253483, loss_shrink_maps: 0.624872, loss_threshold_maps: 0.494010, loss_binary_maps: 0.124561, avg_reader_cost: 1.55446 s, avg_batch_cost: 1.78925 s, avg_samples: 12.5, ips: 6.98615 samples/s, eta: 2:35:19
[2024/07/28 02:02:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:02:56] ppocr INFO: epoch: [990/1500], global_step: 2970, lr: 0.001000, loss: 1.268064, loss_shrink_maps: 0.634020, loss_threshold_maps: 0.495727, loss_binary_maps: 0.126343, avg_reader_cost: 1.51957 s, avg_batch_cost: 1.74748 s, avg_samples: 12.5, ips: 7.15315 samples/s, eta: 2:35:01
[2024/07/28 02:02:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:03:06] ppocr INFO: epoch: [991/1500], global_step: 2973, lr: 0.001000, loss: 1.300737, loss_shrink_maps: 0.644659, loss_threshold_maps: 0.512480, loss_binary_maps: 0.128808, avg_reader_cost: 1.53492 s, avg_batch_cost: 1.78366 s, avg_samples: 12.5, ips: 7.00808 samples/s, eta: 2:34:42
[2024/07/28 02:03:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:03:15] ppocr INFO: epoch: [992/1500], global_step: 2976, lr: 0.001000, loss: 1.305337, loss_shrink_maps: 0.661328, loss_threshold_maps: 0.517929, loss_binary_maps: 0.131281, avg_reader_cost: 1.55161 s, avg_batch_cost: 1.78166 s, avg_samples: 12.5, ips: 7.01594 samples/s, eta: 2:34:24
[2024/07/28 02:03:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:03:24] ppocr INFO: epoch: [993/1500], global_step: 2979, lr: 0.001000, loss: 1.318960, loss_shrink_maps: 0.681665, loss_threshold_maps: 0.522575, loss_binary_maps: 0.135150, avg_reader_cost: 1.52998 s, avg_batch_cost: 1.77616 s, avg_samples: 12.5, ips: 7.03767 samples/s, eta: 2:34:05
[2024/07/28 02:03:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:03:32] ppocr INFO: epoch: [994/1500], global_step: 2980, lr: 0.001000, loss: 1.318960, loss_shrink_maps: 0.681665, loss_threshold_maps: 0.522575, loss_binary_maps: 0.135150, avg_reader_cost: 0.44598 s, avg_batch_cost: 0.52874 s, avg_samples: 4.8, ips: 9.07821 samples/s, eta: 2:33:59
[2024/07/28 02:03:33] ppocr INFO: epoch: [994/1500], global_step: 2982, lr: 0.001000, loss: 1.318960, loss_shrink_maps: 0.681665, loss_threshold_maps: 0.518507, loss_binary_maps: 0.135150, avg_reader_cost: 1.14884 s, avg_batch_cost: 1.29462 s, avg_samples: 7.7, ips: 5.94769 samples/s, eta: 2:33:47
[2024/07/28 02:03:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:03:43] ppocr INFO: epoch: [995/1500], global_step: 2985, lr: 0.001000, loss: 1.314598, loss_shrink_maps: 0.681665, loss_threshold_maps: 0.508785, loss_binary_maps: 0.135150, avg_reader_cost: 1.60379 s, avg_batch_cost: 1.83537 s, avg_samples: 12.5, ips: 6.81062 samples/s, eta: 2:33:29
[2024/07/28 02:03:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:03:52] ppocr INFO: epoch: [996/1500], global_step: 2988, lr: 0.001000, loss: 1.333186, loss_shrink_maps: 0.696661, loss_threshold_maps: 0.509363, loss_binary_maps: 0.138848, avg_reader_cost: 1.54078 s, avg_batch_cost: 1.77905 s, avg_samples: 12.5, ips: 7.02623 samples/s, eta: 2:33:10
[2024/07/28 02:03:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:04:01] ppocr INFO: epoch: [997/1500], global_step: 2990, lr: 0.001000, loss: 1.327742, loss_shrink_maps: 0.691152, loss_threshold_maps: 0.503713, loss_binary_maps: 0.137184, avg_reader_cost: 0.91636 s, avg_batch_cost: 1.10316 s, avg_samples: 9.6, ips: 8.70225 samples/s, eta: 2:32:58
[2024/07/28 02:04:01] ppocr INFO: epoch: [997/1500], global_step: 2991, lr: 0.001000, loss: 1.327742, loss_shrink_maps: 0.691152, loss_threshold_maps: 0.500980, loss_binary_maps: 0.137184, avg_reader_cost: 0.59703 s, avg_batch_cost: 0.65215 s, avg_samples: 2.9, ips: 4.44680 samples/s, eta: 2:32:52
[2024/07/28 02:04:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:04:10] ppocr INFO: epoch: [998/1500], global_step: 2994, lr: 0.001000, loss: 1.320345, loss_shrink_maps: 0.672755, loss_threshold_maps: 0.508437, loss_binary_maps: 0.134162, avg_reader_cost: 1.53943 s, avg_batch_cost: 1.80419 s, avg_samples: 12.5, ips: 6.92831 samples/s, eta: 2:32:33
[2024/07/28 02:04:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:04:20] ppocr INFO: epoch: [999/1500], global_step: 2997, lr: 0.001000, loss: 1.314496, loss_shrink_maps: 0.659276, loss_threshold_maps: 0.508437, loss_binary_maps: 0.131724, avg_reader_cost: 1.55256 s, avg_batch_cost: 1.78159 s, avg_samples: 12.5, ips: 7.01620 samples/s, eta: 2:32:15
[2024/07/28 02:04:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:04:29] ppocr INFO: epoch: [1000/1500], global_step: 3000, lr: 0.001000, loss: 1.309451, loss_shrink_maps: 0.659276, loss_threshold_maps: 0.509496, loss_binary_maps: 0.131724, avg_reader_cost: 1.54068 s, avg_batch_cost: 1.76941 s, avg_samples: 12.5, ips: 7.06449 samples/s, eta: 2:31:56

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.72it/s]
eval model::   1%|          | 4/500 [00:00<00:24, 20.22it/s]
eval model::   1%|          | 6/500 [00:00<00:25, 19.67it/s]
eval model::   2%|▏         | 8/500 [00:00<00:26, 18.47it/s]
eval model::   2%|▏         | 11/500 [00:00<00:29, 16.86it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.51it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.33it/s]
eval model::   4%|▍         | 19/500 [00:01<00:28, 16.81it/s]
eval model::   4%|▍         | 21/500 [00:01<00:27, 17.20it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.24it/s]
eval model::   5%|▌         | 27/500 [00:01<00:24, 18.99it/s]
eval model::   6%|▌         | 30/500 [00:01<00:23, 20.10it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 19.78it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 19.01it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 17.98it/s]
eval model::   8%|▊         | 41/500 [00:02<00:25, 18.22it/s]
eval model::   9%|▊         | 43/500 [00:02<00:28, 16.07it/s]
eval model::   9%|▉         | 46/500 [00:02<00:26, 17.30it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 19.84it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 20.52it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 21.17it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:22, 20.06it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.58it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.88it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.58it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.88it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.28it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 20.54it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.97it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.36it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.40it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 19.73it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:21, 18.85it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.76it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.79it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.69it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.60it/s]
eval model::  21%|██        | 106/500 [00:05<00:16, 23.47it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:16, 23.81it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:16, 23.10it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:17, 21.90it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:17, 22.34it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:17, 22.04it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 20.49it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.10it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 19.12it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 18.66it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:17, 20.30it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:17, 20.16it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:16, 21.32it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:15, 22.56it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.36it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.57it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 18.64it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:15, 21.63it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:21, 15.64it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:20, 16.80it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 18.76it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 20.50it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 18.41it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:20, 15.86it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:20, 15.98it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:21, 14.91it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:18, 17.34it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.64it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:16, 19.30it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 20.59it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 20.17it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.24it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.86it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 21.67it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:23, 12.28it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:22, 12.86it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:21, 13.42it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.36it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.08it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:24, 11.66it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:19, 14.25it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:19, 13.95it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.86it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 22.13it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:11, 23.85it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:10, 25.24it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:10, 24.36it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 21.27it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:11, 22.94it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:11, 21.12it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 22.14it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.94it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 21.86it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 20.65it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 19.05it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:12, 19.01it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.47it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 19.77it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 18.98it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 18.43it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:10, 20.08it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:10, 20.34it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:18, 11.46it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:15, 13.87it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:13, 15.74it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:14, 14.04it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:11, 17.96it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 17.53it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 18.55it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:08, 21.40it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 20.45it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 20.03it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 20.75it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 21.08it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 21.56it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 22.16it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 20.74it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:08, 20.64it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 21.41it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:08, 20.08it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 15.47it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 17.31it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 17.21it/s]
eval model::  70%|███████   | 350/500 [00:18<00:07, 19.05it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 21.23it/s]
eval model::  71%|███████   | 356/500 [00:18<00:06, 20.67it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:06, 22.10it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:06, 20.04it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 19.22it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:07, 18.74it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 20.17it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 19.84it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:06, 20.37it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 20.52it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:04, 23.89it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 14.40it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 16.29it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 17.21it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:06, 17.22it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 18.95it/s]
eval model::  80%|████████  | 402/500 [00:21<00:04, 19.73it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 21.28it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.32it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 21.95it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 22.98it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 21.40it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 15.27it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 16.09it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 14.60it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 14.94it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 17.86it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:04, 16.70it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:02, 21.97it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 24.53it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 23.58it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 24.26it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 23.41it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.39it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:01, 23.00it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 22.07it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 22.18it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 20.63it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 20.15it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 20.70it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 22.32it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 21.02it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 21.11it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 20.66it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 19.99it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 20.56it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 20.31it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 17.19it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 17.26it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 19.19it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.16it/s]
[2024/07/28 02:04:55] ppocr INFO: cur metric, precision: 0.7003058103975535, recall: 0.6615310544053924, hmean: 0.6803664273334983, fps: 47.04513578458488
[2024/07/28 02:04:55] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 02:04:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:05:04] ppocr INFO: epoch: [1001/1500], global_step: 3003, lr: 0.001000, loss: 1.309451, loss_shrink_maps: 0.655358, loss_threshold_maps: 0.509496, loss_binary_maps: 0.130805, avg_reader_cost: 1.94310 s, avg_batch_cost: 2.37008 s, avg_samples: 12.5, ips: 5.27409 samples/s, eta: 2:31:41
[2024/07/28 02:05:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:05:14] ppocr INFO: epoch: [1002/1500], global_step: 3006, lr: 0.001000, loss: 1.295505, loss_shrink_maps: 0.649877, loss_threshold_maps: 0.507045, loss_binary_maps: 0.129518, avg_reader_cost: 1.53811 s, avg_batch_cost: 1.76945 s, avg_samples: 12.5, ips: 7.06434 samples/s, eta: 2:31:22
[2024/07/28 02:05:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:05:23] ppocr INFO: epoch: [1003/1500], global_step: 3009, lr: 0.001000, loss: 1.264618, loss_shrink_maps: 0.634030, loss_threshold_maps: 0.503254, loss_binary_maps: 0.126076, avg_reader_cost: 1.48983 s, avg_batch_cost: 1.73451 s, avg_samples: 12.5, ips: 7.20665 samples/s, eta: 2:31:04
[2024/07/28 02:05:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:05:30] ppocr INFO: epoch: [1004/1500], global_step: 3010, lr: 0.001000, loss: 1.264618, loss_shrink_maps: 0.634030, loss_threshold_maps: 0.500011, loss_binary_maps: 0.126076, avg_reader_cost: 0.42597 s, avg_batch_cost: 0.50859 s, avg_samples: 4.8, ips: 9.43781 samples/s, eta: 2:30:57
[2024/07/28 02:05:32] ppocr INFO: epoch: [1004/1500], global_step: 3012, lr: 0.001000, loss: 1.267984, loss_shrink_maps: 0.634030, loss_threshold_maps: 0.493939, loss_binary_maps: 0.126076, avg_reader_cost: 1.10840 s, avg_batch_cost: 1.25404 s, avg_samples: 7.7, ips: 6.14014 samples/s, eta: 2:30:45
[2024/07/28 02:05:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:05:41] ppocr INFO: epoch: [1005/1500], global_step: 3015, lr: 0.001000, loss: 1.212736, loss_shrink_maps: 0.619868, loss_threshold_maps: 0.475732, loss_binary_maps: 0.122980, avg_reader_cost: 1.55690 s, avg_batch_cost: 1.79275 s, avg_samples: 12.5, ips: 6.97252 samples/s, eta: 2:30:27
[2024/07/28 02:05:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:05:50] ppocr INFO: epoch: [1006/1500], global_step: 3018, lr: 0.001000, loss: 1.193096, loss_shrink_maps: 0.602340, loss_threshold_maps: 0.472421, loss_binary_maps: 0.119832, avg_reader_cost: 1.53430 s, avg_batch_cost: 1.76430 s, avg_samples: 12.5, ips: 7.08494 samples/s, eta: 2:30:08
[2024/07/28 02:05:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:05:59] ppocr INFO: epoch: [1007/1500], global_step: 3020, lr: 0.001000, loss: 1.193096, loss_shrink_maps: 0.602340, loss_threshold_maps: 0.472421, loss_binary_maps: 0.119832, avg_reader_cost: 0.92088 s, avg_batch_cost: 1.09406 s, avg_samples: 9.6, ips: 8.77462 samples/s, eta: 2:29:56
[2024/07/28 02:06:00] ppocr INFO: epoch: [1007/1500], global_step: 3021, lr: 0.001000, loss: 1.186129, loss_shrink_maps: 0.587517, loss_threshold_maps: 0.472421, loss_binary_maps: 0.117097, avg_reader_cost: 0.59262 s, avg_batch_cost: 0.64746 s, avg_samples: 2.9, ips: 4.47901 samples/s, eta: 2:29:50
[2024/07/28 02:06:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:06:09] ppocr INFO: epoch: [1008/1500], global_step: 3024, lr: 0.001000, loss: 1.212736, loss_shrink_maps: 0.609736, loss_threshold_maps: 0.474399, loss_binary_maps: 0.121448, avg_reader_cost: 1.55776 s, avg_batch_cost: 1.81153 s, avg_samples: 12.5, ips: 6.90023 samples/s, eta: 2:29:31
[2024/07/28 02:06:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:06:18] ppocr INFO: epoch: [1009/1500], global_step: 3027, lr: 0.001000, loss: 1.217816, loss_shrink_maps: 0.595761, loss_threshold_maps: 0.476069, loss_binary_maps: 0.118811, avg_reader_cost: 1.52656 s, avg_batch_cost: 1.75964 s, avg_samples: 12.5, ips: 7.10372 samples/s, eta: 2:29:13
[2024/07/28 02:06:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:06:27] ppocr INFO: epoch: [1010/1500], global_step: 3030, lr: 0.001000, loss: 1.252552, loss_shrink_maps: 0.609736, loss_threshold_maps: 0.483916, loss_binary_maps: 0.121448, avg_reader_cost: 1.50008 s, avg_batch_cost: 1.72829 s, avg_samples: 12.5, ips: 7.23259 samples/s, eta: 2:28:54
[2024/07/28 02:06:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:06:37] ppocr INFO: epoch: [1011/1500], global_step: 3033, lr: 0.001000, loss: 1.217816, loss_shrink_maps: 0.595761, loss_threshold_maps: 0.477317, loss_binary_maps: 0.118713, avg_reader_cost: 1.53961 s, avg_batch_cost: 1.80586 s, avg_samples: 12.5, ips: 6.92192 samples/s, eta: 2:28:36
[2024/07/28 02:06:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:06:46] ppocr INFO: epoch: [1012/1500], global_step: 3036, lr: 0.001000, loss: 1.263831, loss_shrink_maps: 0.624476, loss_threshold_maps: 0.502133, loss_binary_maps: 0.124462, avg_reader_cost: 1.50990 s, avg_batch_cost: 1.77025 s, avg_samples: 12.5, ips: 7.06115 samples/s, eta: 2:28:17
[2024/07/28 02:06:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:06:56] ppocr INFO: epoch: [1013/1500], global_step: 3039, lr: 0.001000, loss: 1.320330, loss_shrink_maps: 0.672170, loss_threshold_maps: 0.508350, loss_binary_maps: 0.133975, avg_reader_cost: 1.56755 s, avg_batch_cost: 1.79664 s, avg_samples: 12.5, ips: 6.95745 samples/s, eta: 2:27:59
[2024/07/28 02:06:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:07:04] ppocr INFO: epoch: [1014/1500], global_step: 3040, lr: 0.001000, loss: 1.285534, loss_shrink_maps: 0.642278, loss_threshold_maps: 0.511362, loss_binary_maps: 0.127973, avg_reader_cost: 0.47539 s, avg_batch_cost: 0.55979 s, avg_samples: 4.8, ips: 8.57462 samples/s, eta: 2:27:53
[2024/07/28 02:07:05] ppocr INFO: epoch: [1014/1500], global_step: 3042, lr: 0.001000, loss: 1.320330, loss_shrink_maps: 0.672170, loss_threshold_maps: 0.511648, loss_binary_maps: 0.133975, avg_reader_cost: 1.21102 s, avg_batch_cost: 1.35715 s, avg_samples: 7.7, ips: 5.67364 samples/s, eta: 2:27:41
[2024/07/28 02:07:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:07:14] ppocr INFO: epoch: [1015/1500], global_step: 3045, lr: 0.001000, loss: 1.269183, loss_shrink_maps: 0.626099, loss_threshold_maps: 0.499219, loss_binary_maps: 0.124923, avg_reader_cost: 1.52857 s, avg_batch_cost: 1.75686 s, avg_samples: 12.5, ips: 7.11496 samples/s, eta: 2:27:23
[2024/07/28 02:07:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:07:24] ppocr INFO: epoch: [1016/1500], global_step: 3048, lr: 0.001000, loss: 1.201767, loss_shrink_maps: 0.608244, loss_threshold_maps: 0.492766, loss_binary_maps: 0.121243, avg_reader_cost: 1.54136 s, avg_batch_cost: 1.76944 s, avg_samples: 12.5, ips: 7.06438 samples/s, eta: 2:27:04
[2024/07/28 02:07:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:07:33] ppocr INFO: epoch: [1017/1500], global_step: 3050, lr: 0.001000, loss: 1.201767, loss_shrink_maps: 0.608244, loss_threshold_maps: 0.492766, loss_binary_maps: 0.121243, avg_reader_cost: 0.97761 s, avg_batch_cost: 1.20193 s, avg_samples: 9.6, ips: 7.98713 samples/s, eta: 2:26:52
[2024/07/28 02:07:33] ppocr INFO: epoch: [1017/1500], global_step: 3051, lr: 0.001000, loss: 1.237056, loss_shrink_maps: 0.631918, loss_threshold_maps: 0.492766, loss_binary_maps: 0.126266, avg_reader_cost: 0.64663 s, avg_batch_cost: 0.70142 s, avg_samples: 2.9, ips: 4.13446 samples/s, eta: 2:26:46
[2024/07/28 02:07:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:07:43] ppocr INFO: epoch: [1018/1500], global_step: 3054, lr: 0.001000, loss: 1.237056, loss_shrink_maps: 0.631918, loss_threshold_maps: 0.492766, loss_binary_maps: 0.126266, avg_reader_cost: 1.49868 s, avg_batch_cost: 1.74297 s, avg_samples: 12.5, ips: 7.17166 samples/s, eta: 2:26:28
[2024/07/28 02:07:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:07:52] ppocr INFO: epoch: [1019/1500], global_step: 3057, lr: 0.001000, loss: 1.237056, loss_shrink_maps: 0.631918, loss_threshold_maps: 0.492766, loss_binary_maps: 0.126266, avg_reader_cost: 1.52943 s, avg_batch_cost: 1.80305 s, avg_samples: 12.5, ips: 6.93269 samples/s, eta: 2:26:09
[2024/07/28 02:07:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:08:01] ppocr INFO: epoch: [1020/1500], global_step: 3060, lr: 0.001000, loss: 1.196820, loss_shrink_maps: 0.612281, loss_threshold_maps: 0.480438, loss_binary_maps: 0.122442, avg_reader_cost: 1.49705 s, avg_batch_cost: 1.72700 s, avg_samples: 12.5, ips: 7.23800 samples/s, eta: 2:25:51

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:02,  7.94it/s]
eval model::   1%|          | 3/500 [00:00<00:35, 14.13it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.39it/s]
eval model::   2%|▏         | 8/500 [00:00<00:30, 16.30it/s]
eval model::   2%|▏         | 11/500 [00:00<00:31, 15.61it/s]
eval model::   3%|▎         | 13/500 [00:00<00:31, 15.54it/s]
eval model::   3%|▎         | 15/500 [00:00<00:31, 15.53it/s]
eval model::   4%|▎         | 18/500 [00:01<00:26, 18.19it/s]
eval model::   4%|▍         | 20/500 [00:01<00:27, 17.57it/s]
eval model::   5%|▍         | 23/500 [00:01<00:24, 19.32it/s]
eval model::   5%|▌         | 25/500 [00:01<00:26, 18.10it/s]
eval model::   6%|▌         | 28/500 [00:01<00:25, 18.58it/s]
eval model::   6%|▌         | 31/500 [00:01<00:26, 17.61it/s]
eval model::   7%|▋         | 34/500 [00:01<00:26, 17.78it/s]
eval model::   7%|▋         | 36/500 [00:02<00:26, 17.47it/s]
eval model::   8%|▊         | 38/500 [00:02<00:28, 16.21it/s]
eval model::   8%|▊         | 40/500 [00:02<00:29, 15.70it/s]
eval model::   8%|▊         | 42/500 [00:02<00:29, 15.73it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 17.87it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 16.91it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.28it/s]
eval model::  11%|█         | 53/500 [00:03<00:22, 19.92it/s]
eval model::  11%|█         | 56/500 [00:03<00:21, 20.68it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:22, 19.79it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 16.05it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.31it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.53it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:22, 19.19it/s]
eval model::  14%|█▍        | 72/500 [00:04<00:21, 19.97it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:21, 19.39it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 21.00it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.18it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 20.16it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.38it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:23, 17.54it/s]
eval model::  18%|█▊        | 91/500 [00:05<00:22, 18.04it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:22, 18.18it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:19, 20.78it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.53it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.08it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 22.03it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.12it/s]
eval model::  22%|██▏       | 111/500 [00:06<00:20, 19.11it/s]
eval model::  23%|██▎       | 114/500 [00:06<00:19, 19.67it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:20, 18.87it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:20, 18.56it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:19, 19.23it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:22, 16.72it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:20, 18.04it/s]
eval model::  26%|██▌       | 130/500 [00:07<00:22, 16.72it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:21, 17.12it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:18, 19.63it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:19, 18.85it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 18.97it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 20.41it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:18, 18.90it/s]
eval model::  30%|██▉       | 148/500 [00:08<00:19, 18.29it/s]
eval model::  30%|███       | 151/500 [00:08<00:19, 17.53it/s]
eval model::  31%|███       | 155/500 [00:08<00:16, 20.55it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:15, 21.68it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:23, 14.25it/s]
eval model::  33%|███▎      | 164/500 [00:09<00:20, 16.22it/s]
eval model::  33%|███▎      | 167/500 [00:09<00:18, 17.85it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:16, 19.49it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:19, 16.90it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:22, 14.35it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:19, 16.19it/s]
eval model::  36%|███▌      | 180/500 [00:10<00:22, 14.08it/s]
eval model::  37%|███▋      | 183/500 [00:10<00:19, 16.33it/s]
eval model::  37%|███▋      | 186/500 [00:10<00:17, 18.31it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:16, 18.97it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:15, 19.65it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.92it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.34it/s]
eval model::  40%|████      | 201/500 [00:11<00:14, 21.16it/s]
eval model::  41%|████      | 205/500 [00:11<00:17, 16.86it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:17, 17.16it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:17, 16.32it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:16, 17.02it/s]
eval model::  43%|████▎     | 215/500 [00:12<00:20, 13.94it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:22, 12.76it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:20, 13.94it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:20, 13.61it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:17, 16.08it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.65it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:13, 19.64it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 21.68it/s]
eval model::  47%|████▋     | 235/500 [00:13<00:13, 19.76it/s]
eval model::  48%|████▊     | 239/500 [00:13<00:12, 21.74it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:14, 18.08it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:13, 19.51it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.03it/s]
eval model::  50%|█████     | 251/500 [00:13<00:12, 20.68it/s]
eval model::  51%|█████     | 254/500 [00:14<00:11, 22.33it/s]
eval model::  51%|█████▏    | 257/500 [00:14<00:11, 20.56it/s]
eval model::  52%|█████▏    | 260/500 [00:14<00:11, 21.57it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:11, 21.05it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:11, 20.44it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:14, 16.50it/s]
eval model::  54%|█████▍    | 272/500 [00:15<00:12, 17.65it/s]
eval model::  55%|█████▌    | 275/500 [00:15<00:12, 17.91it/s]
eval model::  56%|█████▌    | 278/500 [00:15<00:11, 19.42it/s]
eval model::  56%|█████▌    | 281/500 [00:15<00:10, 20.80it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:09, 21.94it/s]
eval model::  57%|█████▋    | 287/500 [00:16<00:17, 12.43it/s]
eval model::  58%|█████▊    | 290/500 [00:16<00:14, 14.61it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:14, 14.43it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:14, 14.21it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:16, 12.32it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:13, 15.46it/s]
eval model::  60%|██████    | 301/500 [00:16<00:13, 15.10it/s]
eval model::  61%|██████    | 304/500 [00:17<00:11, 16.87it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:10, 18.62it/s]
eval model::  62%|██████▏   | 310/500 [00:17<00:10, 18.90it/s]
eval model::  62%|██████▏   | 312/500 [00:17<00:10, 18.61it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:09, 18.73it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:09, 19.74it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 20.06it/s]
eval model::  65%|██████▍   | 324/500 [00:18<00:09, 19.39it/s]
eval model::  65%|██████▌   | 327/500 [00:18<00:08, 21.56it/s]
eval model::  66%|██████▌   | 330/500 [00:18<00:08, 20.55it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:08, 20.61it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:08, 19.99it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:08, 19.64it/s]
eval model::  68%|██████▊   | 341/500 [00:19<00:10, 14.60it/s]
eval model::  69%|██████▉   | 344/500 [00:19<00:09, 16.52it/s]
eval model::  69%|██████▉   | 347/500 [00:19<00:08, 17.60it/s]
eval model::  70%|███████   | 350/500 [00:19<00:07, 18.87it/s]
eval model::  71%|███████   | 353/500 [00:19<00:07, 20.93it/s]
eval model::  71%|███████   | 356/500 [00:19<00:06, 21.06it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:06, 22.42it/s]
eval model::  72%|███████▏  | 362/500 [00:20<00:07, 18.72it/s]
eval model::  73%|███████▎  | 365/500 [00:20<00:07, 18.48it/s]
eval model::  73%|███████▎  | 367/500 [00:20<00:08, 16.19it/s]
eval model::  74%|███████▍  | 370/500 [00:20<00:07, 18.43it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 17.58it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 20.09it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 23.04it/s]
eval model::  76%|███████▋  | 382/500 [00:21<00:05, 21.06it/s]
eval model::  77%|███████▋  | 385/500 [00:21<00:05, 22.71it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:08, 13.90it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:07, 15.47it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 16.23it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 16.62it/s]
eval model::  80%|███████▉  | 398/500 [00:22<00:05, 18.76it/s]
eval model::  80%|████████  | 401/500 [00:22<00:05, 17.84it/s]
eval model::  81%|████████  | 404/500 [00:22<00:04, 19.52it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 20.45it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:03, 22.51it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:04, 21.61it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 20.60it/s]
eval model::  84%|████████▍ | 419/500 [00:23<00:04, 19.84it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:05, 14.50it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.08it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.42it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 17.35it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 16.16it/s]
eval model::  87%|████████▋ | 435/500 [00:24<00:03, 20.29it/s]
eval model::  88%|████████▊ | 438/500 [00:24<00:02, 22.06it/s]
eval model::  88%|████████▊ | 441/500 [00:24<00:02, 20.46it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 22.28it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 23.46it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 20.40it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 21.72it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:02, 21.10it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:02, 18.46it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 17.06it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 17.05it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 18.54it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 18.63it/s]
eval model::  95%|█████████▍| 474/500 [00:26<00:01, 17.99it/s]
eval model::  95%|█████████▌| 477/500 [00:26<00:01, 19.55it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:00, 20.83it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 20.56it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 19.42it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 20.50it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 17.55it/s]
eval model::  99%|█████████▉| 495/500 [00:27<00:00, 18.33it/s]
eval model:: 100%|█████████▉| 498/500 [00:27<00:00, 20.21it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.29it/s]
[2024/07/28 02:08:28] ppocr INFO: cur metric, precision: 0.6714628297362111, recall: 0.6740491092922485, hmean: 0.6727534839019702, fps: 46.95328828980308
[2024/07/28 02:08:28] ppocr INFO: best metric, hmean: 0.7048391183177097, precision: 0.7438502673796792, recall: 0.6697159364467983, fps: 46.08540547152116, best_epoch: 580
[2024/07/28 02:08:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:08:36] ppocr INFO: epoch: [1021/1500], global_step: 3063, lr: 0.001000, loss: 1.178416, loss_shrink_maps: 0.596659, loss_threshold_maps: 0.479192, loss_binary_maps: 0.119176, avg_reader_cost: 1.59551 s, avg_batch_cost: 1.90836 s, avg_samples: 12.5, ips: 6.55012 samples/s, eta: 2:25:33
[2024/07/28 02:08:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:08:46] ppocr INFO: epoch: [1022/1500], global_step: 3066, lr: 0.001000, loss: 1.216134, loss_shrink_maps: 0.612281, loss_threshold_maps: 0.481932, loss_binary_maps: 0.122442, avg_reader_cost: 1.60999 s, avg_batch_cost: 1.91293 s, avg_samples: 12.5, ips: 6.53449 samples/s, eta: 2:25:15
[2024/07/28 02:08:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:08:56] ppocr INFO: epoch: [1023/1500], global_step: 3069, lr: 0.001000, loss: 1.216134, loss_shrink_maps: 0.605467, loss_threshold_maps: 0.481932, loss_binary_maps: 0.121190, avg_reader_cost: 1.60965 s, avg_batch_cost: 1.86130 s, avg_samples: 12.5, ips: 6.71574 samples/s, eta: 2:24:57
[2024/07/28 02:08:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:09:03] ppocr INFO: epoch: [1024/1500], global_step: 3070, lr: 0.001000, loss: 1.216134, loss_shrink_maps: 0.605467, loss_threshold_maps: 0.481932, loss_binary_maps: 0.121190, avg_reader_cost: 0.41921 s, avg_batch_cost: 0.50508 s, avg_samples: 4.8, ips: 9.50344 samples/s, eta: 2:24:50
[2024/07/28 02:09:05] ppocr INFO: epoch: [1024/1500], global_step: 3072, lr: 0.001000, loss: 1.177151, loss_shrink_maps: 0.591278, loss_threshold_maps: 0.481932, loss_binary_maps: 0.117745, avg_reader_cost: 1.10202 s, avg_batch_cost: 1.24869 s, avg_samples: 7.7, ips: 6.16646 samples/s, eta: 2:24:38
[2024/07/28 02:09:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:09:14] ppocr INFO: epoch: [1025/1500], global_step: 3075, lr: 0.001000, loss: 1.177151, loss_shrink_maps: 0.596659, loss_threshold_maps: 0.481932, loss_binary_maps: 0.119176, avg_reader_cost: 1.52199 s, avg_batch_cost: 1.75812 s, avg_samples: 12.5, ips: 7.10987 samples/s, eta: 2:24:20
[2024/07/28 02:09:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:09:24] ppocr INFO: epoch: [1026/1500], global_step: 3078, lr: 0.001000, loss: 1.176161, loss_shrink_maps: 0.596659, loss_threshold_maps: 0.476141, loss_binary_maps: 0.119176, avg_reader_cost: 1.58672 s, avg_batch_cost: 1.89465 s, avg_samples: 12.5, ips: 6.59753 samples/s, eta: 2:24:02
[2024/07/28 02:09:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:09:33] ppocr INFO: epoch: [1027/1500], global_step: 3080, lr: 0.001000, loss: 1.176161, loss_shrink_maps: 0.596659, loss_threshold_maps: 0.475740, loss_binary_maps: 0.118928, avg_reader_cost: 0.96032 s, avg_batch_cost: 1.14433 s, avg_samples: 9.6, ips: 8.38918 samples/s, eta: 2:23:49
[2024/07/28 02:09:33] ppocr INFO: epoch: [1027/1500], global_step: 3081, lr: 0.001000, loss: 1.163992, loss_shrink_maps: 0.595055, loss_threshold_maps: 0.470612, loss_binary_maps: 0.118402, avg_reader_cost: 0.61793 s, avg_batch_cost: 0.67264 s, avg_samples: 2.9, ips: 4.31139 samples/s, eta: 2:23:44
[2024/07/28 02:09:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:09:43] ppocr INFO: epoch: [1028/1500], global_step: 3084, lr: 0.001000, loss: 1.211006, loss_shrink_maps: 0.617683, loss_threshold_maps: 0.468120, loss_binary_maps: 0.123044, avg_reader_cost: 1.54957 s, avg_batch_cost: 1.77786 s, avg_samples: 12.5, ips: 7.03094 samples/s, eta: 2:23:25
[2024/07/28 02:09:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:09:52] ppocr INFO: epoch: [1029/1500], global_step: 3087, lr: 0.001000, loss: 1.192017, loss_shrink_maps: 0.607430, loss_threshold_maps: 0.468120, loss_binary_maps: 0.120856, avg_reader_cost: 1.56669 s, avg_batch_cost: 1.81330 s, avg_samples: 12.5, ips: 6.89351 samples/s, eta: 2:23:07
[2024/07/28 02:09:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:10:02] ppocr INFO: epoch: [1030/1500], global_step: 3090, lr: 0.001000, loss: 1.211006, loss_shrink_maps: 0.617683, loss_threshold_maps: 0.477804, loss_binary_maps: 0.123044, avg_reader_cost: 1.57073 s, avg_batch_cost: 1.81156 s, avg_samples: 12.5, ips: 6.90012 samples/s, eta: 2:22:49
[2024/07/28 02:10:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:10:12] ppocr INFO: epoch: [1031/1500], global_step: 3093, lr: 0.001000, loss: 1.246340, loss_shrink_maps: 0.629549, loss_threshold_maps: 0.480345, loss_binary_maps: 0.125209, avg_reader_cost: 1.72889 s, avg_batch_cost: 1.97162 s, avg_samples: 12.5, ips: 6.33998 samples/s, eta: 2:22:31
[2024/07/28 02:10:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:10:21] ppocr INFO: epoch: [1032/1500], global_step: 3096, lr: 0.001000, loss: 1.246340, loss_shrink_maps: 0.629549, loss_threshold_maps: 0.480345, loss_binary_maps: 0.125209, avg_reader_cost: 1.50182 s, avg_batch_cost: 1.75136 s, avg_samples: 12.5, ips: 7.13729 samples/s, eta: 2:22:13
[2024/07/28 02:10:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:10:31] ppocr INFO: epoch: [1033/1500], global_step: 3099, lr: 0.001000, loss: 1.285873, loss_shrink_maps: 0.644076, loss_threshold_maps: 0.486397, loss_binary_maps: 0.128235, avg_reader_cost: 1.54133 s, avg_batch_cost: 1.76915 s, avg_samples: 12.5, ips: 7.06556 samples/s, eta: 2:21:54
[2024/07/28 02:10:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:10:38] ppocr INFO: epoch: [1034/1500], global_step: 3100, lr: 0.001000, loss: 1.297879, loss_shrink_maps: 0.644076, loss_threshold_maps: 0.493310, loss_binary_maps: 0.128235, avg_reader_cost: 0.41871 s, avg_batch_cost: 0.52137 s, avg_samples: 4.8, ips: 9.20658 samples/s, eta: 2:21:48
[2024/07/28 02:10:40] ppocr INFO: epoch: [1034/1500], global_step: 3102, lr: 0.001000, loss: 1.285873, loss_shrink_maps: 0.643206, loss_threshold_maps: 0.495084, loss_binary_maps: 0.127753, avg_reader_cost: 1.13383 s, avg_batch_cost: 1.27934 s, avg_samples: 7.7, ips: 6.01874 samples/s, eta: 2:21:36
[2024/07/28 02:10:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:10:49] ppocr INFO: epoch: [1035/1500], global_step: 3105, lr: 0.001000, loss: 1.257668, loss_shrink_maps: 0.627883, loss_threshold_maps: 0.495084, loss_binary_maps: 0.124367, avg_reader_cost: 1.54401 s, avg_batch_cost: 1.80390 s, avg_samples: 12.5, ips: 6.92944 samples/s, eta: 2:21:17
[2024/07/28 02:10:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:10:59] ppocr INFO: epoch: [1036/1500], global_step: 3108, lr: 0.001000, loss: 1.252431, loss_shrink_maps: 0.627883, loss_threshold_maps: 0.489978, loss_binary_maps: 0.124367, avg_reader_cost: 1.67163 s, avg_batch_cost: 1.90009 s, avg_samples: 12.5, ips: 6.57863 samples/s, eta: 2:21:00
[2024/07/28 02:11:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:11:08] ppocr INFO: epoch: [1037/1500], global_step: 3110, lr: 0.001000, loss: 1.230310, loss_shrink_maps: 0.619199, loss_threshold_maps: 0.487076, loss_binary_maps: 0.123360, avg_reader_cost: 0.93904 s, avg_batch_cost: 1.11316 s, avg_samples: 9.6, ips: 8.62407 samples/s, eta: 2:20:47
[2024/07/28 02:11:08] ppocr INFO: epoch: [1037/1500], global_step: 3111, lr: 0.001000, loss: 1.231018, loss_shrink_maps: 0.619199, loss_threshold_maps: 0.489978, loss_binary_maps: 0.123360, avg_reader_cost: 0.60226 s, avg_batch_cost: 0.65764 s, avg_samples: 2.9, ips: 4.40971 samples/s, eta: 2:20:41
[2024/07/28 02:11:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:11:17] ppocr INFO: epoch: [1038/1500], global_step: 3114, lr: 0.001000, loss: 1.231018, loss_shrink_maps: 0.624721, loss_threshold_maps: 0.487890, loss_binary_maps: 0.124154, avg_reader_cost: 1.54289 s, avg_batch_cost: 1.77556 s, avg_samples: 12.5, ips: 7.04005 samples/s, eta: 2:20:23
[2024/07/28 02:11:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:11:27] ppocr INFO: epoch: [1039/1500], global_step: 3117, lr: 0.001000, loss: 1.231018, loss_shrink_maps: 0.624721, loss_threshold_maps: 0.487890, loss_binary_maps: 0.124154, avg_reader_cost: 1.58579 s, avg_batch_cost: 1.87903 s, avg_samples: 12.5, ips: 6.65238 samples/s, eta: 2:20:05
[2024/07/28 02:11:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:11:36] ppocr INFO: epoch: [1040/1500], global_step: 3120, lr: 0.001000, loss: 1.221574, loss_shrink_maps: 0.613674, loss_threshold_maps: 0.478956, loss_binary_maps: 0.122126, avg_reader_cost: 1.51277 s, avg_batch_cost: 1.74151 s, avg_samples: 12.5, ips: 7.17769 samples/s, eta: 2:19:46

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:04,  7.79it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 16.88it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.39it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.52it/s]
eval model::   2%|▏         | 11/500 [00:00<00:31, 15.43it/s]
eval model::   3%|▎         | 13/500 [00:00<00:30, 15.88it/s]
eval model::   3%|▎         | 15/500 [00:00<00:30, 16.00it/s]
eval model::   4%|▎         | 18/500 [00:01<00:26, 18.31it/s]
eval model::   4%|▍         | 20/500 [00:01<00:29, 16.54it/s]
eval model::   5%|▍         | 23/500 [00:01<00:26, 18.32it/s]
eval model::   5%|▌         | 25/500 [00:01<00:29, 16.25it/s]
eval model::   6%|▌         | 28/500 [00:01<00:26, 17.52it/s]
eval model::   6%|▌         | 31/500 [00:01<00:26, 17.53it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.31it/s]
eval model::   7%|▋         | 37/500 [00:02<00:24, 18.79it/s]
eval model::   8%|▊         | 39/500 [00:02<00:28, 16.30it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.79it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.95it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 17.35it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 18.92it/s]
eval model::  11%|█         | 53/500 [00:03<00:22, 19.70it/s]
eval model::  11%|█         | 56/500 [00:03<00:21, 20.29it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:21, 20.49it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:25, 17.19it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:26, 16.33it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 19.13it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 21.26it/s]
eval model::  15%|█▍        | 73/500 [00:04<00:19, 21.98it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:19, 21.55it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.73it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.18it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.47it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:22, 18.71it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:24, 16.72it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:23, 17.40it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.30it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.13it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.62it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.83it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 20.92it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:20, 19.22it/s]
eval model::  23%|██▎       | 114/500 [00:06<00:18, 20.97it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:19, 19.41it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:21, 17.93it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 19.25it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 18.18it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.61it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:21, 17.34it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:18, 19.71it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.37it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.59it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.71it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.31it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.80it/s]
eval model::  31%|███       | 153/500 [00:08<00:18, 18.34it/s]
eval model::  31%|███       | 156/500 [00:08<00:16, 20.61it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:15, 21.74it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:19, 17.63it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:20, 16.78it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:19, 17.41it/s]
eval model::  34%|███▍      | 169/500 [00:09<00:17, 19.38it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:17, 19.17it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 16.98it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.62it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.44it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:18, 16.92it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:16, 19.40it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:15, 19.73it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:14, 20.77it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:13, 22.05it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.29it/s]
eval model::  40%|████      | 200/500 [00:10<00:15, 19.79it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 19.86it/s]
eval model::  41%|████      | 206/500 [00:11<00:22, 12.91it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:22, 13.18it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:21, 13.52it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.58it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.58it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:22, 12.45it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:18, 14.85it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.25it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:17, 15.92it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:16, 16.70it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 19.41it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:14, 18.53it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:13, 19.69it/s]
eval model::  48%|████▊     | 239/500 [00:13<00:12, 20.94it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:14, 17.47it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:13, 18.50it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 19.67it/s]
eval model::  50%|█████     | 251/500 [00:13<00:13, 19.13it/s]
eval model::  51%|█████     | 253/500 [00:13<00:12, 19.16it/s]
eval model::  51%|█████     | 256/500 [00:14<00:11, 20.57it/s]
eval model::  52%|█████▏    | 260/500 [00:14<00:10, 23.54it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:10, 23.17it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:11, 21.05it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 19.74it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:10, 20.80it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.67it/s]
eval model::  55%|█████▌    | 277/500 [00:15<00:11, 18.86it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:11, 18.78it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 20.24it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:18, 11.51it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:17, 12.47it/s]
eval model::  58%|█████▊    | 290/500 [00:16<00:13, 15.05it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:13, 15.80it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:13, 15.64it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:14, 13.87it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:11, 16.77it/s]
eval model::  60%|██████    | 301/500 [00:16<00:14, 13.89it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 15.86it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:10, 18.02it/s]
eval model::  62%|██████▏   | 310/500 [00:17<00:09, 19.30it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:09, 20.43it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 19.43it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 20.61it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 19.13it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 20.32it/s]
eval model::  66%|██████▌   | 328/500 [00:18<00:08, 19.53it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:08, 19.95it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:07, 21.49it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 20.29it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 16.00it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 17.30it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:08, 18.41it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:07, 19.42it/s]
eval model::  70%|███████   | 352/500 [00:19<00:06, 21.20it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 21.14it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 21.07it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.34it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.47it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:07, 18.07it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:06, 18.89it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:06, 18.84it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:06, 18.39it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 21.01it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 23.32it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 20.38it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:05, 22.68it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:08, 13.56it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:07, 15.56it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 16.27it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:06, 16.90it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 18.38it/s]
eval model::  80%|████████  | 402/500 [00:22<00:05, 17.00it/s]
eval model::  81%|████████  | 405/500 [00:22<00:05, 18.44it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:04, 19.77it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:04, 21.32it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 22.78it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 20.27it/s]
eval model::  84%|████████▍ | 420/500 [00:23<00:05, 13.96it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:05, 14.81it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.77it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:04, 15.21it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 17.27it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 16.26it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 19.19it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:02, 21.73it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:02, 20.11it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 22.27it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 22.93it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 19.83it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 21.65it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 23.29it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 24.86it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:01, 19.97it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:01, 17.97it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 18.38it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 18.94it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.97it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.64it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:00, 21.67it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 22.74it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 22.15it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 22.67it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 19.11it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 20.08it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 22.14it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.54it/s]
[2024/07/28 02:12:03] ppocr INFO: cur metric, precision: 0.727589641434263, recall: 0.7034183919114106, hmean: 0.7152998776009791, fps: 45.69782460781328
[2024/07/28 02:12:03] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/28 02:12:03] ppocr INFO: best metric, hmean: 0.7152998776009791, precision: 0.727589641434263, recall: 0.7034183919114106, fps: 45.69782460781328, best_epoch: 1040
[2024/07/28 02:12:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:12:12] ppocr INFO: epoch: [1041/1500], global_step: 3123, lr: 0.001000, loss: 1.221574, loss_shrink_maps: 0.615238, loss_threshold_maps: 0.475283, loss_binary_maps: 0.123029, avg_reader_cost: 1.58941 s, avg_batch_cost: 1.92428 s, avg_samples: 12.5, ips: 6.49593 samples/s, eta: 2:19:28
[2024/07/28 02:12:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:12:22] ppocr INFO: epoch: [1042/1500], global_step: 3126, lr: 0.001000, loss: 1.230446, loss_shrink_maps: 0.631328, loss_threshold_maps: 0.476695, loss_binary_maps: 0.125783, avg_reader_cost: 1.59413 s, avg_batch_cost: 1.87794 s, avg_samples: 12.5, ips: 6.65624 samples/s, eta: 2:19:10
[2024/07/28 02:12:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:12:31] ppocr INFO: epoch: [1043/1500], global_step: 3129, lr: 0.001000, loss: 1.248200, loss_shrink_maps: 0.636098, loss_threshold_maps: 0.480995, loss_binary_maps: 0.126258, avg_reader_cost: 1.62183 s, avg_batch_cost: 1.88721 s, avg_samples: 12.5, ips: 6.62352 samples/s, eta: 2:18:52
[2024/07/28 02:12:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:12:40] ppocr INFO: epoch: [1044/1500], global_step: 3130, lr: 0.001000, loss: 1.260409, loss_shrink_maps: 0.636571, loss_threshold_maps: 0.486009, loss_binary_maps: 0.126258, avg_reader_cost: 0.43474 s, avg_batch_cost: 0.54224 s, avg_samples: 4.8, ips: 8.85218 samples/s, eta: 2:18:46
[2024/07/28 02:12:41] ppocr INFO: epoch: [1044/1500], global_step: 3132, lr: 0.001000, loss: 1.264644, loss_shrink_maps: 0.640888, loss_threshold_maps: 0.491752, loss_binary_maps: 0.127961, avg_reader_cost: 1.17620 s, avg_batch_cost: 1.32206 s, avg_samples: 7.7, ips: 5.82427 samples/s, eta: 2:18:34
[2024/07/28 02:12:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:12:51] ppocr INFO: epoch: [1045/1500], global_step: 3135, lr: 0.001000, loss: 1.264644, loss_shrink_maps: 0.650413, loss_threshold_maps: 0.498226, loss_binary_maps: 0.129958, avg_reader_cost: 1.61449 s, avg_batch_cost: 1.84247 s, avg_samples: 12.5, ips: 6.78437 samples/s, eta: 2:18:16
[2024/07/28 02:12:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:13:00] ppocr INFO: epoch: [1046/1500], global_step: 3138, lr: 0.001000, loss: 1.281904, loss_shrink_maps: 0.662912, loss_threshold_maps: 0.498226, loss_binary_maps: 0.132486, avg_reader_cost: 1.51806 s, avg_batch_cost: 1.76198 s, avg_samples: 12.5, ips: 7.09430 samples/s, eta: 2:17:58
[2024/07/28 02:13:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:13:09] ppocr INFO: epoch: [1047/1500], global_step: 3140, lr: 0.001000, loss: 1.264644, loss_shrink_maps: 0.650413, loss_threshold_maps: 0.498226, loss_binary_maps: 0.129958, avg_reader_cost: 0.94481 s, avg_batch_cost: 1.14688 s, avg_samples: 9.6, ips: 8.37054 samples/s, eta: 2:17:45
[2024/07/28 02:13:09] ppocr INFO: epoch: [1047/1500], global_step: 3141, lr: 0.001000, loss: 1.267846, loss_shrink_maps: 0.651514, loss_threshold_maps: 0.500670, loss_binary_maps: 0.129958, avg_reader_cost: 0.61905 s, avg_batch_cost: 0.67374 s, avg_samples: 2.9, ips: 4.30436 samples/s, eta: 2:17:39
[2024/07/28 02:13:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:13:19] ppocr INFO: epoch: [1048/1500], global_step: 3144, lr: 0.001000, loss: 1.283658, loss_shrink_maps: 0.662423, loss_threshold_maps: 0.500670, loss_binary_maps: 0.132154, avg_reader_cost: 1.50495 s, avg_batch_cost: 1.73417 s, avg_samples: 12.5, ips: 7.20807 samples/s, eta: 2:17:21
[2024/07/28 02:13:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:13:28] ppocr INFO: epoch: [1049/1500], global_step: 3147, lr: 0.001000, loss: 1.310687, loss_shrink_maps: 0.665542, loss_threshold_maps: 0.501400, loss_binary_maps: 0.132377, avg_reader_cost: 1.56105 s, avg_batch_cost: 1.79019 s, avg_samples: 12.5, ips: 6.98251 samples/s, eta: 2:17:02
[2024/07/28 02:13:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:13:37] ppocr INFO: epoch: [1050/1500], global_step: 3150, lr: 0.001000, loss: 1.288370, loss_shrink_maps: 0.651027, loss_threshold_maps: 0.501400, loss_binary_maps: 0.129849, avg_reader_cost: 1.49268 s, avg_batch_cost: 1.75332 s, avg_samples: 12.5, ips: 7.12931 samples/s, eta: 2:16:44
[2024/07/28 02:13:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:13:47] ppocr INFO: epoch: [1051/1500], global_step: 3153, lr: 0.001000, loss: 1.274312, loss_shrink_maps: 0.639191, loss_threshold_maps: 0.500546, loss_binary_maps: 0.127121, avg_reader_cost: 1.52112 s, avg_batch_cost: 1.78529 s, avg_samples: 12.5, ips: 7.00166 samples/s, eta: 2:16:25
[2024/07/28 02:13:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:13:56] ppocr INFO: epoch: [1052/1500], global_step: 3156, lr: 0.001000, loss: 1.251564, loss_shrink_maps: 0.624319, loss_threshold_maps: 0.492829, loss_binary_maps: 0.124851, avg_reader_cost: 1.50609 s, avg_batch_cost: 1.75779 s, avg_samples: 12.5, ips: 7.11119 samples/s, eta: 2:16:07
[2024/07/28 02:13:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:14:06] ppocr INFO: epoch: [1053/1500], global_step: 3159, lr: 0.001000, loss: 1.246918, loss_shrink_maps: 0.620887, loss_threshold_maps: 0.491440, loss_binary_maps: 0.123909, avg_reader_cost: 1.49959 s, avg_batch_cost: 1.73342 s, avg_samples: 12.5, ips: 7.21119 samples/s, eta: 2:15:48
[2024/07/28 02:14:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:14:13] ppocr INFO: epoch: [1054/1500], global_step: 3160, lr: 0.001000, loss: 1.246918, loss_shrink_maps: 0.620887, loss_threshold_maps: 0.491440, loss_binary_maps: 0.123909, avg_reader_cost: 0.41671 s, avg_batch_cost: 0.51214 s, avg_samples: 4.8, ips: 9.37243 samples/s, eta: 2:15:42
[2024/07/28 02:14:15] ppocr INFO: epoch: [1054/1500], global_step: 3162, lr: 0.001000, loss: 1.214264, loss_shrink_maps: 0.606155, loss_threshold_maps: 0.488114, loss_binary_maps: 0.121523, avg_reader_cost: 1.11553 s, avg_batch_cost: 1.26113 s, avg_samples: 7.7, ips: 6.10565 samples/s, eta: 2:15:30
[2024/07/28 02:14:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:14:24] ppocr INFO: epoch: [1055/1500], global_step: 3165, lr: 0.001000, loss: 1.188984, loss_shrink_maps: 0.601555, loss_threshold_maps: 0.484066, loss_binary_maps: 0.120061, avg_reader_cost: 1.54258 s, avg_batch_cost: 1.77367 s, avg_samples: 12.5, ips: 7.04754 samples/s, eta: 2:15:11
[2024/07/28 02:14:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:14:34] ppocr INFO: epoch: [1056/1500], global_step: 3168, lr: 0.001000, loss: 1.181847, loss_shrink_maps: 0.586720, loss_threshold_maps: 0.485916, loss_binary_maps: 0.116662, avg_reader_cost: 1.51172 s, avg_batch_cost: 1.76013 s, avg_samples: 12.5, ips: 7.10174 samples/s, eta: 2:14:53
[2024/07/28 02:14:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:14:43] ppocr INFO: epoch: [1057/1500], global_step: 3170, lr: 0.001000, loss: 1.172354, loss_shrink_maps: 0.571536, loss_threshold_maps: 0.477054, loss_binary_maps: 0.113591, avg_reader_cost: 0.95316 s, avg_batch_cost: 1.19369 s, avg_samples: 9.6, ips: 8.04231 samples/s, eta: 2:14:41
[2024/07/28 02:14:43] ppocr INFO: epoch: [1057/1500], global_step: 3171, lr: 0.001000, loss: 1.172354, loss_shrink_maps: 0.571536, loss_threshold_maps: 0.477054, loss_binary_maps: 0.113591, avg_reader_cost: 0.64240 s, avg_batch_cost: 0.69747 s, avg_samples: 2.9, ips: 4.15787 samples/s, eta: 2:14:35
[2024/07/28 02:14:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:14:53] ppocr INFO: epoch: [1058/1500], global_step: 3174, lr: 0.001000, loss: 1.172354, loss_shrink_maps: 0.571536, loss_threshold_maps: 0.477054, loss_binary_maps: 0.113591, avg_reader_cost: 1.55472 s, avg_batch_cost: 1.79030 s, avg_samples: 12.5, ips: 6.98205 samples/s, eta: 2:14:17
[2024/07/28 02:14:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:15:02] ppocr INFO: epoch: [1059/1500], global_step: 3177, lr: 0.001000, loss: 1.188984, loss_shrink_maps: 0.586720, loss_threshold_maps: 0.481868, loss_binary_maps: 0.116662, avg_reader_cost: 1.50298 s, avg_batch_cost: 1.73446 s, avg_samples: 12.5, ips: 7.20685 samples/s, eta: 2:13:58
[2024/07/28 02:15:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:15:11] ppocr INFO: epoch: [1060/1500], global_step: 3180, lr: 0.001000, loss: 1.194971, loss_shrink_maps: 0.586720, loss_threshold_maps: 0.485929, loss_binary_maps: 0.116662, avg_reader_cost: 1.51553 s, avg_batch_cost: 1.75845 s, avg_samples: 12.5, ips: 7.10852 samples/s, eta: 2:13:40

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:50,  9.79it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.23it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.51it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 18.15it/s]
eval model::   2%|▏         | 10/500 [00:00<00:26, 18.66it/s]
eval model::   2%|▏         | 12/500 [00:00<00:27, 17.74it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.49it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.60it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.14it/s]
eval model::   5%|▍         | 24/500 [00:01<00:20, 22.81it/s]
eval model::   5%|▌         | 27/500 [00:01<00:23, 20.27it/s]
eval model::   6%|▌         | 30/500 [00:01<00:22, 20.59it/s]
eval model::   7%|▋         | 33/500 [00:01<00:24, 19.08it/s]
eval model::   7%|▋         | 35/500 [00:01<00:25, 18.22it/s]
eval model::   8%|▊         | 38/500 [00:01<00:24, 18.61it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.37it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.78it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.20it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 17.65it/s]
eval model::  10%|▉         | 49/500 [00:02<00:25, 17.64it/s]
eval model::  10%|█         | 51/500 [00:02<00:25, 17.87it/s]
eval model::  11%|█         | 54/500 [00:02<00:24, 18.56it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:21, 20.21it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:22, 19.40it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:27, 15.77it/s]
eval model::  13%|█▎        | 65/500 [00:03<00:24, 17.93it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:19, 21.70it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:18, 23.01it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:18, 22.94it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:18, 22.88it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.91it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 20.04it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 18.79it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:21, 18.80it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.00it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:23, 17.40it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:21, 18.83it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.67it/s]
eval model::  20%|██        | 101/500 [00:05<00:22, 17.51it/s]
eval model::  21%|██        | 104/500 [00:05<00:19, 19.92it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 20.71it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 20.53it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 20.83it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:18, 21.33it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 21.24it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:17, 21.22it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:20, 17.95it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 20.71it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 18.47it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:19, 19.06it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:19, 18.40it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 19.69it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:16, 21.68it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:16, 21.00it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:18, 18.69it/s]
eval model::  30%|███       | 151/500 [00:07<00:20, 16.84it/s]
eval model::  31%|███       | 154/500 [00:07<00:17, 19.34it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:16, 20.97it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:24, 13.74it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:21, 15.36it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:19, 16.93it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 18.99it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:18, 18.18it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:20, 15.97it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:18, 17.65it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:20, 15.46it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:17, 17.69it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 19.76it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.34it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:14, 21.32it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.14it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:15, 19.30it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.60it/s]
eval model::  41%|████      | 205/500 [00:10<00:18, 15.55it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:18, 16.22it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:18, 16.01it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:18, 15.95it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.64it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.83it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.65it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.14it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.30it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 17.00it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.30it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 22.71it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:11, 22.31it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.34it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 19.89it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:11, 21.69it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.61it/s]
eval model::  50%|█████     | 251/500 [00:13<00:12, 20.58it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 20.80it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 20.45it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:11, 20.78it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:11, 20.02it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:12, 18.19it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:13, 17.37it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.25it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.32it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:13, 17.09it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 18.97it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:10, 20.16it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:19, 10.71it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:15, 13.33it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:13, 15.32it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:16, 12.79it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 16.20it/s]
eval model::  60%|██████    | 302/500 [00:16<00:12, 16.23it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.58it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.38it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 19.37it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 20.51it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 20.05it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 21.33it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.43it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 21.50it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 21.16it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 20.24it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 21.11it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 20.61it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 15.61it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:08, 17.81it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 19.64it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 21.55it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 21.24it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 21.05it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 21.65it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.71it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:08, 16.73it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:08, 16.67it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 18.38it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:07, 18.25it/s]
eval model::  75%|███████▍  | 374/500 [00:20<00:06, 19.75it/s]
eval model::  75%|███████▌  | 377/500 [00:20<00:05, 22.15it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 21.40it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:04, 24.51it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 15.86it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 17.89it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:05, 18.70it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 19.07it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 19.94it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 19.47it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 20.33it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.29it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 22.93it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 24.27it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:03, 21.50it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.99it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 15.84it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 15.23it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 16.21it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:03, 18.92it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 17.18it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:03, 20.97it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 22.96it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 23.43it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 23.49it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 23.20it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 23.15it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:01, 23.01it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 22.95it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 20.99it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 17.96it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.88it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.78it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 20.75it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 19.43it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 20.49it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 21.51it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 20.78it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 19.57it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 21.81it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 17.39it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 18.77it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 20.80it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.93it/s]
[2024/07/28 02:15:38] ppocr INFO: cur metric, precision: 0.7350608143839239, recall: 0.6692344727973039, hmean: 0.7006048387096775, fps: 46.07790838244493
[2024/07/28 02:15:38] ppocr INFO: best metric, hmean: 0.7152998776009791, precision: 0.727589641434263, recall: 0.7034183919114106, fps: 45.69782460781328, best_epoch: 1040
[2024/07/28 02:15:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:15:46] ppocr INFO: epoch: [1061/1500], global_step: 3183, lr: 0.001000, loss: 1.189730, loss_shrink_maps: 0.581114, loss_threshold_maps: 0.485929, loss_binary_maps: 0.115696, avg_reader_cost: 1.82177 s, avg_batch_cost: 2.14703 s, avg_samples: 12.5, ips: 5.82200 samples/s, eta: 2:13:23
[2024/07/28 02:15:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:15:55] ppocr INFO: epoch: [1062/1500], global_step: 3186, lr: 0.001000, loss: 1.213994, loss_shrink_maps: 0.618473, loss_threshold_maps: 0.493752, loss_binary_maps: 0.123025, avg_reader_cost: 1.50218 s, avg_batch_cost: 1.73048 s, avg_samples: 12.5, ips: 7.22341 samples/s, eta: 2:13:04
[2024/07/28 02:15:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:16:05] ppocr INFO: epoch: [1063/1500], global_step: 3189, lr: 0.001000, loss: 1.226635, loss_shrink_maps: 0.626288, loss_threshold_maps: 0.493752, loss_binary_maps: 0.124477, avg_reader_cost: 1.57322 s, avg_batch_cost: 1.83053 s, avg_samples: 12.5, ips: 6.82864 samples/s, eta: 2:12:46
[2024/07/28 02:16:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:16:13] ppocr INFO: epoch: [1064/1500], global_step: 3190, lr: 0.001000, loss: 1.229184, loss_shrink_maps: 0.626288, loss_threshold_maps: 0.493752, loss_binary_maps: 0.124477, avg_reader_cost: 0.43346 s, avg_batch_cost: 0.55476 s, avg_samples: 4.8, ips: 8.65245 samples/s, eta: 2:12:40
[2024/07/28 02:16:15] ppocr INFO: epoch: [1064/1500], global_step: 3192, lr: 0.001000, loss: 1.234466, loss_shrink_maps: 0.630178, loss_threshold_maps: 0.493752, loss_binary_maps: 0.125050, avg_reader_cost: 1.20169 s, avg_batch_cost: 1.34812 s, avg_samples: 7.7, ips: 5.71167 samples/s, eta: 2:12:28
[2024/07/28 02:16:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:16:24] ppocr INFO: epoch: [1065/1500], global_step: 3195, lr: 0.001000, loss: 1.225718, loss_shrink_maps: 0.615986, loss_threshold_maps: 0.486913, loss_binary_maps: 0.122654, avg_reader_cost: 1.51866 s, avg_batch_cost: 1.75085 s, avg_samples: 12.5, ips: 7.13938 samples/s, eta: 2:12:09
[2024/07/28 02:16:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:16:33] ppocr INFO: epoch: [1066/1500], global_step: 3198, lr: 0.001000, loss: 1.205225, loss_shrink_maps: 0.610015, loss_threshold_maps: 0.475961, loss_binary_maps: 0.121709, avg_reader_cost: 1.50859 s, avg_batch_cost: 1.73660 s, avg_samples: 12.5, ips: 7.19797 samples/s, eta: 2:11:51
[2024/07/28 02:16:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:16:42] ppocr INFO: epoch: [1067/1500], global_step: 3200, lr: 0.001000, loss: 1.178357, loss_shrink_maps: 0.588635, loss_threshold_maps: 0.469199, loss_binary_maps: 0.117503, avg_reader_cost: 0.95497 s, avg_batch_cost: 1.13143 s, avg_samples: 9.6, ips: 8.48487 samples/s, eta: 2:11:38
[2024/07/28 02:16:43] ppocr INFO: epoch: [1067/1500], global_step: 3201, lr: 0.001000, loss: 1.189289, loss_shrink_maps: 0.595626, loss_threshold_maps: 0.469199, loss_binary_maps: 0.118842, avg_reader_cost: 0.61131 s, avg_batch_cost: 0.66600 s, avg_samples: 2.9, ips: 4.35438 samples/s, eta: 2:11:32
[2024/07/28 02:16:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:16:52] ppocr INFO: epoch: [1068/1500], global_step: 3204, lr: 0.001000, loss: 1.200538, loss_shrink_maps: 0.601939, loss_threshold_maps: 0.475497, loss_binary_maps: 0.120183, avg_reader_cost: 1.57927 s, avg_batch_cost: 1.82893 s, avg_samples: 12.5, ips: 6.83462 samples/s, eta: 2:11:14
[2024/07/28 02:16:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:17:01] ppocr INFO: epoch: [1069/1500], global_step: 3207, lr: 0.001000, loss: 1.189289, loss_shrink_maps: 0.595626, loss_threshold_maps: 0.470289, loss_binary_maps: 0.118842, avg_reader_cost: 1.56325 s, avg_batch_cost: 1.80588 s, avg_samples: 12.5, ips: 6.92183 samples/s, eta: 2:10:56
[2024/07/28 02:17:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:17:11] ppocr INFO: epoch: [1070/1500], global_step: 3210, lr: 0.001000, loss: 1.204558, loss_shrink_maps: 0.601935, loss_threshold_maps: 0.475497, loss_binary_maps: 0.120158, avg_reader_cost: 1.55184 s, avg_batch_cost: 1.80721 s, avg_samples: 12.5, ips: 6.91676 samples/s, eta: 2:10:38
[2024/07/28 02:17:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:17:21] ppocr INFO: epoch: [1071/1500], global_step: 3213, lr: 0.001000, loss: 1.191154, loss_shrink_maps: 0.594546, loss_threshold_maps: 0.470481, loss_binary_maps: 0.118534, avg_reader_cost: 1.59566 s, avg_batch_cost: 1.85827 s, avg_samples: 12.5, ips: 6.72669 samples/s, eta: 2:10:20
[2024/07/28 02:17:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:17:30] ppocr INFO: epoch: [1072/1500], global_step: 3216, lr: 0.001000, loss: 1.204558, loss_shrink_maps: 0.601935, loss_threshold_maps: 0.480019, loss_binary_maps: 0.120158, avg_reader_cost: 1.66171 s, avg_batch_cost: 1.89003 s, avg_samples: 12.5, ips: 6.61367 samples/s, eta: 2:10:02
[2024/07/28 02:17:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:17:40] ppocr INFO: epoch: [1073/1500], global_step: 3219, lr: 0.001000, loss: 1.215515, loss_shrink_maps: 0.608385, loss_threshold_maps: 0.486203, loss_binary_maps: 0.120846, avg_reader_cost: 1.53848 s, avg_batch_cost: 1.83165 s, avg_samples: 12.5, ips: 6.82446 samples/s, eta: 2:09:43
[2024/07/28 02:17:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:17:48] ppocr INFO: epoch: [1074/1500], global_step: 3220, lr: 0.001000, loss: 1.215515, loss_shrink_maps: 0.608385, loss_threshold_maps: 0.486203, loss_binary_maps: 0.120846, avg_reader_cost: 0.41075 s, avg_batch_cost: 0.52673 s, avg_samples: 4.8, ips: 9.11280 samples/s, eta: 2:09:37
[2024/07/28 02:17:50] ppocr INFO: epoch: [1074/1500], global_step: 3222, lr: 0.001000, loss: 1.215515, loss_shrink_maps: 0.608385, loss_threshold_maps: 0.485009, loss_binary_maps: 0.120846, avg_reader_cost: 1.14500 s, avg_batch_cost: 1.29113 s, avg_samples: 7.7, ips: 5.96375 samples/s, eta: 2:09:25
[2024/07/28 02:17:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:17:59] ppocr INFO: epoch: [1075/1500], global_step: 3225, lr: 0.001000, loss: 1.219404, loss_shrink_maps: 0.608795, loss_threshold_maps: 0.486203, loss_binary_maps: 0.121268, avg_reader_cost: 1.53455 s, avg_batch_cost: 1.79499 s, avg_samples: 12.5, ips: 6.96384 samples/s, eta: 2:09:07
[2024/07/28 02:18:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:18:09] ppocr INFO: epoch: [1076/1500], global_step: 3228, lr: 0.001000, loss: 1.233167, loss_shrink_maps: 0.625293, loss_threshold_maps: 0.485992, loss_binary_maps: 0.124524, avg_reader_cost: 1.59404 s, avg_batch_cost: 1.82306 s, avg_samples: 12.5, ips: 6.85659 samples/s, eta: 2:08:49
[2024/07/28 02:18:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:18:17] ppocr INFO: epoch: [1077/1500], global_step: 3230, lr: 0.001000, loss: 1.210982, loss_shrink_maps: 0.603476, loss_threshold_maps: 0.483530, loss_binary_maps: 0.120423, avg_reader_cost: 0.91448 s, avg_batch_cost: 1.08826 s, avg_samples: 9.6, ips: 8.82144 samples/s, eta: 2:08:36
[2024/07/28 02:18:18] ppocr INFO: epoch: [1077/1500], global_step: 3231, lr: 0.001000, loss: 1.204607, loss_shrink_maps: 0.603476, loss_threshold_maps: 0.478208, loss_binary_maps: 0.120423, avg_reader_cost: 0.58954 s, avg_batch_cost: 0.64515 s, avg_samples: 2.9, ips: 4.49505 samples/s, eta: 2:08:30
[2024/07/28 02:18:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:18:27] ppocr INFO: epoch: [1078/1500], global_step: 3234, lr: 0.001000, loss: 1.172057, loss_shrink_maps: 0.576362, loss_threshold_maps: 0.470793, loss_binary_maps: 0.114790, avg_reader_cost: 1.52159 s, avg_batch_cost: 1.76486 s, avg_samples: 12.5, ips: 7.08272 samples/s, eta: 2:08:12
[2024/07/28 02:18:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:18:37] ppocr INFO: epoch: [1079/1500], global_step: 3237, lr: 0.001000, loss: 1.189506, loss_shrink_maps: 0.583835, loss_threshold_maps: 0.473346, loss_binary_maps: 0.116319, avg_reader_cost: 1.61814 s, avg_batch_cost: 1.84568 s, avg_samples: 12.5, ips: 6.77256 samples/s, eta: 2:07:53
[2024/07/28 02:18:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:18:46] ppocr INFO: epoch: [1080/1500], global_step: 3240, lr: 0.001000, loss: 1.189506, loss_shrink_maps: 0.583835, loss_threshold_maps: 0.472029, loss_binary_maps: 0.116319, avg_reader_cost: 1.51063 s, avg_batch_cost: 1.75624 s, avg_samples: 12.5, ips: 7.11750 samples/s, eta: 2:07:35

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:38, 12.88it/s]
eval model::   1%|          | 5/500 [00:00<00:24, 19.86it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 18.00it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.60it/s]
eval model::   3%|▎         | 13/500 [00:00<00:26, 18.08it/s]
eval model::   3%|▎         | 15/500 [00:00<00:26, 18.30it/s]
eval model::   4%|▍         | 19/500 [00:00<00:22, 20.95it/s]
eval model::   4%|▍         | 22/500 [00:01<00:23, 20.45it/s]
eval model::   5%|▌         | 25/500 [00:01<00:22, 20.76it/s]
eval model::   6%|▌         | 28/500 [00:01<00:23, 19.97it/s]
eval model::   6%|▌         | 31/500 [00:01<00:23, 20.10it/s]
eval model::   7%|▋         | 34/500 [00:01<00:23, 19.85it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.36it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.10it/s]
eval model::   8%|▊         | 42/500 [00:02<00:28, 15.90it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 18.13it/s]
eval model::   9%|▉         | 47/500 [00:02<00:27, 16.39it/s]
eval model::  10%|▉         | 49/500 [00:02<00:26, 17.12it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 18.86it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 19.98it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:26, 16.65it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 15.82it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.78it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.35it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 19.73it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.03it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:20, 21.02it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:17, 23.98it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:16, 24.70it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:16, 24.52it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:19, 20.91it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.51it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.79it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.49it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.99it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.40it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 21.96it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.50it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:17, 21.75it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:16, 23.62it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:17, 22.07it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 20.62it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.67it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.36it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 18.77it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:22, 16.53it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:18, 19.83it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:18, 19.99it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:16, 21.12it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:19, 18.23it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:21, 16.43it/s]
eval model::  30%|███       | 150/500 [00:07<00:19, 18.34it/s]
eval model::  30%|███       | 152/500 [00:07<00:21, 16.32it/s]
eval model::  31%|███       | 155/500 [00:08<00:19, 17.73it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:17, 19.25it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:23, 14.61it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:22, 15.10it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:19, 17.32it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:19, 17.47it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 18.35it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:17, 19.12it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:17, 18.14it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.56it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.32it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 18.41it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 20.14it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:14, 21.72it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:13, 22.70it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:14, 20.45it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:13, 22.64it/s]
eval model::  40%|████      | 202/500 [00:10<00:12, 23.99it/s]
eval model::  41%|████      | 205/500 [00:10<00:19, 15.01it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:18, 15.73it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 15.46it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.60it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.00it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.73it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.05it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:21, 12.65it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.17it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 19.90it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 21.61it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 23.33it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:12, 21.37it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:15, 16.32it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 19.69it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 21.04it/s]
eval model::  51%|█████     | 254/500 [00:13<00:12, 19.51it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 20.38it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:11, 21.01it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:12, 19.39it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:12, 19.30it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.63it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.16it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.40it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.47it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.93it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:10, 21.01it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:19, 11.17it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:15, 13.57it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:14, 14.36it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:13, 14.81it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:15, 13.51it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 16.75it/s]
eval model::  60%|██████    | 302/500 [00:16<00:13, 15.04it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 15.74it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 19.90it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:08, 22.15it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:08, 22.73it/s]
eval model::  63%|██████▎   | 317/500 [00:17<00:07, 23.35it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:07, 23.85it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:07, 22.21it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:07, 21.88it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 20.10it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:08, 20.49it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 21.74it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:07, 20.90it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:11, 14.35it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 16.52it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 17.89it/s]
eval model::  70%|███████   | 350/500 [00:18<00:07, 19.81it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 21.14it/s]
eval model::  71%|███████   | 356/500 [00:19<00:07, 18.99it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:07, 19.65it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:08, 16.96it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:08, 15.16it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:09, 14.20it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 16.41it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:07, 17.03it/s]
eval model::  75%|███████▍  | 374/500 [00:20<00:06, 19.05it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 23.37it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:05, 22.54it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:04, 24.50it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:07, 14.43it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:06, 16.46it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 17.48it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 17.73it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 17.21it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 17.65it/s]
eval model::  81%|████████  | 404/500 [00:21<00:05, 17.83it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 19.90it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 20.94it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:03, 21.86it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:03, 22.26it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 20.19it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 15.17it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 14.57it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:04, 15.18it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:03, 18.20it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 17.73it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 20.26it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 22.35it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 21.06it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 24.15it/s]
eval model::  90%|████████▉ | 448/500 [00:24<00:02, 20.54it/s]
eval model::  90%|█████████ | 451/500 [00:24<00:02, 20.91it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 21.92it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 21.69it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:02, 19.73it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 17.95it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 14.84it/s]
eval model::  93%|█████████▎| 467/500 [00:25<00:02, 15.20it/s]
eval model::  94%|█████████▍| 470/500 [00:25<00:01, 17.33it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 18.73it/s]
eval model::  95%|█████████▌| 475/500 [00:25<00:01, 17.97it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 18.38it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:01, 19.40it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 19.10it/s]
eval model::  97%|█████████▋| 485/500 [00:26<00:00, 21.59it/s]
eval model::  98%|█████████▊| 488/500 [00:26<00:00, 21.85it/s]
eval model::  98%|█████████▊| 491/500 [00:26<00:00, 21.18it/s]
eval model::  99%|█████████▉| 494/500 [00:26<00:00, 19.15it/s]
eval model::  99%|█████████▉| 497/500 [00:26<00:00, 21.43it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 21.35it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.68it/s]
[2024/07/28 02:19:13] ppocr INFO: cur metric, precision: 0.7654794520547945, recall: 0.672604718343765, hmean: 0.7160430548436699, fps: 44.8783209422934
[2024/07/28 02:19:13] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/28 02:19:13] ppocr INFO: best metric, hmean: 0.7160430548436699, precision: 0.7654794520547945, recall: 0.672604718343765, fps: 44.8783209422934, best_epoch: 1080
[2024/07/28 02:19:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:19:21] ppocr INFO: epoch: [1081/1500], global_step: 3243, lr: 0.001000, loss: 1.189506, loss_shrink_maps: 0.583835, loss_threshold_maps: 0.472029, loss_binary_maps: 0.116319, avg_reader_cost: 1.56364 s, avg_batch_cost: 1.79238 s, avg_samples: 12.5, ips: 6.97395 samples/s, eta: 2:07:17
[2024/07/28 02:19:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:19:30] ppocr INFO: epoch: [1082/1500], global_step: 3246, lr: 0.001000, loss: 1.189506, loss_shrink_maps: 0.581406, loss_threshold_maps: 0.473039, loss_binary_maps: 0.115860, avg_reader_cost: 1.52490 s, avg_batch_cost: 1.75317 s, avg_samples: 12.5, ips: 7.12993 samples/s, eta: 2:06:58
[2024/07/28 02:19:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:19:40] ppocr INFO: epoch: [1083/1500], global_step: 3249, lr: 0.001000, loss: 1.239222, loss_shrink_maps: 0.625414, loss_threshold_maps: 0.483229, loss_binary_maps: 0.124520, avg_reader_cost: 1.59915 s, avg_batch_cost: 1.84626 s, avg_samples: 12.5, ips: 6.77043 samples/s, eta: 2:06:40
[2024/07/28 02:19:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:19:48] ppocr INFO: epoch: [1084/1500], global_step: 3250, lr: 0.001000, loss: 1.232944, loss_shrink_maps: 0.625414, loss_threshold_maps: 0.482644, loss_binary_maps: 0.124368, avg_reader_cost: 0.42311 s, avg_batch_cost: 0.50806 s, avg_samples: 4.8, ips: 9.44773 samples/s, eta: 2:06:34
[2024/07/28 02:19:49] ppocr INFO: epoch: [1084/1500], global_step: 3252, lr: 0.001000, loss: 1.254514, loss_shrink_maps: 0.634883, loss_threshold_maps: 0.484241, loss_binary_maps: 0.126479, avg_reader_cost: 1.10755 s, avg_batch_cost: 1.25342 s, avg_samples: 7.7, ips: 6.14318 samples/s, eta: 2:06:22
[2024/07/28 02:19:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:19:59] ppocr INFO: epoch: [1085/1500], global_step: 3255, lr: 0.001000, loss: 1.261442, loss_shrink_maps: 0.645208, loss_threshold_maps: 0.479063, loss_binary_maps: 0.128059, avg_reader_cost: 1.61950 s, avg_batch_cost: 1.87487 s, avg_samples: 12.5, ips: 6.66712 samples/s, eta: 2:06:04
[2024/07/28 02:20:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:20:08] ppocr INFO: epoch: [1086/1500], global_step: 3258, lr: 0.001000, loss: 1.254514, loss_shrink_maps: 0.634883, loss_threshold_maps: 0.492188, loss_binary_maps: 0.126479, avg_reader_cost: 1.61960 s, avg_batch_cost: 1.84827 s, avg_samples: 12.5, ips: 6.76307 samples/s, eta: 2:05:45
[2024/07/28 02:20:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:20:18] ppocr INFO: epoch: [1087/1500], global_step: 3260, lr: 0.001000, loss: 1.260877, loss_shrink_maps: 0.645208, loss_threshold_maps: 0.486426, loss_binary_maps: 0.128059, avg_reader_cost: 1.06860 s, avg_batch_cost: 1.29165 s, avg_samples: 9.6, ips: 7.43233 samples/s, eta: 2:05:34
[2024/07/28 02:20:19] ppocr INFO: epoch: [1087/1500], global_step: 3261, lr: 0.001000, loss: 1.248237, loss_shrink_maps: 0.640839, loss_threshold_maps: 0.481153, loss_binary_maps: 0.127469, avg_reader_cost: 0.69156 s, avg_batch_cost: 0.74650 s, avg_samples: 2.9, ips: 3.88481 samples/s, eta: 2:05:28
[2024/07/28 02:20:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:20:28] ppocr INFO: epoch: [1088/1500], global_step: 3264, lr: 0.001000, loss: 1.241827, loss_shrink_maps: 0.632042, loss_threshold_maps: 0.478969, loss_binary_maps: 0.125975, avg_reader_cost: 1.53083 s, avg_batch_cost: 1.76507 s, avg_samples: 12.5, ips: 7.08188 samples/s, eta: 2:05:10
[2024/07/28 02:20:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:20:38] ppocr INFO: epoch: [1089/1500], global_step: 3267, lr: 0.001000, loss: 1.241827, loss_shrink_maps: 0.632042, loss_threshold_maps: 0.481153, loss_binary_maps: 0.125975, avg_reader_cost: 1.56635 s, avg_batch_cost: 1.80068 s, avg_samples: 12.5, ips: 6.94182 samples/s, eta: 2:04:51
[2024/07/28 02:20:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:20:47] ppocr INFO: epoch: [1090/1500], global_step: 3270, lr: 0.001000, loss: 1.240038, loss_shrink_maps: 0.621240, loss_threshold_maps: 0.475355, loss_binary_maps: 0.123651, avg_reader_cost: 1.63123 s, avg_batch_cost: 1.90642 s, avg_samples: 12.5, ips: 6.55680 samples/s, eta: 2:04:33
[2024/07/28 02:20:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:20:58] ppocr INFO: epoch: [1091/1500], global_step: 3273, lr: 0.001000, loss: 1.188123, loss_shrink_maps: 0.596850, loss_threshold_maps: 0.471862, loss_binary_maps: 0.118963, avg_reader_cost: 1.75647 s, avg_batch_cost: 1.98455 s, avg_samples: 12.5, ips: 6.29865 samples/s, eta: 2:04:16
[2024/07/28 02:21:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:21:07] ppocr INFO: epoch: [1092/1500], global_step: 3276, lr: 0.001000, loss: 1.184929, loss_shrink_maps: 0.580863, loss_threshold_maps: 0.467765, loss_binary_maps: 0.116060, avg_reader_cost: 1.52638 s, avg_batch_cost: 1.76996 s, avg_samples: 12.5, ips: 7.06232 samples/s, eta: 2:03:57
[2024/07/28 02:21:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:21:17] ppocr INFO: epoch: [1093/1500], global_step: 3279, lr: 0.001000, loss: 1.135063, loss_shrink_maps: 0.567055, loss_threshold_maps: 0.462839, loss_binary_maps: 0.113318, avg_reader_cost: 1.52626 s, avg_batch_cost: 1.76984 s, avg_samples: 12.5, ips: 7.06279 samples/s, eta: 2:03:39
[2024/07/28 02:21:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:21:25] ppocr INFO: epoch: [1094/1500], global_step: 3280, lr: 0.001000, loss: 1.135063, loss_shrink_maps: 0.567055, loss_threshold_maps: 0.462839, loss_binary_maps: 0.113318, avg_reader_cost: 0.41633 s, avg_batch_cost: 0.50324 s, avg_samples: 4.8, ips: 9.53811 samples/s, eta: 2:03:32
[2024/07/28 02:21:26] ppocr INFO: epoch: [1094/1500], global_step: 3282, lr: 0.001000, loss: 1.135063, loss_shrink_maps: 0.567055, loss_threshold_maps: 0.462839, loss_binary_maps: 0.113318, avg_reader_cost: 1.09779 s, avg_batch_cost: 1.24364 s, avg_samples: 7.7, ips: 6.19153 samples/s, eta: 2:03:20
[2024/07/28 02:21:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:21:36] ppocr INFO: epoch: [1095/1500], global_step: 3285, lr: 0.001000, loss: 1.160865, loss_shrink_maps: 0.575492, loss_threshold_maps: 0.464237, loss_binary_maps: 0.115107, avg_reader_cost: 1.74989 s, avg_batch_cost: 1.97999 s, avg_samples: 12.5, ips: 6.31316 samples/s, eta: 2:03:03
[2024/07/28 02:21:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:21:46] ppocr INFO: epoch: [1096/1500], global_step: 3288, lr: 0.001000, loss: 1.169874, loss_shrink_maps: 0.582766, loss_threshold_maps: 0.462264, loss_binary_maps: 0.116386, avg_reader_cost: 1.53370 s, avg_batch_cost: 1.78606 s, avg_samples: 12.5, ips: 6.99864 samples/s, eta: 2:02:44
[2024/07/28 02:21:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:21:55] ppocr INFO: epoch: [1097/1500], global_step: 3290, lr: 0.001000, loss: 1.166117, loss_shrink_maps: 0.582766, loss_threshold_maps: 0.462264, loss_binary_maps: 0.116139, avg_reader_cost: 0.96521 s, avg_batch_cost: 1.16956 s, avg_samples: 9.6, ips: 8.20822 samples/s, eta: 2:02:32
[2024/07/28 02:21:56] ppocr INFO: epoch: [1097/1500], global_step: 3291, lr: 0.001000, loss: 1.182424, loss_shrink_maps: 0.586722, loss_threshold_maps: 0.462887, loss_binary_maps: 0.116391, avg_reader_cost: 0.63034 s, avg_batch_cost: 0.68535 s, avg_samples: 2.9, ips: 4.23142 samples/s, eta: 2:02:26
[2024/07/28 02:21:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:22:05] ppocr INFO: epoch: [1098/1500], global_step: 3294, lr: 0.001000, loss: 1.203354, loss_shrink_maps: 0.590260, loss_threshold_maps: 0.472226, loss_binary_maps: 0.117726, avg_reader_cost: 1.54625 s, avg_batch_cost: 1.77467 s, avg_samples: 12.5, ips: 7.04356 samples/s, eta: 2:02:08
[2024/07/28 02:22:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:22:14] ppocr INFO: epoch: [1099/1500], global_step: 3297, lr: 0.001000, loss: 1.218849, loss_shrink_maps: 0.622319, loss_threshold_maps: 0.479194, loss_binary_maps: 0.124833, avg_reader_cost: 1.52748 s, avg_batch_cost: 1.76456 s, avg_samples: 12.5, ips: 7.08393 samples/s, eta: 2:01:49
[2024/07/28 02:22:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:22:24] ppocr INFO: epoch: [1100/1500], global_step: 3300, lr: 0.001000, loss: 1.230256, loss_shrink_maps: 0.622319, loss_threshold_maps: 0.484449, loss_binary_maps: 0.124833, avg_reader_cost: 1.60195 s, avg_batch_cost: 1.82937 s, avg_samples: 12.5, ips: 6.83294 samples/s, eta: 2:01:31

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:50,  9.85it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.57it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.95it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.85it/s]
eval model::   2%|▏         | 10/500 [00:00<00:27, 17.82it/s]
eval model::   2%|▏         | 12/500 [00:00<00:28, 16.97it/s]
eval model::   3%|▎         | 14/500 [00:00<00:29, 16.63it/s]
eval model::   3%|▎         | 16/500 [00:00<00:27, 17.49it/s]
eval model::   4%|▍         | 19/500 [00:01<00:26, 17.91it/s]
eval model::   4%|▍         | 21/500 [00:01<00:26, 18.16it/s]
eval model::   5%|▍         | 24/500 [00:01<00:24, 19.40it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.34it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 19.90it/s]
eval model::   6%|▌         | 31/500 [00:01<00:26, 17.39it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 18.73it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.74it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.53it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.62it/s]
eval model::   9%|▉         | 44/500 [00:02<00:26, 16.95it/s]
eval model::   9%|▉         | 46/500 [00:02<00:26, 16.96it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.44it/s]
eval model::  10%|█         | 51/500 [00:02<00:24, 18.62it/s]
eval model::  11%|█         | 54/500 [00:02<00:20, 21.38it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:19, 22.24it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:20, 21.31it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 16.13it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:21, 19.95it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 21.37it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 20.35it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:20, 20.78it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.38it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:17, 23.30it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:17, 23.29it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 20.13it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:20, 20.18it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:19, 21.36it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:18, 22.26it/s]
eval model::  20%|██        | 100/500 [00:05<00:19, 20.92it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 21.55it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.29it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.32it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 20.84it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:19, 19.82it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:18, 21.15it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.65it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:21, 17.52it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:19, 18.95it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:20, 18.04it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.81it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.61it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.78it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.04it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.16it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 19.35it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 20.71it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 18.39it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 20.77it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 20.29it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:19, 16.90it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:19, 17.01it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 18.66it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:17, 19.28it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:18, 17.46it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:20, 15.89it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:17, 17.91it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:19, 16.38it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 19.26it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 21.72it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.98it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:13, 22.80it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:13, 22.30it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.32it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 21.58it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.49it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:20, 14.43it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:19, 14.98it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 14.73it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.75it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:22, 12.52it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:23, 12.26it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.34it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:20, 13.64it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.91it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 20.71it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 22.04it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.90it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 23.08it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:13, 19.28it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:12, 20.63it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.18it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 20.92it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.41it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:12, 19.81it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:11, 21.23it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 21.83it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:11, 20.03it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 19.26it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 20.27it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.30it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 20.79it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.66it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 21.27it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:18, 11.22it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:14, 14.91it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:13, 15.28it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:14, 14.17it/s]
eval model::  60%|██████    | 300/500 [00:16<00:13, 15.30it/s]
eval model::  60%|██████    | 302/500 [00:16<00:12, 15.93it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.49it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.84it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:08, 22.04it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:08, 21.94it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:09, 20.26it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 22.36it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 21.90it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 23.11it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:07, 22.51it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 22.82it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:06, 23.96it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 22.51it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:09, 17.25it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:07, 19.47it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:07, 20.85it/s]
eval model::  70%|███████   | 351/500 [00:18<00:06, 22.24it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 23.12it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:07, 20.27it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:06, 21.51it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:07, 18.89it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:08, 16.46it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 18.04it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 19.34it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:05, 21.46it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 22.36it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:05, 21.54it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:05, 23.14it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:08, 14.07it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 16.30it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 17.40it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 19.14it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 20.64it/s]
eval model::  80%|████████  | 402/500 [00:21<00:04, 19.61it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 20.89it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.38it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 21.96it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:04, 20.27it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 18.97it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 19.14it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:04, 16.19it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 18.34it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:03, 19.56it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 20.18it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 19.28it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:03, 19.96it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 20.50it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 20.65it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 21.79it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 20.89it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 20.89it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 22.55it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 24.35it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 24.36it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 20.54it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 18.93it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 19.84it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 20.88it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 20.89it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:00, 22.60it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 23.61it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 24.88it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 24.57it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 23.68it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 21.14it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 20.75it/s]
eval model:: 100%|█████████▉| 499/500 [00:25<00:00, 21.45it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.47it/s]
[2024/07/28 02:22:50] ppocr INFO: cur metric, precision: 0.7856294536817102, recall: 0.6369764082811747, hmean: 0.7035362935389523, fps: 45.97610735469473
[2024/07/28 02:22:50] ppocr INFO: best metric, hmean: 0.7160430548436699, precision: 0.7654794520547945, recall: 0.672604718343765, fps: 44.8783209422934, best_epoch: 1080
[2024/07/28 02:22:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:22:58] ppocr INFO: epoch: [1101/1500], global_step: 3303, lr: 0.001000, loss: 1.247001, loss_shrink_maps: 0.637774, loss_threshold_maps: 0.500630, loss_binary_maps: 0.126744, avg_reader_cost: 1.85307 s, avg_batch_cost: 2.21275 s, avg_samples: 12.5, ips: 5.64907 samples/s, eta: 2:01:14
[2024/07/28 02:23:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:23:08] ppocr INFO: epoch: [1102/1500], global_step: 3306, lr: 0.001000, loss: 1.247001, loss_shrink_maps: 0.630447, loss_threshold_maps: 0.500630, loss_binary_maps: 0.125882, avg_reader_cost: 1.56244 s, avg_batch_cost: 1.79836 s, avg_samples: 12.5, ips: 6.95078 samples/s, eta: 2:00:56
[2024/07/28 02:23:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:23:17] ppocr INFO: epoch: [1103/1500], global_step: 3309, lr: 0.001000, loss: 1.268326, loss_shrink_maps: 0.654742, loss_threshold_maps: 0.505883, loss_binary_maps: 0.130104, avg_reader_cost: 1.63331 s, avg_batch_cost: 1.86871 s, avg_samples: 12.5, ips: 6.68912 samples/s, eta: 2:00:38
[2024/07/28 02:23:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:23:25] ppocr INFO: epoch: [1104/1500], global_step: 3310, lr: 0.001000, loss: 1.268326, loss_shrink_maps: 0.654742, loss_threshold_maps: 0.505883, loss_binary_maps: 0.130104, avg_reader_cost: 0.42201 s, avg_batch_cost: 0.50668 s, avg_samples: 4.8, ips: 9.47349 samples/s, eta: 2:00:31
[2024/07/28 02:23:27] ppocr INFO: epoch: [1104/1500], global_step: 3312, lr: 0.001000, loss: 1.239013, loss_shrink_maps: 0.620490, loss_threshold_maps: 0.499517, loss_binary_maps: 0.123991, avg_reader_cost: 1.10516 s, avg_batch_cost: 1.25124 s, avg_samples: 7.7, ips: 6.15391 samples/s, eta: 2:00:19
[2024/07/28 02:23:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:23:36] ppocr INFO: epoch: [1105/1500], global_step: 3315, lr: 0.001000, loss: 1.239013, loss_shrink_maps: 0.611789, loss_threshold_maps: 0.499517, loss_binary_maps: 0.121479, avg_reader_cost: 1.50186 s, avg_batch_cost: 1.74904 s, avg_samples: 12.5, ips: 7.14679 samples/s, eta: 2:00:01
[2024/07/28 02:23:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:23:46] ppocr INFO: epoch: [1106/1500], global_step: 3318, lr: 0.001000, loss: 1.208930, loss_shrink_maps: 0.598083, loss_threshold_maps: 0.490188, loss_binary_maps: 0.119642, avg_reader_cost: 1.52283 s, avg_batch_cost: 1.77061 s, avg_samples: 12.5, ips: 7.05970 samples/s, eta: 1:59:43
[2024/07/28 02:23:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:23:54] ppocr INFO: epoch: [1107/1500], global_step: 3320, lr: 0.001000, loss: 1.208930, loss_shrink_maps: 0.598083, loss_threshold_maps: 0.488264, loss_binary_maps: 0.119593, avg_reader_cost: 0.90435 s, avg_batch_cost: 1.07908 s, avg_samples: 9.6, ips: 8.89644 samples/s, eta: 1:59:30
[2024/07/28 02:23:55] ppocr INFO: epoch: [1107/1500], global_step: 3321, lr: 0.001000, loss: 1.208930, loss_shrink_maps: 0.598083, loss_threshold_maps: 0.488264, loss_binary_maps: 0.119593, avg_reader_cost: 0.58555 s, avg_batch_cost: 0.64063 s, avg_samples: 2.9, ips: 4.52676 samples/s, eta: 1:59:24
[2024/07/28 02:23:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:24:05] ppocr INFO: epoch: [1108/1500], global_step: 3324, lr: 0.001000, loss: 1.208930, loss_shrink_maps: 0.596964, loss_threshold_maps: 0.488264, loss_binary_maps: 0.119326, avg_reader_cost: 1.62379 s, avg_batch_cost: 1.85288 s, avg_samples: 12.5, ips: 6.74625 samples/s, eta: 1:59:06
[2024/07/28 02:24:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:24:14] ppocr INFO: epoch: [1109/1500], global_step: 3327, lr: 0.001000, loss: 1.186326, loss_shrink_maps: 0.593796, loss_threshold_maps: 0.484573, loss_binary_maps: 0.118348, avg_reader_cost: 1.56283 s, avg_batch_cost: 1.80164 s, avg_samples: 12.5, ips: 6.93814 samples/s, eta: 1:58:48
[2024/07/28 02:24:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:24:24] ppocr INFO: epoch: [1110/1500], global_step: 3330, lr: 0.001000, loss: 1.160749, loss_shrink_maps: 0.576203, loss_threshold_maps: 0.482126, loss_binary_maps: 0.114192, avg_reader_cost: 1.53528 s, avg_batch_cost: 1.76571 s, avg_samples: 12.5, ips: 7.07931 samples/s, eta: 1:58:29
[2024/07/28 02:24:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:24:34] ppocr INFO: epoch: [1111/1500], global_step: 3333, lr: 0.001000, loss: 1.234140, loss_shrink_maps: 0.619886, loss_threshold_maps: 0.486052, loss_binary_maps: 0.122788, avg_reader_cost: 1.59641 s, avg_batch_cost: 1.83654 s, avg_samples: 12.5, ips: 6.80628 samples/s, eta: 1:58:11
[2024/07/28 02:24:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:24:43] ppocr INFO: epoch: [1112/1500], global_step: 3336, lr: 0.001000, loss: 1.238964, loss_shrink_maps: 0.619503, loss_threshold_maps: 0.492448, loss_binary_maps: 0.122715, avg_reader_cost: 1.55394 s, avg_batch_cost: 1.78750 s, avg_samples: 12.5, ips: 6.99300 samples/s, eta: 1:57:53
[2024/07/28 02:24:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:24:53] ppocr INFO: epoch: [1113/1500], global_step: 3339, lr: 0.001000, loss: 1.210144, loss_shrink_maps: 0.610002, loss_threshold_maps: 0.485972, loss_binary_maps: 0.121424, avg_reader_cost: 1.56867 s, avg_batch_cost: 1.80447 s, avg_samples: 12.5, ips: 6.92724 samples/s, eta: 1:57:34
[2024/07/28 02:24:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:25:01] ppocr INFO: epoch: [1114/1500], global_step: 3340, lr: 0.001000, loss: 1.210144, loss_shrink_maps: 0.610002, loss_threshold_maps: 0.485972, loss_binary_maps: 0.121424, avg_reader_cost: 0.42328 s, avg_batch_cost: 0.50678 s, avg_samples: 4.8, ips: 9.47162 samples/s, eta: 1:57:28
[2024/07/28 02:25:02] ppocr INFO: epoch: [1114/1500], global_step: 3342, lr: 0.001000, loss: 1.190858, loss_shrink_maps: 0.594167, loss_threshold_maps: 0.481841, loss_binary_maps: 0.117974, avg_reader_cost: 1.10518 s, avg_batch_cost: 1.25123 s, avg_samples: 7.7, ips: 6.15395 samples/s, eta: 1:57:16
[2024/07/28 02:25:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:25:12] ppocr INFO: epoch: [1115/1500], global_step: 3345, lr: 0.001000, loss: 1.184625, loss_shrink_maps: 0.591687, loss_threshold_maps: 0.480552, loss_binary_maps: 0.117372, avg_reader_cost: 1.53770 s, avg_batch_cost: 1.76648 s, avg_samples: 12.5, ips: 7.07620 samples/s, eta: 1:56:57
[2024/07/28 02:25:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:25:21] ppocr INFO: epoch: [1116/1500], global_step: 3348, lr: 0.001000, loss: 1.198678, loss_shrink_maps: 0.597507, loss_threshold_maps: 0.477900, loss_binary_maps: 0.118508, avg_reader_cost: 1.56668 s, avg_batch_cost: 1.80743 s, avg_samples: 12.5, ips: 6.91589 samples/s, eta: 1:56:39
[2024/07/28 02:25:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:25:30] ppocr INFO: epoch: [1117/1500], global_step: 3350, lr: 0.001000, loss: 1.198678, loss_shrink_maps: 0.602686, loss_threshold_maps: 0.477900, loss_binary_maps: 0.119581, avg_reader_cost: 0.94678 s, avg_batch_cost: 1.11958 s, avg_samples: 9.6, ips: 8.57468 samples/s, eta: 1:56:27
[2024/07/28 02:25:31] ppocr INFO: epoch: [1117/1500], global_step: 3351, lr: 0.001000, loss: 1.192445, loss_shrink_maps: 0.597507, loss_threshold_maps: 0.477900, loss_binary_maps: 0.118508, avg_reader_cost: 0.60547 s, avg_batch_cost: 0.66033 s, avg_samples: 2.9, ips: 4.39174 samples/s, eta: 1:56:21
[2024/07/28 02:25:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:25:40] ppocr INFO: epoch: [1118/1500], global_step: 3354, lr: 0.001000, loss: 1.192445, loss_shrink_maps: 0.596122, loss_threshold_maps: 0.477900, loss_binary_maps: 0.118902, avg_reader_cost: 1.57377 s, avg_batch_cost: 1.81991 s, avg_samples: 12.5, ips: 6.86848 samples/s, eta: 1:56:02
[2024/07/28 02:25:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:25:50] ppocr INFO: epoch: [1119/1500], global_step: 3357, lr: 0.001000, loss: 1.186398, loss_shrink_maps: 0.595390, loss_threshold_maps: 0.473759, loss_binary_maps: 0.118670, avg_reader_cost: 1.54039 s, avg_batch_cost: 1.76892 s, avg_samples: 12.5, ips: 7.06647 samples/s, eta: 1:55:44
[2024/07/28 02:25:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:25:59] ppocr INFO: epoch: [1120/1500], global_step: 3360, lr: 0.001000, loss: 1.186398, loss_shrink_maps: 0.593642, loss_threshold_maps: 0.477900, loss_binary_maps: 0.118300, avg_reader_cost: 1.53044 s, avg_batch_cost: 1.77791 s, avg_samples: 12.5, ips: 7.03073 samples/s, eta: 1:55:26

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:50,  9.79it/s]
eval model::   1%|          | 4/500 [00:00<00:25, 19.39it/s]
eval model::   1%|▏         | 7/500 [00:00<00:22, 21.53it/s]
eval model::   2%|▏         | 10/500 [00:00<00:24, 20.26it/s]
eval model::   3%|▎         | 13/500 [00:00<00:26, 18.44it/s]
eval model::   3%|▎         | 15/500 [00:00<00:27, 17.49it/s]
eval model::   4%|▎         | 18/500 [00:00<00:23, 20.29it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.74it/s]
eval model::   5%|▌         | 25/500 [00:01<00:22, 21.26it/s]
eval model::   6%|▌         | 28/500 [00:01<00:23, 20.37it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.48it/s]
eval model::   7%|▋         | 33/500 [00:01<00:24, 18.72it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 19.32it/s]
eval model::   8%|▊         | 39/500 [00:02<00:25, 17.86it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.67it/s]
eval model::   9%|▉         | 44/500 [00:02<00:26, 17.02it/s]
eval model::   9%|▉         | 46/500 [00:02<00:28, 16.00it/s]
eval model::  10%|▉         | 49/500 [00:02<00:25, 17.80it/s]
eval model::  10%|█         | 51/500 [00:02<00:24, 18.27it/s]
eval model::  11%|█         | 54/500 [00:02<00:22, 19.71it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:23, 18.75it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:26, 16.37it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:33, 13.26it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:34, 12.58it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:29, 14.91it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:25, 16.63it/s]
eval model::  14%|█▍        | 72/500 [00:04<00:23, 18.60it/s]
eval model::  15%|█▍        | 74/500 [00:04<00:22, 18.80it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:21, 19.40it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:19, 21.75it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.65it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.50it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.37it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:21, 19.05it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:21, 18.59it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:21, 18.62it/s]
eval model::  20%|██        | 100/500 [00:05<00:23, 17.36it/s]
eval model::  21%|██        | 103/500 [00:05<00:20, 18.91it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 20.71it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 20.96it/s]
eval model::  22%|██▏       | 112/500 [00:06<00:19, 19.47it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:23, 16.57it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:22, 16.87it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:20, 18.27it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:19, 19.69it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:22, 16.51it/s]
eval model::  26%|██▌       | 128/500 [00:07<00:21, 17.19it/s]
eval model::  26%|██▌       | 130/500 [00:07<00:25, 14.55it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:31, 11.73it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:26, 13.82it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:26, 13.47it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:24, 14.47it/s]
eval model::  28%|██▊       | 142/500 [00:08<00:21, 16.50it/s]
eval model::  29%|██▉       | 145/500 [00:08<00:19, 18.59it/s]
eval model::  29%|██▉       | 147/500 [00:08<00:19, 17.86it/s]
eval model::  30%|███       | 150/500 [00:08<00:17, 20.16it/s]
eval model::  31%|███       | 153/500 [00:08<00:17, 20.39it/s]
eval model::  31%|███       | 156/500 [00:08<00:15, 22.71it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 21.25it/s]
eval model::  32%|███▏      | 162/500 [00:09<00:23, 14.46it/s]
eval model::  33%|███▎      | 165/500 [00:09<00:21, 15.56it/s]
eval model::  34%|███▎      | 168/500 [00:09<00:19, 17.04it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:19, 17.29it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 15.68it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.28it/s]
eval model::  36%|███▌      | 179/500 [00:10<00:17, 18.16it/s]
eval model::  36%|███▌      | 181/500 [00:10<00:19, 16.19it/s]
eval model::  37%|███▋      | 184/500 [00:10<00:17, 18.26it/s]
eval model::  37%|███▋      | 187/500 [00:10<00:16, 19.37it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:14, 20.82it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:13, 22.54it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:13, 21.93it/s]
eval model::  40%|███▉      | 199/500 [00:11<00:12, 23.65it/s]
eval model::  40%|████      | 202/500 [00:11<00:12, 24.29it/s]
eval model::  41%|████      | 205/500 [00:11<00:19, 15.22it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:18, 15.79it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:17, 16.58it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:18, 15.92it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.30it/s]
eval model::  43%|████▎     | 215/500 [00:12<00:18, 15.31it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:21, 13.41it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:19, 14.72it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:22, 12.35it/s]
eval model::  45%|████▍     | 223/500 [00:12<00:21, 12.74it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:19, 14.25it/s]
eval model::  45%|████▌     | 227/500 [00:13<00:17, 15.20it/s]
eval model::  46%|████▌     | 230/500 [00:13<00:14, 18.58it/s]
eval model::  47%|████▋     | 233/500 [00:13<00:13, 20.20it/s]
eval model::  47%|████▋     | 236/500 [00:13<00:13, 20.13it/s]
eval model::  48%|████▊     | 239/500 [00:13<00:12, 21.65it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:12, 19.97it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.36it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 20.46it/s]
eval model::  50%|█████     | 251/500 [00:14<00:12, 20.14it/s]
eval model::  51%|█████     | 254/500 [00:14<00:11, 20.94it/s]
eval model::  51%|█████▏    | 257/500 [00:14<00:11, 20.30it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:10, 22.42it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:11, 20.10it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 20.56it/s]
eval model::  54%|█████▍    | 270/500 [00:15<00:11, 19.92it/s]
eval model::  55%|█████▍    | 273/500 [00:15<00:11, 20.42it/s]
eval model::  55%|█████▌    | 276/500 [00:15<00:11, 19.78it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:11, 19.76it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:09, 21.78it/s]
eval model::  57%|█████▋    | 286/500 [00:16<00:16, 13.00it/s]
eval model::  58%|█████▊    | 289/500 [00:16<00:13, 15.24it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:12, 16.40it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:15, 13.09it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 16.19it/s]
eval model::  60%|██████    | 302/500 [00:17<00:13, 14.94it/s]
eval model::  61%|██████    | 304/500 [00:17<00:12, 15.30it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:11, 17.40it/s]
eval model::  62%|██████▏   | 310/500 [00:17<00:09, 19.03it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:09, 18.96it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:10, 17.31it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:09, 19.58it/s]
eval model::  64%|██████▍   | 322/500 [00:18<00:09, 18.78it/s]
eval model::  65%|██████▌   | 325/500 [00:18<00:09, 19.41it/s]
eval model::  66%|██████▌   | 328/500 [00:18<00:09, 17.98it/s]
eval model::  66%|██████▌   | 330/500 [00:18<00:09, 18.34it/s]
eval model::  66%|██████▋   | 332/500 [00:18<00:09, 17.87it/s]
eval model::  67%|██████▋   | 335/500 [00:18<00:08, 19.72it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 19.31it/s]
eval model::  68%|██████▊   | 340/500 [00:19<00:10, 14.88it/s]
eval model::  69%|██████▊   | 343/500 [00:19<00:09, 17.44it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:07, 19.50it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:07, 19.36it/s]
eval model::  70%|███████   | 352/500 [00:19<00:07, 19.79it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 20.05it/s]
eval model::  72%|███████▏  | 358/500 [00:20<00:06, 20.29it/s]
eval model::  72%|███████▏  | 361/500 [00:20<00:07, 18.25it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:07, 17.78it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:08, 16.65it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:06, 18.82it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:06, 18.57it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:06, 18.86it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 21.66it/s]
eval model::  76%|███████▌  | 380/500 [00:21<00:05, 21.70it/s]
eval model::  77%|███████▋  | 383/500 [00:21<00:05, 23.17it/s]
eval model::  77%|███████▋  | 386/500 [00:21<00:07, 14.85it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:06, 17.10it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:05, 18.34it/s]
eval model::  79%|███████▉  | 395/500 [00:22<00:05, 19.63it/s]
eval model::  80%|███████▉  | 398/500 [00:22<00:04, 21.49it/s]
eval model::  80%|████████  | 401/500 [00:22<00:04, 20.53it/s]
eval model::  81%|████████  | 404/500 [00:22<00:04, 21.96it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 21.25it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:03, 23.55it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 23.62it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:03, 22.16it/s]
eval model::  84%|████████▍ | 420/500 [00:23<00:05, 15.06it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:05, 15.46it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.80it/s]
eval model::  85%|████████▌ | 427/500 [00:23<00:04, 16.92it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 19.11it/s]
eval model::  87%|████████▋ | 433/500 [00:24<00:03, 18.59it/s]
eval model::  87%|████████▋ | 436/500 [00:24<00:03, 19.68it/s]
eval model::  88%|████████▊ | 439/500 [00:24<00:03, 19.49it/s]
eval model::  88%|████████▊ | 442/500 [00:24<00:02, 19.49it/s]
eval model::  89%|████████▉ | 445/500 [00:24<00:02, 20.48it/s]
eval model::  90%|████████▉ | 448/500 [00:24<00:02, 19.36it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 18.52it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 20.09it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:02, 21.66it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:01, 21.52it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:01, 19.92it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:01, 17.88it/s]
eval model::  93%|█████████▎| 467/500 [00:25<00:01, 18.28it/s]
eval model::  94%|█████████▍| 470/500 [00:25<00:01, 20.04it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 20.79it/s]
eval model::  95%|█████████▌| 476/500 [00:26<00:01, 21.01it/s]
eval model::  96%|█████████▌| 479/500 [00:26<00:00, 21.50it/s]
eval model::  96%|█████████▋| 482/500 [00:26<00:00, 21.12it/s]
eval model::  97%|█████████▋| 485/500 [00:26<00:00, 23.02it/s]
eval model::  98%|█████████▊| 488/500 [00:26<00:00, 22.02it/s]
eval model::  98%|█████████▊| 491/500 [00:26<00:00, 21.42it/s]
eval model::  99%|█████████▉| 494/500 [00:26<00:00, 19.97it/s]
eval model::  99%|█████████▉| 497/500 [00:27<00:00, 20.81it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.38it/s]
[2024/07/28 02:26:27] ppocr INFO: cur metric, precision: 0.7218508997429306, recall: 0.6759749638902263, hmean: 0.6981601193436101, fps: 43.506547862498984
[2024/07/28 02:26:27] ppocr INFO: best metric, hmean: 0.7160430548436699, precision: 0.7654794520547945, recall: 0.672604718343765, fps: 44.8783209422934, best_epoch: 1080
[2024/07/28 02:26:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:26:35] ppocr INFO: epoch: [1121/1500], global_step: 3363, lr: 0.001000, loss: 1.198798, loss_shrink_maps: 0.607078, loss_threshold_maps: 0.484785, loss_binary_maps: 0.120910, avg_reader_cost: 1.48948 s, avg_batch_cost: 1.74024 s, avg_samples: 12.5, ips: 7.18292 samples/s, eta: 1:55:07
[2024/07/28 02:26:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:26:44] ppocr INFO: epoch: [1122/1500], global_step: 3366, lr: 0.001000, loss: 1.198798, loss_shrink_maps: 0.605763, loss_threshold_maps: 0.492366, loss_binary_maps: 0.120687, avg_reader_cost: 1.55187 s, avg_batch_cost: 1.83032 s, avg_samples: 12.5, ips: 6.82941 samples/s, eta: 1:54:49
[2024/07/28 02:26:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:26:54] ppocr INFO: epoch: [1123/1500], global_step: 3369, lr: 0.001000, loss: 1.243986, loss_shrink_maps: 0.630872, loss_threshold_maps: 0.499739, loss_binary_maps: 0.125489, avg_reader_cost: 1.53697 s, avg_batch_cost: 1.76726 s, avg_samples: 12.5, ips: 7.07310 samples/s, eta: 1:54:31
[2024/07/28 02:26:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:27:02] ppocr INFO: epoch: [1124/1500], global_step: 3370, lr: 0.001000, loss: 1.234070, loss_shrink_maps: 0.617586, loss_threshold_maps: 0.495162, loss_binary_maps: 0.123048, avg_reader_cost: 0.37846 s, avg_batch_cost: 0.52660 s, avg_samples: 4.8, ips: 9.11512 samples/s, eta: 1:54:24
[2024/07/28 02:27:03] ppocr INFO: epoch: [1124/1500], global_step: 3372, lr: 0.001000, loss: 1.234070, loss_shrink_maps: 0.617586, loss_threshold_maps: 0.487625, loss_binary_maps: 0.123048, avg_reader_cost: 1.14438 s, avg_batch_cost: 1.29023 s, avg_samples: 7.7, ips: 5.96792 samples/s, eta: 1:54:12
[2024/07/28 02:27:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:27:13] ppocr INFO: epoch: [1125/1500], global_step: 3375, lr: 0.001000, loss: 1.234070, loss_shrink_maps: 0.617586, loss_threshold_maps: 0.487625, loss_binary_maps: 0.123048, avg_reader_cost: 1.49782 s, avg_batch_cost: 1.73039 s, avg_samples: 12.5, ips: 7.22380 samples/s, eta: 1:53:54
[2024/07/28 02:27:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:27:22] ppocr INFO: epoch: [1126/1500], global_step: 3378, lr: 0.001000, loss: 1.242893, loss_shrink_maps: 0.641649, loss_threshold_maps: 0.489084, loss_binary_maps: 0.127710, avg_reader_cost: 1.52314 s, avg_batch_cost: 1.77849 s, avg_samples: 12.5, ips: 7.02842 samples/s, eta: 1:53:35
[2024/07/28 02:27:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:27:31] ppocr INFO: epoch: [1127/1500], global_step: 3380, lr: 0.001000, loss: 1.242893, loss_shrink_maps: 0.624170, loss_threshold_maps: 0.489084, loss_binary_maps: 0.124095, avg_reader_cost: 0.95986 s, avg_batch_cost: 1.13334 s, avg_samples: 9.6, ips: 8.47050 samples/s, eta: 1:53:23
[2024/07/28 02:27:32] ppocr INFO: epoch: [1127/1500], global_step: 3381, lr: 0.001000, loss: 1.242893, loss_shrink_maps: 0.624170, loss_threshold_maps: 0.489084, loss_binary_maps: 0.124095, avg_reader_cost: 0.61238 s, avg_batch_cost: 0.66762 s, avg_samples: 2.9, ips: 4.34379 samples/s, eta: 1:53:17
[2024/07/28 02:27:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:27:42] ppocr INFO: epoch: [1128/1500], global_step: 3384, lr: 0.001000, loss: 1.242893, loss_shrink_maps: 0.624170, loss_threshold_maps: 0.485090, loss_binary_maps: 0.124095, avg_reader_cost: 1.52880 s, avg_batch_cost: 1.77181 s, avg_samples: 12.5, ips: 7.05494 samples/s, eta: 1:52:59
[2024/07/28 02:27:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:27:51] ppocr INFO: epoch: [1129/1500], global_step: 3387, lr: 0.001000, loss: 1.230682, loss_shrink_maps: 0.623779, loss_threshold_maps: 0.476971, loss_binary_maps: 0.124089, avg_reader_cost: 1.57338 s, avg_batch_cost: 1.80768 s, avg_samples: 12.5, ips: 6.91494 samples/s, eta: 1:52:40
[2024/07/28 02:27:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:28:01] ppocr INFO: epoch: [1130/1500], global_step: 3390, lr: 0.001000, loss: 1.209952, loss_shrink_maps: 0.617430, loss_threshold_maps: 0.476971, loss_binary_maps: 0.122710, avg_reader_cost: 1.52964 s, avg_batch_cost: 1.75940 s, avg_samples: 12.5, ips: 7.10468 samples/s, eta: 1:52:22
[2024/07/28 02:28:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:28:11] ppocr INFO: epoch: [1131/1500], global_step: 3393, lr: 0.001000, loss: 1.213006, loss_shrink_maps: 0.617430, loss_threshold_maps: 0.476971, loss_binary_maps: 0.122710, avg_reader_cost: 1.61478 s, avg_batch_cost: 1.93965 s, avg_samples: 12.5, ips: 6.44445 samples/s, eta: 1:52:04
[2024/07/28 02:28:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:28:21] ppocr INFO: epoch: [1132/1500], global_step: 3396, lr: 0.001000, loss: 1.209564, loss_shrink_maps: 0.613711, loss_threshold_maps: 0.480477, loss_binary_maps: 0.122170, avg_reader_cost: 1.63428 s, avg_batch_cost: 1.88993 s, avg_samples: 12.5, ips: 6.61401 samples/s, eta: 1:51:46
[2024/07/28 02:28:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:28:30] ppocr INFO: epoch: [1133/1500], global_step: 3399, lr: 0.001000, loss: 1.198314, loss_shrink_maps: 0.602473, loss_threshold_maps: 0.474155, loss_binary_maps: 0.120393, avg_reader_cost: 1.50933 s, avg_batch_cost: 1.75689 s, avg_samples: 12.5, ips: 7.11485 samples/s, eta: 1:51:28
[2024/07/28 02:28:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:28:38] ppocr INFO: epoch: [1134/1500], global_step: 3400, lr: 0.001000, loss: 1.198314, loss_shrink_maps: 0.602473, loss_threshold_maps: 0.472770, loss_binary_maps: 0.120393, avg_reader_cost: 0.43350 s, avg_batch_cost: 0.52278 s, avg_samples: 4.8, ips: 9.18166 samples/s, eta: 1:51:21
[2024/07/28 02:28:40] ppocr INFO: epoch: [1134/1500], global_step: 3402, lr: 0.001000, loss: 1.198314, loss_shrink_maps: 0.599383, loss_threshold_maps: 0.472770, loss_binary_maps: 0.119314, avg_reader_cost: 1.13682 s, avg_batch_cost: 1.28292 s, avg_samples: 7.7, ips: 6.00194 samples/s, eta: 1:51:09
[2024/07/28 02:28:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:28:50] ppocr INFO: epoch: [1135/1500], global_step: 3405, lr: 0.001000, loss: 1.197371, loss_shrink_maps: 0.594774, loss_threshold_maps: 0.474155, loss_binary_maps: 0.118635, avg_reader_cost: 1.69116 s, avg_batch_cost: 1.93830 s, avg_samples: 12.5, ips: 6.44895 samples/s, eta: 1:50:52
[2024/07/28 02:28:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:29:00] ppocr INFO: epoch: [1136/1500], global_step: 3408, lr: 0.001000, loss: 1.191938, loss_shrink_maps: 0.586472, loss_threshold_maps: 0.475519, loss_binary_maps: 0.116945, avg_reader_cost: 1.53925 s, avg_batch_cost: 1.80038 s, avg_samples: 12.5, ips: 6.94299 samples/s, eta: 1:50:33
[2024/07/28 02:29:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:29:08] ppocr INFO: epoch: [1137/1500], global_step: 3410, lr: 0.001000, loss: 1.176448, loss_shrink_maps: 0.576948, loss_threshold_maps: 0.472770, loss_binary_maps: 0.114952, avg_reader_cost: 0.94289 s, avg_batch_cost: 1.11617 s, avg_samples: 9.6, ips: 8.60082 samples/s, eta: 1:50:21
[2024/07/28 02:29:09] ppocr INFO: epoch: [1137/1500], global_step: 3411, lr: 0.001000, loss: 1.161826, loss_shrink_maps: 0.573837, loss_threshold_maps: 0.470281, loss_binary_maps: 0.114168, avg_reader_cost: 0.60376 s, avg_batch_cost: 0.65862 s, avg_samples: 2.9, ips: 4.40318 samples/s, eta: 1:50:15
[2024/07/28 02:29:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:29:19] ppocr INFO: epoch: [1138/1500], global_step: 3414, lr: 0.001000, loss: 1.161826, loss_shrink_maps: 0.573837, loss_threshold_maps: 0.470281, loss_binary_maps: 0.114168, avg_reader_cost: 1.52255 s, avg_batch_cost: 1.75124 s, avg_samples: 12.5, ips: 7.13780 samples/s, eta: 1:49:57
[2024/07/28 02:29:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:29:28] ppocr INFO: epoch: [1139/1500], global_step: 3417, lr: 0.001000, loss: 1.177340, loss_shrink_maps: 0.582888, loss_threshold_maps: 0.471314, loss_binary_maps: 0.116178, avg_reader_cost: 1.57335 s, avg_batch_cost: 1.80200 s, avg_samples: 12.5, ips: 6.93672 samples/s, eta: 1:49:38
[2024/07/28 02:29:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:29:38] ppocr INFO: epoch: [1140/1500], global_step: 3420, lr: 0.001000, loss: 1.155703, loss_shrink_maps: 0.568524, loss_threshold_maps: 0.471314, loss_binary_maps: 0.113053, avg_reader_cost: 1.52145 s, avg_batch_cost: 1.75073 s, avg_samples: 12.5, ips: 7.13986 samples/s, eta: 1:49:20

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:57,  8.62it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.48it/s]
eval model::   1%|▏         | 7/500 [00:00<00:24, 19.90it/s]
eval model::   2%|▏         | 9/500 [00:00<00:26, 18.67it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.45it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.47it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.61it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 19.19it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.32it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 19.92it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 21.66it/s]
eval model::   6%|▋         | 32/500 [00:01<00:25, 18.40it/s]
eval model::   7%|▋         | 34/500 [00:01<00:27, 16.94it/s]
eval model::   7%|▋         | 36/500 [00:01<00:27, 16.79it/s]
eval model::   8%|▊         | 38/500 [00:02<00:27, 16.88it/s]
eval model::   8%|▊         | 40/500 [00:02<00:28, 16.10it/s]
eval model::   8%|▊         | 42/500 [00:02<00:30, 14.92it/s]
eval model::   9%|▉         | 45/500 [00:02<00:26, 17.28it/s]
eval model::   9%|▉         | 47/500 [00:02<00:27, 16.20it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.40it/s]
eval model::  11%|█         | 53/500 [00:02<00:22, 19.51it/s]
eval model::  11%|█         | 56/500 [00:03<00:24, 18.09it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:25, 17.19it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:24, 17.74it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:30, 14.16it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:29, 14.85it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:25, 17.28it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:24, 17.32it/s]
eval model::  14%|█▍        | 72/500 [00:04<00:23, 18.02it/s]
eval model::  15%|█▍        | 74/500 [00:04<00:23, 17.97it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:20, 20.27it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:17, 23.81it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:16, 25.14it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:18, 22.79it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:21, 19.49it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:22, 18.14it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.88it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.10it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 19.94it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.22it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.45it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 20.92it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:19, 19.78it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:18, 20.18it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 19.97it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 19.23it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 19.74it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:20, 18.26it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:21, 17.54it/s]
eval model::  27%|██▋       | 134/500 [00:07<00:18, 19.35it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:18, 19.83it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:17, 20.26it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.57it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.91it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.37it/s]
eval model::  31%|███       | 153/500 [00:08<00:18, 18.58it/s]
eval model::  31%|███       | 156/500 [00:08<00:16, 20.89it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:15, 21.76it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:20, 16.39it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:20, 16.65it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 18.20it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 19.73it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:17, 18.93it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.42it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.02it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:19, 16.04it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:19, 16.52it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.49it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:17, 18.08it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:16, 18.48it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:15, 19.98it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:15, 19.44it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 21.15it/s]
eval model::  40%|████      | 202/500 [00:10<00:12, 23.12it/s]
eval model::  41%|████      | 205/500 [00:11<00:22, 13.34it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:19, 14.91it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 15.38it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.67it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.86it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:20, 13.56it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:17, 15.89it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:19, 14.22it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.90it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 20.82it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:13, 20.02it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 20.87it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.02it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:14, 17.60it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 19.93it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.73it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 21.36it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.69it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:12, 18.93it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:12, 18.99it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:11, 19.81it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:12, 19.11it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:12, 18.34it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:12, 17.91it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 19.66it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.24it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 18.40it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:13, 16.62it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:12, 17.60it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:21, 10.10it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:16, 12.48it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:14, 14.45it/s]
eval model::  59%|█████▊    | 293/500 [00:16<00:14, 13.90it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:17, 11.88it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:13, 14.46it/s]
eval model::  60%|██████    | 300/500 [00:16<00:13, 15.00it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.47it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 17.95it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 20.22it/s]
eval model::  62%|██████▏   | 311/500 [00:17<00:09, 20.09it/s]
eval model::  63%|██████▎   | 314/500 [00:17<00:09, 20.45it/s]
eval model::  63%|██████▎   | 317/500 [00:17<00:08, 21.09it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:08, 21.97it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:08, 21.22it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:08, 20.89it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 20.24it/s]
eval model::  66%|██████▋   | 332/500 [00:18<00:08, 20.87it/s]
eval model::  67%|██████▋   | 335/500 [00:18<00:07, 22.68it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 20.05it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:10, 14.72it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 17.60it/s]
eval model::  70%|██████▉   | 348/500 [00:19<00:08, 18.32it/s]
eval model::  70%|███████   | 351/500 [00:19<00:07, 19.88it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 21.25it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 21.96it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.92it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.15it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:08, 16.40it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:07, 17.10it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:07, 17.58it/s]
eval model::  75%|███████▍  | 374/500 [00:20<00:06, 19.00it/s]
eval model::  75%|███████▌  | 377/500 [00:20<00:06, 20.18it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:06, 17.94it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 20.27it/s]
eval model::  77%|███████▋  | 386/500 [00:21<00:08, 13.39it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:06, 16.90it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:05, 18.16it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 18.81it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:04, 21.00it/s]
eval model::  80%|████████  | 402/500 [00:21<00:04, 20.68it/s]
eval model::  81%|████████  | 405/500 [00:22<00:04, 22.40it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:04, 21.74it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:04, 21.96it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:04, 19.55it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 18.22it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 18.24it/s]
eval model::  84%|████████▍ | 421/500 [00:23<00:06, 12.63it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.23it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.78it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 17.31it/s]
eval model::  86%|████████▌ | 431/500 [00:23<00:04, 16.94it/s]
eval model::  87%|████████▋ | 434/500 [00:23<00:03, 19.91it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:02, 21.04it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:03, 17.80it/s]
eval model::  89%|████████▊ | 443/500 [00:24<00:02, 19.61it/s]
eval model::  89%|████████▉ | 446/500 [00:24<00:02, 20.01it/s]
eval model::  90%|████████▉ | 449/500 [00:24<00:02, 17.21it/s]
eval model::  90%|█████████ | 452/500 [00:24<00:02, 18.43it/s]
eval model::  91%|█████████ | 455/500 [00:24<00:02, 20.08it/s]
eval model::  92%|█████████▏| 458/500 [00:24<00:01, 21.72it/s]
eval model::  92%|█████████▏| 461/500 [00:25<00:01, 20.84it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:01, 18.63it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 16.62it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 16.68it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 17.49it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 17.63it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 19.35it/s]
eval model::  96%|█████████▌| 479/500 [00:26<00:01, 20.63it/s]
eval model::  96%|█████████▋| 482/500 [00:26<00:00, 20.36it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 22.01it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 23.28it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 19.46it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 20.85it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 22.21it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.48it/s]
[2024/07/28 02:30:05] ppocr INFO: cur metric, precision: 0.7388978063135366, recall: 0.6649012999518537, hmean: 0.6999493157627978, fps: 44.71578345500319
[2024/07/28 02:30:05] ppocr INFO: best metric, hmean: 0.7160430548436699, precision: 0.7654794520547945, recall: 0.672604718343765, fps: 44.8783209422934, best_epoch: 1080
[2024/07/28 02:30:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:30:13] ppocr INFO: epoch: [1141/1500], global_step: 3423, lr: 0.001000, loss: 1.142617, loss_shrink_maps: 0.566594, loss_threshold_maps: 0.459018, loss_binary_maps: 0.112626, avg_reader_cost: 1.47629 s, avg_batch_cost: 1.70509 s, avg_samples: 12.5, ips: 7.33099 samples/s, eta: 1:49:01
[2024/07/28 02:30:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:30:22] ppocr INFO: epoch: [1142/1500], global_step: 3426, lr: 0.001000, loss: 1.143928, loss_shrink_maps: 0.566594, loss_threshold_maps: 0.474364, loss_binary_maps: 0.112626, avg_reader_cost: 1.52940 s, avg_batch_cost: 1.77057 s, avg_samples: 12.5, ips: 7.05987 samples/s, eta: 1:48:43
[2024/07/28 02:30:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:30:32] ppocr INFO: epoch: [1143/1500], global_step: 3429, lr: 0.001000, loss: 1.177340, loss_shrink_maps: 0.582888, loss_threshold_maps: 0.476123, loss_binary_maps: 0.116178, avg_reader_cost: 1.53282 s, avg_batch_cost: 1.80218 s, avg_samples: 12.5, ips: 6.93604 samples/s, eta: 1:48:25
[2024/07/28 02:30:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:30:40] ppocr INFO: epoch: [1144/1500], global_step: 3430, lr: 0.001000, loss: 1.195960, loss_shrink_maps: 0.590261, loss_threshold_maps: 0.488392, loss_binary_maps: 0.117704, avg_reader_cost: 0.40642 s, avg_batch_cost: 0.50427 s, avg_samples: 4.8, ips: 9.51867 samples/s, eta: 1:48:18
[2024/07/28 02:30:41] ppocr INFO: epoch: [1144/1500], global_step: 3432, lr: 0.001000, loss: 1.210467, loss_shrink_maps: 0.600534, loss_threshold_maps: 0.486082, loss_binary_maps: 0.119408, avg_reader_cost: 1.09951 s, avg_batch_cost: 1.24507 s, avg_samples: 7.7, ips: 6.18437 samples/s, eta: 1:48:06
[2024/07/28 02:30:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:30:51] ppocr INFO: epoch: [1145/1500], global_step: 3435, lr: 0.001000, loss: 1.217997, loss_shrink_maps: 0.605531, loss_threshold_maps: 0.482432, loss_binary_maps: 0.120094, avg_reader_cost: 1.49204 s, avg_batch_cost: 1.74431 s, avg_samples: 12.5, ips: 7.16618 samples/s, eta: 1:47:48
[2024/07/28 02:30:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:31:00] ppocr INFO: epoch: [1146/1500], global_step: 3438, lr: 0.001000, loss: 1.223845, loss_shrink_maps: 0.605531, loss_threshold_maps: 0.486082, loss_binary_maps: 0.120094, avg_reader_cost: 1.52278 s, avg_batch_cost: 1.76968 s, avg_samples: 12.5, ips: 7.06341 samples/s, eta: 1:47:29
[2024/07/28 02:31:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:31:09] ppocr INFO: epoch: [1147/1500], global_step: 3440, lr: 0.001000, loss: 1.223845, loss_shrink_maps: 0.605531, loss_threshold_maps: 0.486082, loss_binary_maps: 0.120094, avg_reader_cost: 0.91109 s, avg_batch_cost: 1.15276 s, avg_samples: 9.6, ips: 8.32783 samples/s, eta: 1:47:17
[2024/07/28 02:31:10] ppocr INFO: epoch: [1147/1500], global_step: 3441, lr: 0.001000, loss: 1.209605, loss_shrink_maps: 0.605465, loss_threshold_maps: 0.482674, loss_binary_maps: 0.119954, avg_reader_cost: 0.62194 s, avg_batch_cost: 0.67679 s, avg_samples: 2.9, ips: 4.28495 samples/s, eta: 1:47:11
[2024/07/28 02:31:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:31:19] ppocr INFO: epoch: [1148/1500], global_step: 3444, lr: 0.001000, loss: 1.223845, loss_shrink_maps: 0.612156, loss_threshold_maps: 0.482674, loss_binary_maps: 0.121206, avg_reader_cost: 1.52000 s, avg_batch_cost: 1.77484 s, avg_samples: 12.5, ips: 7.04291 samples/s, eta: 1:46:53
[2024/07/28 02:31:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:31:29] ppocr INFO: epoch: [1149/1500], global_step: 3447, lr: 0.001000, loss: 1.223845, loss_shrink_maps: 0.612156, loss_threshold_maps: 0.482674, loss_binary_maps: 0.121206, avg_reader_cost: 1.55315 s, avg_batch_cost: 1.78116 s, avg_samples: 12.5, ips: 7.01790 samples/s, eta: 1:46:34
[2024/07/28 02:31:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:31:38] ppocr INFO: epoch: [1150/1500], global_step: 3450, lr: 0.001000, loss: 1.208221, loss_shrink_maps: 0.618092, loss_threshold_maps: 0.480595, loss_binary_maps: 0.122585, avg_reader_cost: 1.48273 s, avg_batch_cost: 1.71082 s, avg_samples: 12.5, ips: 7.30642 samples/s, eta: 1:46:16
[2024/07/28 02:31:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:31:49] ppocr INFO: epoch: [1151/1500], global_step: 3453, lr: 0.001000, loss: 1.201247, loss_shrink_maps: 0.604219, loss_threshold_maps: 0.484035, loss_binary_maps: 0.120331, avg_reader_cost: 1.67087 s, avg_batch_cost: 1.89877 s, avg_samples: 12.5, ips: 6.58319 samples/s, eta: 1:45:58
[2024/07/28 02:31:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:31:58] ppocr INFO: epoch: [1152/1500], global_step: 3456, lr: 0.001000, loss: 1.161994, loss_shrink_maps: 0.587670, loss_threshold_maps: 0.460459, loss_binary_maps: 0.117144, avg_reader_cost: 1.62757 s, avg_batch_cost: 1.87336 s, avg_samples: 12.5, ips: 6.67249 samples/s, eta: 1:45:40
[2024/07/28 02:32:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:32:08] ppocr INFO: epoch: [1153/1500], global_step: 3459, lr: 0.001000, loss: 1.157054, loss_shrink_maps: 0.572418, loss_threshold_maps: 0.471141, loss_binary_maps: 0.114193, avg_reader_cost: 1.52998 s, avg_batch_cost: 1.75939 s, avg_samples: 12.5, ips: 7.10472 samples/s, eta: 1:45:21
[2024/07/28 02:32:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:32:16] ppocr INFO: epoch: [1154/1500], global_step: 3460, lr: 0.001000, loss: 1.157054, loss_shrink_maps: 0.572418, loss_threshold_maps: 0.460459, loss_binary_maps: 0.114203, avg_reader_cost: 0.41578 s, avg_batch_cost: 0.52619 s, avg_samples: 4.8, ips: 9.12214 samples/s, eta: 1:45:15
[2024/07/28 02:32:17] ppocr INFO: epoch: [1154/1500], global_step: 3462, lr: 0.001000, loss: 1.177500, loss_shrink_maps: 0.588288, loss_threshold_maps: 0.471430, loss_binary_maps: 0.117583, avg_reader_cost: 1.14470 s, avg_batch_cost: 1.29142 s, avg_samples: 7.7, ips: 5.96241 samples/s, eta: 1:45:03
[2024/07/28 02:32:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:32:27] ppocr INFO: epoch: [1155/1500], global_step: 3465, lr: 0.001000, loss: 1.197965, loss_shrink_maps: 0.607737, loss_threshold_maps: 0.484324, loss_binary_maps: 0.120977, avg_reader_cost: 1.56514 s, avg_batch_cost: 1.86388 s, avg_samples: 12.5, ips: 6.70643 samples/s, eta: 1:44:45
[2024/07/28 02:32:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:32:37] ppocr INFO: epoch: [1156/1500], global_step: 3468, lr: 0.001000, loss: 1.197965, loss_shrink_maps: 0.607737, loss_threshold_maps: 0.473760, loss_binary_maps: 0.120977, avg_reader_cost: 1.54033 s, avg_batch_cost: 1.79273 s, avg_samples: 12.5, ips: 6.97262 samples/s, eta: 1:44:27
[2024/07/28 02:32:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:32:46] ppocr INFO: epoch: [1157/1500], global_step: 3470, lr: 0.001000, loss: 1.208358, loss_shrink_maps: 0.608307, loss_threshold_maps: 0.482327, loss_binary_maps: 0.121565, avg_reader_cost: 0.92078 s, avg_batch_cost: 1.15517 s, avg_samples: 9.6, ips: 8.31049 samples/s, eta: 1:44:14
[2024/07/28 02:32:46] ppocr INFO: epoch: [1157/1500], global_step: 3471, lr: 0.001000, loss: 1.201474, loss_shrink_maps: 0.593048, loss_threshold_maps: 0.482327, loss_binary_maps: 0.118556, avg_reader_cost: 0.62348 s, avg_batch_cost: 0.67833 s, avg_samples: 2.9, ips: 4.27523 samples/s, eta: 1:44:08
[2024/07/28 02:32:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:32:56] ppocr INFO: epoch: [1158/1500], global_step: 3474, lr: 0.001000, loss: 1.238403, loss_shrink_maps: 0.633880, loss_threshold_maps: 0.486056, loss_binary_maps: 0.126363, avg_reader_cost: 1.58275 s, avg_batch_cost: 1.81071 s, avg_samples: 12.5, ips: 6.90335 samples/s, eta: 1:43:50
[2024/07/28 02:32:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:33:06] ppocr INFO: epoch: [1159/1500], global_step: 3477, lr: 0.001000, loss: 1.277666, loss_shrink_maps: 0.652940, loss_threshold_maps: 0.489545, loss_binary_maps: 0.130266, avg_reader_cost: 1.58108 s, avg_batch_cost: 1.84936 s, avg_samples: 12.5, ips: 6.75911 samples/s, eta: 1:43:32
[2024/07/28 02:33:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:33:16] ppocr INFO: epoch: [1160/1500], global_step: 3480, lr: 0.001000, loss: 1.238403, loss_shrink_maps: 0.633880, loss_threshold_maps: 0.486056, loss_binary_maps: 0.126363, avg_reader_cost: 1.57657 s, avg_batch_cost: 1.81422 s, avg_samples: 12.5, ips: 6.89001 samples/s, eta: 1:43:14

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.62it/s]
eval model::   1%|          | 4/500 [00:00<00:25, 19.28it/s]
eval model::   1%|          | 6/500 [00:00<00:25, 19.17it/s]
eval model::   2%|▏         | 8/500 [00:00<00:26, 18.89it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.18it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.31it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.94it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.84it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 22.00it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.62it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.14it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.33it/s]
eval model::   7%|▋         | 34/500 [00:01<00:27, 17.14it/s]
eval model::   7%|▋         | 37/500 [00:01<00:24, 18.94it/s]
eval model::   8%|▊         | 39/500 [00:02<00:26, 17.39it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.86it/s]
eval model::   9%|▉         | 44/500 [00:02<00:26, 17.44it/s]
eval model::   9%|▉         | 46/500 [00:02<00:27, 16.32it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.50it/s]
eval model::  10%|█         | 51/500 [00:02<00:26, 16.79it/s]
eval model::  11%|█         | 54/500 [00:02<00:24, 18.29it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:22, 19.38it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:24, 18.10it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.61it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.22it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:24, 17.47it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 19.84it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.36it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:19, 21.40it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.85it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.11it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.27it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 20.20it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 17.99it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:22, 17.86it/s]
eval model::  19%|█▉        | 94/500 [00:05<00:22, 17.68it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:21, 18.75it/s]
eval model::  20%|██        | 100/500 [00:05<00:24, 16.44it/s]
eval model::  20%|██        | 102/500 [00:05<00:23, 16.87it/s]
eval model::  21%|██        | 105/500 [00:05<00:21, 18.67it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:20, 19.18it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 19.53it/s]
eval model::  23%|██▎       | 114/500 [00:06<00:19, 19.97it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:18, 20.39it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:21, 18.05it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:19, 19.82it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:22, 16.40it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:20, 17.82it/s]
eval model::  26%|██▌       | 131/500 [00:07<00:22, 16.27it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:23, 15.81it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:21, 17.18it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:19, 18.26it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.73it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.47it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 17.73it/s]
eval model::  30%|███       | 150/500 [00:08<00:18, 18.64it/s]
eval model::  30%|███       | 152/500 [00:08<00:19, 17.84it/s]
eval model::  31%|███       | 156/500 [00:08<00:16, 21.29it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:15, 22.31it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:23, 14.09it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:22, 15.04it/s]
eval model::  33%|███▎      | 167/500 [00:09<00:20, 16.24it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:17, 18.79it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:18, 17.93it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:22, 14.34it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:19, 16.88it/s]
eval model::  36%|███▋      | 182/500 [00:10<00:21, 14.76it/s]
eval model::  37%|███▋      | 186/500 [00:10<00:18, 17.05it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:18, 16.62it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:18, 16.82it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:16, 19.00it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:17, 17.45it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:15, 18.87it/s]
eval model::  40%|████      | 202/500 [00:11<00:14, 20.98it/s]
eval model::  41%|████      | 205/500 [00:11<00:21, 13.82it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:19, 14.77it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 14.60it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.78it/s]
eval model::  43%|████▎     | 215/500 [00:12<00:21, 13.37it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:23, 11.79it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:22, 12.50it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:24, 11.62it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:19, 14.27it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:18, 14.87it/s]
eval model::  46%|████▌     | 229/500 [00:13<00:15, 17.91it/s]
eval model::  46%|████▋     | 232/500 [00:13<00:12, 20.70it/s]
eval model::  47%|████▋     | 235/500 [00:13<00:13, 20.09it/s]
eval model::  48%|████▊     | 238/500 [00:13<00:12, 20.33it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:12, 20.95it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:14, 18.28it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:12, 20.68it/s]
eval model::  50%|█████     | 250/500 [00:14<00:12, 19.44it/s]
eval model::  51%|█████     | 253/500 [00:14<00:11, 21.16it/s]
eval model::  51%|█████     | 256/500 [00:14<00:11, 21.73it/s]
eval model::  52%|█████▏    | 259/500 [00:14<00:10, 23.40it/s]
eval model::  52%|█████▏    | 262/500 [00:14<00:10, 23.77it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:10, 22.32it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:10, 22.71it/s]
eval model::  54%|█████▍    | 271/500 [00:15<00:11, 19.73it/s]
eval model::  55%|█████▍    | 274/500 [00:15<00:10, 20.71it/s]
eval model::  55%|█████▌    | 277/500 [00:15<00:11, 18.71it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:11, 18.67it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 20.93it/s]
eval model::  57%|█████▋    | 285/500 [00:16<00:17, 12.30it/s]
eval model::  58%|█████▊    | 288/500 [00:16<00:14, 14.20it/s]
eval model::  58%|█████▊    | 291/500 [00:16<00:12, 16.82it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:12, 16.57it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:13, 15.01it/s]
eval model::  60%|██████    | 300/500 [00:16<00:12, 15.86it/s]
eval model::  61%|██████    | 303/500 [00:16<00:11, 17.66it/s]
eval model::  61%|██████    | 305/500 [00:17<00:11, 17.25it/s]
eval model::  62%|██████▏   | 308/500 [00:17<00:10, 18.46it/s]
eval model::  62%|██████▏   | 311/500 [00:17<00:09, 20.46it/s]
eval model::  63%|██████▎   | 314/500 [00:17<00:09, 19.98it/s]
eval model::  63%|██████▎   | 317/500 [00:17<00:09, 20.11it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:08, 21.04it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:09, 19.28it/s]
eval model::  65%|██████▌   | 326/500 [00:18<00:08, 20.68it/s]
eval model::  66%|██████▌   | 329/500 [00:18<00:08, 20.21it/s]
eval model::  66%|██████▋   | 332/500 [00:18<00:08, 20.96it/s]
eval model::  67%|██████▋   | 335/500 [00:18<00:07, 21.15it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 20.21it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:10, 15.74it/s]
eval model::  69%|██████▊   | 343/500 [00:19<00:09, 16.48it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:08, 18.36it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:07, 19.57it/s]
eval model::  70%|███████   | 352/500 [00:19<00:06, 21.61it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 22.26it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 22.41it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.89it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:07, 18.42it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:08, 16.00it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:07, 18.12it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:06, 18.40it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 19.60it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:06, 20.25it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:06, 19.46it/s]
eval model::  77%|███████▋  | 384/500 [00:21<00:05, 20.84it/s]
eval model::  77%|███████▋  | 387/500 [00:21<00:08, 13.12it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:07, 15.25it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:06, 15.71it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 16.33it/s]
eval model::  79%|███████▉  | 397/500 [00:22<00:05, 18.42it/s]
eval model::  80%|████████  | 400/500 [00:22<00:05, 17.69it/s]
eval model::  80%|████████  | 402/500 [00:22<00:05, 18.18it/s]
eval model::  81%|████████  | 404/500 [00:22<00:05, 18.56it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 18.77it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 21.15it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:04, 19.76it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 19.78it/s]
eval model::  84%|████████▍ | 419/500 [00:23<00:04, 18.26it/s]
eval model::  84%|████████▍ | 421/500 [00:23<00:06, 11.45it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 12.97it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 13.37it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 15.79it/s]
eval model::  86%|████████▌ | 431/500 [00:24<00:04, 15.66it/s]
eval model::  87%|████████▋ | 435/500 [00:24<00:03, 19.21it/s]
eval model::  88%|████████▊ | 438/500 [00:24<00:02, 21.36it/s]
eval model::  88%|████████▊ | 441/500 [00:24<00:02, 19.68it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 21.60it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 21.87it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 19.66it/s]
eval model::  91%|█████████ | 453/500 [00:25<00:02, 21.28it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:01, 22.25it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:01, 22.50it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 16.49it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:02, 16.61it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 15.68it/s]
eval model::  94%|█████████▍| 469/500 [00:26<00:01, 17.25it/s]
eval model::  94%|█████████▍| 472/500 [00:26<00:01, 19.24it/s]
eval model::  95%|█████████▌| 475/500 [00:26<00:01, 19.61it/s]
eval model::  96%|█████████▌| 478/500 [00:26<00:01, 20.57it/s]
eval model::  96%|█████████▌| 481/500 [00:26<00:00, 21.81it/s]
eval model::  97%|█████████▋| 484/500 [00:26<00:00, 22.55it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 22.70it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 23.75it/s]
eval model::  99%|█████████▊| 493/500 [00:27<00:00, 20.20it/s]
eval model::  99%|█████████▉| 496/500 [00:27<00:00, 20.15it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 23.92it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.26it/s]
[2024/07/28 02:33:43] ppocr INFO: cur metric, precision: 0.6982047549733139, recall: 0.6928261916225325, hmean: 0.695505074915418, fps: 44.90063975241479
[2024/07/28 02:33:43] ppocr INFO: best metric, hmean: 0.7160430548436699, precision: 0.7654794520547945, recall: 0.672604718343765, fps: 44.8783209422934, best_epoch: 1080
[2024/07/28 02:33:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:33:50] ppocr INFO: epoch: [1161/1500], global_step: 3483, lr: 0.001000, loss: 1.229315, loss_shrink_maps: 0.628148, loss_threshold_maps: 0.484602, loss_binary_maps: 0.125133, avg_reader_cost: 1.42127 s, avg_batch_cost: 1.65126 s, avg_samples: 12.5, ips: 7.56998 samples/s, eta: 1:42:55
[2024/07/28 02:33:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:34:00] ppocr INFO: epoch: [1162/1500], global_step: 3486, lr: 0.001000, loss: 1.240829, loss_shrink_maps: 0.622882, loss_threshold_maps: 0.484602, loss_binary_maps: 0.123928, avg_reader_cost: 1.64360 s, avg_batch_cost: 1.88883 s, avg_samples: 12.5, ips: 6.61784 samples/s, eta: 1:42:37
[2024/07/28 02:34:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:34:10] ppocr INFO: epoch: [1163/1500], global_step: 3489, lr: 0.001000, loss: 1.219840, loss_shrink_maps: 0.600923, loss_threshold_maps: 0.479640, loss_binary_maps: 0.120041, avg_reader_cost: 1.49732 s, avg_batch_cost: 1.74085 s, avg_samples: 12.5, ips: 7.18040 samples/s, eta: 1:42:19
[2024/07/28 02:34:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:34:18] ppocr INFO: epoch: [1164/1500], global_step: 3490, lr: 0.001000, loss: 1.174239, loss_shrink_maps: 0.581531, loss_threshold_maps: 0.475582, loss_binary_maps: 0.116044, avg_reader_cost: 0.39781 s, avg_batch_cost: 0.49675 s, avg_samples: 4.8, ips: 9.66279 samples/s, eta: 1:42:12
[2024/07/28 02:34:19] ppocr INFO: epoch: [1164/1500], global_step: 3492, lr: 0.001000, loss: 1.152555, loss_shrink_maps: 0.588441, loss_threshold_maps: 0.455728, loss_binary_maps: 0.117351, avg_reader_cost: 1.08476 s, avg_batch_cost: 1.23029 s, avg_samples: 7.7, ips: 6.25868 samples/s, eta: 1:42:00
[2024/07/28 02:34:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:34:29] ppocr INFO: epoch: [1165/1500], global_step: 3495, lr: 0.001000, loss: 1.108559, loss_shrink_maps: 0.561444, loss_threshold_maps: 0.446917, loss_binary_maps: 0.111988, avg_reader_cost: 1.52010 s, avg_batch_cost: 1.75467 s, avg_samples: 12.5, ips: 7.12384 samples/s, eta: 1:41:42
[2024/07/28 02:34:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:34:38] ppocr INFO: epoch: [1166/1500], global_step: 3498, lr: 0.001000, loss: 1.194958, loss_shrink_maps: 0.603342, loss_threshold_maps: 0.469701, loss_binary_maps: 0.120096, avg_reader_cost: 1.51531 s, avg_batch_cost: 1.75497 s, avg_samples: 12.5, ips: 7.12262 samples/s, eta: 1:41:23
[2024/07/28 02:34:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:34:48] ppocr INFO: epoch: [1167/1500], global_step: 3500, lr: 0.001000, loss: 1.216842, loss_shrink_maps: 0.608914, loss_threshold_maps: 0.475861, loss_binary_maps: 0.121348, avg_reader_cost: 1.01311 s, avg_batch_cost: 1.23271 s, avg_samples: 9.6, ips: 7.78773 samples/s, eta: 1:41:11
[2024/07/28 02:34:49] ppocr INFO: epoch: [1167/1500], global_step: 3501, lr: 0.001000, loss: 1.216842, loss_shrink_maps: 0.608914, loss_threshold_maps: 0.475861, loss_binary_maps: 0.121348, avg_reader_cost: 0.66263 s, avg_batch_cost: 0.71705 s, avg_samples: 2.9, ips: 4.04436 samples/s, eta: 1:41:06
[2024/07/28 02:34:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:34:58] ppocr INFO: epoch: [1168/1500], global_step: 3504, lr: 0.001000, loss: 1.194958, loss_shrink_maps: 0.604424, loss_threshold_maps: 0.475861, loss_binary_maps: 0.120096, avg_reader_cost: 1.49526 s, avg_batch_cost: 1.74220 s, avg_samples: 12.5, ips: 7.17486 samples/s, eta: 1:40:47
[2024/07/28 02:35:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:35:08] ppocr INFO: epoch: [1169/1500], global_step: 3507, lr: 0.001000, loss: 1.174302, loss_shrink_maps: 0.599024, loss_threshold_maps: 0.470439, loss_binary_maps: 0.119280, avg_reader_cost: 1.54779 s, avg_batch_cost: 1.77671 s, avg_samples: 12.5, ips: 7.03546 samples/s, eta: 1:40:29
[2024/07/28 02:35:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:35:17] ppocr INFO: epoch: [1170/1500], global_step: 3510, lr: 0.001000, loss: 1.231891, loss_shrink_maps: 0.629867, loss_threshold_maps: 0.481206, loss_binary_maps: 0.125119, avg_reader_cost: 1.53062 s, avg_batch_cost: 1.75905 s, avg_samples: 12.5, ips: 7.10610 samples/s, eta: 1:40:10
[2024/07/28 02:35:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:35:27] ppocr INFO: epoch: [1171/1500], global_step: 3513, lr: 0.001000, loss: 1.286019, loss_shrink_maps: 0.651782, loss_threshold_maps: 0.492605, loss_binary_maps: 0.129416, avg_reader_cost: 1.54125 s, avg_batch_cost: 1.77790 s, avg_samples: 12.5, ips: 7.03075 samples/s, eta: 1:39:52
[2024/07/28 02:35:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:35:37] ppocr INFO: epoch: [1172/1500], global_step: 3516, lr: 0.001000, loss: 1.289562, loss_shrink_maps: 0.651782, loss_threshold_maps: 0.493665, loss_binary_maps: 0.129416, avg_reader_cost: 1.68017 s, avg_batch_cost: 1.92994 s, avg_samples: 12.5, ips: 6.47688 samples/s, eta: 1:39:34
[2024/07/28 02:35:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:35:47] ppocr INFO: epoch: [1173/1500], global_step: 3519, lr: 0.001000, loss: 1.251600, loss_shrink_maps: 0.630093, loss_threshold_maps: 0.493665, loss_binary_maps: 0.125382, avg_reader_cost: 1.52410 s, avg_batch_cost: 1.77682 s, avg_samples: 12.5, ips: 7.03503 samples/s, eta: 1:39:16
[2024/07/28 02:35:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:35:55] ppocr INFO: epoch: [1174/1500], global_step: 3520, lr: 0.001000, loss: 1.227778, loss_shrink_maps: 0.610169, loss_threshold_maps: 0.487485, loss_binary_maps: 0.121303, avg_reader_cost: 0.43475 s, avg_batch_cost: 0.51971 s, avg_samples: 4.8, ips: 9.23590 samples/s, eta: 1:39:09
[2024/07/28 02:35:56] ppocr INFO: epoch: [1174/1500], global_step: 3522, lr: 0.001000, loss: 1.212613, loss_shrink_maps: 0.610169, loss_threshold_maps: 0.485584, loss_binary_maps: 0.121303, avg_reader_cost: 1.13049 s, avg_batch_cost: 1.27592 s, avg_samples: 7.7, ips: 6.03486 samples/s, eta: 1:38:57
[2024/07/28 02:36:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:36:06] ppocr INFO: epoch: [1175/1500], global_step: 3525, lr: 0.001000, loss: 1.212613, loss_shrink_maps: 0.610169, loss_threshold_maps: 0.487485, loss_binary_maps: 0.121303, avg_reader_cost: 1.56457 s, avg_batch_cost: 1.79277 s, avg_samples: 12.5, ips: 6.97246 samples/s, eta: 1:38:39
[2024/07/28 02:36:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:36:16] ppocr INFO: epoch: [1176/1500], global_step: 3528, lr: 0.001000, loss: 1.196890, loss_shrink_maps: 0.601462, loss_threshold_maps: 0.482910, loss_binary_maps: 0.119539, avg_reader_cost: 1.49857 s, avg_batch_cost: 1.72913 s, avg_samples: 12.5, ips: 7.22906 samples/s, eta: 1:38:21
[2024/07/28 02:36:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:36:25] ppocr INFO: epoch: [1177/1500], global_step: 3530, lr: 0.001000, loss: 1.184586, loss_shrink_maps: 0.597582, loss_threshold_maps: 0.478718, loss_binary_maps: 0.119279, avg_reader_cost: 0.89843 s, avg_batch_cost: 1.08255 s, avg_samples: 9.6, ips: 8.86794 samples/s, eta: 1:38:08
[2024/07/28 02:36:25] ppocr INFO: epoch: [1177/1500], global_step: 3531, lr: 0.001000, loss: 1.196890, loss_shrink_maps: 0.603873, loss_threshold_maps: 0.482910, loss_binary_maps: 0.120647, avg_reader_cost: 0.58704 s, avg_batch_cost: 0.64179 s, avg_samples: 2.9, ips: 4.51860 samples/s, eta: 1:38:02
[2024/07/28 02:36:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:36:35] ppocr INFO: epoch: [1178/1500], global_step: 3534, lr: 0.001000, loss: 1.196890, loss_shrink_maps: 0.603873, loss_threshold_maps: 0.473951, loss_binary_maps: 0.120647, avg_reader_cost: 1.58388 s, avg_batch_cost: 1.87126 s, avg_samples: 12.5, ips: 6.68001 samples/s, eta: 1:37:44
[2024/07/28 02:36:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:36:45] ppocr INFO: epoch: [1179/1500], global_step: 3537, lr: 0.001000, loss: 1.184586, loss_shrink_maps: 0.597582, loss_threshold_maps: 0.468432, loss_binary_maps: 0.119279, avg_reader_cost: 1.56633 s, avg_batch_cost: 1.82897 s, avg_samples: 12.5, ips: 6.83444 samples/s, eta: 1:37:26
[2024/07/28 02:36:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:36:54] ppocr INFO: epoch: [1180/1500], global_step: 3540, lr: 0.001000, loss: 1.218692, loss_shrink_maps: 0.615418, loss_threshold_maps: 0.472184, loss_binary_maps: 0.122246, avg_reader_cost: 1.51210 s, avg_batch_cost: 1.74620 s, avg_samples: 12.5, ips: 7.15841 samples/s, eta: 1:37:08

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:37, 13.20it/s]
eval model::   1%|          | 5/500 [00:00<00:23, 20.88it/s]
eval model::   2%|▏         | 8/500 [00:00<00:25, 19.57it/s]
eval model::   2%|▏         | 11/500 [00:00<00:24, 20.10it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.45it/s]
eval model::   3%|▎         | 17/500 [00:00<00:24, 19.42it/s]
eval model::   4%|▍         | 19/500 [00:00<00:24, 19.29it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.28it/s]
eval model::   5%|▍         | 23/500 [00:01<00:25, 18.94it/s]
eval model::   5%|▌         | 25/500 [00:01<00:29, 16.06it/s]
eval model::   6%|▌         | 28/500 [00:01<00:26, 17.69it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.40it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.51it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 20.12it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.62it/s]
eval model::   8%|▊         | 42/500 [00:02<00:28, 16.14it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 17.63it/s]
eval model::   9%|▉         | 47/500 [00:02<00:27, 16.42it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 18.90it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 20.42it/s]
eval model::  11%|█         | 56/500 [00:03<00:22, 19.60it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:23, 18.86it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 15.75it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.34it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.37it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.44it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 19.72it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:21, 20.17it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 20.87it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 21.72it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 20.14it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:23, 17.40it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:25, 15.95it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:24, 16.61it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:25, 15.98it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:22, 17.95it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.29it/s]
eval model::  20%|██        | 101/500 [00:05<00:22, 17.84it/s]
eval model::  21%|██        | 104/500 [00:05<00:19, 19.81it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 22.16it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 21.68it/s]
eval model::  23%|██▎       | 113/500 [00:06<00:17, 22.37it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:17, 21.78it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:16, 22.76it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:17, 21.01it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:22, 16.99it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:21, 17.26it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:21, 17.06it/s]
eval model::  26%|██▌       | 131/500 [00:07<00:23, 15.41it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:25, 14.57it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:22, 16.31it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:21, 16.58it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:20, 17.22it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:19, 18.56it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:20, 17.37it/s]
eval model::  29%|██▉       | 147/500 [00:08<00:23, 14.99it/s]
eval model::  30%|██▉       | 149/500 [00:08<00:21, 15.96it/s]
eval model::  30%|███       | 151/500 [00:08<00:23, 14.89it/s]
eval model::  31%|███       | 154/500 [00:08<00:20, 17.24it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:17, 19.15it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:17, 19.27it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:26, 12.81it/s]
eval model::  33%|███▎      | 163/500 [00:09<00:24, 13.78it/s]
eval model::  33%|███▎      | 165/500 [00:09<00:22, 15.00it/s]
eval model::  34%|███▎      | 168/500 [00:09<00:19, 17.20it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:18, 17.45it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:19, 16.70it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.13it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.61it/s]
eval model::  36%|███▌      | 181/500 [00:10<00:21, 14.65it/s]
eval model::  37%|███▋      | 184/500 [00:10<00:17, 17.66it/s]
eval model::  37%|███▋      | 187/500 [00:10<00:16, 18.76it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:16, 18.39it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:15, 19.47it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:16, 18.41it/s]
eval model::  40%|███▉      | 199/500 [00:11<00:15, 19.17it/s]
eval model::  40%|████      | 201/500 [00:11<00:16, 18.64it/s]
eval model::  41%|████      | 204/500 [00:11<00:13, 21.19it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:24, 11.75it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:22, 12.67it/s]
eval model::  42%|████▏     | 211/500 [00:12<00:22, 13.06it/s]
eval model::  43%|████▎     | 213/500 [00:12<00:20, 13.69it/s]
eval model::  43%|████▎     | 215/500 [00:12<00:23, 11.98it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:28,  9.86it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:25, 10.91it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:25, 10.89it/s]
eval model::  45%|████▍     | 223/500 [00:13<00:22, 12.39it/s]
eval model::  45%|████▌     | 226/500 [00:13<00:18, 14.73it/s]
eval model::  46%|████▌     | 229/500 [00:13<00:15, 17.48it/s]
eval model::  46%|████▋     | 232/500 [00:13<00:13, 19.44it/s]
eval model::  47%|████▋     | 235/500 [00:13<00:13, 19.41it/s]
eval model::  48%|████▊     | 238/500 [00:13<00:12, 21.38it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:12, 20.10it/s]
eval model::  49%|████▉     | 244/500 [00:14<00:14, 17.44it/s]
eval model::  49%|████▉     | 246/500 [00:14<00:14, 17.76it/s]
eval model::  50%|████▉     | 249/500 [00:14<00:14, 16.86it/s]
eval model::  50%|█████     | 252/500 [00:14<00:13, 18.38it/s]
eval model::  51%|█████     | 255/500 [00:14<00:13, 18.47it/s]
eval model::  52%|█████▏    | 258/500 [00:14<00:12, 20.05it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:11, 20.93it/s]
eval model::  53%|█████▎    | 264/500 [00:15<00:11, 21.02it/s]
eval model::  53%|█████▎    | 267/500 [00:15<00:10, 22.19it/s]
eval model::  54%|█████▍    | 270/500 [00:15<00:10, 22.23it/s]
eval model::  55%|█████▍    | 273/500 [00:15<00:09, 22.94it/s]
eval model::  55%|█████▌    | 276/500 [00:15<00:10, 21.76it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:10, 22.05it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:09, 23.67it/s]
eval model::  57%|█████▋    | 285/500 [00:16<00:16, 13.36it/s]
eval model::  58%|█████▊    | 288/500 [00:16<00:13, 15.25it/s]
eval model::  58%|█████▊    | 291/500 [00:16<00:11, 17.68it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:12, 17.15it/s]
eval model::  59%|█████▉    | 297/500 [00:17<00:13, 14.72it/s]
eval model::  60%|██████    | 300/500 [00:17<00:13, 14.55it/s]
eval model::  60%|██████    | 302/500 [00:17<00:12, 15.35it/s]
eval model::  61%|██████    | 304/500 [00:17<00:12, 16.12it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:10, 17.69it/s]
eval model::  62%|██████▏   | 309/500 [00:17<00:10, 18.11it/s]
eval model::  62%|██████▏   | 311/500 [00:17<00:10, 18.51it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:11, 16.99it/s]
eval model::  63%|██████▎   | 315/500 [00:18<00:11, 16.29it/s]
eval model::  64%|██████▎   | 318/500 [00:18<00:10, 17.47it/s]
eval model::  64%|██████▍   | 321/500 [00:18<00:09, 18.53it/s]
eval model::  65%|██████▍   | 323/500 [00:18<00:09, 18.24it/s]
eval model::  65%|██████▌   | 326/500 [00:18<00:08, 19.95it/s]
eval model::  66%|██████▌   | 329/500 [00:18<00:08, 20.30it/s]
eval model::  66%|██████▋   | 332/500 [00:18<00:08, 20.72it/s]
eval model::  67%|██████▋   | 335/500 [00:19<00:07, 21.56it/s]
eval model::  68%|██████▊   | 338/500 [00:19<00:08, 18.22it/s]
eval model::  68%|██████▊   | 340/500 [00:19<00:12, 12.58it/s]
eval model::  68%|██████▊   | 342/500 [00:19<00:11, 13.63it/s]
eval model::  69%|██████▉   | 345/500 [00:19<00:09, 16.61it/s]
eval model::  70%|██████▉   | 348/500 [00:19<00:08, 18.96it/s]
eval model::  70%|███████   | 352/500 [00:20<00:06, 22.33it/s]
eval model::  71%|███████   | 355/500 [00:20<00:06, 22.84it/s]
eval model::  72%|███████▏  | 358/500 [00:20<00:06, 22.38it/s]
eval model::  72%|███████▏  | 361/500 [00:20<00:07, 19.67it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:07, 18.97it/s]
eval model::  73%|███████▎  | 367/500 [00:20<00:08, 15.88it/s]
eval model::  74%|███████▍  | 370/500 [00:21<00:07, 17.54it/s]
eval model::  74%|███████▍  | 372/500 [00:21<00:07, 17.98it/s]
eval model::  75%|███████▌  | 375/500 [00:21<00:06, 19.25it/s]
eval model::  76%|███████▌  | 379/500 [00:21<00:05, 22.40it/s]
eval model::  76%|███████▋  | 382/500 [00:21<00:06, 19.13it/s]
eval model::  77%|███████▋  | 385/500 [00:21<00:05, 21.09it/s]
eval model::  78%|███████▊  | 388/500 [00:22<00:08, 13.23it/s]
eval model::  78%|███████▊  | 391/500 [00:22<00:07, 14.96it/s]
eval model::  79%|███████▉  | 394/500 [00:22<00:06, 15.37it/s]
eval model::  79%|███████▉  | 396/500 [00:22<00:06, 15.51it/s]
eval model::  80%|███████▉  | 399/500 [00:22<00:05, 17.52it/s]
eval model::  80%|████████  | 401/500 [00:22<00:06, 16.48it/s]
eval model::  81%|████████  | 404/500 [00:22<00:05, 19.19it/s]
eval model::  81%|████████▏ | 407/500 [00:23<00:04, 20.33it/s]
eval model::  82%|████████▏ | 410/500 [00:23<00:04, 21.56it/s]
eval model::  83%|████████▎ | 413/500 [00:23<00:04, 21.32it/s]
eval model::  83%|████████▎ | 416/500 [00:23<00:04, 20.25it/s]
eval model::  84%|████████▍ | 419/500 [00:23<00:04, 17.68it/s]
eval model::  84%|████████▍ | 421/500 [00:24<00:06, 11.95it/s]
eval model::  85%|████████▍ | 424/500 [00:24<00:06, 12.60it/s]
eval model::  85%|████████▌ | 426/500 [00:24<00:05, 13.19it/s]
eval model::  86%|████████▌ | 429/500 [00:24<00:04, 15.62it/s]
eval model::  86%|████████▌ | 431/500 [00:24<00:04, 14.47it/s]
eval model::  87%|████████▋ | 434/500 [00:24<00:03, 17.29it/s]
eval model::  87%|████████▋ | 437/500 [00:24<00:03, 18.96it/s]
eval model::  88%|████████▊ | 440/500 [00:25<00:03, 18.87it/s]
eval model::  89%|████████▉ | 444/500 [00:25<00:02, 21.77it/s]
eval model::  89%|████████▉ | 447/500 [00:25<00:02, 22.48it/s]
eval model::  90%|█████████ | 450/500 [00:25<00:02, 19.75it/s]
eval model::  91%|█████████ | 453/500 [00:25<00:02, 19.46it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:02, 21.08it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:01, 22.68it/s]
eval model::  92%|█████████▏| 462/500 [00:26<00:01, 20.15it/s]
eval model::  93%|█████████▎| 465/500 [00:26<00:01, 19.31it/s]
eval model::  94%|█████████▎| 468/500 [00:26<00:01, 20.69it/s]
eval model::  94%|█████████▍| 471/500 [00:26<00:01, 22.51it/s]
eval model::  95%|█████████▍| 474/500 [00:26<00:01, 21.65it/s]
eval model::  95%|█████████▌| 477/500 [00:26<00:00, 23.42it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:00, 25.06it/s]
eval model::  97%|█████████▋| 483/500 [00:27<00:00, 26.18it/s]
eval model::  97%|█████████▋| 486/500 [00:27<00:00, 24.64it/s]
eval model::  98%|█████████▊| 489/500 [00:27<00:00, 24.81it/s]
eval model::  98%|█████████▊| 492/500 [00:27<00:00, 20.39it/s]
eval model::  99%|█████████▉| 495/500 [00:27<00:00, 21.21it/s]
eval model:: 100%|█████████▉| 498/500 [00:27<00:00, 22.91it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.00it/s]
[2024/07/28 02:37:22] ppocr INFO: cur metric, precision: 0.6938579654510557, recall: 0.6961964371689937, hmean: 0.6950252343186735, fps: 44.55747857199829
[2024/07/28 02:37:22] ppocr INFO: best metric, hmean: 0.7160430548436699, precision: 0.7654794520547945, recall: 0.672604718343765, fps: 44.8783209422934, best_epoch: 1080
[2024/07/28 02:37:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:37:30] ppocr INFO: epoch: [1181/1500], global_step: 3543, lr: 0.001000, loss: 1.229552, loss_shrink_maps: 0.622002, loss_threshold_maps: 0.477501, loss_binary_maps: 0.123629, avg_reader_cost: 1.48764 s, avg_batch_cost: 1.71589 s, avg_samples: 12.5, ips: 7.28485 samples/s, eta: 1:36:49
[2024/07/28 02:37:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:37:40] ppocr INFO: epoch: [1182/1500], global_step: 3546, lr: 0.001000, loss: 1.234081, loss_shrink_maps: 0.622940, loss_threshold_maps: 0.480145, loss_binary_maps: 0.124131, avg_reader_cost: 1.51184 s, avg_batch_cost: 1.75607 s, avg_samples: 12.5, ips: 7.11815 samples/s, eta: 1:36:31
[2024/07/28 02:37:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:37:49] ppocr INFO: epoch: [1183/1500], global_step: 3549, lr: 0.001000, loss: 1.241272, loss_shrink_maps: 0.633721, loss_threshold_maps: 0.484250, loss_binary_maps: 0.126355, avg_reader_cost: 1.48803 s, avg_batch_cost: 1.71964 s, avg_samples: 12.5, ips: 7.26897 samples/s, eta: 1:36:12
[2024/07/28 02:37:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:37:57] ppocr INFO: epoch: [1184/1500], global_step: 3550, lr: 0.001000, loss: 1.256880, loss_shrink_maps: 0.645685, loss_threshold_maps: 0.484250, loss_binary_maps: 0.128460, avg_reader_cost: 0.41587 s, avg_batch_cost: 0.52157 s, avg_samples: 4.8, ips: 9.20304 samples/s, eta: 1:36:06
[2024/07/28 02:37:59] ppocr INFO: epoch: [1184/1500], global_step: 3552, lr: 0.001000, loss: 1.251379, loss_shrink_maps: 0.633721, loss_threshold_maps: 0.484250, loss_binary_maps: 0.126355, avg_reader_cost: 1.13469 s, avg_batch_cost: 1.28046 s, avg_samples: 7.7, ips: 6.01346 samples/s, eta: 1:35:54
[2024/07/28 02:38:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:38:09] ppocr INFO: epoch: [1185/1500], global_step: 3555, lr: 0.001000, loss: 1.251379, loss_shrink_maps: 0.633107, loss_threshold_maps: 0.480145, loss_binary_maps: 0.126094, avg_reader_cost: 1.59856 s, avg_batch_cost: 1.82656 s, avg_samples: 12.5, ips: 6.84346 samples/s, eta: 1:35:36
[2024/07/28 02:38:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:38:18] ppocr INFO: epoch: [1186/1500], global_step: 3558, lr: 0.001000, loss: 1.238062, loss_shrink_maps: 0.627027, loss_threshold_maps: 0.480679, loss_binary_maps: 0.125112, avg_reader_cost: 1.56020 s, avg_batch_cost: 1.80792 s, avg_samples: 12.5, ips: 6.91403 samples/s, eta: 1:35:17
[2024/07/28 02:38:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:38:28] ppocr INFO: epoch: [1187/1500], global_step: 3560, lr: 0.001000, loss: 1.229714, loss_shrink_maps: 0.626089, loss_threshold_maps: 0.479639, loss_binary_maps: 0.124611, avg_reader_cost: 0.91890 s, avg_batch_cost: 1.11006 s, avg_samples: 9.6, ips: 8.64820 samples/s, eta: 1:35:05
[2024/07/28 02:38:28] ppocr INFO: epoch: [1187/1500], global_step: 3561, lr: 0.001000, loss: 1.185389, loss_shrink_maps: 0.592735, loss_threshold_maps: 0.479621, loss_binary_maps: 0.118444, avg_reader_cost: 0.60055 s, avg_batch_cost: 0.65535 s, avg_samples: 2.9, ips: 4.42514 samples/s, eta: 1:34:59
[2024/07/28 02:38:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:38:38] ppocr INFO: epoch: [1188/1500], global_step: 3564, lr: 0.001000, loss: 1.176317, loss_shrink_maps: 0.584001, loss_threshold_maps: 0.479621, loss_binary_maps: 0.116827, avg_reader_cost: 1.59825 s, avg_batch_cost: 1.83793 s, avg_samples: 12.5, ips: 6.80114 samples/s, eta: 1:34:41
[2024/07/28 02:38:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:38:48] ppocr INFO: epoch: [1189/1500], global_step: 3567, lr: 0.001000, loss: 1.140872, loss_shrink_maps: 0.565029, loss_threshold_maps: 0.474282, loss_binary_maps: 0.113059, avg_reader_cost: 1.57851 s, avg_batch_cost: 1.82508 s, avg_samples: 12.5, ips: 6.84902 samples/s, eta: 1:34:23
[2024/07/28 02:38:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:38:58] ppocr INFO: epoch: [1190/1500], global_step: 3570, lr: 0.001000, loss: 1.134489, loss_shrink_maps: 0.550443, loss_threshold_maps: 0.467340, loss_binary_maps: 0.110047, avg_reader_cost: 1.54942 s, avg_batch_cost: 1.77775 s, avg_samples: 12.5, ips: 7.03135 samples/s, eta: 1:34:04
[2024/07/28 02:39:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:39:07] ppocr INFO: epoch: [1191/1500], global_step: 3573, lr: 0.001000, loss: 1.124456, loss_shrink_maps: 0.554735, loss_threshold_maps: 0.464634, loss_binary_maps: 0.110897, avg_reader_cost: 1.50329 s, avg_batch_cost: 1.74403 s, avg_samples: 12.5, ips: 7.16730 samples/s, eta: 1:33:46
[2024/07/28 02:39:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:39:17] ppocr INFO: epoch: [1192/1500], global_step: 3576, lr: 0.001000, loss: 1.140872, loss_shrink_maps: 0.564384, loss_threshold_maps: 0.467340, loss_binary_maps: 0.112826, avg_reader_cost: 1.71460 s, avg_batch_cost: 1.97138 s, avg_samples: 12.5, ips: 6.34075 samples/s, eta: 1:33:28
[2024/07/28 02:39:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:39:27] ppocr INFO: epoch: [1193/1500], global_step: 3579, lr: 0.001000, loss: 1.174709, loss_shrink_maps: 0.574679, loss_threshold_maps: 0.469137, loss_binary_maps: 0.114878, avg_reader_cost: 1.51396 s, avg_batch_cost: 1.74708 s, avg_samples: 12.5, ips: 7.15478 samples/s, eta: 1:33:10
[2024/07/28 02:39:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:39:35] ppocr INFO: epoch: [1194/1500], global_step: 3580, lr: 0.001000, loss: 1.210154, loss_shrink_maps: 0.594527, loss_threshold_maps: 0.474167, loss_binary_maps: 0.118690, avg_reader_cost: 0.43446 s, avg_batch_cost: 0.51652 s, avg_samples: 4.8, ips: 9.29299 samples/s, eta: 1:33:03
[2024/07/28 02:39:37] ppocr INFO: epoch: [1194/1500], global_step: 3582, lr: 0.001000, loss: 1.174709, loss_shrink_maps: 0.574679, loss_threshold_maps: 0.466981, loss_binary_maps: 0.114878, avg_reader_cost: 1.12434 s, avg_batch_cost: 1.27011 s, avg_samples: 7.7, ips: 6.06248 samples/s, eta: 1:32:51
[2024/07/28 02:39:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:39:47] ppocr INFO: epoch: [1195/1500], global_step: 3585, lr: 0.001000, loss: 1.172350, loss_shrink_maps: 0.578060, loss_threshold_maps: 0.466981, loss_binary_maps: 0.115212, avg_reader_cost: 1.55694 s, avg_batch_cost: 1.80406 s, avg_samples: 12.5, ips: 6.92880 samples/s, eta: 1:32:33
[2024/07/28 02:39:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:39:56] ppocr INFO: epoch: [1196/1500], global_step: 3588, lr: 0.001000, loss: 1.185618, loss_shrink_maps: 0.592578, loss_threshold_maps: 0.478467, loss_binary_maps: 0.117816, avg_reader_cost: 1.52413 s, avg_batch_cost: 1.79038 s, avg_samples: 12.5, ips: 6.98175 samples/s, eta: 1:32:15
[2024/07/28 02:40:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:40:06] ppocr INFO: epoch: [1197/1500], global_step: 3590, lr: 0.001000, loss: 1.185618, loss_shrink_maps: 0.592578, loss_threshold_maps: 0.478467, loss_binary_maps: 0.117816, avg_reader_cost: 1.00926 s, avg_batch_cost: 1.18275 s, avg_samples: 9.6, ips: 8.11669 samples/s, eta: 1:32:03
[2024/07/28 02:40:06] ppocr INFO: epoch: [1197/1500], global_step: 3591, lr: 0.001000, loss: 1.172350, loss_shrink_maps: 0.578060, loss_threshold_maps: 0.473437, loss_binary_maps: 0.115212, avg_reader_cost: 0.63712 s, avg_batch_cost: 0.69181 s, avg_samples: 2.9, ips: 4.19187 samples/s, eta: 1:31:57
[2024/07/28 02:40:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:40:16] ppocr INFO: epoch: [1198/1500], global_step: 3594, lr: 0.001000, loss: 1.183416, loss_shrink_maps: 0.595897, loss_threshold_maps: 0.473854, loss_binary_maps: 0.118401, avg_reader_cost: 1.55103 s, avg_batch_cost: 1.79462 s, avg_samples: 12.5, ips: 6.96528 samples/s, eta: 1:31:39
[2024/07/28 02:40:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:40:26] ppocr INFO: epoch: [1199/1500], global_step: 3597, lr: 0.001000, loss: 1.175231, loss_shrink_maps: 0.587675, loss_threshold_maps: 0.469195, loss_binary_maps: 0.116616, avg_reader_cost: 1.55461 s, avg_batch_cost: 1.82156 s, avg_samples: 12.5, ips: 6.86226 samples/s, eta: 1:31:20
[2024/07/28 02:40:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:40:35] ppocr INFO: epoch: [1200/1500], global_step: 3600, lr: 0.001000, loss: 1.175231, loss_shrink_maps: 0.587675, loss_threshold_maps: 0.469123, loss_binary_maps: 0.116616, avg_reader_cost: 1.54066 s, avg_batch_cost: 1.78153 s, avg_samples: 12.5, ips: 7.01644 samples/s, eta: 1:31:02

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:52,  9.58it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.27it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.60it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.98it/s]
eval model::   2%|▏         | 11/500 [00:00<00:24, 19.89it/s]
eval model::   3%|▎         | 14/500 [00:00<00:23, 20.32it/s]
eval model::   4%|▎         | 18/500 [00:00<00:20, 23.83it/s]
eval model::   4%|▍         | 21/500 [00:01<00:22, 21.64it/s]
eval model::   5%|▍         | 24/500 [00:01<00:21, 22.12it/s]
eval model::   5%|▌         | 27/500 [00:01<00:23, 19.88it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 21.72it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 19.53it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 18.69it/s]
eval model::   8%|▊         | 38/500 [00:01<00:24, 18.64it/s]
eval model::   8%|▊         | 40/500 [00:02<00:28, 16.01it/s]
eval model::   8%|▊         | 42/500 [00:02<00:31, 14.53it/s]
eval model::   9%|▉         | 45/500 [00:02<00:27, 16.27it/s]
eval model::   9%|▉         | 47/500 [00:02<00:28, 15.84it/s]
eval model::  10%|█         | 50/500 [00:02<00:26, 16.90it/s]
eval model::  11%|█         | 53/500 [00:02<00:24, 18.17it/s]
eval model::  11%|█         | 56/500 [00:02<00:22, 19.36it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:24, 17.79it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:29, 14.70it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:31, 13.83it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:27, 15.58it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:25, 17.21it/s]
eval model::  14%|█▍        | 72/500 [00:04<00:23, 17.95it/s]
eval model::  15%|█▍        | 74/500 [00:04<00:24, 17.62it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:22, 18.41it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:20, 20.90it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:18, 21.96it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:20, 20.15it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.61it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:21, 18.87it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:22, 18.25it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.95it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.30it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.02it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 20.86it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:20, 19.37it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:20, 18.89it/s]
eval model::  23%|██▎       | 114/500 [00:06<00:19, 20.24it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:19, 19.67it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 20.43it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.97it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.72it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 18.78it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:21, 16.85it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:20, 17.97it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:20, 17.47it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:21, 17.05it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 18.86it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:18, 18.96it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.34it/s]
eval model::  30%|███       | 150/500 [00:08<00:17, 20.15it/s]
eval model::  31%|███       | 153/500 [00:08<00:18, 19.10it/s]
eval model::  31%|███       | 156/500 [00:08<00:16, 21.39it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 21.00it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:21, 15.38it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:21, 15.40it/s]
eval model::  33%|███▎      | 167/500 [00:09<00:20, 16.42it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:18, 18.11it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:18, 17.62it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:18, 17.56it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.89it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.98it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:18, 16.77it/s]
eval model::  37%|███▋      | 185/500 [00:10<00:16, 19.27it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:16, 19.46it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:15, 20.13it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:13, 22.10it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:15, 18.95it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 21.43it/s]
eval model::  41%|████      | 205/500 [00:11<00:18, 16.32it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:17, 16.53it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 16.06it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.47it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:19, 14.62it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.20it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:19, 14.43it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:20, 13.39it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:17, 16.07it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 16.63it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:15, 17.22it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:13, 19.74it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:14, 18.26it/s]
eval model::  47%|████▋     | 236/500 [00:13<00:14, 18.58it/s]
eval model::  48%|████▊     | 239/500 [00:13<00:12, 21.19it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 19.38it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.92it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.74it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 21.00it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.69it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 20.58it/s]
eval model::  52%|█████▏    | 260/500 [00:14<00:11, 21.56it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:10, 21.59it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:10, 22.42it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.88it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 19.47it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:13, 17.23it/s]
eval model::  56%|█████▌    | 278/500 [00:15<00:11, 19.27it/s]
eval model::  56%|█████▌    | 281/500 [00:15<00:10, 21.10it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:09, 22.67it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:16, 12.77it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:13, 15.25it/s]
eval model::  59%|█████▊    | 293/500 [00:16<00:12, 16.90it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:14, 14.11it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 16.73it/s]
eval model::  60%|██████    | 302/500 [00:16<00:12, 16.13it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 16.20it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:11, 17.22it/s]
eval model::  62%|██████▏   | 310/500 [00:17<00:10, 18.58it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:09, 18.90it/s]
eval model::  63%|██████▎   | 315/500 [00:17<00:10, 17.94it/s]
eval model::  64%|██████▎   | 318/500 [00:17<00:09, 20.01it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 20.57it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 19.65it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:08, 21.01it/s]
eval model::  66%|██████▌   | 330/500 [00:18<00:08, 19.78it/s]
eval model::  67%|██████▋   | 333/500 [00:18<00:07, 21.42it/s]
eval model::  67%|██████▋   | 336/500 [00:18<00:07, 22.44it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:06, 23.14it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 15.57it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:09, 17.13it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 17.58it/s]
eval model::  70%|███████   | 351/500 [00:19<00:07, 19.31it/s]
eval model::  71%|███████   | 354/500 [00:19<00:06, 20.98it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 19.76it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:06, 21.41it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 18.27it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:08, 16.02it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:07, 17.80it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:07, 17.88it/s]
eval model::  75%|███████▍  | 374/500 [00:20<00:06, 20.36it/s]
eval model::  75%|███████▌  | 377/500 [00:20<00:05, 22.06it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 21.17it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 23.09it/s]
eval model::  77%|███████▋  | 386/500 [00:21<00:07, 15.59it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:06, 17.69it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:05, 18.22it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:05, 19.92it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:04, 20.48it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 17.16it/s]
eval model::  81%|████████  | 404/500 [00:21<00:05, 18.37it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 18.80it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:04, 21.86it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:04, 21.36it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 19.58it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.29it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 14.97it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.90it/s]
eval model::  85%|████████▌ | 427/500 [00:23<00:04, 16.85it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 18.53it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 18.67it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 20.63it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 22.19it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 22.17it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 25.47it/s]
eval model::  90%|████████▉ | 448/500 [00:24<00:02, 23.83it/s]
eval model::  90%|█████████ | 451/500 [00:24<00:02, 23.90it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:01, 23.94it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 22.48it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 20.53it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 16.87it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 15.36it/s]
eval model::  93%|█████████▎| 467/500 [00:25<00:02, 15.89it/s]
eval model::  94%|█████████▍| 470/500 [00:25<00:01, 18.55it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 19.74it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 20.53it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:00, 21.62it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 21.72it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 23.48it/s]
eval model::  98%|█████████▊| 488/500 [00:26<00:00, 21.36it/s]
eval model::  98%|█████████▊| 491/500 [00:26<00:00, 19.08it/s]
eval model::  99%|█████████▉| 494/500 [00:26<00:00, 18.10it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 21.66it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.71it/s]
[2024/07/28 02:41:02] ppocr INFO: cur metric, precision: 0.7568, recall: 0.6831969186326432, hmean: 0.7181174089068827, fps: 44.212831257019886
[2024/07/28 02:41:02] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/28 02:41:02] ppocr INFO: best metric, hmean: 0.7181174089068827, precision: 0.7568, recall: 0.6831969186326432, fps: 44.212831257019886, best_epoch: 1200
[2024/07/28 02:41:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:41:04] ppocr INFO: save model in ./output/db_mv3/iter_epoch_1200
[2024/07/28 02:41:11] ppocr INFO: epoch: [1201/1500], global_step: 3603, lr: 0.001000, loss: 1.183416, loss_shrink_maps: 0.595897, loss_threshold_maps: 0.478770, loss_binary_maps: 0.118401, avg_reader_cost: 1.52589 s, avg_batch_cost: 1.75647 s, avg_samples: 12.5, ips: 7.11653 samples/s, eta: 1:30:44
[2024/07/28 02:41:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:41:20] ppocr INFO: epoch: [1202/1500], global_step: 3606, lr: 0.001000, loss: 1.179496, loss_shrink_maps: 0.588644, loss_threshold_maps: 0.478770, loss_binary_maps: 0.117351, avg_reader_cost: 1.51928 s, avg_batch_cost: 1.76192 s, avg_samples: 12.5, ips: 7.09454 samples/s, eta: 1:30:25
[2024/07/28 02:41:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:41:30] ppocr INFO: epoch: [1203/1500], global_step: 3609, lr: 0.001000, loss: 1.167532, loss_shrink_maps: 0.585427, loss_threshold_maps: 0.470245, loss_binary_maps: 0.116089, avg_reader_cost: 1.53040 s, avg_batch_cost: 1.76750 s, avg_samples: 12.5, ips: 7.07214 samples/s, eta: 1:30:07
[2024/07/28 02:41:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:41:38] ppocr INFO: epoch: [1204/1500], global_step: 3610, lr: 0.001000, loss: 1.147610, loss_shrink_maps: 0.568694, loss_threshold_maps: 0.469123, loss_binary_maps: 0.113237, avg_reader_cost: 0.41360 s, avg_batch_cost: 0.50908 s, avg_samples: 4.8, ips: 9.42879 samples/s, eta: 1:30:01
[2024/07/28 02:41:40] ppocr INFO: epoch: [1204/1500], global_step: 3612, lr: 0.001000, loss: 1.167532, loss_shrink_maps: 0.585558, loss_threshold_maps: 0.470245, loss_binary_maps: 0.116616, avg_reader_cost: 1.10945 s, avg_batch_cost: 1.25483 s, avg_samples: 7.7, ips: 6.13631 samples/s, eta: 1:29:49
[2024/07/28 02:41:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:41:50] ppocr INFO: epoch: [1205/1500], global_step: 3615, lr: 0.001000, loss: 1.154326, loss_shrink_maps: 0.584591, loss_threshold_maps: 0.476550, loss_binary_maps: 0.116494, avg_reader_cost: 1.62609 s, avg_batch_cost: 1.89471 s, avg_samples: 12.5, ips: 6.59733 samples/s, eta: 1:29:31
[2024/07/28 02:41:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:41:59] ppocr INFO: epoch: [1206/1500], global_step: 3618, lr: 0.001000, loss: 1.167532, loss_shrink_maps: 0.586527, loss_threshold_maps: 0.472320, loss_binary_maps: 0.117422, avg_reader_cost: 1.49525 s, avg_batch_cost: 1.72399 s, avg_samples: 12.5, ips: 7.25064 samples/s, eta: 1:29:12
[2024/07/28 02:42:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:42:09] ppocr INFO: epoch: [1207/1500], global_step: 3620, lr: 0.001000, loss: 1.173139, loss_shrink_maps: 0.591293, loss_threshold_maps: 0.472320, loss_binary_maps: 0.117904, avg_reader_cost: 1.04063 s, avg_batch_cost: 1.21370 s, avg_samples: 9.6, ips: 7.90969 samples/s, eta: 1:29:00
[2024/07/28 02:42:10] ppocr INFO: epoch: [1207/1500], global_step: 3621, lr: 0.001000, loss: 1.173139, loss_shrink_maps: 0.591293, loss_threshold_maps: 0.472320, loss_binary_maps: 0.117904, avg_reader_cost: 0.65221 s, avg_batch_cost: 0.70710 s, avg_samples: 2.9, ips: 4.10128 samples/s, eta: 1:28:54
[2024/07/28 02:42:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:42:19] ppocr INFO: epoch: [1208/1500], global_step: 3624, lr: 0.001000, loss: 1.178063, loss_shrink_maps: 0.596152, loss_threshold_maps: 0.470912, loss_binary_maps: 0.118592, avg_reader_cost: 1.52097 s, avg_batch_cost: 1.78758 s, avg_samples: 12.5, ips: 6.99269 samples/s, eta: 1:28:36
[2024/07/28 02:42:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:42:29] ppocr INFO: epoch: [1209/1500], global_step: 3627, lr: 0.001000, loss: 1.190440, loss_shrink_maps: 0.599666, loss_threshold_maps: 0.472354, loss_binary_maps: 0.119382, avg_reader_cost: 1.57439 s, avg_batch_cost: 1.80329 s, avg_samples: 12.5, ips: 6.93178 samples/s, eta: 1:28:18
[2024/07/28 02:42:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:42:39] ppocr INFO: epoch: [1210/1500], global_step: 3630, lr: 0.001000, loss: 1.198100, loss_shrink_maps: 0.601822, loss_threshold_maps: 0.474582, loss_binary_maps: 0.119802, avg_reader_cost: 1.54756 s, avg_batch_cost: 1.80034 s, avg_samples: 12.5, ips: 6.94312 samples/s, eta: 1:27:59
[2024/07/28 02:42:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:42:49] ppocr INFO: epoch: [1211/1500], global_step: 3633, lr: 0.001000, loss: 1.198100, loss_shrink_maps: 0.601822, loss_threshold_maps: 0.472354, loss_binary_maps: 0.119802, avg_reader_cost: 1.57134 s, avg_batch_cost: 1.84327 s, avg_samples: 12.5, ips: 6.78143 samples/s, eta: 1:27:41
[2024/07/28 02:42:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:42:59] ppocr INFO: epoch: [1212/1500], global_step: 3636, lr: 0.001000, loss: 1.190440, loss_shrink_maps: 0.599666, loss_threshold_maps: 0.469649, loss_binary_maps: 0.119382, avg_reader_cost: 1.53533 s, avg_batch_cost: 1.78648 s, avg_samples: 12.5, ips: 6.99701 samples/s, eta: 1:27:23
[2024/07/28 02:43:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:43:08] ppocr INFO: epoch: [1213/1500], global_step: 3639, lr: 0.001000, loss: 1.190440, loss_shrink_maps: 0.601629, loss_threshold_maps: 0.470683, loss_binary_maps: 0.119802, avg_reader_cost: 1.51015 s, avg_batch_cost: 1.74992 s, avg_samples: 12.5, ips: 7.14317 samples/s, eta: 1:27:05
[2024/07/28 02:43:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:43:16] ppocr INFO: epoch: [1214/1500], global_step: 3640, lr: 0.001000, loss: 1.186577, loss_shrink_maps: 0.601629, loss_threshold_maps: 0.469649, loss_binary_maps: 0.119802, avg_reader_cost: 0.42520 s, avg_batch_cost: 0.50800 s, avg_samples: 4.8, ips: 9.44887 samples/s, eta: 1:26:58
[2024/07/28 02:43:18] ppocr INFO: epoch: [1214/1500], global_step: 3642, lr: 0.001000, loss: 1.179634, loss_shrink_maps: 0.600227, loss_threshold_maps: 0.460505, loss_binary_maps: 0.119802, avg_reader_cost: 1.10744 s, avg_batch_cost: 1.25331 s, avg_samples: 7.7, ips: 6.14372 samples/s, eta: 1:26:46
[2024/07/28 02:43:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:43:28] ppocr INFO: epoch: [1215/1500], global_step: 3645, lr: 0.001000, loss: 1.179634, loss_shrink_maps: 0.600572, loss_threshold_maps: 0.460505, loss_binary_maps: 0.119805, avg_reader_cost: 1.53894 s, avg_batch_cost: 1.80235 s, avg_samples: 12.5, ips: 6.93538 samples/s, eta: 1:26:28
[2024/07/28 02:43:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:43:37] ppocr INFO: epoch: [1216/1500], global_step: 3648, lr: 0.001000, loss: 1.179634, loss_shrink_maps: 0.596190, loss_threshold_maps: 0.460505, loss_binary_maps: 0.119192, avg_reader_cost: 1.50645 s, avg_batch_cost: 1.76100 s, avg_samples: 12.5, ips: 7.09826 samples/s, eta: 1:26:10
[2024/07/28 02:43:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:43:47] ppocr INFO: epoch: [1217/1500], global_step: 3650, lr: 0.001000, loss: 1.179634, loss_shrink_maps: 0.596190, loss_threshold_maps: 0.457398, loss_binary_maps: 0.119192, avg_reader_cost: 0.94536 s, avg_batch_cost: 1.19838 s, avg_samples: 9.6, ips: 8.01080 samples/s, eta: 1:25:58
[2024/07/28 02:43:48] ppocr INFO: epoch: [1217/1500], global_step: 3651, lr: 0.001000, loss: 1.179634, loss_shrink_maps: 0.596190, loss_threshold_maps: 0.460083, loss_binary_maps: 0.119192, avg_reader_cost: 0.64469 s, avg_batch_cost: 0.69971 s, avg_samples: 2.9, ips: 4.14456 samples/s, eta: 1:25:52
[2024/07/28 02:43:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:43:57] ppocr INFO: epoch: [1218/1500], global_step: 3654, lr: 0.001000, loss: 1.179634, loss_shrink_maps: 0.600572, loss_threshold_maps: 0.460083, loss_binary_maps: 0.120008, avg_reader_cost: 1.53037 s, avg_batch_cost: 1.76773 s, avg_samples: 12.5, ips: 7.07121 samples/s, eta: 1:25:33
[2024/07/28 02:44:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:44:07] ppocr INFO: epoch: [1219/1500], global_step: 3657, lr: 0.001000, loss: 1.173199, loss_shrink_maps: 0.596190, loss_threshold_maps: 0.455669, loss_binary_maps: 0.119192, avg_reader_cost: 1.52517 s, avg_batch_cost: 1.75518 s, avg_samples: 12.5, ips: 7.12178 samples/s, eta: 1:25:15
[2024/07/28 02:44:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:44:17] ppocr INFO: epoch: [1220/1500], global_step: 3660, lr: 0.001000, loss: 1.189290, loss_shrink_maps: 0.596262, loss_threshold_maps: 0.480281, loss_binary_maps: 0.119299, avg_reader_cost: 1.51265 s, avg_batch_cost: 1.74168 s, avg_samples: 12.5, ips: 7.17699 samples/s, eta: 1:24:57

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:52,  9.57it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.49it/s]
eval model::   1%|▏         | 7/500 [00:00<00:24, 20.39it/s]
eval model::   2%|▏         | 9/500 [00:00<00:27, 18.07it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.97it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.15it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.76it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.59it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 21.95it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 19.91it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 20.50it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 18.72it/s]
eval model::   7%|▋         | 34/500 [00:01<00:26, 17.86it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 20.07it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.49it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.61it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.41it/s]
eval model::   9%|▉         | 47/500 [00:02<00:24, 18.49it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 19.02it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 18.86it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 19.66it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:22, 19.69it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:25, 17.00it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:32, 13.45it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:30, 14.28it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:25, 17.29it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.24it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 21.78it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:19, 22.02it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:18, 22.58it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:17, 23.69it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:19, 21.63it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:19, 20.77it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:21, 19.07it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:20, 19.46it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.61it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.69it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.90it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 20.34it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:19, 19.71it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:20, 19.12it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:19, 19.85it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:19, 19.30it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.92it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.74it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 18.04it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 20.35it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 18.26it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 20.21it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:16, 21.63it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:16, 22.13it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.55it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 19.13it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 20.65it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 19.52it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 20.83it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:17, 19.89it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:24, 14.07it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:21, 15.81it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:18, 18.11it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:18, 17.91it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:18, 17.79it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 17.02it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.24it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:18, 16.98it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.07it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.30it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 20.94it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 20.20it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.97it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.93it/s]
eval model::  41%|████      | 205/500 [00:10<00:21, 13.62it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:20, 14.51it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:19, 15.03it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:18, 15.23it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.34it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.34it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:23, 12.19it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:19, 14.71it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.67it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 17.00it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:13, 19.74it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:11, 24.22it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:11, 23.61it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:10, 25.12it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 22.54it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:13, 18.65it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:12, 20.05it/s]
eval model::  50%|█████     | 250/500 [00:13<00:12, 20.05it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 21.99it/s]
eval model::  51%|█████     | 256/500 [00:13<00:10, 23.06it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 24.10it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:09, 25.21it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:10, 21.76it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:10, 22.57it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:11, 20.10it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:11, 19.73it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 17.65it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:12, 17.45it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 18.96it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:19, 11.30it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:15, 13.35it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:13, 15.78it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:13, 15.74it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:14, 14.27it/s]
eval model::  60%|██████    | 300/500 [00:16<00:11, 16.81it/s]
eval model::  61%|██████    | 303/500 [00:16<00:10, 19.12it/s]
eval model::  61%|██████    | 306/500 [00:16<00:09, 19.65it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:09, 20.90it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:08, 22.35it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:07, 23.22it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:07, 24.41it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:07, 23.91it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:07, 24.37it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:06, 24.80it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:07, 21.94it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 22.75it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 22.05it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 20.60it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 15.57it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 17.79it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 18.65it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 20.49it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.31it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 20.83it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 18.98it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 19.38it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:06, 19.36it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.85it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 21.02it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 23.86it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:05, 22.41it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:05, 23.19it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:07, 14.70it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 17.88it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 18.34it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 19.70it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 21.12it/s]
eval model::  80%|████████  | 402/500 [00:21<00:04, 20.03it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 20.74it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.62it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 22.68it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 22.61it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 21.25it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 14.25it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 14.64it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 13.81it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:05, 14.14it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:04, 16.73it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:04, 15.93it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 19.95it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 22.17it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 20.46it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 22.65it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 23.38it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 21.29it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:01, 23.94it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:01, 26.46it/s]
eval model::  92%|█████████▏| 461/500 [00:23<00:01, 25.00it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:01, 20.76it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 18.65it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 20.77it/s]
eval model::  95%|█████████▍| 473/500 [00:24<00:01, 20.62it/s]
eval model::  95%|█████████▌| 476/500 [00:24<00:01, 20.23it/s]
eval model::  96%|█████████▌| 479/500 [00:24<00:00, 21.40it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 20.93it/s]
eval model::  97%|█████████▋| 485/500 [00:25<00:00, 22.56it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 21.56it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 18.78it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 15.76it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 17.23it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 20.65it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.25it/s]
[2024/07/28 02:44:43] ppocr INFO: cur metric, precision: 0.7535847052575677, recall: 0.6831969186326432, hmean: 0.7166666666666667, fps: 46.7602991068739
[2024/07/28 02:44:43] ppocr INFO: best metric, hmean: 0.7181174089068827, precision: 0.7568, recall: 0.6831969186326432, fps: 44.212831257019886, best_epoch: 1200
[2024/07/28 02:44:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:44:52] ppocr INFO: epoch: [1221/1500], global_step: 3663, lr: 0.001000, loss: 1.186368, loss_shrink_maps: 0.593281, loss_threshold_maps: 0.480281, loss_binary_maps: 0.118517, avg_reader_cost: 2.07115 s, avg_batch_cost: 2.51063 s, avg_samples: 12.5, ips: 4.97883 samples/s, eta: 1:24:40
[2024/07/28 02:44:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:45:02] ppocr INFO: epoch: [1222/1500], global_step: 3666, lr: 0.001000, loss: 1.186368, loss_shrink_maps: 0.593281, loss_threshold_maps: 0.480716, loss_binary_maps: 0.118517, avg_reader_cost: 1.49712 s, avg_batch_cost: 1.73648 s, avg_samples: 12.5, ips: 7.19846 samples/s, eta: 1:24:22
[2024/07/28 02:45:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:45:12] ppocr INFO: epoch: [1223/1500], global_step: 3669, lr: 0.001000, loss: 1.154122, loss_shrink_maps: 0.577147, loss_threshold_maps: 0.458977, loss_binary_maps: 0.115162, avg_reader_cost: 1.60048 s, avg_batch_cost: 1.87689 s, avg_samples: 12.5, ips: 6.65997 samples/s, eta: 1:24:03
[2024/07/28 02:45:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:45:20] ppocr INFO: epoch: [1224/1500], global_step: 3670, lr: 0.001000, loss: 1.154122, loss_shrink_maps: 0.577147, loss_threshold_maps: 0.458977, loss_binary_maps: 0.115162, avg_reader_cost: 0.43771 s, avg_batch_cost: 0.54115 s, avg_samples: 4.8, ips: 8.86998 samples/s, eta: 1:23:57
[2024/07/28 02:45:22] ppocr INFO: epoch: [1224/1500], global_step: 3672, lr: 0.001000, loss: 1.173608, loss_shrink_maps: 0.577147, loss_threshold_maps: 0.463250, loss_binary_maps: 0.115162, avg_reader_cost: 1.17393 s, avg_batch_cost: 1.31999 s, avg_samples: 7.7, ips: 5.83338 samples/s, eta: 1:23:45
[2024/07/28 02:45:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:45:32] ppocr INFO: epoch: [1225/1500], global_step: 3675, lr: 0.001000, loss: 1.185725, loss_shrink_maps: 0.585352, loss_threshold_maps: 0.467476, loss_binary_maps: 0.116923, avg_reader_cost: 1.62121 s, avg_batch_cost: 1.91121 s, avg_samples: 12.5, ips: 6.54037 samples/s, eta: 1:23:27
[2024/07/28 02:45:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:45:42] ppocr INFO: epoch: [1226/1500], global_step: 3678, lr: 0.001000, loss: 1.150084, loss_shrink_maps: 0.569146, loss_threshold_maps: 0.463250, loss_binary_maps: 0.113462, avg_reader_cost: 1.51618 s, avg_batch_cost: 1.78172 s, avg_samples: 12.5, ips: 7.01568 samples/s, eta: 1:23:09
[2024/07/28 02:45:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:45:51] ppocr INFO: epoch: [1227/1500], global_step: 3680, lr: 0.001000, loss: 1.150084, loss_shrink_maps: 0.569146, loss_threshold_maps: 0.463250, loss_binary_maps: 0.113462, avg_reader_cost: 1.00039 s, avg_batch_cost: 1.20193 s, avg_samples: 9.6, ips: 7.98716 samples/s, eta: 1:22:57
[2024/07/28 02:45:52] ppocr INFO: epoch: [1227/1500], global_step: 3681, lr: 0.001000, loss: 1.142412, loss_shrink_maps: 0.567146, loss_threshold_maps: 0.458977, loss_binary_maps: 0.113053, avg_reader_cost: 0.64707 s, avg_batch_cost: 0.70193 s, avg_samples: 2.9, ips: 4.13149 samples/s, eta: 1:22:51
[2024/07/28 02:45:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:46:01] ppocr INFO: epoch: [1228/1500], global_step: 3684, lr: 0.001000, loss: 1.187504, loss_shrink_maps: 0.587643, loss_threshold_maps: 0.455073, loss_binary_maps: 0.117149, avg_reader_cost: 1.48608 s, avg_batch_cost: 1.73206 s, avg_samples: 12.5, ips: 7.21683 samples/s, eta: 1:22:33
[2024/07/28 02:46:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:46:11] ppocr INFO: epoch: [1229/1500], global_step: 3687, lr: 0.001000, loss: 1.200845, loss_shrink_maps: 0.591423, loss_threshold_maps: 0.465196, loss_binary_maps: 0.117954, avg_reader_cost: 1.64238 s, avg_batch_cost: 1.90490 s, avg_samples: 12.5, ips: 6.56202 samples/s, eta: 1:22:15
[2024/07/28 02:46:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:46:21] ppocr INFO: epoch: [1230/1500], global_step: 3690, lr: 0.001000, loss: 1.247528, loss_shrink_maps: 0.630971, loss_threshold_maps: 0.477446, loss_binary_maps: 0.125742, avg_reader_cost: 1.52163 s, avg_batch_cost: 1.75064 s, avg_samples: 12.5, ips: 7.14023 samples/s, eta: 1:21:56
[2024/07/28 02:46:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:46:31] ppocr INFO: epoch: [1231/1500], global_step: 3693, lr: 0.001000, loss: 1.224068, loss_shrink_maps: 0.608679, loss_threshold_maps: 0.475236, loss_binary_maps: 0.121346, avg_reader_cost: 1.50076 s, avg_batch_cost: 1.76571 s, avg_samples: 12.5, ips: 7.07931 samples/s, eta: 1:21:38
[2024/07/28 02:46:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:46:41] ppocr INFO: epoch: [1232/1500], global_step: 3696, lr: 0.001000, loss: 1.183654, loss_shrink_maps: 0.593773, loss_threshold_maps: 0.460011, loss_binary_maps: 0.118625, avg_reader_cost: 1.54258 s, avg_batch_cost: 1.78433 s, avg_samples: 12.5, ips: 7.00544 samples/s, eta: 1:21:20
[2024/07/28 02:46:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:46:50] ppocr INFO: epoch: [1233/1500], global_step: 3699, lr: 0.001000, loss: 1.208223, loss_shrink_maps: 0.608679, loss_threshold_maps: 0.467981, loss_binary_maps: 0.121346, avg_reader_cost: 1.50154 s, avg_batch_cost: 1.74423 s, avg_samples: 12.5, ips: 7.16650 samples/s, eta: 1:21:01
[2024/07/28 02:46:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:46:59] ppocr INFO: epoch: [1234/1500], global_step: 3700, lr: 0.001000, loss: 1.211208, loss_shrink_maps: 0.621437, loss_threshold_maps: 0.465190, loss_binary_maps: 0.123990, avg_reader_cost: 0.45592 s, avg_batch_cost: 0.53809 s, avg_samples: 4.8, ips: 8.92042 samples/s, eta: 1:20:55
[2024/07/28 02:47:00] ppocr INFO: epoch: [1234/1500], global_step: 3702, lr: 0.001000, loss: 1.211208, loss_shrink_maps: 0.621437, loss_threshold_maps: 0.465190, loss_binary_maps: 0.123990, avg_reader_cost: 1.16730 s, avg_batch_cost: 1.31275 s, avg_samples: 7.7, ips: 5.86554 samples/s, eta: 1:20:43
[2024/07/28 02:47:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:47:10] ppocr INFO: epoch: [1235/1500], global_step: 3705, lr: 0.001000, loss: 1.210101, loss_shrink_maps: 0.614480, loss_threshold_maps: 0.472446, loss_binary_maps: 0.122566, avg_reader_cost: 1.54185 s, avg_batch_cost: 1.77131 s, avg_samples: 12.5, ips: 7.05692 samples/s, eta: 1:20:25
[2024/07/28 02:47:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:47:20] ppocr INFO: epoch: [1236/1500], global_step: 3708, lr: 0.001000, loss: 1.162485, loss_shrink_maps: 0.600728, loss_threshold_maps: 0.449993, loss_binary_maps: 0.119656, avg_reader_cost: 1.67524 s, avg_batch_cost: 1.90368 s, avg_samples: 12.5, ips: 6.56622 samples/s, eta: 1:20:07
[2024/07/28 02:47:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:47:30] ppocr INFO: epoch: [1237/1500], global_step: 3710, lr: 0.001000, loss: 1.133588, loss_shrink_maps: 0.591336, loss_threshold_maps: 0.449993, loss_binary_maps: 0.117852, avg_reader_cost: 0.99591 s, avg_batch_cost: 1.16907 s, avg_samples: 9.6, ips: 8.21168 samples/s, eta: 1:19:55
[2024/07/28 02:47:30] ppocr INFO: epoch: [1237/1500], global_step: 3711, lr: 0.001000, loss: 1.113742, loss_shrink_maps: 0.574802, loss_threshold_maps: 0.437069, loss_binary_maps: 0.115162, avg_reader_cost: 0.63014 s, avg_batch_cost: 0.68487 s, avg_samples: 2.9, ips: 4.23436 samples/s, eta: 1:19:49
[2024/07/28 02:47:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:47:40] ppocr INFO: epoch: [1238/1500], global_step: 3714, lr: 0.001000, loss: 1.118811, loss_shrink_maps: 0.560516, loss_threshold_maps: 0.454974, loss_binary_maps: 0.111836, avg_reader_cost: 1.65992 s, avg_batch_cost: 1.88848 s, avg_samples: 12.5, ips: 6.61907 samples/s, eta: 1:19:31
[2024/07/28 02:47:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:47:50] ppocr INFO: epoch: [1239/1500], global_step: 3717, lr: 0.001000, loss: 1.138657, loss_shrink_maps: 0.582288, loss_threshold_maps: 0.469416, loss_binary_maps: 0.116203, avg_reader_cost: 1.50268 s, avg_batch_cost: 1.74073 s, avg_samples: 12.5, ips: 7.18088 samples/s, eta: 1:19:12
[2024/07/28 02:47:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:48:00] ppocr INFO: epoch: [1240/1500], global_step: 3720, lr: 0.001000, loss: 1.109664, loss_shrink_maps: 0.555905, loss_threshold_maps: 0.451900, loss_binary_maps: 0.110745, avg_reader_cost: 1.51479 s, avg_batch_cost: 1.77789 s, avg_samples: 12.5, ips: 7.03082 samples/s, eta: 1:18:54

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:54,  9.12it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.49it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.14it/s]
eval model::   2%|▏         | 8/500 [00:00<00:26, 18.43it/s]
eval model::   2%|▏         | 10/500 [00:00<00:27, 17.78it/s]
eval model::   2%|▏         | 12/500 [00:00<00:30, 16.21it/s]
eval model::   3%|▎         | 14/500 [00:00<00:30, 15.90it/s]
eval model::   3%|▎         | 16/500 [00:00<00:29, 16.52it/s]
eval model::   4%|▍         | 19/500 [00:01<00:26, 18.01it/s]
eval model::   4%|▍         | 22/500 [00:01<00:24, 19.52it/s]
eval model::   5%|▌         | 25/500 [00:01<00:23, 19.82it/s]
eval model::   6%|▌         | 28/500 [00:01<00:22, 21.20it/s]
eval model::   6%|▌         | 31/500 [00:01<00:22, 21.18it/s]
eval model::   7%|▋         | 34/500 [00:01<00:22, 20.54it/s]
eval model::   7%|▋         | 37/500 [00:01<00:20, 22.47it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.79it/s]
eval model::   9%|▊         | 43/500 [00:02<00:26, 17.20it/s]
eval model::   9%|▉         | 46/500 [00:02<00:25, 17.67it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 19.71it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 19.77it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 20.61it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 18.52it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.34it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.20it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:21, 20.17it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 21.34it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:19, 21.80it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:18, 22.36it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:20, 20.62it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 20.18it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 19.86it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:22, 18.60it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:24, 16.47it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:24, 16.98it/s]
eval model::  19%|█▉        | 94/500 [00:05<00:24, 16.76it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:22, 17.97it/s]
eval model::  20%|██        | 100/500 [00:05<00:22, 17.85it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 21.24it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 22.43it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 21.25it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 21.36it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:19, 19.87it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 21.17it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 20.29it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:20, 18.08it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 19.77it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 18.84it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:20, 18.35it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:19, 18.92it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:19, 19.04it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:19, 18.80it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 20.55it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:19, 18.63it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:21, 16.25it/s]
eval model::  30%|███       | 151/500 [00:07<00:20, 17.14it/s]
eval model::  31%|███       | 154/500 [00:08<00:18, 19.01it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:16, 20.22it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:28, 12.00it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:24, 13.71it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:20, 16.40it/s]
eval model::  34%|███▍      | 169/500 [00:09<00:18, 17.64it/s]
eval model::  34%|███▍      | 172/500 [00:09<00:17, 18.23it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:19, 16.73it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:17, 18.21it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:18, 16.91it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:16, 19.24it/s]
eval model::  37%|███▋      | 187/500 [00:10<00:15, 20.44it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:14, 21.09it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:13, 22.46it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:14, 20.52it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:15, 18.90it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 20.74it/s]
eval model::  41%|████      | 205/500 [00:11<00:21, 13.63it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:20, 14.53it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:19, 15.16it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 14.38it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.75it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:22, 12.94it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:24, 11.78it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:19, 14.26it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:21, 12.69it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:17, 15.65it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:15, 17.14it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:13, 19.83it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 21.01it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 21.76it/s]
eval model::  48%|████▊     | 240/500 [00:13<00:11, 21.70it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:13, 19.15it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:12, 20.05it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:13, 19.22it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 19.99it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.14it/s]
eval model::  52%|█████▏    | 258/500 [00:14<00:11, 21.33it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:11, 19.96it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:11, 20.58it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:10, 21.92it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 20.77it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:10, 20.70it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 18.54it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:11, 19.16it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:11, 19.76it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:17, 12.08it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.45it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 17.05it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:14, 14.38it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:11, 17.68it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 17.20it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 17.48it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.73it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.16it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:08, 21.24it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 19.55it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 20.32it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.00it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 19.66it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:09, 18.31it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:09, 18.75it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:08, 20.50it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 18.63it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:08, 18.08it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:11, 13.84it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 16.64it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 19.02it/s]
eval model::  70%|███████   | 351/500 [00:19<00:06, 22.35it/s]
eval model::  71%|███████   | 354/500 [00:19<00:06, 24.00it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:07, 19.75it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:07, 19.75it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 19.33it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:08, 16.09it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:07, 18.02it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 17.95it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 20.06it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 23.19it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 22.63it/s]
eval model::  77%|███████▋  | 386/500 [00:21<00:07, 14.55it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:07, 15.35it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:06, 16.24it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 16.86it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 17.13it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 19.36it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 18.66it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 20.23it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 18.80it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 20.11it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:04, 20.14it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 19.18it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 18.73it/s]
eval model::  84%|████████▍ | 420/500 [00:23<00:06, 11.44it/s]
eval model::  85%|████████▍ | 423/500 [00:23<00:05, 13.99it/s]
eval model::  85%|████████▌ | 425/500 [00:23<00:05, 13.52it/s]
eval model::  86%|████████▌ | 428/500 [00:23<00:04, 16.45it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:04, 16.96it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 17.21it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 18.96it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 20.73it/s]
eval model::  88%|████████▊ | 441/500 [00:24<00:02, 20.86it/s]
eval model::  89%|████████▉ | 445/500 [00:24<00:02, 23.86it/s]
eval model::  90%|████████▉ | 448/500 [00:24<00:02, 23.26it/s]
eval model::  90%|█████████ | 451/500 [00:24<00:02, 22.51it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 21.73it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 22.73it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 20.43it/s]
eval model::  93%|█████████▎| 463/500 [00:25<00:02, 18.15it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 16.68it/s]
eval model::  93%|█████████▎| 467/500 [00:25<00:02, 16.49it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 20.02it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 19.55it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 21.70it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:00, 22.43it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 19.87it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 19.03it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 19.43it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 16.82it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 18.45it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 22.06it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.50it/s]
[2024/07/28 02:48:27] ppocr INFO: cur metric, precision: 0.7239263803680982, recall: 0.6817525276841598, hmean: 0.7022067939499133, fps: 44.857389614489485
[2024/07/28 02:48:27] ppocr INFO: best metric, hmean: 0.7181174089068827, precision: 0.7568, recall: 0.6831969186326432, fps: 44.212831257019886, best_epoch: 1200
[2024/07/28 02:48:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:48:35] ppocr INFO: epoch: [1241/1500], global_step: 3723, lr: 0.001000, loss: 1.125507, loss_shrink_maps: 0.555905, loss_threshold_maps: 0.451900, loss_binary_maps: 0.110876, avg_reader_cost: 1.48284 s, avg_batch_cost: 1.71447 s, avg_samples: 12.5, ips: 7.29087 samples/s, eta: 1:18:35
[2024/07/28 02:48:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:48:45] ppocr INFO: epoch: [1242/1500], global_step: 3726, lr: 0.001000, loss: 1.125036, loss_shrink_maps: 0.555905, loss_threshold_maps: 0.450514, loss_binary_maps: 0.110876, avg_reader_cost: 1.55015 s, avg_batch_cost: 1.80060 s, avg_samples: 12.5, ips: 6.94212 samples/s, eta: 1:18:17
[2024/07/28 02:48:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:48:55] ppocr INFO: epoch: [1243/1500], global_step: 3729, lr: 0.001000, loss: 1.123495, loss_shrink_maps: 0.554600, loss_threshold_maps: 0.452860, loss_binary_maps: 0.110749, avg_reader_cost: 1.50930 s, avg_batch_cost: 1.74260 s, avg_samples: 12.5, ips: 7.17320 samples/s, eta: 1:17:59
[2024/07/28 02:48:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:49:03] ppocr INFO: epoch: [1244/1500], global_step: 3730, lr: 0.001000, loss: 1.132171, loss_shrink_maps: 0.555905, loss_threshold_maps: 0.452860, loss_binary_maps: 0.111205, avg_reader_cost: 0.41535 s, avg_batch_cost: 0.51196 s, avg_samples: 4.8, ips: 9.37573 samples/s, eta: 1:17:53
[2024/07/28 02:49:05] ppocr INFO: epoch: [1244/1500], global_step: 3732, lr: 0.001000, loss: 1.134183, loss_shrink_maps: 0.555905, loss_threshold_maps: 0.458378, loss_binary_maps: 0.111205, avg_reader_cost: 1.11521 s, avg_batch_cost: 1.26125 s, avg_samples: 7.7, ips: 6.10506 samples/s, eta: 1:17:41
[2024/07/28 02:49:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:49:15] ppocr INFO: epoch: [1245/1500], global_step: 3735, lr: 0.001000, loss: 1.140276, loss_shrink_maps: 0.562438, loss_threshold_maps: 0.458378, loss_binary_maps: 0.112016, avg_reader_cost: 1.54701 s, avg_batch_cost: 1.77664 s, avg_samples: 12.5, ips: 7.03576 samples/s, eta: 1:17:22
[2024/07/28 02:49:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:49:24] ppocr INFO: epoch: [1246/1500], global_step: 3738, lr: 0.001000, loss: 1.154876, loss_shrink_maps: 0.569342, loss_threshold_maps: 0.456967, loss_binary_maps: 0.113246, avg_reader_cost: 1.50297 s, avg_batch_cost: 1.73857 s, avg_samples: 12.5, ips: 7.18982 samples/s, eta: 1:17:04
[2024/07/28 02:49:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:49:33] ppocr INFO: epoch: [1247/1500], global_step: 3740, lr: 0.001000, loss: 1.154876, loss_shrink_maps: 0.569342, loss_threshold_maps: 0.464058, loss_binary_maps: 0.113246, avg_reader_cost: 0.91085 s, avg_batch_cost: 1.08656 s, avg_samples: 9.6, ips: 8.83519 samples/s, eta: 1:16:51
[2024/07/28 02:49:34] ppocr INFO: epoch: [1247/1500], global_step: 3741, lr: 0.001000, loss: 1.140276, loss_shrink_maps: 0.563971, loss_threshold_maps: 0.464058, loss_binary_maps: 0.112206, avg_reader_cost: 0.58889 s, avg_batch_cost: 0.64376 s, avg_samples: 2.9, ips: 4.50477 samples/s, eta: 1:16:45
[2024/07/28 02:49:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:49:44] ppocr INFO: epoch: [1248/1500], global_step: 3744, lr: 0.001000, loss: 1.154876, loss_shrink_maps: 0.577274, loss_threshold_maps: 0.464058, loss_binary_maps: 0.115006, avg_reader_cost: 1.56800 s, avg_batch_cost: 1.81415 s, avg_samples: 12.5, ips: 6.89026 samples/s, eta: 1:16:27
[2024/07/28 02:49:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:49:54] ppocr INFO: epoch: [1249/1500], global_step: 3747, lr: 0.001000, loss: 1.173792, loss_shrink_maps: 0.594269, loss_threshold_maps: 0.464646, loss_binary_maps: 0.118401, avg_reader_cost: 1.56177 s, avg_batch_cost: 1.81719 s, avg_samples: 12.5, ips: 6.87877 samples/s, eta: 1:16:09
[2024/07/28 02:49:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:50:04] ppocr INFO: epoch: [1250/1500], global_step: 3750, lr: 0.001000, loss: 1.158558, loss_shrink_maps: 0.583940, loss_threshold_maps: 0.461122, loss_binary_maps: 0.116316, avg_reader_cost: 1.60066 s, avg_batch_cost: 1.84994 s, avg_samples: 12.5, ips: 6.75698 samples/s, eta: 1:15:51
[2024/07/28 02:50:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:50:14] ppocr INFO: epoch: [1251/1500], global_step: 3753, lr: 0.001000, loss: 1.131322, loss_shrink_maps: 0.567462, loss_threshold_maps: 0.453710, loss_binary_maps: 0.113642, avg_reader_cost: 1.60529 s, avg_batch_cost: 1.83606 s, avg_samples: 12.5, ips: 6.80807 samples/s, eta: 1:15:33
[2024/07/28 02:50:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:50:24] ppocr INFO: epoch: [1252/1500], global_step: 3756, lr: 0.001000, loss: 1.155335, loss_shrink_maps: 0.583940, loss_threshold_maps: 0.461122, loss_binary_maps: 0.116316, avg_reader_cost: 1.48222 s, avg_batch_cost: 1.72111 s, avg_samples: 12.5, ips: 7.26276 samples/s, eta: 1:15:14
[2024/07/28 02:50:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:50:34] ppocr INFO: epoch: [1253/1500], global_step: 3759, lr: 0.001000, loss: 1.131322, loss_shrink_maps: 0.567462, loss_threshold_maps: 0.456854, loss_binary_maps: 0.113642, avg_reader_cost: 1.60332 s, avg_batch_cost: 1.83175 s, avg_samples: 12.5, ips: 6.82408 samples/s, eta: 1:14:56
[2024/07/28 02:50:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:50:42] ppocr INFO: epoch: [1254/1500], global_step: 3760, lr: 0.001000, loss: 1.118259, loss_shrink_maps: 0.560107, loss_threshold_maps: 0.451817, loss_binary_maps: 0.112161, avg_reader_cost: 0.41094 s, avg_batch_cost: 0.52238 s, avg_samples: 4.8, ips: 9.18865 samples/s, eta: 1:14:50
[2024/07/28 02:50:43] ppocr INFO: epoch: [1254/1500], global_step: 3762, lr: 0.001000, loss: 1.117432, loss_shrink_maps: 0.558179, loss_threshold_maps: 0.451817, loss_binary_maps: 0.111568, avg_reader_cost: 1.13577 s, avg_batch_cost: 1.28000 s, avg_samples: 7.7, ips: 6.01561 samples/s, eta: 1:14:38
[2024/07/28 02:50:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:50:53] ppocr INFO: epoch: [1255/1500], global_step: 3765, lr: 0.001000, loss: 1.103992, loss_shrink_maps: 0.554578, loss_threshold_maps: 0.443118, loss_binary_maps: 0.110428, avg_reader_cost: 1.50035 s, avg_batch_cost: 1.74059 s, avg_samples: 12.5, ips: 7.18149 samples/s, eta: 1:14:20
[2024/07/28 02:50:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:51:03] ppocr INFO: epoch: [1256/1500], global_step: 3768, lr: 0.001000, loss: 1.068816, loss_shrink_maps: 0.531647, loss_threshold_maps: 0.432119, loss_binary_maps: 0.105990, avg_reader_cost: 1.54295 s, avg_batch_cost: 1.78284 s, avg_samples: 12.5, ips: 7.01130 samples/s, eta: 1:14:01
[2024/07/28 02:51:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:51:12] ppocr INFO: epoch: [1257/1500], global_step: 3770, lr: 0.001000, loss: 1.068816, loss_shrink_maps: 0.534994, loss_threshold_maps: 0.432119, loss_binary_maps: 0.106251, avg_reader_cost: 0.90749 s, avg_batch_cost: 1.10230 s, avg_samples: 9.6, ips: 8.70908 samples/s, eta: 1:13:49
[2024/07/28 02:51:13] ppocr INFO: epoch: [1257/1500], global_step: 3771, lr: 0.001000, loss: 1.068816, loss_shrink_maps: 0.534994, loss_threshold_maps: 0.432119, loss_binary_maps: 0.106251, avg_reader_cost: 0.59694 s, avg_batch_cost: 0.65160 s, avg_samples: 2.9, ips: 4.45059 samples/s, eta: 1:13:43
[2024/07/28 02:51:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:51:22] ppocr INFO: epoch: [1258/1500], global_step: 3774, lr: 0.001000, loss: 1.066647, loss_shrink_maps: 0.531647, loss_threshold_maps: 0.432119, loss_binary_maps: 0.105990, avg_reader_cost: 1.47716 s, avg_batch_cost: 1.72007 s, avg_samples: 12.5, ips: 7.26713 samples/s, eta: 1:13:25
[2024/07/28 02:51:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:51:32] ppocr INFO: epoch: [1259/1500], global_step: 3777, lr: 0.001000, loss: 1.082660, loss_shrink_maps: 0.534994, loss_threshold_maps: 0.433552, loss_binary_maps: 0.106251, avg_reader_cost: 1.51723 s, avg_batch_cost: 1.74624 s, avg_samples: 12.5, ips: 7.15825 samples/s, eta: 1:13:06
[2024/07/28 02:51:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:51:42] ppocr INFO: epoch: [1260/1500], global_step: 3780, lr: 0.001000, loss: 1.155143, loss_shrink_maps: 0.573212, loss_threshold_maps: 0.445069, loss_binary_maps: 0.114955, avg_reader_cost: 1.53928 s, avg_batch_cost: 1.80584 s, avg_samples: 12.5, ips: 6.92199 samples/s, eta: 1:12:48

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.64it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 16.55it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.34it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.03it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.26it/s]
eval model::   3%|▎         | 13/500 [00:00<00:26, 18.58it/s]
eval model::   3%|▎         | 15/500 [00:00<00:26, 18.16it/s]
eval model::   4%|▎         | 18/500 [00:01<00:26, 18.50it/s]
eval model::   4%|▍         | 20/500 [00:01<00:27, 17.57it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.24it/s]
eval model::   5%|▌         | 26/500 [00:01<00:25, 18.87it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.34it/s]
eval model::   6%|▋         | 32/500 [00:01<00:25, 18.32it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.31it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 19.65it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.31it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.16it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.65it/s]
eval model::  10%|▉         | 48/500 [00:02<00:23, 19.00it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.72it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 20.51it/s]
eval model::  11%|█         | 56/500 [00:02<00:20, 21.27it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:22, 19.84it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:27, 15.87it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:26, 16.40it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:21, 19.92it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 20.85it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:20, 20.71it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:20, 20.75it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:20, 20.86it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:20, 20.34it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:21, 19.68it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 18.82it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:24, 17.07it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:23, 17.27it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:21, 18.39it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:21, 18.70it/s]
eval model::  20%|██        | 100/500 [00:05<00:22, 17.60it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 19.93it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.05it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.81it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:16, 23.24it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:16, 22.72it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:15, 24.49it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 20.43it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 19.94it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:17, 20.92it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:18, 20.51it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:18, 20.37it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:16, 21.43it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:17, 20.58it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.32it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:18, 19.34it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:18, 19.25it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 17.70it/s]
eval model::  30%|███       | 151/500 [00:07<00:21, 16.38it/s]
eval model::  31%|███       | 154/500 [00:07<00:18, 18.45it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:17, 19.44it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:19, 17.41it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:26, 12.87it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:22, 15.15it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:20, 16.13it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:17, 18.39it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:18, 17.55it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:22, 14.15it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:20, 15.93it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:22, 14.24it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:19, 16.64it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.50it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:17, 18.34it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:16, 18.54it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:16, 18.82it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:16, 18.94it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.12it/s]
eval model::  40%|████      | 200/500 [00:10<00:15, 19.35it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 20.35it/s]
eval model::  41%|████      | 206/500 [00:11<00:24, 11.76it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:24, 12.01it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:23, 12.38it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:20, 13.68it/s]
eval model::  43%|████▎     | 215/500 [00:12<00:23, 12.00it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:24, 11.51it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:22, 12.56it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:22, 12.54it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:17, 15.38it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:17, 16.07it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:14, 19.05it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.71it/s]
eval model::  47%|████▋     | 236/500 [00:13<00:11, 22.42it/s]
eval model::  48%|████▊     | 239/500 [00:13<00:10, 23.93it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 19.63it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:13, 19.61it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:12, 20.75it/s]
eval model::  50%|█████     | 251/500 [00:13<00:13, 18.97it/s]
eval model::  51%|█████     | 254/500 [00:13<00:12, 20.46it/s]
eval model::  51%|█████▏    | 257/500 [00:14<00:11, 21.27it/s]
eval model::  52%|█████▏    | 260/500 [00:14<00:11, 20.87it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:11, 20.75it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:11, 19.61it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:13, 17.58it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 18.17it/s]
eval model::  55%|█████▌    | 275/500 [00:15<00:12, 18.53it/s]
eval model::  56%|█████▌    | 278/500 [00:15<00:10, 20.67it/s]
eval model::  56%|█████▌    | 281/500 [00:15<00:10, 21.37it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:09, 22.02it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:16, 12.56it/s]
eval model::  58%|█████▊    | 290/500 [00:16<00:14, 14.80it/s]
eval model::  59%|█████▊    | 293/500 [00:16<00:13, 15.52it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:16, 12.23it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:13, 14.83it/s]
eval model::  60%|██████    | 300/500 [00:16<00:14, 14.16it/s]
eval model::  60%|██████    | 302/500 [00:16<00:13, 14.61it/s]
eval model::  61%|██████    | 304/500 [00:17<00:12, 15.13it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:10, 17.57it/s]
eval model::  62%|██████▏   | 310/500 [00:17<00:09, 19.09it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:09, 19.81it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 19.06it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 21.12it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.96it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:07, 22.23it/s]
eval model::  66%|██████▌   | 328/500 [00:18<00:08, 20.28it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:08, 19.71it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:08, 20.20it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 19.85it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 15.22it/s]
eval model::  69%|██████▊   | 343/500 [00:19<00:09, 16.63it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:08, 18.15it/s]
eval model::  70%|██████▉   | 348/500 [00:19<00:08, 17.73it/s]
eval model::  70%|███████   | 351/500 [00:19<00:07, 19.63it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 19.79it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 18.79it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:07, 18.25it/s]
eval model::  72%|███████▏  | 362/500 [00:20<00:08, 17.14it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:08, 16.50it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:08, 15.39it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:07, 17.19it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:07, 17.44it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:07, 18.02it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 18.00it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:06, 19.43it/s]
eval model::  76%|███████▌  | 380/500 [00:21<00:06, 17.41it/s]
eval model::  77%|███████▋  | 383/500 [00:21<00:05, 19.91it/s]
eval model::  77%|███████▋  | 386/500 [00:21<00:08, 13.18it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:07, 14.21it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:07, 15.29it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 16.16it/s]
eval model::  79%|███████▉  | 395/500 [00:22<00:06, 16.39it/s]
eval model::  80%|███████▉  | 398/500 [00:22<00:05, 19.57it/s]
eval model::  80%|████████  | 401/500 [00:22<00:06, 15.66it/s]
eval model::  81%|████████  | 404/500 [00:22<00:05, 16.87it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 19.21it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:04, 20.30it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:04, 20.45it/s]
eval model::  83%|████████▎ | 416/500 [00:23<00:04, 19.35it/s]
eval model::  84%|████████▍ | 419/500 [00:23<00:04, 17.94it/s]
eval model::  84%|████████▍ | 421/500 [00:23<00:06, 12.62it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 12.79it/s]
eval model::  85%|████████▌ | 426/500 [00:24<00:05, 13.81it/s]
eval model::  86%|████████▌ | 429/500 [00:24<00:04, 16.28it/s]
eval model::  86%|████████▌ | 431/500 [00:24<00:04, 16.37it/s]
eval model::  87%|████████▋ | 434/500 [00:24<00:03, 19.22it/s]
eval model::  87%|████████▋ | 437/500 [00:24<00:03, 18.90it/s]
eval model::  88%|████████▊ | 440/500 [00:24<00:03, 17.82it/s]
eval model::  89%|████████▊ | 443/500 [00:24<00:02, 19.97it/s]
eval model::  89%|████████▉ | 446/500 [00:24<00:02, 19.82it/s]
eval model::  90%|████████▉ | 449/500 [00:25<00:02, 18.76it/s]
eval model::  90%|█████████ | 452/500 [00:25<00:02, 20.53it/s]
eval model::  91%|█████████ | 455/500 [00:25<00:02, 21.50it/s]
eval model::  92%|█████████▏| 458/500 [00:25<00:01, 21.35it/s]
eval model::  92%|█████████▏| 461/500 [00:25<00:01, 22.21it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:01, 18.30it/s]
eval model::  93%|█████████▎| 466/500 [00:26<00:01, 17.78it/s]
eval model::  94%|█████████▎| 468/500 [00:26<00:01, 17.63it/s]
eval model::  94%|█████████▍| 471/500 [00:26<00:01, 19.77it/s]
eval model::  95%|█████████▍| 474/500 [00:26<00:01, 20.41it/s]
eval model::  95%|█████████▌| 477/500 [00:26<00:01, 21.92it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:00, 22.35it/s]
eval model::  97%|█████████▋| 483/500 [00:26<00:00, 23.10it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 21.30it/s]
eval model::  98%|█████████▊| 490/500 [00:27<00:00, 24.23it/s]
eval model::  99%|█████████▊| 493/500 [00:27<00:00, 22.44it/s]
eval model::  99%|█████████▉| 496/500 [00:27<00:00, 21.86it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 25.93it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.22it/s]
[2024/07/28 02:52:10] ppocr INFO: cur metric, precision: 0.7166998011928429, recall: 0.6942705825710159, hmean: 0.7053069210075814, fps: 45.13136029252815
[2024/07/28 02:52:10] ppocr INFO: best metric, hmean: 0.7181174089068827, precision: 0.7568, recall: 0.6831969186326432, fps: 44.212831257019886, best_epoch: 1200
[2024/07/28 02:52:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:52:17] ppocr INFO: epoch: [1261/1500], global_step: 3783, lr: 0.001000, loss: 1.166007, loss_shrink_maps: 0.581128, loss_threshold_maps: 0.454058, loss_binary_maps: 0.116129, avg_reader_cost: 1.48926 s, avg_batch_cost: 1.72185 s, avg_samples: 12.5, ips: 7.25965 samples/s, eta: 1:12:30
[2024/07/28 02:52:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:52:27] ppocr INFO: epoch: [1262/1500], global_step: 3786, lr: 0.001000, loss: 1.166007, loss_shrink_maps: 0.581128, loss_threshold_maps: 0.443280, loss_binary_maps: 0.116129, avg_reader_cost: 1.50410 s, avg_batch_cost: 1.75540 s, avg_samples: 12.5, ips: 7.12090 samples/s, eta: 1:12:11
[2024/07/28 02:52:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:52:37] ppocr INFO: epoch: [1263/1500], global_step: 3789, lr: 0.001000, loss: 1.155143, loss_shrink_maps: 0.573212, loss_threshold_maps: 0.443280, loss_binary_maps: 0.114955, avg_reader_cost: 1.51130 s, avg_batch_cost: 1.74279 s, avg_samples: 12.5, ips: 7.17242 samples/s, eta: 1:11:53
[2024/07/28 02:52:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:52:46] ppocr INFO: epoch: [1264/1500], global_step: 3790, lr: 0.001000, loss: 1.166007, loss_shrink_maps: 0.581128, loss_threshold_maps: 0.452256, loss_binary_maps: 0.116129, avg_reader_cost: 0.37905 s, avg_batch_cost: 0.55574 s, avg_samples: 4.8, ips: 8.63720 samples/s, eta: 1:11:47
[2024/07/28 02:52:47] ppocr INFO: epoch: [1264/1500], global_step: 3792, lr: 0.001000, loss: 1.181787, loss_shrink_maps: 0.582806, loss_threshold_maps: 0.454102, loss_binary_maps: 0.116129, avg_reader_cost: 1.20258 s, avg_batch_cost: 1.34833 s, avg_samples: 7.7, ips: 5.71076 samples/s, eta: 1:11:35
[2024/07/28 02:52:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:52:57] ppocr INFO: epoch: [1265/1500], global_step: 3795, lr: 0.001000, loss: 1.194647, loss_shrink_maps: 0.588489, loss_threshold_maps: 0.452256, loss_binary_maps: 0.117036, avg_reader_cost: 1.64749 s, avg_batch_cost: 1.95726 s, avg_samples: 12.5, ips: 6.38649 samples/s, eta: 1:11:17
[2024/07/28 02:53:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:53:08] ppocr INFO: epoch: [1266/1500], global_step: 3798, lr: 0.001000, loss: 1.194647, loss_shrink_maps: 0.583437, loss_threshold_maps: 0.452256, loss_binary_maps: 0.116648, avg_reader_cost: 1.56402 s, avg_batch_cost: 1.85070 s, avg_samples: 12.5, ips: 6.75421 samples/s, eta: 1:10:59
[2024/07/28 02:53:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:53:18] ppocr INFO: epoch: [1267/1500], global_step: 3800, lr: 0.001000, loss: 1.189633, loss_shrink_maps: 0.583437, loss_threshold_maps: 0.452256, loss_binary_maps: 0.116318, avg_reader_cost: 0.98375 s, avg_batch_cost: 1.27749 s, avg_samples: 9.6, ips: 7.51473 samples/s, eta: 1:10:47
[2024/07/28 02:53:18] ppocr INFO: epoch: [1267/1500], global_step: 3801, lr: 0.001000, loss: 1.156509, loss_shrink_maps: 0.570948, loss_threshold_maps: 0.451093, loss_binary_maps: 0.113764, avg_reader_cost: 0.68454 s, avg_batch_cost: 0.73909 s, avg_samples: 2.9, ips: 3.92373 samples/s, eta: 1:10:41
[2024/07/28 02:53:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:53:28] ppocr INFO: epoch: [1268/1500], global_step: 3804, lr: 0.001000, loss: 1.125972, loss_shrink_maps: 0.563598, loss_threshold_maps: 0.451093, loss_binary_maps: 0.112447, avg_reader_cost: 1.64076 s, avg_batch_cost: 1.88396 s, avg_samples: 12.5, ips: 6.63495 samples/s, eta: 1:10:23
[2024/07/28 02:53:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:53:38] ppocr INFO: epoch: [1269/1500], global_step: 3807, lr: 0.001000, loss: 1.153133, loss_shrink_maps: 0.570948, loss_threshold_maps: 0.464835, loss_binary_maps: 0.113764, avg_reader_cost: 1.51683 s, avg_batch_cost: 1.76147 s, avg_samples: 12.5, ips: 7.09633 samples/s, eta: 1:10:05
[2024/07/28 02:53:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:53:48] ppocr INFO: epoch: [1270/1500], global_step: 3810, lr: 0.001000, loss: 1.182816, loss_shrink_maps: 0.578144, loss_threshold_maps: 0.476379, loss_binary_maps: 0.115109, avg_reader_cost: 1.51508 s, avg_batch_cost: 1.77418 s, avg_samples: 12.5, ips: 7.04552 samples/s, eta: 1:09:46
[2024/07/28 02:53:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:53:58] ppocr INFO: epoch: [1271/1500], global_step: 3813, lr: 0.001000, loss: 1.182816, loss_shrink_maps: 0.584101, loss_threshold_maps: 0.476379, loss_binary_maps: 0.116182, avg_reader_cost: 1.51427 s, avg_batch_cost: 1.77051 s, avg_samples: 12.5, ips: 7.06010 samples/s, eta: 1:09:28
[2024/07/28 02:54:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:54:08] ppocr INFO: epoch: [1272/1500], global_step: 3816, lr: 0.001000, loss: 1.182816, loss_shrink_maps: 0.584101, loss_threshold_maps: 0.479460, loss_binary_maps: 0.116182, avg_reader_cost: 1.52483 s, avg_batch_cost: 1.75542 s, avg_samples: 12.5, ips: 7.12080 samples/s, eta: 1:09:10
[2024/07/28 02:54:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:54:18] ppocr INFO: epoch: [1273/1500], global_step: 3819, lr: 0.001000, loss: 1.190304, loss_shrink_maps: 0.584516, loss_threshold_maps: 0.484188, loss_binary_maps: 0.116182, avg_reader_cost: 1.48453 s, avg_batch_cost: 1.73470 s, avg_samples: 12.5, ips: 7.20584 samples/s, eta: 1:08:51
[2024/07/28 02:54:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:54:26] ppocr INFO: epoch: [1274/1500], global_step: 3820, lr: 0.001000, loss: 1.190304, loss_shrink_maps: 0.584516, loss_threshold_maps: 0.481358, loss_binary_maps: 0.116182, avg_reader_cost: 0.41854 s, avg_batch_cost: 0.50126 s, avg_samples: 4.8, ips: 9.57595 samples/s, eta: 1:08:45
[2024/07/28 02:54:27] ppocr INFO: epoch: [1274/1500], global_step: 3822, lr: 0.001000, loss: 1.190304, loss_shrink_maps: 0.584516, loss_threshold_maps: 0.481358, loss_binary_maps: 0.116182, avg_reader_cost: 1.09403 s, avg_batch_cost: 1.23917 s, avg_samples: 7.7, ips: 6.21386 samples/s, eta: 1:08:33
[2024/07/28 02:54:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:54:38] ppocr INFO: epoch: [1275/1500], global_step: 3825, lr: 0.001000, loss: 1.183196, loss_shrink_maps: 0.579634, loss_threshold_maps: 0.481358, loss_binary_maps: 0.115441, avg_reader_cost: 1.63779 s, avg_batch_cost: 1.92509 s, avg_samples: 12.5, ips: 6.49319 samples/s, eta: 1:08:15
[2024/07/28 02:54:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:54:47] ppocr INFO: epoch: [1276/1500], global_step: 3828, lr: 0.001000, loss: 1.158629, loss_shrink_maps: 0.571167, loss_threshold_maps: 0.466112, loss_binary_maps: 0.113539, avg_reader_cost: 1.50481 s, avg_batch_cost: 1.73621 s, avg_samples: 12.5, ips: 7.19961 samples/s, eta: 1:07:57
[2024/07/28 02:54:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:54:57] ppocr INFO: epoch: [1277/1500], global_step: 3830, lr: 0.001000, loss: 1.144670, loss_shrink_maps: 0.566206, loss_threshold_maps: 0.464134, loss_binary_maps: 0.112629, avg_reader_cost: 0.96571 s, avg_batch_cost: 1.13875 s, avg_samples: 9.6, ips: 8.43027 samples/s, eta: 1:07:44
[2024/07/28 02:54:57] ppocr INFO: epoch: [1277/1500], global_step: 3831, lr: 0.001000, loss: 1.158629, loss_shrink_maps: 0.575499, loss_threshold_maps: 0.455291, loss_binary_maps: 0.114759, avg_reader_cost: 0.61490 s, avg_batch_cost: 0.66984 s, avg_samples: 2.9, ips: 4.32940 samples/s, eta: 1:07:38
[2024/07/28 02:55:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:55:07] ppocr INFO: epoch: [1278/1500], global_step: 3834, lr: 0.001000, loss: 1.121657, loss_shrink_maps: 0.542303, loss_threshold_maps: 0.452395, loss_binary_maps: 0.108174, avg_reader_cost: 1.50400 s, avg_batch_cost: 1.73952 s, avg_samples: 12.5, ips: 7.18591 samples/s, eta: 1:07:20
[2024/07/28 02:55:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:55:17] ppocr INFO: epoch: [1279/1500], global_step: 3837, lr: 0.001000, loss: 1.121657, loss_shrink_maps: 0.542303, loss_threshold_maps: 0.453483, loss_binary_maps: 0.108174, avg_reader_cost: 1.60192 s, avg_batch_cost: 1.82980 s, avg_samples: 12.5, ips: 6.83133 samples/s, eta: 1:07:02
[2024/07/28 02:55:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:55:27] ppocr INFO: epoch: [1280/1500], global_step: 3840, lr: 0.001000, loss: 1.090117, loss_shrink_maps: 0.536015, loss_threshold_maps: 0.446961, loss_binary_maps: 0.107022, avg_reader_cost: 1.51356 s, avg_batch_cost: 1.76069 s, avg_samples: 12.5, ips: 7.09947 samples/s, eta: 1:06:44

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:58,  8.59it/s]
eval model::   1%|          | 4/500 [00:00<00:30, 16.52it/s]
eval model::   1%|          | 6/500 [00:00<00:31, 15.76it/s]
eval model::   2%|▏         | 8/500 [00:00<00:29, 16.43it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.05it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.48it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.37it/s]
eval model::   4%|▍         | 20/500 [00:01<00:26, 18.10it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.32it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.46it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 19.96it/s]
eval model::   6%|▋         | 32/500 [00:01<00:26, 17.74it/s]
eval model::   7%|▋         | 34/500 [00:01<00:26, 17.56it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.14it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.17it/s]
eval model::   9%|▊         | 43/500 [00:02<00:26, 17.56it/s]
eval model::   9%|▉         | 46/500 [00:02<00:24, 18.48it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 19.54it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 20.23it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.53it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 19.18it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 15.77it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 16.15it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.81it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.70it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 22.08it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:19, 22.26it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:17, 23.91it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 22.99it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 20.71it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.17it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.82it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.66it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:19, 20.68it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.88it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.13it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.00it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 21.45it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 20.59it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:19, 19.72it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:17, 21.24it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:17, 21.50it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:21, 17.77it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:20, 17.91it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.58it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 16.84it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:19, 18.70it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.53it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.64it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:18, 18.94it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:18, 19.04it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:19, 17.69it/s]
eval model::  30%|███       | 151/500 [00:07<00:20, 16.85it/s]
eval model::  31%|███       | 154/500 [00:08<00:17, 19.29it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:15, 21.65it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:21, 15.48it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.37it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 19.18it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 19.24it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 19.11it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:18, 17.77it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:17, 17.95it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.29it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:21, 14.54it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:18, 17.49it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 19.16it/s]
eval model::  38%|███▊      | 190/500 [00:10<00:16, 18.64it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:15, 19.84it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:16, 18.35it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:16, 18.58it/s]
eval model::  40%|████      | 201/500 [00:10<00:15, 19.32it/s]
eval model::  41%|████      | 204/500 [00:10<00:14, 20.87it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:26, 11.01it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:23, 12.28it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:23, 12.45it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:20, 13.68it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:22, 12.46it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:24, 11.66it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:19, 14.09it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:21, 13.08it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:16, 17.04it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 20.66it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.91it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.88it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.77it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 19.55it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.86it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 22.79it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 22.04it/s]
eval model::  51%|█████     | 254/500 [00:13<00:10, 23.25it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:09, 24.48it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:09, 25.74it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:09, 24.28it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:10, 21.28it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 19.50it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 19.56it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.77it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.17it/s]
eval model::  56%|█████▌    | 281/500 [00:15<00:11, 19.26it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:11, 18.18it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:24,  8.84it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:20, 10.25it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:14, 14.24it/s]
eval model::  59%|█████▊    | 293/500 [00:16<00:13, 15.03it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:16, 12.39it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 16.71it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 16.53it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.84it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.37it/s]
eval model::  62%|██████▏   | 311/500 [00:17<00:09, 20.73it/s]
eval model::  63%|██████▎   | 314/500 [00:17<00:09, 19.83it/s]
eval model::  63%|██████▎   | 317/500 [00:17<00:08, 21.38it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:08, 21.82it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:08, 19.82it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:08, 19.94it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 20.37it/s]
eval model::  66%|██████▋   | 332/500 [00:18<00:07, 21.77it/s]
eval model::  67%|██████▋   | 335/500 [00:18<00:07, 21.62it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 19.88it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:09, 16.05it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:08, 17.79it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 18.67it/s]
eval model::  70%|███████   | 350/500 [00:19<00:07, 20.97it/s]
eval model::  71%|███████   | 354/500 [00:19<00:06, 23.69it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:06, 21.89it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:06, 21.99it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:06, 19.81it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:08, 16.29it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:07, 18.25it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:06, 18.46it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 21.55it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 21.66it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 22.43it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:07, 15.63it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:06, 17.35it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:05, 18.21it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:05, 19.51it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:04, 21.56it/s]
eval model::  80%|████████  | 402/500 [00:21<00:04, 20.87it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 21.46it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 22.62it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:03, 22.37it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 22.13it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:03, 20.88it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 13.52it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 14.50it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 14.52it/s]
eval model::  85%|████████▌ | 427/500 [00:23<00:04, 17.04it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 19.67it/s]
eval model::  87%|████████▋ | 433/500 [00:23<00:03, 21.21it/s]
eval model::  87%|████████▋ | 437/500 [00:23<00:02, 24.27it/s]
eval model::  88%|████████▊ | 440/500 [00:23<00:02, 22.70it/s]
eval model::  89%|████████▊ | 443/500 [00:23<00:02, 24.13it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 23.52it/s]
eval model::  90%|████████▉ | 449/500 [00:24<00:02, 21.23it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 23.26it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 22.94it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 22.77it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:01, 20.02it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:01, 18.48it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 18.35it/s]
eval model::  94%|█████████▍| 470/500 [00:25<00:01, 20.60it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 20.83it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 20.60it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:00, 22.41it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 20.88it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 22.02it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 22.86it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 20.46it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 21.26it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 22.62it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.93it/s]
[2024/07/28 02:55:53] ppocr INFO: cur metric, precision: 0.7455403987408185, recall: 0.6841598459316321, hmean: 0.7135325131810192, fps: 46.58489082265975
[2024/07/28 02:55:53] ppocr INFO: best metric, hmean: 0.7181174089068827, precision: 0.7568, recall: 0.6831969186326432, fps: 44.212831257019886, best_epoch: 1200
[2024/07/28 02:55:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:56:03] ppocr INFO: epoch: [1281/1500], global_step: 3843, lr: 0.001000, loss: 1.110625, loss_shrink_maps: 0.536015, loss_threshold_maps: 0.453483, loss_binary_maps: 0.107022, avg_reader_cost: 2.00434 s, avg_batch_cost: 2.46054 s, avg_samples: 12.5, ips: 5.08018 samples/s, eta: 1:06:26
[2024/07/28 02:56:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:56:13] ppocr INFO: epoch: [1282/1500], global_step: 3846, lr: 0.001000, loss: 1.126236, loss_shrink_maps: 0.563353, loss_threshold_maps: 0.458169, loss_binary_maps: 0.112237, avg_reader_cost: 1.56292 s, avg_batch_cost: 1.79769 s, avg_samples: 12.5, ips: 6.95336 samples/s, eta: 1:06:08
[2024/07/28 02:56:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:56:23] ppocr INFO: epoch: [1283/1500], global_step: 3849, lr: 0.001000, loss: 1.129808, loss_shrink_maps: 0.568304, loss_threshold_maps: 0.463010, loss_binary_maps: 0.113262, avg_reader_cost: 1.50216 s, avg_batch_cost: 1.77046 s, avg_samples: 12.5, ips: 7.06032 samples/s, eta: 1:05:50
[2024/07/28 02:56:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:56:31] ppocr INFO: epoch: [1284/1500], global_step: 3850, lr: 0.001000, loss: 1.154719, loss_shrink_maps: 0.578567, loss_threshold_maps: 0.468887, loss_binary_maps: 0.115342, avg_reader_cost: 0.39611 s, avg_batch_cost: 0.51151 s, avg_samples: 4.8, ips: 9.38391 samples/s, eta: 1:05:44
[2024/07/28 02:56:33] ppocr INFO: epoch: [1284/1500], global_step: 3852, lr: 0.001000, loss: 1.147824, loss_shrink_maps: 0.566396, loss_threshold_maps: 0.472969, loss_binary_maps: 0.112929, avg_reader_cost: 1.11470 s, avg_batch_cost: 1.26076 s, avg_samples: 7.7, ips: 6.10744 samples/s, eta: 1:05:32
[2024/07/28 02:56:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:56:43] ppocr INFO: epoch: [1285/1500], global_step: 3855, lr: 0.001000, loss: 1.163709, loss_shrink_maps: 0.578816, loss_threshold_maps: 0.468887, loss_binary_maps: 0.115520, avg_reader_cost: 1.51117 s, avg_batch_cost: 1.75793 s, avg_samples: 12.5, ips: 7.11062 samples/s, eta: 1:05:13
[2024/07/28 02:56:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:56:53] ppocr INFO: epoch: [1286/1500], global_step: 3858, lr: 0.001000, loss: 1.164586, loss_shrink_maps: 0.584852, loss_threshold_maps: 0.470535, loss_binary_maps: 0.116623, avg_reader_cost: 1.59239 s, avg_batch_cost: 1.82592 s, avg_samples: 12.5, ips: 6.84586 samples/s, eta: 1:04:55
[2024/07/28 02:56:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:57:02] ppocr INFO: epoch: [1287/1500], global_step: 3860, lr: 0.001000, loss: 1.164586, loss_shrink_maps: 0.584852, loss_threshold_maps: 0.468648, loss_binary_maps: 0.116623, avg_reader_cost: 0.93549 s, avg_batch_cost: 1.12551 s, avg_samples: 9.6, ips: 8.52948 samples/s, eta: 1:04:43
[2024/07/28 02:57:03] ppocr INFO: epoch: [1287/1500], global_step: 3861, lr: 0.001000, loss: 1.164586, loss_shrink_maps: 0.584852, loss_threshold_maps: 0.468648, loss_binary_maps: 0.116623, avg_reader_cost: 0.60857 s, avg_batch_cost: 0.66322 s, avg_samples: 2.9, ips: 4.37260 samples/s, eta: 1:04:37
[2024/07/28 02:57:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:57:13] ppocr INFO: epoch: [1288/1500], global_step: 3864, lr: 0.001000, loss: 1.171589, loss_shrink_maps: 0.592192, loss_threshold_maps: 0.470900, loss_binary_maps: 0.117933, avg_reader_cost: 1.57304 s, avg_batch_cost: 1.80598 s, avg_samples: 12.5, ips: 6.92146 samples/s, eta: 1:04:19
[2024/07/28 02:57:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:57:22] ppocr INFO: epoch: [1289/1500], global_step: 3867, lr: 0.001000, loss: 1.164586, loss_shrink_maps: 0.584852, loss_threshold_maps: 0.468648, loss_binary_maps: 0.116623, avg_reader_cost: 1.52024 s, avg_batch_cost: 1.74932 s, avg_samples: 12.5, ips: 7.14564 samples/s, eta: 1:04:00
[2024/07/28 02:57:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:57:32] ppocr INFO: epoch: [1290/1500], global_step: 3870, lr: 0.001000, loss: 1.164586, loss_shrink_maps: 0.584852, loss_threshold_maps: 0.468151, loss_binary_maps: 0.116623, avg_reader_cost: 1.52872 s, avg_batch_cost: 1.78916 s, avg_samples: 12.5, ips: 6.98652 samples/s, eta: 1:03:42
[2024/07/28 02:57:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:57:42] ppocr INFO: epoch: [1291/1500], global_step: 3873, lr: 0.001000, loss: 1.169711, loss_shrink_maps: 0.584864, loss_threshold_maps: 0.468151, loss_binary_maps: 0.116695, avg_reader_cost: 1.50926 s, avg_batch_cost: 1.74959 s, avg_samples: 12.5, ips: 7.14454 samples/s, eta: 1:03:24
[2024/07/28 02:57:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:57:52] ppocr INFO: epoch: [1292/1500], global_step: 3876, lr: 0.001000, loss: 1.193831, loss_shrink_maps: 0.593483, loss_threshold_maps: 0.468151, loss_binary_maps: 0.118118, avg_reader_cost: 1.52279 s, avg_batch_cost: 1.75134 s, avg_samples: 12.5, ips: 7.13740 samples/s, eta: 1:03:05
[2024/07/28 02:57:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:58:02] ppocr INFO: epoch: [1293/1500], global_step: 3879, lr: 0.001000, loss: 1.163741, loss_shrink_maps: 0.586890, loss_threshold_maps: 0.459935, loss_binary_maps: 0.117024, avg_reader_cost: 1.56258 s, avg_batch_cost: 1.79632 s, avg_samples: 12.5, ips: 6.95869 samples/s, eta: 1:02:47
[2024/07/28 02:58:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:58:10] ppocr INFO: epoch: [1294/1500], global_step: 3880, lr: 0.001000, loss: 1.187052, loss_shrink_maps: 0.593483, loss_threshold_maps: 0.464770, loss_binary_maps: 0.118118, avg_reader_cost: 0.40297 s, avg_batch_cost: 0.51332 s, avg_samples: 4.8, ips: 9.35086 samples/s, eta: 1:02:41
[2024/07/28 02:58:12] ppocr INFO: epoch: [1294/1500], global_step: 3882, lr: 0.001000, loss: 1.163741, loss_shrink_maps: 0.586890, loss_threshold_maps: 0.459935, loss_binary_maps: 0.117024, avg_reader_cost: 1.11841 s, avg_batch_cost: 1.26464 s, avg_samples: 7.7, ips: 6.08870 samples/s, eta: 1:02:29
[2024/07/28 02:58:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:58:22] ppocr INFO: epoch: [1295/1500], global_step: 3885, lr: 0.001000, loss: 1.163741, loss_shrink_maps: 0.586890, loss_threshold_maps: 0.459935, loss_binary_maps: 0.117024, avg_reader_cost: 1.58574 s, avg_batch_cost: 1.81520 s, avg_samples: 12.5, ips: 6.88630 samples/s, eta: 1:02:11
[2024/07/28 02:58:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:58:32] ppocr INFO: epoch: [1296/1500], global_step: 3888, lr: 0.001000, loss: 1.164759, loss_shrink_maps: 0.586890, loss_threshold_maps: 0.467539, loss_binary_maps: 0.117024, avg_reader_cost: 1.58132 s, avg_batch_cost: 1.83762 s, avg_samples: 12.5, ips: 6.80227 samples/s, eta: 1:01:53
[2024/07/28 02:58:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:58:41] ppocr INFO: epoch: [1297/1500], global_step: 3890, lr: 0.001000, loss: 1.153558, loss_shrink_maps: 0.577076, loss_threshold_maps: 0.466303, loss_binary_maps: 0.115121, avg_reader_cost: 0.95482 s, avg_batch_cost: 1.14825 s, avg_samples: 9.6, ips: 8.36053 samples/s, eta: 1:01:40
[2024/07/28 02:58:42] ppocr INFO: epoch: [1297/1500], global_step: 3891, lr: 0.001000, loss: 1.151426, loss_shrink_maps: 0.569423, loss_threshold_maps: 0.459906, loss_binary_maps: 0.113547, avg_reader_cost: 0.61973 s, avg_batch_cost: 0.67525 s, avg_samples: 2.9, ips: 4.29473 samples/s, eta: 1:01:34
[2024/07/28 02:58:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:58:52] ppocr INFO: epoch: [1298/1500], global_step: 3894, lr: 0.001000, loss: 1.151426, loss_shrink_maps: 0.568616, loss_threshold_maps: 0.462674, loss_binary_maps: 0.113547, avg_reader_cost: 1.55722 s, avg_batch_cost: 1.84220 s, avg_samples: 12.5, ips: 6.78537 samples/s, eta: 1:01:16
[2024/07/28 02:58:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:59:02] ppocr INFO: epoch: [1299/1500], global_step: 3897, lr: 0.001000, loss: 1.151426, loss_shrink_maps: 0.575347, loss_threshold_maps: 0.462674, loss_binary_maps: 0.114841, avg_reader_cost: 1.58733 s, avg_batch_cost: 1.83391 s, avg_samples: 12.5, ips: 6.81602 samples/s, eta: 1:00:58
[2024/07/28 02:59:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:59:11] ppocr INFO: epoch: [1300/1500], global_step: 3900, lr: 0.001000, loss: 1.167178, loss_shrink_maps: 0.584547, loss_threshold_maps: 0.471258, loss_binary_maps: 0.116446, avg_reader_cost: 1.48497 s, avg_batch_cost: 1.71466 s, avg_samples: 12.5, ips: 7.29007 samples/s, eta: 1:00:40

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:52,  9.44it/s]
eval model::   1%|          | 4/500 [00:00<00:30, 16.32it/s]
eval model::   1%|▏         | 7/500 [00:00<00:27, 18.00it/s]
eval model::   2%|▏         | 9/500 [00:00<00:27, 18.12it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.55it/s]
eval model::   3%|▎         | 13/500 [00:00<00:28, 17.05it/s]
eval model::   3%|▎         | 15/500 [00:00<00:27, 17.85it/s]
eval model::   3%|▎         | 17/500 [00:00<00:26, 18.24it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 18.75it/s]
eval model::   5%|▍         | 23/500 [00:01<00:24, 19.60it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 20.44it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 20.63it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.40it/s]
eval model::   7%|▋         | 34/500 [00:01<00:23, 19.42it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 21.87it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.22it/s]
eval model::   8%|▊         | 42/500 [00:02<00:29, 15.79it/s]
eval model::   9%|▉         | 44/500 [00:02<00:27, 16.43it/s]
eval model::   9%|▉         | 46/500 [00:02<00:27, 16.35it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 19.13it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 20.81it/s]
eval model::  11%|█         | 55/500 [00:02<00:19, 22.62it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:21, 21.04it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 16.21it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 16.15it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.72it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 19.94it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 20.93it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 21.19it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:18, 23.01it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:17, 24.22it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.91it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:20, 20.37it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:23, 17.80it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.90it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:20, 19.86it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.79it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.33it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 21.95it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:17, 22.01it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:17, 22.07it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:16, 23.08it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:16, 22.53it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:17, 21.86it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:16, 23.02it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 18.94it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 20.15it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:22, 16.44it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:20, 17.99it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:19, 18.37it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:19, 18.31it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.82it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.30it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:17, 19.59it/s]
eval model::  30%|███       | 151/500 [00:07<00:20, 17.29it/s]
eval model::  31%|███       | 154/500 [00:07<00:17, 19.88it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:16, 21.32it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:23, 14.60it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:20, 16.56it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:17, 18.86it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 19.66it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 18.84it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:19, 16.33it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:18, 17.87it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:20, 15.86it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:17, 18.08it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 19.71it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.32it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:13, 22.53it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 20.75it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:13, 21.70it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.61it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.49it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:21, 13.69it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:20, 14.53it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 14.97it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.52it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 14.06it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.34it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.28it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:20, 13.89it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 17.16it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:14, 19.23it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 24.21it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:10, 24.62it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:10, 25.89it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 20.99it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:10, 23.31it/s]
eval model::  50%|█████     | 250/500 [00:13<00:10, 22.76it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 21.84it/s]
eval model::  51%|█████     | 256/500 [00:13<00:10, 22.28it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 21.91it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 20.66it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:11, 20.96it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:11, 20.74it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:11, 20.30it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:11, 20.49it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 17.86it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 19.18it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:11, 19.63it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:20, 10.57it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:16, 12.92it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:13, 15.41it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:14, 13.80it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:11, 17.59it/s]
eval model::  60%|██████    | 302/500 [00:16<00:11, 17.22it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 18.75it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:08, 21.95it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:07, 23.69it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 23.03it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:07, 25.27it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:07, 22.28it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:07, 23.49it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 20.93it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 19.89it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 21.87it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:07, 21.31it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:10, 14.73it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:10, 15.54it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 17.83it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 18.39it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 20.17it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 22.28it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:07, 19.90it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:06, 20.96it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 18.22it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 17.26it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:08, 15.14it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:07, 17.10it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:07, 17.36it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 20.07it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 23.63it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 20.84it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:08, 13.95it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 16.32it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 17.42it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 19.37it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:04, 21.10it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 19.39it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 20.77it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 22.04it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 24.47it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 24.49it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 20.86it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:06, 13.03it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:05, 14.97it/s]
eval model::  85%|████████▌ | 425/500 [00:22<00:05, 13.95it/s]
eval model::  86%|████████▌ | 428/500 [00:22<00:04, 16.64it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 17.94it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:02, 21.94it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 24.23it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 25.23it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 26.19it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 23.13it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 22.72it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:01, 24.18it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 22.82it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 22.66it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 18.62it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 17.58it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 17.34it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 18.07it/s]
eval model::  95%|█████████▍| 473/500 [00:24<00:01, 18.29it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 18.20it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:01, 19.72it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 21.15it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 21.15it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 21.47it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 20.19it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 17.76it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 17.76it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 19.42it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 19.15it/s]
[2024/07/28 02:59:37] ppocr INFO: cur metric, precision: 0.7566844919786097, recall: 0.6812710640346654, hmean: 0.71700025335698, fps: 46.498502987539545
[2024/07/28 02:59:37] ppocr INFO: best metric, hmean: 0.7181174089068827, precision: 0.7568, recall: 0.6831969186326432, fps: 44.212831257019886, best_epoch: 1200
[2024/07/28 02:59:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:59:47] ppocr INFO: epoch: [1301/1500], global_step: 3903, lr: 0.001000, loss: 1.173487, loss_shrink_maps: 0.590119, loss_threshold_maps: 0.469313, loss_binary_maps: 0.117489, avg_reader_cost: 2.04666 s, avg_batch_cost: 2.44982 s, avg_samples: 12.5, ips: 5.10242 samples/s, eta: 1:00:22
[2024/07/28 02:59:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 02:59:57] ppocr INFO: epoch: [1302/1500], global_step: 3906, lr: 0.001000, loss: 1.174978, loss_shrink_maps: 0.590119, loss_threshold_maps: 0.467900, loss_binary_maps: 0.117489, avg_reader_cost: 1.51035 s, avg_batch_cost: 1.73861 s, avg_samples: 12.5, ips: 7.18964 samples/s, eta: 1:00:04
[2024/07/28 03:00:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:00:06] ppocr INFO: epoch: [1303/1500], global_step: 3909, lr: 0.001000, loss: 1.167784, loss_shrink_maps: 0.588224, loss_threshold_maps: 0.459181, loss_binary_maps: 0.117223, avg_reader_cost: 1.50717 s, avg_batch_cost: 1.73540 s, avg_samples: 12.5, ips: 7.20295 samples/s, eta: 0:59:46
[2024/07/28 03:00:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:00:15] ppocr INFO: epoch: [1304/1500], global_step: 3910, lr: 0.001000, loss: 1.167784, loss_shrink_maps: 0.588224, loss_threshold_maps: 0.459181, loss_binary_maps: 0.117223, avg_reader_cost: 0.42576 s, avg_batch_cost: 0.51582 s, avg_samples: 4.8, ips: 9.30556 samples/s, eta: 0:59:39
[2024/07/28 03:00:16] ppocr INFO: epoch: [1304/1500], global_step: 3912, lr: 0.001000, loss: 1.174978, loss_shrink_maps: 0.588224, loss_threshold_maps: 0.465798, loss_binary_maps: 0.117223, avg_reader_cost: 1.12262 s, avg_batch_cost: 1.26801 s, avg_samples: 7.7, ips: 6.07249 samples/s, eta: 0:59:27
[2024/07/28 03:00:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:00:26] ppocr INFO: epoch: [1305/1500], global_step: 3915, lr: 0.001000, loss: 1.174978, loss_shrink_maps: 0.588634, loss_threshold_maps: 0.465798, loss_binary_maps: 0.117307, avg_reader_cost: 1.54233 s, avg_batch_cost: 1.77935 s, avg_samples: 12.5, ips: 7.02503 samples/s, eta: 0:59:09
[2024/07/28 03:00:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:00:37] ppocr INFO: epoch: [1306/1500], global_step: 3918, lr: 0.001000, loss: 1.167784, loss_shrink_maps: 0.575032, loss_threshold_maps: 0.459181, loss_binary_maps: 0.114499, avg_reader_cost: 1.64444 s, avg_batch_cost: 1.87337 s, avg_samples: 12.5, ips: 6.67247 samples/s, eta: 0:58:51
[2024/07/28 03:00:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:00:46] ppocr INFO: epoch: [1307/1500], global_step: 3920, lr: 0.001000, loss: 1.162787, loss_shrink_maps: 0.571258, loss_threshold_maps: 0.453544, loss_binary_maps: 0.113729, avg_reader_cost: 0.93968 s, avg_batch_cost: 1.11555 s, avg_samples: 9.6, ips: 8.60562 samples/s, eta: 0:58:39
[2024/07/28 03:00:47] ppocr INFO: epoch: [1307/1500], global_step: 3921, lr: 0.001000, loss: 1.167784, loss_shrink_maps: 0.575032, loss_threshold_maps: 0.459181, loss_binary_maps: 0.114499, avg_reader_cost: 0.60385 s, avg_batch_cost: 0.65848 s, avg_samples: 2.9, ips: 4.40406 samples/s, eta: 0:58:33
[2024/07/28 03:00:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:00:57] ppocr INFO: epoch: [1308/1500], global_step: 3924, lr: 0.001000, loss: 1.142469, loss_shrink_maps: 0.566852, loss_threshold_maps: 0.453544, loss_binary_maps: 0.113103, avg_reader_cost: 1.60226 s, avg_batch_cost: 1.83093 s, avg_samples: 12.5, ips: 6.82711 samples/s, eta: 0:58:15
[2024/07/28 03:01:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:01:07] ppocr INFO: epoch: [1309/1500], global_step: 3927, lr: 0.001000, loss: 1.159176, loss_shrink_maps: 0.566852, loss_threshold_maps: 0.452999, loss_binary_maps: 0.113103, avg_reader_cost: 1.56764 s, avg_batch_cost: 1.79663 s, avg_samples: 12.5, ips: 6.95747 samples/s, eta: 0:57:56
[2024/07/28 03:01:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:01:17] ppocr INFO: epoch: [1310/1500], global_step: 3930, lr: 0.001000, loss: 1.171455, loss_shrink_maps: 0.566852, loss_threshold_maps: 0.462039, loss_binary_maps: 0.113103, avg_reader_cost: 1.62499 s, avg_batch_cost: 1.85289 s, avg_samples: 12.5, ips: 6.74624 samples/s, eta: 0:57:38
[2024/07/28 03:01:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:01:27] ppocr INFO: epoch: [1311/1500], global_step: 3933, lr: 0.001000, loss: 1.146470, loss_shrink_maps: 0.564700, loss_threshold_maps: 0.462039, loss_binary_maps: 0.112767, avg_reader_cost: 1.55692 s, avg_batch_cost: 1.79450 s, avg_samples: 12.5, ips: 6.96572 samples/s, eta: 0:57:20
[2024/07/28 03:01:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:01:36] ppocr INFO: epoch: [1312/1500], global_step: 3936, lr: 0.001000, loss: 1.134190, loss_shrink_maps: 0.562287, loss_threshold_maps: 0.457701, loss_binary_maps: 0.112259, avg_reader_cost: 1.48430 s, avg_batch_cost: 1.71361 s, avg_samples: 12.5, ips: 7.29455 samples/s, eta: 0:57:02
[2024/07/28 03:01:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:01:46] ppocr INFO: epoch: [1313/1500], global_step: 3939, lr: 0.001000, loss: 1.165397, loss_shrink_maps: 0.579208, loss_threshold_maps: 0.466417, loss_binary_maps: 0.115513, avg_reader_cost: 1.50564 s, avg_batch_cost: 1.74783 s, avg_samples: 12.5, ips: 7.15173 samples/s, eta: 0:56:43
[2024/07/28 03:01:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:01:55] ppocr INFO: epoch: [1314/1500], global_step: 3940, lr: 0.001000, loss: 1.146470, loss_shrink_maps: 0.564700, loss_threshold_maps: 0.466417, loss_binary_maps: 0.112767, avg_reader_cost: 0.41957 s, avg_batch_cost: 0.50222 s, avg_samples: 4.8, ips: 9.55750 samples/s, eta: 0:56:37
[2024/07/28 03:01:56] ppocr INFO: epoch: [1314/1500], global_step: 3942, lr: 0.001000, loss: 1.139584, loss_shrink_maps: 0.564700, loss_threshold_maps: 0.461854, loss_binary_maps: 0.112767, avg_reader_cost: 1.09554 s, avg_batch_cost: 1.24097 s, avg_samples: 7.7, ips: 6.20481 samples/s, eta: 0:56:25
[2024/07/28 03:02:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:02:06] ppocr INFO: epoch: [1315/1500], global_step: 3945, lr: 0.001000, loss: 1.139584, loss_shrink_maps: 0.562287, loss_threshold_maps: 0.466231, loss_binary_maps: 0.112259, avg_reader_cost: 1.63163 s, avg_batch_cost: 1.85956 s, avg_samples: 12.5, ips: 6.72200 samples/s, eta: 0:56:07
[2024/07/28 03:02:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:02:16] ppocr INFO: epoch: [1316/1500], global_step: 3948, lr: 0.001000, loss: 1.139584, loss_shrink_maps: 0.562287, loss_threshold_maps: 0.468952, loss_binary_maps: 0.112259, avg_reader_cost: 1.51307 s, avg_batch_cost: 1.74762 s, avg_samples: 12.5, ips: 7.15257 samples/s, eta: 0:55:49
[2024/07/28 03:02:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:02:26] ppocr INFO: epoch: [1317/1500], global_step: 3950, lr: 0.001000, loss: 1.121745, loss_shrink_maps: 0.546954, loss_threshold_maps: 0.466231, loss_binary_maps: 0.109021, avg_reader_cost: 0.93467 s, avg_batch_cost: 1.10750 s, avg_samples: 9.6, ips: 8.66818 samples/s, eta: 0:55:36
[2024/07/28 03:02:26] ppocr INFO: epoch: [1317/1500], global_step: 3951, lr: 0.001000, loss: 1.120380, loss_shrink_maps: 0.538627, loss_threshold_maps: 0.459820, loss_binary_maps: 0.107265, avg_reader_cost: 0.59925 s, avg_batch_cost: 0.65394 s, avg_samples: 2.9, ips: 4.43463 samples/s, eta: 0:55:30
[2024/07/28 03:02:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:02:36] ppocr INFO: epoch: [1318/1500], global_step: 3954, lr: 0.001000, loss: 1.087575, loss_shrink_maps: 0.533913, loss_threshold_maps: 0.439046, loss_binary_maps: 0.106075, avg_reader_cost: 1.54121 s, avg_batch_cost: 1.81773 s, avg_samples: 12.5, ips: 6.87669 samples/s, eta: 0:55:12
[2024/07/28 03:02:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:02:46] ppocr INFO: epoch: [1319/1500], global_step: 3957, lr: 0.001000, loss: 1.092174, loss_shrink_maps: 0.535893, loss_threshold_maps: 0.446718, loss_binary_maps: 0.106783, avg_reader_cost: 1.52388 s, avg_batch_cost: 1.75237 s, avg_samples: 12.5, ips: 7.13320 samples/s, eta: 0:54:54
[2024/07/28 03:02:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:02:56] ppocr INFO: epoch: [1320/1500], global_step: 3960, lr: 0.001000, loss: 1.092174, loss_shrink_maps: 0.537913, loss_threshold_maps: 0.446718, loss_binary_maps: 0.107265, avg_reader_cost: 1.52054 s, avg_batch_cost: 1.76079 s, avg_samples: 12.5, ips: 7.09907 samples/s, eta: 0:54:35

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.27it/s]
eval model::   1%|          | 3/500 [00:00<00:35, 14.12it/s]
eval model::   1%|          | 6/500 [00:00<00:29, 16.96it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.80it/s]
eval model::   2%|▏         | 11/500 [00:00<00:29, 16.85it/s]
eval model::   3%|▎         | 14/500 [00:00<00:28, 17.04it/s]
eval model::   3%|▎         | 17/500 [00:00<00:25, 18.77it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 18.59it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.44it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 21.05it/s]
eval model::   6%|▌         | 29/500 [00:01<00:24, 19.55it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 19.67it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 19.03it/s]
eval model::   8%|▊         | 38/500 [00:02<00:23, 19.88it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.51it/s]
eval model::   9%|▊         | 43/500 [00:02<00:26, 17.05it/s]
eval model::   9%|▉         | 46/500 [00:02<00:25, 17.70it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 19.87it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 20.26it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.68it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 19.06it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:28, 15.45it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.32it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.13it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 21.35it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 20.14it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:19, 21.38it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:19, 21.24it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:22, 18.78it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:21, 19.22it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:22, 18.22it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:25, 16.17it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:24, 16.95it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:22, 18.18it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:20, 19.66it/s]
eval model::  20%|██        | 101/500 [00:05<00:21, 18.52it/s]
eval model::  21%|██        | 104/500 [00:05<00:19, 20.28it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 22.19it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 21.71it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:17, 22.00it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:17, 21.64it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:16, 23.52it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:17, 21.53it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:20, 18.57it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 20.48it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:18, 20.05it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:17, 20.55it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:18, 19.34it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:19, 18.46it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:18, 19.39it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:19, 18.56it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.29it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.50it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 20.18it/s]
eval model::  31%|███       | 156/500 [00:08<00:16, 21.32it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 20.89it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:22, 14.75it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:22, 15.21it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:19, 16.93it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:17, 18.90it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:17, 18.22it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:19, 16.34it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:17, 18.29it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:19, 16.41it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 19.20it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 21.72it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 22.15it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:14, 21.93it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 21.06it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.01it/s]
eval model::  40%|████      | 201/500 [00:10<00:15, 19.66it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 21.61it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:23, 12.23it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:22, 13.08it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:21, 13.26it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:20, 14.17it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:23, 12.27it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:24, 11.44it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:19, 14.21it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.83it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.69it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:12, 21.40it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 22.54it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:11, 22.28it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.99it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 19.19it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 21.11it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 22.68it/s]
eval model::  50%|█████     | 251/500 [00:13<00:11, 22.12it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 20.80it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 20.68it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 22.21it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:11, 20.00it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:12, 19.36it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.57it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 19.15it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.53it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 17.19it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:14, 15.74it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:12, 17.80it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:19, 11.27it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:15, 13.65it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 16.18it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:13, 15.69it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:14, 13.79it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 16.44it/s]
eval model::  60%|██████    | 301/500 [00:16<00:13, 15.20it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 17.54it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:09, 19.41it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:08, 21.47it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 22.88it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:08, 21.13it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:07, 22.77it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.99it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:07, 22.06it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 20.82it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 19.56it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 21.53it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 19.51it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 14.69it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.32it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:07, 19.26it/s]
eval model::  70%|███████   | 351/500 [00:18<00:06, 22.01it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 21.95it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 21.20it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:08, 17.37it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:08, 17.00it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:08, 16.36it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 17.22it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:07, 17.12it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 18.27it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:06, 20.20it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:06, 19.20it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 20.61it/s]
eval model::  77%|███████▋  | 387/500 [00:21<00:08, 12.71it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:07, 15.13it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 16.86it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 17.34it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 18.97it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 17.62it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 19.55it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:04, 21.60it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:03, 23.38it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 24.48it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:03, 22.09it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 15.36it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:04, 15.96it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:04, 15.32it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:04, 16.21it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 19.23it/s]
eval model::  87%|████████▋ | 433/500 [00:23<00:03, 19.89it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:03, 20.81it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 22.43it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 23.07it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 22.84it/s]
eval model::  90%|████████▉ | 448/500 [00:24<00:02, 21.76it/s]
eval model::  90%|█████████ | 451/500 [00:24<00:02, 22.43it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:01, 23.65it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 24.21it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 23.07it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 19.84it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:01, 17.47it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 17.83it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 18.74it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 18.92it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 20.42it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:01, 18.68it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:01, 18.79it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 20.01it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 19.30it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 20.47it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 18.19it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 17.44it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 18.76it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.69it/s]
[2024/07/28 03:03:23] ppocr INFO: cur metric, precision: 0.7213114754098361, recall: 0.6990852190659606, hmean: 0.7100244498777506, fps: 46.0676883652964
[2024/07/28 03:03:23] ppocr INFO: best metric, hmean: 0.7181174089068827, precision: 0.7568, recall: 0.6831969186326432, fps: 44.212831257019886, best_epoch: 1200
[2024/07/28 03:03:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:03:32] ppocr INFO: epoch: [1321/1500], global_step: 3963, lr: 0.001000, loss: 1.079990, loss_shrink_maps: 0.533913, loss_threshold_maps: 0.439046, loss_binary_maps: 0.106075, avg_reader_cost: 1.68552 s, avg_batch_cost: 1.98424 s, avg_samples: 12.5, ips: 6.29964 samples/s, eta: 0:54:17
[2024/07/28 03:03:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:03:42] ppocr INFO: epoch: [1322/1500], global_step: 3966, lr: 0.001000, loss: 1.079990, loss_shrink_maps: 0.534539, loss_threshold_maps: 0.435928, loss_binary_maps: 0.106427, avg_reader_cost: 1.53735 s, avg_batch_cost: 1.77432 s, avg_samples: 12.5, ips: 7.04496 samples/s, eta: 0:53:59
[2024/07/28 03:03:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:03:51] ppocr INFO: epoch: [1323/1500], global_step: 3969, lr: 0.001000, loss: 1.076328, loss_shrink_maps: 0.533907, loss_threshold_maps: 0.436312, loss_binary_maps: 0.106109, avg_reader_cost: 1.48449 s, avg_batch_cost: 1.72167 s, avg_samples: 12.5, ips: 7.26037 samples/s, eta: 0:53:41
[2024/07/28 03:03:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:04:00] ppocr INFO: epoch: [1324/1500], global_step: 3970, lr: 0.001000, loss: 1.074758, loss_shrink_maps: 0.532370, loss_threshold_maps: 0.431175, loss_binary_maps: 0.106109, avg_reader_cost: 0.40460 s, avg_batch_cost: 0.50132 s, avg_samples: 4.8, ips: 9.57471 samples/s, eta: 0:53:35
[2024/07/28 03:04:01] ppocr INFO: epoch: [1324/1500], global_step: 3972, lr: 0.001000, loss: 1.074758, loss_shrink_maps: 0.532370, loss_threshold_maps: 0.431175, loss_binary_maps: 0.106109, avg_reader_cost: 1.09401 s, avg_batch_cost: 1.23976 s, avg_samples: 7.7, ips: 6.21086 samples/s, eta: 0:53:23
[2024/07/28 03:04:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:04:11] ppocr INFO: epoch: [1325/1500], global_step: 3975, lr: 0.001000, loss: 1.065720, loss_shrink_maps: 0.528917, loss_threshold_maps: 0.426586, loss_binary_maps: 0.105463, avg_reader_cost: 1.49502 s, avg_batch_cost: 1.73258 s, avg_samples: 12.5, ips: 7.21465 samples/s, eta: 0:53:04
[2024/07/28 03:04:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:04:21] ppocr INFO: epoch: [1326/1500], global_step: 3978, lr: 0.001000, loss: 1.074758, loss_shrink_maps: 0.532370, loss_threshold_maps: 0.432092, loss_binary_maps: 0.106109, avg_reader_cost: 1.54018 s, avg_batch_cost: 1.77827 s, avg_samples: 12.5, ips: 7.02930 samples/s, eta: 0:52:46
[2024/07/28 03:04:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:04:30] ppocr INFO: epoch: [1327/1500], global_step: 3980, lr: 0.001000, loss: 1.074758, loss_shrink_maps: 0.538586, loss_threshold_maps: 0.432092, loss_binary_maps: 0.107121, avg_reader_cost: 0.96591 s, avg_batch_cost: 1.14863 s, avg_samples: 9.6, ips: 8.35780 samples/s, eta: 0:52:34
[2024/07/28 03:04:31] ppocr INFO: epoch: [1327/1500], global_step: 3981, lr: 0.001000, loss: 1.065720, loss_shrink_maps: 0.538586, loss_threshold_maps: 0.432092, loss_binary_maps: 0.107121, avg_reader_cost: 0.62039 s, avg_batch_cost: 0.67482 s, avg_samples: 2.9, ips: 4.29747 samples/s, eta: 0:52:28
[2024/07/28 03:04:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:04:41] ppocr INFO: epoch: [1328/1500], global_step: 3984, lr: 0.001000, loss: 1.104219, loss_shrink_maps: 0.544628, loss_threshold_maps: 0.451946, loss_binary_maps: 0.108180, avg_reader_cost: 1.59998 s, avg_batch_cost: 1.83832 s, avg_samples: 12.5, ips: 6.79969 samples/s, eta: 0:52:10
[2024/07/28 03:04:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:04:51] ppocr INFO: epoch: [1329/1500], global_step: 3987, lr: 0.001000, loss: 1.104219, loss_shrink_maps: 0.544628, loss_threshold_maps: 0.448631, loss_binary_maps: 0.108180, avg_reader_cost: 1.55494 s, avg_batch_cost: 1.82900 s, avg_samples: 12.5, ips: 6.83435 samples/s, eta: 0:51:51
[2024/07/28 03:04:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:05:01] ppocr INFO: epoch: [1330/1500], global_step: 3990, lr: 0.001000, loss: 1.136938, loss_shrink_maps: 0.565117, loss_threshold_maps: 0.451946, loss_binary_maps: 0.111984, avg_reader_cost: 1.57801 s, avg_batch_cost: 1.80692 s, avg_samples: 12.5, ips: 6.91784 samples/s, eta: 0:51:33
[2024/07/28 03:05:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:05:11] ppocr INFO: epoch: [1331/1500], global_step: 3993, lr: 0.001000, loss: 1.181784, loss_shrink_maps: 0.586892, loss_threshold_maps: 0.456266, loss_binary_maps: 0.116566, avg_reader_cost: 1.52114 s, avg_batch_cost: 1.75328 s, avg_samples: 12.5, ips: 7.12949 samples/s, eta: 0:51:15
[2024/07/28 03:05:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:05:21] ppocr INFO: epoch: [1332/1500], global_step: 3996, lr: 0.001000, loss: 1.181784, loss_shrink_maps: 0.591420, loss_threshold_maps: 0.456266, loss_binary_maps: 0.117849, avg_reader_cost: 1.62819 s, avg_batch_cost: 1.87426 s, avg_samples: 12.5, ips: 6.66930 samples/s, eta: 0:50:57
[2024/07/28 03:05:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:05:31] ppocr INFO: epoch: [1333/1500], global_step: 3999, lr: 0.001000, loss: 1.128039, loss_shrink_maps: 0.564150, loss_threshold_maps: 0.441866, loss_binary_maps: 0.112173, avg_reader_cost: 1.53478 s, avg_batch_cost: 1.76308 s, avg_samples: 12.5, ips: 7.08986 samples/s, eta: 0:50:39
[2024/07/28 03:05:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:05:40] ppocr INFO: epoch: [1334/1500], global_step: 4000, lr: 0.001000, loss: 1.128039, loss_shrink_maps: 0.562911, loss_threshold_maps: 0.441866, loss_binary_maps: 0.112130, avg_reader_cost: 0.45668 s, avg_batch_cost: 0.57157 s, avg_samples: 4.8, ips: 8.39785 samples/s, eta: 0:50:32
[2024/07/28 03:05:42] ppocr INFO: epoch: [1334/1500], global_step: 4002, lr: 0.001000, loss: 1.166424, loss_shrink_maps: 0.587928, loss_threshold_maps: 0.446409, loss_binary_maps: 0.117217, avg_reader_cost: 1.23540 s, avg_batch_cost: 1.38111 s, avg_samples: 7.7, ips: 5.57524 samples/s, eta: 0:50:21
[2024/07/28 03:05:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:05:52] ppocr INFO: epoch: [1335/1500], global_step: 4005, lr: 0.001000, loss: 1.184529, loss_shrink_maps: 0.595332, loss_threshold_maps: 0.447163, loss_binary_maps: 0.118668, avg_reader_cost: 1.50504 s, avg_batch_cost: 1.73410 s, avg_samples: 12.5, ips: 7.20835 samples/s, eta: 0:50:02
[2024/07/28 03:05:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:06:01] ppocr INFO: epoch: [1336/1500], global_step: 4008, lr: 0.001000, loss: 1.184529, loss_shrink_maps: 0.595745, loss_threshold_maps: 0.462280, loss_binary_maps: 0.118818, avg_reader_cost: 1.48171 s, avg_batch_cost: 1.72635 s, avg_samples: 12.5, ips: 7.24070 samples/s, eta: 0:49:44
[2024/07/28 03:06:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:06:11] ppocr INFO: epoch: [1337/1500], global_step: 4010, lr: 0.001000, loss: 1.180238, loss_shrink_maps: 0.593836, loss_threshold_maps: 0.462280, loss_binary_maps: 0.118461, avg_reader_cost: 0.92009 s, avg_batch_cost: 1.11678 s, avg_samples: 9.6, ips: 8.59616 samples/s, eta: 0:49:32
[2024/07/28 03:06:11] ppocr INFO: epoch: [1337/1500], global_step: 4011, lr: 0.001000, loss: 1.162854, loss_shrink_maps: 0.587928, loss_threshold_maps: 0.454995, loss_binary_maps: 0.117217, avg_reader_cost: 0.60413 s, avg_batch_cost: 0.65894 s, avg_samples: 2.9, ips: 4.40098 samples/s, eta: 0:49:26
[2024/07/28 03:06:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:06:22] ppocr INFO: epoch: [1338/1500], global_step: 4014, lr: 0.001000, loss: 1.144837, loss_shrink_maps: 0.570042, loss_threshold_maps: 0.445139, loss_binary_maps: 0.113647, avg_reader_cost: 1.66573 s, avg_batch_cost: 1.95318 s, avg_samples: 12.5, ips: 6.39981 samples/s, eta: 0:49:08
[2024/07/28 03:06:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:06:32] ppocr INFO: epoch: [1339/1500], global_step: 4017, lr: 0.001000, loss: 1.180621, loss_shrink_maps: 0.584108, loss_threshold_maps: 0.463280, loss_binary_maps: 0.116554, avg_reader_cost: 1.55828 s, avg_batch_cost: 1.81970 s, avg_samples: 12.5, ips: 6.86927 samples/s, eta: 0:48:49
[2024/07/28 03:06:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:06:42] ppocr INFO: epoch: [1340/1500], global_step: 4020, lr: 0.001000, loss: 1.190375, loss_shrink_maps: 0.598607, loss_threshold_maps: 0.476808, loss_binary_maps: 0.119673, avg_reader_cost: 1.51742 s, avg_batch_cost: 1.74587 s, avg_samples: 12.5, ips: 7.15977 samples/s, eta: 0:48:31

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.38it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.20it/s]
eval model::   1%|▏         | 7/500 [00:00<00:23, 21.26it/s]
eval model::   2%|▏         | 10/500 [00:00<00:25, 19.14it/s]
eval model::   2%|▏         | 12/500 [00:00<00:25, 18.90it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.42it/s]
eval model::   4%|▎         | 18/500 [00:00<00:21, 21.96it/s]
eval model::   4%|▍         | 21/500 [00:01<00:24, 19.37it/s]
eval model::   5%|▍         | 24/500 [00:01<00:22, 21.03it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 21.49it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 21.70it/s]
eval model::   7%|▋         | 33/500 [00:01<00:21, 21.26it/s]
eval model::   7%|▋         | 36/500 [00:01<00:23, 19.50it/s]
eval model::   8%|▊         | 39/500 [00:01<00:24, 18.92it/s]
eval model::   8%|▊         | 42/500 [00:02<00:24, 18.36it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.34it/s]
eval model::  10%|▉         | 48/500 [00:02<00:21, 20.75it/s]
eval model::  10%|█         | 51/500 [00:02<00:21, 21.12it/s]
eval model::  11%|█         | 54/500 [00:02<00:20, 22.18it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:21, 20.89it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:23, 18.54it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:31, 13.94it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:29, 14.54it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:23, 18.38it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 20.47it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:22, 19.14it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:20, 20.63it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 23.16it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:17, 24.14it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:19, 20.89it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.20it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.69it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:20, 20.16it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.87it/s]
eval model::  20%|██        | 102/500 [00:05<00:20, 19.28it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.45it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.64it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:18, 21.19it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:20, 19.21it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:18, 20.11it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:20, 18.30it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:20, 18.34it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:23, 15.96it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:20, 17.80it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:21, 17.20it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:22, 16.61it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:18, 19.89it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.84it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 19.08it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:19, 18.62it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.60it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.63it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.44it/s]
eval model::  30%|███       | 152/500 [00:07<00:19, 17.75it/s]
eval model::  31%|███       | 154/500 [00:07<00:19, 18.00it/s]
eval model::  31%|███       | 156/500 [00:08<00:18, 18.17it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:18, 18.72it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:27, 12.55it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:22, 15.26it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:19, 16.94it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:17, 19.00it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:17, 18.32it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:20, 15.78it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:17, 18.08it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:20, 15.86it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:18, 16.76it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.66it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.24it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:14, 21.39it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 21.15it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:13, 21.74it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 21.89it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.50it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:23, 12.44it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:21, 13.28it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 14.19it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.24it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:22, 12.65it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:24, 11.59it/s]
eval model::  44%|████▍     | 219/500 [00:11<00:21, 13.12it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:21, 12.77it/s]
eval model::  45%|████▍     | 224/500 [00:12<00:17, 15.84it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:14, 18.45it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 20.64it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 20.66it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 20.69it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.20it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 19.46it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 19.86it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:11, 21.15it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 21.95it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.41it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 21.25it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 21.88it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:12, 18.49it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 19.96it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:12, 19.11it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 18.99it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:13, 16.09it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:12, 17.89it/s]
eval model::  56%|█████▌    | 280/500 [00:15<00:12, 18.17it/s]
eval model::  57%|█████▋    | 283/500 [00:15<00:11, 19.59it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:19, 11.04it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:15, 13.58it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:14, 14.15it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:13, 15.04it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:15, 12.98it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 15.90it/s]
eval model::  60%|██████    | 301/500 [00:16<00:13, 15.00it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.65it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.89it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.82it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:08, 21.58it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:08, 21.19it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:07, 22.95it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.99it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:07, 21.93it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 21.09it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:07, 21.55it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:07, 21.52it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 20.53it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:11, 14.49it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.19it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 17.77it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:08, 17.95it/s]
eval model::  70%|███████   | 351/500 [00:19<00:07, 20.11it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 20.80it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 20.73it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.76it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.70it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 17.64it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:07, 18.06it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:06, 19.38it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:06, 19.34it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 21.25it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 21.63it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 23.36it/s]
eval model::  77%|███████▋  | 386/500 [00:21<00:08, 13.77it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:06, 16.23it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:06, 16.47it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:06, 16.89it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 19.28it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 18.75it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 20.11it/s]
eval model::  81%|████████▏ | 407/500 [00:22<00:04, 20.75it/s]
eval model::  82%|████████▏ | 410/500 [00:22<00:03, 22.57it/s]
eval model::  83%|████████▎ | 413/500 [00:22<00:03, 21.96it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 20.00it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 19.46it/s]
eval model::  84%|████████▍ | 422/500 [00:23<00:05, 13.01it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 13.06it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.06it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 17.11it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:04, 16.68it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 17.86it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:03, 19.29it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:03, 18.95it/s]
eval model::  89%|████████▉ | 444/500 [00:24<00:02, 20.23it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 20.95it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 19.84it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 21.68it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:01, 22.76it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 21.90it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 18.17it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:01, 18.24it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 16.97it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 16.78it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 17.30it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 17.81it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 20.20it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:01, 20.03it/s]
eval model::  96%|█████████▋| 482/500 [00:26<00:00, 19.68it/s]
eval model::  97%|█████████▋| 485/500 [00:26<00:00, 19.74it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 17.32it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 18.89it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 16.09it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 17.85it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 20.28it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.51it/s]
[2024/07/28 03:07:09] ppocr INFO: cur metric, precision: 0.736788096459723, recall: 0.6913818006740491, hmean: 0.7133631395926479, fps: 45.903713378463245
[2024/07/28 03:07:09] ppocr INFO: best metric, hmean: 0.7181174089068827, precision: 0.7568, recall: 0.6831969186326432, fps: 44.212831257019886, best_epoch: 1200
[2024/07/28 03:07:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:07:17] ppocr INFO: epoch: [1341/1500], global_step: 4023, lr: 0.001000, loss: 1.158652, loss_shrink_maps: 0.571718, loss_threshold_maps: 0.462206, loss_binary_maps: 0.114078, avg_reader_cost: 1.54468 s, avg_batch_cost: 1.79843 s, avg_samples: 12.5, ips: 6.95049 samples/s, eta: 0:48:13
[2024/07/28 03:07:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:07:28] ppocr INFO: epoch: [1342/1500], global_step: 4026, lr: 0.001000, loss: 1.148320, loss_shrink_maps: 0.571718, loss_threshold_maps: 0.457041, loss_binary_maps: 0.114078, avg_reader_cost: 1.54604 s, avg_batch_cost: 1.78860 s, avg_samples: 12.5, ips: 6.98870 samples/s, eta: 0:47:55
[2024/07/28 03:07:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:07:37] ppocr INFO: epoch: [1343/1500], global_step: 4029, lr: 0.001000, loss: 1.130039, loss_shrink_maps: 0.569371, loss_threshold_maps: 0.457041, loss_binary_maps: 0.113448, avg_reader_cost: 1.52725 s, avg_batch_cost: 1.75572 s, avg_samples: 12.5, ips: 7.11960 samples/s, eta: 0:47:36
[2024/07/28 03:07:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:07:46] ppocr INFO: epoch: [1344/1500], global_step: 4030, lr: 0.001000, loss: 1.137724, loss_shrink_maps: 0.583437, loss_threshold_maps: 0.457041, loss_binary_maps: 0.116355, avg_reader_cost: 0.41555 s, avg_batch_cost: 0.50584 s, avg_samples: 4.8, ips: 9.48913 samples/s, eta: 0:47:30
[2024/07/28 03:07:47] ppocr INFO: epoch: [1344/1500], global_step: 4032, lr: 0.001000, loss: 1.170289, loss_shrink_maps: 0.587679, loss_threshold_maps: 0.462206, loss_binary_maps: 0.117139, avg_reader_cost: 1.10273 s, avg_batch_cost: 1.24824 s, avg_samples: 7.7, ips: 6.16871 samples/s, eta: 0:47:18
[2024/07/28 03:07:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:07:57] ppocr INFO: epoch: [1345/1500], global_step: 4035, lr: 0.001000, loss: 1.136928, loss_shrink_maps: 0.572338, loss_threshold_maps: 0.471808, loss_binary_maps: 0.114008, avg_reader_cost: 1.49225 s, avg_batch_cost: 1.74020 s, avg_samples: 12.5, ips: 7.18308 samples/s, eta: 0:47:00
[2024/07/28 03:08:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:08:07] ppocr INFO: epoch: [1346/1500], global_step: 4038, lr: 0.001000, loss: 1.155209, loss_shrink_maps: 0.585332, loss_threshold_maps: 0.469481, loss_binary_maps: 0.116510, avg_reader_cost: 1.54622 s, avg_batch_cost: 1.80981 s, avg_samples: 12.5, ips: 6.90682 samples/s, eta: 0:46:42
[2024/07/28 03:08:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:08:17] ppocr INFO: epoch: [1347/1500], global_step: 4040, lr: 0.001000, loss: 1.155209, loss_shrink_maps: 0.585332, loss_threshold_maps: 0.469481, loss_binary_maps: 0.116510, avg_reader_cost: 0.92158 s, avg_batch_cost: 1.09861 s, avg_samples: 9.6, ips: 8.73829 samples/s, eta: 0:46:29
[2024/07/28 03:08:17] ppocr INFO: epoch: [1347/1500], global_step: 4041, lr: 0.001000, loss: 1.155209, loss_shrink_maps: 0.585332, loss_threshold_maps: 0.466001, loss_binary_maps: 0.116510, avg_reader_cost: 0.59489 s, avg_batch_cost: 0.65000 s, avg_samples: 2.9, ips: 4.46151 samples/s, eta: 0:46:23
[2024/07/28 03:08:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:08:27] ppocr INFO: epoch: [1348/1500], global_step: 4044, lr: 0.001000, loss: 1.112575, loss_shrink_maps: 0.555501, loss_threshold_maps: 0.458554, loss_binary_maps: 0.110672, avg_reader_cost: 1.48243 s, avg_batch_cost: 1.73132 s, avg_samples: 12.5, ips: 7.21993 samples/s, eta: 0:46:05
[2024/07/28 03:08:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:08:37] ppocr INFO: epoch: [1349/1500], global_step: 4047, lr: 0.001000, loss: 1.095497, loss_shrink_maps: 0.538242, loss_threshold_maps: 0.455529, loss_binary_maps: 0.107205, avg_reader_cost: 1.55551 s, avg_batch_cost: 1.83583 s, avg_samples: 12.5, ips: 6.80889 samples/s, eta: 0:45:47
[2024/07/28 03:08:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:08:48] ppocr INFO: epoch: [1350/1500], global_step: 4050, lr: 0.001000, loss: 1.095497, loss_shrink_maps: 0.544506, loss_threshold_maps: 0.442302, loss_binary_maps: 0.108648, avg_reader_cost: 1.73966 s, avg_batch_cost: 1.99180 s, avg_samples: 12.5, ips: 6.27572 samples/s, eta: 0:45:29
[2024/07/28 03:08:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:08:59] ppocr INFO: epoch: [1351/1500], global_step: 4053, lr: 0.001000, loss: 1.095497, loss_shrink_maps: 0.549357, loss_threshold_maps: 0.442302, loss_binary_maps: 0.109349, avg_reader_cost: 1.66366 s, avg_batch_cost: 1.89199 s, avg_samples: 12.5, ips: 6.60679 samples/s, eta: 0:45:11
[2024/07/28 03:09:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:09:09] ppocr INFO: epoch: [1352/1500], global_step: 4056, lr: 0.001000, loss: 1.072155, loss_shrink_maps: 0.541095, loss_threshold_maps: 0.436296, loss_binary_maps: 0.107886, avg_reader_cost: 1.63584 s, avg_batch_cost: 1.87137 s, avg_samples: 12.5, ips: 6.67961 samples/s, eta: 0:44:53
[2024/07/28 03:09:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:09:19] ppocr INFO: epoch: [1353/1500], global_step: 4059, lr: 0.001000, loss: 1.052342, loss_shrink_maps: 0.519756, loss_threshold_maps: 0.432994, loss_binary_maps: 0.103697, avg_reader_cost: 1.52799 s, avg_batch_cost: 1.76768 s, avg_samples: 12.5, ips: 7.07140 samples/s, eta: 0:44:34
[2024/07/28 03:09:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:09:27] ppocr INFO: epoch: [1354/1500], global_step: 4060, lr: 0.001000, loss: 1.042979, loss_shrink_maps: 0.518038, loss_threshold_maps: 0.431650, loss_binary_maps: 0.103231, avg_reader_cost: 0.41024 s, avg_batch_cost: 0.49846 s, avg_samples: 4.8, ips: 9.62956 samples/s, eta: 0:44:28
[2024/07/28 03:09:29] ppocr INFO: epoch: [1354/1500], global_step: 4062, lr: 0.001000, loss: 1.057370, loss_shrink_maps: 0.520926, loss_threshold_maps: 0.432994, loss_binary_maps: 0.103785, avg_reader_cost: 1.08839 s, avg_batch_cost: 1.23430 s, avg_samples: 7.7, ips: 6.23834 samples/s, eta: 0:44:16
[2024/07/28 03:09:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:09:39] ppocr INFO: epoch: [1355/1500], global_step: 4065, lr: 0.001000, loss: 1.071343, loss_shrink_maps: 0.541095, loss_threshold_maps: 0.435090, loss_binary_maps: 0.107886, avg_reader_cost: 1.55127 s, avg_batch_cost: 1.78051 s, avg_samples: 12.5, ips: 7.02046 samples/s, eta: 0:43:58
[2024/07/28 03:09:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:09:49] ppocr INFO: epoch: [1356/1500], global_step: 4068, lr: 0.001000, loss: 1.066472, loss_shrink_maps: 0.532041, loss_threshold_maps: 0.447399, loss_binary_maps: 0.105889, avg_reader_cost: 1.51007 s, avg_batch_cost: 1.73957 s, avg_samples: 12.5, ips: 7.18567 samples/s, eta: 0:43:40
[2024/07/28 03:09:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:09:59] ppocr INFO: epoch: [1357/1500], global_step: 4070, lr: 0.001000, loss: 1.066472, loss_shrink_maps: 0.532041, loss_threshold_maps: 0.448775, loss_binary_maps: 0.105889, avg_reader_cost: 0.93063 s, avg_batch_cost: 1.12380 s, avg_samples: 9.6, ips: 8.54246 samples/s, eta: 0:43:27
[2024/07/28 03:09:59] ppocr INFO: epoch: [1357/1500], global_step: 4071, lr: 0.001000, loss: 1.066472, loss_shrink_maps: 0.532041, loss_threshold_maps: 0.448775, loss_binary_maps: 0.105889, avg_reader_cost: 0.60754 s, avg_batch_cost: 0.66281 s, avg_samples: 2.9, ips: 4.37533 samples/s, eta: 0:43:21
[2024/07/28 03:10:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:10:09] ppocr INFO: epoch: [1358/1500], global_step: 4074, lr: 0.001000, loss: 1.103176, loss_shrink_maps: 0.548392, loss_threshold_maps: 0.451808, loss_binary_maps: 0.109265, avg_reader_cost: 1.63301 s, avg_batch_cost: 1.87558 s, avg_samples: 12.5, ips: 6.66462 samples/s, eta: 0:43:03
[2024/07/28 03:10:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:10:19] ppocr INFO: epoch: [1359/1500], global_step: 4077, lr: 0.001000, loss: 1.144864, loss_shrink_maps: 0.576725, loss_threshold_maps: 0.451808, loss_binary_maps: 0.115031, avg_reader_cost: 1.53279 s, avg_batch_cost: 1.76056 s, avg_samples: 12.5, ips: 7.10001 samples/s, eta: 0:42:45
[2024/07/28 03:10:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:10:29] ppocr INFO: epoch: [1360/1500], global_step: 4080, lr: 0.001000, loss: 1.160761, loss_shrink_maps: 0.584651, loss_threshold_maps: 0.456354, loss_binary_maps: 0.116062, avg_reader_cost: 1.49442 s, avg_batch_cost: 1.72970 s, avg_samples: 12.5, ips: 7.22670 samples/s, eta: 0:42:27

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:54,  9.11it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 17.72it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.03it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.86it/s]
eval model::   2%|▏         | 10/500 [00:00<00:26, 18.15it/s]
eval model::   2%|▏         | 12/500 [00:00<00:26, 18.11it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.65it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.90it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.69it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 22.19it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 20.85it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 21.63it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 19.58it/s]
eval model::   7%|▋         | 35/500 [00:01<00:23, 19.55it/s]
eval model::   8%|▊         | 38/500 [00:01<00:22, 20.54it/s]
eval model::   8%|▊         | 41/500 [00:02<00:24, 19.05it/s]
eval model::   9%|▊         | 43/500 [00:02<00:27, 16.85it/s]
eval model::   9%|▉         | 46/500 [00:02<00:25, 17.82it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 20.26it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 20.09it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 19.49it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 18.46it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.29it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.66it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:20, 20.80it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 22.12it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:18, 23.27it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:17, 23.76it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:17, 24.75it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:17, 24.28it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:18, 22.87it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:19, 21.17it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:19, 20.51it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:19, 21.10it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:18, 21.34it/s]
eval model::  20%|██        | 100/500 [00:05<00:21, 19.00it/s]
eval model::  20%|██        | 102/500 [00:05<00:21, 18.85it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.80it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:16, 23.00it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:17, 22.64it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:20, 18.59it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:19, 19.78it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:20, 18.15it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:20, 18.35it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:25, 14.51it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:23, 15.68it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:26, 13.73it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:26, 14.13it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:21, 17.13it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:20, 17.45it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:20, 17.65it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.04it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:19, 18.02it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:21, 16.48it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 18.62it/s]
eval model::  30%|███       | 152/500 [00:07<00:20, 16.60it/s]
eval model::  31%|███       | 155/500 [00:08<00:17, 19.47it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:16, 21.03it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:22, 14.79it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:22, 15.18it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:19, 17.50it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:18, 17.82it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:17, 18.55it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 15.67it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:18, 17.26it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:17, 17.98it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:15, 20.31it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 22.15it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:13, 22.84it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:13, 22.12it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 20.48it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.91it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.61it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.23it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:19, 14.72it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:19, 15.01it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 14.96it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.37it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 14.12it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 13.46it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 16.48it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 14.67it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:14, 18.68it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:11, 23.01it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 24.01it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:11, 23.36it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:10, 23.97it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:13, 18.48it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:13, 19.31it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:13, 19.37it/s]
eval model::  50%|█████     | 251/500 [00:13<00:13, 18.54it/s]
eval model::  51%|█████     | 253/500 [00:13<00:13, 18.77it/s]
eval model::  51%|█████     | 255/500 [00:13<00:13, 18.19it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:12, 19.68it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:12, 18.99it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:13, 18.03it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:13, 16.86it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:14, 16.62it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:15, 15.05it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:14, 16.06it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:14, 15.93it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:13, 16.76it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:13, 16.40it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:11, 18.86it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:15, 13.97it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:14, 14.92it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:11, 17.88it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:10, 19.39it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:13, 14.84it/s]
eval model::  60%|██████    | 300/500 [00:16<00:11, 17.02it/s]
eval model::  61%|██████    | 303/500 [00:16<00:10, 18.45it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 19.08it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:08, 21.33it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:08, 22.48it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 21.86it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:07, 23.42it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 22.18it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 21.82it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 23.66it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:07, 22.93it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:08, 20.61it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 20.59it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:07, 20.43it/s]
eval model::  68%|██████▊   | 342/500 [00:18<00:09, 16.66it/s]
eval model::  69%|██████▉   | 345/500 [00:18<00:08, 18.89it/s]
eval model::  70%|██████▉   | 348/500 [00:18<00:07, 19.97it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 22.84it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 22.58it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 21.95it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:06, 19.94it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 19.10it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 18.97it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:06, 19.00it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:06, 19.58it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:05, 21.29it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 22.98it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 20.85it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 22.69it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:07, 15.35it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 17.61it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:05, 18.37it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 20.06it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:04, 21.70it/s]
eval model::  80%|████████  | 401/500 [00:21<00:04, 20.21it/s]
eval model::  81%|████████  | 404/500 [00:21<00:04, 21.44it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 23.23it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:03, 25.48it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 24.63it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 22.61it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 19.90it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:03, 20.49it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:03, 20.52it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 22.33it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 21.78it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 22.64it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 23.85it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 24.51it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 26.64it/s]
eval model::  90%|████████▉ | 449/500 [00:23<00:02, 22.26it/s]
eval model::  90%|█████████ | 452/500 [00:23<00:02, 21.60it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:02, 21.85it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:01, 22.16it/s]
eval model::  92%|█████████▏| 461/500 [00:23<00:01, 20.80it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 20.93it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 17.69it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 19.65it/s]
eval model::  95%|█████████▍| 473/500 [00:24<00:01, 20.50it/s]
eval model::  95%|█████████▌| 476/500 [00:24<00:01, 20.96it/s]
eval model::  96%|█████████▌| 479/500 [00:24<00:00, 22.46it/s]
eval model::  96%|█████████▋| 482/500 [00:24<00:00, 21.26it/s]
eval model::  97%|█████████▋| 485/500 [00:24<00:00, 22.91it/s]
eval model::  98%|█████████▊| 488/500 [00:25<00:00, 21.61it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 19.34it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 18.22it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 17.51it/s]
eval model:: 100%|█████████▉| 499/500 [00:25<00:00, 19.29it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.41it/s]
[2024/07/28 03:10:55] ppocr INFO: cur metric, precision: 0.7631284916201118, recall: 0.6576793452094367, hmean: 0.7064908197569175, fps: 45.4435385022865
[2024/07/28 03:10:55] ppocr INFO: best metric, hmean: 0.7181174089068827, precision: 0.7568, recall: 0.6831969186326432, fps: 44.212831257019886, best_epoch: 1200
[2024/07/28 03:10:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:11:05] ppocr INFO: epoch: [1361/1500], global_step: 4083, lr: 0.001000, loss: 1.156480, loss_shrink_maps: 0.581505, loss_threshold_maps: 0.451808, loss_binary_maps: 0.115925, avg_reader_cost: 2.04849 s, avg_batch_cost: 2.45170 s, avg_samples: 12.5, ips: 5.09850 samples/s, eta: 0:42:09
[2024/07/28 03:11:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:11:15] ppocr INFO: epoch: [1362/1500], global_step: 4086, lr: 0.001000, loss: 1.144864, loss_shrink_maps: 0.576725, loss_threshold_maps: 0.451574, loss_binary_maps: 0.115031, avg_reader_cost: 1.50973 s, avg_batch_cost: 1.76451 s, avg_samples: 12.5, ips: 7.08411 samples/s, eta: 0:41:51
[2024/07/28 03:11:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:11:25] ppocr INFO: epoch: [1363/1500], global_step: 4089, lr: 0.001000, loss: 1.143264, loss_shrink_maps: 0.563657, loss_threshold_maps: 0.451574, loss_binary_maps: 0.112023, avg_reader_cost: 1.55387 s, avg_batch_cost: 1.78131 s, avg_samples: 12.5, ips: 7.01730 samples/s, eta: 0:41:33
[2024/07/28 03:11:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:11:33] ppocr INFO: epoch: [1364/1500], global_step: 4090, lr: 0.001000, loss: 1.135365, loss_shrink_maps: 0.563657, loss_threshold_maps: 0.449152, loss_binary_maps: 0.112023, avg_reader_cost: 0.43578 s, avg_batch_cost: 0.51894 s, avg_samples: 4.8, ips: 9.24964 samples/s, eta: 0:41:26
[2024/07/28 03:11:35] ppocr INFO: epoch: [1364/1500], global_step: 4092, lr: 0.001000, loss: 1.135365, loss_shrink_maps: 0.563657, loss_threshold_maps: 0.449152, loss_binary_maps: 0.112023, avg_reader_cost: 1.12895 s, avg_batch_cost: 1.27453 s, avg_samples: 7.7, ips: 6.04145 samples/s, eta: 0:41:14
[2024/07/28 03:11:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:11:45] ppocr INFO: epoch: [1365/1500], global_step: 4095, lr: 0.001000, loss: 1.095557, loss_shrink_maps: 0.549355, loss_threshold_maps: 0.443444, loss_binary_maps: 0.110003, avg_reader_cost: 1.58695 s, avg_batch_cost: 1.86546 s, avg_samples: 12.5, ips: 6.70076 samples/s, eta: 0:40:56
[2024/07/28 03:11:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:11:55] ppocr INFO: epoch: [1366/1500], global_step: 4098, lr: 0.001000, loss: 1.105958, loss_shrink_maps: 0.549355, loss_threshold_maps: 0.445563, loss_binary_maps: 0.110003, avg_reader_cost: 1.53131 s, avg_batch_cost: 1.81417 s, avg_samples: 12.5, ips: 6.89019 samples/s, eta: 0:40:38
[2024/07/28 03:11:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:12:04] ppocr INFO: epoch: [1367/1500], global_step: 4100, lr: 0.001000, loss: 1.105958, loss_shrink_maps: 0.549355, loss_threshold_maps: 0.445563, loss_binary_maps: 0.110003, avg_reader_cost: 0.90286 s, avg_batch_cost: 1.09203 s, avg_samples: 9.6, ips: 8.79094 samples/s, eta: 0:40:26
[2024/07/28 03:12:05] ppocr INFO: epoch: [1367/1500], global_step: 4101, lr: 0.001000, loss: 1.123259, loss_shrink_maps: 0.556701, loss_threshold_maps: 0.449007, loss_binary_maps: 0.110979, avg_reader_cost: 0.59186 s, avg_batch_cost: 0.64658 s, avg_samples: 2.9, ips: 4.48514 samples/s, eta: 0:40:20
[2024/07/28 03:12:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:12:16] ppocr INFO: epoch: [1368/1500], global_step: 4104, lr: 0.001000, loss: 1.138888, loss_shrink_maps: 0.563657, loss_threshold_maps: 0.455750, loss_binary_maps: 0.112023, avg_reader_cost: 1.67481 s, avg_batch_cost: 1.90359 s, avg_samples: 12.5, ips: 6.56655 samples/s, eta: 0:40:02
[2024/07/28 03:12:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:12:26] ppocr INFO: epoch: [1369/1500], global_step: 4107, lr: 0.001000, loss: 1.116747, loss_shrink_maps: 0.556312, loss_threshold_maps: 0.449847, loss_binary_maps: 0.111047, avg_reader_cost: 1.53020 s, avg_batch_cost: 1.76719 s, avg_samples: 12.5, ips: 7.07337 samples/s, eta: 0:39:43
[2024/07/28 03:12:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:12:36] ppocr INFO: epoch: [1370/1500], global_step: 4110, lr: 0.001000, loss: 1.146309, loss_shrink_maps: 0.573060, loss_threshold_maps: 0.458642, loss_binary_maps: 0.114210, avg_reader_cost: 1.56904 s, avg_batch_cost: 1.79788 s, avg_samples: 12.5, ips: 6.95265 samples/s, eta: 0:39:25
[2024/07/28 03:12:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:12:46] ppocr INFO: epoch: [1371/1500], global_step: 4113, lr: 0.001000, loss: 1.116506, loss_shrink_maps: 0.550533, loss_threshold_maps: 0.455721, loss_binary_maps: 0.109656, avg_reader_cost: 1.54048 s, avg_batch_cost: 1.77057 s, avg_samples: 12.5, ips: 7.05987 samples/s, eta: 0:39:07
[2024/07/28 03:12:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:12:56] ppocr INFO: epoch: [1372/1500], global_step: 4116, lr: 0.001000, loss: 1.138646, loss_shrink_maps: 0.561645, loss_threshold_maps: 0.458642, loss_binary_maps: 0.111552, avg_reader_cost: 1.52171 s, avg_batch_cost: 1.77559 s, avg_samples: 12.5, ips: 7.03989 samples/s, eta: 0:38:49
[2024/07/28 03:12:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:13:06] ppocr INFO: epoch: [1373/1500], global_step: 4119, lr: 0.001000, loss: 1.146309, loss_shrink_maps: 0.573060, loss_threshold_maps: 0.458975, loss_binary_maps: 0.114210, avg_reader_cost: 1.64794 s, avg_batch_cost: 1.87699 s, avg_samples: 12.5, ips: 6.65961 samples/s, eta: 0:38:31
[2024/07/28 03:13:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:13:15] ppocr INFO: epoch: [1374/1500], global_step: 4120, lr: 0.001000, loss: 1.141229, loss_shrink_maps: 0.574021, loss_threshold_maps: 0.458642, loss_binary_maps: 0.114186, avg_reader_cost: 0.40715 s, avg_batch_cost: 0.49852 s, avg_samples: 4.8, ips: 9.62844 samples/s, eta: 0:38:24
[2024/07/28 03:13:16] ppocr INFO: epoch: [1374/1500], global_step: 4122, lr: 0.001000, loss: 1.129481, loss_shrink_maps: 0.570480, loss_threshold_maps: 0.458642, loss_binary_maps: 0.113364, avg_reader_cost: 1.08798 s, avg_batch_cost: 1.23367 s, avg_samples: 7.7, ips: 6.24151 samples/s, eta: 0:38:12
[2024/07/28 03:13:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:13:26] ppocr INFO: epoch: [1375/1500], global_step: 4125, lr: 0.001000, loss: 1.141229, loss_shrink_maps: 0.574021, loss_threshold_maps: 0.461130, loss_binary_maps: 0.114186, avg_reader_cost: 1.55850 s, avg_batch_cost: 1.84091 s, avg_samples: 12.5, ips: 6.79012 samples/s, eta: 0:37:54
[2024/07/28 03:13:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:13:36] ppocr INFO: epoch: [1376/1500], global_step: 4128, lr: 0.001000, loss: 1.141229, loss_shrink_maps: 0.574021, loss_threshold_maps: 0.458947, loss_binary_maps: 0.114186, avg_reader_cost: 1.57860 s, avg_batch_cost: 1.81255 s, avg_samples: 12.5, ips: 6.89637 samples/s, eta: 0:37:36
[2024/07/28 03:13:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:13:46] ppocr INFO: epoch: [1377/1500], global_step: 4130, lr: 0.001000, loss: 1.127395, loss_shrink_maps: 0.562322, loss_threshold_maps: 0.458503, loss_binary_maps: 0.111711, avg_reader_cost: 0.92785 s, avg_batch_cost: 1.10126 s, avg_samples: 9.6, ips: 8.71727 samples/s, eta: 0:37:24
[2024/07/28 03:13:47] ppocr INFO: epoch: [1377/1500], global_step: 4131, lr: 0.001000, loss: 1.131480, loss_shrink_maps: 0.570480, loss_threshold_maps: 0.464949, loss_binary_maps: 0.113364, avg_reader_cost: 0.59640 s, avg_batch_cost: 0.65078 s, avg_samples: 2.9, ips: 4.45622 samples/s, eta: 0:37:18
[2024/07/28 03:13:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:13:57] ppocr INFO: epoch: [1378/1500], global_step: 4134, lr: 0.001000, loss: 1.140083, loss_shrink_maps: 0.570480, loss_threshold_maps: 0.471837, loss_binary_maps: 0.113364, avg_reader_cost: 1.53223 s, avg_batch_cost: 1.76433 s, avg_samples: 12.5, ips: 7.08483 samples/s, eta: 0:36:59
[2024/07/28 03:14:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:14:07] ppocr INFO: epoch: [1379/1500], global_step: 4137, lr: 0.001000, loss: 1.135221, loss_shrink_maps: 0.570480, loss_threshold_maps: 0.471837, loss_binary_maps: 0.113364, avg_reader_cost: 1.53827 s, avg_batch_cost: 1.78999 s, avg_samples: 12.5, ips: 6.98329 samples/s, eta: 0:36:41
[2024/07/28 03:14:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:14:17] ppocr INFO: epoch: [1380/1500], global_step: 4140, lr: 0.001000, loss: 1.124541, loss_shrink_maps: 0.552971, loss_threshold_maps: 0.462354, loss_binary_maps: 0.109808, avg_reader_cost: 1.53198 s, avg_batch_cost: 1.79308 s, avg_samples: 12.5, ips: 6.97125 samples/s, eta: 0:36:23

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:55,  8.94it/s]
eval model::   1%|          | 3/500 [00:00<00:37, 13.28it/s]
eval model::   1%|          | 5/500 [00:00<00:34, 14.19it/s]
eval model::   1%|▏         | 7/500 [00:00<00:30, 15.93it/s]
eval model::   2%|▏         | 9/500 [00:00<00:28, 17.20it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.57it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.44it/s]
eval model::   3%|▎         | 17/500 [00:00<00:22, 21.15it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 19.00it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.64it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 20.10it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.39it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 18.95it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.18it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 20.08it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.46it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 18.01it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 18.97it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 17.61it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.28it/s]
eval model::  10%|█         | 52/500 [00:02<00:25, 17.27it/s]
eval model::  11%|█         | 54/500 [00:02<00:25, 17.56it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:22, 19.42it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:24, 17.94it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:30, 14.45it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:30, 14.42it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:25, 17.06it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:22, 19.36it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 20.13it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:21, 19.83it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 20.67it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 21.34it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 20.61it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.53it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:25, 16.03it/s]
eval model::  18%|█▊        | 91/500 [00:05<00:26, 15.44it/s]
eval model::  19%|█▊        | 93/500 [00:05<00:27, 15.07it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:25, 15.96it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:22, 18.02it/s]
eval model::  20%|██        | 100/500 [00:05<00:23, 16.68it/s]
eval model::  21%|██        | 103/500 [00:05<00:20, 19.02it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 19.74it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:19, 19.86it/s]
eval model::  22%|██▏       | 112/500 [00:06<00:19, 19.88it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:18, 21.07it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:16, 22.95it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:16, 22.49it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:17, 21.53it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:19, 19.45it/s]
eval model::  26%|██▌       | 130/500 [00:07<00:20, 17.81it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:22, 16.23it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:19, 18.27it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:19, 18.53it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 19.51it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 20.55it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 19.68it/s]
eval model::  30%|██▉       | 149/500 [00:08<00:18, 18.57it/s]
eval model::  30%|███       | 151/500 [00:08<00:22, 15.48it/s]
eval model::  31%|███       | 155/500 [00:08<00:18, 18.64it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:17, 19.50it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:22, 14.78it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:22, 14.66it/s]
eval model::  33%|███▎      | 165/500 [00:09<00:21, 15.51it/s]
eval model::  34%|███▎      | 168/500 [00:09<00:18, 17.57it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:17, 18.65it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 15.88it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:18, 17.87it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:20, 15.36it/s]
eval model::  36%|███▋      | 182/500 [00:10<00:20, 15.80it/s]
eval model::  37%|███▋      | 185/500 [00:10<00:17, 18.47it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:17, 18.33it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:15, 19.87it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 20.54it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.35it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 21.21it/s]
eval model::  41%|████      | 205/500 [00:11<00:19, 15.16it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:18, 16.16it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 16.07it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:16, 17.14it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.00it/s]
eval model::  43%|████▎     | 217/500 [00:12<00:21, 13.15it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:17, 15.59it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:19, 14.00it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 16.61it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:15, 18.11it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:13, 20.29it/s]
eval model::  47%|████▋     | 234/500 [00:13<00:14, 18.75it/s]
eval model::  47%|████▋     | 237/500 [00:13<00:13, 19.85it/s]
eval model::  48%|████▊     | 240/500 [00:13<00:13, 18.65it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:16, 15.33it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:15, 16.88it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:13, 19.17it/s]
eval model::  50%|█████     | 251/500 [00:13<00:13, 18.73it/s]
eval model::  51%|█████     | 253/500 [00:14<00:13, 18.90it/s]
eval model::  51%|█████     | 256/500 [00:14<00:12, 18.95it/s]
eval model::  52%|█████▏    | 258/500 [00:14<00:12, 18.76it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:11, 20.09it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:12, 19.54it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 20.48it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 20.07it/s]
eval model::  55%|█████▍    | 273/500 [00:15<00:10, 21.03it/s]
eval model::  55%|█████▌    | 276/500 [00:15<00:12, 18.55it/s]
eval model::  56%|█████▌    | 278/500 [00:15<00:11, 18.63it/s]
eval model::  56%|█████▌    | 280/500 [00:15<00:11, 18.61it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:11, 18.70it/s]
eval model::  57%|█████▋    | 285/500 [00:16<00:20, 10.59it/s]
eval model::  58%|█████▊    | 288/500 [00:16<00:16, 13.15it/s]
eval model::  58%|█████▊    | 291/500 [00:16<00:13, 15.26it/s]
eval model::  59%|█████▊    | 293/500 [00:16<00:13, 15.13it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:16, 12.07it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:13, 14.90it/s]
eval model::  60%|██████    | 300/500 [00:17<00:13, 14.33it/s]
eval model::  60%|██████    | 302/500 [00:17<00:13, 14.18it/s]
eval model::  61%|██████    | 304/500 [00:17<00:13, 14.34it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:11, 16.77it/s]
eval model::  62%|██████▏   | 310/500 [00:17<00:10, 18.36it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:09, 20.14it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 18.45it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:09, 20.11it/s]
eval model::  64%|██████▍   | 322/500 [00:18<00:09, 18.16it/s]
eval model::  65%|██████▍   | 324/500 [00:18<00:09, 18.26it/s]
eval model::  65%|██████▌   | 327/500 [00:18<00:08, 19.30it/s]
eval model::  66%|██████▌   | 329/500 [00:18<00:09, 18.37it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:09, 18.37it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:08, 20.21it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 21.29it/s]
eval model::  68%|██████▊   | 340/500 [00:19<00:11, 13.94it/s]
eval model::  69%|██████▊   | 343/500 [00:19<00:10, 15.46it/s]
eval model::  69%|██████▉   | 346/500 [00:19<00:08, 17.75it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:08, 18.86it/s]
eval model::  70%|███████   | 352/500 [00:19<00:07, 19.96it/s]
eval model::  71%|███████   | 355/500 [00:19<00:07, 20.28it/s]
eval model::  72%|███████▏  | 358/500 [00:20<00:06, 20.89it/s]
eval model::  72%|███████▏  | 361/500 [00:20<00:08, 16.43it/s]
eval model::  73%|███████▎  | 364/500 [00:20<00:08, 16.77it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:08, 16.34it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:07, 17.82it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:06, 18.30it/s]
eval model::  75%|███████▌  | 375/500 [00:21<00:06, 20.45it/s]
eval model::  76%|███████▌  | 378/500 [00:21<00:05, 22.27it/s]
eval model::  76%|███████▌  | 381/500 [00:21<00:05, 22.87it/s]
eval model::  77%|███████▋  | 385/500 [00:21<00:04, 26.49it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:07, 14.86it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:06, 16.15it/s]
eval model::  79%|███████▉  | 394/500 [00:22<00:06, 17.35it/s]
eval model::  79%|███████▉  | 397/500 [00:22<00:05, 18.63it/s]
eval model::  80%|████████  | 400/500 [00:22<00:05, 19.25it/s]
eval model::  81%|████████  | 403/500 [00:22<00:04, 21.48it/s]
eval model::  81%|████████  | 406/500 [00:22<00:04, 22.29it/s]
eval model::  82%|████████▏ | 409/500 [00:22<00:03, 23.96it/s]
eval model::  82%|████████▏ | 412/500 [00:22<00:03, 25.35it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:03, 23.65it/s]
eval model::  84%|████████▎ | 418/500 [00:23<00:03, 20.57it/s]
eval model::  84%|████████▍ | 421/500 [00:23<00:05, 14.34it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.25it/s]
eval model::  85%|████████▌ | 427/500 [00:23<00:04, 16.48it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 17.89it/s]
eval model::  87%|████████▋ | 433/500 [00:24<00:03, 17.85it/s]
eval model::  87%|████████▋ | 436/500 [00:24<00:03, 19.09it/s]
eval model::  88%|████████▊ | 439/500 [00:24<00:02, 21.04it/s]
eval model::  88%|████████▊ | 442/500 [00:24<00:02, 21.95it/s]
eval model::  89%|████████▉ | 445/500 [00:24<00:02, 20.55it/s]
eval model::  90%|████████▉ | 448/500 [00:24<00:02, 19.02it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 18.87it/s]
eval model::  91%|█████████ | 453/500 [00:25<00:02, 19.73it/s]
eval model::  91%|█████████ | 456/500 [00:25<00:02, 21.15it/s]
eval model::  92%|█████████▏| 459/500 [00:25<00:01, 20.72it/s]
eval model::  92%|█████████▏| 462/500 [00:25<00:02, 18.04it/s]
eval model::  93%|█████████▎| 464/500 [00:25<00:01, 18.29it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:01, 17.47it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 17.43it/s]
eval model::  94%|█████████▍| 471/500 [00:26<00:01, 19.50it/s]
eval model::  95%|█████████▍| 474/500 [00:26<00:01, 18.77it/s]
eval model::  95%|█████████▌| 477/500 [00:26<00:01, 20.33it/s]
eval model::  96%|█████████▌| 480/500 [00:26<00:01, 18.61it/s]
eval model::  96%|█████████▋| 482/500 [00:26<00:01, 17.83it/s]
eval model::  97%|█████████▋| 485/500 [00:26<00:00, 19.76it/s]
eval model::  98%|█████████▊| 488/500 [00:26<00:00, 20.21it/s]
eval model::  98%|█████████▊| 491/500 [00:27<00:00, 20.55it/s]
eval model::  99%|█████████▉| 494/500 [00:27<00:00, 18.40it/s]
eval model::  99%|█████████▉| 497/500 [00:27<00:00, 19.97it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.18it/s]
[2024/07/28 03:14:44] ppocr INFO: cur metric, precision: 0.729923273657289, recall: 0.687048627828599, hmean: 0.7078373015873016, fps: 44.40780930428425
[2024/07/28 03:14:44] ppocr INFO: best metric, hmean: 0.7181174089068827, precision: 0.7568, recall: 0.6831969186326432, fps: 44.212831257019886, best_epoch: 1200
[2024/07/28 03:14:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:14:52] ppocr INFO: epoch: [1381/1500], global_step: 4143, lr: 0.001000, loss: 1.114106, loss_shrink_maps: 0.543620, loss_threshold_maps: 0.458130, loss_binary_maps: 0.108230, avg_reader_cost: 1.44749 s, avg_batch_cost: 1.68886 s, avg_samples: 12.5, ips: 7.40146 samples/s, eta: 0:36:05
[2024/07/28 03:14:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:15:02] ppocr INFO: epoch: [1382/1500], global_step: 4146, lr: 0.001000, loss: 1.107902, loss_shrink_maps: 0.541932, loss_threshold_maps: 0.453225, loss_binary_maps: 0.108230, avg_reader_cost: 1.53555 s, avg_batch_cost: 1.76991 s, avg_samples: 12.5, ips: 7.06251 samples/s, eta: 0:35:46
[2024/07/28 03:15:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:15:13] ppocr INFO: epoch: [1383/1500], global_step: 4149, lr: 0.001000, loss: 1.094973, loss_shrink_maps: 0.538183, loss_threshold_maps: 0.447018, loss_binary_maps: 0.107545, avg_reader_cost: 1.56556 s, avg_batch_cost: 1.79550 s, avg_samples: 12.5, ips: 6.96186 samples/s, eta: 0:35:28
[2024/07/28 03:15:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:15:21] ppocr INFO: epoch: [1384/1500], global_step: 4150, lr: 0.001000, loss: 1.094973, loss_shrink_maps: 0.538183, loss_threshold_maps: 0.451255, loss_binary_maps: 0.107545, avg_reader_cost: 0.40413 s, avg_batch_cost: 0.51683 s, avg_samples: 4.8, ips: 9.28747 samples/s, eta: 0:35:22
[2024/07/28 03:15:23] ppocr INFO: epoch: [1384/1500], global_step: 4152, lr: 0.001000, loss: 1.094973, loss_shrink_maps: 0.538183, loss_threshold_maps: 0.443252, loss_binary_maps: 0.107545, avg_reader_cost: 1.12516 s, avg_batch_cost: 1.27104 s, avg_samples: 7.7, ips: 6.05802 samples/s, eta: 0:35:10
[2024/07/28 03:15:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:15:33] ppocr INFO: epoch: [1385/1500], global_step: 4155, lr: 0.001000, loss: 1.094973, loss_shrink_maps: 0.541932, loss_threshold_maps: 0.449061, loss_binary_maps: 0.108174, avg_reader_cost: 1.56851 s, avg_batch_cost: 1.81314 s, avg_samples: 12.5, ips: 6.89412 samples/s, eta: 0:34:52
[2024/07/28 03:15:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:15:43] ppocr INFO: epoch: [1386/1500], global_step: 4158, lr: 0.001000, loss: 1.084375, loss_shrink_maps: 0.536438, loss_threshold_maps: 0.435422, loss_binary_maps: 0.106782, avg_reader_cost: 1.51788 s, avg_batch_cost: 1.76092 s, avg_samples: 12.5, ips: 7.09857 samples/s, eta: 0:34:34
[2024/07/28 03:15:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:15:53] ppocr INFO: epoch: [1387/1500], global_step: 4160, lr: 0.001000, loss: 1.078357, loss_shrink_maps: 0.531984, loss_threshold_maps: 0.433620, loss_binary_maps: 0.105827, avg_reader_cost: 1.03798 s, avg_batch_cost: 1.21164 s, avg_samples: 9.6, ips: 7.92315 samples/s, eta: 0:34:21
[2024/07/28 03:15:54] ppocr INFO: epoch: [1387/1500], global_step: 4161, lr: 0.001000, loss: 1.078317, loss_shrink_maps: 0.528149, loss_threshold_maps: 0.432693, loss_binary_maps: 0.105462, avg_reader_cost: 0.65214 s, avg_batch_cost: 0.70636 s, avg_samples: 2.9, ips: 4.10556 samples/s, eta: 0:34:15
[2024/07/28 03:15:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:16:04] ppocr INFO: epoch: [1388/1500], global_step: 4164, lr: 0.001000, loss: 1.085684, loss_shrink_maps: 0.540264, loss_threshold_maps: 0.446018, loss_binary_maps: 0.107765, avg_reader_cost: 1.51118 s, avg_batch_cost: 1.77155 s, avg_samples: 12.5, ips: 7.05596 samples/s, eta: 0:33:57
[2024/07/28 03:16:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:16:14] ppocr INFO: epoch: [1389/1500], global_step: 4167, lr: 0.001000, loss: 1.085684, loss_shrink_maps: 0.544030, loss_threshold_maps: 0.443285, loss_binary_maps: 0.107964, avg_reader_cost: 1.51760 s, avg_batch_cost: 1.77088 s, avg_samples: 12.5, ips: 7.05864 samples/s, eta: 0:33:39
[2024/07/28 03:16:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:16:24] ppocr INFO: epoch: [1390/1500], global_step: 4170, lr: 0.001000, loss: 1.093004, loss_shrink_maps: 0.547378, loss_threshold_maps: 0.443285, loss_binary_maps: 0.109024, avg_reader_cost: 1.51224 s, avg_batch_cost: 1.75001 s, avg_samples: 12.5, ips: 7.14281 samples/s, eta: 0:33:21
[2024/07/28 03:16:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:16:34] ppocr INFO: epoch: [1391/1500], global_step: 4173, lr: 0.001000, loss: 1.085684, loss_shrink_maps: 0.544030, loss_threshold_maps: 0.442633, loss_binary_maps: 0.107964, avg_reader_cost: 1.53051 s, avg_batch_cost: 1.80635 s, avg_samples: 12.5, ips: 6.92002 samples/s, eta: 0:33:03
[2024/07/28 03:16:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:16:44] ppocr INFO: epoch: [1392/1500], global_step: 4176, lr: 0.001000, loss: 1.093004, loss_shrink_maps: 0.547378, loss_threshold_maps: 0.442633, loss_binary_maps: 0.109024, avg_reader_cost: 1.53014 s, avg_batch_cost: 1.76244 s, avg_samples: 12.5, ips: 7.09244 samples/s, eta: 0:32:44
[2024/07/28 03:16:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:16:54] ppocr INFO: epoch: [1393/1500], global_step: 4179, lr: 0.001000, loss: 1.123434, loss_shrink_maps: 0.563784, loss_threshold_maps: 0.447783, loss_binary_maps: 0.112088, avg_reader_cost: 1.53327 s, avg_batch_cost: 1.77127 s, avg_samples: 12.5, ips: 7.05708 samples/s, eta: 0:32:26
[2024/07/28 03:16:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:17:03] ppocr INFO: epoch: [1394/1500], global_step: 4180, lr: 0.001000, loss: 1.123434, loss_shrink_maps: 0.563784, loss_threshold_maps: 0.447783, loss_binary_maps: 0.112088, avg_reader_cost: 0.42640 s, avg_batch_cost: 0.50940 s, avg_samples: 4.8, ips: 9.42288 samples/s, eta: 0:32:20
[2024/07/28 03:17:04] ppocr INFO: epoch: [1394/1500], global_step: 4182, lr: 0.001000, loss: 1.150048, loss_shrink_maps: 0.572709, loss_threshold_maps: 0.449460, loss_binary_maps: 0.114430, avg_reader_cost: 1.10994 s, avg_batch_cost: 1.25583 s, avg_samples: 7.7, ips: 6.13143 samples/s, eta: 0:32:08
[2024/07/28 03:17:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:17:14] ppocr INFO: epoch: [1395/1500], global_step: 4185, lr: 0.001000, loss: 1.143702, loss_shrink_maps: 0.567919, loss_threshold_maps: 0.444857, loss_binary_maps: 0.113236, avg_reader_cost: 1.54769 s, avg_batch_cost: 1.78742 s, avg_samples: 12.5, ips: 6.99333 samples/s, eta: 0:31:50
[2024/07/28 03:17:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:17:25] ppocr INFO: epoch: [1396/1500], global_step: 4188, lr: 0.001000, loss: 1.140866, loss_shrink_maps: 0.569770, loss_threshold_maps: 0.452637, loss_binary_maps: 0.113687, avg_reader_cost: 1.69637 s, avg_batch_cost: 1.96813 s, avg_samples: 12.5, ips: 6.35121 samples/s, eta: 0:31:32
[2024/07/28 03:17:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:17:35] ppocr INFO: epoch: [1397/1500], global_step: 4190, lr: 0.001000, loss: 1.137570, loss_shrink_maps: 0.567173, loss_threshold_maps: 0.451257, loss_binary_maps: 0.112848, avg_reader_cost: 0.93020 s, avg_batch_cost: 1.10443 s, avg_samples: 9.6, ips: 8.69223 samples/s, eta: 0:31:19
[2024/07/28 03:17:36] ppocr INFO: epoch: [1397/1500], global_step: 4191, lr: 0.001000, loss: 1.137570, loss_shrink_maps: 0.567173, loss_threshold_maps: 0.451257, loss_binary_maps: 0.112848, avg_reader_cost: 0.59802 s, avg_batch_cost: 0.65317 s, avg_samples: 2.9, ips: 4.43991 samples/s, eta: 0:31:13
[2024/07/28 03:17:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:17:45] ppocr INFO: epoch: [1398/1500], global_step: 4194, lr: 0.001000, loss: 1.137570, loss_shrink_maps: 0.567173, loss_threshold_maps: 0.451257, loss_binary_maps: 0.112848, avg_reader_cost: 1.50680 s, avg_batch_cost: 1.73869 s, avg_samples: 12.5, ips: 7.18932 samples/s, eta: 0:30:55
[2024/07/28 03:17:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:17:56] ppocr INFO: epoch: [1399/1500], global_step: 4197, lr: 0.001000, loss: 1.137570, loss_shrink_maps: 0.569154, loss_threshold_maps: 0.450770, loss_binary_maps: 0.113309, avg_reader_cost: 1.57493 s, avg_batch_cost: 1.82573 s, avg_samples: 12.5, ips: 6.84659 samples/s, eta: 0:30:37
[2024/07/28 03:17:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:18:06] ppocr INFO: epoch: [1400/1500], global_step: 4200, lr: 0.001000, loss: 1.116874, loss_shrink_maps: 0.566868, loss_threshold_maps: 0.445181, loss_binary_maps: 0.112817, avg_reader_cost: 1.59476 s, avg_batch_cost: 1.82373 s, avg_samples: 12.5, ips: 6.85408 samples/s, eta: 0:30:19

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:13,  6.74it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 16.84it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.45it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.18it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.73it/s]
eval model::   3%|▎         | 14/500 [00:00<00:27, 17.70it/s]
eval model::   3%|▎         | 16/500 [00:00<00:26, 18.01it/s]
eval model::   4%|▍         | 19/500 [00:01<00:24, 19.86it/s]
eval model::   4%|▍         | 22/500 [00:01<00:24, 19.62it/s]
eval model::   5%|▌         | 25/500 [00:01<00:25, 18.86it/s]
eval model::   6%|▌         | 28/500 [00:01<00:24, 19.28it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.18it/s]
eval model::   7%|▋         | 34/500 [00:01<00:26, 17.74it/s]
eval model::   7%|▋         | 37/500 [00:02<00:23, 19.62it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.25it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 17.80it/s]
eval model::   9%|▉         | 44/500 [00:02<00:24, 18.28it/s]
eval model::   9%|▉         | 46/500 [00:02<00:27, 16.78it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.39it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 19.65it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 20.26it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 18.63it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.49it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.55it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.60it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.75it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 20.95it/s]
eval model::  15%|█▌        | 75/500 [00:04<00:20, 20.62it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:19, 21.97it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:18, 23.12it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:17, 24.05it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:19, 21.65it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:19, 21.48it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:18, 21.80it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:18, 22.44it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:17, 22.48it/s]
eval model::  20%|██        | 102/500 [00:05<00:19, 20.71it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 22.61it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.00it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 20.73it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 20.66it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:19, 19.29it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:20, 18.63it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:20, 18.05it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:20, 18.06it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:21, 17.45it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:19, 19.31it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:20, 18.19it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:21, 17.31it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:19, 18.47it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:19, 18.13it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:20, 17.47it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:18, 19.15it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.09it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.27it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.41it/s]
eval model::  31%|███       | 153/500 [00:08<00:20, 17.19it/s]
eval model::  31%|███       | 155/500 [00:08<00:19, 17.45it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:17, 19.99it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:19, 17.00it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:18, 18.44it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:16, 19.94it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 19.18it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:18, 17.60it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:18, 17.49it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.64it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:18, 17.10it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 20.83it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.25it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.71it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.74it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.40it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.53it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.44it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:24, 12.14it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:22, 12.93it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:21, 13.32it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:20, 13.94it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.56it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:23, 12.11it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 14.88it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.71it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 17.00it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:14, 18.84it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:11, 23.36it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:11, 23.68it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:10, 25.18it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:10, 24.43it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:11, 22.06it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:10, 23.46it/s]
eval model::  50%|█████     | 250/500 [00:13<00:11, 21.00it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 22.12it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 21.15it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 22.50it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:10, 21.99it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:10, 21.87it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:11, 20.53it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:11, 19.38it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 19.22it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:13, 17.20it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 18.64it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:11, 18.93it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:10, 20.27it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:18, 11.63it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:15, 13.86it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:13, 15.88it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 16.24it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:14, 14.43it/s]
eval model::  60%|██████    | 300/500 [00:16<00:11, 16.97it/s]
eval model::  61%|██████    | 303/500 [00:16<00:10, 18.20it/s]
eval model::  61%|██████    | 305/500 [00:16<00:10, 18.03it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:09, 20.07it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:08, 22.15it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 21.27it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 22.35it/s]
eval model::  64%|██████▍   | 321/500 [00:17<00:08, 21.85it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:07, 22.91it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:07, 23.97it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:06, 24.30it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:06, 24.90it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:06, 24.55it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 17.42it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:07, 19.75it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 21.33it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.74it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 21.81it/s]
eval model::  71%|███████   | 355/500 [00:18<00:07, 20.71it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:07, 20.13it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 17.92it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.84it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:08, 16.32it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 18.51it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 19.30it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:05, 20.94it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 21.87it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:06, 19.14it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 21.10it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 14.39it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 16.53it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:06, 17.13it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 18.59it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 19.49it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 19.37it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 20.28it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 22.18it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:03, 24.73it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:03, 22.99it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 19.78it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:05, 13.69it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 14.46it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:04, 15.59it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:04, 16.18it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:04, 16.17it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 19.50it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 21.22it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 22.02it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 26.07it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 25.66it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 23.33it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:01, 24.26it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 25.28it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 24.32it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 20.97it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 19.20it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 18.40it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 19.96it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 21.23it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:00, 22.60it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 21.25it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 21.18it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 20.95it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 21.30it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 18.03it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 19.55it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 21.89it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.25it/s]
[2024/07/28 03:18:32] ppocr INFO: cur metric, precision: 0.7494714587737844, recall: 0.6827154549831488, hmean: 0.7145376669186193, fps: 46.83552900558406
[2024/07/28 03:18:32] ppocr INFO: best metric, hmean: 0.7181174089068827, precision: 0.7568, recall: 0.6831969186326432, fps: 44.212831257019886, best_epoch: 1200
[2024/07/28 03:18:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:18:42] ppocr INFO: epoch: [1401/1500], global_step: 4203, lr: 0.001000, loss: 1.080242, loss_shrink_maps: 0.530277, loss_threshold_maps: 0.445181, loss_binary_maps: 0.105367, avg_reader_cost: 2.07401 s, avg_batch_cost: 2.57394 s, avg_samples: 12.5, ips: 4.85637 samples/s, eta: 0:30:01
[2024/07/28 03:18:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:18:52] ppocr INFO: epoch: [1402/1500], global_step: 4206, lr: 0.001000, loss: 1.072896, loss_shrink_maps: 0.534038, loss_threshold_maps: 0.445181, loss_binary_maps: 0.106192, avg_reader_cost: 1.51030 s, avg_batch_cost: 1.73918 s, avg_samples: 12.5, ips: 7.18730 samples/s, eta: 0:29:43
[2024/07/28 03:18:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:19:02] ppocr INFO: epoch: [1403/1500], global_step: 4209, lr: 0.001000, loss: 1.072896, loss_shrink_maps: 0.541745, loss_threshold_maps: 0.446812, loss_binary_maps: 0.107420, avg_reader_cost: 1.49947 s, avg_batch_cost: 1.73759 s, avg_samples: 12.5, ips: 7.19386 samples/s, eta: 0:29:24
[2024/07/28 03:19:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:19:11] ppocr INFO: epoch: [1404/1500], global_step: 4210, lr: 0.001000, loss: 1.072896, loss_shrink_maps: 0.541745, loss_threshold_maps: 0.442531, loss_binary_maps: 0.107420, avg_reader_cost: 0.45243 s, avg_batch_cost: 0.54550 s, avg_samples: 4.8, ips: 8.79929 samples/s, eta: 0:29:18
[2024/07/28 03:19:12] ppocr INFO: epoch: [1404/1500], global_step: 4212, lr: 0.001000, loss: 1.072896, loss_shrink_maps: 0.541745, loss_threshold_maps: 0.442531, loss_binary_maps: 0.107420, avg_reader_cost: 1.18309 s, avg_batch_cost: 1.32894 s, avg_samples: 7.7, ips: 5.79409 samples/s, eta: 0:29:06
[2024/07/28 03:19:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:19:23] ppocr INFO: epoch: [1405/1500], global_step: 4215, lr: 0.001000, loss: 1.067701, loss_shrink_maps: 0.534038, loss_threshold_maps: 0.440162, loss_binary_maps: 0.106192, avg_reader_cost: 1.56734 s, avg_batch_cost: 1.79603 s, avg_samples: 12.5, ips: 6.95980 samples/s, eta: 0:28:48
[2024/07/28 03:19:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:19:33] ppocr INFO: epoch: [1406/1500], global_step: 4218, lr: 0.001000, loss: 1.055275, loss_shrink_maps: 0.516775, loss_threshold_maps: 0.435059, loss_binary_maps: 0.102739, avg_reader_cost: 1.51780 s, avg_batch_cost: 1.74584 s, avg_samples: 12.5, ips: 7.15987 samples/s, eta: 0:28:30
[2024/07/28 03:19:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:19:42] ppocr INFO: epoch: [1407/1500], global_step: 4220, lr: 0.001000, loss: 1.041950, loss_shrink_maps: 0.498566, loss_threshold_maps: 0.433407, loss_binary_maps: 0.099403, avg_reader_cost: 0.94586 s, avg_batch_cost: 1.11924 s, avg_samples: 9.6, ips: 8.57722 samples/s, eta: 0:28:18
[2024/07/28 03:19:43] ppocr INFO: epoch: [1407/1500], global_step: 4221, lr: 0.001000, loss: 1.041950, loss_shrink_maps: 0.498566, loss_threshold_maps: 0.433407, loss_binary_maps: 0.099403, avg_reader_cost: 0.60528 s, avg_batch_cost: 0.66019 s, avg_samples: 2.9, ips: 4.39266 samples/s, eta: 0:28:12
[2024/07/28 03:19:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:19:53] ppocr INFO: epoch: [1408/1500], global_step: 4224, lr: 0.001000, loss: 1.028550, loss_shrink_maps: 0.496081, loss_threshold_maps: 0.429788, loss_binary_maps: 0.098678, avg_reader_cost: 1.53752 s, avg_batch_cost: 1.76929 s, avg_samples: 12.5, ips: 7.06496 samples/s, eta: 0:27:53
[2024/07/28 03:19:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:20:03] ppocr INFO: epoch: [1409/1500], global_step: 4227, lr: 0.001000, loss: 1.020208, loss_shrink_maps: 0.496081, loss_threshold_maps: 0.429501, loss_binary_maps: 0.098678, avg_reader_cost: 1.50392 s, avg_batch_cost: 1.75468 s, avg_samples: 12.5, ips: 7.12380 samples/s, eta: 0:27:35
[2024/07/28 03:20:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:20:14] ppocr INFO: epoch: [1410/1500], global_step: 4230, lr: 0.001000, loss: 1.009662, loss_shrink_maps: 0.492918, loss_threshold_maps: 0.418651, loss_binary_maps: 0.098378, avg_reader_cost: 1.64215 s, avg_batch_cost: 1.87121 s, avg_samples: 12.5, ips: 6.68015 samples/s, eta: 0:27:17
[2024/07/28 03:20:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:20:24] ppocr INFO: epoch: [1411/1500], global_step: 4233, lr: 0.001000, loss: 1.011580, loss_shrink_maps: 0.496081, loss_threshold_maps: 0.418651, loss_binary_maps: 0.098678, avg_reader_cost: 1.59448 s, avg_batch_cost: 1.91446 s, avg_samples: 12.5, ips: 6.52925 samples/s, eta: 0:26:59
[2024/07/28 03:20:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:20:34] ppocr INFO: epoch: [1412/1500], global_step: 4236, lr: 0.001000, loss: 1.006550, loss_shrink_maps: 0.491303, loss_threshold_maps: 0.411772, loss_binary_maps: 0.097876, avg_reader_cost: 1.55862 s, avg_batch_cost: 1.78654 s, avg_samples: 12.5, ips: 6.99677 samples/s, eta: 0:26:41
[2024/07/28 03:20:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:20:45] ppocr INFO: epoch: [1413/1500], global_step: 4239, lr: 0.001000, loss: 1.009662, loss_shrink_maps: 0.494466, loss_threshold_maps: 0.414957, loss_binary_maps: 0.098175, avg_reader_cost: 1.51641 s, avg_batch_cost: 1.75654 s, avg_samples: 12.5, ips: 7.11625 samples/s, eta: 0:26:22
[2024/07/28 03:20:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:20:54] ppocr INFO: epoch: [1414/1500], global_step: 4240, lr: 0.001000, loss: 1.011292, loss_shrink_maps: 0.501640, loss_threshold_maps: 0.414957, loss_binary_maps: 0.099798, avg_reader_cost: 0.45006 s, avg_batch_cost: 0.53604 s, avg_samples: 4.8, ips: 8.95459 samples/s, eta: 0:26:16
[2024/07/28 03:20:55] ppocr INFO: epoch: [1414/1500], global_step: 4242, lr: 0.001000, loss: 1.033622, loss_shrink_maps: 0.516605, loss_threshold_maps: 0.418651, loss_binary_maps: 0.103115, avg_reader_cost: 1.16345 s, avg_batch_cost: 1.30927 s, avg_samples: 7.7, ips: 5.88113 samples/s, eta: 0:26:04
[2024/07/28 03:20:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:21:05] ppocr INFO: epoch: [1415/1500], global_step: 4245, lr: 0.001000, loss: 1.099002, loss_shrink_maps: 0.544404, loss_threshold_maps: 0.440621, loss_binary_maps: 0.108781, avg_reader_cost: 1.53983 s, avg_batch_cost: 1.77176 s, avg_samples: 12.5, ips: 7.05514 samples/s, eta: 0:25:46
[2024/07/28 03:21:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:21:15] ppocr INFO: epoch: [1416/1500], global_step: 4248, lr: 0.001000, loss: 1.089830, loss_shrink_maps: 0.543166, loss_threshold_maps: 0.440621, loss_binary_maps: 0.108249, avg_reader_cost: 1.57760 s, avg_batch_cost: 1.80598 s, avg_samples: 12.5, ips: 6.92145 samples/s, eta: 0:25:28
[2024/07/28 03:21:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:21:25] ppocr INFO: epoch: [1417/1500], global_step: 4250, lr: 0.001000, loss: 1.089830, loss_shrink_maps: 0.543581, loss_threshold_maps: 0.436063, loss_binary_maps: 0.108781, avg_reader_cost: 0.91923 s, avg_batch_cost: 1.09999 s, avg_samples: 9.6, ips: 8.72735 samples/s, eta: 0:25:16
[2024/07/28 03:21:25] ppocr INFO: epoch: [1417/1500], global_step: 4251, lr: 0.001000, loss: 1.089830, loss_shrink_maps: 0.543581, loss_threshold_maps: 0.440621, loss_binary_maps: 0.108781, avg_reader_cost: 0.59570 s, avg_batch_cost: 0.65050 s, avg_samples: 2.9, ips: 4.45812 samples/s, eta: 0:25:10
[2024/07/28 03:21:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:21:36] ppocr INFO: epoch: [1418/1500], global_step: 4254, lr: 0.001000, loss: 1.099002, loss_shrink_maps: 0.547330, loss_threshold_maps: 0.445739, loss_binary_maps: 0.109021, avg_reader_cost: 1.52506 s, avg_batch_cost: 1.78786 s, avg_samples: 12.5, ips: 6.99158 samples/s, eta: 0:24:51
[2024/07/28 03:21:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:21:46] ppocr INFO: epoch: [1419/1500], global_step: 4257, lr: 0.001000, loss: 1.132897, loss_shrink_maps: 0.554879, loss_threshold_maps: 0.465415, loss_binary_maps: 0.110572, avg_reader_cost: 1.58999 s, avg_batch_cost: 1.86196 s, avg_samples: 12.5, ips: 6.71337 samples/s, eta: 0:24:33
[2024/07/28 03:21:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:21:56] ppocr INFO: epoch: [1420/1500], global_step: 4260, lr: 0.001000, loss: 1.119348, loss_shrink_maps: 0.552025, loss_threshold_maps: 0.459799, loss_binary_maps: 0.109995, avg_reader_cost: 1.51481 s, avg_batch_cost: 1.75643 s, avg_samples: 12.5, ips: 7.11673 samples/s, eta: 0:24:15

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:57,  8.63it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.00it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.81it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.38it/s]
eval model::   2%|▏         | 10/500 [00:00<00:27, 17.68it/s]
eval model::   3%|▎         | 13/500 [00:00<00:25, 19.14it/s]
eval model::   3%|▎         | 16/500 [00:00<00:22, 21.28it/s]
eval model::   4%|▍         | 19/500 [00:00<00:21, 22.30it/s]
eval model::   4%|▍         | 22/500 [00:01<00:22, 21.16it/s]
eval model::   5%|▌         | 25/500 [00:01<00:24, 19.63it/s]
eval model::   6%|▌         | 28/500 [00:01<00:23, 19.84it/s]
eval model::   6%|▌         | 31/500 [00:01<00:23, 19.54it/s]
eval model::   7%|▋         | 34/500 [00:01<00:22, 20.44it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 21.70it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.97it/s]
eval model::   8%|▊         | 42/500 [00:02<00:26, 17.23it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.54it/s]
eval model::  10%|▉         | 48/500 [00:02<00:23, 18.92it/s]
eval model::  10%|█         | 51/500 [00:02<00:22, 20.11it/s]
eval model::  11%|█         | 55/500 [00:02<00:19, 22.99it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:21, 20.85it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:26, 16.49it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:26, 16.47it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.15it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.16it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 20.69it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 20.55it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:21, 20.02it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:19, 21.19it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:20, 19.87it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.58it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.58it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:21, 18.78it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.93it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:20, 19.70it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:19, 20.25it/s]
eval model::  20%|██        | 102/500 [00:05<00:21, 18.37it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 20.63it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.44it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:18, 21.47it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:17, 22.08it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:16, 23.19it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:17, 22.05it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 20.58it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.38it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 18.92it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 17.78it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:19, 19.14it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:18, 19.78it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 19.78it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:16, 21.21it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:16, 21.05it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:17, 19.50it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 17.54it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 20.47it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:16, 20.42it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:24, 13.58it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:23, 14.45it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:20, 16.53it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:19, 16.88it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:18, 17.41it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:21, 15.32it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:21, 14.98it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:20, 15.72it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:23, 13.79it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:19, 16.55it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:17, 18.35it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 19.67it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:14, 21.79it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 21.20it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.64it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.55it/s]
eval model::  41%|████      | 204/500 [00:10<00:14, 20.87it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:21, 13.43it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:21, 13.62it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 13.96it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:20, 14.23it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:23, 12.13it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:26, 10.64it/s]
eval model::  44%|████▍     | 219/500 [00:12<00:23, 12.20it/s]
eval model::  44%|████▍     | 221/500 [00:12<00:24, 11.24it/s]
eval model::  45%|████▍     | 223/500 [00:12<00:21, 12.85it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:17, 15.26it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 19.46it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 20.74it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.88it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:10, 23.79it/s]
eval model::  48%|████▊     | 242/500 [00:13<00:13, 18.66it/s]
eval model::  49%|████▉     | 245/500 [00:13<00:12, 20.14it/s]
eval model::  50%|████▉     | 248/500 [00:13<00:11, 21.13it/s]
eval model::  50%|█████     | 251/500 [00:13<00:12, 19.77it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.31it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.78it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:10, 22.90it/s]
eval model::  53%|█████▎    | 263/500 [00:14<00:10, 23.32it/s]
eval model::  53%|█████▎    | 266/500 [00:14<00:11, 19.91it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:12, 18.65it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 19.84it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 18.05it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 18.04it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:12, 18.34it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:11, 19.57it/s]
eval model::  57%|█████▋    | 284/500 [00:15<00:11, 18.91it/s]
eval model::  57%|█████▋    | 286/500 [00:15<00:23,  9.09it/s]
eval model::  58%|█████▊    | 289/500 [00:15<00:18, 11.65it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:15, 13.29it/s]
eval model::  59%|█████▉    | 294/500 [00:16<00:14, 13.76it/s]
eval model::  59%|█████▉    | 296/500 [00:16<00:16, 12.40it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 15.59it/s]
eval model::  60%|██████    | 301/500 [00:16<00:14, 14.04it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 15.93it/s]
eval model::  61%|██████▏   | 307/500 [00:17<00:10, 18.10it/s]
eval model::  62%|██████▏   | 310/500 [00:17<00:09, 20.03it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:08, 20.99it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 19.84it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 21.55it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 20.40it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:07, 23.22it/s]
eval model::  66%|██████▌   | 329/500 [00:18<00:07, 21.59it/s]
eval model::  66%|██████▋   | 332/500 [00:18<00:07, 22.36it/s]
eval model::  67%|██████▋   | 335/500 [00:18<00:07, 21.36it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 19.87it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:09, 15.99it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:08, 17.55it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 18.82it/s]
eval model::  70%|███████   | 350/500 [00:19<00:07, 20.84it/s]
eval model::  71%|███████   | 353/500 [00:19<00:06, 22.38it/s]
eval model::  71%|███████   | 356/500 [00:19<00:07, 20.05it/s]
eval model::  72%|███████▏  | 359/500 [00:19<00:06, 20.87it/s]
eval model::  72%|███████▏  | 362/500 [00:19<00:07, 17.35it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:08, 15.36it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:09, 14.59it/s]
eval model::  74%|███████▎  | 368/500 [00:20<00:08, 15.58it/s]
eval model::  74%|███████▍  | 370/500 [00:20<00:07, 16.26it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:08, 15.86it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 18.06it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:06, 19.78it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:06, 19.09it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 21.29it/s]
eval model::  77%|███████▋  | 387/500 [00:21<00:08, 13.95it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:06, 16.43it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:06, 16.75it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 18.65it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 20.15it/s]
eval model::  80%|████████  | 402/500 [00:22<00:04, 19.86it/s]
eval model::  81%|████████  | 405/500 [00:22<00:04, 20.28it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:04, 19.79it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:04, 20.30it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:04, 20.51it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 19.20it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 19.16it/s]
eval model::  84%|████████▍ | 421/500 [00:23<00:06, 12.67it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.27it/s]
eval model::  85%|████████▌ | 427/500 [00:23<00:04, 16.41it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 18.47it/s]
eval model::  87%|████████▋ | 433/500 [00:23<00:03, 19.07it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:03, 21.24it/s]
eval model::  88%|████████▊ | 439/500 [00:24<00:02, 21.71it/s]
eval model::  88%|████████▊ | 442/500 [00:24<00:02, 20.96it/s]
eval model::  89%|████████▉ | 445/500 [00:24<00:02, 21.76it/s]
eval model::  90%|████████▉ | 448/500 [00:24<00:02, 20.46it/s]
eval model::  90%|█████████ | 451/500 [00:24<00:02, 19.05it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 20.21it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:02, 21.40it/s]
eval model::  92%|█████████▏| 460/500 [00:25<00:01, 21.38it/s]
eval model::  93%|█████████▎| 463/500 [00:25<00:02, 17.84it/s]
eval model::  93%|█████████▎| 465/500 [00:25<00:02, 17.19it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 18.67it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 21.14it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 19.86it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:00, 22.58it/s]
eval model::  96%|█████████▌| 481/500 [00:26<00:00, 22.72it/s]
eval model::  97%|█████████▋| 484/500 [00:26<00:00, 21.85it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 20.95it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 20.92it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 18.89it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 18.17it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 20.29it/s]
eval model:: 100%|██████████| 500/500 [00:27<00:00, 18.51it/s]
[2024/07/28 03:22:23] ppocr INFO: cur metric, precision: 0.7272727272727273, recall: 0.7010110736639383, hmean: 0.7139004658004413, fps: 44.43815155604262
[2024/07/28 03:22:23] ppocr INFO: best metric, hmean: 0.7181174089068827, precision: 0.7568, recall: 0.6831969186326432, fps: 44.212831257019886, best_epoch: 1200
[2024/07/28 03:22:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:22:32] ppocr INFO: epoch: [1421/1500], global_step: 4263, lr: 0.001000, loss: 1.093628, loss_shrink_maps: 0.548856, loss_threshold_maps: 0.443709, loss_binary_maps: 0.109302, avg_reader_cost: 1.54088 s, avg_batch_cost: 1.81137 s, avg_samples: 12.5, ips: 6.90084 samples/s, eta: 0:23:57
[2024/07/28 03:22:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:22:42] ppocr INFO: epoch: [1422/1500], global_step: 4266, lr: 0.001000, loss: 1.093628, loss_shrink_maps: 0.548856, loss_threshold_maps: 0.452768, loss_binary_maps: 0.109302, avg_reader_cost: 1.54755 s, avg_batch_cost: 1.79707 s, avg_samples: 12.5, ips: 6.95577 samples/s, eta: 0:23:39
[2024/07/28 03:22:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:22:52] ppocr INFO: epoch: [1423/1500], global_step: 4269, lr: 0.001000, loss: 1.124857, loss_shrink_maps: 0.556106, loss_threshold_maps: 0.455582, loss_binary_maps: 0.110772, avg_reader_cost: 1.56801 s, avg_batch_cost: 1.80541 s, avg_samples: 12.5, ips: 6.92364 samples/s, eta: 0:23:20
[2024/07/28 03:22:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:23:01] ppocr INFO: epoch: [1424/1500], global_step: 4270, lr: 0.001000, loss: 1.144602, loss_shrink_maps: 0.558868, loss_threshold_maps: 0.459777, loss_binary_maps: 0.111498, avg_reader_cost: 0.41056 s, avg_batch_cost: 0.50886 s, avg_samples: 4.8, ips: 9.43285 samples/s, eta: 0:23:14
[2024/07/28 03:23:03] ppocr INFO: epoch: [1424/1500], global_step: 4272, lr: 0.001000, loss: 1.124857, loss_shrink_maps: 0.556106, loss_threshold_maps: 0.455582, loss_binary_maps: 0.110772, avg_reader_cost: 1.10933 s, avg_batch_cost: 1.25540 s, avg_samples: 7.7, ips: 6.13351 samples/s, eta: 0:23:02
[2024/07/28 03:23:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:23:13] ppocr INFO: epoch: [1425/1500], global_step: 4275, lr: 0.001000, loss: 1.099527, loss_shrink_maps: 0.550644, loss_threshold_maps: 0.452768, loss_binary_maps: 0.109542, avg_reader_cost: 1.61493 s, avg_batch_cost: 1.88239 s, avg_samples: 12.5, ips: 6.64048 samples/s, eta: 0:22:44
[2024/07/28 03:23:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:23:24] ppocr INFO: epoch: [1426/1500], global_step: 4278, lr: 0.001000, loss: 1.087080, loss_shrink_maps: 0.544543, loss_threshold_maps: 0.444192, loss_binary_maps: 0.108270, avg_reader_cost: 1.65275 s, avg_batch_cost: 1.91657 s, avg_samples: 12.5, ips: 6.52208 samples/s, eta: 0:22:26
[2024/07/28 03:23:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:23:33] ppocr INFO: epoch: [1427/1500], global_step: 4280, lr: 0.001000, loss: 1.076008, loss_shrink_maps: 0.537333, loss_threshold_maps: 0.437670, loss_binary_maps: 0.107037, avg_reader_cost: 0.89738 s, avg_batch_cost: 1.07186 s, avg_samples: 9.6, ips: 8.95643 samples/s, eta: 0:22:14
[2024/07/28 03:23:34] ppocr INFO: epoch: [1427/1500], global_step: 4281, lr: 0.001000, loss: 1.076008, loss_shrink_maps: 0.537333, loss_threshold_maps: 0.437670, loss_binary_maps: 0.107037, avg_reader_cost: 0.58167 s, avg_batch_cost: 0.63658 s, avg_samples: 2.9, ips: 4.55561 samples/s, eta: 0:22:08
[2024/07/28 03:23:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:23:44] ppocr INFO: epoch: [1428/1500], global_step: 4284, lr: 0.001000, loss: 1.068051, loss_shrink_maps: 0.531784, loss_threshold_maps: 0.434879, loss_binary_maps: 0.106066, avg_reader_cost: 1.50252 s, avg_batch_cost: 1.76223 s, avg_samples: 12.5, ips: 7.09329 samples/s, eta: 0:21:49
[2024/07/28 03:23:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:23:54] ppocr INFO: epoch: [1429/1500], global_step: 4287, lr: 0.001000, loss: 1.079124, loss_shrink_maps: 0.538850, loss_threshold_maps: 0.436418, loss_binary_maps: 0.107540, avg_reader_cost: 1.56015 s, avg_batch_cost: 1.79020 s, avg_samples: 12.5, ips: 6.98246 samples/s, eta: 0:21:31
[2024/07/28 03:23:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:24:04] ppocr INFO: epoch: [1430/1500], global_step: 4290, lr: 0.001000, loss: 1.064565, loss_shrink_maps: 0.531784, loss_threshold_maps: 0.434280, loss_binary_maps: 0.106066, avg_reader_cost: 1.56860 s, avg_batch_cost: 1.79949 s, avg_samples: 12.5, ips: 6.94641 samples/s, eta: 0:21:13
[2024/07/28 03:24:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:24:15] ppocr INFO: epoch: [1431/1500], global_step: 4293, lr: 0.001000, loss: 1.100999, loss_shrink_maps: 0.544399, loss_threshold_maps: 0.440286, loss_binary_maps: 0.108607, avg_reader_cost: 1.69496 s, avg_batch_cost: 1.96884 s, avg_samples: 12.5, ips: 6.34891 samples/s, eta: 0:20:55
[2024/07/28 03:24:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:24:25] ppocr INFO: epoch: [1432/1500], global_step: 4296, lr: 0.001000, loss: 1.111784, loss_shrink_maps: 0.544297, loss_threshold_maps: 0.442953, loss_binary_maps: 0.108969, avg_reader_cost: 1.49335 s, avg_batch_cost: 1.72176 s, avg_samples: 12.5, ips: 7.26002 samples/s, eta: 0:20:37
[2024/07/28 03:24:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:24:35] ppocr INFO: epoch: [1433/1500], global_step: 4299, lr: 0.001000, loss: 1.118312, loss_shrink_maps: 0.556659, loss_threshold_maps: 0.448754, loss_binary_maps: 0.111367, avg_reader_cost: 1.53725 s, avg_batch_cost: 1.76719 s, avg_samples: 12.5, ips: 7.07339 samples/s, eta: 0:20:18
[2024/07/28 03:24:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:24:44] ppocr INFO: epoch: [1434/1500], global_step: 4300, lr: 0.001000, loss: 1.118312, loss_shrink_maps: 0.556659, loss_threshold_maps: 0.454890, loss_binary_maps: 0.111367, avg_reader_cost: 0.40866 s, avg_batch_cost: 0.51101 s, avg_samples: 4.8, ips: 9.39320 samples/s, eta: 0:20:12
[2024/07/28 03:24:46] ppocr INFO: epoch: [1434/1500], global_step: 4302, lr: 0.001000, loss: 1.112828, loss_shrink_maps: 0.544297, loss_threshold_maps: 0.448754, loss_binary_maps: 0.108969, avg_reader_cost: 1.11450 s, avg_batch_cost: 1.26128 s, avg_samples: 7.7, ips: 6.10490 samples/s, eta: 0:20:00
[2024/07/28 03:24:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:24:56] ppocr INFO: epoch: [1435/1500], global_step: 4305, lr: 0.001000, loss: 1.126877, loss_shrink_maps: 0.568455, loss_threshold_maps: 0.455942, loss_binary_maps: 0.113747, avg_reader_cost: 1.56291 s, avg_batch_cost: 1.81611 s, avg_samples: 12.5, ips: 6.88285 samples/s, eta: 0:19:42
[2024/07/28 03:25:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:25:06] ppocr INFO: epoch: [1436/1500], global_step: 4308, lr: 0.001000, loss: 1.150082, loss_shrink_maps: 0.574811, loss_threshold_maps: 0.460397, loss_binary_maps: 0.114844, avg_reader_cost: 1.51314 s, avg_batch_cost: 1.74353 s, avg_samples: 12.5, ips: 7.16938 samples/s, eta: 0:19:24
[2024/07/28 03:25:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:25:16] ppocr INFO: epoch: [1437/1500], global_step: 4310, lr: 0.001000, loss: 1.143436, loss_shrink_maps: 0.567648, loss_threshold_maps: 0.456813, loss_binary_maps: 0.113169, avg_reader_cost: 0.91955 s, avg_batch_cost: 1.11174 s, avg_samples: 9.6, ips: 8.63515 samples/s, eta: 0:19:12
[2024/07/28 03:25:16] ppocr INFO: epoch: [1437/1500], global_step: 4311, lr: 0.001000, loss: 1.122589, loss_shrink_maps: 0.554863, loss_threshold_maps: 0.453497, loss_binary_maps: 0.110675, avg_reader_cost: 0.60172 s, avg_batch_cost: 0.65755 s, avg_samples: 2.9, ips: 4.41030 samples/s, eta: 0:19:06
[2024/07/28 03:25:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:25:26] ppocr INFO: epoch: [1438/1500], global_step: 4314, lr: 0.001000, loss: 1.112302, loss_shrink_maps: 0.554863, loss_threshold_maps: 0.460736, loss_binary_maps: 0.110675, avg_reader_cost: 1.49917 s, avg_batch_cost: 1.75465 s, avg_samples: 12.5, ips: 7.12391 samples/s, eta: 0:18:47
[2024/07/28 03:25:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:25:36] ppocr INFO: epoch: [1439/1500], global_step: 4317, lr: 0.001000, loss: 1.112302, loss_shrink_maps: 0.554863, loss_threshold_maps: 0.460736, loss_binary_maps: 0.110675, avg_reader_cost: 1.50933 s, avg_batch_cost: 1.73830 s, avg_samples: 12.5, ips: 7.19092 samples/s, eta: 0:18:29
[2024/07/28 03:25:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:25:47] ppocr INFO: epoch: [1440/1500], global_step: 4320, lr: 0.001000, loss: 1.113401, loss_shrink_maps: 0.549993, loss_threshold_maps: 0.460212, loss_binary_maps: 0.109880, avg_reader_cost: 1.56452 s, avg_batch_cost: 1.79723 s, avg_samples: 12.5, ips: 6.95516 samples/s, eta: 0:18:11

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:57,  8.67it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.30it/s]
eval model::   1%|          | 6/500 [00:00<00:32, 15.40it/s]
eval model::   2%|▏         | 8/500 [00:00<00:31, 15.61it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.48it/s]
eval model::   3%|▎         | 13/500 [00:00<00:27, 17.59it/s]
eval model::   3%|▎         | 15/500 [00:00<00:28, 16.85it/s]
eval model::   4%|▎         | 18/500 [00:01<00:24, 19.51it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.47it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 21.66it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.72it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 20.65it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 20.24it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 18.82it/s]
eval model::   8%|▊         | 38/500 [00:02<00:22, 20.34it/s]
eval model::   8%|▊         | 41/500 [00:02<00:24, 18.70it/s]
eval model::   9%|▊         | 43/500 [00:02<00:26, 17.26it/s]
eval model::   9%|▉         | 46/500 [00:02<00:25, 17.72it/s]
eval model::  10%|▉         | 49/500 [00:02<00:23, 19.41it/s]
eval model::  10%|█         | 51/500 [00:02<00:23, 18.78it/s]
eval model::  11%|█         | 54/500 [00:02<00:22, 19.45it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:22, 20.08it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:22, 19.18it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:28, 15.51it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:27, 16.13it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 19.06it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 20.59it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 21.12it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 22.17it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:18, 22.22it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:18, 22.98it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:17, 23.13it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:18, 21.76it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:20, 19.55it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:19, 20.47it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:18, 21.64it/s]
eval model::  20%|██        | 100/500 [00:05<00:19, 20.12it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 21.33it/s]
eval model::  21%|██        | 106/500 [00:05<00:17, 22.10it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:17, 22.35it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:17, 21.80it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:17, 21.85it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:16, 23.78it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:19, 19.86it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 19.58it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.51it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 19.07it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:21, 17.31it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:19, 18.54it/s]
eval model::  27%|██▋       | 137/500 [00:06<00:19, 18.35it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 19.62it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:16, 21.53it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 20.25it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:18, 19.39it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 17.71it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 20.93it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:15, 22.47it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:24, 14.03it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:22, 14.71it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:20, 16.43it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 18.86it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 18.81it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:20, 15.99it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:19, 16.60it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:22, 14.23it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:19, 16.38it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:17, 18.00it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:17, 18.29it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:15, 20.07it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 21.65it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:13, 22.06it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.38it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.25it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:21, 13.48it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:21, 13.79it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 14.33it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:19, 14.70it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:21, 13.44it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:22, 12.38it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 14.93it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:20, 13.87it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:16, 17.15it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:14, 19.27it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 22.05it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 21.08it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 22.52it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 21.65it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:13, 18.33it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:12, 19.97it/s]
eval model::  50%|█████     | 250/500 [00:13<00:12, 19.62it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 21.33it/s]
eval model::  51%|█████     | 256/500 [00:13<00:10, 22.31it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 23.74it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:09, 24.98it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:09, 23.95it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:10, 23.08it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:10, 22.28it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 20.24it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 20.27it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.63it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 20.74it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:19, 11.05it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:15, 13.59it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:13, 15.05it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:15, 12.95it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:13, 15.32it/s]
eval model::  60%|██████    | 302/500 [00:16<00:13, 14.87it/s]
eval model::  61%|██████    | 305/500 [00:16<00:11, 16.90it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:10, 19.04it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:09, 20.05it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:08, 21.05it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:08, 21.42it/s]
eval model::  64%|██████▍   | 320/500 [00:17<00:07, 22.84it/s]
eval model::  65%|██████▍   | 323/500 [00:17<00:08, 20.86it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:09, 19.24it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:09, 18.72it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:08, 19.91it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 21.07it/s]
eval model::  68%|██████▊   | 338/500 [00:18<00:08, 18.88it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:11, 14.28it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 17.18it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 19.30it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 21.10it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 21.95it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.55it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:06, 21.33it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.10it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.45it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:08, 15.98it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:07, 17.24it/s]
eval model::  74%|███████▍  | 371/500 [00:19<00:07, 17.66it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 20.05it/s]
eval model::  75%|███████▌  | 377/500 [00:20<00:05, 22.11it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 21.05it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 22.19it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:08, 13.66it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:06, 16.13it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:06, 17.02it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:05, 18.23it/s]
eval model::  80%|███████▉  | 398/500 [00:21<00:05, 20.33it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 18.03it/s]
eval model::  81%|████████  | 404/500 [00:21<00:05, 19.15it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 20.85it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 22.42it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:03, 22.16it/s]
eval model::  83%|████████▎ | 416/500 [00:22<00:04, 20.44it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 19.21it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 13.76it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:05, 13.80it/s]
eval model::  85%|████████▌ | 427/500 [00:22<00:04, 15.98it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 17.93it/s]
eval model::  87%|████████▋ | 433/500 [00:23<00:03, 18.23it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:03, 19.95it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 20.76it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 21.79it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 23.36it/s]
eval model::  90%|████████▉ | 448/500 [00:23<00:02, 22.62it/s]
eval model::  90%|█████████ | 451/500 [00:23<00:02, 21.79it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 21.73it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 23.06it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 21.68it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:02, 17.57it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.76it/s]
eval model::  93%|█████████▎| 467/500 [00:24<00:01, 16.64it/s]
eval model::  94%|█████████▍| 470/500 [00:25<00:01, 18.37it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 19.86it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 21.16it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:00, 21.61it/s]
eval model::  96%|█████████▋| 482/500 [00:25<00:00, 21.32it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 21.09it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 21.04it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 18.29it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 19.58it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 21.29it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.92it/s]
[2024/07/28 03:26:13] ppocr INFO: cur metric, precision: 0.715211970074813, recall: 0.6904188733750601, hmean: 0.7025967662910338, fps: 45.8403418580426
[2024/07/28 03:26:13] ppocr INFO: best metric, hmean: 0.7181174089068827, precision: 0.7568, recall: 0.6831969186326432, fps: 44.212831257019886, best_epoch: 1200
[2024/07/28 03:26:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:26:22] ppocr INFO: epoch: [1441/1500], global_step: 4323, lr: 0.001000, loss: 1.113401, loss_shrink_maps: 0.549993, loss_threshold_maps: 0.467062, loss_binary_maps: 0.109880, avg_reader_cost: 1.79406 s, avg_batch_cost: 2.16562 s, avg_samples: 12.5, ips: 5.77202 samples/s, eta: 0:17:53
[2024/07/28 03:26:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:26:32] ppocr INFO: epoch: [1442/1500], global_step: 4326, lr: 0.001000, loss: 1.108189, loss_shrink_maps: 0.546475, loss_threshold_maps: 0.456431, loss_binary_maps: 0.109380, avg_reader_cost: 1.48053 s, avg_batch_cost: 1.71049 s, avg_samples: 12.5, ips: 7.30787 samples/s, eta: 0:17:35
[2024/07/28 03:26:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:26:42] ppocr INFO: epoch: [1443/1500], global_step: 4329, lr: 0.001000, loss: 1.122690, loss_shrink_maps: 0.550816, loss_threshold_maps: 0.456431, loss_binary_maps: 0.110149, avg_reader_cost: 1.58099 s, avg_batch_cost: 1.80949 s, avg_samples: 12.5, ips: 6.90803 samples/s, eta: 0:17:16
[2024/07/28 03:26:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:26:51] ppocr INFO: epoch: [1444/1500], global_step: 4330, lr: 0.001000, loss: 1.132120, loss_shrink_maps: 0.561823, loss_threshold_maps: 0.458499, loss_binary_maps: 0.112075, avg_reader_cost: 0.40192 s, avg_batch_cost: 0.49474 s, avg_samples: 4.8, ips: 9.70208 samples/s, eta: 0:17:10
[2024/07/28 03:26:53] ppocr INFO: epoch: [1444/1500], global_step: 4332, lr: 0.001000, loss: 1.122690, loss_shrink_maps: 0.546475, loss_threshold_maps: 0.456431, loss_binary_maps: 0.109380, avg_reader_cost: 1.08082 s, avg_batch_cost: 1.22654 s, avg_samples: 7.7, ips: 6.27781 samples/s, eta: 0:16:58
[2024/07/28 03:26:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:27:03] ppocr INFO: epoch: [1445/1500], global_step: 4335, lr: 0.001000, loss: 1.132120, loss_shrink_maps: 0.558306, loss_threshold_maps: 0.456431, loss_binary_maps: 0.111574, avg_reader_cost: 1.52023 s, avg_batch_cost: 1.75624 s, avg_samples: 12.5, ips: 7.11749 samples/s, eta: 0:16:40
[2024/07/28 03:27:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:27:13] ppocr INFO: epoch: [1446/1500], global_step: 4338, lr: 0.001000, loss: 1.130199, loss_shrink_maps: 0.571898, loss_threshold_maps: 0.446185, loss_binary_maps: 0.114267, avg_reader_cost: 1.56963 s, avg_batch_cost: 1.79850 s, avg_samples: 12.5, ips: 6.95025 samples/s, eta: 0:16:22
[2024/07/28 03:27:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:27:23] ppocr INFO: epoch: [1447/1500], global_step: 4340, lr: 0.001000, loss: 1.138150, loss_shrink_maps: 0.582051, loss_threshold_maps: 0.446185, loss_binary_maps: 0.116510, avg_reader_cost: 0.96392 s, avg_batch_cost: 1.13706 s, avg_samples: 9.6, ips: 8.44286 samples/s, eta: 0:16:10
[2024/07/28 03:27:23] ppocr INFO: epoch: [1447/1500], global_step: 4341, lr: 0.001000, loss: 1.138150, loss_shrink_maps: 0.588310, loss_threshold_maps: 0.446185, loss_binary_maps: 0.117677, avg_reader_cost: 0.61421 s, avg_batch_cost: 0.66902 s, avg_samples: 2.9, ips: 4.33468 samples/s, eta: 0:16:04
[2024/07/28 03:27:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:27:34] ppocr INFO: epoch: [1448/1500], global_step: 4344, lr: 0.001000, loss: 1.138150, loss_shrink_maps: 0.588310, loss_threshold_maps: 0.460107, loss_binary_maps: 0.117677, avg_reader_cost: 1.50815 s, avg_batch_cost: 1.74404 s, avg_samples: 12.5, ips: 7.16726 samples/s, eta: 0:15:45
[2024/07/28 03:27:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:27:44] ppocr INFO: epoch: [1449/1500], global_step: 4347, lr: 0.001000, loss: 1.145799, loss_shrink_maps: 0.577506, loss_threshold_maps: 0.462098, loss_binary_maps: 0.115522, avg_reader_cost: 1.49958 s, avg_batch_cost: 1.75029 s, avg_samples: 12.5, ips: 7.14167 samples/s, eta: 0:15:27
[2024/07/28 03:27:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:27:55] ppocr INFO: epoch: [1450/1500], global_step: 4350, lr: 0.001000, loss: 1.124428, loss_shrink_maps: 0.546424, loss_threshold_maps: 0.446513, loss_binary_maps: 0.109119, avg_reader_cost: 1.71421 s, avg_batch_cost: 1.95551 s, avg_samples: 12.5, ips: 6.39218 samples/s, eta: 0:15:09
[2024/07/28 03:27:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:28:06] ppocr INFO: epoch: [1451/1500], global_step: 4353, lr: 0.001000, loss: 1.129691, loss_shrink_maps: 0.564365, loss_threshold_maps: 0.445516, loss_binary_maps: 0.112699, avg_reader_cost: 1.62012 s, avg_batch_cost: 1.86560 s, avg_samples: 12.5, ips: 6.70024 samples/s, eta: 0:14:51
[2024/07/28 03:28:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:28:16] ppocr INFO: epoch: [1452/1500], global_step: 4356, lr: 0.001000, loss: 1.143428, loss_shrink_maps: 0.580283, loss_threshold_maps: 0.463172, loss_binary_maps: 0.115957, avg_reader_cost: 1.51147 s, avg_batch_cost: 1.76957 s, avg_samples: 12.5, ips: 7.06387 samples/s, eta: 0:14:33
[2024/07/28 03:28:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:28:26] ppocr INFO: epoch: [1453/1500], global_step: 4359, lr: 0.001000, loss: 1.143428, loss_shrink_maps: 0.563245, loss_threshold_maps: 0.466131, loss_binary_maps: 0.112572, avg_reader_cost: 1.55302 s, avg_batch_cost: 1.78283 s, avg_samples: 12.5, ips: 7.01133 samples/s, eta: 0:14:14
[2024/07/28 03:28:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:28:35] ppocr INFO: epoch: [1454/1500], global_step: 4360, lr: 0.001000, loss: 1.143428, loss_shrink_maps: 0.563245, loss_threshold_maps: 0.466131, loss_binary_maps: 0.112572, avg_reader_cost: 0.41056 s, avg_batch_cost: 0.51732 s, avg_samples: 4.8, ips: 9.27856 samples/s, eta: 0:14:08
[2024/07/28 03:28:36] ppocr INFO: epoch: [1454/1500], global_step: 4362, lr: 0.001000, loss: 1.158076, loss_shrink_maps: 0.578298, loss_threshold_maps: 0.466131, loss_binary_maps: 0.115766, avg_reader_cost: 1.12596 s, avg_batch_cost: 1.27159 s, avg_samples: 7.7, ips: 6.05541 samples/s, eta: 0:13:56
[2024/07/28 03:28:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:28:47] ppocr INFO: epoch: [1455/1500], global_step: 4365, lr: 0.001000, loss: 1.196660, loss_shrink_maps: 0.588257, loss_threshold_maps: 0.479375, loss_binary_maps: 0.117589, avg_reader_cost: 1.80100 s, avg_batch_cost: 2.02942 s, avg_samples: 12.5, ips: 6.15941 samples/s, eta: 0:13:38
[2024/07/28 03:28:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:28:58] ppocr INFO: epoch: [1456/1500], global_step: 4368, lr: 0.001000, loss: 1.241111, loss_shrink_maps: 0.630735, loss_threshold_maps: 0.498004, loss_binary_maps: 0.125429, avg_reader_cost: 1.50039 s, avg_batch_cost: 1.75307 s, avg_samples: 12.5, ips: 7.13036 samples/s, eta: 0:13:20
[2024/07/28 03:29:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:29:08] ppocr INFO: epoch: [1457/1500], global_step: 4370, lr: 0.001000, loss: 1.261841, loss_shrink_maps: 0.636298, loss_threshold_maps: 0.499428, loss_binary_maps: 0.126722, avg_reader_cost: 0.93560 s, avg_batch_cost: 1.10905 s, avg_samples: 9.6, ips: 8.65607 samples/s, eta: 0:13:08
[2024/07/28 03:29:08] ppocr INFO: epoch: [1457/1500], global_step: 4371, lr: 0.001000, loss: 1.261841, loss_shrink_maps: 0.637236, loss_threshold_maps: 0.499428, loss_binary_maps: 0.127395, avg_reader_cost: 0.60024 s, avg_batch_cost: 0.65523 s, avg_samples: 2.9, ips: 4.42594 samples/s, eta: 0:13:02
[2024/07/28 03:29:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:29:19] ppocr INFO: epoch: [1458/1500], global_step: 4374, lr: 0.001000, loss: 1.241111, loss_shrink_maps: 0.633258, loss_threshold_maps: 0.499428, loss_binary_maps: 0.126463, avg_reader_cost: 1.63261 s, avg_batch_cost: 1.97716 s, avg_samples: 12.5, ips: 6.32219 samples/s, eta: 0:12:44
[2024/07/28 03:29:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:29:30] ppocr INFO: epoch: [1459/1500], global_step: 4377, lr: 0.001000, loss: 1.258954, loss_shrink_maps: 0.637236, loss_threshold_maps: 0.499428, loss_binary_maps: 0.127395, avg_reader_cost: 1.59017 s, avg_batch_cost: 1.85152 s, avg_samples: 12.5, ips: 6.75122 samples/s, eta: 0:12:25
[2024/07/28 03:29:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:29:40] ppocr INFO: epoch: [1460/1500], global_step: 4380, lr: 0.001000, loss: 1.242568, loss_shrink_maps: 0.633471, loss_threshold_maps: 0.498004, loss_binary_maps: 0.126730, avg_reader_cost: 1.52614 s, avg_batch_cost: 1.75448 s, avg_samples: 12.5, ips: 7.12460 samples/s, eta: 0:12:07

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:58,  8.54it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 19.04it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.16it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 18.02it/s]
eval model::   2%|▏         | 11/500 [00:00<00:24, 19.73it/s]
eval model::   3%|▎         | 13/500 [00:00<00:25, 19.12it/s]
eval model::   3%|▎         | 16/500 [00:00<00:24, 19.95it/s]
eval model::   4%|▍         | 19/500 [00:01<00:25, 18.92it/s]
eval model::   4%|▍         | 21/500 [00:01<00:25, 18.69it/s]
eval model::   5%|▍         | 23/500 [00:01<00:25, 18.82it/s]
eval model::   5%|▌         | 25/500 [00:01<00:25, 18.93it/s]
eval model::   6%|▌         | 28/500 [00:01<00:24, 19.39it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.31it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.17it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 19.84it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.95it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 17.81it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.51it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 17.19it/s]
eval model::  10%|█         | 50/500 [00:02<00:25, 17.84it/s]
eval model::  11%|█         | 53/500 [00:02<00:22, 19.49it/s]
eval model::  11%|█         | 56/500 [00:02<00:22, 19.90it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:24, 17.88it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:29, 15.11it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:28, 15.41it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:21, 19.81it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:21, 20.10it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 20.63it/s]
eval model::  15%|█▌        | 76/500 [00:04<00:19, 22.07it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:17, 23.42it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:17, 24.30it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 21.41it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.05it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 17.81it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:23, 16.99it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:22, 17.77it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:21, 18.97it/s]
eval model::  20%|██        | 101/500 [00:05<00:23, 16.80it/s]
eval model::  21%|██        | 104/500 [00:05<00:21, 18.34it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:19, 19.88it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:19, 20.38it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 21.10it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:19, 19.78it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:18, 20.58it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:17, 21.65it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:19, 19.29it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 20.21it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 18.96it/s]
eval model::  27%|██▋       | 133/500 [00:07<00:20, 17.48it/s]
eval model::  27%|██▋       | 136/500 [00:07<00:19, 18.63it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:19, 18.94it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 18.97it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:16, 21.01it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:18, 19.66it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:17, 20.20it/s]
eval model::  30%|███       | 152/500 [00:07<00:17, 19.40it/s]
eval model::  31%|███       | 155/500 [00:08<00:15, 21.70it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:14, 23.14it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:21, 15.85it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:19, 17.61it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 18.97it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:15, 21.03it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:16, 19.40it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.98it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.74it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:19, 16.55it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.42it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.06it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:14, 20.72it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.59it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:15, 19.96it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.14it/s]
eval model::  41%|████      | 205/500 [00:10<00:19, 15.43it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:17, 16.49it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:17, 16.25it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:16, 17.27it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.23it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 12.99it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:18, 15.44it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:19, 14.47it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.35it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:13, 19.43it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 20.89it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:13, 20.12it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:13, 19.84it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:13, 19.64it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:14, 17.93it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:13, 18.34it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:13, 18.28it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 19.77it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.33it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 20.84it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 22.88it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:09, 23.64it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:10, 23.26it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:10, 21.06it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 20.61it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 18.19it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:12, 18.27it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 19.55it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:19, 11.17it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:15, 13.35it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 16.10it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:15, 13.25it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:12, 16.57it/s]
eval model::  60%|██████    | 302/500 [00:16<00:12, 15.81it/s]
eval model::  61%|██████    | 304/500 [00:16<00:12, 15.89it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 17.75it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:10, 18.52it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 19.47it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 18.68it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:09, 19.00it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 19.18it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 21.21it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 20.81it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 20.40it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 20.96it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:08, 19.18it/s]
eval model::  68%|██████▊   | 339/500 [00:18<00:08, 19.06it/s]
eval model::  68%|██████▊   | 341/500 [00:18<00:11, 14.04it/s]
eval model::  69%|██████▉   | 344/500 [00:18<00:09, 16.81it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:08, 18.91it/s]
eval model::  70%|███████   | 351/500 [00:18<00:07, 20.82it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 22.76it/s]
eval model::  71%|███████▏  | 357/500 [00:19<00:06, 20.54it/s]
eval model::  72%|███████▏  | 360/500 [00:19<00:06, 20.86it/s]
eval model::  73%|███████▎  | 363/500 [00:19<00:07, 18.59it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:07, 18.14it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:08, 15.52it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:07, 17.47it/s]
eval model::  75%|███████▍  | 373/500 [00:20<00:06, 19.37it/s]
eval model::  75%|███████▌  | 376/500 [00:20<00:05, 21.77it/s]
eval model::  76%|███████▌  | 379/500 [00:20<00:05, 22.84it/s]
eval model::  76%|███████▋  | 382/500 [00:20<00:05, 21.05it/s]
eval model::  77%|███████▋  | 385/500 [00:20<00:05, 21.04it/s]
eval model::  78%|███████▊  | 388/500 [00:21<00:09, 12.30it/s]
eval model::  78%|███████▊  | 391/500 [00:21<00:07, 14.72it/s]
eval model::  79%|███████▉  | 394/500 [00:21<00:06, 16.04it/s]
eval model::  79%|███████▉  | 397/500 [00:21<00:05, 18.64it/s]
eval model::  80%|████████  | 400/500 [00:21<00:05, 17.81it/s]
eval model::  81%|████████  | 403/500 [00:21<00:05, 18.91it/s]
eval model::  81%|████████  | 406/500 [00:21<00:04, 19.02it/s]
eval model::  82%|████████▏ | 409/500 [00:22<00:04, 20.14it/s]
eval model::  82%|████████▏ | 412/500 [00:22<00:04, 21.41it/s]
eval model::  83%|████████▎ | 415/500 [00:22<00:04, 19.75it/s]
eval model::  84%|████████▎ | 418/500 [00:22<00:04, 18.08it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 13.61it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:04, 15.69it/s]
eval model::  85%|████████▌ | 425/500 [00:23<00:04, 15.36it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:03, 19.42it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 19.29it/s]
eval model::  87%|████████▋ | 435/500 [00:23<00:03, 20.71it/s]
eval model::  88%|████████▊ | 438/500 [00:23<00:02, 22.09it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 20.52it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 21.12it/s]
eval model::  89%|████████▉ | 447/500 [00:24<00:02, 20.95it/s]
eval model::  90%|█████████ | 450/500 [00:24<00:02, 19.70it/s]
eval model::  91%|█████████ | 453/500 [00:24<00:02, 20.69it/s]
eval model::  91%|█████████ | 456/500 [00:24<00:02, 21.45it/s]
eval model::  92%|█████████▏| 459/500 [00:24<00:01, 20.60it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:02, 16.46it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 17.13it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:02, 16.99it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 17.63it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 20.05it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.82it/s]
eval model::  95%|█████████▌| 477/500 [00:25<00:01, 21.21it/s]
eval model::  96%|█████████▌| 480/500 [00:25<00:00, 20.70it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 20.42it/s]
eval model::  97%|█████████▋| 486/500 [00:26<00:00, 18.34it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 20.53it/s]
eval model::  98%|█████████▊| 492/500 [00:26<00:00, 18.44it/s]
eval model::  99%|█████████▉| 494/500 [00:26<00:00, 18.71it/s]
eval model::  99%|█████████▉| 496/500 [00:26<00:00, 18.55it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 20.72it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.66it/s]
[2024/07/28 03:30:07] ppocr INFO: cur metric, precision: 0.7625, recall: 0.6754935002407318, hmean: 0.7163645647178964, fps: 44.16669251820965
[2024/07/28 03:30:07] ppocr INFO: best metric, hmean: 0.7181174089068827, precision: 0.7568, recall: 0.6831969186326432, fps: 44.212831257019886, best_epoch: 1200
[2024/07/28 03:30:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:30:15] ppocr INFO: epoch: [1461/1500], global_step: 4383, lr: 0.001000, loss: 1.242568, loss_shrink_maps: 0.633471, loss_threshold_maps: 0.492656, loss_binary_maps: 0.126730, avg_reader_cost: 1.48365 s, avg_batch_cost: 1.71178 s, avg_samples: 12.5, ips: 7.30233 samples/s, eta: 0:11:49
[2024/07/28 03:30:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:30:25] ppocr INFO: epoch: [1462/1500], global_step: 4386, lr: 0.001000, loss: 1.242568, loss_shrink_maps: 0.633471, loss_threshold_maps: 0.473829, loss_binary_maps: 0.126730, avg_reader_cost: 1.49012 s, avg_batch_cost: 1.72150 s, avg_samples: 12.5, ips: 7.26110 samples/s, eta: 0:11:31
[2024/07/28 03:30:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:30:36] ppocr INFO: epoch: [1463/1500], global_step: 4389, lr: 0.001000, loss: 1.232884, loss_shrink_maps: 0.633471, loss_threshold_maps: 0.473829, loss_binary_maps: 0.126730, avg_reader_cost: 1.52381 s, avg_batch_cost: 1.75287 s, avg_samples: 12.5, ips: 7.13114 samples/s, eta: 0:11:13
[2024/07/28 03:30:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:30:45] ppocr INFO: epoch: [1464/1500], global_step: 4390, lr: 0.001000, loss: 1.229148, loss_shrink_maps: 0.622908, loss_threshold_maps: 0.472047, loss_binary_maps: 0.124154, avg_reader_cost: 0.41674 s, avg_batch_cost: 0.53560 s, avg_samples: 4.8, ips: 8.96189 samples/s, eta: 0:11:06
[2024/07/28 03:30:46] ppocr INFO: epoch: [1464/1500], global_step: 4392, lr: 0.001000, loss: 1.176825, loss_shrink_maps: 0.593189, loss_threshold_maps: 0.472047, loss_binary_maps: 0.118302, avg_reader_cost: 1.16239 s, avg_batch_cost: 1.30815 s, avg_samples: 7.7, ips: 5.88615 samples/s, eta: 0:10:54
[2024/07/28 03:30:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:30:57] ppocr INFO: epoch: [1465/1500], global_step: 4395, lr: 0.001000, loss: 1.177238, loss_shrink_maps: 0.591567, loss_threshold_maps: 0.465929, loss_binary_maps: 0.117989, avg_reader_cost: 1.63880 s, avg_batch_cost: 1.92613 s, avg_samples: 12.5, ips: 6.48971 samples/s, eta: 0:10:36
[2024/07/28 03:31:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:31:07] ppocr INFO: epoch: [1466/1500], global_step: 4398, lr: 0.001000, loss: 1.144596, loss_shrink_maps: 0.565285, loss_threshold_maps: 0.459984, loss_binary_maps: 0.112442, avg_reader_cost: 1.53463 s, avg_batch_cost: 1.79264 s, avg_samples: 12.5, ips: 6.97294 samples/s, eta: 0:10:18
[2024/07/28 03:31:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:31:17] ppocr INFO: epoch: [1467/1500], global_step: 4400, lr: 0.001000, loss: 1.152918, loss_shrink_maps: 0.572960, loss_threshold_maps: 0.459984, loss_binary_maps: 0.114163, avg_reader_cost: 0.94366 s, avg_batch_cost: 1.11690 s, avg_samples: 9.6, ips: 8.59518 samples/s, eta: 0:10:06
[2024/07/28 03:31:17] ppocr INFO: epoch: [1467/1500], global_step: 4401, lr: 0.001000, loss: 1.144596, loss_shrink_maps: 0.565285, loss_threshold_maps: 0.456384, loss_binary_maps: 0.112442, avg_reader_cost: 0.60399 s, avg_batch_cost: 0.65893 s, avg_samples: 2.9, ips: 4.40108 samples/s, eta: 0:10:00
[2024/07/28 03:31:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:31:28] ppocr INFO: epoch: [1468/1500], global_step: 4404, lr: 0.001000, loss: 1.125519, loss_shrink_maps: 0.556395, loss_threshold_maps: 0.450791, loss_binary_maps: 0.110774, avg_reader_cost: 1.53859 s, avg_batch_cost: 1.79001 s, avg_samples: 12.5, ips: 6.98320 samples/s, eta: 0:09:42
[2024/07/28 03:31:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:31:38] ppocr INFO: epoch: [1469/1500], global_step: 4407, lr: 0.001000, loss: 1.152918, loss_shrink_maps: 0.569088, loss_threshold_maps: 0.459984, loss_binary_maps: 0.113237, avg_reader_cost: 1.53341 s, avg_batch_cost: 1.76146 s, avg_samples: 12.5, ips: 7.09639 samples/s, eta: 0:09:23
[2024/07/28 03:31:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:31:48] ppocr INFO: epoch: [1470/1500], global_step: 4410, lr: 0.001000, loss: 1.125519, loss_shrink_maps: 0.556143, loss_threshold_maps: 0.456384, loss_binary_maps: 0.110661, avg_reader_cost: 1.53820 s, avg_batch_cost: 1.76629 s, avg_samples: 12.5, ips: 7.07698 samples/s, eta: 0:09:05
[2024/07/28 03:31:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:31:58] ppocr INFO: epoch: [1471/1500], global_step: 4413, lr: 0.001000, loss: 1.153108, loss_shrink_maps: 0.569088, loss_threshold_maps: 0.457068, loss_binary_maps: 0.113237, avg_reader_cost: 1.53037 s, avg_batch_cost: 1.76794 s, avg_samples: 12.5, ips: 7.07038 samples/s, eta: 0:08:47
[2024/07/28 03:32:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:32:09] ppocr INFO: epoch: [1472/1500], global_step: 4416, lr: 0.001000, loss: 1.144787, loss_shrink_maps: 0.564537, loss_threshold_maps: 0.461637, loss_binary_maps: 0.112328, avg_reader_cost: 1.51662 s, avg_batch_cost: 1.76876 s, avg_samples: 12.5, ips: 7.06708 samples/s, eta: 0:08:29
[2024/07/28 03:32:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:32:19] ppocr INFO: epoch: [1473/1500], global_step: 4419, lr: 0.001000, loss: 1.091354, loss_shrink_maps: 0.551595, loss_threshold_maps: 0.461348, loss_binary_maps: 0.109905, avg_reader_cost: 1.55576 s, avg_batch_cost: 1.78417 s, avg_samples: 12.5, ips: 7.00605 samples/s, eta: 0:08:11
[2024/07/28 03:32:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:32:28] ppocr INFO: epoch: [1474/1500], global_step: 4420, lr: 0.001000, loss: 1.091354, loss_shrink_maps: 0.551595, loss_threshold_maps: 0.461348, loss_binary_maps: 0.109905, avg_reader_cost: 0.41313 s, avg_batch_cost: 0.50949 s, avg_samples: 4.8, ips: 9.42121 samples/s, eta: 0:08:04
[2024/07/28 03:32:29] ppocr INFO: epoch: [1474/1500], global_step: 4422, lr: 0.001000, loss: 1.104328, loss_shrink_maps: 0.551595, loss_threshold_maps: 0.462582, loss_binary_maps: 0.109905, avg_reader_cost: 1.10999 s, avg_batch_cost: 1.25567 s, avg_samples: 7.7, ips: 6.13216 samples/s, eta: 0:07:52
[2024/07/28 03:32:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:32:39] ppocr INFO: epoch: [1475/1500], global_step: 4425, lr: 0.001000, loss: 1.124940, loss_shrink_maps: 0.554006, loss_threshold_maps: 0.462582, loss_binary_maps: 0.110239, avg_reader_cost: 1.49121 s, avg_batch_cost: 1.72066 s, avg_samples: 12.5, ips: 7.26465 samples/s, eta: 0:07:34
[2024/07/28 03:32:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:32:50] ppocr INFO: epoch: [1476/1500], global_step: 4428, lr: 0.001000, loss: 1.124940, loss_shrink_maps: 0.554006, loss_threshold_maps: 0.459540, loss_binary_maps: 0.110239, avg_reader_cost: 1.57003 s, avg_batch_cost: 1.79836 s, avg_samples: 12.5, ips: 6.95076 samples/s, eta: 0:07:16
[2024/07/28 03:32:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:32:59] ppocr INFO: epoch: [1477/1500], global_step: 4430, lr: 0.001000, loss: 1.124940, loss_shrink_maps: 0.554006, loss_threshold_maps: 0.456150, loss_binary_maps: 0.110239, avg_reader_cost: 0.90173 s, avg_batch_cost: 1.09591 s, avg_samples: 9.6, ips: 8.75988 samples/s, eta: 0:07:04
[2024/07/28 03:33:00] ppocr INFO: epoch: [1477/1500], global_step: 4431, lr: 0.001000, loss: 1.104328, loss_shrink_maps: 0.547926, loss_threshold_maps: 0.455167, loss_binary_maps: 0.109154, avg_reader_cost: 0.59392 s, avg_batch_cost: 0.64845 s, avg_samples: 2.9, ips: 4.47218 samples/s, eta: 0:06:58
[2024/07/28 03:33:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:33:10] ppocr INFO: epoch: [1478/1500], global_step: 4434, lr: 0.001000, loss: 1.104328, loss_shrink_maps: 0.542905, loss_threshold_maps: 0.450170, loss_binary_maps: 0.107750, avg_reader_cost: 1.56997 s, avg_batch_cost: 1.80049 s, avg_samples: 12.5, ips: 6.94257 samples/s, eta: 0:06:40
[2024/07/28 03:33:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:33:21] ppocr INFO: epoch: [1479/1500], global_step: 4437, lr: 0.001000, loss: 1.081116, loss_shrink_maps: 0.539966, loss_threshold_maps: 0.442473, loss_binary_maps: 0.107193, avg_reader_cost: 1.51736 s, avg_batch_cost: 1.76424 s, avg_samples: 12.5, ips: 7.08522 samples/s, eta: 0:06:21
[2024/07/28 03:33:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:33:31] ppocr INFO: epoch: [1480/1500], global_step: 4440, lr: 0.001000, loss: 1.102623, loss_shrink_maps: 0.544017, loss_threshold_maps: 0.444807, loss_binary_maps: 0.108182, avg_reader_cost: 1.64355 s, avg_batch_cost: 1.87428 s, avg_samples: 12.5, ips: 6.66922 samples/s, eta: 0:06:03

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:59,  8.34it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.48it/s]
eval model::   1%|          | 6/500 [00:00<00:30, 16.11it/s]
eval model::   2%|▏         | 9/500 [00:00<00:26, 18.75it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 18.04it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.60it/s]
eval model::   3%|▎         | 17/500 [00:00<00:22, 21.37it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.43it/s]
eval model::   5%|▍         | 24/500 [00:01<00:20, 23.02it/s]
eval model::   5%|▌         | 27/500 [00:01<00:21, 21.71it/s]
eval model::   6%|▌         | 30/500 [00:01<00:21, 21.84it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 20.23it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 18.76it/s]
eval model::   8%|▊         | 39/500 [00:02<00:26, 17.48it/s]
eval model::   8%|▊         | 42/500 [00:02<00:27, 16.61it/s]
eval model::   9%|▉         | 44/500 [00:02<00:26, 17.25it/s]
eval model::   9%|▉         | 46/500 [00:02<00:28, 15.81it/s]
eval model::  10%|▉         | 49/500 [00:02<00:24, 18.08it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 19.64it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.91it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:22, 19.80it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:27, 16.25it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.94it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.71it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 22.23it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:21, 20.33it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:20, 20.67it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:20, 20.69it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:21, 19.67it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:21, 19.55it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:22, 18.52it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:24, 16.55it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:24, 16.87it/s]
eval model::  19%|█▉        | 95/500 [00:05<00:23, 16.91it/s]
eval model::  20%|█▉        | 98/500 [00:05<00:21, 19.03it/s]
eval model::  20%|██        | 100/500 [00:05<00:24, 16.37it/s]
eval model::  21%|██        | 103/500 [00:05<00:21, 18.80it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 19.86it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 21.39it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:19, 20.20it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:18, 20.72it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:16, 22.58it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:19, 19.47it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:19, 19.11it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.13it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 18.80it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:22, 16.67it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:19, 18.65it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:20, 17.57it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:20, 17.79it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 18.99it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:18, 19.21it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.38it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.25it/s]
eval model::  31%|███       | 153/500 [00:08<00:16, 20.62it/s]
eval model::  31%|███       | 156/500 [00:08<00:15, 21.98it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:18, 18.71it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:24, 13.99it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:20, 16.37it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 18.12it/s]
eval model::  34%|███▍      | 170/500 [00:09<00:16, 19.45it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:17, 18.53it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:20, 15.93it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:19, 16.23it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:21, 15.04it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:20, 15.76it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:16, 18.68it/s]
eval model::  38%|███▊      | 188/500 [00:10<00:16, 19.37it/s]
eval model::  38%|███▊      | 191/500 [00:10<00:15, 20.30it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:14, 20.50it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:15, 20.01it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.31it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.10it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:20, 14.03it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:19, 14.79it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:19, 14.99it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.18it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:20, 13.96it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:21, 12.94it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:18, 15.34it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:19, 14.14it/s]
eval model::  45%|████▌     | 226/500 [00:12<00:15, 17.61it/s]
eval model::  46%|████▌     | 229/500 [00:12<00:13, 19.69it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 21.89it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:13, 20.17it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:12, 21.04it/s]
eval model::  48%|████▊     | 241/500 [00:13<00:12, 20.66it/s]
eval model::  49%|████▉     | 244/500 [00:13<00:13, 19.04it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:12, 20.32it/s]
eval model::  50%|█████     | 250/500 [00:13<00:12, 20.58it/s]
eval model::  51%|█████     | 253/500 [00:13<00:11, 22.27it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 21.28it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 22.71it/s]
eval model::  52%|█████▏    | 262/500 [00:14<00:10, 23.71it/s]
eval model::  53%|█████▎    | 265/500 [00:14<00:10, 21.48it/s]
eval model::  54%|█████▎    | 268/500 [00:14<00:10, 21.44it/s]
eval model::  54%|█████▍    | 271/500 [00:14<00:12, 19.03it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:11, 19.95it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:12, 18.35it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:12, 18.18it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:11, 19.61it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:18, 11.82it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.46it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:12, 16.71it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:14, 13.90it/s]
eval model::  60%|█████▉    | 299/500 [00:16<00:11, 17.39it/s]
eval model::  60%|██████    | 302/500 [00:16<00:12, 16.23it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.47it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 19.08it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.11it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 20.42it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:08, 20.98it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 21.13it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:09, 18.70it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 20.55it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 19.71it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:08, 20.40it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 21.65it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 20.46it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:10, 15.52it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 16.78it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 18.76it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:07, 20.35it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 21.45it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 21.92it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 20.19it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:07, 18.37it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 18.17it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 18.41it/s]
eval model::  74%|███████▎  | 368/500 [00:19<00:07, 18.70it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 18.84it/s]
eval model::  74%|███████▍  | 372/500 [00:20<00:06, 18.71it/s]
eval model::  75%|███████▌  | 375/500 [00:20<00:06, 20.79it/s]
eval model::  76%|███████▌  | 378/500 [00:20<00:05, 22.70it/s]
eval model::  76%|███████▌  | 381/500 [00:20<00:05, 21.03it/s]
eval model::  77%|███████▋  | 384/500 [00:20<00:05, 22.67it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 15.00it/s]
eval model::  78%|███████▊  | 390/500 [00:21<00:06, 17.31it/s]
eval model::  79%|███████▊  | 393/500 [00:21<00:05, 18.31it/s]
eval model::  79%|███████▉  | 396/500 [00:21<00:05, 18.78it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:05, 18.62it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 17.76it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 19.79it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 19.03it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:04, 20.04it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:04, 21.23it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 18.07it/s]
eval model::  84%|████████▍ | 419/500 [00:22<00:04, 18.35it/s]
eval model::  84%|████████▍ | 421/500 [00:22<00:06, 12.14it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 13.53it/s]
eval model::  85%|████████▌ | 426/500 [00:23<00:05, 14.64it/s]
eval model::  86%|████████▌ | 429/500 [00:23<00:04, 17.70it/s]
eval model::  86%|████████▋ | 432/500 [00:23<00:03, 18.10it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:02, 21.62it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 23.43it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 23.08it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 23.17it/s]
eval model::  90%|████████▉ | 448/500 [00:24<00:02, 22.30it/s]
eval model::  90%|█████████ | 451/500 [00:24<00:02, 22.32it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 22.53it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 23.18it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 21.64it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 18.57it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 16.47it/s]
eval model::  93%|█████████▎| 467/500 [00:25<00:01, 17.05it/s]
eval model::  94%|█████████▍| 470/500 [00:25<00:01, 19.81it/s]
eval model::  95%|█████████▍| 473/500 [00:25<00:01, 19.56it/s]
eval model::  95%|█████████▌| 476/500 [00:25<00:01, 18.57it/s]
eval model::  96%|█████████▌| 479/500 [00:25<00:01, 18.96it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 19.10it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 19.84it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 18.02it/s]
eval model::  98%|█████████▊| 489/500 [00:26<00:00, 17.72it/s]
eval model::  98%|█████████▊| 491/500 [00:26<00:00, 16.59it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 15.85it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 16.35it/s]
eval model:: 100%|█████████▉| 499/500 [00:26<00:00, 20.86it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.68it/s]
[2024/07/28 03:33:58] ppocr INFO: cur metric, precision: 0.722502522704339, recall: 0.6894559460760713, hmean: 0.7055925104705593, fps: 45.28838523062977
[2024/07/28 03:33:58] ppocr INFO: best metric, hmean: 0.7181174089068827, precision: 0.7568, recall: 0.6831969186326432, fps: 44.212831257019886, best_epoch: 1200
[2024/07/28 03:34:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:34:08] ppocr INFO: epoch: [1481/1500], global_step: 4443, lr: 0.001000, loss: 1.106564, loss_shrink_maps: 0.553358, loss_threshold_maps: 0.444807, loss_binary_maps: 0.110041, avg_reader_cost: 1.59738 s, avg_batch_cost: 1.85663 s, avg_samples: 12.5, ips: 6.73263 samples/s, eta: 0:05:45
[2024/07/28 03:34:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:34:18] ppocr INFO: epoch: [1482/1500], global_step: 4446, lr: 0.001000, loss: 1.106564, loss_shrink_maps: 0.553358, loss_threshold_maps: 0.445001, loss_binary_maps: 0.110041, avg_reader_cost: 1.55862 s, avg_batch_cost: 1.78705 s, avg_samples: 12.5, ips: 6.99477 samples/s, eta: 0:05:27
[2024/07/28 03:34:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:34:28] ppocr INFO: epoch: [1483/1500], global_step: 4449, lr: 0.001000, loss: 1.120575, loss_shrink_maps: 0.553774, loss_threshold_maps: 0.446891, loss_binary_maps: 0.110492, avg_reader_cost: 1.49871 s, avg_batch_cost: 1.72717 s, avg_samples: 12.5, ips: 7.23729 samples/s, eta: 0:05:09
[2024/07/28 03:34:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:34:37] ppocr INFO: epoch: [1484/1500], global_step: 4450, lr: 0.001000, loss: 1.106564, loss_shrink_maps: 0.543984, loss_threshold_maps: 0.445001, loss_binary_maps: 0.108427, avg_reader_cost: 0.42773 s, avg_batch_cost: 0.53312 s, avg_samples: 4.8, ips: 9.00353 samples/s, eta: 0:05:03
[2024/07/28 03:34:39] ppocr INFO: epoch: [1484/1500], global_step: 4452, lr: 0.001000, loss: 1.122203, loss_shrink_maps: 0.560951, loss_threshold_maps: 0.449230, loss_binary_maps: 0.111555, avg_reader_cost: 1.15744 s, avg_batch_cost: 1.30313 s, avg_samples: 7.7, ips: 5.90884 samples/s, eta: 0:04:50
[2024/07/28 03:34:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:34:49] ppocr INFO: epoch: [1485/1500], global_step: 4455, lr: 0.001000, loss: 1.166969, loss_shrink_maps: 0.574775, loss_threshold_maps: 0.468083, loss_binary_maps: 0.114940, avg_reader_cost: 1.56082 s, avg_batch_cost: 1.80439 s, avg_samples: 12.5, ips: 6.92754 samples/s, eta: 0:04:32
[2024/07/28 03:34:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:34:59] ppocr INFO: epoch: [1486/1500], global_step: 4458, lr: 0.001000, loss: 1.176056, loss_shrink_maps: 0.586437, loss_threshold_maps: 0.470364, loss_binary_maps: 0.117093, avg_reader_cost: 1.49175 s, avg_batch_cost: 1.74390 s, avg_samples: 12.5, ips: 7.16785 samples/s, eta: 0:04:14
[2024/07/28 03:35:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:35:09] ppocr INFO: epoch: [1487/1500], global_step: 4460, lr: 0.001000, loss: 1.176056, loss_shrink_maps: 0.586437, loss_threshold_maps: 0.470364, loss_binary_maps: 0.117093, avg_reader_cost: 0.92021 s, avg_batch_cost: 1.09492 s, avg_samples: 9.6, ips: 8.76774 samples/s, eta: 0:04:02
[2024/07/28 03:35:10] ppocr INFO: epoch: [1487/1500], global_step: 4461, lr: 0.001000, loss: 1.180835, loss_shrink_maps: 0.588204, loss_threshold_maps: 0.472082, loss_binary_maps: 0.117320, avg_reader_cost: 0.59316 s, avg_batch_cost: 0.64809 s, avg_samples: 2.9, ips: 4.47468 samples/s, eta: 0:03:56
[2024/07/28 03:35:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:35:20] ppocr INFO: epoch: [1488/1500], global_step: 4464, lr: 0.001000, loss: 1.188157, loss_shrink_maps: 0.600408, loss_threshold_maps: 0.470364, loss_binary_maps: 0.119888, avg_reader_cost: 1.55948 s, avg_batch_cost: 1.79459 s, avg_samples: 12.5, ips: 6.96537 samples/s, eta: 0:03:38
[2024/07/28 03:35:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:35:31] ppocr INFO: epoch: [1489/1500], global_step: 4467, lr: 0.001000, loss: 1.188157, loss_shrink_maps: 0.589292, loss_threshold_maps: 0.466952, loss_binary_maps: 0.117643, avg_reader_cost: 1.60776 s, avg_batch_cost: 1.83597 s, avg_samples: 12.5, ips: 6.80837 samples/s, eta: 0:03:20
[2024/07/28 03:35:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:35:41] ppocr INFO: epoch: [1490/1500], global_step: 4470, lr: 0.001000, loss: 1.195446, loss_shrink_maps: 0.601372, loss_threshold_maps: 0.467356, loss_binary_maps: 0.120095, avg_reader_cost: 1.59788 s, avg_batch_cost: 1.85539 s, avg_samples: 12.5, ips: 6.73712 samples/s, eta: 0:03:01
[2024/07/28 03:35:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:35:51] ppocr INFO: epoch: [1491/1500], global_step: 4473, lr: 0.001000, loss: 1.180864, loss_shrink_maps: 0.602379, loss_threshold_maps: 0.455262, loss_binary_maps: 0.120486, avg_reader_cost: 1.53077 s, avg_batch_cost: 1.77481 s, avg_samples: 12.5, ips: 7.04299 samples/s, eta: 0:02:43
[2024/07/28 03:35:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:36:02] ppocr INFO: epoch: [1492/1500], global_step: 4476, lr: 0.001000, loss: 1.201694, loss_shrink_maps: 0.617280, loss_threshold_maps: 0.461206, loss_binary_maps: 0.122841, avg_reader_cost: 1.57202 s, avg_batch_cost: 1.83390 s, avg_samples: 12.5, ips: 6.81609 samples/s, eta: 0:02:25
[2024/07/28 03:36:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:36:12] ppocr INFO: epoch: [1493/1500], global_step: 4479, lr: 0.001000, loss: 1.194618, loss_shrink_maps: 0.602379, loss_threshold_maps: 0.461206, loss_binary_maps: 0.120486, avg_reader_cost: 1.49030 s, avg_batch_cost: 1.74762 s, avg_samples: 12.5, ips: 7.15257 samples/s, eta: 0:02:07
[2024/07/28 03:36:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:36:21] ppocr INFO: epoch: [1494/1500], global_step: 4480, lr: 0.001000, loss: 1.194618, loss_shrink_maps: 0.602379, loss_threshold_maps: 0.460883, loss_binary_maps: 0.120486, avg_reader_cost: 0.42422 s, avg_batch_cost: 0.51991 s, avg_samples: 4.8, ips: 9.23237 samples/s, eta: 0:02:01
[2024/07/28 03:36:23] ppocr INFO: epoch: [1494/1500], global_step: 4482, lr: 0.001000, loss: 1.194618, loss_shrink_maps: 0.602256, loss_threshold_maps: 0.451724, loss_binary_maps: 0.120370, avg_reader_cost: 1.13117 s, avg_batch_cost: 1.27710 s, avg_samples: 7.7, ips: 6.02926 samples/s, eta: 0:01:49
[2024/07/28 03:36:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:36:33] ppocr INFO: epoch: [1495/1500], global_step: 4485, lr: 0.001000, loss: 1.194618, loss_shrink_maps: 0.602256, loss_threshold_maps: 0.460883, loss_binary_maps: 0.120370, avg_reader_cost: 1.54389 s, avg_batch_cost: 1.80591 s, avg_samples: 12.5, ips: 6.92172 samples/s, eta: 0:01:30
[2024/07/28 03:36:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:36:44] ppocr INFO: epoch: [1496/1500], global_step: 4488, lr: 0.001000, loss: 1.183950, loss_shrink_maps: 0.594420, loss_threshold_maps: 0.457676, loss_binary_maps: 0.118264, avg_reader_cost: 1.49595 s, avg_batch_cost: 1.74696 s, avg_samples: 12.5, ips: 7.15528 samples/s, eta: 0:01:12
[2024/07/28 03:36:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:36:53] ppocr INFO: epoch: [1497/1500], global_step: 4490, lr: 0.001000, loss: 1.183950, loss_shrink_maps: 0.594420, loss_threshold_maps: 0.465592, loss_binary_maps: 0.118264, avg_reader_cost: 0.91684 s, avg_batch_cost: 1.08943 s, avg_samples: 9.6, ips: 8.81195 samples/s, eta: 0:01:00
[2024/07/28 03:36:54] ppocr INFO: epoch: [1497/1500], global_step: 4491, lr: 0.001000, loss: 1.183950, loss_shrink_maps: 0.590347, loss_threshold_maps: 0.465592, loss_binary_maps: 0.117533, avg_reader_cost: 0.59017 s, avg_batch_cost: 0.64501 s, avg_samples: 2.9, ips: 4.49609 samples/s, eta: 0:00:54
[2024/07/28 03:36:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:37:04] ppocr INFO: epoch: [1498/1500], global_step: 4494, lr: 0.001000, loss: 1.133682, loss_shrink_maps: 0.562351, loss_threshold_maps: 0.449332, loss_binary_maps: 0.112008, avg_reader_cost: 1.53221 s, avg_batch_cost: 1.76541 s, avg_samples: 12.5, ips: 7.08051 samples/s, eta: 0:00:36
[2024/07/28 03:37:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:37:15] ppocr INFO: epoch: [1499/1500], global_step: 4497, lr: 0.001000, loss: 1.090888, loss_shrink_maps: 0.541108, loss_threshold_maps: 0.445383, loss_binary_maps: 0.107828, avg_reader_cost: 1.51009 s, avg_batch_cost: 1.74685 s, avg_samples: 12.5, ips: 7.15573 samples/s, eta: 0:00:18
[2024/07/28 03:37:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:37:25] ppocr INFO: epoch: [1500/1500], global_step: 4500, lr: 0.001000, loss: 1.090888, loss_shrink_maps: 0.541108, loss_threshold_maps: 0.445225, loss_binary_maps: 0.107828, avg_reader_cost: 1.51975 s, avg_batch_cost: 1.76996 s, avg_samples: 12.5, ips: 7.06229 samples/s, eta: 0:00:00

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:00,  8.25it/s]
eval model::   1%|          | 3/500 [00:00<00:35, 13.90it/s]
eval model::   1%|          | 6/500 [00:00<00:30, 16.26it/s]
eval model::   2%|▏         | 8/500 [00:00<00:30, 16.27it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.32it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 18.69it/s]
eval model::   4%|▎         | 18/500 [00:00<00:21, 21.95it/s]
eval model::   4%|▍         | 21/500 [00:01<00:23, 20.27it/s]
eval model::   5%|▍         | 24/500 [00:01<00:23, 20.28it/s]
eval model::   5%|▌         | 27/500 [00:01<00:24, 19.67it/s]
eval model::   6%|▌         | 30/500 [00:01<00:22, 21.31it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 19.87it/s]
eval model::   7%|▋         | 36/500 [00:01<00:24, 19.06it/s]
eval model::   8%|▊         | 38/500 [00:02<00:24, 18.86it/s]
eval model::   8%|▊         | 40/500 [00:02<00:26, 17.51it/s]
eval model::   8%|▊         | 42/500 [00:02<00:29, 15.71it/s]
eval model::   9%|▉         | 45/500 [00:02<00:25, 17.79it/s]
eval model::   9%|▉         | 47/500 [00:02<00:26, 17.26it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 19.12it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 20.42it/s]
eval model::  11%|█         | 56/500 [00:02<00:21, 20.92it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:24, 18.23it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:29, 14.88it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:29, 14.65it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:22, 18.93it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:19, 22.35it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:19, 21.98it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:18, 23.01it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:18, 22.51it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:19, 21.23it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:20, 20.15it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:23, 17.58it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:24, 16.68it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:24, 16.45it/s]
eval model::  19%|█▉        | 96/500 [00:05<00:22, 17.89it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.80it/s]
eval model::  20%|██        | 102/500 [00:05<00:21, 18.47it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 20.05it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:21, 18.66it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:21, 18.39it/s]
eval model::  23%|██▎       | 115/500 [00:06<00:20, 18.80it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:18, 20.86it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 20.01it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 19.90it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.43it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:20, 18.49it/s]
eval model::  26%|██▋       | 132/500 [00:07<00:21, 17.42it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:18, 19.43it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.38it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.70it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.41it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.85it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.72it/s]
eval model::  31%|███       | 153/500 [00:08<00:18, 18.51it/s]
eval model::  31%|███▏      | 157/500 [00:08<00:16, 21.19it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:23, 14.62it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:20, 16.27it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:19, 16.89it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:17, 18.69it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:18, 18.09it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 16.25it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:20, 16.11it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.83it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:18, 17.44it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.09it/s]
eval model::  38%|███▊      | 189/500 [00:10<00:16, 19.39it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:15, 20.25it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.65it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 20.88it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 21.35it/s]
eval model::  41%|████      | 204/500 [00:10<00:12, 22.78it/s]
eval model::  41%|████▏     | 207/500 [00:11<00:21, 13.72it/s]
eval model::  42%|████▏     | 209/500 [00:11<00:20, 14.36it/s]
eval model::  42%|████▏     | 211/500 [00:11<00:20, 14.23it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:18, 15.18it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:23, 12.29it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:24, 11.56it/s]
eval model::  44%|████▍     | 220/500 [00:12<00:19, 14.08it/s]
eval model::  44%|████▍     | 222/500 [00:12<00:21, 12.91it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:17, 16.00it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:14, 18.34it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 20.75it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:13, 19.44it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 21.67it/s]
eval model::  48%|████▊     | 240/500 [00:13<00:11, 22.33it/s]
eval model::  49%|████▊     | 243/500 [00:13<00:13, 18.36it/s]
eval model::  49%|████▉     | 246/500 [00:13<00:12, 20.00it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:12, 20.24it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 21.07it/s]
eval model::  51%|█████     | 255/500 [00:13<00:10, 22.47it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:10, 22.10it/s]
eval model::  52%|█████▏    | 261/500 [00:14<00:11, 21.27it/s]
eval model::  53%|█████▎    | 264/500 [00:14<00:11, 20.96it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:11, 21.04it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:12, 18.53it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 19.98it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 18.03it/s]
eval model::  56%|█████▌    | 279/500 [00:15<00:11, 18.87it/s]
eval model::  56%|█████▋    | 282/500 [00:15<00:10, 20.25it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:20, 10.46it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:16, 12.90it/s]
eval model::  58%|█████▊    | 292/500 [00:16<00:13, 15.93it/s]
eval model::  59%|█████▉    | 295/500 [00:16<00:14, 14.26it/s]
eval model::  60%|█████▉    | 298/500 [00:16<00:12, 16.28it/s]
eval model::  60%|██████    | 301/500 [00:16<00:12, 15.39it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.88it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 18.93it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.85it/s]
eval model::  63%|██████▎   | 313/500 [00:17<00:08, 21.79it/s]
eval model::  63%|██████▎   | 316/500 [00:17<00:09, 19.65it/s]
eval model::  64%|██████▍   | 319/500 [00:17<00:08, 20.78it/s]
eval model::  64%|██████▍   | 322/500 [00:17<00:08, 19.93it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:08, 20.98it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 19.92it/s]
eval model::  66%|██████▌   | 331/500 [00:18<00:08, 19.88it/s]
eval model::  67%|██████▋   | 334/500 [00:18<00:07, 21.18it/s]
eval model::  67%|██████▋   | 337/500 [00:18<00:07, 20.91it/s]
eval model::  68%|██████▊   | 340/500 [00:18<00:11, 14.14it/s]
eval model::  69%|██████▊   | 343/500 [00:18<00:09, 15.71it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:08, 18.15it/s]
eval model::  70%|██████▉   | 349/500 [00:19<00:07, 20.07it/s]
eval model::  70%|███████   | 352/500 [00:19<00:06, 21.89it/s]
eval model::  71%|███████   | 355/500 [00:19<00:06, 21.64it/s]
eval model::  72%|███████▏  | 358/500 [00:19<00:07, 20.17it/s]
eval model::  72%|███████▏  | 361/500 [00:19<00:08, 16.99it/s]
eval model::  73%|███████▎  | 364/500 [00:19<00:07, 17.29it/s]
eval model::  73%|███████▎  | 366/500 [00:20<00:08, 16.47it/s]
eval model::  74%|███████▍  | 369/500 [00:20<00:07, 18.18it/s]
eval model::  74%|███████▍  | 371/500 [00:20<00:07, 17.84it/s]
eval model::  75%|███████▍  | 374/500 [00:20<00:06, 19.48it/s]
eval model::  75%|███████▌  | 377/500 [00:20<00:05, 21.79it/s]
eval model::  76%|███████▌  | 380/500 [00:20<00:05, 21.17it/s]
eval model::  77%|███████▋  | 383/500 [00:20<00:05, 22.86it/s]
eval model::  77%|███████▋  | 386/500 [00:21<00:07, 15.36it/s]
eval model::  78%|███████▊  | 389/500 [00:21<00:06, 17.91it/s]
eval model::  78%|███████▊  | 392/500 [00:21<00:05, 18.39it/s]
eval model::  79%|███████▉  | 395/500 [00:21<00:05, 19.84it/s]
eval model::  80%|███████▉  | 399/500 [00:21<00:04, 22.60it/s]
eval model::  80%|████████  | 402/500 [00:21<00:04, 20.68it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 21.64it/s]
eval model::  82%|████████▏ | 408/500 [00:22<00:04, 21.57it/s]
eval model::  82%|████████▏ | 411/500 [00:22<00:04, 21.38it/s]
eval model::  83%|████████▎ | 414/500 [00:22<00:03, 22.22it/s]
eval model::  83%|████████▎ | 417/500 [00:22<00:04, 20.28it/s]
eval model::  84%|████████▍ | 420/500 [00:22<00:05, 13.67it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:05, 14.34it/s]
eval model::  85%|████████▍ | 424/500 [00:23<00:05, 14.56it/s]
eval model::  85%|████████▌ | 427/500 [00:23<00:04, 16.84it/s]
eval model::  86%|████████▌ | 430/500 [00:23<00:03, 18.92it/s]
eval model::  87%|████████▋ | 433/500 [00:23<00:03, 19.58it/s]
eval model::  87%|████████▋ | 436/500 [00:23<00:02, 21.75it/s]
eval model::  88%|████████▊ | 439/500 [00:23<00:02, 22.98it/s]
eval model::  88%|████████▊ | 442/500 [00:23<00:02, 23.14it/s]
eval model::  89%|████████▉ | 445/500 [00:23<00:02, 23.98it/s]
eval model::  90%|████████▉ | 448/500 [00:24<00:02, 22.36it/s]
eval model::  90%|█████████ | 451/500 [00:24<00:02, 22.31it/s]
eval model::  91%|█████████ | 454/500 [00:24<00:02, 21.52it/s]
eval model::  91%|█████████▏| 457/500 [00:24<00:01, 22.95it/s]
eval model::  92%|█████████▏| 460/500 [00:24<00:01, 23.79it/s]
eval model::  93%|█████████▎| 463/500 [00:24<00:01, 19.21it/s]
eval model::  93%|█████████▎| 466/500 [00:25<00:01, 18.06it/s]
eval model::  94%|█████████▎| 468/500 [00:25<00:01, 17.71it/s]
eval model::  94%|█████████▍| 471/500 [00:25<00:01, 19.21it/s]
eval model::  95%|█████████▍| 474/500 [00:25<00:01, 18.58it/s]
eval model::  96%|█████████▌| 478/500 [00:25<00:01, 21.25it/s]
eval model::  96%|█████████▌| 481/500 [00:25<00:00, 19.41it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 19.85it/s]
eval model::  97%|█████████▋| 487/500 [00:26<00:00, 18.17it/s]
eval model::  98%|█████████▊| 490/500 [00:26<00:00, 20.15it/s]
eval model::  99%|█████████▊| 493/500 [00:26<00:00, 17.80it/s]
eval model::  99%|█████████▉| 495/500 [00:26<00:00, 17.11it/s]
eval model:: 100%|█████████▉| 498/500 [00:26<00:00, 18.83it/s]
eval model:: 100%|██████████| 500/500 [00:26<00:00, 18.66it/s]
[2024/07/28 03:37:52] ppocr INFO: cur metric, precision: 0.7367367367367368, recall: 0.7087144920558498, hmean: 0.7224539877300615, fps: 45.878396147141586
[2024/07/28 03:37:52] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/07/28 03:37:52] ppocr INFO: best metric, hmean: 0.7224539877300615, precision: 0.7367367367367368, recall: 0.7087144920558498, fps: 45.878396147141586, best_epoch: 1500
[2024/07/28 03:37:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/07/28 03:37:54] ppocr INFO: best metric, hmean: 0.7224539877300615, precision: 0.7367367367367368, recall: 0.7087144920558498, fps: 45.878396147141586, best_epoch: 1500
I0728 03:37:56.256302 192577 tcp_store.cc:289] receive shutdown event and so quit from MasterDaemon run loop
[2024/08/01 17:43:09] ppocr INFO: Architecture : 
[2024/08/01 17:43:09] ppocr INFO:     Backbone : 
[2024/08/01 17:43:09] ppocr INFO:         model_name : large
[2024/08/01 17:43:09] ppocr INFO:         name : MobileNetV3
[2024/08/01 17:43:09] ppocr INFO:         scale : 0.5
[2024/08/01 17:43:09] ppocr INFO:     Head : 
[2024/08/01 17:43:09] ppocr INFO:         k : 50
[2024/08/01 17:43:09] ppocr INFO:         name : DBHead
[2024/08/01 17:43:09] ppocr INFO:     Neck : 
[2024/08/01 17:43:09] ppocr INFO:         name : DBFPN
[2024/08/01 17:43:09] ppocr INFO:         out_channels : 256
[2024/08/01 17:43:09] ppocr INFO:     Transform : None
[2024/08/01 17:43:09] ppocr INFO:     algorithm : DB
[2024/08/01 17:43:09] ppocr INFO:     model_type : det
[2024/08/01 17:43:09] ppocr INFO: Eval : 
[2024/08/01 17:43:09] ppocr INFO:     dataset : 
[2024/08/01 17:43:09] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/08/01 17:43:09] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
[2024/08/01 17:43:09] ppocr INFO:         name : SimpleDataSet
[2024/08/01 17:43:09] ppocr INFO:         transforms : 
[2024/08/01 17:43:09] ppocr INFO:             DecodeImage : 
[2024/08/01 17:43:09] ppocr INFO:                 channel_first : False
[2024/08/01 17:43:09] ppocr INFO:                 img_mode : BGR
[2024/08/01 17:43:09] ppocr INFO:             DetLabelEncode : None
[2024/08/01 17:43:09] ppocr INFO:             DetResizeForTest : 
[2024/08/01 17:43:09] ppocr INFO:                 image_shape : [736, 1280]
[2024/08/01 17:43:09] ppocr INFO:             NormalizeImage : 
[2024/08/01 17:43:09] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/08/01 17:43:09] ppocr INFO:                 order : hwc
[2024/08/01 17:43:09] ppocr INFO:                 scale : 1./255.
[2024/08/01 17:43:09] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/08/01 17:43:09] ppocr INFO:             ToCHWImage : None
[2024/08/01 17:43:09] ppocr INFO:             KeepKeys : 
[2024/08/01 17:43:09] ppocr INFO:                 keep_keys : ['image', 'shape', 'polys', 'ignore_tags']
[2024/08/01 17:43:09] ppocr INFO:     loader : 
[2024/08/01 17:43:09] ppocr INFO:         batch_size_per_card : 1
[2024/08/01 17:43:09] ppocr INFO:         drop_last : False
[2024/08/01 17:43:09] ppocr INFO:         num_workers : 0
[2024/08/01 17:43:09] ppocr INFO:         shuffle : False
[2024/08/01 17:43:09] ppocr INFO:         use_shared_memory : True
[2024/08/01 17:43:09] ppocr INFO: Global : 
[2024/08/01 17:43:09] ppocr INFO:     cal_metric_during_train : False
[2024/08/01 17:43:09] ppocr INFO:     checkpoints : None
[2024/08/01 17:43:09] ppocr INFO:     distributed : True
[2024/08/01 17:43:09] ppocr INFO:     epoch_num : 100
[2024/08/01 17:43:09] ppocr INFO:     eval_batch_step : [0, 60]
[2024/08/01 17:43:09] ppocr INFO:     infer_img : doc/imgs_en/img_10.jpg
[2024/08/01 17:43:09] ppocr INFO:     log_smooth_window : 20
[2024/08/01 17:43:09] ppocr INFO:     pretrained_model : ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/08/01 17:43:09] ppocr INFO:     print_batch_step : 10
[2024/08/01 17:43:09] ppocr INFO:     save_epoch_step : 1200
[2024/08/01 17:43:09] ppocr INFO:     save_inference_dir : None
[2024/08/01 17:43:09] ppocr INFO:     save_model_dir : ./output/db_mv3/
[2024/08/01 17:43:09] ppocr INFO:     save_res_path : ./output/det_db/predicts_db.txt
[2024/08/01 17:43:09] ppocr INFO:     use_gpu : True
[2024/08/01 17:43:09] ppocr INFO:     use_visualdl : False
[2024/08/01 17:43:09] ppocr INFO:     use_xpu : False
[2024/08/01 17:43:09] ppocr INFO: Loss : 
[2024/08/01 17:43:09] ppocr INFO:     alpha : 5
[2024/08/01 17:43:09] ppocr INFO:     balance_loss : True
[2024/08/01 17:43:09] ppocr INFO:     beta : 10
[2024/08/01 17:43:09] ppocr INFO:     main_loss_type : DiceLoss
[2024/08/01 17:43:09] ppocr INFO:     name : DBLoss
[2024/08/01 17:43:09] ppocr INFO:     ohem_ratio : 3
[2024/08/01 17:43:09] ppocr INFO: Metric : 
[2024/08/01 17:43:09] ppocr INFO:     main_indicator : hmean
[2024/08/01 17:43:09] ppocr INFO:     name : DetMetric
[2024/08/01 17:43:09] ppocr INFO: Optimizer : 
[2024/08/01 17:43:09] ppocr INFO:     beta1 : 0.9
[2024/08/01 17:43:09] ppocr INFO:     beta2 : 0.999
[2024/08/01 17:43:09] ppocr INFO:     lr : 
[2024/08/01 17:43:09] ppocr INFO:         learning_rate : 0.001
[2024/08/01 17:43:09] ppocr INFO:     name : Adam
[2024/08/01 17:43:09] ppocr INFO:     regularizer : 
[2024/08/01 17:43:09] ppocr INFO:         factor : 0
[2024/08/01 17:43:09] ppocr INFO:         name : L2
[2024/08/01 17:43:09] ppocr INFO: PostProcess : 
[2024/08/01 17:43:09] ppocr INFO:     box_thresh : 0.6
[2024/08/01 17:43:09] ppocr INFO:     max_candidates : 1000
[2024/08/01 17:43:09] ppocr INFO:     name : DBPostProcess
[2024/08/01 17:43:09] ppocr INFO:     thresh : 0.3
[2024/08/01 17:43:09] ppocr INFO:     unclip_ratio : 1.5
[2024/08/01 17:43:09] ppocr INFO: Train : 
[2024/08/01 17:43:09] ppocr INFO:     dataset : 
[2024/08/01 17:43:09] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/08/01 17:43:09] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/08/01 17:43:09] ppocr INFO:         name : SimpleDataSet
[2024/08/01 17:43:09] ppocr INFO:         ratio_list : [1.0]
[2024/08/01 17:43:09] ppocr INFO:         transforms : 
[2024/08/01 17:43:09] ppocr INFO:             DecodeImage : 
[2024/08/01 17:43:09] ppocr INFO:                 channel_first : False
[2024/08/01 17:43:09] ppocr INFO:                 img_mode : BGR
[2024/08/01 17:43:09] ppocr INFO:             DetLabelEncode : None
[2024/08/01 17:43:09] ppocr INFO:             IaaAugment : 
[2024/08/01 17:43:09] ppocr INFO:                 augmenter_args : 
[2024/08/01 17:43:09] ppocr INFO:                     args : 
[2024/08/01 17:43:09] ppocr INFO:                         p : 0.5
[2024/08/01 17:43:09] ppocr INFO:                     type : Fliplr
[2024/08/01 17:43:09] ppocr INFO:                     args : 
[2024/08/01 17:43:09] ppocr INFO:                         rotate : [-10, 10]
[2024/08/01 17:43:09] ppocr INFO:                     type : Affine
[2024/08/01 17:43:09] ppocr INFO:                     args : 
[2024/08/01 17:43:09] ppocr INFO:                         size : [0.5, 3]
[2024/08/01 17:43:09] ppocr INFO:                     type : Resize
[2024/08/01 17:43:09] ppocr INFO:             EastRandomCropData : 
[2024/08/01 17:43:09] ppocr INFO:                 keep_ratio : True
[2024/08/01 17:43:09] ppocr INFO:                 max_tries : 50
[2024/08/01 17:43:09] ppocr INFO:                 size : [640, 640]
[2024/08/01 17:43:09] ppocr INFO:             MakeBorderMap : 
[2024/08/01 17:43:09] ppocr INFO:                 shrink_ratio : 0.4
[2024/08/01 17:43:09] ppocr INFO:                 thresh_max : 0.7
[2024/08/01 17:43:09] ppocr INFO:                 thresh_min : 0.3
[2024/08/01 17:43:09] ppocr INFO:             MakeShrinkMap : 
[2024/08/01 17:43:09] ppocr INFO:                 min_text_size : 8
[2024/08/01 17:43:09] ppocr INFO:                 shrink_ratio : 0.4
[2024/08/01 17:43:09] ppocr INFO:             NormalizeImage : 
[2024/08/01 17:43:09] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/08/01 17:43:09] ppocr INFO:                 order : hwc
[2024/08/01 17:43:09] ppocr INFO:                 scale : 1./255.
[2024/08/01 17:43:09] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/08/01 17:43:09] ppocr INFO:             ToCHWImage : None
[2024/08/01 17:43:09] ppocr INFO:             KeepKeys : 
[2024/08/01 17:43:09] ppocr INFO:                 keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask']
[2024/08/01 17:43:09] ppocr INFO:     loader : 
[2024/08/01 17:43:09] ppocr INFO:         batch_size_per_card : 48
[2024/08/01 17:43:09] ppocr INFO:         drop_last : False
[2024/08/01 17:43:09] ppocr INFO:         num_workers : 8
[2024/08/01 17:43:09] ppocr INFO:         shuffle : True
[2024/08/01 17:43:09] ppocr INFO:         use_shared_memory : True
[2024/08/01 17:43:09] ppocr INFO: profiler_options : None
[2024/08/01 17:43:09] ppocr INFO: train with paddle 2.5.2 and device Place(gpu:0)
======================= Modified FLAGS detected =======================
FLAGS(name='FLAGS_selected_gpus', current_value='0', default_value='')
FLAGS(name='FLAGS_cudnn_batchnorm_spatial_persistent', current_value=True, default_value=False)
=======================================================================
I0801 17:43:09.953569   736 tcp_utils.cc:181] The server starts to listen on IP_ANY:62911
I0801 17:43:09.953749   736 tcp_utils.cc:130] Successfully connected to 10.8.145.246:62911


--------------------------------------
C++ Traceback (most recent call last):
--------------------------------------
0   phi::distributed::CreateOrGetGlobalTCPStore()
1   phi::distributed::TCPStore::TCPStore(std::string, unsigned short, bool, unsigned long, int)
2   phi::distributed::TCPStore::waitWorkers()
3   phi::distributed::TCPStore::get(std::string const&)
4   void phi::distributed::tcputils::receive_bytes<unsigned long>(int, unsigned long*, unsigned long)

----------------------
Error Message Summary:
----------------------
FatalError: `Termination signal` is detected by the operating system.
  [TimeInfo: *** Aborted at 1722505391 (unix time) try "date -d @1722505391" if you are using GNU date ***]
  [SignalInfo: *** SIGTERM (@0x28f) received by PID 736 (TID 0x7fbca936b740) from PID 655 ***]

[2024/08/01 17:45:05] ppocr INFO: Architecture : 
[2024/08/01 17:45:05] ppocr INFO:     Backbone : 
[2024/08/01 17:45:05] ppocr INFO:         model_name : large
[2024/08/01 17:45:05] ppocr INFO:         name : MobileNetV3
[2024/08/01 17:45:05] ppocr INFO:         scale : 0.5
[2024/08/01 17:45:05] ppocr INFO:     Head : 
[2024/08/01 17:45:05] ppocr INFO:         k : 50
[2024/08/01 17:45:05] ppocr INFO:         name : DBHead
[2024/08/01 17:45:05] ppocr INFO:     Neck : 
[2024/08/01 17:45:05] ppocr INFO:         name : DBFPN
[2024/08/01 17:45:05] ppocr INFO:         out_channels : 256
[2024/08/01 17:45:05] ppocr INFO:     Transform : None
[2024/08/01 17:45:05] ppocr INFO:     algorithm : DB
[2024/08/01 17:45:05] ppocr INFO:     model_type : det
[2024/08/01 17:45:05] ppocr INFO: Eval : 
[2024/08/01 17:45:05] ppocr INFO:     dataset : 
[2024/08/01 17:45:05] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/08/01 17:45:05] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
[2024/08/01 17:45:05] ppocr INFO:         name : SimpleDataSet
[2024/08/01 17:45:05] ppocr INFO:         transforms : 
[2024/08/01 17:45:05] ppocr INFO:             DecodeImage : 
[2024/08/01 17:45:05] ppocr INFO:                 channel_first : False
[2024/08/01 17:45:05] ppocr INFO:                 img_mode : BGR
[2024/08/01 17:45:05] ppocr INFO:             DetLabelEncode : None
[2024/08/01 17:45:05] ppocr INFO:             DetResizeForTest : 
[2024/08/01 17:45:05] ppocr INFO:                 image_shape : [736, 1280]
[2024/08/01 17:45:05] ppocr INFO:             NormalizeImage : 
[2024/08/01 17:45:05] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/08/01 17:45:05] ppocr INFO:                 order : hwc
[2024/08/01 17:45:05] ppocr INFO:                 scale : 1./255.
[2024/08/01 17:45:05] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/08/01 17:45:05] ppocr INFO:             ToCHWImage : None
[2024/08/01 17:45:05] ppocr INFO:             KeepKeys : 
[2024/08/01 17:45:05] ppocr INFO:                 keep_keys : ['image', 'shape', 'polys', 'ignore_tags']
[2024/08/01 17:45:05] ppocr INFO:     loader : 
[2024/08/01 17:45:05] ppocr INFO:         batch_size_per_card : 1
[2024/08/01 17:45:05] ppocr INFO:         drop_last : False
[2024/08/01 17:45:05] ppocr INFO:         num_workers : 0
[2024/08/01 17:45:05] ppocr INFO:         shuffle : False
[2024/08/01 17:45:05] ppocr INFO:         use_shared_memory : True
[2024/08/01 17:45:05] ppocr INFO: Global : 
[2024/08/01 17:45:05] ppocr INFO:     cal_metric_during_train : False
[2024/08/01 17:45:05] ppocr INFO:     checkpoints : None
[2024/08/01 17:45:05] ppocr INFO:     distributed : True
[2024/08/01 17:45:05] ppocr INFO:     epoch_num : 100
[2024/08/01 17:45:05] ppocr INFO:     eval_batch_step : [0, 60]
[2024/08/01 17:45:05] ppocr INFO:     infer_img : doc/imgs_en/img_10.jpg
[2024/08/01 17:45:05] ppocr INFO:     log_smooth_window : 20
[2024/08/01 17:45:05] ppocr INFO:     pretrained_model : ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/08/01 17:45:05] ppocr INFO:     print_batch_step : 10
[2024/08/01 17:45:05] ppocr INFO:     save_epoch_step : 1200
[2024/08/01 17:45:05] ppocr INFO:     save_inference_dir : None
[2024/08/01 17:45:05] ppocr INFO:     save_model_dir : ./output/db_mv3/
[2024/08/01 17:45:05] ppocr INFO:     save_res_path : ./output/det_db/predicts_db.txt
[2024/08/01 17:45:05] ppocr INFO:     use_gpu : True
[2024/08/01 17:45:05] ppocr INFO:     use_visualdl : False
[2024/08/01 17:45:05] ppocr INFO:     use_xpu : False
[2024/08/01 17:45:05] ppocr INFO: Loss : 
[2024/08/01 17:45:05] ppocr INFO:     alpha : 5
[2024/08/01 17:45:05] ppocr INFO:     balance_loss : True
[2024/08/01 17:45:05] ppocr INFO:     beta : 10
[2024/08/01 17:45:05] ppocr INFO:     main_loss_type : DiceLoss
[2024/08/01 17:45:05] ppocr INFO:     name : DBLoss
[2024/08/01 17:45:05] ppocr INFO:     ohem_ratio : 3
[2024/08/01 17:45:05] ppocr INFO: Metric : 
[2024/08/01 17:45:05] ppocr INFO:     main_indicator : hmean
[2024/08/01 17:45:05] ppocr INFO:     name : DetMetric
[2024/08/01 17:45:05] ppocr INFO: Optimizer : 
[2024/08/01 17:45:05] ppocr INFO:     beta1 : 0.9
[2024/08/01 17:45:05] ppocr INFO:     beta2 : 0.999
[2024/08/01 17:45:05] ppocr INFO:     lr : 
[2024/08/01 17:45:05] ppocr INFO:         learning_rate : 0.001
[2024/08/01 17:45:05] ppocr INFO:     name : Adam
[2024/08/01 17:45:05] ppocr INFO:     regularizer : 
[2024/08/01 17:45:05] ppocr INFO:         factor : 0
[2024/08/01 17:45:05] ppocr INFO:         name : L2
[2024/08/01 17:45:05] ppocr INFO: PostProcess : 
[2024/08/01 17:45:05] ppocr INFO:     box_thresh : 0.6
[2024/08/01 17:45:05] ppocr INFO:     max_candidates : 1000
[2024/08/01 17:45:05] ppocr INFO:     name : DBPostProcess
[2024/08/01 17:45:05] ppocr INFO:     thresh : 0.3
[2024/08/01 17:45:05] ppocr INFO:     unclip_ratio : 1.5
[2024/08/01 17:45:05] ppocr INFO: Train : 
[2024/08/01 17:45:05] ppocr INFO:     dataset : 
[2024/08/01 17:45:05] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/08/01 17:45:05] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/08/01 17:45:05] ppocr INFO:         name : SimpleDataSet
[2024/08/01 17:45:05] ppocr INFO:         ratio_list : [1.0]
[2024/08/01 17:45:05] ppocr INFO:         transforms : 
[2024/08/01 17:45:05] ppocr INFO:             DecodeImage : 
[2024/08/01 17:45:05] ppocr INFO:                 channel_first : False
[2024/08/01 17:45:05] ppocr INFO:                 img_mode : BGR
[2024/08/01 17:45:05] ppocr INFO:             DetLabelEncode : None
[2024/08/01 17:45:05] ppocr INFO:             IaaAugment : 
[2024/08/01 17:45:05] ppocr INFO:                 augmenter_args : 
[2024/08/01 17:45:05] ppocr INFO:                     args : 
[2024/08/01 17:45:05] ppocr INFO:                         p : 0.5
[2024/08/01 17:45:05] ppocr INFO:                     type : Fliplr
[2024/08/01 17:45:05] ppocr INFO:                     args : 
[2024/08/01 17:45:05] ppocr INFO:                         rotate : [-10, 10]
[2024/08/01 17:45:05] ppocr INFO:                     type : Affine
[2024/08/01 17:45:05] ppocr INFO:                     args : 
[2024/08/01 17:45:05] ppocr INFO:                         size : [0.5, 3]
[2024/08/01 17:45:05] ppocr INFO:                     type : Resize
[2024/08/01 17:45:05] ppocr INFO:             EastRandomCropData : 
[2024/08/01 17:45:05] ppocr INFO:                 keep_ratio : True
[2024/08/01 17:45:05] ppocr INFO:                 max_tries : 50
[2024/08/01 17:45:05] ppocr INFO:                 size : [640, 640]
[2024/08/01 17:45:05] ppocr INFO:             MakeBorderMap : 
[2024/08/01 17:45:05] ppocr INFO:                 shrink_ratio : 0.4
[2024/08/01 17:45:05] ppocr INFO:                 thresh_max : 0.7
[2024/08/01 17:45:05] ppocr INFO:                 thresh_min : 0.3
[2024/08/01 17:45:05] ppocr INFO:             MakeShrinkMap : 
[2024/08/01 17:45:05] ppocr INFO:                 min_text_size : 8
[2024/08/01 17:45:05] ppocr INFO:                 shrink_ratio : 0.4
[2024/08/01 17:45:05] ppocr INFO:             NormalizeImage : 
[2024/08/01 17:45:05] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/08/01 17:45:05] ppocr INFO:                 order : hwc
[2024/08/01 17:45:05] ppocr INFO:                 scale : 1./255.
[2024/08/01 17:45:05] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/08/01 17:45:05] ppocr INFO:             ToCHWImage : None
[2024/08/01 17:45:05] ppocr INFO:             KeepKeys : 
[2024/08/01 17:45:05] ppocr INFO:                 keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask']
[2024/08/01 17:45:05] ppocr INFO:     loader : 
[2024/08/01 17:45:05] ppocr INFO:         batch_size_per_card : 48
[2024/08/01 17:45:05] ppocr INFO:         drop_last : False
[2024/08/01 17:45:05] ppocr INFO:         num_workers : 8
[2024/08/01 17:45:05] ppocr INFO:         shuffle : True
[2024/08/01 17:45:05] ppocr INFO:         use_shared_memory : True
[2024/08/01 17:45:05] ppocr INFO: profiler_options : None
[2024/08/01 17:45:05] ppocr INFO: train with paddle 2.5.2 and device Place(gpu:0)
======================= Modified FLAGS detected =======================
FLAGS(name='FLAGS_selected_gpus', current_value='0', default_value='')
FLAGS(name='FLAGS_cudnn_batchnorm_spatial_persistent', current_value=True, default_value=False)
=======================================================================
I0801 17:45:05.476492  1057 tcp_utils.cc:181] The server starts to listen on IP_ANY:49295
I0801 17:45:05.476715  1057 tcp_utils.cc:130] Successfully connected to 10.8.145.246:49295
I0801 17:45:08.597571  1057 process_group_nccl.cc:120] ProcessGroupNCCL pg_timeout_ 1800000
[2024/08/01 17:45:08] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/08/01 17:45:08] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
W0801 17:45:08.610193  1057 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 90.2, Driver API Version: 50724.2, Runtime API Version: 50724.2
[2024/08/01 17:45:10] ppocr INFO: load pretrain successful from ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/08/01 17:45:10] ppocr INFO: train dataloader has 3 iters
[2024/08/01 17:45:10] ppocr INFO: valid dataloader has 500 iters
[2024/08/01 17:45:10] ppocr INFO: During the training process, after the 0th iteration, an evaluation is run every 60 iterations
[2024/08/01 17:46:27] ppocr INFO: epoch: [1/100], global_step: 3, lr: 0.001000, loss: 9.286121, loss_shrink_maps: 4.932228, loss_threshold_maps: 3.392218, loss_binary_maps: 0.985882, avg_reader_cost: 12.85591 s, avg_batch_cost: 18.91408 s, avg_samples: 12.5, ips: 0.66088 samples/s, eta: 5:12:04
[2024/08/01 17:46:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:46:36] ppocr INFO: epoch: [2/100], global_step: 6, lr: 0.001000, loss: 8.548527, loss_shrink_maps: 4.929990, loss_threshold_maps: 2.632422, loss_binary_maps: 0.986115, avg_reader_cost: 2.20346 s, avg_batch_cost: 2.42593 s, avg_samples: 12.5, ips: 5.15267 samples/s, eta: 2:54:16
[2024/08/01 17:46:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:46:46] ppocr INFO: epoch: [3/100], global_step: 9, lr: 0.001000, loss: 7.804622, loss_shrink_maps: 4.927752, loss_threshold_maps: 1.853062, loss_binary_maps: 0.985882, avg_reader_cost: 2.19835 s, avg_batch_cost: 2.41719 s, avg_samples: 12.5, ips: 5.17130 samples/s, eta: 2:08:01
[2024/08/01 17:46:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:46:54] ppocr INFO: epoch: [4/100], global_step: 10, lr: 0.001000, loss: 7.545996, loss_shrink_maps: 4.928834, loss_threshold_maps: 1.624225, loss_binary_maps: 0.986062, avg_reader_cost: 0.68740 s, avg_batch_cost: 0.77541 s, avg_samples: 4.8, ips: 6.19027 samples/s, eta: 1:58:34
[2024/08/01 17:46:55] ppocr INFO: epoch: [4/100], global_step: 12, lr: 0.001000, loss: 7.237197, loss_shrink_maps: 4.927601, loss_threshold_maps: 1.334146, loss_binary_maps: 0.985817, avg_reader_cost: 1.63828 s, avg_batch_cost: 1.77769 s, avg_samples: 7.7, ips: 4.33147 samples/s, eta: 1:45:14
[2024/08/01 17:46:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:47:05] ppocr INFO: epoch: [5/100], global_step: 15, lr: 0.001000, loss: 7.130876, loss_shrink_maps: 4.927450, loss_threshold_maps: 1.216004, loss_binary_maps: 0.985418, avg_reader_cost: 2.27306 s, avg_batch_cost: 2.55231 s, avg_samples: 12.5, ips: 4.89753 samples/s, eta: 1:31:23
[2024/08/01 17:47:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:47:14] ppocr INFO: epoch: [6/100], global_step: 18, lr: 0.001000, loss: 7.104102, loss_shrink_maps: 4.924126, loss_threshold_maps: 1.187511, loss_binary_maps: 0.984908, avg_reader_cost: 2.21714 s, avg_batch_cost: 2.43917 s, avg_samples: 12.5, ips: 5.12469 samples/s, eta: 1:21:43
[2024/08/01 17:47:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:47:23] ppocr INFO: epoch: [7/100], global_step: 20, lr: 0.001000, loss: 7.077874, loss_shrink_maps: 4.920578, loss_threshold_maps: 1.158228, loss_binary_maps: 0.984056, avg_reader_cost: 1.42181 s, avg_batch_cost: 1.58732 s, avg_samples: 9.6, ips: 6.04795 samples/s, eta: 1:16:44
[2024/08/01 17:47:24] ppocr INFO: epoch: [7/100], global_step: 21, lr: 0.001000, loss: 7.067647, loss_shrink_maps: 4.916796, loss_threshold_maps: 1.156397, loss_binary_maps: 0.982678, avg_reader_cost: 0.83747 s, avg_batch_cost: 0.88965 s, avg_samples: 2.9, ips: 3.25971 samples/s, eta: 1:14:47
[2024/08/01 17:47:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:47:33] ppocr INFO: epoch: [8/100], global_step: 24, lr: 0.001000, loss: 7.047196, loss_shrink_maps: 4.914110, loss_threshold_maps: 1.148854, loss_binary_maps: 0.979226, avg_reader_cost: 2.22181 s, avg_batch_cost: 2.44873 s, avg_samples: 12.5, ips: 5.10469 samples/s, eta: 1:09:26
[2024/08/01 17:47:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:47:43] ppocr INFO: epoch: [9/100], global_step: 27, lr: 0.001000, loss: 7.011276, loss_shrink_maps: 4.911048, loss_threshold_maps: 1.123974, loss_binary_maps: 0.974819, avg_reader_cost: 2.26004 s, avg_batch_cost: 2.51024 s, avg_samples: 12.5, ips: 4.97961 samples/s, eta: 1:05:16
[2024/08/01 17:47:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:47:52] ppocr INFO: epoch: [10/100], global_step: 30, lr: 0.001000, loss: 6.940498, loss_shrink_maps: 4.902378, loss_threshold_maps: 1.077619, loss_binary_maps: 0.970784, avg_reader_cost: 2.21147 s, avg_batch_cost: 2.44738 s, avg_samples: 12.5, ips: 5.10749 samples/s, eta: 1:01:46
[2024/08/01 17:47:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:48:02] ppocr INFO: epoch: [11/100], global_step: 33, lr: 0.001000, loss: 6.868617, loss_shrink_maps: 4.885881, loss_threshold_maps: 1.038050, loss_binary_maps: 0.960971, avg_reader_cost: 2.22404 s, avg_batch_cost: 2.44256 s, avg_samples: 12.5, ips: 5.11758 samples/s, eta: 0:58:49
[2024/08/01 17:48:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:48:12] ppocr INFO: epoch: [12/100], global_step: 36, lr: 0.001000, loss: 6.817448, loss_shrink_maps: 4.864040, loss_threshold_maps: 1.009396, loss_binary_maps: 0.956228, avg_reader_cost: 2.26320 s, avg_batch_cost: 2.48832 s, avg_samples: 12.5, ips: 5.02348 samples/s, eta: 0:56:21
[2024/08/01 17:48:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:48:21] ppocr INFO: epoch: [13/100], global_step: 39, lr: 0.001000, loss: 6.791888, loss_shrink_maps: 4.857442, loss_threshold_maps: 0.978082, loss_binary_maps: 0.947377, avg_reader_cost: 2.31176 s, avg_batch_cost: 2.53060 s, avg_samples: 12.5, ips: 4.93953 samples/s, eta: 0:54:15
[2024/08/01 17:48:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:48:29] ppocr INFO: epoch: [14/100], global_step: 40, lr: 0.001000, loss: 6.778063, loss_shrink_maps: 4.856284, loss_threshold_maps: 0.968342, loss_binary_maps: 0.946886, avg_reader_cost: 0.65116 s, avg_batch_cost: 0.74874 s, avg_samples: 4.8, ips: 6.41073 samples/s, eta: 0:53:30
[2024/08/01 17:48:31] ppocr INFO: epoch: [14/100], global_step: 42, lr: 0.001000, loss: 6.729544, loss_shrink_maps: 4.851938, loss_threshold_maps: 0.954702, loss_binary_maps: 0.946146, avg_reader_cost: 1.58592 s, avg_batch_cost: 1.72608 s, avg_samples: 7.7, ips: 4.46097 samples/s, eta: 0:52:20
[2024/08/01 17:48:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:48:40] ppocr INFO: epoch: [15/100], global_step: 45, lr: 0.001000, loss: 6.668135, loss_shrink_maps: 4.836268, loss_threshold_maps: 0.933070, loss_binary_maps: 0.927373, avg_reader_cost: 2.23363 s, avg_batch_cost: 2.45210 s, avg_samples: 12.5, ips: 5.09768 samples/s, eta: 0:50:35
[2024/08/01 17:48:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:48:50] ppocr INFO: epoch: [16/100], global_step: 48, lr: 0.001000, loss: 6.658508, loss_shrink_maps: 4.823704, loss_threshold_maps: 0.926389, loss_binary_maps: 0.910777, avg_reader_cost: 2.16004 s, avg_batch_cost: 2.40701 s, avg_samples: 12.5, ips: 5.19315 samples/s, eta: 0:48:58
[2024/08/01 17:48:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:48:58] ppocr INFO: epoch: [17/100], global_step: 50, lr: 0.001000, loss: 6.639746, loss_shrink_maps: 4.820710, loss_threshold_maps: 0.918938, loss_binary_maps: 0.902400, avg_reader_cost: 1.34973 s, avg_batch_cost: 1.52841 s, avg_samples: 9.6, ips: 6.28102 samples/s, eta: 0:47:55
[2024/08/01 17:48:59] ppocr INFO: epoch: [17/100], global_step: 51, lr: 0.001000, loss: 6.622952, loss_shrink_maps: 4.820710, loss_threshold_maps: 0.912991, loss_binary_maps: 0.890444, avg_reader_cost: 0.80877 s, avg_batch_cost: 0.86062 s, avg_samples: 2.9, ips: 3.36965 samples/s, eta: 0:47:29
[2024/08/01 17:49:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:49:08] ppocr INFO: epoch: [18/100], global_step: 54, lr: 0.001000, loss: 6.563270, loss_shrink_maps: 4.818290, loss_threshold_maps: 0.883130, loss_binary_maps: 0.867894, avg_reader_cost: 2.23478 s, avg_batch_cost: 2.49605 s, avg_samples: 12.5, ips: 5.00791 samples/s, eta: 0:46:12
[2024/08/01 17:49:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:49:18] ppocr INFO: epoch: [19/100], global_step: 57, lr: 0.001000, loss: 6.473013, loss_shrink_maps: 4.817668, loss_threshold_maps: 0.883047, loss_binary_maps: 0.807621, avg_reader_cost: 2.22523 s, avg_batch_cost: 2.44413 s, avg_samples: 12.5, ips: 5.11430 samples/s, eta: 0:44:58
[2024/08/01 17:49:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:49:27] ppocr INFO: epoch: [20/100], global_step: 60, lr: 0.001000, loss: 6.351774, loss_shrink_maps: 4.817668, loss_threshold_maps: 0.874931, loss_binary_maps: 0.712683, avg_reader_cost: 2.18743 s, avg_batch_cost: 2.42843 s, avg_samples: 12.5, ips: 5.14736 samples/s, eta: 0:43:49

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<02:00,  4.15it/s]
eval model::   0%|          | 2/500 [00:00<01:25,  5.84it/s]
eval model::   1%|          | 3/500 [00:00<01:13,  6.74it/s]
eval model::   1%|          | 4/500 [00:00<01:04,  7.69it/s]
eval model::   1%|          | 6/500 [00:00<00:55,  8.90it/s]
eval model::   2%|▏         | 8/500 [00:00<00:52,  9.41it/s]
eval model::   2%|▏         | 9/500 [00:01<00:53,  9.15it/s]
eval model::   2%|▏         | 10/500 [00:01<00:55,  8.85it/s]
eval model::   2%|▏         | 11/500 [00:01<00:54,  8.94it/s]
eval model::   2%|▏         | 12/500 [00:01<00:53,  9.15it/s]
eval model::   3%|▎         | 13/500 [00:01<00:53,  9.10it/s]
eval model::   3%|▎         | 14/500 [00:01<00:54,  8.89it/s]
eval model::   3%|▎         | 15/500 [00:01<00:55,  8.79it/s]
eval model::   3%|▎         | 16/500 [00:01<00:55,  8.78it/s]
eval model::   3%|▎         | 17/500 [00:01<00:54,  8.94it/s]
eval model::   4%|▎         | 18/500 [00:02<00:53,  9.06it/s]
eval model::   4%|▍         | 19/500 [00:02<00:53,  9.03it/s]
eval model::   4%|▍         | 20/500 [00:02<00:51,  9.24it/s]
eval model::   4%|▍         | 21/500 [00:02<00:53,  9.03it/s]
eval model::   4%|▍         | 22/500 [00:02<00:52,  9.12it/s]
eval model::   5%|▍         | 24/500 [00:02<00:47, 10.07it/s]
eval model::   5%|▌         | 25/500 [00:02<00:48,  9.87it/s]
eval model::   5%|▌         | 26/500 [00:02<00:48,  9.76it/s]
eval model::   6%|▌         | 28/500 [00:03<00:48,  9.79it/s]
eval model::   6%|▌         | 30/500 [00:03<00:46, 10.19it/s]
eval model::   6%|▋         | 32/500 [00:03<00:46, 10.12it/s]
eval model::   7%|▋         | 34/500 [00:03<00:46, 10.04it/s]
eval model::   7%|▋         | 36/500 [00:03<00:46,  9.99it/s]
eval model::   8%|▊         | 38/500 [00:04<00:46, 10.03it/s]
eval model::   8%|▊         | 40/500 [00:04<00:46, 10.00it/s]
eval model::   8%|▊         | 42/500 [00:04<00:44, 10.18it/s]
eval model::   9%|▉         | 44/500 [00:04<00:45, 10.06it/s]
eval model::   9%|▉         | 46/500 [00:04<00:45, 10.00it/s]
eval model::  10%|▉         | 48/500 [00:05<00:47,  9.61it/s]
eval model::  10%|▉         | 49/500 [00:05<00:47,  9.47it/s]
eval model::  10%|█         | 50/500 [00:05<00:48,  9.31it/s]
eval model::  10%|█         | 51/500 [00:05<00:49,  9.12it/s]
eval model::  10%|█         | 52/500 [00:05<00:48,  9.26it/s]
eval model::  11%|█         | 53/500 [00:05<00:49,  8.97it/s]
eval model::  11%|█         | 54/500 [00:05<00:49,  9.01it/s]
eval model::  11%|█         | 55/500 [00:05<00:49,  8.96it/s]
eval model::  11%|█         | 56/500 [00:06<00:48,  9.09it/s]
eval model::  11%|█▏        | 57/500 [00:06<00:47,  9.33it/s]
eval model::  12%|█▏        | 58/500 [00:06<00:48,  9.21it/s]
eval model::  12%|█▏        | 59/500 [00:06<00:47,  9.30it/s]
eval model::  12%|█▏        | 61/500 [00:06<00:46,  9.51it/s]
eval model::  12%|█▏        | 62/500 [00:06<00:45,  9.56it/s]
eval model::  13%|█▎        | 63/500 [00:06<00:47,  9.28it/s]
eval model::  13%|█▎        | 64/500 [00:06<00:47,  9.24it/s]
eval model::  13%|█▎        | 65/500 [00:06<00:46,  9.39it/s]
eval model::  13%|█▎        | 67/500 [00:07<00:45,  9.56it/s]
eval model::  14%|█▍        | 69/500 [00:07<00:44,  9.78it/s]
eval model::  14%|█▍        | 70/500 [00:07<00:44,  9.66it/s]
eval model::  14%|█▍        | 71/500 [00:07<00:44,  9.74it/s]
eval model::  14%|█▍        | 72/500 [00:07<00:44,  9.66it/s]
eval model::  15%|█▍        | 74/500 [00:07<00:43,  9.85it/s]
eval model::  15%|█▌        | 76/500 [00:08<00:42, 10.09it/s]
eval model::  16%|█▌        | 78/500 [00:08<00:42,  9.93it/s]
eval model::  16%|█▌        | 80/500 [00:08<00:42,  9.80it/s]
eval model::  16%|█▋        | 82/500 [00:08<00:42,  9.93it/s]
eval model::  17%|█▋        | 83/500 [00:08<00:42,  9.91it/s]
eval model::  17%|█▋        | 84/500 [00:08<00:42,  9.68it/s]
eval model::  17%|█▋        | 85/500 [00:09<00:43,  9.58it/s]
eval model::  17%|█▋        | 87/500 [00:09<00:42,  9.70it/s]
eval model::  18%|█▊        | 88/500 [00:09<00:42,  9.74it/s]
eval model::  18%|█▊        | 90/500 [00:09<00:41,  9.79it/s]
eval model::  18%|█▊        | 91/500 [00:09<00:42,  9.73it/s]
eval model::  18%|█▊        | 92/500 [00:09<00:42,  9.53it/s]
eval model::  19%|█▊        | 93/500 [00:09<00:43,  9.46it/s]
eval model::  19%|█▉        | 94/500 [00:09<00:43,  9.41it/s]
eval model::  19%|█▉        | 96/500 [00:10<00:41,  9.67it/s]
eval model::  20%|█▉        | 98/500 [00:10<00:39, 10.12it/s]
eval model::  20%|██        | 100/500 [00:10<00:38, 10.30it/s]
eval model::  20%|██        | 102/500 [00:10<00:39, 10.11it/s]
eval model::  21%|██        | 104/500 [00:10<00:37, 10.44it/s]
eval model::  21%|██        | 106/500 [00:11<00:38, 10.33it/s]
eval model::  22%|██▏       | 108/500 [00:11<00:37, 10.45it/s]
eval model::  22%|██▏       | 110/500 [00:11<00:39,  9.89it/s]
eval model::  22%|██▏       | 111/500 [00:11<00:40,  9.70it/s]
eval model::  23%|██▎       | 113/500 [00:11<00:38, 10.13it/s]
eval model::  23%|██▎       | 115/500 [00:12<00:38,  9.91it/s]
eval model::  23%|██▎       | 116/500 [00:12<00:39,  9.81it/s]
eval model::  24%|██▎       | 118/500 [00:12<00:38,  9.98it/s]
eval model::  24%|██▍       | 119/500 [00:12<00:38,  9.97it/s]
eval model::  24%|██▍       | 121/500 [00:12<00:37, 10.17it/s]
eval model::  25%|██▍       | 123/500 [00:12<00:37, 10.06it/s]
eval model::  25%|██▌       | 125/500 [00:13<00:37, 10.06it/s]
eval model::  25%|██▌       | 127/500 [00:13<00:37,  9.92it/s]
eval model::  26%|██▌       | 128/500 [00:13<00:37,  9.89it/s]
eval model::  26%|██▌       | 129/500 [00:13<00:37,  9.88it/s]
eval model::  26%|██▌       | 130/500 [00:13<00:38,  9.69it/s]
eval model::  26%|██▋       | 132/500 [00:13<00:37,  9.69it/s]
eval model::  27%|██▋       | 133/500 [00:13<00:37,  9.73it/s]
eval model::  27%|██▋       | 134/500 [00:13<00:37,  9.78it/s]
eval model::  27%|██▋       | 135/500 [00:14<00:38,  9.51it/s]
eval model::  27%|██▋       | 137/500 [00:14<00:37,  9.57it/s]
eval model::  28%|██▊       | 138/500 [00:14<00:37,  9.61it/s]
eval model::  28%|██▊       | 140/500 [00:14<00:37,  9.70it/s]
eval model::  28%|██▊       | 141/500 [00:14<00:37,  9.65it/s]
eval model::  28%|██▊       | 142/500 [00:14<00:37,  9.63it/s]
eval model::  29%|██▉       | 144/500 [00:15<00:36,  9.68it/s]
eval model::  29%|██▉       | 146/500 [00:15<00:36,  9.75it/s]
eval model::  30%|██▉       | 148/500 [00:15<00:36,  9.75it/s]
eval model::  30%|██▉       | 149/500 [00:15<00:36,  9.62it/s]
eval model::  30%|███       | 151/500 [00:15<00:35,  9.75it/s]
eval model::  30%|███       | 152/500 [00:15<00:36,  9.57it/s]
eval model::  31%|███       | 153/500 [00:15<00:37,  9.26it/s]
eval model::  31%|███       | 154/500 [00:16<00:37,  9.26it/s]
eval model::  31%|███       | 155/500 [00:16<00:38,  9.06it/s]
eval model::  31%|███       | 156/500 [00:16<00:37,  9.10it/s]
eval model::  32%|███▏      | 158/500 [00:16<00:36,  9.29it/s]
eval model::  32%|███▏      | 159/500 [00:16<00:37,  9.09it/s]
eval model::  32%|███▏      | 160/500 [00:16<00:37,  9.04it/s]
eval model::  32%|███▏      | 161/500 [00:16<00:37,  9.10it/s]
eval model::  32%|███▏      | 162/500 [00:16<00:38,  8.73it/s]
eval model::  33%|███▎      | 163/500 [00:17<00:37,  8.90it/s]
eval model::  33%|███▎      | 165/500 [00:17<00:37,  9.03it/s]
eval model::  33%|███▎      | 166/500 [00:17<00:37,  8.97it/s]
eval model::  33%|███▎      | 167/500 [00:17<00:36,  9.12it/s]
eval model::  34%|███▍      | 169/500 [00:17<00:35,  9.20it/s]
eval model::  34%|███▍      | 170/500 [00:17<00:35,  9.17it/s]
eval model::  34%|███▍      | 171/500 [00:17<00:36,  8.98it/s]
eval model::  34%|███▍      | 172/500 [00:18<00:36,  8.93it/s]
eval model::  35%|███▍      | 174/500 [00:18<00:34,  9.47it/s]
eval model::  35%|███▌      | 175/500 [00:18<00:35,  9.18it/s]
eval model::  35%|███▌      | 177/500 [00:18<00:34,  9.27it/s]
eval model::  36%|███▌      | 178/500 [00:18<00:34,  9.34it/s]
eval model::  36%|███▌      | 180/500 [00:18<00:33,  9.69it/s]
eval model::  36%|███▌      | 181/500 [00:18<00:32,  9.69it/s]
eval model::  36%|███▋      | 182/500 [00:19<00:34,  9.20it/s]
eval model::  37%|███▋      | 183/500 [00:19<00:34,  9.08it/s]
eval model::  37%|███▋      | 184/500 [00:19<00:34,  9.26it/s]
eval model::  37%|███▋      | 186/500 [00:19<00:32,  9.61it/s]
eval model::  38%|███▊      | 188/500 [00:19<00:31,  9.93it/s]
eval model::  38%|███▊      | 189/500 [00:19<00:31,  9.93it/s]
eval model::  38%|███▊      | 190/500 [00:19<00:32,  9.59it/s]
eval model::  38%|███▊      | 192/500 [00:20<00:36,  8.46it/s]
eval model::  39%|███▊      | 193/500 [00:20<00:35,  8.71it/s]
eval model::  39%|███▉      | 195/500 [00:20<00:33,  9.23it/s]
eval model::  39%|███▉      | 196/500 [00:20<00:33,  9.17it/s]
eval model::  39%|███▉      | 197/500 [00:20<00:33,  9.18it/s]
eval model::  40%|███▉      | 198/500 [00:20<00:32,  9.35it/s]
eval model::  40%|████      | 200/500 [00:21<00:31,  9.56it/s]
eval model::  40%|████      | 201/500 [00:21<00:31,  9.39it/s]
eval model::  41%|████      | 203/500 [00:21<00:31,  9.57it/s]
eval model::  41%|████      | 205/500 [00:21<00:30,  9.69it/s]
eval model::  41%|████▏     | 207/500 [00:21<00:30,  9.57it/s]
eval model::  42%|████▏     | 208/500 [00:21<00:30,  9.57it/s]
eval model::  42%|████▏     | 209/500 [00:21<00:30,  9.52it/s]
eval model::  42%|████▏     | 210/500 [00:22<00:30,  9.37it/s]
eval model::  42%|████▏     | 211/500 [00:22<00:30,  9.38it/s]
eval model::  43%|████▎     | 213/500 [00:22<00:29,  9.78it/s]
eval model::  43%|████▎     | 214/500 [00:22<00:29,  9.74it/s]
eval model::  43%|████▎     | 215/500 [00:22<00:29,  9.76it/s]
eval model::  43%|████▎     | 216/500 [00:22<00:29,  9.74it/s]
eval model::  43%|████▎     | 217/500 [00:22<00:29,  9.49it/s]
eval model::  44%|████▍     | 219/500 [00:22<00:27, 10.12it/s]
eval model::  44%|████▍     | 221/500 [00:23<00:27, 10.02it/s]
eval model::  45%|████▍     | 223/500 [00:23<00:27, 10.15it/s]
eval model::  45%|████▌     | 225/500 [00:23<00:28,  9.80it/s]
eval model::  45%|████▌     | 226/500 [00:23<00:28,  9.70it/s]
eval model::  46%|████▌     | 228/500 [00:23<00:28,  9.69it/s]
eval model::  46%|████▌     | 229/500 [00:24<00:27,  9.73it/s]
eval model::  46%|████▌     | 231/500 [00:24<00:26, 10.03it/s]
eval model::  47%|████▋     | 233/500 [00:24<00:26, 10.21it/s]
eval model::  47%|████▋     | 235/500 [00:24<00:25, 10.44it/s]
eval model::  47%|████▋     | 237/500 [00:24<00:25, 10.38it/s]
eval model::  48%|████▊     | 239/500 [00:24<00:24, 10.65it/s]
eval model::  48%|████▊     | 241/500 [00:25<00:24, 10.65it/s]
eval model::  49%|████▊     | 243/500 [00:25<00:24, 10.42it/s]
eval model::  49%|████▉     | 245/500 [00:25<00:25,  9.89it/s]
eval model::  49%|████▉     | 246/500 [00:25<00:25,  9.82it/s]
eval model::  49%|████▉     | 247/500 [00:25<00:26,  9.58it/s]
eval model::  50%|████▉     | 248/500 [00:25<00:26,  9.47it/s]
eval model::  50%|████▉     | 249/500 [00:26<00:26,  9.54it/s]
eval model::  50%|█████     | 250/500 [00:26<00:25,  9.64it/s]
eval model::  50%|█████     | 252/500 [00:26<00:24,  9.97it/s]
eval model::  51%|█████     | 253/500 [00:26<00:24,  9.90it/s]
eval model::  51%|█████     | 255/500 [00:26<00:23, 10.23it/s]
eval model::  51%|█████▏    | 257/500 [00:26<00:23, 10.40it/s]
eval model::  52%|█████▏    | 259/500 [00:26<00:24,  9.99it/s]
eval model::  52%|█████▏    | 260/500 [00:27<00:24,  9.79it/s]
eval model::  52%|█████▏    | 261/500 [00:27<00:24,  9.75it/s]
eval model::  52%|█████▏    | 262/500 [00:27<00:24,  9.65it/s]
eval model::  53%|█████▎    | 263/500 [00:27<00:25,  9.41it/s]
eval model::  53%|█████▎    | 264/500 [00:27<00:25,  9.38it/s]
eval model::  53%|█████▎    | 265/500 [00:27<00:25,  9.29it/s]
eval model::  53%|█████▎    | 266/500 [00:27<00:24,  9.44it/s]
eval model::  53%|█████▎    | 267/500 [00:27<00:24,  9.48it/s]
eval model::  54%|█████▎    | 268/500 [00:27<00:24,  9.59it/s]
eval model::  54%|█████▍    | 269/500 [00:28<00:23,  9.69it/s]
eval model::  54%|█████▍    | 271/500 [00:28<00:24,  9.44it/s]
eval model::  54%|█████▍    | 272/500 [00:28<00:24,  9.29it/s]
eval model::  55%|█████▍    | 273/500 [00:28<00:25,  8.82it/s]
eval model::  55%|█████▍    | 274/500 [00:28<00:26,  8.57it/s]
eval model::  55%|█████▌    | 275/500 [00:28<00:26,  8.37it/s]
eval model::  55%|█████▌    | 276/500 [00:28<00:27,  8.27it/s]
eval model::  55%|█████▌    | 277/500 [00:29<00:27,  8.05it/s]
eval model::  56%|█████▌    | 278/500 [00:29<00:28,  7.85it/s]
eval model::  56%|█████▌    | 279/500 [00:29<00:27,  7.98it/s]
eval model::  56%|█████▌    | 280/500 [00:29<00:28,  7.79it/s]
eval model::  56%|█████▌    | 281/500 [00:29<00:28,  7.70it/s]
eval model::  56%|█████▋    | 282/500 [00:29<00:27,  7.94it/s]
eval model::  57%|█████▋    | 283/500 [00:29<00:26,  8.20it/s]
eval model::  57%|█████▋    | 284/500 [00:29<00:26,  8.11it/s]
eval model::  57%|█████▋    | 285/500 [00:30<00:27,  7.87it/s]
eval model::  57%|█████▋    | 286/500 [00:30<00:27,  7.71it/s]
eval model::  57%|█████▋    | 287/500 [00:30<00:27,  7.85it/s]
eval model::  58%|█████▊    | 288/500 [00:30<00:27,  7.69it/s]
eval model::  58%|█████▊    | 289/500 [00:30<00:26,  7.94it/s]
eval model::  58%|█████▊    | 290/500 [00:30<00:26,  8.02it/s]
eval model::  58%|█████▊    | 291/500 [00:30<00:25,  8.13it/s]
eval model::  58%|█████▊    | 292/500 [00:30<00:25,  8.17it/s]
eval model::  59%|█████▊    | 293/500 [00:31<00:24,  8.39it/s]
eval model::  59%|█████▉    | 294/500 [00:31<00:24,  8.32it/s]
eval model::  59%|█████▉    | 295/500 [00:31<00:24,  8.27it/s]
eval model::  59%|█████▉    | 296/500 [00:31<00:25,  8.07it/s]
eval model::  59%|█████▉    | 297/500 [00:31<00:24,  8.12it/s]
eval model::  60%|█████▉    | 298/500 [00:31<00:23,  8.45it/s]
eval model::  60%|█████▉    | 299/500 [00:31<00:23,  8.63it/s]
eval model::  60%|██████    | 300/500 [00:31<00:23,  8.38it/s]
eval model::  60%|██████    | 301/500 [00:31<00:24,  8.05it/s]
eval model::  60%|██████    | 302/500 [00:32<00:23,  8.29it/s]
eval model::  61%|██████    | 303/500 [00:32<00:23,  8.36it/s]
eval model::  61%|██████    | 304/500 [00:32<00:24,  7.94it/s]
eval model::  61%|██████    | 305/500 [00:32<00:24,  8.08it/s]
eval model::  61%|██████    | 306/500 [00:32<00:23,  8.11it/s]
eval model::  61%|██████▏   | 307/500 [00:32<00:22,  8.44it/s]
eval model::  62%|██████▏   | 308/500 [00:32<00:22,  8.53it/s]
eval model::  62%|██████▏   | 309/500 [00:32<00:22,  8.44it/s]
eval model::  62%|██████▏   | 310/500 [00:33<00:22,  8.50it/s]
eval model::  62%|██████▏   | 311/500 [00:33<00:21,  8.62it/s]
eval model::  62%|██████▏   | 312/500 [00:33<00:21,  8.57it/s]
eval model::  63%|██████▎   | 313/500 [00:33<00:21,  8.71it/s]
eval model::  63%|██████▎   | 314/500 [00:33<00:20,  8.99it/s]
eval model::  63%|██████▎   | 315/500 [00:33<00:21,  8.71it/s]
eval model::  63%|██████▎   | 316/500 [00:33<00:20,  8.78it/s]
eval model::  63%|██████▎   | 317/500 [00:33<00:20,  8.75it/s]
eval model::  64%|██████▎   | 318/500 [00:33<00:20,  8.73it/s]
eval model::  64%|██████▍   | 319/500 [00:34<00:21,  8.37it/s]
eval model::  64%|██████▍   | 320/500 [00:34<00:22,  8.11it/s]
eval model::  64%|██████▍   | 321/500 [00:34<00:22,  7.81it/s]
eval model::  64%|██████▍   | 322/500 [00:34<00:22,  7.96it/s]
eval model::  65%|██████▍   | 323/500 [00:34<00:21,  8.42it/s]
eval model::  65%|██████▍   | 324/500 [00:34<00:21,  8.36it/s]
eval model::  65%|██████▌   | 325/500 [00:34<00:20,  8.62it/s]
eval model::  65%|██████▌   | 326/500 [00:34<00:20,  8.49it/s]
eval model::  65%|██████▌   | 327/500 [00:35<00:19,  8.85it/s]
eval model::  66%|██████▌   | 328/500 [00:35<00:19,  8.71it/s]
eval model::  66%|██████▌   | 329/500 [00:35<00:19,  8.70it/s]
eval model::  66%|██████▌   | 330/500 [00:35<00:19,  8.93it/s]
eval model::  66%|██████▌   | 331/500 [00:35<00:19,  8.75it/s]
eval model::  66%|██████▋   | 332/500 [00:35<00:19,  8.80it/s]
eval model::  67%|██████▋   | 333/500 [00:35<00:19,  8.77it/s]
eval model::  67%|██████▋   | 334/500 [00:35<00:19,  8.60it/s]
eval model::  67%|██████▋   | 335/500 [00:35<00:19,  8.36it/s]
eval model::  67%|██████▋   | 336/500 [00:36<00:19,  8.29it/s]
eval model::  67%|██████▋   | 337/500 [00:36<00:19,  8.25it/s]
eval model::  68%|██████▊   | 338/500 [00:36<00:19,  8.38it/s]
eval model::  68%|██████▊   | 339/500 [00:36<00:19,  8.20it/s]
eval model::  68%|██████▊   | 340/500 [00:36<00:18,  8.50it/s]
eval model::  68%|██████▊   | 341/500 [00:36<00:18,  8.62it/s]
eval model::  68%|██████▊   | 342/500 [00:36<00:17,  8.85it/s]
eval model::  69%|██████▊   | 343/500 [00:36<00:18,  8.68it/s]
eval model::  69%|██████▉   | 344/500 [00:37<00:17,  8.85it/s]
eval model::  69%|██████▉   | 345/500 [00:37<00:16,  9.16it/s]
eval model::  69%|██████▉   | 346/500 [00:37<00:17,  8.99it/s]
eval model::  69%|██████▉   | 347/500 [00:37<00:17,  8.52it/s]
eval model::  70%|██████▉   | 348/500 [00:37<00:17,  8.61it/s]
eval model::  70%|██████▉   | 349/500 [00:37<00:18,  8.30it/s]
eval model::  70%|███████   | 350/500 [00:37<00:18,  7.90it/s]
eval model::  70%|███████   | 351/500 [00:37<00:19,  7.79it/s]
eval model::  70%|███████   | 352/500 [00:38<00:19,  7.74it/s]
eval model::  71%|███████   | 353/500 [00:38<00:17,  8.20it/s]
eval model::  71%|███████   | 354/500 [00:38<00:18,  7.94it/s]
eval model::  71%|███████   | 355/500 [00:38<00:17,  8.06it/s]
eval model::  71%|███████▏  | 357/500 [00:38<00:15,  9.12it/s]
eval model::  72%|███████▏  | 358/500 [00:38<00:15,  8.97it/s]
eval model::  72%|███████▏  | 360/500 [00:38<00:15,  9.03it/s]
eval model::  72%|███████▏  | 362/500 [00:39<00:15,  9.08it/s]
eval model::  73%|███████▎  | 364/500 [00:39<00:14,  9.64it/s]
eval model::  73%|███████▎  | 366/500 [00:39<00:13,  9.58it/s]
eval model::  73%|███████▎  | 367/500 [00:39<00:13,  9.58it/s]
eval model::  74%|███████▎  | 368/500 [00:39<00:13,  9.43it/s]
eval model::  74%|███████▍  | 369/500 [00:39<00:14,  9.31it/s]
eval model::  74%|███████▍  | 370/500 [00:39<00:13,  9.39it/s]
eval model::  74%|███████▍  | 371/500 [00:40<00:14,  9.20it/s]
eval model::  74%|███████▍  | 372/500 [00:40<00:13,  9.27it/s]
eval model::  75%|███████▍  | 374/500 [00:40<00:13,  9.38it/s]
eval model::  75%|███████▌  | 375/500 [00:40<00:13,  9.13it/s]
eval model::  75%|███████▌  | 376/500 [00:40<00:13,  9.10it/s]
eval model::  75%|███████▌  | 377/500 [00:40<00:13,  9.15it/s]
eval model::  76%|███████▌  | 378/500 [00:40<00:13,  9.22it/s]
eval model::  76%|███████▌  | 379/500 [00:40<00:12,  9.37it/s]
eval model::  76%|███████▌  | 380/500 [00:41<00:13,  9.18it/s]
eval model::  76%|███████▌  | 381/500 [00:41<00:13,  8.86it/s]
eval model::  76%|███████▋  | 382/500 [00:41<00:13,  9.06it/s]
eval model::  77%|███████▋  | 383/500 [00:41<00:13,  8.87it/s]
eval model::  77%|███████▋  | 384/500 [00:41<00:13,  8.71it/s]
eval model::  77%|███████▋  | 385/500 [00:41<00:13,  8.59it/s]
eval model::  77%|███████▋  | 386/500 [00:41<00:13,  8.54it/s]
eval model::  77%|███████▋  | 387/500 [00:41<00:13,  8.53it/s]
eval model::  78%|███████▊  | 388/500 [00:41<00:13,  8.59it/s]
eval model::  78%|███████▊  | 389/500 [00:42<00:13,  8.43it/s]
eval model::  78%|███████▊  | 390/500 [00:42<00:13,  8.25it/s]
eval model::  78%|███████▊  | 391/500 [00:42<00:13,  8.28it/s]
eval model::  78%|███████▊  | 392/500 [00:42<00:12,  8.44it/s]
eval model::  79%|███████▊  | 393/500 [00:42<00:12,  8.54it/s]
eval model::  79%|███████▉  | 394/500 [00:42<00:11,  8.93it/s]
eval model::  79%|███████▉  | 395/500 [00:42<00:12,  8.62it/s]
eval model::  79%|███████▉  | 397/500 [00:42<00:11,  9.27it/s]
eval model::  80%|███████▉  | 398/500 [00:43<00:11,  9.15it/s]
eval model::  80%|███████▉  | 399/500 [00:43<00:11,  9.10it/s]
eval model::  80%|████████  | 400/500 [00:43<00:11,  9.00it/s]
eval model::  80%|████████  | 401/500 [00:43<00:11,  8.87it/s]
eval model::  80%|████████  | 402/500 [00:43<00:10,  9.16it/s]
eval model::  81%|████████  | 403/500 [00:43<00:10,  9.04it/s]
eval model::  81%|████████  | 404/500 [00:43<00:10,  9.13it/s]
eval model::  81%|████████  | 405/500 [00:43<00:10,  9.20it/s]
eval model::  81%|████████  | 406/500 [00:44<00:10,  9.03it/s]
eval model::  81%|████████▏ | 407/500 [00:44<00:10,  8.81it/s]
eval model::  82%|████████▏ | 408/500 [00:44<00:10,  8.86it/s]
eval model::  82%|████████▏ | 409/500 [00:44<00:09,  9.15it/s]
eval model::  82%|████████▏ | 410/500 [00:44<00:10,  8.46it/s]
eval model::  82%|████████▏ | 411/500 [00:44<00:10,  8.67it/s]
eval model::  82%|████████▏ | 412/500 [00:44<00:10,  8.28it/s]
eval model::  83%|████████▎ | 413/500 [00:44<00:10,  8.21it/s]
eval model::  83%|████████▎ | 415/500 [00:45<00:09,  8.64it/s]
eval model::  83%|████████▎ | 416/500 [00:45<00:10,  8.39it/s]
eval model::  83%|████████▎ | 417/500 [00:45<00:09,  8.70it/s]
eval model::  84%|████████▎ | 418/500 [00:45<00:09,  8.81it/s]
eval model::  84%|████████▍ | 419/500 [00:45<00:08,  9.00it/s]
eval model::  84%|████████▍ | 420/500 [00:45<00:09,  8.58it/s]
eval model::  84%|████████▍ | 421/500 [00:45<00:09,  8.37it/s]
eval model::  84%|████████▍ | 422/500 [00:45<00:09,  8.28it/s]
eval model::  85%|████████▍ | 423/500 [00:45<00:08,  8.58it/s]
eval model::  85%|████████▍ | 424/500 [00:46<00:08,  8.48it/s]
eval model::  85%|████████▌ | 425/500 [00:46<00:08,  8.57it/s]
eval model::  85%|████████▌ | 426/500 [00:46<00:08,  8.70it/s]
eval model::  85%|████████▌ | 427/500 [00:46<00:08,  8.54it/s]
eval model::  86%|████████▌ | 428/500 [00:46<00:08,  8.15it/s]
eval model::  86%|████████▌ | 429/500 [00:46<00:08,  8.38it/s]
eval model::  86%|████████▌ | 431/500 [00:46<00:07,  9.04it/s]
eval model::  87%|████████▋ | 433/500 [00:47<00:07,  9.56it/s]
eval model::  87%|████████▋ | 435/500 [00:47<00:06,  9.92it/s]
eval model::  87%|████████▋ | 436/500 [00:47<00:06,  9.88it/s]
eval model::  87%|████████▋ | 437/500 [00:47<00:06,  9.52it/s]
eval model::  88%|████████▊ | 438/500 [00:47<00:06,  9.48it/s]
eval model::  88%|████████▊ | 440/500 [00:47<00:05, 10.08it/s]
eval model::  88%|████████▊ | 441/500 [00:47<00:05,  9.98it/s]
eval model::  89%|████████▊ | 443/500 [00:48<00:05, 10.39it/s]
eval model::  89%|████████▉ | 445/500 [00:48<00:05,  9.82it/s]
eval model::  89%|████████▉ | 446/500 [00:48<00:05,  9.65it/s]
eval model::  89%|████████▉ | 447/500 [00:48<00:05,  9.49it/s]
eval model::  90%|████████▉ | 449/500 [00:48<00:04, 10.25it/s]
eval model::  90%|█████████ | 451/500 [00:48<00:04, 10.28it/s]
eval model::  91%|█████████ | 453/500 [00:49<00:04, 10.44it/s]
eval model::  91%|█████████ | 455/500 [00:49<00:04, 10.56it/s]
eval model::  91%|█████████▏| 457/500 [00:49<00:04,  9.86it/s]
eval model::  92%|█████████▏| 458/500 [00:49<00:04,  9.78it/s]
eval model::  92%|█████████▏| 459/500 [00:49<00:04,  9.37it/s]
eval model::  92%|█████████▏| 461/500 [00:49<00:03,  9.85it/s]
eval model::  92%|█████████▏| 462/500 [00:50<00:04,  9.38it/s]
eval model::  93%|█████████▎| 464/500 [00:50<00:03,  9.64it/s]
eval model::  93%|█████████▎| 465/500 [00:50<00:03,  9.70it/s]
eval model::  93%|█████████▎| 467/500 [00:50<00:03, 10.26it/s]
eval model::  94%|█████████▍| 469/500 [00:50<00:02, 10.62it/s]
eval model::  94%|█████████▍| 471/500 [00:50<00:02, 10.19it/s]
eval model::  95%|█████████▍| 473/500 [00:51<00:02, 10.21it/s]
eval model::  95%|█████████▌| 475/500 [00:51<00:02, 10.36it/s]
eval model::  95%|█████████▌| 477/500 [00:51<00:02, 10.25it/s]
eval model::  96%|█████████▌| 479/500 [00:51<00:02, 10.43it/s]
eval model::  96%|█████████▌| 481/500 [00:51<00:01, 10.20it/s]
eval model::  97%|█████████▋| 483/500 [00:52<00:01, 10.03it/s]
eval model::  97%|█████████▋| 485/500 [00:52<00:01,  9.85it/s]
eval model::  97%|█████████▋| 486/500 [00:52<00:01,  9.75it/s]
eval model::  98%|█████████▊| 488/500 [00:52<00:01,  9.81it/s]
eval model::  98%|█████████▊| 489/500 [00:52<00:01,  9.82it/s]
eval model::  98%|█████████▊| 491/500 [00:52<00:00,  9.73it/s]
eval model::  99%|█████████▊| 493/500 [00:53<00:00,  9.85it/s]
eval model::  99%|█████████▉| 494/500 [00:53<00:00,  9.68it/s]
eval model::  99%|█████████▉| 495/500 [00:53<00:00,  9.50it/s]
eval model::  99%|█████████▉| 497/500 [00:53<00:00,  9.98it/s]
eval model:: 100%|█████████▉| 499/500 [00:53<00:00, 10.47it/s]
eval model:: 100%|██████████| 500/500 [00:53<00:00,  9.30it/s]
[2024/08/01 17:50:21] ppocr INFO: cur metric, precision: 0, recall: 0.0, hmean: 0, fps: 56.70311941619866
[2024/08/01 17:50:21] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 17:50:21] ppocr INFO: best metric, hmean: 0, precision: 0, recall: 0.0, fps: 56.70311941619866, best_epoch: 20
[2024/08/01 17:50:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:50:36] ppocr INFO: epoch: [21/100], global_step: 63, lr: 0.001000, loss: 6.246375, loss_shrink_maps: 4.821410, loss_threshold_maps: 0.868740, loss_binary_maps: 0.510149, avg_reader_cost: 3.24343 s, avg_batch_cost: 4.29778 s, avg_samples: 12.5, ips: 2.90848 samples/s, eta: 0:43:54
[2024/08/01 17:50:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:50:44] ppocr INFO: epoch: [22/100], global_step: 66, lr: 0.001000, loss: 6.210634, loss_shrink_maps: 4.817578, loss_threshold_maps: 0.868740, loss_binary_maps: 0.480297, avg_reader_cost: 1.55981 s, avg_batch_cost: 1.78959 s, avg_samples: 12.5, ips: 6.98483 samples/s, eta: 0:42:26
[2024/08/01 17:50:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:50:51] ppocr INFO: epoch: [23/100], global_step: 69, lr: 0.001000, loss: 6.186344, loss_shrink_maps: 4.825064, loss_threshold_maps: 0.873637, loss_binary_maps: 0.462218, avg_reader_cost: 1.60264 s, avg_batch_cost: 1.91454 s, avg_samples: 12.5, ips: 6.52900 samples/s, eta: 0:41:08
[2024/08/01 17:50:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:50:57] ppocr INFO: epoch: [24/100], global_step: 70, lr: 0.001000, loss: 6.186344, loss_shrink_maps: 4.829183, loss_threshold_maps: 0.877313, loss_binary_maps: 0.462218, avg_reader_cost: 0.42745 s, avg_batch_cost: 0.51434 s, avg_samples: 4.8, ips: 9.33242 samples/s, eta: 0:40:39
[2024/08/01 17:50:58] ppocr INFO: epoch: [24/100], global_step: 72, lr: 0.001000, loss: 6.184086, loss_shrink_maps: 4.829183, loss_threshold_maps: 0.879005, loss_binary_maps: 0.454171, avg_reader_cost: 1.11738 s, avg_batch_cost: 1.25714 s, avg_samples: 7.7, ips: 6.12499 samples/s, eta: 0:39:51
[2024/08/01 17:50:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:51:05] ppocr INFO: epoch: [25/100], global_step: 75, lr: 0.001000, loss: 6.153066, loss_shrink_maps: 4.817578, loss_threshold_maps: 0.879005, loss_binary_maps: 0.403712, avg_reader_cost: 1.53132 s, avg_batch_cost: 1.76569 s, avg_samples: 12.5, ips: 7.07939 samples/s, eta: 0:38:38
[2024/08/01 17:51:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:51:13] ppocr INFO: epoch: [26/100], global_step: 78, lr: 0.001000, loss: 6.095283, loss_shrink_maps: 4.815012, loss_threshold_maps: 0.877313, loss_binary_maps: 0.394333, avg_reader_cost: 1.55916 s, avg_batch_cost: 1.77713 s, avg_samples: 12.5, ips: 7.03381 samples/s, eta: 0:37:30
[2024/08/01 17:51:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:51:20] ppocr INFO: epoch: [27/100], global_step: 80, lr: 0.001000, loss: 6.040155, loss_shrink_maps: 4.807832, loss_threshold_maps: 0.862050, loss_binary_maps: 0.385620, avg_reader_cost: 0.94487 s, avg_batch_cost: 1.16765 s, avg_samples: 9.6, ips: 8.22163 samples/s, eta: 0:36:46
[2024/08/01 17:51:20] ppocr INFO: epoch: [27/100], global_step: 81, lr: 0.001000, loss: 6.034917, loss_shrink_maps: 4.802644, loss_threshold_maps: 0.857663, loss_binary_maps: 0.387033, avg_reader_cost: 0.62777 s, avg_batch_cost: 0.68009 s, avg_samples: 2.9, ips: 4.26416 samples/s, eta: 0:36:27
[2024/08/01 17:51:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:51:27] ppocr INFO: epoch: [28/100], global_step: 84, lr: 0.001000, loss: 6.004999, loss_shrink_maps: 4.760480, loss_threshold_maps: 0.827622, loss_binary_maps: 0.382736, avg_reader_cost: 1.51718 s, avg_batch_cost: 1.74777 s, avg_samples: 12.5, ips: 7.15197 samples/s, eta: 0:35:25
[2024/08/01 17:51:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:51:35] ppocr INFO: epoch: [29/100], global_step: 87, lr: 0.001000, loss: 5.960230, loss_shrink_maps: 4.744919, loss_threshold_maps: 0.824065, loss_binary_maps: 0.374527, avg_reader_cost: 1.57940 s, avg_batch_cost: 1.83344 s, avg_samples: 12.5, ips: 6.81779 samples/s, eta: 0:34:28
[2024/08/01 17:51:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:51:42] ppocr INFO: epoch: [30/100], global_step: 90, lr: 0.001000, loss: 5.896573, loss_shrink_maps: 4.721136, loss_threshold_maps: 0.823350, loss_binary_maps: 0.365361, avg_reader_cost: 1.54142 s, avg_batch_cost: 1.77970 s, avg_samples: 12.5, ips: 7.02367 samples/s, eta: 0:33:32
[2024/08/01 17:51:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:51:49] ppocr INFO: epoch: [31/100], global_step: 93, lr: 0.001000, loss: 5.877733, loss_shrink_maps: 4.681227, loss_threshold_maps: 0.822547, loss_binary_maps: 0.365361, avg_reader_cost: 1.52164 s, avg_batch_cost: 1.74048 s, avg_samples: 12.5, ips: 7.18194 samples/s, eta: 0:32:38
[2024/08/01 17:51:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:51:56] ppocr INFO: epoch: [32/100], global_step: 96, lr: 0.001000, loss: 5.854583, loss_shrink_maps: 4.637784, loss_threshold_maps: 0.823350, loss_binary_maps: 0.378998, avg_reader_cost: 1.50499 s, avg_batch_cost: 1.74903 s, avg_samples: 12.5, ips: 7.14683 samples/s, eta: 0:31:47
[2024/08/01 17:51:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:52:03] ppocr INFO: epoch: [33/100], global_step: 99, lr: 0.001000, loss: 5.775094, loss_shrink_maps: 4.570210, loss_threshold_maps: 0.816176, loss_binary_maps: 0.379037, avg_reader_cost: 1.54504 s, avg_batch_cost: 1.76606 s, avg_samples: 12.5, ips: 7.07788 samples/s, eta: 0:30:58
[2024/08/01 17:52:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:52:09] ppocr INFO: epoch: [34/100], global_step: 100, lr: 0.001000, loss: 5.763016, loss_shrink_maps: 4.561550, loss_threshold_maps: 0.823468, loss_binary_maps: 0.379037, avg_reader_cost: 0.44845 s, avg_batch_cost: 0.52719 s, avg_samples: 4.8, ips: 9.10490 samples/s, eta: 0:30:40
[2024/08/01 17:52:11] ppocr INFO: epoch: [34/100], global_step: 102, lr: 0.001000, loss: 5.742966, loss_shrink_maps: 4.524618, loss_threshold_maps: 0.823468, loss_binary_maps: 0.364334, avg_reader_cost: 1.14198 s, avg_batch_cost: 1.28130 s, avg_samples: 7.7, ips: 6.00952 samples/s, eta: 0:30:11
[2024/08/01 17:52:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:52:18] ppocr INFO: epoch: [35/100], global_step: 105, lr: 0.001000, loss: 5.669571, loss_shrink_maps: 4.507236, loss_threshold_maps: 0.824728, loss_binary_maps: 0.370966, avg_reader_cost: 1.58504 s, avg_batch_cost: 1.86516 s, avg_samples: 12.5, ips: 6.70185 samples/s, eta: 0:29:27
[2024/08/01 17:52:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:52:26] ppocr INFO: epoch: [36/100], global_step: 108, lr: 0.001000, loss: 5.504646, loss_shrink_maps: 4.384252, loss_threshold_maps: 0.795745, loss_binary_maps: 0.367287, avg_reader_cost: 1.60318 s, avg_batch_cost: 1.86544 s, avg_samples: 12.5, ips: 6.70084 samples/s, eta: 0:28:45
[2024/08/01 17:52:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:52:32] ppocr INFO: epoch: [37/100], global_step: 110, lr: 0.001000, loss: 5.487755, loss_shrink_maps: 4.276102, loss_threshold_maps: 0.795865, loss_binary_maps: 0.367287, avg_reader_cost: 0.94205 s, avg_batch_cost: 1.12038 s, avg_samples: 9.6, ips: 8.56850 samples/s, eta: 0:28:15
[2024/08/01 17:52:33] ppocr INFO: epoch: [37/100], global_step: 111, lr: 0.001000, loss: 5.467844, loss_shrink_maps: 4.272153, loss_threshold_maps: 0.795595, loss_binary_maps: 0.367287, avg_reader_cost: 0.60411 s, avg_batch_cost: 0.65705 s, avg_samples: 2.9, ips: 4.41367 samples/s, eta: 0:28:02
[2024/08/01 17:52:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:52:41] ppocr INFO: epoch: [38/100], global_step: 114, lr: 0.001000, loss: 5.356896, loss_shrink_maps: 4.210736, loss_threshold_maps: 0.795072, loss_binary_maps: 0.350336, avg_reader_cost: 1.63229 s, avg_batch_cost: 1.97290 s, avg_samples: 12.5, ips: 6.33585 samples/s, eta: 0:27:24
[2024/08/01 17:52:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:52:48] ppocr INFO: epoch: [39/100], global_step: 117, lr: 0.001000, loss: 5.262022, loss_shrink_maps: 4.137803, loss_threshold_maps: 0.783977, loss_binary_maps: 0.347792, avg_reader_cost: 1.58468 s, avg_batch_cost: 1.81424 s, avg_samples: 12.5, ips: 6.88994 samples/s, eta: 0:26:45
[2024/08/01 17:52:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:52:56] ppocr INFO: epoch: [40/100], global_step: 120, lr: 0.001000, loss: 5.102067, loss_shrink_maps: 3.975166, loss_threshold_maps: 0.783977, loss_binary_maps: 0.347554, avg_reader_cost: 1.60789 s, avg_batch_cost: 1.88224 s, avg_samples: 12.5, ips: 6.64101 samples/s, eta: 0:26:07

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:01,  8.12it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.33it/s]
eval model::   1%|          | 6/500 [00:00<00:29, 16.77it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.12it/s]
eval model::   2%|▏         | 11/500 [00:00<00:25, 18.83it/s]
eval model::   3%|▎         | 13/500 [00:00<00:27, 17.44it/s]
eval model::   3%|▎         | 16/500 [00:00<00:26, 18.53it/s]
eval model::   4%|▍         | 19/500 [00:01<00:25, 19.05it/s]
eval model::   4%|▍         | 21/500 [00:01<00:27, 17.47it/s]
eval model::   5%|▍         | 24/500 [00:01<00:24, 19.30it/s]
eval model::   5%|▌         | 27/500 [00:01<00:23, 19.93it/s]
eval model::   6%|▌         | 29/500 [00:01<00:24, 19.55it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.32it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.34it/s]
eval model::   7%|▋         | 37/500 [00:02<00:24, 18.92it/s]
eval model::   8%|▊         | 39/500 [00:02<00:26, 17.47it/s]
eval model::   8%|▊         | 41/500 [00:02<00:25, 17.74it/s]
eval model::   9%|▊         | 43/500 [00:02<00:27, 16.53it/s]
eval model::   9%|▉         | 46/500 [00:02<00:25, 17.67it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 20.18it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 20.06it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 20.14it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:21, 20.50it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:23, 18.66it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:23, 18.41it/s]
eval model::  13%|█▎        | 65/500 [00:03<00:23, 18.77it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:22, 19.22it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:21, 20.16it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:20, 20.50it/s]
eval model::  15%|█▌        | 77/500 [00:04<00:21, 19.40it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:21, 19.37it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 20.45it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.22it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.00it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 18.52it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:21, 18.63it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:21, 18.89it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:20, 19.68it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:22, 17.96it/s]
eval model::  20%|██        | 101/500 [00:05<00:22, 17.72it/s]
eval model::  21%|██        | 104/500 [00:05<00:19, 20.49it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.12it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 21.11it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 20.55it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:19, 19.74it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:18, 20.07it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:18, 19.91it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:18, 20.18it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 20.51it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:18, 20.25it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:17, 20.61it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:18, 19.76it/s]
eval model::  28%|██▊       | 140/500 [00:07<00:18, 19.51it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 20.84it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 20.71it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:17, 19.91it/s]
eval model::  30%|███       | 152/500 [00:07<00:19, 18.18it/s]
eval model::  31%|███       | 155/500 [00:08<00:17, 19.63it/s]
eval model::  32%|███▏      | 158/500 [00:08<00:16, 20.29it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:18, 18.71it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:18, 18.70it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.19it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 18.57it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:19, 17.22it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:18, 17.44it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:20, 16.01it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:18, 17.86it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:19, 16.82it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 18.74it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 21.11it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:13, 23.05it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:13, 23.19it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:13, 22.83it/s]
eval model::  40%|███▉      | 198/500 [00:10<00:14, 21.56it/s]
eval model::  40%|████      | 201/500 [00:10<00:13, 22.10it/s]
eval model::  41%|████      | 205/500 [00:10<00:17, 16.99it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:16, 17.80it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:15, 18.13it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:14, 19.88it/s]
eval model::  43%|████▎     | 216/500 [00:11<00:17, 16.67it/s]
eval model::  44%|████▎     | 218/500 [00:11<00:17, 16.49it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:15, 17.54it/s]
eval model::  45%|████▍     | 223/500 [00:11<00:15, 17.76it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:14, 19.30it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:12, 22.49it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:11, 22.54it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:11, 22.79it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 23.46it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:13, 19.07it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:12, 20.60it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 22.67it/s]
eval model::  50%|█████     | 251/500 [00:12<00:12, 20.37it/s]
eval model::  51%|█████     | 254/500 [00:13<00:11, 21.23it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.22it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:11, 21.31it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:12, 19.36it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:12, 18.26it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:12, 18.49it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:12, 17.84it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:12, 17.79it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.52it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 19.30it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 18.86it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 19.82it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:12, 16.77it/s]
eval model::  58%|█████▊    | 288/500 [00:14<00:11, 18.59it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:10, 20.52it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:10, 19.79it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:09, 20.91it/s]
eval model::  60%|██████    | 300/500 [00:15<00:11, 17.33it/s]
eval model::  61%|██████    | 303/500 [00:15<00:10, 18.95it/s]
eval model::  61%|██████    | 306/500 [00:15<00:09, 20.39it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:08, 21.74it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 20.48it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 21.14it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:09, 19.89it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 20.58it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 19.66it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:08, 20.56it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:08, 20.30it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:08, 19.79it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:08, 19.45it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:08, 19.24it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 17.25it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:08, 19.61it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 20.20it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:07, 21.41it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 22.16it/s]
eval model::  71%|███████   | 355/500 [00:18<00:07, 20.01it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:07, 20.07it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 18.83it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 17.44it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:07, 18.34it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 20.72it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 20.16it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 22.25it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:05, 22.01it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:04, 23.51it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:06, 18.17it/s]
eval model::  78%|███████▊  | 389/500 [00:19<00:05, 19.21it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:05, 19.26it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 20.60it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:04, 22.16it/s]
eval model::  80%|████████  | 401/500 [00:20<00:04, 20.65it/s]
eval model::  81%|████████  | 404/500 [00:20<00:04, 19.70it/s]
eval model::  81%|████████▏ | 407/500 [00:20<00:04, 19.67it/s]
eval model::  82%|████████▏ | 410/500 [00:20<00:04, 20.43it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:04, 19.06it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:04, 19.53it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 18.39it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 16.74it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 17.45it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 18.03it/s]
eval model::  85%|████████▌ | 427/500 [00:21<00:03, 20.45it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 20.84it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:03, 20.08it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:03, 19.17it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:03, 18.81it/s]
eval model::  88%|████████▊ | 440/500 [00:22<00:03, 17.70it/s]
eval model::  89%|████████▊ | 443/500 [00:22<00:02, 19.70it/s]
eval model::  89%|████████▉ | 446/500 [00:22<00:02, 19.86it/s]
eval model::  90%|████████▉ | 449/500 [00:23<00:02, 18.60it/s]
eval model::  90%|█████████ | 452/500 [00:23<00:02, 19.98it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:02, 19.58it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:01, 21.28it/s]
eval model::  92%|█████████▏| 461/500 [00:23<00:01, 21.47it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 21.24it/s]
eval model::  93%|█████████▎| 467/500 [00:23<00:01, 19.89it/s]
eval model::  94%|█████████▍| 470/500 [00:24<00:01, 20.98it/s]
eval model::  95%|█████████▍| 473/500 [00:24<00:01, 21.31it/s]
eval model::  95%|█████████▌| 476/500 [00:24<00:01, 21.12it/s]
eval model::  96%|█████████▌| 479/500 [00:24<00:00, 22.09it/s]
eval model::  96%|█████████▋| 482/500 [00:24<00:00, 22.06it/s]
eval model::  97%|█████████▋| 485/500 [00:24<00:00, 21.92it/s]
eval model::  98%|█████████▊| 488/500 [00:24<00:00, 20.27it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 18.80it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 20.16it/s]
eval model::  99%|█████████▉| 497/500 [00:25<00:00, 21.45it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 20.39it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.61it/s]
[2024/08/01 17:53:21] ppocr INFO: cur metric, precision: 0.22295081967213115, recall: 0.130958112662494, hmean: 0.16499848346982104, fps: 44.93138701044406
[2024/08/01 17:53:21] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 17:53:21] ppocr INFO: best metric, hmean: 0.16499848346982104, precision: 0.22295081967213115, recall: 0.130958112662494, fps: 44.93138701044406, best_epoch: 40
[2024/08/01 17:53:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:53:28] ppocr INFO: epoch: [41/100], global_step: 123, lr: 0.001000, loss: 5.013711, loss_shrink_maps: 3.900425, loss_threshold_maps: 0.791662, loss_binary_maps: 0.345926, avg_reader_cost: 1.55501 s, avg_batch_cost: 1.77387 s, avg_samples: 12.5, ips: 7.04675 samples/s, eta: 0:25:29
[2024/08/01 17:53:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:53:35] ppocr INFO: epoch: [42/100], global_step: 126, lr: 0.001000, loss: 4.917653, loss_shrink_maps: 3.761256, loss_threshold_maps: 0.793332, loss_binary_maps: 0.344768, avg_reader_cost: 1.54325 s, avg_batch_cost: 1.76445 s, avg_samples: 12.5, ips: 7.08436 samples/s, eta: 0:24:52
[2024/08/01 17:53:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:53:43] ppocr INFO: epoch: [43/100], global_step: 129, lr: 0.001000, loss: 4.813151, loss_shrink_maps: 3.702121, loss_threshold_maps: 0.793332, loss_binary_maps: 0.343558, avg_reader_cost: 1.52052 s, avg_batch_cost: 1.75167 s, avg_samples: 12.5, ips: 7.13603 samples/s, eta: 0:24:15
[2024/08/01 17:53:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:53:48] ppocr INFO: epoch: [44/100], global_step: 130, lr: 0.001000, loss: 4.810200, loss_shrink_maps: 3.696463, loss_threshold_maps: 0.785647, loss_binary_maps: 0.343558, avg_reader_cost: 0.42727 s, avg_batch_cost: 0.51753 s, avg_samples: 4.8, ips: 9.27480 samples/s, eta: 0:24:02
[2024/08/01 17:53:50] ppocr INFO: epoch: [44/100], global_step: 132, lr: 0.001000, loss: 4.772030, loss_shrink_maps: 3.589952, loss_threshold_maps: 0.793332, loss_binary_maps: 0.344768, avg_reader_cost: 1.12297 s, avg_batch_cost: 1.26250 s, avg_samples: 7.7, ips: 6.09903 samples/s, eta: 0:23:40
[2024/08/01 17:53:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:53:57] ppocr INFO: epoch: [45/100], global_step: 135, lr: 0.001000, loss: 4.592910, loss_shrink_maps: 3.459525, loss_threshold_maps: 0.783712, loss_binary_maps: 0.343558, avg_reader_cost: 1.54752 s, avg_batch_cost: 1.78079 s, avg_samples: 12.5, ips: 7.01935 samples/s, eta: 0:23:05
[2024/08/01 17:53:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:54:04] ppocr INFO: epoch: [46/100], global_step: 138, lr: 0.001000, loss: 4.477604, loss_shrink_maps: 3.358388, loss_threshold_maps: 0.777170, loss_binary_maps: 0.336770, avg_reader_cost: 1.54333 s, avg_batch_cost: 1.76163 s, avg_samples: 12.5, ips: 7.09571 samples/s, eta: 0:22:31
[2024/08/01 17:54:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:54:11] ppocr INFO: epoch: [47/100], global_step: 140, lr: 0.001000, loss: 4.174245, loss_shrink_maps: 3.057818, loss_threshold_maps: 0.775730, loss_binary_maps: 0.329912, avg_reader_cost: 0.94198 s, avg_batch_cost: 1.10834 s, avg_samples: 9.6, ips: 8.66160 samples/s, eta: 0:22:08
[2024/08/01 17:54:11] ppocr INFO: epoch: [47/100], global_step: 141, lr: 0.001000, loss: 4.174245, loss_shrink_maps: 3.057599, loss_threshold_maps: 0.773347, loss_binary_maps: 0.329912, avg_reader_cost: 0.59814 s, avg_batch_cost: 0.65080 s, avg_samples: 2.9, ips: 4.45608 samples/s, eta: 0:21:57
[2024/08/01 17:54:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:54:19] ppocr INFO: epoch: [48/100], global_step: 144, lr: 0.001000, loss: 3.932898, loss_shrink_maps: 2.859950, loss_threshold_maps: 0.771016, loss_binary_maps: 0.318970, avg_reader_cost: 1.60583 s, avg_batch_cost: 1.82395 s, avg_samples: 12.5, ips: 6.85324 samples/s, eta: 0:21:25
[2024/08/01 17:54:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:54:26] ppocr INFO: epoch: [49/100], global_step: 147, lr: 0.001000, loss: 3.757834, loss_shrink_maps: 2.709851, loss_threshold_maps: 0.766270, loss_binary_maps: 0.311008, avg_reader_cost: 1.55615 s, avg_batch_cost: 1.77678 s, avg_samples: 12.5, ips: 7.03518 samples/s, eta: 0:20:53
[2024/08/01 17:54:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:54:33] ppocr INFO: epoch: [50/100], global_step: 150, lr: 0.001000, loss: 3.741022, loss_shrink_maps: 2.650275, loss_threshold_maps: 0.760824, loss_binary_maps: 0.316353, avg_reader_cost: 1.52191 s, avg_batch_cost: 1.76198 s, avg_samples: 12.5, ips: 7.09431 samples/s, eta: 0:20:22
[2024/08/01 17:54:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:54:41] ppocr INFO: epoch: [51/100], global_step: 153, lr: 0.001000, loss: 3.669412, loss_shrink_maps: 2.579703, loss_threshold_maps: 0.765692, loss_binary_maps: 0.314351, avg_reader_cost: 1.63428 s, avg_batch_cost: 1.85759 s, avg_samples: 12.5, ips: 6.72915 samples/s, eta: 0:19:52
[2024/08/01 17:54:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:54:48] ppocr INFO: epoch: [52/100], global_step: 156, lr: 0.001000, loss: 3.402554, loss_shrink_maps: 2.382327, loss_threshold_maps: 0.756391, loss_binary_maps: 0.314351, avg_reader_cost: 1.52596 s, avg_batch_cost: 1.74469 s, avg_samples: 12.5, ips: 7.16458 samples/s, eta: 0:19:21
[2024/08/01 17:54:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:54:55] ppocr INFO: epoch: [53/100], global_step: 159, lr: 0.001000, loss: 3.361134, loss_shrink_maps: 2.272924, loss_threshold_maps: 0.765692, loss_binary_maps: 0.314122, avg_reader_cost: 1.53452 s, avg_batch_cost: 1.78002 s, avg_samples: 12.5, ips: 7.02241 samples/s, eta: 0:18:51
[2024/08/01 17:54:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:55:01] ppocr INFO: epoch: [54/100], global_step: 160, lr: 0.001000, loss: 3.357454, loss_shrink_maps: 2.255252, loss_threshold_maps: 0.765692, loss_binary_maps: 0.314122, avg_reader_cost: 0.42923 s, avg_batch_cost: 0.50767 s, avg_samples: 4.8, ips: 9.45500 samples/s, eta: 0:18:41
[2024/08/01 17:55:03] ppocr INFO: epoch: [54/100], global_step: 162, lr: 0.001000, loss: 3.326994, loss_shrink_maps: 2.189058, loss_threshold_maps: 0.775286, loss_binary_maps: 0.310779, avg_reader_cost: 1.10308 s, avg_batch_cost: 1.24259 s, avg_samples: 7.7, ips: 6.19672 samples/s, eta: 0:18:22
[2024/08/01 17:55:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:55:10] ppocr INFO: epoch: [55/100], global_step: 165, lr: 0.001000, loss: 3.272557, loss_shrink_maps: 2.152653, loss_threshold_maps: 0.780274, loss_binary_maps: 0.306106, avg_reader_cost: 1.64286 s, avg_batch_cost: 1.91193 s, avg_samples: 12.5, ips: 6.53789 samples/s, eta: 0:17:54
[2024/08/01 17:55:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:55:17] ppocr INFO: epoch: [56/100], global_step: 168, lr: 0.001000, loss: 3.186116, loss_shrink_maps: 2.099789, loss_threshold_maps: 0.772851, loss_binary_maps: 0.304385, avg_reader_cost: 1.52204 s, avg_batch_cost: 1.74309 s, avg_samples: 12.5, ips: 7.17115 samples/s, eta: 0:17:25
[2024/08/01 17:55:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:55:24] ppocr INFO: epoch: [57/100], global_step: 170, lr: 0.001000, loss: 3.100752, loss_shrink_maps: 2.033987, loss_threshold_maps: 0.772097, loss_binary_maps: 0.293253, avg_reader_cost: 0.94066 s, avg_batch_cost: 1.13010 s, avg_samples: 9.6, ips: 8.49482 samples/s, eta: 0:17:05
[2024/08/01 17:55:25] ppocr INFO: epoch: [57/100], global_step: 171, lr: 0.001000, loss: 3.098502, loss_shrink_maps: 2.006058, loss_threshold_maps: 0.772097, loss_binary_maps: 0.294432, avg_reader_cost: 0.60932 s, avg_batch_cost: 0.66166 s, avg_samples: 2.9, ips: 4.38290 samples/s, eta: 0:16:57
[2024/08/01 17:55:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:55:32] ppocr INFO: epoch: [58/100], global_step: 174, lr: 0.001000, loss: 3.023936, loss_shrink_maps: 1.943461, loss_threshold_maps: 0.769614, loss_binary_maps: 0.293253, avg_reader_cost: 1.52857 s, avg_batch_cost: 1.76433 s, avg_samples: 12.5, ips: 7.08483 samples/s, eta: 0:16:29
[2024/08/01 17:55:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:55:39] ppocr INFO: epoch: [59/100], global_step: 177, lr: 0.001000, loss: 2.898122, loss_shrink_maps: 1.842262, loss_threshold_maps: 0.769614, loss_binary_maps: 0.290709, avg_reader_cost: 1.77343 s, avg_batch_cost: 1.99340 s, avg_samples: 12.5, ips: 6.27069 samples/s, eta: 0:16:02
[2024/08/01 17:55:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:55:47] ppocr INFO: epoch: [60/100], global_step: 180, lr: 0.001000, loss: 2.843680, loss_shrink_maps: 1.776176, loss_threshold_maps: 0.759556, loss_binary_maps: 0.282956, avg_reader_cost: 1.46923 s, avg_batch_cost: 1.69968 s, avg_samples: 12.5, ips: 7.35433 samples/s, eta: 0:15:35

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:52,  9.45it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 19.01it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.57it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.19it/s]
eval model::   2%|▏         | 11/500 [00:00<00:25, 19.43it/s]
eval model::   3%|▎         | 14/500 [00:00<00:24, 19.84it/s]
eval model::   3%|▎         | 17/500 [00:00<00:22, 21.73it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.30it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 21.66it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 21.38it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 21.95it/s]
eval model::   6%|▋         | 32/500 [00:01<00:21, 21.32it/s]
eval model::   7%|▋         | 35/500 [00:01<00:23, 19.85it/s]
eval model::   8%|▊         | 38/500 [00:01<00:22, 20.92it/s]
eval model::   8%|▊         | 41/500 [00:02<00:25, 17.87it/s]
eval model::   9%|▊         | 43/500 [00:02<00:25, 18.07it/s]
eval model::   9%|▉         | 46/500 [00:02<00:24, 18.45it/s]
eval model::  10%|▉         | 49/500 [00:02<00:21, 20.90it/s]
eval model::  10%|█         | 52/500 [00:02<00:20, 21.39it/s]
eval model::  11%|█         | 56/500 [00:02<00:19, 22.42it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:22, 19.80it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:22, 19.72it/s]
eval model::  13%|█▎        | 65/500 [00:03<00:20, 21.41it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:19, 21.63it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 20.86it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:24, 17.74it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:23, 18.27it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:21, 19.62it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:20, 19.88it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:21, 19.68it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 17.99it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:21, 18.55it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:22, 18.15it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:21, 18.61it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:22, 17.53it/s]
eval model::  20%|██        | 101/500 [00:05<00:24, 16.15it/s]
eval model::  21%|██        | 104/500 [00:05<00:21, 18.81it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:19, 20.38it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:19, 20.50it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 21.33it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:18, 20.78it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:17, 21.23it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:19, 19.64it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:19, 19.19it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 20.11it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 19.15it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:18, 20.21it/s]
eval model::  27%|██▋       | 137/500 [00:06<00:18, 19.38it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:20, 18.04it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.38it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.84it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:16, 21.30it/s]
eval model::  30%|███       | 151/500 [00:07<00:17, 20.26it/s]
eval model::  31%|███       | 154/500 [00:07<00:15, 21.88it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:15, 21.49it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:16, 20.04it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:17, 19.67it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.54it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 19.77it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 19.10it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:18, 17.75it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:16, 19.21it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:16, 19.82it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:14, 21.74it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.50it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:16, 19.03it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:15, 19.47it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:15, 19.28it/s]
eval model::  39%|███▉      | 197/500 [00:09<00:15, 19.22it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:15, 19.32it/s]
eval model::  40%|████      | 201/500 [00:10<00:16, 18.38it/s]
eval model::  41%|████      | 204/500 [00:10<00:14, 19.89it/s]
eval model::  41%|████      | 206/500 [00:10<00:21, 13.59it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:18, 15.54it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:17, 16.30it/s]
eval model::  43%|████▎     | 214/500 [00:11<00:15, 18.39it/s]
eval model::  43%|████▎     | 216/500 [00:11<00:17, 16.54it/s]
eval model::  44%|████▎     | 218/500 [00:11<00:18, 15.53it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:16, 17.19it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:15, 18.30it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:16, 16.96it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:13, 19.59it/s]
eval model::  46%|████▋     | 232/500 [00:11<00:12, 20.84it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:13, 19.31it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:13, 20.01it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 22.21it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:12, 20.53it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:11, 21.79it/s]
eval model::  50%|█████     | 250/500 [00:12<00:13, 19.21it/s]
eval model::  51%|█████     | 254/500 [00:13<00:10, 22.88it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.94it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:11, 21.37it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 21.60it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:11, 20.19it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:11, 20.48it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:11, 19.02it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:12, 18.60it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 18.54it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 19.23it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 20.65it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:12, 16.97it/s]
eval model::  58%|█████▊    | 288/500 [00:14<00:11, 18.12it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:10, 19.59it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:10, 18.88it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:11, 18.23it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:09, 20.50it/s]
eval model::  60%|██████    | 302/500 [00:15<00:10, 19.39it/s]
eval model::  61%|██████    | 305/500 [00:15<00:10, 19.07it/s]
eval model::  62%|██████▏   | 308/500 [00:15<00:09, 20.80it/s]
eval model::  62%|██████▏   | 311/500 [00:15<00:09, 20.65it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:09, 19.78it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:09, 19.77it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:09, 18.69it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:09, 17.87it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:08, 20.28it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:08, 19.57it/s]
eval model::  66%|██████▌   | 331/500 [00:16<00:07, 21.49it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 21.63it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:07, 20.90it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 17.36it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:08, 19.22it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 21.17it/s]
eval model::  70%|███████   | 350/500 [00:17<00:06, 23.20it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 22.18it/s]
eval model::  71%|███████   | 356/500 [00:18<00:07, 19.06it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:07, 19.86it/s]
eval model::  72%|███████▏  | 362/500 [00:18<00:06, 20.00it/s]
eval model::  73%|███████▎  | 365/500 [00:18<00:07, 18.48it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:07, 18.40it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:06, 20.30it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:05, 22.15it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 21.79it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 22.23it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 21.87it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 21.31it/s]
eval model::  78%|███████▊  | 388/500 [00:19<00:07, 15.22it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:06, 16.79it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:05, 19.22it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:04, 22.46it/s]
eval model::  80%|████████  | 401/500 [00:20<00:04, 22.66it/s]
eval model::  81%|████████  | 404/500 [00:20<00:04, 23.50it/s]
eval model::  81%|████████▏ | 407/500 [00:20<00:04, 22.60it/s]
eval model::  82%|████████▏ | 410/500 [00:20<00:04, 21.65it/s]
eval model::  83%|████████▎ | 413/500 [00:20<00:04, 19.99it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:04, 18.55it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 17.88it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 16.50it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 16.38it/s]
eval model::  85%|████████▌ | 425/500 [00:21<00:04, 17.73it/s]
eval model::  86%|████████▌ | 428/500 [00:21<00:03, 20.42it/s]
eval model::  86%|████████▌ | 431/500 [00:21<00:03, 22.50it/s]
eval model::  87%|████████▋ | 434/500 [00:22<00:02, 23.43it/s]
eval model::  87%|████████▋ | 437/500 [00:22<00:02, 22.08it/s]
eval model::  88%|████████▊ | 440/500 [00:22<00:03, 18.78it/s]
eval model::  89%|████████▊ | 443/500 [00:22<00:02, 21.01it/s]
eval model::  89%|████████▉ | 446/500 [00:22<00:02, 21.58it/s]
eval model::  90%|████████▉ | 449/500 [00:22<00:02, 21.16it/s]
eval model::  90%|█████████ | 452/500 [00:22<00:02, 21.67it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:02, 21.79it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:01, 22.98it/s]
eval model::  92%|█████████▏| 461/500 [00:23<00:01, 21.39it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 19.05it/s]
eval model::  93%|█████████▎| 466/500 [00:23<00:01, 18.23it/s]
eval model::  94%|█████████▍| 469/500 [00:23<00:01, 19.82it/s]
eval model::  94%|█████████▍| 472/500 [00:23<00:01, 20.55it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 21.76it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 20.43it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:01, 18.13it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 18.73it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 18.23it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 20.79it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 21.34it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 22.47it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 21.47it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.77it/s]
[2024/08/01 17:56:12] ppocr INFO: cur metric, precision: 0.6271604938271605, recall: 0.36687530091478093, hmean: 0.4629404617253949, fps: 44.60411811862789
[2024/08/01 17:56:12] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 17:56:12] ppocr INFO: best metric, hmean: 0.4629404617253949, precision: 0.6271604938271605, recall: 0.36687530091478093, fps: 44.60411811862789, best_epoch: 60
[2024/08/01 17:56:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:56:19] ppocr INFO: epoch: [61/100], global_step: 183, lr: 0.001000, loss: 2.792534, loss_shrink_maps: 1.755670, loss_threshold_maps: 0.753185, loss_binary_maps: 0.283688, avg_reader_cost: 1.55247 s, avg_batch_cost: 1.77067 s, avg_samples: 12.5, ips: 7.05946 samples/s, eta: 0:15:08
[2024/08/01 17:56:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:56:27] ppocr INFO: epoch: [62/100], global_step: 186, lr: 0.001000, loss: 2.752594, loss_shrink_maps: 1.720897, loss_threshold_maps: 0.752956, loss_binary_maps: 0.279250, avg_reader_cost: 1.69251 s, avg_batch_cost: 1.91173 s, avg_samples: 12.5, ips: 6.53857 samples/s, eta: 0:14:42
[2024/08/01 17:56:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:56:34] ppocr INFO: epoch: [63/100], global_step: 189, lr: 0.001000, loss: 2.676908, loss_shrink_maps: 1.647927, loss_threshold_maps: 0.752956, loss_binary_maps: 0.273353, avg_reader_cost: 1.56534 s, avg_batch_cost: 1.79999 s, avg_samples: 12.5, ips: 6.94447 samples/s, eta: 0:14:16
[2024/08/01 17:56:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:56:40] ppocr INFO: epoch: [64/100], global_step: 190, lr: 0.001000, loss: 2.625532, loss_shrink_maps: 1.618552, loss_threshold_maps: 0.750702, loss_binary_maps: 0.269597, avg_reader_cost: 0.43119 s, avg_batch_cost: 0.50998 s, avg_samples: 4.8, ips: 9.41213 samples/s, eta: 0:14:06
[2024/08/01 17:56:41] ppocr INFO: epoch: [64/100], global_step: 192, lr: 0.001000, loss: 2.606832, loss_shrink_maps: 1.599669, loss_threshold_maps: 0.750702, loss_binary_maps: 0.269597, avg_reader_cost: 1.10812 s, avg_batch_cost: 1.24797 s, avg_samples: 7.7, ips: 6.17001 samples/s, eta: 0:13:49
[2024/08/01 17:56:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:56:49] ppocr INFO: epoch: [65/100], global_step: 195, lr: 0.001000, loss: 2.606832, loss_shrink_maps: 1.599669, loss_threshold_maps: 0.758246, loss_binary_maps: 0.269597, avg_reader_cost: 1.60982 s, avg_batch_cost: 1.82860 s, avg_samples: 12.5, ips: 6.83584 samples/s, eta: 0:13:24
[2024/08/01 17:56:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:56:56] ppocr INFO: epoch: [66/100], global_step: 198, lr: 0.001000, loss: 2.606832, loss_shrink_maps: 1.599669, loss_threshold_maps: 0.758246, loss_binary_maps: 0.269597, avg_reader_cost: 1.51711 s, avg_batch_cost: 1.76934 s, avg_samples: 12.5, ips: 7.06479 samples/s, eta: 0:12:58
[2024/08/01 17:56:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:57:03] ppocr INFO: epoch: [67/100], global_step: 200, lr: 0.001000, loss: 2.615722, loss_shrink_maps: 1.607543, loss_threshold_maps: 0.755183, loss_binary_maps: 0.276641, avg_reader_cost: 1.01705 s, avg_batch_cost: 1.20149 s, avg_samples: 9.6, ips: 7.99011 samples/s, eta: 0:12:41
[2024/08/01 17:57:04] ppocr INFO: epoch: [67/100], global_step: 201, lr: 0.001000, loss: 2.606832, loss_shrink_maps: 1.581619, loss_threshold_maps: 0.755183, loss_binary_maps: 0.276641, avg_reader_cost: 0.64468 s, avg_batch_cost: 0.69674 s, avg_samples: 2.9, ips: 4.16221 samples/s, eta: 0:12:33
[2024/08/01 17:57:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:57:11] ppocr INFO: epoch: [68/100], global_step: 204, lr: 0.001000, loss: 2.615722, loss_shrink_maps: 1.595874, loss_threshold_maps: 0.759664, loss_binary_maps: 0.285357, avg_reader_cost: 1.52103 s, avg_batch_cost: 1.74601 s, avg_samples: 12.5, ips: 7.15918 samples/s, eta: 0:12:08
[2024/08/01 17:57:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:57:19] ppocr INFO: epoch: [69/100], global_step: 207, lr: 0.001000, loss: 2.581047, loss_shrink_maps: 1.540290, loss_threshold_maps: 0.763395, loss_binary_maps: 0.276206, avg_reader_cost: 1.63766 s, avg_batch_cost: 1.89827 s, avg_samples: 12.5, ips: 6.58493 samples/s, eta: 0:11:43
[2024/08/01 17:57:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:57:26] ppocr INFO: epoch: [70/100], global_step: 210, lr: 0.001000, loss: 2.670115, loss_shrink_maps: 1.616660, loss_threshold_maps: 0.778639, loss_binary_maps: 0.286601, avg_reader_cost: 1.56350 s, avg_batch_cost: 1.78212 s, avg_samples: 12.5, ips: 7.01413 samples/s, eta: 0:11:19
[2024/08/01 17:57:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:57:33] ppocr INFO: epoch: [71/100], global_step: 213, lr: 0.001000, loss: 2.642823, loss_shrink_maps: 1.604991, loss_threshold_maps: 0.769228, loss_binary_maps: 0.283805, avg_reader_cost: 1.49166 s, avg_batch_cost: 1.73735 s, avg_samples: 12.5, ips: 7.19487 samples/s, eta: 0:10:54
[2024/08/01 17:57:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:57:40] ppocr INFO: epoch: [72/100], global_step: 216, lr: 0.001000, loss: 2.628230, loss_shrink_maps: 1.580254, loss_threshold_maps: 0.769228, loss_binary_maps: 0.283672, avg_reader_cost: 1.50262 s, avg_batch_cost: 1.74138 s, avg_samples: 12.5, ips: 7.17821 samples/s, eta: 0:10:29
[2024/08/01 17:57:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:57:48] ppocr INFO: epoch: [73/100], global_step: 219, lr: 0.001000, loss: 2.556266, loss_shrink_maps: 1.539362, loss_threshold_maps: 0.768479, loss_binary_maps: 0.277668, avg_reader_cost: 1.63636 s, avg_batch_cost: 1.85537 s, avg_samples: 12.5, ips: 6.73720 samples/s, eta: 0:10:05
[2024/08/01 17:57:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:57:54] ppocr INFO: epoch: [74/100], global_step: 220, lr: 0.001000, loss: 2.555165, loss_shrink_maps: 1.533696, loss_threshold_maps: 0.768479, loss_binary_maps: 0.277464, avg_reader_cost: 0.44255 s, avg_batch_cost: 0.52207 s, avg_samples: 4.8, ips: 9.19411 samples/s, eta: 0:09:57
[2024/08/01 17:57:56] ppocr INFO: epoch: [74/100], global_step: 222, lr: 0.001000, loss: 2.538739, loss_shrink_maps: 1.491951, loss_threshold_maps: 0.765522, loss_binary_maps: 0.268607, avg_reader_cost: 1.13246 s, avg_batch_cost: 1.27239 s, avg_samples: 7.7, ips: 6.05160 samples/s, eta: 0:09:41
[2024/08/01 17:57:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:58:03] ppocr INFO: epoch: [75/100], global_step: 225, lr: 0.001000, loss: 2.467374, loss_shrink_maps: 1.444626, loss_threshold_maps: 0.760291, loss_binary_maps: 0.264442, avg_reader_cost: 1.50995 s, avg_batch_cost: 1.74114 s, avg_samples: 12.5, ips: 7.17919 samples/s, eta: 0:09:17
[2024/08/01 17:58:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:58:10] ppocr INFO: epoch: [76/100], global_step: 228, lr: 0.001000, loss: 2.426688, loss_shrink_maps: 1.416160, loss_threshold_maps: 0.748964, loss_binary_maps: 0.260958, avg_reader_cost: 1.53072 s, avg_batch_cost: 1.75967 s, avg_samples: 12.5, ips: 7.10362 samples/s, eta: 0:08:53
[2024/08/01 17:58:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:58:17] ppocr INFO: epoch: [77/100], global_step: 230, lr: 0.001000, loss: 2.426688, loss_shrink_maps: 1.416160, loss_threshold_maps: 0.743827, loss_binary_maps: 0.260958, avg_reader_cost: 0.93616 s, avg_batch_cost: 1.12900 s, avg_samples: 9.6, ips: 8.50306 samples/s, eta: 0:08:37
[2024/08/01 17:58:17] ppocr INFO: epoch: [77/100], global_step: 231, lr: 0.001000, loss: 2.451796, loss_shrink_maps: 1.430924, loss_threshold_maps: 0.743827, loss_binary_maps: 0.263259, avg_reader_cost: 0.60888 s, avg_batch_cost: 0.66096 s, avg_samples: 2.9, ips: 4.38755 samples/s, eta: 0:08:30
[2024/08/01 17:58:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:58:25] ppocr INFO: epoch: [78/100], global_step: 234, lr: 0.001000, loss: 2.446901, loss_shrink_maps: 1.428773, loss_threshold_maps: 0.740955, loss_binary_maps: 0.254834, avg_reader_cost: 1.57521 s, avg_batch_cost: 1.86379 s, avg_samples: 12.5, ips: 6.70675 samples/s, eta: 0:08:07
[2024/08/01 17:58:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:58:32] ppocr INFO: epoch: [79/100], global_step: 237, lr: 0.001000, loss: 2.446901, loss_shrink_maps: 1.428773, loss_threshold_maps: 0.740913, loss_binary_maps: 0.254305, avg_reader_cost: 1.52857 s, avg_batch_cost: 1.74842 s, avg_samples: 12.5, ips: 7.14932 samples/s, eta: 0:07:43
[2024/08/01 17:58:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:58:40] ppocr INFO: epoch: [80/100], global_step: 240, lr: 0.001000, loss: 2.446901, loss_shrink_maps: 1.436827, loss_threshold_maps: 0.740913, loss_binary_maps: 0.254305, avg_reader_cost: 1.50979 s, avg_batch_cost: 1.72927 s, avg_samples: 12.5, ips: 7.22848 samples/s, eta: 0:07:20

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:57,  8.61it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.00it/s]
eval model::   1%|          | 6/500 [00:00<00:30, 15.98it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.18it/s]
eval model::   2%|▏         | 11/500 [00:00<00:25, 19.44it/s]
eval model::   3%|▎         | 13/500 [00:00<00:25, 19.39it/s]
eval model::   3%|▎         | 16/500 [00:00<00:23, 20.50it/s]
eval model::   4%|▍         | 19/500 [00:00<00:23, 20.78it/s]
eval model::   4%|▍         | 22/500 [00:01<00:21, 22.14it/s]
eval model::   5%|▌         | 25/500 [00:01<00:23, 20.62it/s]
eval model::   6%|▌         | 28/500 [00:01<00:22, 20.99it/s]
eval model::   6%|▌         | 31/500 [00:01<00:22, 20.60it/s]
eval model::   7%|▋         | 34/500 [00:01<00:22, 20.82it/s]
eval model::   7%|▋         | 37/500 [00:01<00:20, 22.22it/s]
eval model::   8%|▊         | 40/500 [00:01<00:22, 20.62it/s]
eval model::   9%|▊         | 43/500 [00:02<00:21, 21.31it/s]
eval model::   9%|▉         | 46/500 [00:02<00:22, 20.13it/s]
eval model::  10%|▉         | 49/500 [00:02<00:20, 22.04it/s]
eval model::  10%|█         | 52/500 [00:02<00:20, 21.80it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.90it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:20, 21.65it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:23, 18.33it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:21, 19.92it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:20, 21.21it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 21.70it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 21.15it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.61it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:20, 20.95it/s]
eval model::  16%|█▋        | 82/500 [00:03<00:20, 20.42it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:19, 20.81it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:19, 20.74it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:20, 20.32it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:20, 20.26it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:20, 20.03it/s]
eval model::  20%|██        | 100/500 [00:04<00:20, 19.09it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 21.20it/s]
eval model::  21%|██        | 106/500 [00:05<00:20, 19.66it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:19, 19.99it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:19, 19.54it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:19, 19.57it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:19, 19.37it/s]
eval model::  24%|██▍       | 119/500 [00:05<00:19, 19.98it/s]
eval model::  24%|██▍       | 122/500 [00:05<00:18, 20.68it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:19, 19.67it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:17, 20.89it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 18.88it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:17, 20.48it/s]
eval model::  27%|██▋       | 137/500 [00:06<00:17, 20.73it/s]
eval model::  28%|██▊       | 140/500 [00:06<00:17, 21.02it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:16, 21.19it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 20.73it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:17, 20.27it/s]
eval model::  30%|███       | 152/500 [00:07<00:16, 20.65it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 21.16it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:15, 21.75it/s]
eval model::  32%|███▏      | 161/500 [00:07<00:16, 20.73it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:16, 20.79it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:16, 20.30it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:15, 21.87it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:15, 21.65it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:15, 20.41it/s]
eval model::  36%|███▌      | 179/500 [00:08<00:15, 20.35it/s]
eval model::  36%|███▋      | 182/500 [00:08<00:16, 18.82it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:14, 21.08it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:14, 21.80it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:14, 21.15it/s]
eval model::  39%|███▉      | 194/500 [00:09<00:13, 22.65it/s]
eval model::  39%|███▉      | 197/500 [00:09<00:14, 20.90it/s]
eval model::  40%|████      | 200/500 [00:09<00:14, 21.25it/s]
eval model::  41%|████      | 203/500 [00:09<00:14, 20.67it/s]
eval model::  41%|████      | 206/500 [00:10<00:20, 14.50it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:18, 15.66it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:18, 15.72it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:17, 16.44it/s]
eval model::  43%|████▎     | 215/500 [00:10<00:18, 15.53it/s]
eval model::  43%|████▎     | 217/500 [00:10<00:18, 15.50it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 17.72it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:15, 17.44it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:13, 20.74it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:11, 24.53it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:10, 25.30it/s]
eval model::  47%|████▋     | 236/500 [00:11<00:10, 24.38it/s]
eval model::  48%|████▊     | 239/500 [00:11<00:11, 22.36it/s]
eval model::  48%|████▊     | 242/500 [00:11<00:11, 21.81it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:11, 22.06it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:12, 20.87it/s]
eval model::  50%|█████     | 251/500 [00:12<00:13, 19.12it/s]
eval model::  51%|█████     | 254/500 [00:12<00:12, 20.18it/s]
eval model::  51%|█████▏    | 257/500 [00:12<00:11, 21.50it/s]
eval model::  52%|█████▏    | 260/500 [00:12<00:11, 21.23it/s]
eval model::  53%|█████▎    | 263/500 [00:12<00:11, 20.53it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:12, 19.44it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:12, 19.25it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:11, 19.37it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:12, 18.70it/s]
eval model::  55%|█████▌    | 275/500 [00:13<00:12, 18.47it/s]
eval model::  56%|█████▌    | 278/500 [00:13<00:10, 20.73it/s]
eval model::  56%|█████▌    | 281/500 [00:13<00:10, 21.46it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:09, 21.98it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:12, 17.61it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:09, 21.30it/s]
eval model::  59%|█████▉    | 294/500 [00:14<00:10, 20.07it/s]
eval model::  59%|█████▉    | 297/500 [00:14<00:10, 19.51it/s]
eval model::  60%|██████    | 300/500 [00:14<00:09, 20.42it/s]
eval model::  61%|██████    | 303/500 [00:14<00:09, 21.39it/s]
eval model::  61%|██████    | 306/500 [00:15<00:09, 21.03it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:08, 22.32it/s]
eval model::  62%|██████▏   | 312/500 [00:15<00:08, 22.00it/s]
eval model::  63%|██████▎   | 315/500 [00:15<00:09, 20.49it/s]
eval model::  64%|██████▎   | 318/500 [00:15<00:08, 22.52it/s]
eval model::  64%|██████▍   | 321/500 [00:15<00:08, 20.73it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:09, 19.21it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:08, 20.48it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:08, 20.09it/s]
eval model::  67%|██████▋   | 333/500 [00:16<00:08, 20.40it/s]
eval model::  67%|██████▋   | 336/500 [00:16<00:08, 20.47it/s]
eval model::  68%|██████▊   | 339/500 [00:16<00:07, 20.57it/s]
eval model::  68%|██████▊   | 342/500 [00:16<00:09, 17.30it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:08, 19.07it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:07, 20.45it/s]
eval model::  70%|███████   | 351/500 [00:17<00:07, 20.77it/s]
eval model::  71%|███████   | 354/500 [00:17<00:06, 21.14it/s]
eval model::  71%|███████▏  | 357/500 [00:17<00:06, 20.86it/s]
eval model::  72%|███████▏  | 360/500 [00:17<00:06, 20.21it/s]
eval model::  73%|███████▎  | 363/500 [00:17<00:06, 20.60it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:06, 19.25it/s]
eval model::  74%|███████▍  | 369/500 [00:18<00:06, 20.68it/s]
eval model::  74%|███████▍  | 372/500 [00:18<00:06, 20.79it/s]
eval model::  75%|███████▌  | 375/500 [00:18<00:05, 20.94it/s]
eval model::  76%|███████▌  | 378/500 [00:18<00:05, 20.62it/s]
eval model::  76%|███████▌  | 381/500 [00:18<00:05, 20.17it/s]
eval model::  77%|███████▋  | 384/500 [00:18<00:05, 20.98it/s]
eval model::  77%|███████▋  | 387/500 [00:19<00:06, 17.22it/s]
eval model::  78%|███████▊  | 390/500 [00:19<00:05, 18.58it/s]
eval model::  79%|███████▊  | 393/500 [00:19<00:05, 19.96it/s]
eval model::  79%|███████▉  | 397/500 [00:19<00:04, 23.18it/s]
eval model::  80%|████████  | 400/500 [00:19<00:04, 21.36it/s]
eval model::  81%|████████  | 403/500 [00:19<00:04, 22.72it/s]
eval model::  81%|████████  | 406/500 [00:19<00:04, 21.91it/s]
eval model::  82%|████████▏ | 409/500 [00:20<00:04, 21.59it/s]
eval model::  82%|████████▏ | 412/500 [00:20<00:04, 21.01it/s]
eval model::  83%|████████▎ | 415/500 [00:20<00:04, 20.71it/s]
eval model::  84%|████████▎ | 418/500 [00:20<00:04, 19.63it/s]
eval model::  84%|████████▍ | 420/500 [00:20<00:04, 18.23it/s]
eval model::  85%|████████▍ | 423/500 [00:20<00:04, 19.14it/s]
eval model::  85%|████████▌ | 425/500 [00:21<00:03, 18.83it/s]
eval model::  86%|████████▌ | 429/500 [00:21<00:03, 22.48it/s]
eval model::  86%|████████▋ | 432/500 [00:21<00:03, 22.59it/s]
eval model::  87%|████████▋ | 435/500 [00:21<00:03, 21.31it/s]
eval model::  88%|████████▊ | 438/500 [00:21<00:02, 21.30it/s]
eval model::  88%|████████▊ | 441/500 [00:21<00:03, 19.42it/s]
eval model::  89%|████████▉ | 445/500 [00:21<00:02, 21.10it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 21.77it/s]
eval model::  90%|█████████ | 451/500 [00:22<00:02, 21.14it/s]
eval model::  91%|█████████ | 454/500 [00:22<00:02, 20.43it/s]
eval model::  91%|█████████▏| 457/500 [00:22<00:02, 20.93it/s]
eval model::  92%|█████████▏| 460/500 [00:22<00:01, 20.30it/s]
eval model::  93%|█████████▎| 463/500 [00:22<00:01, 18.82it/s]
eval model::  93%|█████████▎| 465/500 [00:22<00:01, 17.76it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 20.12it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 19.64it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 20.11it/s]
eval model::  95%|█████████▌| 477/500 [00:23<00:01, 20.81it/s]
eval model::  96%|█████████▌| 480/500 [00:23<00:00, 20.27it/s]
eval model::  97%|█████████▋| 483/500 [00:23<00:00, 21.84it/s]
eval model::  97%|█████████▋| 486/500 [00:23<00:00, 19.90it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 21.01it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 20.70it/s]
eval model::  99%|█████████▉| 495/500 [00:24<00:00, 21.62it/s]
eval model:: 100%|█████████▉| 498/500 [00:24<00:00, 22.63it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 20.34it/s]
[2024/08/01 17:59:04] ppocr INFO: cur metric, precision: 0.674928503336511, recall: 0.3408762638420799, hmean: 0.45297504798464483, fps: 45.82421840900974
[2024/08/01 17:59:04] ppocr INFO: best metric, hmean: 0.4629404617253949, precision: 0.6271604938271605, recall: 0.36687530091478093, fps: 44.60411811862789, best_epoch: 60
[2024/08/01 17:59:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:59:12] ppocr INFO: epoch: [81/100], global_step: 243, lr: 0.001000, loss: 2.483587, loss_shrink_maps: 1.478066, loss_threshold_maps: 0.747631, loss_binary_maps: 0.258378, avg_reader_cost: 1.66642 s, avg_batch_cost: 1.93322 s, avg_samples: 12.5, ips: 6.46590 samples/s, eta: 0:06:57
[2024/08/01 17:59:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:59:19] ppocr INFO: epoch: [82/100], global_step: 246, lr: 0.001000, loss: 2.456248, loss_shrink_maps: 1.465448, loss_threshold_maps: 0.738726, loss_binary_maps: 0.253970, avg_reader_cost: 1.50433 s, avg_batch_cost: 1.74321 s, avg_samples: 12.5, ips: 7.17068 samples/s, eta: 0:06:34
[2024/08/01 17:59:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:59:26] ppocr INFO: epoch: [83/100], global_step: 249, lr: 0.001000, loss: 2.456248, loss_shrink_maps: 1.465448, loss_threshold_maps: 0.735728, loss_binary_maps: 0.254207, avg_reader_cost: 1.59250 s, avg_batch_cost: 1.81266 s, avg_samples: 12.5, ips: 6.89596 samples/s, eta: 0:06:12
[2024/08/01 17:59:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:59:33] ppocr INFO: epoch: [84/100], global_step: 250, lr: 0.001000, loss: 2.456248, loss_shrink_maps: 1.463114, loss_threshold_maps: 0.740869, loss_binary_maps: 0.254207, avg_reader_cost: 0.45611 s, avg_batch_cost: 0.53550 s, avg_samples: 4.8, ips: 8.96357 samples/s, eta: 0:06:04
[2024/08/01 17:59:34] ppocr INFO: epoch: [84/100], global_step: 252, lr: 0.001000, loss: 2.427996, loss_shrink_maps: 1.437432, loss_threshold_maps: 0.740869, loss_binary_maps: 0.254207, avg_reader_cost: 1.15823 s, avg_batch_cost: 1.29735 s, avg_samples: 7.7, ips: 5.93518 samples/s, eta: 0:05:49
[2024/08/01 17:59:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:59:42] ppocr INFO: epoch: [85/100], global_step: 255, lr: 0.001000, loss: 2.395367, loss_shrink_maps: 1.404630, loss_threshold_maps: 0.746343, loss_binary_maps: 0.253366, avg_reader_cost: 1.59973 s, avg_batch_cost: 1.88607 s, avg_samples: 12.5, ips: 6.62753 samples/s, eta: 0:05:27
[2024/08/01 17:59:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:59:49] ppocr INFO: epoch: [86/100], global_step: 258, lr: 0.001000, loss: 2.379710, loss_shrink_maps: 1.403372, loss_threshold_maps: 0.740654, loss_binary_maps: 0.253366, avg_reader_cost: 1.57976 s, avg_batch_cost: 1.82883 s, avg_samples: 12.5, ips: 6.83498 samples/s, eta: 0:05:04
[2024/08/01 17:59:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 17:59:56] ppocr INFO: epoch: [87/100], global_step: 260, lr: 0.001000, loss: 2.361672, loss_shrink_maps: 1.394174, loss_threshold_maps: 0.733426, loss_binary_maps: 0.251277, avg_reader_cost: 0.95708 s, avg_batch_cost: 1.14947 s, avg_samples: 9.6, ips: 8.35166 samples/s, eta: 0:04:49
[2024/08/01 17:59:57] ppocr INFO: epoch: [87/100], global_step: 261, lr: 0.001000, loss: 2.379710, loss_shrink_maps: 1.403372, loss_threshold_maps: 0.740654, loss_binary_maps: 0.253366, avg_reader_cost: 0.61861 s, avg_batch_cost: 0.67061 s, avg_samples: 2.9, ips: 4.32439 samples/s, eta: 0:04:42
[2024/08/01 17:59:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:00:04] ppocr INFO: epoch: [88/100], global_step: 264, lr: 0.001000, loss: 2.389462, loss_shrink_maps: 1.403372, loss_threshold_maps: 0.745238, loss_binary_maps: 0.253366, avg_reader_cost: 1.54106 s, avg_batch_cost: 1.76317 s, avg_samples: 12.5, ips: 7.08952 samples/s, eta: 0:04:20
[2024/08/01 18:00:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:00:11] ppocr INFO: epoch: [89/100], global_step: 267, lr: 0.001000, loss: 2.396676, loss_shrink_maps: 1.403372, loss_threshold_maps: 0.745238, loss_binary_maps: 0.258030, avg_reader_cost: 1.53821 s, avg_batch_cost: 1.78828 s, avg_samples: 12.5, ips: 6.98997 samples/s, eta: 0:03:58
[2024/08/01 18:00:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:00:19] ppocr INFO: epoch: [90/100], global_step: 270, lr: 0.001000, loss: 2.361672, loss_shrink_maps: 1.361840, loss_threshold_maps: 0.745238, loss_binary_maps: 0.251005, avg_reader_cost: 1.55324 s, avg_batch_cost: 1.77347 s, avg_samples: 12.5, ips: 7.04833 samples/s, eta: 0:03:35
[2024/08/01 18:00:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:00:27] ppocr INFO: epoch: [91/100], global_step: 273, lr: 0.001000, loss: 2.337704, loss_shrink_maps: 1.345922, loss_threshold_maps: 0.741381, loss_binary_maps: 0.250517, avg_reader_cost: 1.65841 s, avg_batch_cost: 1.89050 s, avg_samples: 12.5, ips: 6.61201 samples/s, eta: 0:03:14
[2024/08/01 18:00:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:00:34] ppocr INFO: epoch: [92/100], global_step: 276, lr: 0.001000, loss: 2.272242, loss_shrink_maps: 1.312488, loss_threshold_maps: 0.715765, loss_binary_maps: 0.246946, avg_reader_cost: 1.54275 s, avg_batch_cost: 1.78528 s, avg_samples: 12.5, ips: 7.00171 samples/s, eta: 0:02:52
[2024/08/01 18:00:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:00:41] ppocr INFO: epoch: [93/100], global_step: 279, lr: 0.001000, loss: 2.280554, loss_shrink_maps: 1.312488, loss_threshold_maps: 0.730981, loss_binary_maps: 0.250180, avg_reader_cost: 1.58474 s, avg_batch_cost: 1.80534 s, avg_samples: 12.5, ips: 6.92392 samples/s, eta: 0:02:30
[2024/08/01 18:00:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:00:47] ppocr INFO: epoch: [94/100], global_step: 280, lr: 0.001000, loss: 2.280554, loss_shrink_maps: 1.312488, loss_threshold_maps: 0.730981, loss_binary_maps: 0.250180, avg_reader_cost: 0.44562 s, avg_batch_cost: 0.52424 s, avg_samples: 4.8, ips: 9.15604 samples/s, eta: 0:02:23
[2024/08/01 18:00:49] ppocr INFO: epoch: [94/100], global_step: 282, lr: 0.001000, loss: 2.257910, loss_shrink_maps: 1.311358, loss_threshold_maps: 0.721059, loss_binary_maps: 0.248900, avg_reader_cost: 1.13688 s, avg_batch_cost: 1.27704 s, avg_samples: 7.7, ips: 6.02959 samples/s, eta: 0:02:08
[2024/08/01 18:00:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:00:56] ppocr INFO: epoch: [95/100], global_step: 285, lr: 0.001000, loss: 2.251881, loss_shrink_maps: 1.305762, loss_threshold_maps: 0.710019, loss_binary_maps: 0.245806, avg_reader_cost: 1.53885 s, avg_batch_cost: 1.76425 s, avg_samples: 12.5, ips: 7.08514 samples/s, eta: 0:01:47
[2024/08/01 18:00:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:01:03] ppocr INFO: epoch: [96/100], global_step: 288, lr: 0.001000, loss: 2.250419, loss_shrink_maps: 1.305937, loss_threshold_maps: 0.701561, loss_binary_maps: 0.245806, avg_reader_cost: 1.54637 s, avg_batch_cost: 1.76706 s, avg_samples: 12.5, ips: 7.07388 samples/s, eta: 0:01:25
[2024/08/01 18:01:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:01:10] ppocr INFO: epoch: [97/100], global_step: 290, lr: 0.001000, loss: 2.239207, loss_shrink_maps: 1.288913, loss_threshold_maps: 0.701561, loss_binary_maps: 0.243384, avg_reader_cost: 0.94299 s, avg_batch_cost: 1.12499 s, avg_samples: 9.6, ips: 8.53340 samples/s, eta: 0:01:11
[2024/08/01 18:01:11] ppocr INFO: epoch: [97/100], global_step: 291, lr: 0.001000, loss: 2.250419, loss_shrink_maps: 1.305937, loss_threshold_maps: 0.710396, loss_binary_maps: 0.248279, avg_reader_cost: 0.60653 s, avg_batch_cost: 0.65912 s, avg_samples: 2.9, ips: 4.39980 samples/s, eta: 0:01:04
[2024/08/01 18:01:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:01:18] ppocr INFO: epoch: [98/100], global_step: 294, lr: 0.001000, loss: 2.239207, loss_shrink_maps: 1.288913, loss_threshold_maps: 0.716359, loss_binary_maps: 0.243384, avg_reader_cost: 1.50841 s, avg_batch_cost: 1.73455 s, avg_samples: 12.5, ips: 7.20649 samples/s, eta: 0:00:42
[2024/08/01 18:01:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:01:26] ppocr INFO: epoch: [99/100], global_step: 297, lr: 0.001000, loss: 2.254140, loss_shrink_maps: 1.308305, loss_threshold_maps: 0.719154, loss_binary_maps: 0.248279, avg_reader_cost: 1.55645 s, avg_batch_cost: 1.80226 s, avg_samples: 12.5, ips: 6.93573 samples/s, eta: 0:00:21
[2024/08/01 18:01:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:01:33] ppocr INFO: epoch: [100/100], global_step: 300, lr: 0.001000, loss: 2.203037, loss_shrink_maps: 1.251604, loss_threshold_maps: 0.707306, loss_binary_maps: 0.238437, avg_reader_cost: 1.52444 s, avg_batch_cost: 1.76492 s, avg_samples: 12.5, ips: 7.08246 samples/s, eta: 0:00:00

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:50,  9.92it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.78it/s]
eval model::   1%|          | 6/500 [00:00<00:25, 19.02it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.43it/s]
eval model::   2%|▏         | 11/500 [00:00<00:27, 17.75it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 19.26it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.19it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 18.76it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 20.98it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 20.18it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.38it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 19.88it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 19.05it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.51it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.43it/s]
eval model::   8%|▊         | 42/500 [00:02<00:24, 18.48it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.53it/s]
eval model::  10%|▉         | 48/500 [00:02<00:22, 20.37it/s]
eval model::  10%|█         | 51/500 [00:02<00:20, 21.58it/s]
eval model::  11%|█         | 54/500 [00:02<00:22, 20.23it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:20, 21.26it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:21, 20.73it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:22, 19.29it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:21, 20.17it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.82it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 20.04it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:21, 20.12it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:20, 20.74it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:20, 20.18it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:21, 18.91it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:21, 19.33it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:21, 18.78it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:21, 18.76it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:21, 18.59it/s]
eval model::  20%|█▉        | 98/500 [00:04<00:20, 19.41it/s]
eval model::  20%|██        | 100/500 [00:05<00:20, 19.45it/s]
eval model::  21%|██        | 103/500 [00:05<00:18, 21.64it/s]
eval model::  21%|██        | 106/500 [00:05<00:18, 21.78it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 21.07it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:19, 20.25it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:18, 20.97it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:17, 21.71it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:18, 20.46it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:18, 20.33it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:17, 21.58it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:19, 19.39it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:16, 21.65it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:17, 20.72it/s]
eval model::  28%|██▊       | 139/500 [00:06<00:18, 19.88it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:17, 20.29it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.72it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 19.02it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 18.31it/s]
eval model::  31%|███       | 154/500 [00:07<00:16, 20.56it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:15, 21.70it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:17, 19.73it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:16, 20.12it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:15, 21.25it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 20.66it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:16, 20.33it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:15, 20.64it/s]
eval model::  36%|███▌      | 178/500 [00:08<00:15, 21.45it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:17, 17.91it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:16, 19.60it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 19.03it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:15, 19.38it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:15, 19.88it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:15, 19.26it/s]
eval model::  40%|███▉      | 199/500 [00:09<00:14, 21.01it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 22.79it/s]
eval model::  41%|████      | 205/500 [00:10<00:16, 17.84it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:15, 18.54it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:15, 18.76it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:15, 18.40it/s]
eval model::  43%|████▎     | 215/500 [00:10<00:15, 18.12it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:17, 15.94it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 18.03it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:16, 16.67it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:13, 19.67it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:13, 20.44it/s]
eval model::  46%|████▋     | 232/500 [00:11<00:11, 22.70it/s]
eval model::  47%|████▋     | 235/500 [00:11<00:13, 20.19it/s]
eval model::  48%|████▊     | 238/500 [00:11<00:11, 22.05it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 22.72it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:11, 21.51it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:12, 20.59it/s]
eval model::  50%|█████     | 250/500 [00:12<00:12, 19.55it/s]
eval model::  51%|█████     | 253/500 [00:12<00:11, 21.14it/s]
eval model::  51%|█████     | 256/500 [00:12<00:11, 21.36it/s]
eval model::  52%|█████▏    | 259/500 [00:12<00:11, 20.39it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:12, 19.74it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:12, 19.53it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:12, 19.30it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:10, 20.95it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:11, 20.16it/s]
eval model::  55%|█████▌    | 276/500 [00:13<00:11, 19.28it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 19.45it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 20.64it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:14, 14.44it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:13, 15.37it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:11, 18.79it/s]
eval model::  59%|█████▉    | 294/500 [00:14<00:10, 19.07it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:10, 19.19it/s]
eval model::  60%|██████    | 300/500 [00:15<00:10, 18.99it/s]
eval model::  61%|██████    | 303/500 [00:15<00:09, 20.51it/s]
eval model::  61%|██████    | 306/500 [00:15<00:09, 21.26it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:08, 22.22it/s]
eval model::  62%|██████▏   | 312/500 [00:15<00:08, 21.76it/s]
eval model::  63%|██████▎   | 315/500 [00:15<00:08, 21.13it/s]
eval model::  64%|██████▎   | 318/500 [00:15<00:08, 22.09it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:07, 23.14it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 21.41it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:07, 22.20it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:08, 20.90it/s]
eval model::  67%|██████▋   | 333/500 [00:16<00:07, 21.07it/s]
eval model::  67%|██████▋   | 336/500 [00:16<00:08, 20.43it/s]
eval model::  68%|██████▊   | 339/500 [00:16<00:07, 21.87it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:08, 18.50it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:07, 20.55it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:07, 20.36it/s]
eval model::  70%|███████   | 351/500 [00:17<00:07, 20.12it/s]
eval model::  71%|███████   | 354/500 [00:17<00:06, 21.23it/s]
eval model::  71%|███████▏  | 357/500 [00:17<00:07, 20.10it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:07, 19.61it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:07, 19.40it/s]
eval model::  73%|███████▎  | 365/500 [00:18<00:07, 18.78it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:07, 17.52it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:06, 19.72it/s]
eval model::  75%|███████▍  | 373/500 [00:18<00:06, 20.12it/s]
eval model::  75%|███████▌  | 376/500 [00:18<00:05, 21.07it/s]
eval model::  76%|███████▌  | 379/500 [00:18<00:05, 21.68it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 20.76it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 19.40it/s]
eval model::  77%|███████▋  | 387/500 [00:19<00:06, 16.84it/s]
eval model::  78%|███████▊  | 390/500 [00:19<00:06, 17.89it/s]
eval model::  79%|███████▊  | 393/500 [00:19<00:05, 19.91it/s]
eval model::  79%|███████▉  | 396/500 [00:19<00:05, 20.49it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 21.23it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 20.96it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 21.17it/s]
eval model::  82%|████████▏ | 408/500 [00:20<00:04, 20.09it/s]
eval model::  82%|████████▏ | 411/500 [00:20<00:04, 19.69it/s]
eval model::  83%|████████▎ | 413/500 [00:20<00:04, 19.17it/s]
eval model::  83%|████████▎ | 416/500 [00:20<00:04, 20.15it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:03, 20.88it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 18.51it/s]
eval model::  85%|████████▌ | 425/500 [00:21<00:03, 19.21it/s]
eval model::  86%|████████▌ | 428/500 [00:21<00:03, 20.36it/s]
eval model::  86%|████████▌ | 431/500 [00:21<00:03, 20.30it/s]
eval model::  87%|████████▋ | 434/500 [00:21<00:02, 22.30it/s]
eval model::  88%|████████▊ | 438/500 [00:21<00:02, 24.14it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 21.18it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 23.12it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 22.28it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 20.16it/s]
eval model::  91%|█████████ | 453/500 [00:22<00:02, 20.40it/s]
eval model::  91%|█████████ | 456/500 [00:22<00:02, 21.26it/s]
eval model::  92%|█████████▏| 459/500 [00:22<00:01, 21.24it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:02, 17.55it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:02, 17.16it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 19.03it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 21.26it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 21.33it/s]
eval model::  95%|█████████▌| 477/500 [00:23<00:01, 21.40it/s]
eval model::  96%|█████████▌| 480/500 [00:23<00:00, 20.45it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 21.03it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 19.35it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 20.55it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 20.12it/s]
eval model::  99%|█████████▉| 495/500 [00:24<00:00, 20.33it/s]
eval model:: 100%|█████████▉| 498/500 [00:24<00:00, 21.35it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 20.02it/s]
[2024/08/01 18:01:58] ppocr INFO: cur metric, precision: 0.6960203217612193, recall: 0.39576311988444873, hmean: 0.5046040515653776, fps: 45.53875554706853
[2024/08/01 18:01:58] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 18:01:58] ppocr INFO: best metric, hmean: 0.5046040515653776, precision: 0.6960203217612193, recall: 0.39576311988444873, fps: 45.53875554706853, best_epoch: 100
[2024/08/01 18:01:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:01:59] ppocr INFO: best metric, hmean: 0.5046040515653776, precision: 0.6960203217612193, recall: 0.39576311988444873, fps: 45.53875554706853, best_epoch: 100
I0801 18:02:00.495970  1265 tcp_store.cc:289] receive shutdown event and so quit from MasterDaemon run loop
[2024/08/01 18:07:00] ppocr INFO: Architecture : 
[2024/08/01 18:07:00] ppocr INFO:     Backbone : 
[2024/08/01 18:07:00] ppocr INFO:         model_name : large
[2024/08/01 18:07:00] ppocr INFO:         name : MobileNetV3
[2024/08/01 18:07:00] ppocr INFO:         scale : 0.5
[2024/08/01 18:07:00] ppocr INFO:     Head : 
[2024/08/01 18:07:00] ppocr INFO:         k : 50
[2024/08/01 18:07:00] ppocr INFO:         name : DBHead
[2024/08/01 18:07:00] ppocr INFO:     Neck : 
[2024/08/01 18:07:00] ppocr INFO:         name : DBFPN
[2024/08/01 18:07:00] ppocr INFO:         out_channels : 256
[2024/08/01 18:07:00] ppocr INFO:     Transform : None
[2024/08/01 18:07:00] ppocr INFO:     algorithm : DB
[2024/08/01 18:07:00] ppocr INFO:     model_type : det
[2024/08/01 18:07:00] ppocr INFO: Eval : 
[2024/08/01 18:07:00] ppocr INFO:     dataset : 
[2024/08/01 18:07:00] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/08/01 18:07:00] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
[2024/08/01 18:07:00] ppocr INFO:         name : SimpleDataSet
[2024/08/01 18:07:00] ppocr INFO:         transforms : 
[2024/08/01 18:07:00] ppocr INFO:             DecodeImage : 
[2024/08/01 18:07:00] ppocr INFO:                 channel_first : False
[2024/08/01 18:07:00] ppocr INFO:                 img_mode : BGR
[2024/08/01 18:07:00] ppocr INFO:             DetLabelEncode : None
[2024/08/01 18:07:00] ppocr INFO:             DetResizeForTest : 
[2024/08/01 18:07:00] ppocr INFO:                 image_shape : [736, 1280]
[2024/08/01 18:07:00] ppocr INFO:             NormalizeImage : 
[2024/08/01 18:07:00] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/08/01 18:07:00] ppocr INFO:                 order : hwc
[2024/08/01 18:07:00] ppocr INFO:                 scale : 1./255.
[2024/08/01 18:07:00] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/08/01 18:07:00] ppocr INFO:             ToCHWImage : None
[2024/08/01 18:07:00] ppocr INFO:             KeepKeys : 
[2024/08/01 18:07:00] ppocr INFO:                 keep_keys : ['image', 'shape', 'polys', 'ignore_tags']
[2024/08/01 18:07:00] ppocr INFO:     loader : 
[2024/08/01 18:07:00] ppocr INFO:         batch_size_per_card : 1
[2024/08/01 18:07:00] ppocr INFO:         drop_last : False
[2024/08/01 18:07:00] ppocr INFO:         num_workers : 0
[2024/08/01 18:07:00] ppocr INFO:         shuffle : False
[2024/08/01 18:07:00] ppocr INFO:         use_shared_memory : True
[2024/08/01 18:07:00] ppocr INFO: Global : 
[2024/08/01 18:07:00] ppocr INFO:     cal_metric_during_train : False
[2024/08/01 18:07:00] ppocr INFO:     checkpoints : None
[2024/08/01 18:07:00] ppocr INFO:     distributed : True
[2024/08/01 18:07:00] ppocr INFO:     epoch_num : 100
[2024/08/01 18:07:00] ppocr INFO:     eval_batch_step : [0, 60]
[2024/08/01 18:07:00] ppocr INFO:     infer_img : doc/imgs_en/img_10.jpg
[2024/08/01 18:07:00] ppocr INFO:     log_smooth_window : 20
[2024/08/01 18:07:00] ppocr INFO:     pretrained_model : ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/08/01 18:07:00] ppocr INFO:     print_batch_step : 10
[2024/08/01 18:07:00] ppocr INFO:     save_epoch_step : 1200
[2024/08/01 18:07:00] ppocr INFO:     save_inference_dir : None
[2024/08/01 18:07:00] ppocr INFO:     save_model_dir : ./output/db_mv3/
[2024/08/01 18:07:00] ppocr INFO:     save_res_path : ./output/det_db/predicts_db.txt
[2024/08/01 18:07:00] ppocr INFO:     use_gpu : True
[2024/08/01 18:07:00] ppocr INFO:     use_visualdl : False
[2024/08/01 18:07:00] ppocr INFO:     use_xpu : False
[2024/08/01 18:07:00] ppocr INFO: Loss : 
[2024/08/01 18:07:00] ppocr INFO:     alpha : 5
[2024/08/01 18:07:00] ppocr INFO:     balance_loss : True
[2024/08/01 18:07:00] ppocr INFO:     beta : 10
[2024/08/01 18:07:00] ppocr INFO:     main_loss_type : DiceLoss
[2024/08/01 18:07:00] ppocr INFO:     name : DBLoss
[2024/08/01 18:07:00] ppocr INFO:     ohem_ratio : 3
[2024/08/01 18:07:00] ppocr INFO: Metric : 
[2024/08/01 18:07:00] ppocr INFO:     main_indicator : hmean
[2024/08/01 18:07:00] ppocr INFO:     name : DetMetric
[2024/08/01 18:07:00] ppocr INFO: Optimizer : 
[2024/08/01 18:07:00] ppocr INFO:     beta1 : 0.9
[2024/08/01 18:07:00] ppocr INFO:     beta2 : 0.999
[2024/08/01 18:07:00] ppocr INFO:     lr : 
[2024/08/01 18:07:00] ppocr INFO:         learning_rate : 0.001
[2024/08/01 18:07:00] ppocr INFO:     name : Adam
[2024/08/01 18:07:00] ppocr INFO:     regularizer : 
[2024/08/01 18:07:00] ppocr INFO:         factor : 0
[2024/08/01 18:07:00] ppocr INFO:         name : L2
[2024/08/01 18:07:00] ppocr INFO: PostProcess : 
[2024/08/01 18:07:00] ppocr INFO:     box_thresh : 0.6
[2024/08/01 18:07:00] ppocr INFO:     max_candidates : 1000
[2024/08/01 18:07:00] ppocr INFO:     name : DBPostProcess
[2024/08/01 18:07:00] ppocr INFO:     thresh : 0.3
[2024/08/01 18:07:00] ppocr INFO:     unclip_ratio : 1.5
[2024/08/01 18:07:00] ppocr INFO: Train : 
[2024/08/01 18:07:00] ppocr INFO:     dataset : 
[2024/08/01 18:07:00] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/08/01 18:07:00] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/08/01 18:07:00] ppocr INFO:         name : SimpleDataSet
[2024/08/01 18:07:00] ppocr INFO:         ratio_list : [1.0]
[2024/08/01 18:07:00] ppocr INFO:         transforms : 
[2024/08/01 18:07:00] ppocr INFO:             DecodeImage : 
[2024/08/01 18:07:00] ppocr INFO:                 channel_first : False
[2024/08/01 18:07:00] ppocr INFO:                 img_mode : BGR
[2024/08/01 18:07:00] ppocr INFO:             DetLabelEncode : None
[2024/08/01 18:07:00] ppocr INFO:             IaaAugment : 
[2024/08/01 18:07:00] ppocr INFO:                 augmenter_args : 
[2024/08/01 18:07:00] ppocr INFO:                     args : 
[2024/08/01 18:07:00] ppocr INFO:                         p : 0.5
[2024/08/01 18:07:00] ppocr INFO:                     type : Fliplr
[2024/08/01 18:07:00] ppocr INFO:                     args : 
[2024/08/01 18:07:00] ppocr INFO:                         rotate : [-10, 10]
[2024/08/01 18:07:00] ppocr INFO:                     type : Affine
[2024/08/01 18:07:00] ppocr INFO:                     args : 
[2024/08/01 18:07:00] ppocr INFO:                         size : [0.5, 3]
[2024/08/01 18:07:00] ppocr INFO:                     type : Resize
[2024/08/01 18:07:00] ppocr INFO:             EastRandomCropData : 
[2024/08/01 18:07:00] ppocr INFO:                 keep_ratio : True
[2024/08/01 18:07:00] ppocr INFO:                 max_tries : 50
[2024/08/01 18:07:00] ppocr INFO:                 size : [640, 640]
[2024/08/01 18:07:00] ppocr INFO:             MakeBorderMap : 
[2024/08/01 18:07:00] ppocr INFO:                 shrink_ratio : 0.4
[2024/08/01 18:07:00] ppocr INFO:                 thresh_max : 0.7
[2024/08/01 18:07:00] ppocr INFO:                 thresh_min : 0.3
[2024/08/01 18:07:00] ppocr INFO:             MakeShrinkMap : 
[2024/08/01 18:07:00] ppocr INFO:                 min_text_size : 8
[2024/08/01 18:07:00] ppocr INFO:                 shrink_ratio : 0.4
[2024/08/01 18:07:00] ppocr INFO:             NormalizeImage : 
[2024/08/01 18:07:00] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/08/01 18:07:00] ppocr INFO:                 order : hwc
[2024/08/01 18:07:00] ppocr INFO:                 scale : 1./255.
[2024/08/01 18:07:00] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/08/01 18:07:00] ppocr INFO:             ToCHWImage : None
[2024/08/01 18:07:00] ppocr INFO:             KeepKeys : 
[2024/08/01 18:07:00] ppocr INFO:                 keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask']
[2024/08/01 18:07:00] ppocr INFO:     loader : 
[2024/08/01 18:07:00] ppocr INFO:         batch_size_per_card : 48
[2024/08/01 18:07:00] ppocr INFO:         drop_last : False
[2024/08/01 18:07:00] ppocr INFO:         num_workers : 8
[2024/08/01 18:07:00] ppocr INFO:         shuffle : True
[2024/08/01 18:07:00] ppocr INFO:         use_shared_memory : True
[2024/08/01 18:07:00] ppocr INFO: profiler_options : None
[2024/08/01 18:07:00] ppocr INFO: train with paddle 2.5.2 and device Place(gpu:0)
======================= Modified FLAGS detected =======================
FLAGS(name='FLAGS_cudnn_batchnorm_spatial_persistent', current_value=True, default_value=False)
FLAGS(name='FLAGS_selected_gpus', current_value='0', default_value='')
=======================================================================
I0801 18:07:00.283231 56552 tcp_utils.cc:181] The server starts to listen on IP_ANY:52943
I0801 18:07:00.283504 56552 tcp_utils.cc:130] Successfully connected to 10.8.145.246:52943
I0801 18:07:00.458562 56552 process_group_nccl.cc:120] ProcessGroupNCCL pg_timeout_ 1800000
[2024/08/01 18:07:00] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/08/01 18:07:00] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
W0801 18:07:00.478045 56552 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 90.2, Driver API Version: 50724.2, Runtime API Version: 50724.2
[2024/08/01 18:07:02] ppocr INFO: load pretrain successful from ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/08/01 18:07:02] ppocr INFO: train dataloader has 3 iters
[2024/08/01 18:07:02] ppocr INFO: valid dataloader has 500 iters
[2024/08/01 18:07:02] ppocr INFO: During the training process, after the 0th iteration, an evaluation is run every 60 iterations
[2024/08/01 18:07:25] ppocr INFO: epoch: [1/100], global_step: 3, lr: 0.001000, loss: 9.319155, loss_shrink_maps: 4.899740, loss_threshold_maps: 3.451636, loss_binary_maps: 0.981405, avg_reader_cost: 6.10113 s, avg_batch_cost: 6.68934 s, avg_samples: 12.5, ips: 1.86865 samples/s, eta: 1:50:22
[2024/08/01 18:07:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:07:34] ppocr INFO: epoch: [2/100], global_step: 6, lr: 0.001000, loss: 8.332724, loss_shrink_maps: 4.877676, loss_threshold_maps: 2.467809, loss_binary_maps: 0.979844, avg_reader_cost: 2.26724 s, avg_batch_cost: 2.52311 s, avg_samples: 12.5, ips: 4.95421 samples/s, eta: 1:15:14
[2024/08/01 18:07:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:07:44] ppocr INFO: epoch: [3/100], global_step: 9, lr: 0.001000, loss: 7.521289, loss_shrink_maps: 4.860794, loss_threshold_maps: 1.710161, loss_binary_maps: 0.975838, avg_reader_cost: 2.21482 s, avg_batch_cost: 2.46357 s, avg_samples: 12.5, ips: 5.07394 samples/s, eta: 1:02:55
[2024/08/01 18:07:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:07:52] ppocr INFO: epoch: [4/100], global_step: 10, lr: 0.001000, loss: 7.379552, loss_shrink_maps: 4.856158, loss_threshold_maps: 1.565985, loss_binary_maps: 0.974600, avg_reader_cost: 0.68268 s, avg_batch_cost: 0.76209 s, avg_samples: 4.8, ips: 6.29848 samples/s, eta: 1:00:07
[2024/08/01 18:07:53] ppocr INFO: epoch: [4/100], global_step: 12, lr: 0.001000, loss: 7.174122, loss_shrink_maps: 4.847276, loss_threshold_maps: 1.347803, loss_binary_maps: 0.973331, avg_reader_cost: 1.61186 s, avg_batch_cost: 1.75142 s, avg_samples: 7.7, ips: 4.39643 samples/s, eta: 0:56:45
[2024/08/01 18:07:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:08:02] ppocr INFO: epoch: [5/100], global_step: 15, lr: 0.001000, loss: 7.061536, loss_shrink_maps: 4.837828, loss_threshold_maps: 1.236652, loss_binary_maps: 0.972976, avg_reader_cost: 2.21306 s, avg_batch_cost: 2.44836 s, avg_samples: 12.5, ips: 5.10546 samples/s, eta: 0:52:41
[2024/08/01 18:08:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:08:12] ppocr INFO: epoch: [6/100], global_step: 18, lr: 0.001000, loss: 6.925946, loss_shrink_maps: 4.800682, loss_threshold_maps: 1.189876, loss_binary_maps: 0.959426, avg_reader_cost: 2.23025 s, avg_batch_cost: 2.45482 s, avg_samples: 12.5, ips: 5.09203 samples/s, eta: 0:49:51
[2024/08/01 18:08:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:08:21] ppocr INFO: epoch: [7/100], global_step: 20, lr: 0.001000, loss: 6.907336, loss_shrink_maps: 4.775160, loss_threshold_maps: 1.184062, loss_binary_maps: 0.955531, avg_reader_cost: 1.42889 s, avg_batch_cost: 1.60719 s, avg_samples: 9.6, ips: 5.97315 samples/s, eta: 0:48:17
[2024/08/01 18:08:21] ppocr INFO: epoch: [7/100], global_step: 21, lr: 0.001000, loss: 6.887038, loss_shrink_maps: 4.748492, loss_threshold_maps: 1.173221, loss_binary_maps: 0.950754, avg_reader_cost: 0.84759 s, avg_batch_cost: 0.90000 s, avg_samples: 2.9, ips: 3.22223 samples/s, eta: 0:47:49
[2024/08/01 18:08:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:08:31] ppocr INFO: epoch: [8/100], global_step: 24, lr: 0.001000, loss: 6.773968, loss_shrink_maps: 4.683812, loss_threshold_maps: 1.154004, loss_binary_maps: 0.936151, avg_reader_cost: 2.28120 s, avg_batch_cost: 2.49978 s, avg_samples: 12.5, ips: 5.00044 samples/s, eta: 0:46:11
[2024/08/01 18:08:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:08:40] ppocr INFO: epoch: [9/100], global_step: 27, lr: 0.001000, loss: 6.688998, loss_shrink_maps: 4.630290, loss_threshold_maps: 1.137088, loss_binary_maps: 0.914798, avg_reader_cost: 2.20073 s, avg_batch_cost: 2.44471 s, avg_samples: 12.5, ips: 5.11308 samples/s, eta: 0:44:43
[2024/08/01 18:08:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:08:50] ppocr INFO: epoch: [10/100], global_step: 30, lr: 0.001000, loss: 6.464184, loss_shrink_maps: 4.513919, loss_threshold_maps: 1.092908, loss_binary_maps: 0.861604, avg_reader_cost: 2.27593 s, avg_batch_cost: 2.49484 s, avg_samples: 12.5, ips: 5.01034 samples/s, eta: 0:43:33
[2024/08/01 18:08:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:08:59] ppocr INFO: epoch: [11/100], global_step: 33, lr: 0.001000, loss: 6.322528, loss_shrink_maps: 4.452516, loss_threshold_maps: 1.071697, loss_binary_maps: 0.812396, avg_reader_cost: 2.29441 s, avg_batch_cost: 2.51257 s, avg_samples: 12.5, ips: 4.97499 samples/s, eta: 0:42:32
[2024/08/01 18:09:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:09:09] ppocr INFO: epoch: [12/100], global_step: 36, lr: 0.001000, loss: 5.937388, loss_shrink_maps: 4.198843, loss_threshold_maps: 1.034713, loss_binary_maps: 0.712659, avg_reader_cost: 2.25990 s, avg_batch_cost: 2.47828 s, avg_samples: 12.5, ips: 5.04383 samples/s, eta: 0:41:35
[2024/08/01 18:09:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:09:19] ppocr INFO: epoch: [13/100], global_step: 39, lr: 0.001000, loss: 5.627670, loss_shrink_maps: 3.974698, loss_threshold_maps: 0.993390, loss_binary_maps: 0.661709, avg_reader_cost: 2.19511 s, avg_batch_cost: 2.44274 s, avg_samples: 12.5, ips: 5.11721 samples/s, eta: 0:40:40
[2024/08/01 18:09:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:09:27] ppocr INFO: epoch: [14/100], global_step: 40, lr: 0.001000, loss: 5.550260, loss_shrink_maps: 3.921918, loss_threshold_maps: 0.980019, loss_binary_maps: 0.659352, avg_reader_cost: 0.63319 s, avg_batch_cost: 0.75249 s, avg_samples: 4.8, ips: 6.37886 samples/s, eta: 0:40:19
[2024/08/01 18:09:28] ppocr INFO: epoch: [14/100], global_step: 42, lr: 0.001000, loss: 5.418478, loss_shrink_maps: 3.830371, loss_threshold_maps: 0.958994, loss_binary_maps: 0.638701, avg_reader_cost: 1.59311 s, avg_batch_cost: 1.73278 s, avg_samples: 7.7, ips: 4.44372 samples/s, eta: 0:39:53
[2024/08/01 18:09:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:09:38] ppocr INFO: epoch: [15/100], global_step: 45, lr: 0.001000, loss: 5.275356, loss_shrink_maps: 3.712080, loss_threshold_maps: 0.940202, loss_binary_maps: 0.614714, avg_reader_cost: 2.25940 s, avg_batch_cost: 2.49857 s, avg_samples: 12.5, ips: 5.00286 samples/s, eta: 0:39:09
[2024/08/01 18:09:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:09:47] ppocr INFO: epoch: [16/100], global_step: 48, lr: 0.001000, loss: 4.997416, loss_shrink_maps: 3.541642, loss_threshold_maps: 0.918881, loss_binary_maps: 0.565535, avg_reader_cost: 2.22774 s, avg_batch_cost: 2.44643 s, avg_samples: 12.5, ips: 5.10950 samples/s, eta: 0:38:24
[2024/08/01 18:09:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:09:56] ppocr INFO: epoch: [17/100], global_step: 50, lr: 0.001000, loss: 4.698238, loss_shrink_maps: 3.302198, loss_threshold_maps: 0.915165, loss_binary_maps: 0.504008, avg_reader_cost: 1.47197 s, avg_batch_cost: 1.64278 s, avg_samples: 9.6, ips: 5.84375 samples/s, eta: 0:37:57
[2024/08/01 18:09:57] ppocr INFO: epoch: [17/100], global_step: 51, lr: 0.001000, loss: 4.656272, loss_shrink_maps: 3.259141, loss_threshold_maps: 0.915165, loss_binary_maps: 0.490616, avg_reader_cost: 0.86545 s, avg_batch_cost: 0.91739 s, avg_samples: 2.9, ips: 3.16114 samples/s, eta: 0:37:48
[2024/08/01 18:09:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:10:07] ppocr INFO: epoch: [18/100], global_step: 54, lr: 0.001000, loss: 4.445184, loss_shrink_maps: 3.062072, loss_threshold_maps: 0.906554, loss_binary_maps: 0.465108, avg_reader_cost: 2.21975 s, avg_batch_cost: 2.50468 s, avg_samples: 12.5, ips: 4.99066 samples/s, eta: 0:37:10
[2024/08/01 18:10:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:10:16] ppocr INFO: epoch: [19/100], global_step: 57, lr: 0.001000, loss: 4.203848, loss_shrink_maps: 2.862726, loss_threshold_maps: 0.899126, loss_binary_maps: 0.433693, avg_reader_cost: 2.18575 s, avg_batch_cost: 2.40873 s, avg_samples: 12.5, ips: 5.18945 samples/s, eta: 0:36:30
[2024/08/01 18:10:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:10:25] ppocr INFO: epoch: [20/100], global_step: 60, lr: 0.001000, loss: 4.005039, loss_shrink_maps: 2.711213, loss_threshold_maps: 0.890855, loss_binary_maps: 0.404532, avg_reader_cost: 2.19109 s, avg_batch_cost: 2.41133 s, avg_samples: 12.5, ips: 5.18385 samples/s, eta: 0:35:51

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.69it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.71it/s]
eval model::   1%|▏         | 7/500 [00:00<00:23, 21.13it/s]
eval model::   2%|▏         | 10/500 [00:00<00:20, 23.45it/s]
eval model::   3%|▎         | 13/500 [00:00<00:21, 22.42it/s]
eval model::   3%|▎         | 16/500 [00:00<00:20, 23.98it/s]
eval model::   4%|▍         | 19/500 [00:00<00:22, 21.62it/s]
eval model::   4%|▍         | 22/500 [00:01<00:21, 22.25it/s]
eval model::   5%|▌         | 25/500 [00:01<00:22, 20.98it/s]
eval model::   6%|▌         | 28/500 [00:01<00:23, 20.14it/s]
eval model::   6%|▌         | 31/500 [00:01<00:29, 16.05it/s]
eval model::   7%|▋         | 34/500 [00:01<00:26, 17.83it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 19.51it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.20it/s]
eval model::   9%|▊         | 43/500 [00:02<00:24, 19.00it/s]
eval model::   9%|▉         | 46/500 [00:02<00:24, 18.74it/s]
eval model::  10%|▉         | 49/500 [00:02<00:22, 20.20it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 20.85it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 20.60it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:21, 20.26it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:24, 17.78it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:24, 17.75it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:22, 19.06it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:21, 20.21it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.16it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:19, 21.49it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:18, 22.49it/s]
eval model::  16%|█▌        | 81/500 [00:03<00:17, 23.63it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.12it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:18, 22.35it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:18, 22.33it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:17, 22.69it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:18, 21.54it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:20, 20.04it/s]
eval model::  20%|██        | 102/500 [00:04<00:19, 20.61it/s]
eval model::  21%|██        | 105/500 [00:05<00:17, 22.09it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:19, 20.17it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:20, 18.99it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 20.67it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:18, 20.80it/s]
eval model::  24%|██▍       | 120/500 [00:05<00:19, 19.61it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.25it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:18, 20.71it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 21.15it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:17, 20.77it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:17, 20.86it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:17, 21.16it/s]
eval model::  28%|██▊       | 141/500 [00:06<00:18, 19.92it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.02it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:17, 20.32it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.88it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 19.07it/s]
eval model::  31%|███       | 156/500 [00:07<00:17, 19.93it/s]
eval model::  32%|███▏      | 159/500 [00:07<00:16, 20.53it/s]
eval model::  32%|███▏      | 162/500 [00:07<00:16, 20.05it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:15, 21.65it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:16, 19.61it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 18.69it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:17, 18.28it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:16, 19.84it/s]
eval model::  36%|███▌      | 180/500 [00:08<00:16, 19.29it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:15, 21.00it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 21.40it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 21.05it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:15, 20.49it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:19, 15.70it/s]
eval model::  40%|███▉      | 198/500 [00:09<00:16, 18.00it/s]
eval model::  40%|████      | 201/500 [00:10<00:16, 18.18it/s]
eval model::  41%|████      | 204/500 [00:10<00:15, 19.22it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:19, 14.99it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:19, 15.14it/s]
eval model::  42%|████▏     | 212/500 [00:10<00:17, 16.85it/s]
eval model::  43%|████▎     | 215/500 [00:10<00:17, 16.12it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.55it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 18.21it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:15, 17.97it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:13, 20.22it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:12, 21.84it/s]
eval model::  46%|████▋     | 232/500 [00:11<00:10, 24.88it/s]
eval model::  47%|████▋     | 235/500 [00:11<00:10, 24.28it/s]
eval model::  48%|████▊     | 238/500 [00:11<00:11, 23.59it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:12, 21.23it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:11, 21.66it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:11, 22.49it/s]
eval model::  50%|█████     | 250/500 [00:12<00:11, 21.90it/s]
eval model::  51%|█████     | 253/500 [00:12<00:11, 21.77it/s]
eval model::  51%|█████     | 256/500 [00:12<00:12, 20.07it/s]
eval model::  52%|█████▏    | 259/500 [00:12<00:11, 21.11it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 20.62it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:11, 20.96it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:10, 21.42it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:10, 21.29it/s]
eval model::  55%|█████▍    | 274/500 [00:13<00:10, 21.27it/s]
eval model::  55%|█████▌    | 277/500 [00:13<00:10, 20.29it/s]
eval model::  56%|█████▌    | 280/500 [00:13<00:10, 20.04it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:10, 20.25it/s]
eval model::  57%|█████▋    | 286/500 [00:14<00:12, 16.53it/s]
eval model::  58%|█████▊    | 290/500 [00:14<00:10, 19.80it/s]
eval model::  59%|█████▊    | 293/500 [00:14<00:09, 20.84it/s]
eval model::  59%|█████▉    | 296/500 [00:14<00:09, 21.12it/s]
eval model::  60%|█████▉    | 299/500 [00:14<00:08, 23.01it/s]
eval model::  60%|██████    | 302/500 [00:15<00:10, 19.54it/s]
eval model::  61%|██████    | 305/500 [00:15<00:09, 21.00it/s]
eval model::  62%|██████▏   | 308/500 [00:15<00:08, 21.49it/s]
eval model::  62%|██████▏   | 311/500 [00:15<00:08, 22.73it/s]
eval model::  63%|██████▎   | 314/500 [00:15<00:08, 21.21it/s]
eval model::  63%|██████▎   | 317/500 [00:15<00:08, 20.89it/s]
eval model::  64%|██████▍   | 320/500 [00:15<00:08, 20.32it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:08, 20.54it/s]
eval model::  65%|██████▌   | 326/500 [00:16<00:08, 20.18it/s]
eval model::  66%|██████▌   | 329/500 [00:16<00:08, 19.72it/s]
eval model::  66%|██████▋   | 332/500 [00:16<00:08, 19.91it/s]
eval model::  67%|██████▋   | 335/500 [00:16<00:07, 20.89it/s]
eval model::  68%|██████▊   | 338/500 [00:16<00:07, 20.81it/s]
eval model::  68%|██████▊   | 341/500 [00:16<00:08, 18.84it/s]
eval model::  69%|██████▉   | 344/500 [00:17<00:07, 20.86it/s]
eval model::  69%|██████▉   | 347/500 [00:17<00:07, 19.65it/s]
eval model::  70%|███████   | 350/500 [00:17<00:07, 19.91it/s]
eval model::  71%|███████   | 353/500 [00:17<00:06, 21.50it/s]
eval model::  71%|███████   | 356/500 [00:17<00:07, 20.30it/s]
eval model::  72%|███████▏  | 359/500 [00:17<00:06, 20.83it/s]
eval model::  72%|███████▏  | 362/500 [00:17<00:07, 19.01it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.45it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:07, 18.54it/s]
eval model::  74%|███████▍  | 369/500 [00:18<00:06, 20.57it/s]
eval model::  74%|███████▍  | 372/500 [00:18<00:05, 21.60it/s]
eval model::  75%|███████▌  | 375/500 [00:18<00:05, 21.31it/s]
eval model::  76%|███████▌  | 378/500 [00:18<00:05, 22.04it/s]
eval model::  76%|███████▌  | 381/500 [00:18<00:05, 21.49it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:05, 21.74it/s]
eval model::  77%|███████▋  | 387/500 [00:19<00:06, 17.29it/s]
eval model::  78%|███████▊  | 390/500 [00:19<00:05, 18.45it/s]
eval model::  79%|███████▊  | 393/500 [00:19<00:05, 20.43it/s]
eval model::  79%|███████▉  | 396/500 [00:19<00:04, 21.71it/s]
eval model::  80%|███████▉  | 399/500 [00:19<00:04, 20.48it/s]
eval model::  80%|████████  | 402/500 [00:19<00:04, 21.77it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 20.65it/s]
eval model::  82%|████████▏ | 408/500 [00:20<00:04, 20.58it/s]
eval model::  82%|████████▏ | 411/500 [00:20<00:04, 20.83it/s]
eval model::  83%|████████▎ | 414/500 [00:20<00:04, 21.18it/s]
eval model::  83%|████████▎ | 417/500 [00:20<00:03, 20.88it/s]
eval model::  84%|████████▍ | 420/500 [00:20<00:04, 18.53it/s]
eval model::  85%|████████▍ | 423/500 [00:20<00:03, 20.27it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:03, 20.56it/s]
eval model::  86%|████████▌ | 429/500 [00:21<00:03, 22.60it/s]
eval model::  86%|████████▋ | 432/500 [00:21<00:03, 21.77it/s]
eval model::  87%|████████▋ | 435/500 [00:21<00:03, 20.53it/s]
eval model::  88%|████████▊ | 438/500 [00:21<00:03, 19.68it/s]
eval model::  88%|████████▊ | 441/500 [00:21<00:03, 19.15it/s]
eval model::  89%|████████▉ | 444/500 [00:21<00:02, 20.36it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 20.44it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 20.56it/s]
eval model::  91%|█████████ | 453/500 [00:22<00:02, 22.17it/s]
eval model::  91%|█████████ | 456/500 [00:22<00:02, 19.75it/s]
eval model::  92%|█████████▏| 459/500 [00:22<00:01, 21.39it/s]
eval model::  92%|█████████▏| 462/500 [00:22<00:01, 19.66it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:01, 18.70it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 20.15it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 19.90it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 19.60it/s]
eval model::  95%|█████████▌| 477/500 [00:23<00:01, 21.07it/s]
eval model::  96%|█████████▌| 480/500 [00:23<00:00, 21.38it/s]
eval model::  97%|█████████▋| 483/500 [00:23<00:00, 22.26it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 20.24it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 20.68it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 20.40it/s]
eval model::  99%|█████████▉| 495/500 [00:24<00:00, 22.03it/s]
eval model:: 100%|█████████▉| 498/500 [00:24<00:00, 23.27it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 20.29it/s]
[2024/08/01 18:10:50] ppocr INFO: cur metric, precision: 0.4179456906729634, recall: 0.17043813192103996, hmean: 0.2421340629274966, fps: 45.45248943360398
[2024/08/01 18:10:50] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 18:10:50] ppocr INFO: best metric, hmean: 0.2421340629274966, precision: 0.4179456906729634, recall: 0.17043813192103996, fps: 45.45248943360398, best_epoch: 20
[2024/08/01 18:10:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:10:57] ppocr INFO: epoch: [21/100], global_step: 63, lr: 0.001000, loss: 3.829799, loss_shrink_maps: 2.541937, loss_threshold_maps: 0.890855, loss_binary_maps: 0.388472, avg_reader_cost: 1.59575 s, avg_batch_cost: 1.83023 s, avg_samples: 12.5, ips: 6.82976 samples/s, eta: 0:34:52
[2024/08/01 18:10:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:11:05] ppocr INFO: epoch: [22/100], global_step: 66, lr: 0.001000, loss: 3.769218, loss_shrink_maps: 2.493932, loss_threshold_maps: 0.884827, loss_binary_maps: 0.375897, avg_reader_cost: 1.65156 s, avg_batch_cost: 1.87028 s, avg_samples: 12.5, ips: 6.68351 samples/s, eta: 0:33:58
[2024/08/01 18:11:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:11:12] ppocr INFO: epoch: [23/100], global_step: 69, lr: 0.001000, loss: 3.636390, loss_shrink_maps: 2.402104, loss_threshold_maps: 0.880204, loss_binary_maps: 0.364194, avg_reader_cost: 1.52578 s, avg_batch_cost: 1.74805 s, avg_samples: 12.5, ips: 7.15083 samples/s, eta: 0:33:03
[2024/08/01 18:11:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:11:18] ppocr INFO: epoch: [24/100], global_step: 70, lr: 0.001000, loss: 3.622468, loss_shrink_maps: 2.377219, loss_threshold_maps: 0.879671, loss_binary_maps: 0.364194, avg_reader_cost: 0.40074 s, avg_batch_cost: 0.53145 s, avg_samples: 4.8, ips: 9.03184 samples/s, eta: 0:32:43
[2024/08/01 18:11:19] ppocr INFO: epoch: [24/100], global_step: 72, lr: 0.001000, loss: 3.615411, loss_shrink_maps: 2.356580, loss_threshold_maps: 0.876690, loss_binary_maps: 0.359758, avg_reader_cost: 1.15124 s, avg_batch_cost: 1.29153 s, avg_samples: 7.7, ips: 5.96193 samples/s, eta: 0:32:13
[2024/08/01 18:11:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:11:26] ppocr INFO: epoch: [25/100], global_step: 75, lr: 0.001000, loss: 3.488573, loss_shrink_maps: 2.263069, loss_threshold_maps: 0.873825, loss_binary_maps: 0.353313, avg_reader_cost: 1.51339 s, avg_batch_cost: 1.74050 s, avg_samples: 12.5, ips: 7.18183 samples/s, eta: 0:31:24
[2024/08/01 18:11:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:11:33] ppocr INFO: epoch: [26/100], global_step: 78, lr: 0.001000, loss: 3.408530, loss_shrink_maps: 2.200586, loss_threshold_maps: 0.870379, loss_binary_maps: 0.353313, avg_reader_cost: 1.58956 s, avg_batch_cost: 1.85546 s, avg_samples: 12.5, ips: 6.73689 samples/s, eta: 0:30:40
[2024/08/01 18:11:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:11:40] ppocr INFO: epoch: [27/100], global_step: 80, lr: 0.001000, loss: 3.288457, loss_shrink_maps: 2.133621, loss_threshold_maps: 0.849082, loss_binary_maps: 0.346378, avg_reader_cost: 0.91685 s, avg_batch_cost: 1.10424 s, avg_samples: 9.6, ips: 8.69378 samples/s, eta: 0:30:08
[2024/08/01 18:11:40] ppocr INFO: epoch: [27/100], global_step: 81, lr: 0.001000, loss: 3.279466, loss_shrink_maps: 2.106056, loss_threshold_maps: 0.843153, loss_binary_maps: 0.344593, avg_reader_cost: 0.59589 s, avg_batch_cost: 0.64822 s, avg_samples: 2.9, ips: 4.47381 samples/s, eta: 0:29:55
[2024/08/01 18:11:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:11:48] ppocr INFO: epoch: [28/100], global_step: 84, lr: 0.001000, loss: 3.279466, loss_shrink_maps: 2.106056, loss_threshold_maps: 0.841953, loss_binary_maps: 0.346378, avg_reader_cost: 1.52591 s, avg_batch_cost: 1.74529 s, avg_samples: 12.5, ips: 7.16211 samples/s, eta: 0:29:12
[2024/08/01 18:11:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:11:55] ppocr INFO: epoch: [29/100], global_step: 87, lr: 0.001000, loss: 3.262260, loss_shrink_maps: 2.055298, loss_threshold_maps: 0.838604, loss_binary_maps: 0.347711, avg_reader_cost: 1.50188 s, avg_batch_cost: 1.74584 s, avg_samples: 12.5, ips: 7.15987 samples/s, eta: 0:28:31
[2024/08/01 18:11:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:12:02] ppocr INFO: epoch: [30/100], global_step: 90, lr: 0.001000, loss: 3.242962, loss_shrink_maps: 2.032418, loss_threshold_maps: 0.834203, loss_binary_maps: 0.344530, avg_reader_cost: 1.51809 s, avg_batch_cost: 1.73633 s, avg_samples: 12.5, ips: 7.19911 samples/s, eta: 0:27:51
[2024/08/01 18:12:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:12:09] ppocr INFO: epoch: [31/100], global_step: 93, lr: 0.001000, loss: 3.215829, loss_shrink_maps: 2.027420, loss_threshold_maps: 0.836226, loss_binary_maps: 0.344530, avg_reader_cost: 1.51349 s, avg_batch_cost: 1.74046 s, avg_samples: 12.5, ips: 7.18200 samples/s, eta: 0:27:13
[2024/08/01 18:12:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:12:16] ppocr INFO: epoch: [32/100], global_step: 96, lr: 0.001000, loss: 3.167145, loss_shrink_maps: 2.012302, loss_threshold_maps: 0.834203, loss_binary_maps: 0.337574, avg_reader_cost: 1.47428 s, avg_batch_cost: 1.71075 s, avg_samples: 12.5, ips: 7.30672 samples/s, eta: 0:26:35
[2024/08/01 18:12:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:12:23] ppocr INFO: epoch: [33/100], global_step: 99, lr: 0.001000, loss: 3.070911, loss_shrink_maps: 1.922182, loss_threshold_maps: 0.832685, loss_binary_maps: 0.330089, avg_reader_cost: 1.51068 s, avg_batch_cost: 1.75497 s, avg_samples: 12.5, ips: 7.12261 samples/s, eta: 0:26:00
[2024/08/01 18:12:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:12:28] ppocr INFO: epoch: [34/100], global_step: 100, lr: 0.001000, loss: 2.995045, loss_shrink_maps: 1.861519, loss_threshold_maps: 0.832685, loss_binary_maps: 0.324620, avg_reader_cost: 0.41610 s, avg_batch_cost: 0.49482 s, avg_samples: 4.8, ips: 9.70044 samples/s, eta: 0:25:46
[2024/08/01 18:12:30] ppocr INFO: epoch: [34/100], global_step: 102, lr: 0.001000, loss: 2.958931, loss_shrink_maps: 1.814372, loss_threshold_maps: 0.828377, loss_binary_maps: 0.315420, avg_reader_cost: 1.07755 s, avg_batch_cost: 1.21761 s, avg_samples: 7.7, ips: 6.32388 samples/s, eta: 0:25:24
[2024/08/01 18:12:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:12:37] ppocr INFO: epoch: [35/100], global_step: 105, lr: 0.001000, loss: 2.897898, loss_shrink_maps: 1.774559, loss_threshold_maps: 0.821562, loss_binary_maps: 0.310094, avg_reader_cost: 1.49770 s, avg_batch_cost: 1.71676 s, avg_samples: 12.5, ips: 7.28115 samples/s, eta: 0:24:50
[2024/08/01 18:12:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:12:44] ppocr INFO: epoch: [36/100], global_step: 108, lr: 0.001000, loss: 2.897898, loss_shrink_maps: 1.774559, loss_threshold_maps: 0.816544, loss_binary_maps: 0.310094, avg_reader_cost: 1.50742 s, avg_batch_cost: 1.75372 s, avg_samples: 12.5, ips: 7.12770 samples/s, eta: 0:24:18
[2024/08/01 18:12:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:12:51] ppocr INFO: epoch: [37/100], global_step: 110, lr: 0.001000, loss: 2.913584, loss_shrink_maps: 1.780070, loss_threshold_maps: 0.816544, loss_binary_maps: 0.313497, avg_reader_cost: 0.97004 s, avg_batch_cost: 1.16818 s, avg_samples: 9.6, ips: 8.21790 samples/s, eta: 0:23:56
[2024/08/01 18:12:51] ppocr INFO: epoch: [37/100], global_step: 111, lr: 0.001000, loss: 2.913584, loss_shrink_maps: 1.780070, loss_threshold_maps: 0.816544, loss_binary_maps: 0.313497, avg_reader_cost: 0.62913 s, avg_batch_cost: 0.68098 s, avg_samples: 2.9, ips: 4.25859 samples/s, eta: 0:23:48
[2024/08/01 18:12:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:12:58] ppocr INFO: epoch: [38/100], global_step: 114, lr: 0.001000, loss: 2.897898, loss_shrink_maps: 1.774559, loss_threshold_maps: 0.809191, loss_binary_maps: 0.313497, avg_reader_cost: 1.49643 s, avg_batch_cost: 1.71933 s, avg_samples: 12.5, ips: 7.27027 samples/s, eta: 0:23:16
[2024/08/01 18:12:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:13:06] ppocr INFO: epoch: [39/100], global_step: 117, lr: 0.001000, loss: 2.800212, loss_shrink_maps: 1.705390, loss_threshold_maps: 0.800356, loss_binary_maps: 0.302835, avg_reader_cost: 1.67465 s, avg_batch_cost: 1.89318 s, avg_samples: 12.5, ips: 6.60265 samples/s, eta: 0:22:48
[2024/08/01 18:13:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:13:13] ppocr INFO: epoch: [40/100], global_step: 120, lr: 0.001000, loss: 2.744326, loss_shrink_maps: 1.654782, loss_threshold_maps: 0.798770, loss_binary_maps: 0.296375, avg_reader_cost: 1.58496 s, avg_batch_cost: 1.87221 s, avg_samples: 12.5, ips: 6.67660 samples/s, eta: 0:22:20

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:36, 13.69it/s]
eval model::   1%|          | 4/500 [00:00<00:30, 16.02it/s]
eval model::   1%|          | 6/500 [00:00<00:28, 17.18it/s]
eval model::   2%|▏         | 9/500 [00:00<00:24, 20.20it/s]
eval model::   2%|▏         | 12/500 [00:00<00:24, 20.21it/s]
eval model::   3%|▎         | 15/500 [00:00<00:25, 19.03it/s]
eval model::   4%|▎         | 18/500 [00:00<00:24, 19.57it/s]
eval model::   4%|▍         | 20/500 [00:01<00:26, 17.99it/s]
eval model::   5%|▍         | 24/500 [00:01<00:22, 21.33it/s]
eval model::   5%|▌         | 27/500 [00:01<00:22, 20.94it/s]
eval model::   6%|▌         | 30/500 [00:01<00:22, 20.52it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 19.88it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 18.87it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 19.71it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.56it/s]
eval model::   8%|▊         | 42/500 [00:02<00:23, 19.31it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.54it/s]
eval model::   9%|▉         | 47/500 [00:02<00:23, 19.58it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 19.28it/s]
eval model::  10%|█         | 52/500 [00:02<00:23, 19.42it/s]
eval model::  11%|█         | 55/500 [00:02<00:20, 21.38it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:21, 20.18it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:23, 18.97it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:24, 18.18it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:21, 20.55it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 21.19it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:21, 19.98it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:22, 18.81it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:22, 18.94it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:19, 21.03it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:18, 21.98it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:19, 20.85it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:19, 21.45it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:20, 19.81it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:22, 18.29it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:21, 18.54it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:21, 18.55it/s]
eval model::  20%|██        | 101/500 [00:05<00:21, 18.18it/s]
eval model::  21%|██        | 104/500 [00:05<00:21, 18.76it/s]
eval model::  21%|██        | 106/500 [00:05<00:22, 17.69it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:21, 18.16it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:20, 18.74it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 20.72it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:18, 20.55it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 20.32it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.43it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:17, 20.92it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 21.11it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:18, 19.52it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:17, 21.24it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:16, 22.01it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:16, 21.97it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:15, 22.25it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:16, 21.28it/s]
eval model::  30%|███       | 150/500 [00:07<00:15, 22.13it/s]
eval model::  31%|███       | 153/500 [00:07<00:16, 20.93it/s]
eval model::  31%|███       | 156/500 [00:07<00:15, 21.94it/s]
eval model::  32%|███▏      | 159/500 [00:07<00:16, 20.06it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:18, 18.38it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:17, 18.90it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:16, 19.95it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:16, 19.95it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:18, 17.64it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:16, 19.48it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:17, 18.68it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:15, 20.04it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 21.76it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:13, 22.57it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:13, 22.62it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:14, 21.07it/s]
eval model::  40%|███▉      | 198/500 [00:09<00:14, 21.11it/s]
eval model::  40%|████      | 201/500 [00:10<00:15, 19.39it/s]
eval model::  41%|████      | 204/500 [00:10<00:14, 20.41it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:19, 14.71it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:19, 15.26it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:17, 16.09it/s]
eval model::  43%|████▎     | 214/500 [00:10<00:15, 18.07it/s]
eval model::  43%|████▎     | 216/500 [00:11<00:16, 16.84it/s]
eval model::  44%|████▎     | 218/500 [00:11<00:17, 16.08it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:15, 18.22it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:14, 19.38it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:14, 19.13it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:14, 18.46it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:15, 17.79it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:13, 19.74it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.00it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 21.80it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 20.64it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:12, 21.14it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 22.25it/s]
eval model::  50%|█████     | 251/500 [00:12<00:11, 22.20it/s]
eval model::  51%|█████     | 255/500 [00:12<00:10, 23.80it/s]
eval model::  52%|█████▏    | 258/500 [00:12<00:09, 24.79it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:10, 23.71it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:10, 22.02it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:10, 21.70it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:10, 22.47it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:10, 22.35it/s]
eval model::  55%|█████▌    | 276/500 [00:13<00:11, 20.11it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 18.68it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 19.89it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:14, 15.20it/s]
eval model::  58%|█████▊    | 288/500 [00:14<00:11, 17.76it/s]
eval model::  58%|█████▊    | 292/500 [00:14<00:10, 20.53it/s]
eval model::  59%|█████▉    | 295/500 [00:14<00:11, 18.45it/s]
eval model::  60%|█████▉    | 298/500 [00:15<00:09, 20.43it/s]
eval model::  60%|██████    | 301/500 [00:15<00:11, 17.31it/s]
eval model::  61%|██████    | 304/500 [00:15<00:10, 18.02it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:09, 20.23it/s]
eval model::  62%|██████▏   | 311/500 [00:15<00:08, 23.20it/s]
eval model::  63%|██████▎   | 314/500 [00:15<00:07, 24.61it/s]
eval model::  63%|██████▎   | 317/500 [00:15<00:07, 24.83it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:07, 24.01it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:08, 21.06it/s]
eval model::  65%|██████▌   | 326/500 [00:16<00:08, 21.04it/s]
eval model::  66%|██████▌   | 329/500 [00:16<00:08, 19.88it/s]
eval model::  66%|██████▋   | 332/500 [00:16<00:08, 19.37it/s]
eval model::  67%|██████▋   | 335/500 [00:16<00:08, 20.45it/s]
eval model::  68%|██████▊   | 338/500 [00:16<00:08, 19.50it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 17.11it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:07, 19.76it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 20.89it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:06, 23.04it/s]
eval model::  70%|███████   | 352/500 [00:17<00:06, 21.29it/s]
eval model::  71%|███████   | 355/500 [00:17<00:06, 21.04it/s]
eval model::  72%|███████▏  | 358/500 [00:17<00:07, 19.47it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 19.24it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.29it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:06, 20.03it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:06, 20.34it/s]
eval model::  75%|███████▍  | 373/500 [00:18<00:06, 19.76it/s]
eval model::  75%|███████▌  | 376/500 [00:18<00:06, 19.22it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:06, 20.06it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:06, 19.63it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:06, 19.29it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:08, 13.55it/s]
eval model::  78%|███████▊  | 389/500 [00:19<00:06, 16.26it/s]
eval model::  78%|███████▊  | 392/500 [00:19<00:06, 17.49it/s]
eval model::  79%|███████▉  | 394/500 [00:19<00:06, 17.11it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 19.30it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 19.02it/s]
eval model::  80%|████████  | 402/500 [00:20<00:05, 18.74it/s]
eval model::  81%|████████  | 404/500 [00:20<00:05, 18.88it/s]
eval model::  81%|████████▏ | 407/500 [00:20<00:04, 20.37it/s]
eval model::  82%|████████▏ | 410/500 [00:20<00:04, 21.12it/s]
eval model::  83%|████████▎ | 413/500 [00:20<00:04, 20.67it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:04, 20.24it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 19.48it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:04, 17.67it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:03, 19.34it/s]
eval model::  85%|████████▌ | 427/500 [00:21<00:03, 21.28it/s]
eval model::  86%|████████▌ | 430/500 [00:21<00:03, 23.18it/s]
eval model::  87%|████████▋ | 433/500 [00:21<00:02, 23.90it/s]
eval model::  87%|████████▋ | 436/500 [00:21<00:03, 21.10it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 21.92it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 21.48it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 22.12it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 21.08it/s]
eval model::  90%|█████████ | 451/500 [00:22<00:02, 21.31it/s]
eval model::  91%|█████████ | 454/500 [00:22<00:02, 20.38it/s]
eval model::  91%|█████████▏| 457/500 [00:22<00:02, 20.45it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 20.01it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:02, 18.03it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:02, 17.04it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 19.00it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 20.56it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 18.43it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 20.37it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 20.05it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 20.52it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 18.59it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 20.50it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 19.02it/s]
eval model::  99%|█████████▉| 495/500 [00:24<00:00, 20.85it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 20.83it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.85it/s]
[2024/08/01 18:13:38] ppocr INFO: cur metric, precision: 0.4462989840348331, recall: 0.2961001444390948, hmean: 0.35600578871201155, fps: 44.43722218286895
[2024/08/01 18:13:38] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 18:13:38] ppocr INFO: best metric, hmean: 0.35600578871201155, precision: 0.4462989840348331, recall: 0.2961001444390948, fps: 44.43722218286895, best_epoch: 40
[2024/08/01 18:13:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:13:45] ppocr INFO: epoch: [41/100], global_step: 123, lr: 0.001000, loss: 2.859583, loss_shrink_maps: 1.746201, loss_threshold_maps: 0.800356, loss_binary_maps: 0.313899, avg_reader_cost: 1.55946 s, avg_batch_cost: 1.78318 s, avg_samples: 12.5, ips: 7.00995 samples/s, eta: 0:21:51
[2024/08/01 18:13:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:13:53] ppocr INFO: epoch: [42/100], global_step: 126, lr: 0.001000, loss: 2.800212, loss_shrink_maps: 1.705390, loss_threshold_maps: 0.798770, loss_binary_maps: 0.308249, avg_reader_cost: 1.60723 s, avg_batch_cost: 1.89054 s, avg_samples: 12.5, ips: 6.61188 samples/s, eta: 0:21:24
[2024/08/01 18:13:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:14:00] ppocr INFO: epoch: [43/100], global_step: 129, lr: 0.001000, loss: 2.724212, loss_shrink_maps: 1.602625, loss_threshold_maps: 0.795272, loss_binary_maps: 0.292876, avg_reader_cost: 1.54902 s, avg_batch_cost: 1.77008 s, avg_samples: 12.5, ips: 7.06181 samples/s, eta: 0:20:56
[2024/08/01 18:14:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:14:06] ppocr INFO: epoch: [44/100], global_step: 130, lr: 0.001000, loss: 2.689392, loss_shrink_maps: 1.593542, loss_threshold_maps: 0.795272, loss_binary_maps: 0.292876, avg_reader_cost: 0.40773 s, avg_batch_cost: 0.49694 s, avg_samples: 4.8, ips: 9.65906 samples/s, eta: 0:20:46
[2024/08/01 18:14:07] ppocr INFO: epoch: [44/100], global_step: 132, lr: 0.001000, loss: 2.724212, loss_shrink_maps: 1.593542, loss_threshold_maps: 0.797332, loss_binary_maps: 0.296605, avg_reader_cost: 1.08179 s, avg_batch_cost: 1.22136 s, avg_samples: 7.7, ips: 6.30447 samples/s, eta: 0:20:28
[2024/08/01 18:14:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:14:14] ppocr INFO: epoch: [45/100], global_step: 135, lr: 0.001000, loss: 2.684150, loss_shrink_maps: 1.588328, loss_threshold_maps: 0.797332, loss_binary_maps: 0.292876, avg_reader_cost: 1.56459 s, avg_batch_cost: 1.78945 s, avg_samples: 12.5, ips: 6.98537 samples/s, eta: 0:20:01
[2024/08/01 18:14:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:14:22] ppocr INFO: epoch: [46/100], global_step: 138, lr: 0.001000, loss: 2.684150, loss_shrink_maps: 1.588328, loss_threshold_maps: 0.795863, loss_binary_maps: 0.292964, avg_reader_cost: 1.60793 s, avg_batch_cost: 1.88644 s, avg_samples: 12.5, ips: 6.62624 samples/s, eta: 0:19:36
[2024/08/01 18:14:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:14:28] ppocr INFO: epoch: [47/100], global_step: 140, lr: 0.001000, loss: 2.684150, loss_shrink_maps: 1.590146, loss_threshold_maps: 0.792928, loss_binary_maps: 0.296605, avg_reader_cost: 0.92033 s, avg_batch_cost: 1.08662 s, avg_samples: 9.6, ips: 8.83477 samples/s, eta: 0:19:17
[2024/08/01 18:14:29] ppocr INFO: epoch: [47/100], global_step: 141, lr: 0.001000, loss: 2.684150, loss_shrink_maps: 1.590146, loss_threshold_maps: 0.791408, loss_binary_maps: 0.296605, avg_reader_cost: 0.58721 s, avg_batch_cost: 0.64004 s, avg_samples: 2.9, ips: 4.53098 samples/s, eta: 0:19:09
[2024/08/01 18:14:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:14:36] ppocr INFO: epoch: [48/100], global_step: 144, lr: 0.001000, loss: 2.640557, loss_shrink_maps: 1.559869, loss_threshold_maps: 0.786548, loss_binary_maps: 0.289890, avg_reader_cost: 1.51823 s, avg_batch_cost: 1.75166 s, avg_samples: 12.5, ips: 7.13609 samples/s, eta: 0:18:43
[2024/08/01 18:14:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:14:43] ppocr INFO: epoch: [49/100], global_step: 147, lr: 0.001000, loss: 2.662575, loss_shrink_maps: 1.576767, loss_threshold_maps: 0.786548, loss_binary_maps: 0.295986, avg_reader_cost: 1.58080 s, avg_batch_cost: 1.81121 s, avg_samples: 12.5, ips: 6.90148 samples/s, eta: 0:18:17
[2024/08/01 18:14:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:14:50] ppocr INFO: epoch: [50/100], global_step: 150, lr: 0.001000, loss: 2.688912, loss_shrink_maps: 1.601874, loss_threshold_maps: 0.790851, loss_binary_maps: 0.299737, avg_reader_cost: 1.54299 s, avg_batch_cost: 1.78666 s, avg_samples: 12.5, ips: 6.99628 samples/s, eta: 0:17:52
[2024/08/01 18:14:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:14:58] ppocr INFO: epoch: [51/100], global_step: 153, lr: 0.001000, loss: 2.643059, loss_shrink_maps: 1.530937, loss_threshold_maps: 0.780578, loss_binary_maps: 0.287771, avg_reader_cost: 1.54547 s, avg_batch_cost: 1.76373 s, avg_samples: 12.5, ips: 7.08723 samples/s, eta: 0:17:27
[2024/08/01 18:14:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:15:05] ppocr INFO: epoch: [52/100], global_step: 156, lr: 0.001000, loss: 2.609230, loss_shrink_maps: 1.512848, loss_threshold_maps: 0.781693, loss_binary_maps: 0.285174, avg_reader_cost: 1.49095 s, avg_batch_cost: 1.71422 s, avg_samples: 12.5, ips: 7.29194 samples/s, eta: 0:17:02
[2024/08/01 18:15:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:15:12] ppocr INFO: epoch: [53/100], global_step: 159, lr: 0.001000, loss: 2.583328, loss_shrink_maps: 1.511180, loss_threshold_maps: 0.781693, loss_binary_maps: 0.284269, avg_reader_cost: 1.51139 s, avg_batch_cost: 1.75209 s, avg_samples: 12.5, ips: 7.13432 samples/s, eta: 0:16:37
[2024/08/01 18:15:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:15:18] ppocr INFO: epoch: [54/100], global_step: 160, lr: 0.001000, loss: 2.574001, loss_shrink_maps: 1.503162, loss_threshold_maps: 0.780076, loss_binary_maps: 0.283494, avg_reader_cost: 0.41480 s, avg_batch_cost: 0.50557 s, avg_samples: 4.8, ips: 9.49427 samples/s, eta: 0:16:28
[2024/08/01 18:15:19] ppocr INFO: epoch: [54/100], global_step: 162, lr: 0.001000, loss: 2.541956, loss_shrink_maps: 1.474900, loss_threshold_maps: 0.776686, loss_binary_maps: 0.278400, avg_reader_cost: 1.09928 s, avg_batch_cost: 1.23912 s, avg_samples: 7.7, ips: 6.21408 samples/s, eta: 0:16:13
[2024/08/01 18:15:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:15:26] ppocr INFO: epoch: [55/100], global_step: 165, lr: 0.001000, loss: 2.505814, loss_shrink_maps: 1.462624, loss_threshold_maps: 0.767168, loss_binary_maps: 0.277114, avg_reader_cost: 1.49030 s, avg_batch_cost: 1.71333 s, avg_samples: 12.5, ips: 7.29572 samples/s, eta: 0:15:48
[2024/08/01 18:15:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:15:33] ppocr INFO: epoch: [56/100], global_step: 168, lr: 0.001000, loss: 2.505814, loss_shrink_maps: 1.462624, loss_threshold_maps: 0.766094, loss_binary_maps: 0.277114, avg_reader_cost: 1.54625 s, avg_batch_cost: 1.78429 s, avg_samples: 12.5, ips: 7.00558 samples/s, eta: 0:15:25
[2024/08/01 18:15:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:15:40] ppocr INFO: epoch: [57/100], global_step: 170, lr: 0.001000, loss: 2.529082, loss_shrink_maps: 1.479753, loss_threshold_maps: 0.771756, loss_binary_maps: 0.280314, avg_reader_cost: 0.95724 s, avg_batch_cost: 1.12373 s, avg_samples: 9.6, ips: 8.54297 samples/s, eta: 0:15:08
[2024/08/01 18:15:41] ppocr INFO: epoch: [57/100], global_step: 171, lr: 0.001000, loss: 2.529082, loss_shrink_maps: 1.479753, loss_threshold_maps: 0.771756, loss_binary_maps: 0.280314, avg_reader_cost: 0.60569 s, avg_batch_cost: 0.65794 s, avg_samples: 2.9, ips: 4.40773 samples/s, eta: 0:15:01
[2024/08/01 18:15:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:15:48] ppocr INFO: epoch: [58/100], global_step: 174, lr: 0.001000, loss: 2.580708, loss_shrink_maps: 1.493304, loss_threshold_maps: 0.789481, loss_binary_maps: 0.287265, avg_reader_cost: 1.53332 s, avg_batch_cost: 1.75250 s, avg_samples: 12.5, ips: 7.13267 samples/s, eta: 0:14:38
[2024/08/01 18:15:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:15:56] ppocr INFO: epoch: [59/100], global_step: 177, lr: 0.001000, loss: 2.694612, loss_shrink_maps: 1.605105, loss_threshold_maps: 0.792660, loss_binary_maps: 0.299596, avg_reader_cost: 1.59137 s, avg_batch_cost: 1.88508 s, avg_samples: 12.5, ips: 6.63103 samples/s, eta: 0:14:15
[2024/08/01 18:15:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:16:03] ppocr INFO: epoch: [60/100], global_step: 180, lr: 0.001000, loss: 2.669985, loss_shrink_maps: 1.599652, loss_threshold_maps: 0.792660, loss_binary_maps: 0.298280, avg_reader_cost: 1.57663 s, avg_batch_cost: 1.79969 s, avg_samples: 12.5, ips: 6.94565 samples/s, eta: 0:13:53

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:35, 14.00it/s]
eval model::   1%|          | 5/500 [00:00<00:22, 21.77it/s]
eval model::   2%|▏         | 8/500 [00:00<00:24, 20.09it/s]
eval model::   2%|▏         | 11/500 [00:00<00:24, 20.18it/s]
eval model::   3%|▎         | 14/500 [00:00<00:24, 19.74it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.85it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 19.17it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 20.97it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 19.84it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 21.20it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 20.15it/s]
eval model::   7%|▋         | 35/500 [00:01<00:23, 19.48it/s]
eval model::   8%|▊         | 38/500 [00:01<00:21, 21.11it/s]
eval model::   8%|▊         | 41/500 [00:02<00:21, 21.20it/s]
eval model::   9%|▉         | 44/500 [00:02<00:21, 20.93it/s]
eval model::   9%|▉         | 47/500 [00:02<00:23, 19.44it/s]
eval model::  10%|█         | 50/500 [00:02<00:22, 19.80it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 20.54it/s]
eval model::  11%|█         | 56/500 [00:02<00:21, 20.56it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:21, 20.88it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:23, 18.56it/s]
eval model::  13%|█▎        | 65/500 [00:03<00:22, 19.65it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:19, 21.88it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:19, 21.92it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:21, 19.94it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:21, 20.00it/s]
eval model::  16%|█▌        | 80/500 [00:03<00:19, 21.10it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:19, 21.42it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:20, 19.91it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:20, 19.62it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.59it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.55it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:22, 17.83it/s]
eval model::  20%|█▉        | 98/500 [00:04<00:20, 19.87it/s]
eval model::  20%|██        | 101/500 [00:05<00:20, 19.60it/s]
eval model::  21%|██        | 103/500 [00:05<00:20, 19.31it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 19.84it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:20, 18.77it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:21, 18.53it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:21, 18.07it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:19, 19.96it/s]
eval model::  24%|██▎       | 118/500 [00:05<00:19, 19.78it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.33it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:17, 21.72it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:16, 22.87it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:17, 20.72it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:16, 21.91it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:16, 21.93it/s]
eval model::  28%|██▊       | 139/500 [00:06<00:18, 19.74it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.14it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.42it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:17, 19.69it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 18.08it/s]
eval model::  31%|███       | 154/500 [00:07<00:17, 20.30it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:16, 21.03it/s]
eval model::  32%|███▏      | 160/500 [00:07<00:17, 19.52it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:16, 21.05it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.52it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 19.08it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:19, 17.11it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:19, 16.93it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:17, 18.50it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:16, 19.02it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:15, 21.05it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:15, 19.86it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 20.98it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:14, 21.96it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:15, 19.29it/s]
eval model::  40%|███▉      | 198/500 [00:09<00:15, 19.83it/s]
eval model::  40%|████      | 201/500 [00:10<00:14, 20.40it/s]
eval model::  41%|████      | 204/500 [00:10<00:13, 22.52it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:18, 16.07it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:17, 16.69it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:16, 17.23it/s]
eval model::  43%|████▎     | 214/500 [00:10<00:14, 19.28it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:17, 16.15it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 18.10it/s]
eval model::  45%|████▍     | 223/500 [00:11<00:14, 19.24it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:12, 21.18it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:11, 22.83it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:10, 24.63it/s]
eval model::  47%|████▋     | 236/500 [00:11<00:11, 23.14it/s]
eval model::  48%|████▊     | 239/500 [00:11<00:10, 24.38it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:11, 22.43it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:10, 23.91it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 22.66it/s]
eval model::  50%|█████     | 251/500 [00:12<00:11, 22.10it/s]
eval model::  51%|█████     | 254/500 [00:12<00:11, 21.57it/s]
eval model::  51%|█████▏    | 257/500 [00:12<00:11, 21.49it/s]
eval model::  52%|█████▏    | 260/500 [00:12<00:11, 20.98it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:12, 19.75it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:12, 19.15it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:11, 19.42it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:11, 19.90it/s]
eval model::  55%|█████▌    | 275/500 [00:13<00:11, 20.11it/s]
eval model::  56%|█████▌    | 278/500 [00:13<00:10, 21.31it/s]
eval model::  56%|█████▌    | 281/500 [00:13<00:10, 20.44it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 21.08it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:12, 17.11it/s]
eval model::  58%|█████▊    | 291/500 [00:14<00:10, 20.03it/s]
eval model::  59%|█████▉    | 294/500 [00:14<00:10, 20.00it/s]
eval model::  59%|█████▉    | 297/500 [00:14<00:10, 19.66it/s]
eval model::  60%|██████    | 300/500 [00:14<00:10, 19.27it/s]
eval model::  61%|██████    | 303/500 [00:15<00:09, 20.44it/s]
eval model::  61%|██████    | 306/500 [00:15<00:09, 21.42it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:08, 22.10it/s]
eval model::  62%|██████▏   | 312/500 [00:15<00:08, 21.72it/s]
eval model::  63%|██████▎   | 315/500 [00:15<00:09, 20.25it/s]
eval model::  64%|██████▎   | 318/500 [00:15<00:08, 21.04it/s]
eval model::  64%|██████▍   | 321/500 [00:15<00:08, 20.98it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:09, 19.47it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:08, 20.60it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:08, 19.57it/s]
eval model::  67%|██████▋   | 333/500 [00:16<00:08, 20.05it/s]
eval model::  67%|██████▋   | 336/500 [00:16<00:08, 20.46it/s]
eval model::  68%|██████▊   | 339/500 [00:16<00:08, 20.10it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:08, 18.46it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:07, 20.38it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:07, 20.29it/s]
eval model::  70%|███████   | 351/500 [00:17<00:08, 18.20it/s]
eval model::  71%|███████   | 354/500 [00:17<00:07, 20.56it/s]
eval model::  71%|███████▏  | 357/500 [00:17<00:07, 19.93it/s]
eval model::  72%|███████▏  | 360/500 [00:17<00:06, 21.07it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:06, 21.07it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:06, 19.71it/s]
eval model::  74%|███████▍  | 369/500 [00:18<00:06, 20.46it/s]
eval model::  74%|███████▍  | 372/500 [00:18<00:06, 19.45it/s]
eval model::  75%|███████▌  | 376/500 [00:18<00:05, 21.50it/s]
eval model::  76%|███████▌  | 379/500 [00:18<00:05, 21.86it/s]
eval model::  76%|███████▋  | 382/500 [00:18<00:05, 20.68it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 21.54it/s]
eval model::  78%|███████▊  | 388/500 [00:19<00:06, 16.97it/s]
eval model::  78%|███████▊  | 391/500 [00:19<00:05, 18.60it/s]
eval model::  79%|███████▉  | 394/500 [00:19<00:05, 19.53it/s]
eval model::  79%|███████▉  | 397/500 [00:19<00:05, 20.44it/s]
eval model::  80%|████████  | 400/500 [00:19<00:04, 21.45it/s]
eval model::  81%|████████  | 403/500 [00:19<00:04, 23.41it/s]
eval model::  81%|████████  | 406/500 [00:20<00:04, 23.47it/s]
eval model::  82%|████████▏ | 409/500 [00:20<00:04, 21.38it/s]
eval model::  82%|████████▏ | 412/500 [00:20<00:04, 21.99it/s]
eval model::  83%|████████▎ | 415/500 [00:20<00:04, 20.43it/s]
eval model::  84%|████████▎ | 418/500 [00:20<00:04, 18.33it/s]
eval model::  84%|████████▍ | 420/500 [00:20<00:04, 18.00it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 17.59it/s]
eval model::  85%|████████▌ | 425/500 [00:21<00:03, 18.92it/s]
eval model::  86%|████████▌ | 428/500 [00:21<00:03, 20.68it/s]
eval model::  86%|████████▌ | 431/500 [00:21<00:03, 20.55it/s]
eval model::  87%|████████▋ | 434/500 [00:21<00:03, 20.43it/s]
eval model::  87%|████████▋ | 437/500 [00:21<00:03, 20.27it/s]
eval model::  88%|████████▊ | 440/500 [00:21<00:03, 18.66it/s]
eval model::  89%|████████▊ | 443/500 [00:22<00:02, 20.75it/s]
eval model::  89%|████████▉ | 446/500 [00:22<00:02, 18.66it/s]
eval model::  90%|████████▉ | 449/500 [00:22<00:02, 18.07it/s]
eval model::  90%|█████████ | 452/500 [00:22<00:02, 20.30it/s]
eval model::  91%|█████████ | 455/500 [00:22<00:02, 20.61it/s]
eval model::  92%|█████████▏| 458/500 [00:22<00:02, 20.74it/s]
eval model::  92%|█████████▏| 461/500 [00:22<00:01, 21.40it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 20.10it/s]
eval model::  93%|█████████▎| 467/500 [00:23<00:01, 18.17it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 21.30it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 20.91it/s]
eval model::  95%|█████████▌| 477/500 [00:23<00:01, 20.56it/s]
eval model::  96%|█████████▌| 480/500 [00:23<00:00, 20.12it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 20.58it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 19.35it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 20.45it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 19.39it/s]
eval model::  99%|█████████▉| 495/500 [00:24<00:00, 20.35it/s]
eval model:: 100%|█████████▉| 498/500 [00:24<00:00, 20.76it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 20.13it/s]
[2024/08/01 18:16:28] ppocr INFO: cur metric, precision: 0.44954128440366975, recall: 0.25950890707751567, hmean: 0.3290598290598291, fps: 46.10668784767683
[2024/08/01 18:16:28] ppocr INFO: best metric, hmean: 0.35600578871201155, precision: 0.4462989840348331, recall: 0.2961001444390948, fps: 44.43722218286895, best_epoch: 40
[2024/08/01 18:16:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:16:35] ppocr INFO: epoch: [61/100], global_step: 183, lr: 0.001000, loss: 2.741386, loss_shrink_maps: 1.642321, loss_threshold_maps: 0.795108, loss_binary_maps: 0.309163, avg_reader_cost: 1.56411 s, avg_batch_cost: 1.80658 s, avg_samples: 12.5, ips: 6.91914 samples/s, eta: 0:13:30
[2024/08/01 18:16:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:16:42] ppocr INFO: epoch: [62/100], global_step: 186, lr: 0.001000, loss: 2.733625, loss_shrink_maps: 1.646488, loss_threshold_maps: 0.804018, loss_binary_maps: 0.308921, avg_reader_cost: 1.54339 s, avg_batch_cost: 1.77778 s, avg_samples: 12.5, ips: 7.03122 samples/s, eta: 0:13:07
[2024/08/01 18:16:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:16:49] ppocr INFO: epoch: [63/100], global_step: 189, lr: 0.001000, loss: 2.770908, loss_shrink_maps: 1.653917, loss_threshold_maps: 0.808332, loss_binary_maps: 0.311870, avg_reader_cost: 1.48370 s, avg_batch_cost: 1.70926 s, avg_samples: 12.5, ips: 7.31309 samples/s, eta: 0:12:45
[2024/08/01 18:16:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:16:56] ppocr INFO: epoch: [64/100], global_step: 190, lr: 0.001000, loss: 2.733625, loss_shrink_maps: 1.640873, loss_threshold_maps: 0.801866, loss_binary_maps: 0.307920, avg_reader_cost: 0.48396 s, avg_batch_cost: 0.59521 s, avg_samples: 4.8, ips: 8.06442 samples/s, eta: 0:12:37
[2024/08/01 18:16:57] ppocr INFO: epoch: [64/100], global_step: 192, lr: 0.001000, loss: 2.770908, loss_shrink_maps: 1.653917, loss_threshold_maps: 0.808296, loss_binary_maps: 0.311870, avg_reader_cost: 1.27892 s, avg_batch_cost: 1.41926 s, avg_samples: 7.7, ips: 5.42536 samples/s, eta: 0:12:24
[2024/08/01 18:16:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:17:05] ppocr INFO: epoch: [65/100], global_step: 195, lr: 0.001000, loss: 2.719126, loss_shrink_maps: 1.599088, loss_threshold_maps: 0.810944, loss_binary_maps: 0.302728, avg_reader_cost: 1.51380 s, avg_batch_cost: 1.75413 s, avg_samples: 12.5, ips: 7.12603 samples/s, eta: 0:12:01
[2024/08/01 18:17:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:17:12] ppocr INFO: epoch: [66/100], global_step: 198, lr: 0.001000, loss: 2.641654, loss_shrink_maps: 1.545756, loss_threshold_maps: 0.808296, loss_binary_maps: 0.290077, avg_reader_cost: 1.60225 s, avg_batch_cost: 1.85410 s, avg_samples: 12.5, ips: 6.74181 samples/s, eta: 0:11:39
[2024/08/01 18:17:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:17:19] ppocr INFO: epoch: [67/100], global_step: 200, lr: 0.001000, loss: 2.674567, loss_shrink_maps: 1.563972, loss_threshold_maps: 0.810944, loss_binary_maps: 0.295797, avg_reader_cost: 0.91703 s, avg_batch_cost: 1.08924 s, avg_samples: 9.6, ips: 8.81349 samples/s, eta: 0:11:24
[2024/08/01 18:17:19] ppocr INFO: epoch: [67/100], global_step: 201, lr: 0.001000, loss: 2.674567, loss_shrink_maps: 1.563972, loss_threshold_maps: 0.811667, loss_binary_maps: 0.295797, avg_reader_cost: 0.58859 s, avg_batch_cost: 0.64083 s, avg_samples: 2.9, ips: 4.52539 samples/s, eta: 0:11:17
[2024/08/01 18:17:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:17:27] ppocr INFO: epoch: [68/100], global_step: 204, lr: 0.001000, loss: 2.611973, loss_shrink_maps: 1.524972, loss_threshold_maps: 0.810944, loss_binary_maps: 0.292279, avg_reader_cost: 1.55963 s, avg_batch_cost: 1.77864 s, avg_samples: 12.5, ips: 7.02786 samples/s, eta: 0:10:55
[2024/08/01 18:17:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:17:34] ppocr INFO: epoch: [69/100], global_step: 207, lr: 0.001000, loss: 2.586802, loss_shrink_maps: 1.519722, loss_threshold_maps: 0.785335, loss_binary_maps: 0.289134, avg_reader_cost: 1.60770 s, avg_batch_cost: 1.88668 s, avg_samples: 12.5, ips: 6.62539 samples/s, eta: 0:10:34
[2024/08/01 18:17:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:17:42] ppocr INFO: epoch: [70/100], global_step: 210, lr: 0.001000, loss: 2.604886, loss_shrink_maps: 1.522514, loss_threshold_maps: 0.794571, loss_binary_maps: 0.292279, avg_reader_cost: 1.62172 s, avg_batch_cost: 1.89543 s, avg_samples: 12.5, ips: 6.59482 samples/s, eta: 0:10:13
[2024/08/01 18:17:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:17:49] ppocr INFO: epoch: [71/100], global_step: 213, lr: 0.001000, loss: 2.552620, loss_shrink_maps: 1.477390, loss_threshold_maps: 0.785335, loss_binary_maps: 0.282744, avg_reader_cost: 1.47769 s, avg_batch_cost: 1.70345 s, avg_samples: 12.5, ips: 7.33807 samples/s, eta: 0:09:51
[2024/08/01 18:17:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:17:56] ppocr INFO: epoch: [72/100], global_step: 216, lr: 0.001000, loss: 2.514933, loss_shrink_maps: 1.451399, loss_threshold_maps: 0.785335, loss_binary_maps: 0.281126, avg_reader_cost: 1.48051 s, avg_batch_cost: 1.69955 s, avg_samples: 12.5, ips: 7.35489 samples/s, eta: 0:09:30
[2024/08/01 18:17:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:18:03] ppocr INFO: epoch: [73/100], global_step: 219, lr: 0.001000, loss: 2.594768, loss_shrink_maps: 1.537688, loss_threshold_maps: 0.790860, loss_binary_maps: 0.298404, avg_reader_cost: 1.50164 s, avg_batch_cost: 1.72667 s, avg_samples: 12.5, ips: 7.23936 samples/s, eta: 0:09:08
[2024/08/01 18:18:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:18:09] ppocr INFO: epoch: [74/100], global_step: 220, lr: 0.001000, loss: 2.564322, loss_shrink_maps: 1.500784, loss_threshold_maps: 0.788237, loss_binary_maps: 0.289217, avg_reader_cost: 0.40571 s, avg_batch_cost: 0.50885 s, avg_samples: 4.8, ips: 9.43302 samples/s, eta: 0:09:01
[2024/08/01 18:18:10] ppocr INFO: epoch: [74/100], global_step: 222, lr: 0.001000, loss: 2.579374, loss_shrink_maps: 1.510708, loss_threshold_maps: 0.790860, loss_binary_maps: 0.294171, avg_reader_cost: 1.10529 s, avg_batch_cost: 1.24488 s, avg_samples: 7.7, ips: 6.18534 samples/s, eta: 0:08:47
[2024/08/01 18:18:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:18:17] ppocr INFO: epoch: [75/100], global_step: 225, lr: 0.001000, loss: 2.576137, loss_shrink_maps: 1.495310, loss_threshold_maps: 0.788237, loss_binary_maps: 0.292460, avg_reader_cost: 1.51806 s, avg_batch_cost: 1.75170 s, avg_samples: 12.5, ips: 7.13594 samples/s, eta: 0:08:26
[2024/08/01 18:18:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:18:25] ppocr INFO: epoch: [76/100], global_step: 228, lr: 0.001000, loss: 2.576137, loss_shrink_maps: 1.493733, loss_threshold_maps: 0.790860, loss_binary_maps: 0.292460, avg_reader_cost: 1.53554 s, avg_batch_cost: 1.78624 s, avg_samples: 12.5, ips: 6.99794 samples/s, eta: 0:08:05
[2024/08/01 18:18:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:18:31] ppocr INFO: epoch: [77/100], global_step: 230, lr: 0.001000, loss: 2.540976, loss_shrink_maps: 1.470252, loss_threshold_maps: 0.776463, loss_binary_maps: 0.285446, avg_reader_cost: 0.93378 s, avg_batch_cost: 1.10736 s, avg_samples: 9.6, ips: 8.66926 samples/s, eta: 0:07:50
[2024/08/01 18:18:32] ppocr INFO: epoch: [77/100], global_step: 231, lr: 0.001000, loss: 2.497274, loss_shrink_maps: 1.439040, loss_threshold_maps: 0.776463, loss_binary_maps: 0.278949, avg_reader_cost: 0.59761 s, avg_batch_cost: 0.64982 s, avg_samples: 2.9, ips: 4.46279 samples/s, eta: 0:07:44
[2024/08/01 18:18:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:18:39] ppocr INFO: epoch: [78/100], global_step: 234, lr: 0.001000, loss: 2.540976, loss_shrink_maps: 1.470252, loss_threshold_maps: 0.776463, loss_binary_maps: 0.285446, avg_reader_cost: 1.53653 s, avg_batch_cost: 1.77065 s, avg_samples: 12.5, ips: 7.05955 samples/s, eta: 0:07:23
[2024/08/01 18:18:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:18:47] ppocr INFO: epoch: [79/100], global_step: 237, lr: 0.001000, loss: 2.527764, loss_shrink_maps: 1.470252, loss_threshold_maps: 0.762001, loss_binary_maps: 0.285446, avg_reader_cost: 1.55702 s, avg_batch_cost: 1.81551 s, avg_samples: 12.5, ips: 6.88513 samples/s, eta: 0:07:02
[2024/08/01 18:18:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:18:54] ppocr INFO: epoch: [80/100], global_step: 240, lr: 0.001000, loss: 2.455808, loss_shrink_maps: 1.415713, loss_threshold_maps: 0.760621, loss_binary_maps: 0.275684, avg_reader_cost: 1.49700 s, avg_batch_cost: 1.72136 s, avg_samples: 12.5, ips: 7.26171 samples/s, eta: 0:06:41

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:39, 12.66it/s]
eval model::   1%|          | 5/500 [00:00<00:27, 18.13it/s]
eval model::   1%|▏         | 7/500 [00:00<00:26, 18.53it/s]
eval model::   2%|▏         | 9/500 [00:00<00:27, 18.09it/s]
eval model::   2%|▏         | 12/500 [00:00<00:24, 19.74it/s]
eval model::   3%|▎         | 14/500 [00:00<00:26, 18.29it/s]
eval model::   3%|▎         | 16/500 [00:00<00:26, 18.47it/s]
eval model::   4%|▍         | 19/500 [00:00<00:23, 20.75it/s]
eval model::   4%|▍         | 22/500 [00:01<00:22, 21.72it/s]
eval model::   5%|▌         | 25/500 [00:01<00:21, 22.44it/s]
eval model::   6%|▌         | 28/500 [00:01<00:20, 23.16it/s]
eval model::   6%|▌         | 31/500 [00:01<00:23, 20.10it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 19.28it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.79it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.36it/s]
eval model::   9%|▊         | 43/500 [00:02<00:22, 19.88it/s]
eval model::   9%|▉         | 46/500 [00:02<00:21, 20.94it/s]
eval model::  10%|█         | 50/500 [00:02<00:19, 23.51it/s]
eval model::  11%|█         | 53/500 [00:02<00:19, 22.56it/s]
eval model::  11%|█         | 56/500 [00:02<00:20, 21.70it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:20, 21.18it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:23, 18.86it/s]
eval model::  13%|█▎        | 65/500 [00:03<00:22, 19.31it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:20, 21.03it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 20.92it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:22, 19.07it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:22, 19.13it/s]
eval model::  16%|█▌        | 81/500 [00:03<00:19, 21.90it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:18, 22.06it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:19, 21.56it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:18, 21.75it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:19, 21.06it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.94it/s]
eval model::  20%|█▉        | 99/500 [00:04<00:20, 19.99it/s]
eval model::  20%|██        | 102/500 [00:04<00:20, 19.80it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.68it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 20.76it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:21, 18.08it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:19, 19.89it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:18, 20.23it/s]
eval model::  24%|██▍       | 120/500 [00:05<00:20, 18.59it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:18, 20.45it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 19.38it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 19.61it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 18.50it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.44it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:17, 20.29it/s]
eval model::  28%|██▊       | 141/500 [00:06<00:17, 20.32it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.15it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:17, 20.65it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.77it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 18.71it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 20.85it/s]
eval model::  32%|███▏      | 159/500 [00:07<00:15, 21.67it/s]
eval model::  32%|███▏      | 162/500 [00:07<00:16, 20.30it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:15, 21.09it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:15, 21.50it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:15, 20.95it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:17, 19.06it/s]
eval model::  35%|███▌      | 177/500 [00:08<00:16, 20.00it/s]
eval model::  36%|███▌      | 180/500 [00:08<00:16, 19.03it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:14, 21.32it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:14, 21.93it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:14, 22.19it/s]
eval model::  38%|███▊      | 192/500 [00:09<00:13, 22.13it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:14, 20.78it/s]
eval model::  40%|███▉      | 198/500 [00:09<00:14, 21.51it/s]
eval model::  40%|████      | 201/500 [00:09<00:14, 20.93it/s]
eval model::  41%|████      | 204/500 [00:09<00:14, 20.76it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:17, 16.29it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:17, 16.79it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:15, 18.11it/s]
eval model::  43%|████▎     | 215/500 [00:10<00:17, 16.70it/s]
eval model::  43%|████▎     | 217/500 [00:10<00:17, 16.37it/s]
eval model::  44%|████▍     | 220/500 [00:10<00:15, 18.08it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:15, 18.23it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:13, 21.02it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:11, 23.69it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:10, 24.95it/s]
eval model::  47%|████▋     | 236/500 [00:11<00:11, 23.94it/s]
eval model::  48%|████▊     | 239/500 [00:11<00:11, 23.71it/s]
eval model::  48%|████▊     | 242/500 [00:11<00:11, 22.06it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:11, 23.14it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 21.85it/s]
eval model::  50%|█████     | 251/500 [00:12<00:11, 20.87it/s]
eval model::  51%|█████     | 254/500 [00:12<00:11, 20.59it/s]
eval model::  51%|█████▏    | 257/500 [00:12<00:11, 21.41it/s]
eval model::  52%|█████▏    | 260/500 [00:12<00:10, 21.88it/s]
eval model::  53%|█████▎    | 263/500 [00:12<00:11, 20.20it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:11, 19.95it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:12, 18.97it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:12, 18.81it/s]
eval model::  55%|█████▌    | 275/500 [00:13<00:12, 18.23it/s]
eval model::  55%|█████▌    | 277/500 [00:13<00:12, 18.19it/s]
eval model::  56%|█████▌    | 279/500 [00:13<00:12, 17.90it/s]
eval model::  56%|█████▋    | 282/500 [00:13<00:10, 20.11it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:14, 15.16it/s]
eval model::  58%|█████▊    | 288/500 [00:14<00:12, 17.50it/s]
eval model::  58%|█████▊    | 292/500 [00:14<00:09, 20.83it/s]
eval model::  59%|█████▉    | 295/500 [00:14<00:10, 19.03it/s]
eval model::  60%|█████▉    | 298/500 [00:14<00:09, 21.06it/s]
eval model::  60%|██████    | 301/500 [00:14<00:09, 20.02it/s]
eval model::  61%|██████    | 304/500 [00:15<00:09, 20.45it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:08, 21.76it/s]
eval model::  62%|██████▏   | 310/500 [00:15<00:08, 22.00it/s]
eval model::  63%|██████▎   | 313/500 [00:15<00:08, 22.00it/s]
eval model::  63%|██████▎   | 316/500 [00:15<00:08, 21.19it/s]
eval model::  64%|██████▍   | 319/500 [00:15<00:08, 22.51it/s]
eval model::  64%|██████▍   | 322/500 [00:15<00:08, 20.98it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:07, 22.01it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:08, 19.89it/s]
eval model::  66%|██████▌   | 331/500 [00:16<00:08, 19.76it/s]
eval model::  67%|██████▋   | 334/500 [00:16<00:07, 21.51it/s]
eval model::  67%|██████▋   | 337/500 [00:16<00:07, 21.10it/s]
eval model::  68%|██████▊   | 340/500 [00:16<00:08, 17.99it/s]
eval model::  69%|██████▊   | 343/500 [00:16<00:08, 19.28it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 21.20it/s]
eval model::  70%|███████   | 350/500 [00:17<00:06, 23.79it/s]
eval model::  71%|███████   | 353/500 [00:17<00:06, 22.54it/s]
eval model::  71%|███████   | 356/500 [00:17<00:06, 20.65it/s]
eval model::  72%|███████▏  | 359/500 [00:17<00:06, 20.76it/s]
eval model::  72%|███████▏  | 362/500 [00:17<00:06, 20.77it/s]
eval model::  73%|███████▎  | 365/500 [00:17<00:06, 20.00it/s]
eval model::  74%|███████▎  | 368/500 [00:18<00:06, 21.56it/s]
eval model::  74%|███████▍  | 371/500 [00:18<00:05, 21.95it/s]
eval model::  75%|███████▌  | 375/500 [00:18<00:05, 24.73it/s]
eval model::  76%|███████▌  | 378/500 [00:18<00:04, 24.66it/s]
eval model::  76%|███████▌  | 381/500 [00:18<00:04, 23.89it/s]
eval model::  77%|███████▋  | 384/500 [00:18<00:05, 21.56it/s]
eval model::  77%|███████▋  | 387/500 [00:19<00:07, 15.12it/s]
eval model::  78%|███████▊  | 390/500 [00:19<00:06, 17.45it/s]
eval model::  79%|███████▊  | 393/500 [00:19<00:05, 19.73it/s]
eval model::  79%|███████▉  | 396/500 [00:19<00:04, 21.08it/s]
eval model::  80%|███████▉  | 399/500 [00:19<00:04, 22.22it/s]
eval model::  80%|████████  | 402/500 [00:19<00:04, 20.05it/s]
eval model::  81%|████████  | 405/500 [00:19<00:04, 20.76it/s]
eval model::  82%|████████▏ | 409/500 [00:20<00:03, 23.29it/s]
eval model::  82%|████████▏ | 412/500 [00:20<00:03, 22.91it/s]
eval model::  83%|████████▎ | 415/500 [00:20<00:03, 22.66it/s]
eval model::  84%|████████▎ | 418/500 [00:20<00:04, 19.52it/s]
eval model::  84%|████████▍ | 421/500 [00:20<00:04, 16.12it/s]
eval model::  85%|████████▍ | 424/500 [00:20<00:04, 17.86it/s]
eval model::  85%|████████▌ | 427/500 [00:21<00:03, 19.50it/s]
eval model::  86%|████████▌ | 430/500 [00:21<00:03, 20.98it/s]
eval model::  87%|████████▋ | 433/500 [00:21<00:03, 22.12it/s]
eval model::  87%|████████▋ | 436/500 [00:21<00:03, 20.88it/s]
eval model::  88%|████████▊ | 439/500 [00:21<00:03, 20.22it/s]
eval model::  88%|████████▊ | 442/500 [00:21<00:02, 19.42it/s]
eval model::  89%|████████▉ | 445/500 [00:21<00:02, 20.04it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 20.75it/s]
eval model::  90%|█████████ | 451/500 [00:22<00:02, 20.78it/s]
eval model::  91%|█████████ | 454/500 [00:22<00:02, 21.13it/s]
eval model::  91%|█████████▏| 457/500 [00:22<00:01, 22.04it/s]
eval model::  92%|█████████▏| 460/500 [00:22<00:02, 19.36it/s]
eval model::  93%|█████████▎| 463/500 [00:22<00:02, 18.23it/s]
eval model::  93%|█████████▎| 465/500 [00:22<00:02, 17.17it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 19.28it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 19.55it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 19.76it/s]
eval model::  95%|█████████▌| 477/500 [00:23<00:01, 21.03it/s]
eval model::  96%|█████████▌| 480/500 [00:23<00:00, 21.71it/s]
eval model::  97%|█████████▋| 483/500 [00:23<00:00, 22.72it/s]
eval model::  97%|█████████▋| 486/500 [00:23<00:00, 19.74it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 20.62it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 21.26it/s]
eval model::  99%|█████████▉| 495/500 [00:24<00:00, 21.52it/s]
eval model:: 100%|█████████▉| 498/500 [00:24<00:00, 21.84it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 20.36it/s]
[2024/08/01 18:19:19] ppocr INFO: cur metric, precision: 0.6117734724292101, recall: 0.39528165623495426, hmean: 0.48025738520035094, fps: 44.1252897392761
[2024/08/01 18:19:19] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 18:19:19] ppocr INFO: best metric, hmean: 0.48025738520035094, precision: 0.6117734724292101, recall: 0.39528165623495426, fps: 44.1252897392761, best_epoch: 80
[2024/08/01 18:19:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:19:26] ppocr INFO: epoch: [81/100], global_step: 243, lr: 0.001000, loss: 2.396393, loss_shrink_maps: 1.360264, loss_threshold_maps: 0.751727, loss_binary_maps: 0.264412, avg_reader_cost: 1.68287 s, avg_batch_cost: 1.96850 s, avg_samples: 12.5, ips: 6.35002 samples/s, eta: 0:06:21
[2024/08/01 18:19:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:19:33] ppocr INFO: epoch: [82/100], global_step: 246, lr: 0.001000, loss: 2.390742, loss_shrink_maps: 1.360264, loss_threshold_maps: 0.740024, loss_binary_maps: 0.264412, avg_reader_cost: 1.49697 s, avg_batch_cost: 1.71560 s, avg_samples: 12.5, ips: 7.28606 samples/s, eta: 0:06:00
[2024/08/01 18:19:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:19:41] ppocr INFO: epoch: [83/100], global_step: 249, lr: 0.001000, loss: 2.398024, loss_shrink_maps: 1.372147, loss_threshold_maps: 0.740024, loss_binary_maps: 0.268710, avg_reader_cost: 1.50348 s, avg_batch_cost: 1.72413 s, avg_samples: 12.5, ips: 7.25003 samples/s, eta: 0:05:40
[2024/08/01 18:19:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:19:46] ppocr INFO: epoch: [84/100], global_step: 250, lr: 0.001000, loss: 2.404293, loss_shrink_maps: 1.394358, loss_threshold_maps: 0.740024, loss_binary_maps: 0.273819, avg_reader_cost: 0.38260 s, avg_batch_cost: 0.50883 s, avg_samples: 4.8, ips: 9.43334 samples/s, eta: 0:05:33
[2024/08/01 18:19:48] ppocr INFO: epoch: [84/100], global_step: 252, lr: 0.001000, loss: 2.439608, loss_shrink_maps: 1.423297, loss_threshold_maps: 0.746299, loss_binary_maps: 0.278409, avg_reader_cost: 1.10528 s, avg_batch_cost: 1.24464 s, avg_samples: 7.7, ips: 6.18651 samples/s, eta: 0:05:19
[2024/08/01 18:19:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:19:55] ppocr INFO: epoch: [85/100], global_step: 255, lr: 0.001000, loss: 2.420098, loss_shrink_maps: 1.394358, loss_threshold_maps: 0.746299, loss_binary_maps: 0.273819, avg_reader_cost: 1.52414 s, avg_batch_cost: 1.75495 s, avg_samples: 12.5, ips: 7.12271 samples/s, eta: 0:04:59
[2024/08/01 18:19:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:20:03] ppocr INFO: epoch: [86/100], global_step: 258, lr: 0.001000, loss: 2.420098, loss_shrink_maps: 1.394358, loss_threshold_maps: 0.746299, loss_binary_maps: 0.273819, avg_reader_cost: 1.58948 s, avg_batch_cost: 1.80863 s, avg_samples: 12.5, ips: 6.91132 samples/s, eta: 0:04:38
[2024/08/01 18:20:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:20:09] ppocr INFO: epoch: [87/100], global_step: 260, lr: 0.001000, loss: 2.409462, loss_shrink_maps: 1.377930, loss_threshold_maps: 0.744447, loss_binary_maps: 0.270056, avg_reader_cost: 0.91790 s, avg_batch_cost: 1.08467 s, avg_samples: 9.6, ips: 8.85061 samples/s, eta: 0:04:25
[2024/08/01 18:20:10] ppocr INFO: epoch: [87/100], global_step: 261, lr: 0.001000, loss: 2.420098, loss_shrink_maps: 1.394358, loss_threshold_maps: 0.744447, loss_binary_maps: 0.273819, avg_reader_cost: 0.58620 s, avg_batch_cost: 0.63851 s, avg_samples: 2.9, ips: 4.54184 samples/s, eta: 0:04:18
[2024/08/01 18:20:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:20:17] ppocr INFO: epoch: [88/100], global_step: 264, lr: 0.001000, loss: 2.374943, loss_shrink_maps: 1.353950, loss_threshold_maps: 0.739561, loss_binary_maps: 0.264788, avg_reader_cost: 1.61884 s, avg_batch_cost: 1.83812 s, avg_samples: 12.5, ips: 6.80042 samples/s, eta: 0:03:58
[2024/08/01 18:20:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:20:24] ppocr INFO: epoch: [89/100], global_step: 267, lr: 0.001000, loss: 2.409462, loss_shrink_maps: 1.365098, loss_threshold_maps: 0.742023, loss_binary_maps: 0.266683, avg_reader_cost: 1.48039 s, avg_batch_cost: 1.72059 s, avg_samples: 12.5, ips: 7.26495 samples/s, eta: 0:03:38
[2024/08/01 18:20:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:20:32] ppocr INFO: epoch: [90/100], global_step: 270, lr: 0.001000, loss: 2.385764, loss_shrink_maps: 1.353950, loss_threshold_maps: 0.742023, loss_binary_maps: 0.264788, avg_reader_cost: 1.49291 s, avg_batch_cost: 1.71983 s, avg_samples: 12.5, ips: 7.26816 samples/s, eta: 0:03:18
[2024/08/01 18:20:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:20:39] ppocr INFO: epoch: [91/100], global_step: 273, lr: 0.001000, loss: 2.327370, loss_shrink_maps: 1.334060, loss_threshold_maps: 0.739345, loss_binary_maps: 0.261076, avg_reader_cost: 1.54143 s, avg_batch_cost: 1.80413 s, avg_samples: 12.5, ips: 6.92853 samples/s, eta: 0:02:58
[2024/08/01 18:20:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:20:47] ppocr INFO: epoch: [92/100], global_step: 276, lr: 0.001000, loss: 2.343691, loss_shrink_maps: 1.346872, loss_threshold_maps: 0.740283, loss_binary_maps: 0.264314, avg_reader_cost: 1.58525 s, avg_batch_cost: 1.84145 s, avg_samples: 12.5, ips: 6.78814 samples/s, eta: 0:02:38
[2024/08/01 18:20:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:20:54] ppocr INFO: epoch: [93/100], global_step: 279, lr: 0.001000, loss: 2.329264, loss_shrink_maps: 1.338529, loss_threshold_maps: 0.740488, loss_binary_maps: 0.262003, avg_reader_cost: 1.52547 s, avg_batch_cost: 1.74522 s, avg_samples: 12.5, ips: 7.16240 samples/s, eta: 0:02:18
[2024/08/01 18:20:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:21:00] ppocr INFO: epoch: [94/100], global_step: 280, lr: 0.001000, loss: 2.351420, loss_shrink_maps: 1.357253, loss_threshold_maps: 0.744144, loss_binary_maps: 0.264994, avg_reader_cost: 0.41975 s, avg_batch_cost: 0.49864 s, avg_samples: 4.8, ips: 9.62615 samples/s, eta: 0:02:11
[2024/08/01 18:21:01] ppocr INFO: epoch: [94/100], global_step: 282, lr: 0.001000, loss: 2.350816, loss_shrink_maps: 1.357253, loss_threshold_maps: 0.744144, loss_binary_maps: 0.264994, avg_reader_cost: 1.08484 s, avg_batch_cost: 1.22423 s, avg_samples: 7.7, ips: 6.28965 samples/s, eta: 0:01:58
[2024/08/01 18:21:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:21:08] ppocr INFO: epoch: [95/100], global_step: 285, lr: 0.001000, loss: 2.443182, loss_shrink_maps: 1.408644, loss_threshold_maps: 0.749047, loss_binary_maps: 0.277262, avg_reader_cost: 1.50536 s, avg_batch_cost: 1.72401 s, avg_samples: 12.5, ips: 7.25053 samples/s, eta: 0:01:38
[2024/08/01 18:21:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:21:16] ppocr INFO: epoch: [96/100], global_step: 288, lr: 0.001000, loss: 2.353238, loss_shrink_maps: 1.357253, loss_threshold_maps: 0.737373, loss_binary_maps: 0.264994, avg_reader_cost: 1.54814 s, avg_batch_cost: 1.79429 s, avg_samples: 12.5, ips: 6.96655 samples/s, eta: 0:01:18
[2024/08/01 18:21:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:21:23] ppocr INFO: epoch: [97/100], global_step: 290, lr: 0.001000, loss: 2.359910, loss_shrink_maps: 1.361429, loss_threshold_maps: 0.737373, loss_binary_maps: 0.266364, avg_reader_cost: 0.92075 s, avg_batch_cost: 1.09076 s, avg_samples: 9.6, ips: 8.80119 samples/s, eta: 0:01:05
[2024/08/01 18:21:23] ppocr INFO: epoch: [97/100], global_step: 291, lr: 0.001000, loss: 2.385701, loss_shrink_maps: 1.361429, loss_threshold_maps: 0.742244, loss_binary_maps: 0.266364, avg_reader_cost: 0.58916 s, avg_batch_cost: 0.64136 s, avg_samples: 2.9, ips: 4.52161 samples/s, eta: 0:00:58
[2024/08/01 18:21:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:21:30] ppocr INFO: epoch: [98/100], global_step: 294, lr: 0.001000, loss: 2.340175, loss_shrink_maps: 1.339800, loss_threshold_maps: 0.737293, loss_binary_maps: 0.261878, avg_reader_cost: 1.54080 s, avg_batch_cost: 1.77697 s, avg_samples: 12.5, ips: 7.03445 samples/s, eta: 0:00:39
[2024/08/01 18:21:32] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:21:38] ppocr INFO: epoch: [99/100], global_step: 297, lr: 0.001000, loss: 2.332692, loss_shrink_maps: 1.321191, loss_threshold_maps: 0.730704, loss_binary_maps: 0.259471, avg_reader_cost: 1.50092 s, avg_batch_cost: 1.72596 s, avg_samples: 12.5, ips: 7.24235 samples/s, eta: 0:00:19
[2024/08/01 18:21:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:21:45] ppocr INFO: epoch: [100/100], global_step: 300, lr: 0.001000, loss: 2.332692, loss_shrink_maps: 1.321191, loss_threshold_maps: 0.730704, loss_binary_maps: 0.259471, avg_reader_cost: 1.52976 s, avg_batch_cost: 1.76053 s, avg_samples: 12.5, ips: 7.10013 samples/s, eta: 0:00:00

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:55,  9.01it/s]
eval model::   1%|          | 4/500 [00:00<00:28, 17.33it/s]
eval model::   1%|▏         | 7/500 [00:00<00:24, 20.44it/s]
eval model::   2%|▏         | 10/500 [00:00<00:26, 18.76it/s]
eval model::   3%|▎         | 13/500 [00:00<00:24, 19.71it/s]
eval model::   3%|▎         | 16/500 [00:00<00:22, 21.16it/s]
eval model::   4%|▍         | 19/500 [00:00<00:22, 20.95it/s]
eval model::   4%|▍         | 22/500 [00:01<00:23, 20.59it/s]
eval model::   5%|▌         | 25/500 [00:01<00:23, 20.44it/s]
eval model::   6%|▌         | 28/500 [00:01<00:22, 21.22it/s]
eval model::   6%|▌         | 31/500 [00:01<00:23, 19.83it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 18.95it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.79it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.84it/s]
eval model::   9%|▊         | 43/500 [00:02<00:23, 19.53it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.89it/s]
eval model::   9%|▉         | 47/500 [00:02<00:23, 18.88it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 19.53it/s]
eval model::  10%|█         | 52/500 [00:02<00:24, 18.60it/s]
eval model::  11%|█         | 54/500 [00:02<00:23, 18.83it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:22, 19.82it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:23, 18.51it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:25, 17.20it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 15.62it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.11it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 20.68it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:19, 21.42it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 21.10it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:23, 18.07it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:21, 19.63it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:22, 18.53it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.58it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:21, 18.71it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:22, 18.49it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:20, 19.43it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.43it/s]
eval model::  20%|██        | 101/500 [00:05<00:21, 18.80it/s]
eval model::  21%|██        | 104/500 [00:05<00:20, 19.76it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 20.86it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:19, 19.74it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:19, 19.69it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:20, 19.18it/s]
eval model::  23%|██▎       | 116/500 [00:06<00:20, 18.79it/s]
eval model::  24%|██▍       | 119/500 [00:06<00:18, 20.61it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:19, 19.74it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:18, 20.31it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 20.43it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:21, 17.47it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:20, 17.93it/s]
eval model::  27%|██▋       | 135/500 [00:07<00:19, 18.38it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:18, 19.54it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:19, 18.36it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:19, 18.61it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 18.75it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:19, 18.34it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 19.38it/s]
eval model::  30%|███       | 152/500 [00:07<00:18, 19.20it/s]
eval model::  31%|███       | 154/500 [00:08<00:18, 18.87it/s]
eval model::  31%|███       | 156/500 [00:08<00:19, 17.43it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:19, 17.93it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:19, 17.40it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:18, 18.24it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:19, 17.23it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:19, 16.66it/s]
eval model::  34%|███▍      | 171/500 [00:09<00:19, 17.24it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 16.18it/s]
eval model::  35%|███▌      | 177/500 [00:09<00:17, 18.11it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:19, 16.26it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:17, 18.25it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 19.42it/s]
eval model::  38%|███▊      | 189/500 [00:09<00:15, 20.16it/s]
eval model::  38%|███▊      | 192/500 [00:10<00:14, 20.86it/s]
eval model::  39%|███▉      | 195/500 [00:10<00:15, 19.17it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.81it/s]
eval model::  40%|████      | 200/500 [00:10<00:14, 20.09it/s]
eval model::  41%|████      | 203/500 [00:10<00:13, 22.28it/s]
eval model::  41%|████      | 206/500 [00:10<00:18, 16.08it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:18, 15.70it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 15.80it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:16, 17.56it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:17, 16.42it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.61it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 17.27it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:16, 16.39it/s]
eval model::  45%|████▌     | 225/500 [00:12<00:15, 18.02it/s]
eval model::  45%|████▌     | 227/500 [00:12<00:15, 17.98it/s]
eval model::  46%|████▌     | 230/500 [00:12<00:13, 20.44it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.62it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 20.46it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:10, 23.73it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:11, 22.43it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:10, 24.03it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:11, 22.07it/s]
eval model::  50%|█████     | 252/500 [00:13<00:11, 21.15it/s]
eval model::  51%|█████     | 255/500 [00:13<00:12, 20.25it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 21.33it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:11, 20.00it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:13, 17.69it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:12, 18.51it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 19.57it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 20.44it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:10, 20.47it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:10, 21.98it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 21.10it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:14, 14.90it/s]
eval model::  57%|█████▋    | 287/500 [00:15<00:13, 15.39it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:11, 17.99it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:10, 20.06it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:12, 16.29it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:10, 18.91it/s]
eval model::  60%|██████    | 302/500 [00:15<00:10, 18.43it/s]
eval model::  61%|██████    | 305/500 [00:16<00:09, 19.91it/s]
eval model::  62%|██████▏   | 308/500 [00:16<00:08, 21.47it/s]
eval model::  62%|██████▏   | 311/500 [00:16<00:08, 22.64it/s]
eval model::  63%|██████▎   | 314/500 [00:16<00:08, 21.38it/s]
eval model::  63%|██████▎   | 317/500 [00:16<00:08, 22.44it/s]
eval model::  64%|██████▍   | 320/500 [00:16<00:08, 21.47it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:08, 20.84it/s]
eval model::  65%|██████▌   | 326/500 [00:17<00:08, 21.49it/s]
eval model::  66%|██████▌   | 329/500 [00:17<00:08, 19.59it/s]
eval model::  66%|██████▋   | 332/500 [00:17<00:07, 21.26it/s]
eval model::  67%|██████▋   | 335/500 [00:17<00:07, 21.92it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:07, 20.27it/s]
eval model::  68%|██████▊   | 341/500 [00:17<00:08, 18.76it/s]
eval model::  69%|██████▉   | 344/500 [00:17<00:07, 20.53it/s]
eval model::  69%|██████▉   | 347/500 [00:18<00:06, 22.07it/s]
eval model::  70%|███████   | 350/500 [00:18<00:06, 21.95it/s]
eval model::  71%|███████   | 353/500 [00:18<00:06, 22.64it/s]
eval model::  71%|███████   | 356/500 [00:18<00:07, 18.87it/s]
eval model::  72%|███████▏  | 359/500 [00:18<00:07, 18.92it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 18.69it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:07, 18.60it/s]
eval model::  73%|███████▎  | 365/500 [00:19<00:08, 16.59it/s]
eval model::  73%|███████▎  | 367/500 [00:19<00:07, 17.37it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 18.98it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 20.12it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 21.51it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 22.23it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 21.35it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 22.35it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:06, 16.16it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 16.87it/s]
eval model::  79%|███████▊  | 393/500 [00:20<00:05, 18.28it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:05, 18.87it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 20.49it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 20.22it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 21.64it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.13it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 20.21it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:04, 19.78it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 19.41it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 18.67it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:04, 16.64it/s]
eval model::  85%|████████▍ | 424/500 [00:22<00:04, 18.46it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:04, 18.34it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 19.59it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 19.53it/s]
eval model::  87%|████████▋ | 434/500 [00:22<00:03, 21.84it/s]
eval model::  87%|████████▋ | 437/500 [00:22<00:02, 21.91it/s]
eval model::  88%|████████▊ | 440/500 [00:22<00:03, 19.29it/s]
eval model::  89%|████████▊ | 443/500 [00:22<00:02, 21.50it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 20.93it/s]
eval model::  90%|████████▉ | 449/500 [00:23<00:02, 18.98it/s]
eval model::  90%|█████████ | 452/500 [00:23<00:02, 21.05it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:02, 19.74it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:02, 19.96it/s]
eval model::  92%|█████████▏| 461/500 [00:23<00:02, 19.46it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:02, 17.46it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:02, 15.98it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 17.61it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 18.72it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 18.67it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 20.27it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 20.90it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 20.47it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 19.97it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 21.73it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 19.61it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 20.58it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 21.96it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.38it/s]
[2024/08/01 18:22:11] ppocr INFO: cur metric, precision: 0.492258064516129, recall: 0.3673567645642754, hmean: 0.420733388475324, fps: 45.62088636893636
[2024/08/01 18:22:11] ppocr INFO: best metric, hmean: 0.48025738520035094, precision: 0.6117734724292101, recall: 0.39528165623495426, fps: 44.1252897392761, best_epoch: 80
[2024/08/01 18:22:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:22:11] ppocr INFO: best metric, hmean: 0.48025738520035094, precision: 0.6117734724292101, recall: 0.39528165623495426, fps: 44.1252897392761, best_epoch: 80
I0801 18:22:13.097801 56755 tcp_store.cc:289] receive shutdown event and so quit from MasterDaemon run loop
[2024/08/01 18:31:00] ppocr INFO: Architecture : 
[2024/08/01 18:31:00] ppocr INFO:     Backbone : 
[2024/08/01 18:31:00] ppocr INFO:         model_name : large
[2024/08/01 18:31:00] ppocr INFO:         name : MobileNetV3
[2024/08/01 18:31:00] ppocr INFO:         scale : 0.5
[2024/08/01 18:31:00] ppocr INFO:     Head : 
[2024/08/01 18:31:00] ppocr INFO:         k : 50
[2024/08/01 18:31:00] ppocr INFO:         name : DBHead
[2024/08/01 18:31:00] ppocr INFO:     Neck : 
[2024/08/01 18:31:00] ppocr INFO:         name : DBFPN
[2024/08/01 18:31:00] ppocr INFO:         out_channels : 256
[2024/08/01 18:31:00] ppocr INFO:     Transform : None
[2024/08/01 18:31:00] ppocr INFO:     algorithm : DB
[2024/08/01 18:31:00] ppocr INFO:     model_type : det
[2024/08/01 18:31:00] ppocr INFO: Eval : 
[2024/08/01 18:31:00] ppocr INFO:     dataset : 
[2024/08/01 18:31:00] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/08/01 18:31:00] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
[2024/08/01 18:31:00] ppocr INFO:         name : SimpleDataSet
[2024/08/01 18:31:00] ppocr INFO:         transforms : 
[2024/08/01 18:31:00] ppocr INFO:             DecodeImage : 
[2024/08/01 18:31:00] ppocr INFO:                 channel_first : False
[2024/08/01 18:31:00] ppocr INFO:                 img_mode : BGR
[2024/08/01 18:31:00] ppocr INFO:             DetLabelEncode : None
[2024/08/01 18:31:00] ppocr INFO:             DetResizeForTest : 
[2024/08/01 18:31:00] ppocr INFO:                 image_shape : [736, 1280]
[2024/08/01 18:31:00] ppocr INFO:             NormalizeImage : 
[2024/08/01 18:31:00] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/08/01 18:31:00] ppocr INFO:                 order : hwc
[2024/08/01 18:31:00] ppocr INFO:                 scale : 1./255.
[2024/08/01 18:31:00] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/08/01 18:31:00] ppocr INFO:             ToCHWImage : None
[2024/08/01 18:31:00] ppocr INFO:             KeepKeys : 
[2024/08/01 18:31:00] ppocr INFO:                 keep_keys : ['image', 'shape', 'polys', 'ignore_tags']
[2024/08/01 18:31:00] ppocr INFO:     loader : 
[2024/08/01 18:31:00] ppocr INFO:         batch_size_per_card : 1
[2024/08/01 18:31:00] ppocr INFO:         drop_last : False
[2024/08/01 18:31:00] ppocr INFO:         num_workers : 0
[2024/08/01 18:31:00] ppocr INFO:         shuffle : False
[2024/08/01 18:31:00] ppocr INFO:         use_shared_memory : True
[2024/08/01 18:31:00] ppocr INFO: Global : 
[2024/08/01 18:31:00] ppocr INFO:     cal_metric_during_train : False
[2024/08/01 18:31:00] ppocr INFO:     checkpoints : None
[2024/08/01 18:31:00] ppocr INFO:     distributed : True
[2024/08/01 18:31:00] ppocr INFO:     epoch_num : 100
[2024/08/01 18:31:00] ppocr INFO:     eval_batch_step : [0, 60]
[2024/08/01 18:31:00] ppocr INFO:     infer_img : doc/imgs_en/img_10.jpg
[2024/08/01 18:31:00] ppocr INFO:     log_smooth_window : 20
[2024/08/01 18:31:00] ppocr INFO:     pretrained_model : ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/08/01 18:31:00] ppocr INFO:     print_batch_step : 10
[2024/08/01 18:31:00] ppocr INFO:     save_epoch_step : 1200
[2024/08/01 18:31:00] ppocr INFO:     save_inference_dir : None
[2024/08/01 18:31:00] ppocr INFO:     save_model_dir : ./output/db_mv3/
[2024/08/01 18:31:00] ppocr INFO:     save_res_path : ./output/det_db/predicts_db.txt
[2024/08/01 18:31:00] ppocr INFO:     use_gpu : True
[2024/08/01 18:31:00] ppocr INFO:     use_visualdl : False
[2024/08/01 18:31:00] ppocr INFO:     use_xpu : False
[2024/08/01 18:31:00] ppocr INFO: Loss : 
[2024/08/01 18:31:00] ppocr INFO:     alpha : 5
[2024/08/01 18:31:00] ppocr INFO:     balance_loss : True
[2024/08/01 18:31:00] ppocr INFO:     beta : 10
[2024/08/01 18:31:00] ppocr INFO:     main_loss_type : DiceLoss
[2024/08/01 18:31:00] ppocr INFO:     name : DBLoss
[2024/08/01 18:31:00] ppocr INFO:     ohem_ratio : 3
[2024/08/01 18:31:00] ppocr INFO: Metric : 
[2024/08/01 18:31:00] ppocr INFO:     main_indicator : hmean
[2024/08/01 18:31:00] ppocr INFO:     name : DetMetric
[2024/08/01 18:31:00] ppocr INFO: Optimizer : 
[2024/08/01 18:31:00] ppocr INFO:     beta1 : 0.9
[2024/08/01 18:31:00] ppocr INFO:     beta2 : 0.999
[2024/08/01 18:31:00] ppocr INFO:     lr : 
[2024/08/01 18:31:00] ppocr INFO:         learning_rate : 0.001
[2024/08/01 18:31:00] ppocr INFO:     name : Adam
[2024/08/01 18:31:00] ppocr INFO:     regularizer : 
[2024/08/01 18:31:00] ppocr INFO:         factor : 0
[2024/08/01 18:31:00] ppocr INFO:         name : L2
[2024/08/01 18:31:00] ppocr INFO: PostProcess : 
[2024/08/01 18:31:00] ppocr INFO:     box_thresh : 0.6
[2024/08/01 18:31:00] ppocr INFO:     max_candidates : 1000
[2024/08/01 18:31:00] ppocr INFO:     name : DBPostProcess
[2024/08/01 18:31:00] ppocr INFO:     thresh : 0.3
[2024/08/01 18:31:00] ppocr INFO:     unclip_ratio : 1.5
[2024/08/01 18:31:00] ppocr INFO: Train : 
[2024/08/01 18:31:00] ppocr INFO:     dataset : 
[2024/08/01 18:31:00] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/08/01 18:31:00] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/08/01 18:31:00] ppocr INFO:         name : SimpleDataSet
[2024/08/01 18:31:00] ppocr INFO:         ratio_list : [1.0]
[2024/08/01 18:31:00] ppocr INFO:         transforms : 
[2024/08/01 18:31:00] ppocr INFO:             DecodeImage : 
[2024/08/01 18:31:00] ppocr INFO:                 channel_first : False
[2024/08/01 18:31:00] ppocr INFO:                 img_mode : BGR
[2024/08/01 18:31:00] ppocr INFO:             DetLabelEncode : None
[2024/08/01 18:31:00] ppocr INFO:             IaaAugment : 
[2024/08/01 18:31:00] ppocr INFO:                 augmenter_args : 
[2024/08/01 18:31:00] ppocr INFO:                     args : 
[2024/08/01 18:31:00] ppocr INFO:                         p : 0.5
[2024/08/01 18:31:00] ppocr INFO:                     type : Fliplr
[2024/08/01 18:31:00] ppocr INFO:                     args : 
[2024/08/01 18:31:00] ppocr INFO:                         rotate : [-10, 10]
[2024/08/01 18:31:00] ppocr INFO:                     type : Affine
[2024/08/01 18:31:00] ppocr INFO:                     args : 
[2024/08/01 18:31:00] ppocr INFO:                         size : [0.5, 3]
[2024/08/01 18:31:00] ppocr INFO:                     type : Resize
[2024/08/01 18:31:00] ppocr INFO:             EastRandomCropData : 
[2024/08/01 18:31:00] ppocr INFO:                 keep_ratio : True
[2024/08/01 18:31:00] ppocr INFO:                 max_tries : 50
[2024/08/01 18:31:00] ppocr INFO:                 size : [640, 640]
[2024/08/01 18:31:00] ppocr INFO:             MakeBorderMap : 
[2024/08/01 18:31:00] ppocr INFO:                 shrink_ratio : 0.4
[2024/08/01 18:31:00] ppocr INFO:                 thresh_max : 0.7
[2024/08/01 18:31:00] ppocr INFO:                 thresh_min : 0.3
[2024/08/01 18:31:00] ppocr INFO:             MakeShrinkMap : 
[2024/08/01 18:31:00] ppocr INFO:                 min_text_size : 8
[2024/08/01 18:31:00] ppocr INFO:                 shrink_ratio : 0.4
[2024/08/01 18:31:00] ppocr INFO:             NormalizeImage : 
[2024/08/01 18:31:00] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/08/01 18:31:00] ppocr INFO:                 order : hwc
[2024/08/01 18:31:00] ppocr INFO:                 scale : 1./255.
[2024/08/01 18:31:00] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/08/01 18:31:00] ppocr INFO:             ToCHWImage : None
[2024/08/01 18:31:00] ppocr INFO:             KeepKeys : 
[2024/08/01 18:31:00] ppocr INFO:                 keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask']
[2024/08/01 18:31:00] ppocr INFO:     loader : 
[2024/08/01 18:31:00] ppocr INFO:         batch_size_per_card : 48
[2024/08/01 18:31:00] ppocr INFO:         drop_last : False
[2024/08/01 18:31:00] ppocr INFO:         num_workers : 8
[2024/08/01 18:31:00] ppocr INFO:         shuffle : True
[2024/08/01 18:31:00] ppocr INFO:         use_shared_memory : True
[2024/08/01 18:31:00] ppocr INFO: profiler_options : None
[2024/08/01 18:31:00] ppocr INFO: train with paddle 2.5.2 and device Place(gpu:0)
======================= Modified FLAGS detected =======================
FLAGS(name='FLAGS_cudnn_batchnorm_spatial_persistent', current_value=True, default_value=False)
FLAGS(name='FLAGS_selected_gpus', current_value='0', default_value='')
=======================================================================
I0801 18:31:00.364156 110373 tcp_utils.cc:181] The server starts to listen on IP_ANY:63697
I0801 18:31:00.364395 110373 tcp_utils.cc:130] Successfully connected to 10.8.145.246:63697
I0801 18:31:03.395391 110373 process_group_nccl.cc:120] ProcessGroupNCCL pg_timeout_ 1800000
[2024/08/01 18:31:03] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/08/01 18:31:03] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
W0801 18:31:03.406730 110373 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 90.2, Driver API Version: 50724.2, Runtime API Version: 50724.2
[2024/08/01 18:31:04] ppocr INFO: load pretrain successful from ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/08/01 18:31:04] ppocr INFO: train dataloader has 3 iters
[2024/08/01 18:31:04] ppocr INFO: valid dataloader has 500 iters
[2024/08/01 18:31:04] ppocr INFO: During the training process, after the 0th iteration, an evaluation is run every 60 iterations
[2024/08/01 18:31:27] ppocr INFO: epoch: [1/100], global_step: 3, lr: 0.001000, loss: 9.311636, loss_shrink_maps: 4.901662, loss_threshold_maps: 3.441306, loss_binary_maps: 0.983807, avg_reader_cost: 5.81977 s, avg_batch_cost: 6.51965 s, avg_samples: 12.5, ips: 1.91728 samples/s, eta: 1:47:34
[2024/08/01 18:31:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:31:37] ppocr INFO: epoch: [2/100], global_step: 6, lr: 0.001000, loss: 8.466688, loss_shrink_maps: 4.879443, loss_threshold_maps: 2.599884, loss_binary_maps: 0.979790, avg_reader_cost: 2.27395 s, avg_batch_cost: 2.53884 s, avg_samples: 12.5, ips: 4.92352 samples/s, eta: 1:13:58
[2024/08/01 18:31:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:31:46] ppocr INFO: epoch: [3/100], global_step: 9, lr: 0.001000, loss: 7.519474, loss_shrink_maps: 4.863124, loss_threshold_maps: 1.679606, loss_binary_maps: 0.976744, avg_reader_cost: 2.22354 s, avg_batch_cost: 2.51247 s, avg_samples: 12.5, ips: 4.97519 samples/s, eta: 1:02:21
[2024/08/01 18:31:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:31:54] ppocr INFO: epoch: [4/100], global_step: 10, lr: 0.001000, loss: 7.353305, loss_shrink_maps: 4.866306, loss_threshold_maps: 1.529627, loss_binary_maps: 0.977697, avg_reader_cost: 0.66084 s, avg_batch_cost: 0.76427 s, avg_samples: 4.8, ips: 6.28052 samples/s, eta: 0:59:37
[2024/08/01 18:31:56] ppocr INFO: epoch: [4/100], global_step: 12, lr: 0.001000, loss: 7.163844, loss_shrink_maps: 4.855438, loss_threshold_maps: 1.349466, loss_binary_maps: 0.975132, avg_reader_cost: 1.62532 s, avg_batch_cost: 1.78016 s, avg_samples: 7.7, ips: 4.32545 samples/s, eta: 0:56:27
[2024/08/01 18:31:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:32:06] ppocr INFO: epoch: [5/100], global_step: 15, lr: 0.001000, loss: 7.022414, loss_shrink_maps: 4.836698, loss_threshold_maps: 1.214718, loss_binary_maps: 0.970789, avg_reader_cost: 2.27640 s, avg_batch_cost: 2.57588 s, avg_samples: 12.5, ips: 4.85271 samples/s, eta: 0:52:51
[2024/08/01 18:32:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:32:16] ppocr INFO: epoch: [6/100], global_step: 18, lr: 0.001000, loss: 6.958278, loss_shrink_maps: 4.820476, loss_threshold_maps: 1.167517, loss_binary_maps: 0.965038, avg_reader_cost: 2.23115 s, avg_batch_cost: 2.50889 s, avg_samples: 12.5, ips: 4.98228 samples/s, eta: 0:50:08
[2024/08/01 18:32:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:32:25] ppocr INFO: epoch: [7/100], global_step: 20, lr: 0.001000, loss: 6.918981, loss_shrink_maps: 4.791574, loss_threshold_maps: 1.161318, loss_binary_maps: 0.961964, avg_reader_cost: 1.39530 s, avg_batch_cost: 1.60794 s, avg_samples: 9.6, ips: 5.97038 samples/s, eta: 0:48:33
[2024/08/01 18:32:25] ppocr INFO: epoch: [7/100], global_step: 21, lr: 0.001000, loss: 6.889464, loss_shrink_maps: 4.766752, loss_threshold_maps: 1.155881, loss_binary_maps: 0.957538, avg_reader_cost: 0.85247 s, avg_batch_cost: 0.91077 s, avg_samples: 2.9, ips: 3.18411 samples/s, eta: 0:48:05
[2024/08/01 18:32:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:32:35] ppocr INFO: epoch: [8/100], global_step: 24, lr: 0.001000, loss: 6.794288, loss_shrink_maps: 4.735679, loss_threshold_maps: 1.134295, loss_binary_maps: 0.942155, avg_reader_cost: 2.31063 s, avg_batch_cost: 2.55898 s, avg_samples: 12.5, ips: 4.88476 samples/s, eta: 0:46:31
[2024/08/01 18:32:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:32:45] ppocr INFO: epoch: [9/100], global_step: 27, lr: 0.001000, loss: 6.739758, loss_shrink_maps: 4.681714, loss_threshold_maps: 1.101860, loss_binary_maps: 0.930999, avg_reader_cost: 2.23727 s, avg_batch_cost: 2.53368 s, avg_samples: 12.5, ips: 4.93354 samples/s, eta: 0:45:10
[2024/08/01 18:32:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:32:55] ppocr INFO: epoch: [10/100], global_step: 30, lr: 0.001000, loss: 6.499613, loss_shrink_maps: 4.581536, loss_threshold_maps: 1.055003, loss_binary_maps: 0.888690, avg_reader_cost: 2.25060 s, avg_batch_cost: 2.51727 s, avg_samples: 12.5, ips: 4.96569 samples/s, eta: 0:43:59
[2024/08/01 18:32:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:33:04] ppocr INFO: epoch: [11/100], global_step: 33, lr: 0.001000, loss: 6.449403, loss_shrink_maps: 4.547603, loss_threshold_maps: 1.025710, loss_binary_maps: 0.866838, avg_reader_cost: 2.28798 s, avg_batch_cost: 2.53061 s, avg_samples: 12.5, ips: 4.93953 samples/s, eta: 0:42:57
[2024/08/01 18:33:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:33:14] ppocr INFO: epoch: [12/100], global_step: 36, lr: 0.001000, loss: 6.191002, loss_shrink_maps: 4.398135, loss_threshold_maps: 0.987950, loss_binary_maps: 0.790578, avg_reader_cost: 2.24546 s, avg_batch_cost: 2.50070 s, avg_samples: 12.5, ips: 4.99861 samples/s, eta: 0:41:59
[2024/08/01 18:33:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:33:24] ppocr INFO: epoch: [13/100], global_step: 39, lr: 0.001000, loss: 5.892470, loss_shrink_maps: 4.200360, loss_threshold_maps: 0.964301, loss_binary_maps: 0.722674, avg_reader_cost: 2.25928 s, avg_batch_cost: 2.50208 s, avg_samples: 12.5, ips: 4.99584 samples/s, eta: 0:41:06
[2024/08/01 18:33:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:33:32] ppocr INFO: epoch: [14/100], global_step: 40, lr: 0.001000, loss: 5.761197, loss_shrink_maps: 4.108572, loss_threshold_maps: 0.959077, loss_binary_maps: 0.711162, avg_reader_cost: 0.63213 s, avg_batch_cost: 0.74742 s, avg_samples: 4.8, ips: 6.42205 samples/s, eta: 0:40:44
[2024/08/01 18:33:33] ppocr INFO: epoch: [14/100], global_step: 42, lr: 0.001000, loss: 5.505810, loss_shrink_maps: 3.921797, loss_threshold_maps: 0.948719, loss_binary_maps: 0.652212, avg_reader_cost: 1.59237 s, avg_batch_cost: 1.74694 s, avg_samples: 7.7, ips: 4.40770 samples/s, eta: 0:40:17
[2024/08/01 18:33:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:33:43] ppocr INFO: epoch: [15/100], global_step: 45, lr: 0.001000, loss: 5.365054, loss_shrink_maps: 3.804703, loss_threshold_maps: 0.926088, loss_binary_maps: 0.637450, avg_reader_cost: 2.28863 s, avg_batch_cost: 2.56573 s, avg_samples: 12.5, ips: 4.87191 samples/s, eta: 0:39:35
[2024/08/01 18:33:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:33:53] ppocr INFO: epoch: [16/100], global_step: 48, lr: 0.001000, loss: 5.038902, loss_shrink_maps: 3.573594, loss_threshold_maps: 0.912642, loss_binary_maps: 0.558926, avg_reader_cost: 2.32875 s, avg_batch_cost: 2.57205 s, avg_samples: 12.5, ips: 4.85995 samples/s, eta: 0:38:55
[2024/08/01 18:33:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:34:02] ppocr INFO: epoch: [17/100], global_step: 50, lr: 0.001000, loss: 4.823745, loss_shrink_maps: 3.377399, loss_threshold_maps: 0.910908, loss_binary_maps: 0.521755, avg_reader_cost: 1.39635 s, avg_batch_cost: 1.60182 s, avg_samples: 9.6, ips: 5.99319 samples/s, eta: 0:38:24
[2024/08/01 18:34:03] ppocr INFO: epoch: [17/100], global_step: 51, lr: 0.001000, loss: 4.726063, loss_shrink_maps: 3.318785, loss_threshold_maps: 0.910908, loss_binary_maps: 0.519248, avg_reader_cost: 0.85002 s, avg_batch_cost: 0.90848 s, avg_samples: 2.9, ips: 3.19215 samples/s, eta: 0:38:14
[2024/08/01 18:34:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:34:12] ppocr INFO: epoch: [18/100], global_step: 54, lr: 0.001000, loss: 4.485591, loss_shrink_maps: 3.135734, loss_threshold_maps: 0.903998, loss_binary_maps: 0.469538, avg_reader_cost: 2.25079 s, avg_batch_cost: 2.50754 s, avg_samples: 12.5, ips: 4.98496 samples/s, eta: 0:37:35
[2024/08/01 18:34:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:34:22] ppocr INFO: epoch: [19/100], global_step: 57, lr: 0.001000, loss: 4.230521, loss_shrink_maps: 2.942256, loss_threshold_maps: 0.892551, loss_binary_maps: 0.434280, avg_reader_cost: 2.26028 s, avg_batch_cost: 2.50352 s, avg_samples: 12.5, ips: 4.99297 samples/s, eta: 0:36:57
[2024/08/01 18:34:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:34:32] ppocr INFO: epoch: [20/100], global_step: 60, lr: 0.001000, loss: 4.134025, loss_shrink_maps: 2.832201, loss_threshold_maps: 0.876882, loss_binary_maps: 0.421117, avg_reader_cost: 2.24977 s, avg_batch_cost: 2.49578 s, avg_samples: 12.5, ips: 5.00844 samples/s, eta: 0:36:20

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:54,  9.21it/s]
eval model::   1%|          | 4/500 [00:00<00:29, 16.95it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 17.67it/s]
eval model::   2%|▏         | 9/500 [00:00<00:25, 19.56it/s]
eval model::   2%|▏         | 11/500 [00:00<00:25, 19.54it/s]
eval model::   3%|▎         | 13/500 [00:00<00:25, 19.45it/s]
eval model::   3%|▎         | 15/500 [00:00<00:24, 19.46it/s]
eval model::   4%|▎         | 18/500 [00:00<00:22, 21.80it/s]
eval model::   4%|▍         | 21/500 [00:01<00:22, 21.30it/s]
eval model::   5%|▍         | 24/500 [00:01<00:21, 22.65it/s]
eval model::   5%|▌         | 27/500 [00:01<00:20, 23.52it/s]
eval model::   6%|▌         | 30/500 [00:01<00:19, 24.48it/s]
eval model::   7%|▋         | 33/500 [00:01<00:21, 22.01it/s]
eval model::   7%|▋         | 36/500 [00:01<00:21, 21.86it/s]
eval model::   8%|▊         | 39/500 [00:01<00:21, 21.28it/s]
eval model::   8%|▊         | 42/500 [00:02<00:21, 21.07it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.42it/s]
eval model::  10%|▉         | 48/500 [00:02<00:22, 20.23it/s]
eval model::  10%|█         | 51/500 [00:02<00:20, 21.38it/s]
eval model::  11%|█         | 54/500 [00:02<00:21, 21.16it/s]
eval model::  11%|█▏        | 57/500 [00:02<00:20, 22.01it/s]
eval model::  12%|█▏        | 60/500 [00:02<00:20, 21.30it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:24, 18.14it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:20, 21.02it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:20, 21.16it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 21.33it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.25it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:20, 20.89it/s]
eval model::  16%|█▋        | 82/500 [00:03<00:20, 20.48it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:22, 18.82it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 19.68it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.45it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.54it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:21, 18.58it/s]
eval model::  20%|█▉        | 98/500 [00:04<00:19, 20.70it/s]
eval model::  20%|██        | 101/500 [00:04<00:19, 20.72it/s]
eval model::  21%|██        | 104/500 [00:05<00:17, 22.54it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:16, 23.52it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 22.64it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:18, 21.20it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:19, 19.87it/s]
eval model::  24%|██▍       | 119/500 [00:05<00:19, 20.01it/s]
eval model::  24%|██▍       | 122/500 [00:05<00:21, 17.54it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:21, 17.79it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:18, 20.06it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:18, 20.09it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:16, 21.90it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:17, 20.45it/s]
eval model::  28%|██▊       | 139/500 [00:06<00:17, 20.46it/s]
eval model::  28%|██▊       | 142/500 [00:06<00:17, 20.49it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.08it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:18, 19.34it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 18.79it/s]
eval model::  30%|███       | 152/500 [00:07<00:18, 18.66it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 21.35it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:15, 22.26it/s]
eval model::  32%|███▏      | 161/500 [00:07<00:15, 21.99it/s]
eval model::  33%|███▎      | 164/500 [00:08<00:16, 20.48it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:17, 19.41it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:17, 18.43it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:18, 18.14it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:21, 15.18it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:21, 15.19it/s]
eval model::  36%|███▌      | 179/500 [00:08<00:18, 17.55it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:18, 17.65it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:16, 18.96it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 20.73it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:14, 20.70it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:14, 20.67it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:18, 16.19it/s]
eval model::  40%|███▉      | 199/500 [00:09<00:16, 18.24it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 19.87it/s]
eval model::  41%|████      | 205/500 [00:10<00:21, 13.98it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:19, 14.63it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:19, 14.68it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:16, 17.57it/s]
eval model::  43%|████▎     | 216/500 [00:11<00:17, 16.53it/s]
eval model::  44%|████▎     | 218/500 [00:11<00:18, 15.17it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:16, 16.74it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:15, 17.96it/s]
eval model::  45%|████▌     | 227/500 [00:11<00:14, 18.97it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:12, 20.98it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:12, 22.23it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:12, 21.40it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.43it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:11, 23.00it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:11, 21.72it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 21.70it/s]
eval model::  50%|█████     | 251/500 [00:12<00:11, 21.04it/s]
eval model::  51%|█████     | 254/500 [00:12<00:11, 21.61it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 21.61it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:11, 21.77it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 21.60it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:11, 20.30it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:11, 20.09it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:11, 19.83it/s]
eval model::  55%|█████▌    | 275/500 [00:13<00:10, 20.60it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:10, 21.13it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.63it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 20.41it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:12, 17.04it/s]
eval model::  58%|█████▊    | 290/500 [00:14<00:11, 18.68it/s]
eval model::  59%|█████▊    | 293/500 [00:14<00:10, 20.05it/s]
eval model::  59%|█████▉    | 296/500 [00:14<00:10, 19.68it/s]
eval model::  60%|█████▉    | 299/500 [00:15<00:09, 21.09it/s]
eval model::  60%|██████    | 302/500 [00:15<00:10, 19.33it/s]
eval model::  61%|██████    | 305/500 [00:15<00:10, 19.05it/s]
eval model::  62%|██████▏   | 308/500 [00:15<00:09, 19.45it/s]
eval model::  62%|██████▏   | 310/500 [00:15<00:09, 19.52it/s]
eval model::  63%|██████▎   | 313/500 [00:15<00:08, 21.55it/s]
eval model::  63%|██████▎   | 316/500 [00:15<00:08, 20.72it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:07, 22.81it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:08, 21.58it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:08, 21.84it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:08, 20.65it/s]
eval model::  66%|██████▌   | 331/500 [00:16<00:08, 20.94it/s]
eval model::  67%|██████▋   | 334/500 [00:16<00:07, 20.93it/s]
eval model::  67%|██████▋   | 337/500 [00:16<00:07, 20.68it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:08, 18.82it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:07, 20.17it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 21.43it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:07, 21.47it/s]
eval model::  70%|███████   | 352/500 [00:17<00:06, 21.20it/s]
eval model::  71%|███████   | 355/500 [00:17<00:07, 20.60it/s]
eval model::  72%|███████▏  | 358/500 [00:17<00:06, 20.75it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 18.46it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 17.92it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:07, 18.30it/s]
eval model::  74%|███████▍  | 369/500 [00:18<00:06, 19.75it/s]
eval model::  74%|███████▍  | 372/500 [00:18<00:06, 19.83it/s]
eval model::  75%|███████▌  | 375/500 [00:18<00:05, 20.86it/s]
eval model::  76%|███████▌  | 378/500 [00:18<00:05, 22.01it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:05, 20.41it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:05, 20.45it/s]
eval model::  77%|███████▋  | 387/500 [00:19<00:06, 18.19it/s]
eval model::  78%|███████▊  | 390/500 [00:19<00:05, 19.06it/s]
eval model::  79%|███████▊  | 393/500 [00:19<00:05, 20.05it/s]
eval model::  79%|███████▉  | 396/500 [00:19<00:04, 21.17it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 22.13it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 22.00it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 22.03it/s]
eval model::  82%|████████▏ | 408/500 [00:20<00:04, 20.68it/s]
eval model::  82%|████████▏ | 411/500 [00:20<00:04, 20.31it/s]
eval model::  83%|████████▎ | 414/500 [00:20<00:04, 20.68it/s]
eval model::  83%|████████▎ | 417/500 [00:20<00:04, 17.32it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 17.35it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:04, 15.84it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 18.43it/s]
eval model::  85%|████████▌ | 427/500 [00:21<00:03, 20.49it/s]
eval model::  86%|████████▌ | 430/500 [00:21<00:03, 22.26it/s]
eval model::  87%|████████▋ | 433/500 [00:21<00:03, 22.13it/s]
eval model::  87%|████████▋ | 436/500 [00:21<00:02, 22.15it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 21.69it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 19.99it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 20.16it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 19.13it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 19.15it/s]
eval model::  91%|█████████ | 453/500 [00:22<00:02, 19.82it/s]
eval model::  91%|█████████ | 456/500 [00:22<00:02, 20.92it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:01, 20.97it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:01, 19.07it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 19.15it/s]
eval model::  93%|█████████▎| 466/500 [00:23<00:01, 18.67it/s]
eval model::  94%|█████████▍| 469/500 [00:23<00:01, 19.91it/s]
eval model::  94%|█████████▍| 472/500 [00:23<00:01, 20.36it/s]
eval model::  95%|█████████▌| 475/500 [00:23<00:01, 19.64it/s]
eval model::  95%|█████████▌| 477/500 [00:23<00:01, 19.58it/s]
eval model::  96%|█████████▌| 479/500 [00:24<00:01, 19.66it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 19.56it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 19.75it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 19.19it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 20.22it/s]
eval model::  98%|█████████▊| 492/500 [00:24<00:00, 19.70it/s]
eval model::  99%|█████████▉| 495/500 [00:24<00:00, 20.83it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 21.25it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.94it/s]
[2024/08/01 18:34:57] ppocr INFO: cur metric, precision: 0.3586654309545876, recall: 0.18632643235435725, hmean: 0.24524714828897343, fps: 42.78213456158324
[2024/08/01 18:34:57] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 18:34:57] ppocr INFO: best metric, hmean: 0.24524714828897343, precision: 0.3586654309545876, recall: 0.18632643235435725, fps: 42.78213456158324, best_epoch: 20
[2024/08/01 18:34:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:35:04] ppocr INFO: epoch: [21/100], global_step: 63, lr: 0.001000, loss: 4.046984, loss_shrink_maps: 2.766544, loss_threshold_maps: 0.871167, loss_binary_maps: 0.405610, avg_reader_cost: 1.56544 s, avg_batch_cost: 1.82042 s, avg_samples: 12.5, ips: 6.86656 samples/s, eta: 0:35:19
[2024/08/01 18:35:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:35:12] ppocr INFO: epoch: [22/100], global_step: 66, lr: 0.001000, loss: 3.866041, loss_shrink_maps: 2.535505, loss_threshold_maps: 0.868775, loss_binary_maps: 0.393272, avg_reader_cost: 1.56411 s, avg_batch_cost: 1.85486 s, avg_samples: 12.5, ips: 6.73905 samples/s, eta: 0:34:22
[2024/08/01 18:35:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:35:19] ppocr INFO: epoch: [23/100], global_step: 69, lr: 0.001000, loss: 3.718459, loss_shrink_maps: 2.424870, loss_threshold_maps: 0.876882, loss_binary_maps: 0.385653, avg_reader_cost: 1.56334 s, avg_batch_cost: 1.80959 s, avg_samples: 12.5, ips: 6.90763 samples/s, eta: 0:33:28
[2024/08/01 18:35:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:35:25] ppocr INFO: epoch: [24/100], global_step: 70, lr: 0.001000, loss: 3.668140, loss_shrink_maps: 2.408142, loss_threshold_maps: 0.874916, loss_binary_maps: 0.382349, avg_reader_cost: 0.42012 s, avg_batch_cost: 0.52026 s, avg_samples: 4.8, ips: 9.22624 samples/s, eta: 0:33:08
[2024/08/01 18:35:26] ppocr INFO: epoch: [24/100], global_step: 72, lr: 0.001000, loss: 3.628015, loss_shrink_maps: 2.350583, loss_threshold_maps: 0.874916, loss_binary_maps: 0.376778, avg_reader_cost: 1.13688 s, avg_batch_cost: 1.28946 s, avg_samples: 7.7, ips: 5.97147 samples/s, eta: 0:32:37
[2024/08/01 18:35:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:35:34] ppocr INFO: epoch: [25/100], global_step: 75, lr: 0.001000, loss: 3.552265, loss_shrink_maps: 2.295442, loss_threshold_maps: 0.870727, loss_binary_maps: 0.366911, avg_reader_cost: 1.64137 s, avg_batch_cost: 1.88514 s, avg_samples: 12.5, ips: 6.63081 samples/s, eta: 0:31:50
[2024/08/01 18:35:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:35:41] ppocr INFO: epoch: [26/100], global_step: 78, lr: 0.001000, loss: 3.473954, loss_shrink_maps: 2.272506, loss_threshold_maps: 0.865035, loss_binary_maps: 0.354387, avg_reader_cost: 1.53055 s, avg_batch_cost: 1.80719 s, avg_samples: 12.5, ips: 6.91681 samples/s, eta: 0:31:04
[2024/08/01 18:35:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:35:48] ppocr INFO: epoch: [27/100], global_step: 80, lr: 0.001000, loss: 3.426581, loss_shrink_maps: 2.245654, loss_threshold_maps: 0.865035, loss_binary_maps: 0.348521, avg_reader_cost: 1.00917 s, avg_batch_cost: 1.21561 s, avg_samples: 9.6, ips: 7.89728 samples/s, eta: 0:30:34
[2024/08/01 18:35:49] ppocr INFO: epoch: [27/100], global_step: 81, lr: 0.001000, loss: 3.402209, loss_shrink_maps: 2.196910, loss_threshold_maps: 0.865035, loss_binary_maps: 0.354387, avg_reader_cost: 0.65679 s, avg_batch_cost: 0.71490 s, avg_samples: 2.9, ips: 4.05654 samples/s, eta: 0:30:23
[2024/08/01 18:35:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:35:56] ppocr INFO: epoch: [28/100], global_step: 84, lr: 0.001000, loss: 3.345102, loss_shrink_maps: 2.110563, loss_threshold_maps: 0.865035, loss_binary_maps: 0.347278, avg_reader_cost: 1.53867 s, avg_batch_cost: 1.78739 s, avg_samples: 12.5, ips: 6.99345 samples/s, eta: 0:29:39
[2024/08/01 18:35:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:36:04] ppocr INFO: epoch: [29/100], global_step: 87, lr: 0.001000, loss: 3.252011, loss_shrink_maps: 2.064755, loss_threshold_maps: 0.857746, loss_binary_maps: 0.335226, avg_reader_cost: 1.58494 s, avg_batch_cost: 1.85801 s, avg_samples: 12.5, ips: 6.72762 samples/s, eta: 0:29:00
[2024/08/01 18:36:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:36:11] ppocr INFO: epoch: [30/100], global_step: 90, lr: 0.001000, loss: 3.160108, loss_shrink_maps: 2.009127, loss_threshold_maps: 0.836825, loss_binary_maps: 0.324839, avg_reader_cost: 1.55914 s, avg_batch_cost: 1.80996 s, avg_samples: 12.5, ips: 6.90623 samples/s, eta: 0:28:20
[2024/08/01 18:36:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:36:18] ppocr INFO: epoch: [31/100], global_step: 93, lr: 0.001000, loss: 3.105739, loss_shrink_maps: 1.957451, loss_threshold_maps: 0.818913, loss_binary_maps: 0.325512, avg_reader_cost: 1.52931 s, avg_batch_cost: 1.77353 s, avg_samples: 12.5, ips: 7.04809 samples/s, eta: 0:27:41
[2024/08/01 18:36:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:36:26] ppocr INFO: epoch: [32/100], global_step: 96, lr: 0.001000, loss: 3.043463, loss_shrink_maps: 1.880493, loss_threshold_maps: 0.815315, loss_binary_maps: 0.331414, avg_reader_cost: 1.54644 s, avg_batch_cost: 1.79651 s, avg_samples: 12.5, ips: 6.95793 samples/s, eta: 0:27:04
[2024/08/01 18:36:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:36:33] ppocr INFO: epoch: [33/100], global_step: 99, lr: 0.001000, loss: 3.043463, loss_shrink_maps: 1.880493, loss_threshold_maps: 0.813084, loss_binary_maps: 0.330742, avg_reader_cost: 1.62521 s, avg_batch_cost: 1.93127 s, avg_samples: 12.5, ips: 6.47243 samples/s, eta: 0:26:31
[2024/08/01 18:36:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:36:39] ppocr INFO: epoch: [34/100], global_step: 100, lr: 0.001000, loss: 2.961534, loss_shrink_maps: 1.831224, loss_threshold_maps: 0.813084, loss_binary_maps: 0.324840, avg_reader_cost: 0.41554 s, avg_batch_cost: 0.50631 s, avg_samples: 4.8, ips: 9.48036 samples/s, eta: 0:26:17
[2024/08/01 18:36:41] ppocr INFO: epoch: [34/100], global_step: 102, lr: 0.001000, loss: 2.961534, loss_shrink_maps: 1.831224, loss_threshold_maps: 0.820771, loss_binary_maps: 0.324840, avg_reader_cost: 1.11017 s, avg_batch_cost: 1.26564 s, avg_samples: 7.7, ips: 6.08390 samples/s, eta: 0:25:55
[2024/08/01 18:36:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:36:48] ppocr INFO: epoch: [35/100], global_step: 105, lr: 0.001000, loss: 2.989750, loss_shrink_maps: 1.847316, loss_threshold_maps: 0.817728, loss_binary_maps: 0.330691, avg_reader_cost: 1.55334 s, avg_batch_cost: 1.81378 s, avg_samples: 12.5, ips: 6.89167 samples/s, eta: 0:25:22
[2024/08/01 18:36:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:36:56] ppocr INFO: epoch: [36/100], global_step: 108, lr: 0.001000, loss: 3.046692, loss_shrink_maps: 1.880493, loss_threshold_maps: 0.824594, loss_binary_maps: 0.334455, avg_reader_cost: 1.58759 s, avg_batch_cost: 1.84377 s, avg_samples: 12.5, ips: 6.77959 samples/s, eta: 0:24:50
[2024/08/01 18:36:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:37:02] ppocr INFO: epoch: [37/100], global_step: 110, lr: 0.001000, loss: 2.989750, loss_shrink_maps: 1.847316, loss_threshold_maps: 0.820771, loss_binary_maps: 0.330691, avg_reader_cost: 0.93994 s, avg_batch_cost: 1.12455 s, avg_samples: 9.6, ips: 8.53675 samples/s, eta: 0:24:27
[2024/08/01 18:37:03] ppocr INFO: epoch: [37/100], global_step: 111, lr: 0.001000, loss: 2.989750, loss_shrink_maps: 1.847316, loss_threshold_maps: 0.820771, loss_binary_maps: 0.330691, avg_reader_cost: 0.61081 s, avg_batch_cost: 0.66903 s, avg_samples: 2.9, ips: 4.33466 samples/s, eta: 0:24:17
[2024/08/01 18:37:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:37:10] ppocr INFO: epoch: [38/100], global_step: 114, lr: 0.001000, loss: 2.950620, loss_shrink_maps: 1.824088, loss_threshold_maps: 0.817728, loss_binary_maps: 0.327425, avg_reader_cost: 1.54370 s, avg_batch_cost: 1.81750 s, avg_samples: 12.5, ips: 6.87758 samples/s, eta: 0:23:46
[2024/08/01 18:37:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:37:17] ppocr INFO: epoch: [39/100], global_step: 117, lr: 0.001000, loss: 2.912114, loss_shrink_maps: 1.785774, loss_threshold_maps: 0.820771, loss_binary_maps: 0.326718, avg_reader_cost: 1.50053 s, avg_batch_cost: 1.74671 s, avg_samples: 12.5, ips: 7.15633 samples/s, eta: 0:23:14
[2024/08/01 18:37:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:37:25] ppocr INFO: epoch: [40/100], global_step: 120, lr: 0.001000, loss: 2.900538, loss_shrink_maps: 1.764170, loss_threshold_maps: 0.815180, loss_binary_maps: 0.324014, avg_reader_cost: 1.54577 s, avg_batch_cost: 1.81157 s, avg_samples: 12.5, ips: 6.90010 samples/s, eta: 0:22:44

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:42, 11.74it/s]
eval model::   1%|          | 5/500 [00:00<00:28, 17.15it/s]
eval model::   1%|▏         | 7/500 [00:00<00:28, 17.35it/s]
eval model::   2%|▏         | 9/500 [00:00<00:27, 17.62it/s]
eval model::   2%|▏         | 11/500 [00:00<00:28, 17.19it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 19.04it/s]
eval model::   3%|▎         | 17/500 [00:00<00:22, 21.19it/s]
eval model::   4%|▍         | 20/500 [00:01<00:27, 17.65it/s]
eval model::   5%|▍         | 23/500 [00:01<00:24, 19.45it/s]
eval model::   5%|▌         | 26/500 [00:01<00:24, 19.37it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 19.69it/s]
eval model::   6%|▋         | 32/500 [00:01<00:24, 19.22it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 18.86it/s]
eval model::   7%|▋         | 37/500 [00:01<00:23, 19.87it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.46it/s]
eval model::   8%|▊         | 42/500 [00:02<00:24, 18.57it/s]
eval model::   9%|▉         | 45/500 [00:02<00:22, 20.05it/s]
eval model::  10%|▉         | 48/500 [00:02<00:24, 18.34it/s]
eval model::  10%|█         | 50/500 [00:02<00:24, 18.41it/s]
eval model::  10%|█         | 52/500 [00:02<00:24, 18.54it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 20.00it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:22, 19.48it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:23, 18.39it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:24, 18.20it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:21, 19.84it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:19, 21.58it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 20.92it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:20, 20.65it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:20, 20.18it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:20, 20.44it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:21, 19.35it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:22, 18.48it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:22, 18.61it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:22, 17.88it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:19, 20.42it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:19, 20.83it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:18, 21.61it/s]
eval model::  20%|██        | 102/500 [00:05<00:18, 21.24it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.74it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:20, 19.33it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 19.46it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 21.17it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:17, 21.61it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:19, 19.50it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.14it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:17, 20.88it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 21.22it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 18.67it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.73it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 21.04it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.61it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.44it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:17, 20.40it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.48it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 19.90it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 21.31it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 21.27it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:17, 19.39it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:16, 20.77it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:16, 20.58it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:16, 19.71it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:18, 17.65it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.50it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:17, 18.25it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:18, 17.72it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:15, 20.31it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:14, 22.03it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:13, 22.58it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:13, 23.37it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:13, 21.85it/s]
eval model::  40%|███▉      | 199/500 [00:09<00:12, 23.41it/s]
eval model::  40%|████      | 202/500 [00:10<00:14, 21.06it/s]
eval model::  41%|████      | 205/500 [00:10<00:21, 14.03it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:19, 14.80it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:18, 15.35it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:18, 15.86it/s]
eval model::  43%|████▎     | 214/500 [00:11<00:15, 18.06it/s]
eval model::  43%|████▎     | 216/500 [00:11<00:17, 16.56it/s]
eval model::  44%|████▎     | 218/500 [00:11<00:17, 16.32it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 16.70it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:18, 15.25it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:15, 17.79it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:12, 21.71it/s]
eval model::  47%|████▋     | 233/500 [00:12<00:12, 21.34it/s]
eval model::  47%|████▋     | 236/500 [00:12<00:13, 19.86it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:12, 21.18it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 20.83it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:12, 20.88it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 21.35it/s]
eval model::  50%|█████     | 251/500 [00:12<00:12, 19.72it/s]
eval model::  51%|█████     | 254/500 [00:13<00:13, 18.44it/s]
eval model::  51%|█████     | 256/500 [00:13<00:14, 17.20it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:12, 19.08it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:12, 18.86it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 18.73it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:11, 19.94it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:10, 20.94it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:10, 20.65it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:11, 18.99it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.09it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:12, 17.61it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:11, 19.00it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:16, 13.19it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:13, 15.89it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:11, 18.27it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:11, 18.36it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:10, 18.75it/s]
eval model::  60%|██████    | 300/500 [00:15<00:10, 19.92it/s]
eval model::  61%|██████    | 303/500 [00:15<00:09, 19.93it/s]
eval model::  61%|██████    | 306/500 [00:15<00:09, 21.09it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:09, 20.72it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:08, 21.75it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 21.44it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 22.27it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:07, 23.56it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 21.13it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:07, 22.72it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:07, 21.38it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:08, 19.70it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:08, 18.52it/s]
eval model::  68%|██████▊   | 338/500 [00:17<00:09, 17.95it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 16.61it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:08, 18.33it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 20.23it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:06, 21.68it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 21.38it/s]
eval model::  71%|███████   | 355/500 [00:18<00:07, 19.24it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:07, 19.96it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 19.77it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.55it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:06, 19.22it/s]
eval model::  74%|███████▍  | 370/500 [00:19<00:06, 19.73it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 19.77it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 21.07it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 20.79it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 20.18it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 21.37it/s]
eval model::  78%|███████▊  | 388/500 [00:20<00:07, 15.64it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:07, 15.31it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:07, 15.01it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:06, 16.90it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:05, 19.65it/s]
eval model::  80%|████████  | 401/500 [00:20<00:04, 20.45it/s]
eval model::  81%|████████  | 404/500 [00:20<00:04, 21.72it/s]
eval model::  81%|████████▏ | 407/500 [00:20<00:03, 23.70it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:03, 22.95it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:04, 19.63it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:04, 17.91it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:05, 16.36it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 16.10it/s]
eval model::  85%|████████▍ | 423/500 [00:21<00:04, 17.67it/s]
eval model::  85%|████████▌ | 425/500 [00:22<00:04, 17.87it/s]
eval model::  86%|████████▌ | 428/500 [00:22<00:03, 20.46it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 21.17it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:02, 22.25it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 21.89it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 20.84it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 21.00it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 20.63it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 20.00it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 21.04it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:02, 19.90it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:02, 19.60it/s]
eval model::  92%|█████████▏| 461/500 [00:23<00:02, 19.47it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:02, 16.92it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 15.76it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 17.61it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 19.99it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.53it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 19.71it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 20.11it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 22.26it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 19.24it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 21.04it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 21.01it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 20.17it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 21.87it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.49it/s]
[2024/08/01 18:37:50] ppocr INFO: cur metric, precision: 0.5191387559808612, recall: 0.31343283582089554, hmean: 0.39087361152806965, fps: 42.20009767497736
[2024/08/01 18:37:50] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 18:37:50] ppocr INFO: best metric, hmean: 0.39087361152806965, precision: 0.5191387559808612, recall: 0.31343283582089554, fps: 42.20009767497736, best_epoch: 40
[2024/08/01 18:37:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:37:57] ppocr INFO: epoch: [41/100], global_step: 123, lr: 0.001000, loss: 2.830152, loss_shrink_maps: 1.693587, loss_threshold_maps: 0.800355, loss_binary_maps: 0.307020, avg_reader_cost: 1.50989 s, avg_batch_cost: 1.77528 s, avg_samples: 12.5, ips: 7.04116 samples/s, eta: 0:22:14
[2024/08/01 18:37:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:38:05] ppocr INFO: epoch: [42/100], global_step: 126, lr: 0.001000, loss: 2.732768, loss_shrink_maps: 1.623774, loss_threshold_maps: 0.786313, loss_binary_maps: 0.294970, avg_reader_cost: 1.53356 s, avg_batch_cost: 1.80714 s, avg_samples: 12.5, ips: 6.91699 samples/s, eta: 0:21:45
[2024/08/01 18:38:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:38:12] ppocr INFO: epoch: [43/100], global_step: 129, lr: 0.001000, loss: 2.643923, loss_shrink_maps: 1.580450, loss_threshold_maps: 0.781988, loss_binary_maps: 0.290139, avg_reader_cost: 1.57599 s, avg_batch_cost: 1.84578 s, avg_samples: 12.5, ips: 6.77220 samples/s, eta: 0:21:18
[2024/08/01 18:38:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:38:18] ppocr INFO: epoch: [44/100], global_step: 130, lr: 0.001000, loss: 2.621701, loss_shrink_maps: 1.557325, loss_threshold_maps: 0.781988, loss_binary_maps: 0.284211, avg_reader_cost: 0.41544 s, avg_batch_cost: 0.52154 s, avg_samples: 4.8, ips: 9.20346 samples/s, eta: 0:21:07
[2024/08/01 18:38:20] ppocr INFO: epoch: [44/100], global_step: 132, lr: 0.001000, loss: 2.621701, loss_shrink_maps: 1.557325, loss_threshold_maps: 0.784402, loss_binary_maps: 0.284211, avg_reader_cost: 1.14102 s, avg_batch_cost: 1.29664 s, avg_samples: 7.7, ips: 5.93844 samples/s, eta: 0:20:50
[2024/08/01 18:38:21] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:38:27] ppocr INFO: epoch: [45/100], global_step: 135, lr: 0.001000, loss: 2.629364, loss_shrink_maps: 1.565714, loss_threshold_maps: 0.791462, loss_binary_maps: 0.291196, avg_reader_cost: 1.58317 s, avg_batch_cost: 1.82652 s, avg_samples: 12.5, ips: 6.84361 samples/s, eta: 0:20:22
[2024/08/01 18:38:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:38:35] ppocr INFO: epoch: [46/100], global_step: 138, lr: 0.001000, loss: 2.600479, loss_shrink_maps: 1.544780, loss_threshold_maps: 0.785313, loss_binary_maps: 0.283768, avg_reader_cost: 1.61699 s, avg_batch_cost: 1.86117 s, avg_samples: 12.5, ips: 6.71619 samples/s, eta: 0:19:56
[2024/08/01 18:38:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:38:42] ppocr INFO: epoch: [47/100], global_step: 140, lr: 0.001000, loss: 2.629364, loss_shrink_maps: 1.565714, loss_threshold_maps: 0.788173, loss_binary_maps: 0.291196, avg_reader_cost: 0.98192 s, avg_batch_cost: 1.16651 s, avg_samples: 9.6, ips: 8.22965 samples/s, eta: 0:19:38
[2024/08/01 18:38:42] ppocr INFO: epoch: [47/100], global_step: 141, lr: 0.001000, loss: 2.629364, loss_shrink_maps: 1.565714, loss_threshold_maps: 0.788173, loss_binary_maps: 0.291196, avg_reader_cost: 0.63162 s, avg_batch_cost: 0.68989 s, avg_samples: 2.9, ips: 4.20359 samples/s, eta: 0:19:30
[2024/08/01 18:38:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:38:50] ppocr INFO: epoch: [48/100], global_step: 144, lr: 0.001000, loss: 2.643791, loss_shrink_maps: 1.570376, loss_threshold_maps: 0.796349, loss_binary_maps: 0.291169, avg_reader_cost: 1.55264 s, avg_batch_cost: 1.79704 s, avg_samples: 12.5, ips: 6.95588 samples/s, eta: 0:19:03
[2024/08/01 18:38:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:38:57] ppocr INFO: epoch: [49/100], global_step: 147, lr: 0.001000, loss: 2.757683, loss_shrink_maps: 1.653154, loss_threshold_maps: 0.798629, loss_binary_maps: 0.306555, avg_reader_cost: 1.52072 s, avg_batch_cost: 1.78565 s, avg_samples: 12.5, ips: 7.00023 samples/s, eta: 0:18:37
[2024/08/01 18:38:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:39:05] ppocr INFO: epoch: [50/100], global_step: 150, lr: 0.001000, loss: 2.740613, loss_shrink_maps: 1.648338, loss_threshold_maps: 0.798629, loss_binary_maps: 0.306369, avg_reader_cost: 1.68366 s, avg_batch_cost: 1.92649 s, avg_samples: 12.5, ips: 6.48849 samples/s, eta: 0:18:12
[2024/08/01 18:39:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:39:12] ppocr INFO: epoch: [51/100], global_step: 153, lr: 0.001000, loss: 2.651121, loss_shrink_maps: 1.575630, loss_threshold_maps: 0.798629, loss_binary_maps: 0.292680, avg_reader_cost: 1.53109 s, avg_batch_cost: 1.77663 s, avg_samples: 12.5, ips: 7.03579 samples/s, eta: 0:17:47
[2024/08/01 18:39:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:39:20] ppocr INFO: epoch: [52/100], global_step: 156, lr: 0.001000, loss: 2.715518, loss_shrink_maps: 1.605505, loss_threshold_maps: 0.793918, loss_binary_maps: 0.302010, avg_reader_cost: 1.53479 s, avg_batch_cost: 1.77989 s, avg_samples: 12.5, ips: 7.02290 samples/s, eta: 0:17:21
[2024/08/01 18:39:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:39:27] ppocr INFO: epoch: [53/100], global_step: 159, lr: 0.001000, loss: 2.654592, loss_shrink_maps: 1.575630, loss_threshold_maps: 0.788781, loss_binary_maps: 0.294195, avg_reader_cost: 1.57658 s, avg_batch_cost: 1.82198 s, avg_samples: 12.5, ips: 6.86067 samples/s, eta: 0:16:56
[2024/08/01 18:39:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:39:33] ppocr INFO: epoch: [54/100], global_step: 160, lr: 0.001000, loss: 2.654592, loss_shrink_maps: 1.575630, loss_threshold_maps: 0.793918, loss_binary_maps: 0.294195, avg_reader_cost: 0.42734 s, avg_batch_cost: 0.51757 s, avg_samples: 4.8, ips: 9.27410 samples/s, eta: 0:16:47
[2024/08/01 18:39:34] ppocr INFO: epoch: [54/100], global_step: 162, lr: 0.001000, loss: 2.639658, loss_shrink_maps: 1.568874, loss_threshold_maps: 0.788781, loss_binary_maps: 0.294195, avg_reader_cost: 1.13125 s, avg_batch_cost: 1.28428 s, avg_samples: 7.7, ips: 5.99557 samples/s, eta: 0:16:32
[2024/08/01 18:39:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:39:42] ppocr INFO: epoch: [55/100], global_step: 165, lr: 0.001000, loss: 2.611558, loss_shrink_maps: 1.517447, loss_threshold_maps: 0.792250, loss_binary_maps: 0.287616, avg_reader_cost: 1.54994 s, avg_batch_cost: 1.84034 s, avg_samples: 12.5, ips: 6.79224 samples/s, eta: 0:16:07
[2024/08/01 18:39:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:39:49] ppocr INFO: epoch: [56/100], global_step: 168, lr: 0.001000, loss: 2.511963, loss_shrink_maps: 1.454415, loss_threshold_maps: 0.788781, loss_binary_maps: 0.274324, avg_reader_cost: 1.50478 s, avg_batch_cost: 1.75194 s, avg_samples: 12.5, ips: 7.13493 samples/s, eta: 0:15:43
[2024/08/01 18:39:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:39:57] ppocr INFO: epoch: [57/100], global_step: 170, lr: 0.001000, loss: 2.538707, loss_shrink_maps: 1.497145, loss_threshold_maps: 0.792250, loss_binary_maps: 0.282345, avg_reader_cost: 1.04085 s, avg_batch_cost: 1.22556 s, avg_samples: 9.6, ips: 7.83318 samples/s, eta: 0:15:27
[2024/08/01 18:39:57] ppocr INFO: epoch: [57/100], global_step: 171, lr: 0.001000, loss: 2.538707, loss_shrink_maps: 1.497145, loss_threshold_maps: 0.792250, loss_binary_maps: 0.282345, avg_reader_cost: 0.66169 s, avg_batch_cost: 0.71994 s, avg_samples: 2.9, ips: 4.02813 samples/s, eta: 0:15:20
[2024/08/01 18:39:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:40:05] ppocr INFO: epoch: [58/100], global_step: 174, lr: 0.001000, loss: 2.611558, loss_shrink_maps: 1.517447, loss_threshold_maps: 0.794488, loss_binary_maps: 0.287616, avg_reader_cost: 1.61544 s, avg_batch_cost: 1.91010 s, avg_samples: 12.5, ips: 6.54418 samples/s, eta: 0:14:57
[2024/08/01 18:40:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:40:12] ppocr INFO: epoch: [59/100], global_step: 177, lr: 0.001000, loss: 2.556543, loss_shrink_maps: 1.517447, loss_threshold_maps: 0.790738, loss_binary_maps: 0.286310, avg_reader_cost: 1.55679 s, avg_batch_cost: 1.80804 s, avg_samples: 12.5, ips: 6.91357 samples/s, eta: 0:14:33
[2024/08/01 18:40:13] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:40:20] ppocr INFO: epoch: [60/100], global_step: 180, lr: 0.001000, loss: 2.527481, loss_shrink_maps: 1.461832, loss_threshold_maps: 0.766207, loss_binary_maps: 0.276651, avg_reader_cost: 1.52688 s, avg_batch_cost: 1.80495 s, avg_samples: 12.5, ips: 6.92540 samples/s, eta: 0:14:10

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:52,  9.43it/s]
eval model::   1%|          | 4/500 [00:00<00:26, 18.51it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.57it/s]
eval model::   2%|▏         | 9/500 [00:00<00:23, 20.56it/s]
eval model::   2%|▏         | 12/500 [00:00<00:25, 19.41it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 19.01it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 20.67it/s]
eval model::   4%|▍         | 20/500 [00:01<00:23, 20.79it/s]
eval model::   5%|▍         | 23/500 [00:01<00:20, 23.03it/s]
eval model::   5%|▌         | 26/500 [00:01<00:19, 23.83it/s]
eval model::   6%|▌         | 29/500 [00:01<00:21, 22.09it/s]
eval model::   6%|▋         | 32/500 [00:01<00:21, 21.43it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 19.17it/s]
eval model::   8%|▊         | 38/500 [00:01<00:22, 20.40it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.81it/s]
eval model::   9%|▉         | 44/500 [00:02<00:22, 20.05it/s]
eval model::   9%|▉         | 47/500 [00:02<00:23, 18.90it/s]
eval model::  10%|█         | 50/500 [00:02<00:22, 20.41it/s]
eval model::  11%|█         | 53/500 [00:02<00:20, 21.48it/s]
eval model::  11%|█         | 56/500 [00:02<00:21, 20.98it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:20, 21.55it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:22, 19.42it/s]
eval model::  13%|█▎        | 65/500 [00:03<00:21, 19.89it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:19, 21.82it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 21.08it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:20, 20.54it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:21, 19.64it/s]
eval model::  16%|█▌        | 80/500 [00:03<00:20, 20.63it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:19, 21.42it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:21, 19.17it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:21, 19.11it/s]
eval model::  18%|█▊        | 90/500 [00:04<00:24, 17.07it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:23, 17.61it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:23, 17.20it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:21, 18.35it/s]
eval model::  20%|██        | 100/500 [00:05<00:22, 17.71it/s]
eval model::  20%|██        | 102/500 [00:05<00:22, 17.76it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 20.64it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.46it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:17, 21.88it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:16, 22.77it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:17, 21.36it/s]
eval model::  24%|██▍       | 120/500 [00:05<00:19, 19.48it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:19, 19.80it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:19, 19.24it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:18, 20.48it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 19.04it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:19, 18.96it/s]
eval model::  27%|██▋       | 137/500 [00:06<00:20, 17.98it/s]
eval model::  28%|██▊       | 139/500 [00:06<00:19, 18.34it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:18, 19.08it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.40it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 18.77it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.95it/s]
eval model::  30%|███       | 152/500 [00:07<00:19, 18.05it/s]
eval model::  31%|███       | 155/500 [00:07<00:17, 20.24it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:16, 20.16it/s]
eval model::  32%|███▏      | 161/500 [00:08<00:21, 15.42it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:21, 15.35it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:21, 15.38it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:20, 16.34it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:18, 17.90it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:18, 17.27it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:22, 14.27it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:21, 15.16it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:18, 16.96it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:21, 15.07it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:17, 17.66it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:16, 18.94it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:15, 19.64it/s]
eval model::  39%|███▊      | 193/500 [00:10<00:14, 21.59it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:15, 19.78it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:14, 20.87it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 21.68it/s]
eval model::  41%|████      | 205/500 [00:10<00:21, 13.75it/s]
eval model::  42%|████▏     | 208/500 [00:11<00:19, 14.80it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:19, 14.84it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.40it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:17, 16.00it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.35it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:16, 17.03it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:17, 16.19it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:15, 18.06it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:13, 19.62it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:11, 22.40it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 21.02it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 21.88it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 22.45it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:13, 19.66it/s]
eval model::  49%|████▉     | 247/500 [00:13<00:12, 19.81it/s]
eval model::  50%|█████     | 250/500 [00:13<00:13, 18.81it/s]
eval model::  51%|█████     | 253/500 [00:13<00:12, 20.13it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 20.60it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:11, 20.71it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:12, 18.32it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:13, 18.12it/s]
eval model::  53%|█████▎    | 267/500 [00:14<00:12, 19.03it/s]
eval model::  54%|█████▍    | 270/500 [00:14<00:11, 20.24it/s]
eval model::  55%|█████▍    | 273/500 [00:14<00:11, 20.32it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:12, 18.56it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:12, 18.10it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 19.15it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:16, 13.11it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:14, 14.83it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:12, 17.06it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:12, 17.08it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:12, 16.08it/s]
eval model::  60%|██████    | 300/500 [00:15<00:10, 19.27it/s]
eval model::  61%|██████    | 303/500 [00:16<00:10, 18.13it/s]
eval model::  61%|██████    | 306/500 [00:16<00:10, 19.22it/s]
eval model::  62%|██████▏   | 309/500 [00:16<00:10, 18.85it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 19.13it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:09, 19.46it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 21.17it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 21.90it/s]
eval model::  65%|██████▍   | 324/500 [00:17<00:08, 21.55it/s]
eval model::  65%|██████▌   | 327/500 [00:17<00:07, 22.44it/s]
eval model::  66%|██████▌   | 330/500 [00:17<00:08, 20.29it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 21.43it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 22.98it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 21.36it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:08, 19.44it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:06, 22.09it/s]
eval model::  70%|███████   | 350/500 [00:18<00:06, 22.43it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 23.57it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:06, 20.77it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:06, 20.90it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:06, 19.66it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 18.25it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 19.27it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 20.28it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 19.68it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:05, 21.52it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:05, 19.93it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:05, 21.31it/s]
eval model::  77%|███████▋  | 387/500 [00:20<00:07, 15.02it/s]
eval model::  78%|███████▊  | 390/500 [00:20<00:06, 16.84it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 17.02it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 18.29it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:04, 20.52it/s]
eval model::  80%|████████  | 401/500 [00:21<00:05, 17.32it/s]
eval model::  81%|████████  | 404/500 [00:21<00:05, 19.01it/s]
eval model::  81%|████████▏ | 407/500 [00:21<00:04, 20.89it/s]
eval model::  82%|████████▏ | 410/500 [00:21<00:04, 20.97it/s]
eval model::  83%|████████▎ | 413/500 [00:21<00:03, 21.77it/s]
eval model::  83%|████████▎ | 416/500 [00:21<00:03, 22.45it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:03, 21.28it/s]
eval model::  84%|████████▍ | 422/500 [00:22<00:03, 19.78it/s]
eval model::  85%|████████▌ | 425/500 [00:22<00:04, 17.43it/s]
eval model::  86%|████████▌ | 428/500 [00:22<00:03, 19.48it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 19.42it/s]
eval model::  87%|████████▋ | 434/500 [00:22<00:03, 19.84it/s]
eval model::  87%|████████▋ | 437/500 [00:22<00:03, 20.79it/s]
eval model::  88%|████████▊ | 440/500 [00:22<00:03, 19.45it/s]
eval model::  89%|████████▊ | 443/500 [00:23<00:02, 20.57it/s]
eval model::  89%|████████▉ | 446/500 [00:23<00:02, 20.67it/s]
eval model::  90%|████████▉ | 449/500 [00:23<00:02, 18.06it/s]
eval model::  90%|█████████ | 452/500 [00:23<00:02, 19.51it/s]
eval model::  91%|█████████ | 455/500 [00:23<00:02, 19.15it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:01, 21.46it/s]
eval model::  92%|█████████▏| 461/500 [00:23<00:01, 20.92it/s]
eval model::  93%|█████████▎| 464/500 [00:24<00:01, 18.49it/s]
eval model::  93%|█████████▎| 466/500 [00:24<00:01, 17.89it/s]
eval model::  94%|█████████▍| 469/500 [00:24<00:01, 19.43it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 20.13it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 21.14it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:00, 22.15it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 21.65it/s]
eval model::  97%|█████████▋| 484/500 [00:25<00:00, 20.50it/s]
eval model::  97%|█████████▋| 487/500 [00:25<00:00, 19.19it/s]
eval model::  98%|█████████▊| 490/500 [00:25<00:00, 19.98it/s]
eval model::  99%|█████████▊| 493/500 [00:25<00:00, 19.95it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 20.99it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 23.40it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.34it/s]
[2024/08/01 18:40:46] ppocr INFO: cur metric, precision: 0.4854557207498384, recall: 0.36157920077034184, hmean: 0.4144591611479029, fps: 43.48454449021499
[2024/08/01 18:40:46] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 18:40:46] ppocr INFO: best metric, hmean: 0.4144591611479029, precision: 0.4854557207498384, recall: 0.36157920077034184, fps: 43.48454449021499, best_epoch: 60
[2024/08/01 18:40:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:40:53] ppocr INFO: epoch: [61/100], global_step: 183, lr: 0.001000, loss: 2.501282, loss_shrink_maps: 1.421478, loss_threshold_maps: 0.766207, loss_binary_maps: 0.270079, avg_reader_cost: 1.47426 s, avg_batch_cost: 1.73179 s, avg_samples: 12.5, ips: 7.21797 samples/s, eta: 0:13:46
[2024/08/01 18:40:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:41:00] ppocr INFO: epoch: [62/100], global_step: 186, lr: 0.001000, loss: 2.508012, loss_shrink_maps: 1.437702, loss_threshold_maps: 0.757074, loss_binary_maps: 0.274744, avg_reader_cost: 1.57364 s, avg_batch_cost: 1.82563 s, avg_samples: 12.5, ips: 6.84696 samples/s, eta: 0:13:23
[2024/08/01 18:41:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:41:08] ppocr INFO: epoch: [63/100], global_step: 189, lr: 0.001000, loss: 2.538753, loss_shrink_maps: 1.466582, loss_threshold_maps: 0.757074, loss_binary_maps: 0.283109, avg_reader_cost: 1.54917 s, avg_batch_cost: 1.79181 s, avg_samples: 12.5, ips: 6.97617 samples/s, eta: 0:13:00
[2024/08/01 18:41:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:41:13] ppocr INFO: epoch: [64/100], global_step: 190, lr: 0.001000, loss: 2.520784, loss_shrink_maps: 1.466582, loss_threshold_maps: 0.747143, loss_binary_maps: 0.283109, avg_reader_cost: 0.42552 s, avg_batch_cost: 0.51429 s, avg_samples: 4.8, ips: 9.33319 samples/s, eta: 0:12:52
[2024/08/01 18:41:15] ppocr INFO: epoch: [64/100], global_step: 192, lr: 0.001000, loss: 2.464877, loss_shrink_maps: 1.433404, loss_threshold_maps: 0.745705, loss_binary_maps: 0.274398, avg_reader_cost: 1.12588 s, avg_batch_cost: 1.28096 s, avg_samples: 7.7, ips: 6.01113 samples/s, eta: 0:12:37
[2024/08/01 18:41:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:41:22] ppocr INFO: epoch: [65/100], global_step: 195, lr: 0.001000, loss: 2.464877, loss_shrink_maps: 1.433404, loss_threshold_maps: 0.745705, loss_binary_maps: 0.274398, avg_reader_cost: 1.53076 s, avg_batch_cost: 1.77507 s, avg_samples: 12.5, ips: 7.04197 samples/s, eta: 0:12:14
[2024/08/01 18:41:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:41:30] ppocr INFO: epoch: [66/100], global_step: 198, lr: 0.001000, loss: 2.402393, loss_shrink_maps: 1.390111, loss_threshold_maps: 0.747143, loss_binary_maps: 0.266023, avg_reader_cost: 1.56225 s, avg_batch_cost: 1.83810 s, avg_samples: 12.5, ips: 6.80051 samples/s, eta: 0:11:52
[2024/08/01 18:41:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:41:37] ppocr INFO: epoch: [67/100], global_step: 200, lr: 0.001000, loss: 2.464877, loss_shrink_maps: 1.433186, loss_threshold_maps: 0.748735, loss_binary_maps: 0.274398, avg_reader_cost: 0.95020 s, avg_batch_cost: 1.13880 s, avg_samples: 9.6, ips: 8.42996 samples/s, eta: 0:11:37
[2024/08/01 18:41:37] ppocr INFO: epoch: [67/100], global_step: 201, lr: 0.001000, loss: 2.402393, loss_shrink_maps: 1.390111, loss_threshold_maps: 0.747143, loss_binary_maps: 0.266023, avg_reader_cost: 0.61790 s, avg_batch_cost: 0.67601 s, avg_samples: 2.9, ips: 4.28986 samples/s, eta: 0:11:30
[2024/08/01 18:41:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:41:45] ppocr INFO: epoch: [68/100], global_step: 204, lr: 0.001000, loss: 2.331588, loss_shrink_maps: 1.328770, loss_threshold_maps: 0.748180, loss_binary_maps: 0.258273, avg_reader_cost: 1.58117 s, avg_batch_cost: 1.87301 s, avg_samples: 12.5, ips: 6.67375 samples/s, eta: 0:11:08
[2024/08/01 18:41:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:41:53] ppocr INFO: epoch: [69/100], global_step: 207, lr: 0.001000, loss: 2.311538, loss_shrink_maps: 1.310550, loss_threshold_maps: 0.746167, loss_binary_maps: 0.253636, avg_reader_cost: 1.56948 s, avg_batch_cost: 1.82761 s, avg_samples: 12.5, ips: 6.83954 samples/s, eta: 0:10:46
[2024/08/01 18:41:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:42:00] ppocr INFO: epoch: [70/100], global_step: 210, lr: 0.001000, loss: 2.329156, loss_shrink_maps: 1.311264, loss_threshold_maps: 0.752281, loss_binary_maps: 0.254230, avg_reader_cost: 1.53642 s, avg_batch_cost: 1.78267 s, avg_samples: 12.5, ips: 7.01194 samples/s, eta: 0:10:23
[2024/08/01 18:42:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:42:08] ppocr INFO: epoch: [71/100], global_step: 213, lr: 0.001000, loss: 2.331588, loss_shrink_maps: 1.328770, loss_threshold_maps: 0.752281, loss_binary_maps: 0.259096, avg_reader_cost: 1.56777 s, avg_batch_cost: 1.81181 s, avg_samples: 12.5, ips: 6.89916 samples/s, eta: 0:10:02
[2024/08/01 18:42:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:42:15] ppocr INFO: epoch: [72/100], global_step: 216, lr: 0.001000, loss: 2.331522, loss_shrink_maps: 1.318475, loss_threshold_maps: 0.753316, loss_binary_maps: 0.255435, avg_reader_cost: 1.54777 s, avg_batch_cost: 1.79318 s, avg_samples: 12.5, ips: 6.97086 samples/s, eta: 0:09:40
[2024/08/01 18:42:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:42:23] ppocr INFO: epoch: [73/100], global_step: 219, lr: 0.001000, loss: 2.311472, loss_shrink_maps: 1.300255, loss_threshold_maps: 0.746838, loss_binary_maps: 0.251707, avg_reader_cost: 1.58705 s, avg_batch_cost: 1.83152 s, avg_samples: 12.5, ips: 6.82493 samples/s, eta: 0:09:18
[2024/08/01 18:42:23] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:42:28] ppocr INFO: epoch: [74/100], global_step: 220, lr: 0.001000, loss: 2.331522, loss_shrink_maps: 1.318475, loss_threshold_maps: 0.753316, loss_binary_maps: 0.256573, avg_reader_cost: 0.40152 s, avg_batch_cost: 0.52770 s, avg_samples: 4.8, ips: 9.09604 samples/s, eta: 0:09:11
[2024/08/01 18:42:30] ppocr INFO: epoch: [74/100], global_step: 222, lr: 0.001000, loss: 2.360302, loss_shrink_maps: 1.354862, loss_threshold_maps: 0.753316, loss_binary_maps: 0.263823, avg_reader_cost: 1.15285 s, avg_batch_cost: 1.30739 s, avg_samples: 7.7, ips: 5.88958 samples/s, eta: 0:08:57
[2024/08/01 18:42:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:42:38] ppocr INFO: epoch: [75/100], global_step: 225, lr: 0.001000, loss: 2.432237, loss_shrink_maps: 1.407012, loss_threshold_maps: 0.759269, loss_binary_maps: 0.274037, avg_reader_cost: 1.60260 s, avg_batch_cost: 1.89119 s, avg_samples: 12.5, ips: 6.60959 samples/s, eta: 0:08:35
[2024/08/01 18:42:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:42:46] ppocr INFO: epoch: [76/100], global_step: 228, lr: 0.001000, loss: 2.357870, loss_shrink_maps: 1.354862, loss_threshold_maps: 0.752106, loss_binary_maps: 0.263823, avg_reader_cost: 1.63509 s, avg_batch_cost: 1.94524 s, avg_samples: 12.5, ips: 6.42595 samples/s, eta: 0:08:14
[2024/08/01 18:42:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:42:53] ppocr INFO: epoch: [77/100], global_step: 230, lr: 0.001000, loss: 2.355170, loss_shrink_maps: 1.354862, loss_threshold_maps: 0.746838, loss_binary_maps: 0.263823, avg_reader_cost: 0.91529 s, avg_batch_cost: 1.10050 s, avg_samples: 9.6, ips: 8.72332 samples/s, eta: 0:08:00
[2024/08/01 18:42:53] ppocr INFO: epoch: [77/100], global_step: 231, lr: 0.001000, loss: 2.355170, loss_shrink_maps: 1.354862, loss_threshold_maps: 0.746838, loss_binary_maps: 0.263823, avg_reader_cost: 0.59891 s, avg_batch_cost: 0.65713 s, avg_samples: 2.9, ips: 4.41316 samples/s, eta: 0:07:53
[2024/08/01 18:42:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:43:01] ppocr INFO: epoch: [78/100], global_step: 234, lr: 0.001000, loss: 2.313231, loss_shrink_maps: 1.338428, loss_threshold_maps: 0.750355, loss_binary_maps: 0.261390, avg_reader_cost: 1.52929 s, avg_batch_cost: 1.79071 s, avg_samples: 12.5, ips: 6.98046 samples/s, eta: 0:07:31
[2024/08/01 18:43:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:43:08] ppocr INFO: epoch: [79/100], global_step: 237, lr: 0.001000, loss: 2.359019, loss_shrink_maps: 1.354862, loss_threshold_maps: 0.750296, loss_binary_maps: 0.263649, avg_reader_cost: 1.60182 s, avg_batch_cost: 1.86595 s, avg_samples: 12.5, ips: 6.69899 samples/s, eta: 0:07:10
[2024/08/01 18:43:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:43:16] ppocr INFO: epoch: [80/100], global_step: 240, lr: 0.001000, loss: 2.327538, loss_shrink_maps: 1.338428, loss_threshold_maps: 0.750296, loss_binary_maps: 0.261390, avg_reader_cost: 1.53683 s, avg_batch_cost: 1.79004 s, avg_samples: 12.5, ips: 6.98310 samples/s, eta: 0:06:49

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:59,  8.39it/s]
eval model::   1%|          | 4/500 [00:00<00:31, 15.79it/s]
eval model::   1%|▏         | 7/500 [00:00<00:26, 18.77it/s]
eval model::   2%|▏         | 9/500 [00:00<00:25, 19.02it/s]
eval model::   2%|▏         | 11/500 [00:00<00:25, 19.03it/s]
eval model::   3%|▎         | 13/500 [00:00<00:27, 17.72it/s]
eval model::   3%|▎         | 15/500 [00:00<00:27, 17.72it/s]
eval model::   4%|▎         | 18/500 [00:00<00:23, 20.64it/s]
eval model::   4%|▍         | 21/500 [00:01<00:23, 20.67it/s]
eval model::   5%|▍         | 24/500 [00:01<00:22, 20.93it/s]
eval model::   5%|▌         | 27/500 [00:01<00:23, 20.56it/s]
eval model::   6%|▌         | 30/500 [00:01<00:22, 20.79it/s]
eval model::   7%|▋         | 33/500 [00:01<00:23, 19.93it/s]
eval model::   7%|▋         | 36/500 [00:01<00:23, 19.65it/s]
eval model::   8%|▊         | 38/500 [00:01<00:25, 18.42it/s]
eval model::   8%|▊         | 40/500 [00:02<00:25, 18.21it/s]
eval model::   9%|▊         | 43/500 [00:02<00:23, 19.43it/s]
eval model::   9%|▉         | 46/500 [00:02<00:23, 19.52it/s]
eval model::  10%|▉         | 49/500 [00:02<00:21, 21.12it/s]
eval model::  10%|█         | 52/500 [00:02<00:22, 19.55it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 19.88it/s]
eval model::  12%|█▏        | 58/500 [00:03<00:23, 18.90it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:24, 17.88it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:25, 17.10it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:21, 19.81it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 22.51it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 20.53it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:20, 20.80it/s]
eval model::  16%|█▌        | 79/500 [00:04<00:20, 20.85it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:20, 20.44it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 19.83it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 19.94it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:20, 19.51it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:20, 20.25it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:19, 21.15it/s]
eval model::  20%|██        | 100/500 [00:05<00:20, 19.86it/s]
eval model::  21%|██        | 103/500 [00:05<00:19, 20.65it/s]
eval model::  21%|██        | 106/500 [00:05<00:19, 20.22it/s]
eval model::  22%|██▏       | 109/500 [00:05<00:18, 20.69it/s]
eval model::  22%|██▏       | 112/500 [00:05<00:20, 19.04it/s]
eval model::  23%|██▎       | 115/500 [00:05<00:21, 18.32it/s]
eval model::  24%|██▎       | 118/500 [00:06<00:19, 19.64it/s]
eval model::  24%|██▍       | 121/500 [00:06<00:20, 18.48it/s]
eval model::  25%|██▍       | 124/500 [00:06<00:21, 17.50it/s]
eval model::  25%|██▌       | 127/500 [00:06<00:19, 19.57it/s]
eval model::  26%|██▌       | 130/500 [00:06<00:20, 17.93it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:20, 18.23it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.59it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.66it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.51it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.75it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 19.60it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.99it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 18.60it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 20.93it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:16, 20.52it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:17, 19.26it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:18, 18.17it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:19, 17.44it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:17, 19.16it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:17, 18.47it/s]
eval model::  35%|███▍      | 174/500 [00:09<00:20, 16.21it/s]
eval model::  35%|███▌      | 176/500 [00:09<00:19, 16.83it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 18.97it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:17, 17.82it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:15, 19.86it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 20.33it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:14, 20.98it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:13, 22.91it/s]
eval model::  39%|███▉      | 196/500 [00:10<00:14, 21.60it/s]
eval model::  40%|███▉      | 199/500 [00:10<00:13, 21.78it/s]
eval model::  40%|████      | 202/500 [00:10<00:13, 22.38it/s]
eval model::  41%|████      | 205/500 [00:10<00:18, 16.13it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:16, 17.49it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:17, 16.93it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:15, 18.56it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:17, 16.75it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.67it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 18.15it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:16, 17.00it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:14, 19.59it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:14, 18.63it/s]
eval model::  46%|████▌     | 231/500 [00:11<00:13, 20.33it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:12, 21.15it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:11, 22.62it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 23.28it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:13, 19.63it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:12, 20.07it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:13, 18.57it/s]
eval model::  50%|█████     | 251/500 [00:12<00:13, 18.07it/s]
eval model::  51%|█████     | 254/500 [00:13<00:12, 20.11it/s]
eval model::  51%|█████▏    | 257/500 [00:13<00:11, 20.38it/s]
eval model::  52%|█████▏    | 260/500 [00:13<00:11, 21.15it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:11, 21.05it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:11, 20.18it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:10, 21.25it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:11, 19.19it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:11, 19.24it/s]
eval model::  55%|█████▌    | 276/500 [00:14<00:11, 18.99it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 19.93it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 21.21it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:14, 14.69it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:13, 15.39it/s]
eval model::  58%|█████▊    | 290/500 [00:15<00:11, 17.63it/s]
eval model::  59%|█████▊    | 293/500 [00:15<00:10, 19.69it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:11, 17.80it/s]
eval model::  60%|██████    | 300/500 [00:15<00:10, 19.55it/s]
eval model::  61%|██████    | 303/500 [00:15<00:09, 20.78it/s]
eval model::  61%|██████    | 306/500 [00:15<00:08, 21.63it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:09, 20.29it/s]
eval model::  62%|██████▏   | 312/500 [00:16<00:09, 19.95it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 20.76it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 21.19it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 22.05it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 21.09it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:07, 23.05it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:07, 21.95it/s]
eval model::  67%|██████▋   | 333/500 [00:17<00:07, 21.16it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 21.98it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 21.23it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:08, 18.79it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:07, 20.45it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:07, 20.22it/s]
eval model::  70%|███████   | 351/500 [00:17<00:07, 20.51it/s]
eval model::  71%|███████   | 354/500 [00:18<00:06, 21.18it/s]
eval model::  71%|███████▏  | 357/500 [00:18<00:07, 18.93it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:07, 19.79it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:07, 18.96it/s]
eval model::  73%|███████▎  | 365/500 [00:18<00:07, 17.86it/s]
eval model::  74%|███████▎  | 368/500 [00:18<00:06, 19.57it/s]
eval model::  74%|███████▍  | 371/500 [00:18<00:06, 20.19it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 21.00it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:05, 21.07it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:06, 19.83it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:05, 21.35it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:07, 14.76it/s]
eval model::  78%|███████▊  | 389/500 [00:19<00:06, 16.98it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 17.76it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:06, 17.46it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 17.94it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 18.69it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 20.59it/s]
eval model::  81%|████████  | 406/500 [00:20<00:04, 21.04it/s]
eval model::  82%|████████▏ | 409/500 [00:20<00:04, 21.58it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:04, 21.98it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:03, 22.78it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 19.62it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:04, 16.58it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 17.75it/s]
eval model::  85%|████████▌ | 427/500 [00:21<00:03, 19.12it/s]
eval model::  86%|████████▌ | 430/500 [00:22<00:03, 21.12it/s]
eval model::  87%|████████▋ | 433/500 [00:22<00:02, 22.62it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 23.60it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 23.64it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 23.41it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 21.33it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 20.77it/s]
eval model::  90%|█████████ | 451/500 [00:22<00:02, 20.58it/s]
eval model::  91%|█████████ | 454/500 [00:23<00:02, 21.90it/s]
eval model::  91%|█████████▏| 457/500 [00:23<00:01, 23.34it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:01, 22.27it/s]
eval model::  93%|█████████▎| 463/500 [00:23<00:01, 18.71it/s]
eval model::  93%|█████████▎| 466/500 [00:23<00:01, 17.91it/s]
eval model::  94%|█████████▍| 469/500 [00:23<00:01, 19.26it/s]
eval model::  94%|█████████▍| 472/500 [00:24<00:01, 20.02it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 18.60it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 20.10it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 20.01it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 19.00it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 17.97it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 18.82it/s]
eval model::  98%|█████████▊| 491/500 [00:25<00:00, 18.59it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 19.79it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 19.31it/s]
eval model:: 100%|█████████▉| 499/500 [00:25<00:00, 21.63it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.64it/s]
[2024/08/01 18:43:41] ppocr INFO: cur metric, precision: 0.5756958587915818, recall: 0.40828117477130477, hmean: 0.4777464788732394, fps: 43.55140108118018
[2024/08/01 18:43:41] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 18:43:41] ppocr INFO: best metric, hmean: 0.4777464788732394, precision: 0.5756958587915818, recall: 0.40828117477130477, fps: 43.55140108118018, best_epoch: 80
[2024/08/01 18:43:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:43:49] ppocr INFO: epoch: [81/100], global_step: 243, lr: 0.001000, loss: 2.318730, loss_shrink_maps: 1.329317, loss_threshold_maps: 0.749129, loss_binary_maps: 0.258787, avg_reader_cost: 1.60162 s, avg_batch_cost: 1.87977 s, avg_samples: 12.5, ips: 6.64976 samples/s, eta: 0:06:28
[2024/08/01 18:43:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:43:56] ppocr INFO: epoch: [82/100], global_step: 246, lr: 0.001000, loss: 2.318730, loss_shrink_maps: 1.305076, loss_threshold_maps: 0.744377, loss_binary_maps: 0.253913, avg_reader_cost: 1.58209 s, avg_batch_cost: 1.82539 s, avg_samples: 12.5, ips: 6.84786 samples/s, eta: 0:06:07
[2024/08/01 18:43:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:44:04] ppocr INFO: epoch: [83/100], global_step: 249, lr: 0.001000, loss: 2.381673, loss_shrink_maps: 1.341568, loss_threshold_maps: 0.744377, loss_binary_maps: 0.261046, avg_reader_cost: 1.60176 s, avg_batch_cost: 1.87271 s, avg_samples: 12.5, ips: 6.67482 samples/s, eta: 0:05:47
[2024/08/01 18:44:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:44:10] ppocr INFO: epoch: [84/100], global_step: 250, lr: 0.001000, loss: 2.381673, loss_shrink_maps: 1.341568, loss_threshold_maps: 0.744377, loss_binary_maps: 0.261046, avg_reader_cost: 0.41756 s, avg_batch_cost: 0.50759 s, avg_samples: 4.8, ips: 9.45640 samples/s, eta: 0:05:39
[2024/08/01 18:44:11] ppocr INFO: epoch: [84/100], global_step: 252, lr: 0.001000, loss: 2.353767, loss_shrink_maps: 1.320434, loss_threshold_maps: 0.742881, loss_binary_maps: 0.257748, avg_reader_cost: 1.11404 s, avg_batch_cost: 1.27046 s, avg_samples: 7.7, ips: 6.06078 samples/s, eta: 0:05:26
[2024/08/01 18:44:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:44:19] ppocr INFO: epoch: [85/100], global_step: 255, lr: 0.001000, loss: 2.301886, loss_shrink_maps: 1.294086, loss_threshold_maps: 0.731867, loss_binary_maps: 0.251734, avg_reader_cost: 1.54889 s, avg_batch_cost: 1.82807 s, avg_samples: 12.5, ips: 6.83781 samples/s, eta: 0:05:05
[2024/08/01 18:44:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:44:26] ppocr INFO: epoch: [86/100], global_step: 258, lr: 0.001000, loss: 2.285625, loss_shrink_maps: 1.286824, loss_threshold_maps: 0.726836, loss_binary_maps: 0.249740, avg_reader_cost: 1.52347 s, avg_batch_cost: 1.78180 s, avg_samples: 12.5, ips: 7.01537 samples/s, eta: 0:04:44
[2024/08/01 18:44:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:44:34] ppocr INFO: epoch: [87/100], global_step: 260, lr: 0.001000, loss: 2.174765, loss_shrink_maps: 1.236476, loss_threshold_maps: 0.720323, loss_binary_maps: 0.241831, avg_reader_cost: 0.97977 s, avg_batch_cost: 1.16542 s, avg_samples: 9.6, ips: 8.23736 samples/s, eta: 0:04:30
[2024/08/01 18:44:34] ppocr INFO: epoch: [87/100], global_step: 261, lr: 0.001000, loss: 2.174765, loss_shrink_maps: 1.236476, loss_threshold_maps: 0.720323, loss_binary_maps: 0.241831, avg_reader_cost: 0.63115 s, avg_batch_cost: 0.68910 s, avg_samples: 2.9, ips: 4.20838 samples/s, eta: 0:04:24
[2024/08/01 18:44:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:44:42] ppocr INFO: epoch: [88/100], global_step: 264, lr: 0.001000, loss: 2.174765, loss_shrink_maps: 1.236476, loss_threshold_maps: 0.718217, loss_binary_maps: 0.241831, avg_reader_cost: 1.51821 s, avg_batch_cost: 1.76146 s, avg_samples: 12.5, ips: 7.09637 samples/s, eta: 0:04:03
[2024/08/01 18:44:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:44:49] ppocr INFO: epoch: [89/100], global_step: 267, lr: 0.001000, loss: 2.207464, loss_shrink_maps: 1.248974, loss_threshold_maps: 0.720323, loss_binary_maps: 0.243890, avg_reader_cost: 1.52443 s, avg_batch_cost: 1.78275 s, avg_samples: 12.5, ips: 7.01164 samples/s, eta: 0:03:42
[2024/08/01 18:44:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:44:57] ppocr INFO: epoch: [90/100], global_step: 270, lr: 0.001000, loss: 2.240254, loss_shrink_maps: 1.255855, loss_threshold_maps: 0.729933, loss_binary_maps: 0.244755, avg_reader_cost: 1.63227 s, avg_batch_cost: 1.91951 s, avg_samples: 12.5, ips: 6.51209 samples/s, eta: 0:03:22
[2024/08/01 18:44:58] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:45:05] ppocr INFO: epoch: [91/100], global_step: 273, lr: 0.001000, loss: 2.240254, loss_shrink_maps: 1.255855, loss_threshold_maps: 0.733039, loss_binary_maps: 0.244755, avg_reader_cost: 1.72579 s, avg_batch_cost: 1.96742 s, avg_samples: 12.5, ips: 6.35351 samples/s, eta: 0:03:02
[2024/08/01 18:45:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:45:13] ppocr INFO: epoch: [92/100], global_step: 276, lr: 0.001000, loss: 2.240254, loss_shrink_maps: 1.255855, loss_threshold_maps: 0.733039, loss_binary_maps: 0.244755, avg_reader_cost: 1.56925 s, avg_batch_cost: 1.82412 s, avg_samples: 12.5, ips: 6.85263 samples/s, eta: 0:02:41
[2024/08/01 18:45:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:45:20] ppocr INFO: epoch: [93/100], global_step: 279, lr: 0.001000, loss: 2.240254, loss_shrink_maps: 1.265613, loss_threshold_maps: 0.733039, loss_binary_maps: 0.245559, avg_reader_cost: 1.59952 s, avg_batch_cost: 1.86449 s, avg_samples: 12.5, ips: 6.70423 samples/s, eta: 0:02:21
[2024/08/01 18:45:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:45:27] ppocr INFO: epoch: [94/100], global_step: 280, lr: 0.001000, loss: 2.240254, loss_shrink_maps: 1.265613, loss_threshold_maps: 0.733039, loss_binary_maps: 0.245559, avg_reader_cost: 0.42156 s, avg_batch_cost: 0.54059 s, avg_samples: 4.8, ips: 8.87923 samples/s, eta: 0:02:14
[2024/08/01 18:45:28] ppocr INFO: epoch: [94/100], global_step: 282, lr: 0.001000, loss: 2.235088, loss_shrink_maps: 1.265613, loss_threshold_maps: 0.728887, loss_binary_maps: 0.245525, avg_reader_cost: 1.17797 s, avg_batch_cost: 1.33335 s, avg_samples: 7.7, ips: 5.77492 samples/s, eta: 0:02:01
[2024/08/01 18:45:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:45:36] ppocr INFO: epoch: [95/100], global_step: 285, lr: 0.001000, loss: 2.248064, loss_shrink_maps: 1.271666, loss_threshold_maps: 0.733891, loss_binary_maps: 0.248786, avg_reader_cost: 1.54728 s, avg_batch_cost: 1.81453 s, avg_samples: 12.5, ips: 6.88885 samples/s, eta: 0:01:40
[2024/08/01 18:45:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:45:44] ppocr INFO: epoch: [96/100], global_step: 288, lr: 0.001000, loss: 2.235482, loss_shrink_maps: 1.267639, loss_threshold_maps: 0.730886, loss_binary_maps: 0.248786, avg_reader_cost: 1.56094 s, avg_batch_cost: 1.80573 s, avg_samples: 12.5, ips: 6.92240 samples/s, eta: 0:01:20
[2024/08/01 18:45:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:45:51] ppocr INFO: epoch: [97/100], global_step: 290, lr: 0.001000, loss: 2.201778, loss_shrink_maps: 1.252921, loss_threshold_maps: 0.717601, loss_binary_maps: 0.245554, avg_reader_cost: 0.96981 s, avg_batch_cost: 1.16923 s, avg_samples: 9.6, ips: 8.21056 samples/s, eta: 0:01:07
[2024/08/01 18:45:51] ppocr INFO: epoch: [97/100], global_step: 291, lr: 0.001000, loss: 2.235482, loss_shrink_maps: 1.267639, loss_threshold_maps: 0.725062, loss_binary_maps: 0.248786, avg_reader_cost: 0.63322 s, avg_batch_cost: 0.69125 s, avg_samples: 2.9, ips: 4.19528 samples/s, eta: 0:01:00
[2024/08/01 18:45:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:45:59] ppocr INFO: epoch: [98/100], global_step: 294, lr: 0.001000, loss: 2.235482, loss_shrink_maps: 1.267639, loss_threshold_maps: 0.717601, loss_binary_maps: 0.248786, avg_reader_cost: 1.52689 s, avg_batch_cost: 1.79152 s, avg_samples: 12.5, ips: 6.97733 samples/s, eta: 0:00:40
[2024/08/01 18:46:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:46:06] ppocr INFO: epoch: [99/100], global_step: 297, lr: 0.001000, loss: 2.250691, loss_shrink_maps: 1.276110, loss_threshold_maps: 0.720753, loss_binary_maps: 0.250824, avg_reader_cost: 1.52438 s, avg_batch_cost: 1.76807 s, avg_samples: 12.5, ips: 7.06985 samples/s, eta: 0:00:20
[2024/08/01 18:46:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:46:14] ppocr INFO: epoch: [100/100], global_step: 300, lr: 0.001000, loss: 2.255736, loss_shrink_maps: 1.276110, loss_threshold_maps: 0.736116, loss_binary_maps: 0.250824, avg_reader_cost: 1.54734 s, avg_batch_cost: 1.79055 s, avg_samples: 12.5, ips: 6.98110 samples/s, eta: 0:00:00

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:50,  9.86it/s]
eval model::   1%|          | 3/500 [00:00<00:31, 15.74it/s]
eval model::   1%|          | 6/500 [00:00<00:26, 18.92it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.43it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.74it/s]
eval model::   3%|▎         | 14/500 [00:00<00:24, 19.75it/s]
eval model::   3%|▎         | 17/500 [00:00<00:22, 21.53it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.46it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.04it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 20.84it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 20.79it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 19.53it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 18.86it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.63it/s]
eval model::   8%|▊         | 40/500 [00:02<00:24, 18.46it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 18.26it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.53it/s]
eval model::  10%|▉         | 48/500 [00:02<00:23, 19.19it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 19.25it/s]
eval model::  11%|█         | 53/500 [00:02<00:22, 19.72it/s]
eval model::  11%|█         | 56/500 [00:02<00:23, 19.25it/s]
eval model::  12%|█▏        | 59/500 [00:03<00:22, 19.55it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:24, 17.88it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:24, 18.15it/s]
eval model::  13%|█▎        | 67/500 [00:03<00:20, 21.58it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 22.59it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:18, 22.72it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:18, 23.54it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:19, 21.62it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 21.67it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:20, 20.75it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 20.18it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:21, 19.27it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:21, 18.91it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:21, 19.12it/s]
eval model::  20%|█▉        | 98/500 [00:04<00:20, 19.97it/s]
eval model::  20%|██        | 101/500 [00:05<00:19, 20.46it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 21.15it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:17, 22.83it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:18, 21.14it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:17, 21.91it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:17, 22.37it/s]
eval model::  24%|██▍       | 119/500 [00:05<00:17, 21.55it/s]
eval model::  24%|██▍       | 122/500 [00:06<00:17, 21.62it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:18, 20.53it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:17, 21.28it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:19, 19.34it/s]
eval model::  27%|██▋       | 133/500 [00:06<00:19, 19.18it/s]
eval model::  27%|██▋       | 136/500 [00:06<00:17, 20.22it/s]
eval model::  28%|██▊       | 139/500 [00:06<00:17, 20.89it/s]
eval model::  28%|██▊       | 142/500 [00:07<00:16, 21.76it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:17, 20.72it/s]
eval model::  30%|██▉       | 148/500 [00:07<00:17, 19.96it/s]
eval model::  30%|███       | 151/500 [00:07<00:19, 17.97it/s]
eval model::  31%|███       | 154/500 [00:07<00:16, 20.35it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:16, 20.77it/s]
eval model::  32%|███▏      | 160/500 [00:08<00:19, 17.19it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:19, 17.32it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:18, 17.82it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:18, 18.08it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 19.50it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:17, 18.86it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:18, 17.92it/s]
eval model::  36%|███▌      | 178/500 [00:08<00:15, 20.24it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:15, 20.52it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:14, 22.52it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:13, 23.29it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:14, 21.90it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:13, 23.07it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:14, 20.62it/s]
eval model::  40%|███▉      | 199/500 [00:09<00:14, 20.34it/s]
eval model::  40%|████      | 202/500 [00:10<00:15, 19.06it/s]
eval model::  41%|████      | 204/500 [00:10<00:15, 19.18it/s]
eval model::  41%|████      | 206/500 [00:10<00:23, 12.50it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:21, 13.53it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:20, 14.35it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:18, 15.70it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.47it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.50it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 17.59it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:15, 17.49it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:13, 20.25it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:12, 21.63it/s]
eval model::  46%|████▋     | 232/500 [00:11<00:10, 25.04it/s]
eval model::  47%|████▋     | 235/500 [00:11<00:11, 23.54it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:11, 23.71it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 22.70it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:12, 20.01it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:12, 20.48it/s]
eval model::  50%|█████     | 250/500 [00:12<00:13, 18.95it/s]
eval model::  51%|█████     | 253/500 [00:12<00:12, 19.99it/s]
eval model::  51%|█████     | 256/500 [00:12<00:11, 20.98it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 22.12it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:11, 21.15it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:12, 18.25it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:12, 18.02it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:11, 19.97it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:11, 19.22it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:12, 17.61it/s]
eval model::  56%|█████▌    | 278/500 [00:14<00:11, 19.90it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:11, 19.30it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 19.89it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:14, 14.49it/s]
eval model::  58%|█████▊    | 290/500 [00:14<00:12, 17.07it/s]
eval model::  59%|█████▊    | 293/500 [00:14<00:10, 19.08it/s]
eval model::  59%|█████▉    | 296/500 [00:15<00:11, 17.22it/s]
eval model::  60%|██████    | 300/500 [00:15<00:10, 19.75it/s]
eval model::  61%|██████    | 303/500 [00:15<00:09, 20.72it/s]
eval model::  61%|██████    | 306/500 [00:15<00:09, 21.09it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:08, 21.65it/s]
eval model::  62%|██████▏   | 312/500 [00:15<00:08, 21.25it/s]
eval model::  63%|██████▎   | 315/500 [00:16<00:08, 21.13it/s]
eval model::  64%|██████▎   | 318/500 [00:16<00:08, 20.87it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 21.74it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:08, 19.84it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:08, 21.32it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:08, 20.02it/s]
eval model::  67%|██████▋   | 333/500 [00:16<00:07, 21.39it/s]
eval model::  67%|██████▋   | 336/500 [00:17<00:07, 22.11it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:07, 20.64it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:08, 18.23it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 21.11it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:06, 22.73it/s]
eval model::  70%|███████   | 352/500 [00:17<00:06, 21.71it/s]
eval model::  71%|███████   | 355/500 [00:17<00:06, 20.99it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 20.75it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:06, 19.89it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.73it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:07, 18.69it/s]
eval model::  74%|███████▍  | 369/500 [00:18<00:06, 20.32it/s]
eval model::  74%|███████▍  | 372/500 [00:18<00:06, 20.36it/s]
eval model::  75%|███████▌  | 375/500 [00:18<00:05, 21.09it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 22.54it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 20.54it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 21.43it/s]
eval model::  78%|███████▊  | 388/500 [00:19<00:07, 14.46it/s]
eval model::  78%|███████▊  | 391/500 [00:19<00:06, 16.69it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:06, 17.34it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 19.31it/s]
eval model::  80%|████████  | 400/500 [00:20<00:05, 19.64it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 19.49it/s]
eval model::  81%|████████  | 406/500 [00:20<00:04, 20.76it/s]
eval model::  82%|████████▏ | 409/500 [00:20<00:04, 22.70it/s]
eval model::  82%|████████▏ | 412/500 [00:20<00:04, 21.72it/s]
eval model::  83%|████████▎ | 415/500 [00:20<00:04, 21.17it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 19.77it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:04, 16.74it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 18.16it/s]
eval model::  85%|████████▌ | 427/500 [00:21<00:03, 19.28it/s]
eval model::  86%|████████▌ | 430/500 [00:21<00:03, 21.30it/s]
eval model::  87%|████████▋ | 433/500 [00:21<00:02, 22.78it/s]
eval model::  87%|████████▋ | 436/500 [00:22<00:02, 22.43it/s]
eval model::  88%|████████▊ | 439/500 [00:22<00:02, 21.89it/s]
eval model::  88%|████████▊ | 442/500 [00:22<00:02, 21.98it/s]
eval model::  89%|████████▉ | 445/500 [00:22<00:02, 21.21it/s]
eval model::  90%|████████▉ | 448/500 [00:22<00:02, 20.26it/s]
eval model::  90%|█████████ | 451/500 [00:22<00:02, 19.42it/s]
eval model::  91%|█████████ | 454/500 [00:22<00:02, 21.52it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:01, 24.21it/s]
eval model::  92%|█████████▏| 461/500 [00:23<00:01, 22.58it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 19.58it/s]
eval model::  93%|█████████▎| 467/500 [00:23<00:01, 17.36it/s]
eval model::  94%|█████████▍| 471/500 [00:23<00:01, 19.92it/s]
eval model::  95%|█████████▍| 474/500 [00:23<00:01, 19.86it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:00, 22.57it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 23.57it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 23.57it/s]
eval model::  97%|█████████▋| 487/500 [00:24<00:00, 24.22it/s]
eval model::  98%|█████████▊| 490/500 [00:24<00:00, 23.66it/s]
eval model::  99%|█████████▊| 493/500 [00:24<00:00, 22.12it/s]
eval model::  99%|█████████▉| 496/500 [00:24<00:00, 21.85it/s]
eval model:: 100%|█████████▉| 499/500 [00:24<00:00, 22.12it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 20.01it/s]
[2024/08/01 18:46:39] ppocr INFO: cur metric, precision: 0.620617110799439, recall: 0.4260953298025999, hmean: 0.5052811875535255, fps: 43.63615601693568
[2024/08/01 18:46:39] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 18:46:39] ppocr INFO: best metric, hmean: 0.5052811875535255, precision: 0.620617110799439, recall: 0.4260953298025999, fps: 43.63615601693568, best_epoch: 100
[2024/08/01 18:46:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 18:46:40] ppocr INFO: best metric, hmean: 0.5052811875535255, precision: 0.620617110799439, recall: 0.4260953298025999, fps: 43.63615601693568, best_epoch: 100
I0801 18:46:41.780061 110577 tcp_store.cc:289] receive shutdown event and so quit from MasterDaemon run loop
[2024/08/01 20:34:41] ppocr INFO: Architecture : 
[2024/08/01 20:34:41] ppocr INFO:     Backbone : 
[2024/08/01 20:34:41] ppocr INFO:         model_name : large
[2024/08/01 20:34:41] ppocr INFO:         name : MobileNetV3
[2024/08/01 20:34:41] ppocr INFO:         scale : 0.5
[2024/08/01 20:34:41] ppocr INFO:     Head : 
[2024/08/01 20:34:41] ppocr INFO:         k : 50
[2024/08/01 20:34:41] ppocr INFO:         name : DBHead
[2024/08/01 20:34:41] ppocr INFO:     Neck : 
[2024/08/01 20:34:41] ppocr INFO:         name : DBFPN
[2024/08/01 20:34:41] ppocr INFO:         out_channels : 256
[2024/08/01 20:34:41] ppocr INFO:     Transform : None
[2024/08/01 20:34:41] ppocr INFO:     algorithm : DB
[2024/08/01 20:34:41] ppocr INFO:     model_type : det
[2024/08/01 20:34:41] ppocr INFO: Eval : 
[2024/08/01 20:34:41] ppocr INFO:     dataset : 
[2024/08/01 20:34:41] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/08/01 20:34:41] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
[2024/08/01 20:34:41] ppocr INFO:         name : SimpleDataSet
[2024/08/01 20:34:41] ppocr INFO:         transforms : 
[2024/08/01 20:34:41] ppocr INFO:             DecodeImage : 
[2024/08/01 20:34:41] ppocr INFO:                 channel_first : False
[2024/08/01 20:34:41] ppocr INFO:                 img_mode : BGR
[2024/08/01 20:34:41] ppocr INFO:             DetLabelEncode : None
[2024/08/01 20:34:41] ppocr INFO:             DetResizeForTest : 
[2024/08/01 20:34:41] ppocr INFO:                 image_shape : [736, 1280]
[2024/08/01 20:34:41] ppocr INFO:             NormalizeImage : 
[2024/08/01 20:34:41] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/08/01 20:34:41] ppocr INFO:                 order : hwc
[2024/08/01 20:34:41] ppocr INFO:                 scale : 1./255.
[2024/08/01 20:34:41] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/08/01 20:34:41] ppocr INFO:             ToCHWImage : None
[2024/08/01 20:34:41] ppocr INFO:             KeepKeys : 
[2024/08/01 20:34:41] ppocr INFO:                 keep_keys : ['image', 'shape', 'polys', 'ignore_tags']
[2024/08/01 20:34:41] ppocr INFO:     loader : 
[2024/08/01 20:34:41] ppocr INFO:         batch_size_per_card : 1
[2024/08/01 20:34:41] ppocr INFO:         drop_last : False
[2024/08/01 20:34:41] ppocr INFO:         num_workers : 0
[2024/08/01 20:34:41] ppocr INFO:         shuffle : False
[2024/08/01 20:34:41] ppocr INFO:         use_shared_memory : True
[2024/08/01 20:34:41] ppocr INFO: Global : 
[2024/08/01 20:34:41] ppocr INFO:     cal_metric_during_train : False
[2024/08/01 20:34:41] ppocr INFO:     checkpoints : None
[2024/08/01 20:34:41] ppocr INFO:     distributed : True
[2024/08/01 20:34:41] ppocr INFO:     epoch_num : 100
[2024/08/01 20:34:41] ppocr INFO:     eval_batch_step : [0, 60]
[2024/08/01 20:34:41] ppocr INFO:     infer_img : doc/imgs_en/img_10.jpg
[2024/08/01 20:34:41] ppocr INFO:     log_smooth_window : 20
[2024/08/01 20:34:41] ppocr INFO:     pretrained_model : ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/08/01 20:34:41] ppocr INFO:     print_batch_step : 10
[2024/08/01 20:34:41] ppocr INFO:     save_epoch_step : 1200
[2024/08/01 20:34:41] ppocr INFO:     save_inference_dir : None
[2024/08/01 20:34:41] ppocr INFO:     save_model_dir : ./output/db_mv3/
[2024/08/01 20:34:41] ppocr INFO:     save_res_path : ./output/det_db/predicts_db.txt
[2024/08/01 20:34:41] ppocr INFO:     use_gpu : True
[2024/08/01 20:34:41] ppocr INFO:     use_visualdl : False
[2024/08/01 20:34:41] ppocr INFO:     use_xpu : False
[2024/08/01 20:34:41] ppocr INFO: Loss : 
[2024/08/01 20:34:41] ppocr INFO:     alpha : 5
[2024/08/01 20:34:41] ppocr INFO:     balance_loss : True
[2024/08/01 20:34:41] ppocr INFO:     beta : 10
[2024/08/01 20:34:41] ppocr INFO:     main_loss_type : DiceLoss
[2024/08/01 20:34:41] ppocr INFO:     name : DBLoss
[2024/08/01 20:34:41] ppocr INFO:     ohem_ratio : 3
[2024/08/01 20:34:41] ppocr INFO: Metric : 
[2024/08/01 20:34:41] ppocr INFO:     main_indicator : hmean
[2024/08/01 20:34:41] ppocr INFO:     name : DetMetric
[2024/08/01 20:34:41] ppocr INFO: Optimizer : 
[2024/08/01 20:34:41] ppocr INFO:     beta1 : 0.9
[2024/08/01 20:34:41] ppocr INFO:     beta2 : 0.999
[2024/08/01 20:34:41] ppocr INFO:     lr : 
[2024/08/01 20:34:41] ppocr INFO:         learning_rate : 0.001
[2024/08/01 20:34:41] ppocr INFO:     name : Adam
[2024/08/01 20:34:41] ppocr INFO:     regularizer : 
[2024/08/01 20:34:41] ppocr INFO:         factor : 0
[2024/08/01 20:34:41] ppocr INFO:         name : L2
[2024/08/01 20:34:41] ppocr INFO: PostProcess : 
[2024/08/01 20:34:41] ppocr INFO:     box_thresh : 0.6
[2024/08/01 20:34:41] ppocr INFO:     max_candidates : 1000
[2024/08/01 20:34:41] ppocr INFO:     name : DBPostProcess
[2024/08/01 20:34:41] ppocr INFO:     thresh : 0.3
[2024/08/01 20:34:41] ppocr INFO:     unclip_ratio : 1.5
[2024/08/01 20:34:41] ppocr INFO: Train : 
[2024/08/01 20:34:41] ppocr INFO:     dataset : 
[2024/08/01 20:34:41] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/08/01 20:34:41] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/08/01 20:34:41] ppocr INFO:         name : SimpleDataSet
[2024/08/01 20:34:41] ppocr INFO:         ratio_list : [1.0]
[2024/08/01 20:34:41] ppocr INFO:         transforms : 
[2024/08/01 20:34:41] ppocr INFO:             DecodeImage : 
[2024/08/01 20:34:41] ppocr INFO:                 channel_first : False
[2024/08/01 20:34:41] ppocr INFO:                 img_mode : BGR
[2024/08/01 20:34:41] ppocr INFO:             DetLabelEncode : None
[2024/08/01 20:34:41] ppocr INFO:             IaaAugment : 
[2024/08/01 20:34:41] ppocr INFO:                 augmenter_args : 
[2024/08/01 20:34:41] ppocr INFO:                     args : 
[2024/08/01 20:34:41] ppocr INFO:                         p : 0.5
[2024/08/01 20:34:41] ppocr INFO:                     type : Fliplr
[2024/08/01 20:34:41] ppocr INFO:                     args : 
[2024/08/01 20:34:41] ppocr INFO:                         rotate : [-10, 10]
[2024/08/01 20:34:41] ppocr INFO:                     type : Affine
[2024/08/01 20:34:41] ppocr INFO:                     args : 
[2024/08/01 20:34:41] ppocr INFO:                         size : [0.5, 3]
[2024/08/01 20:34:41] ppocr INFO:                     type : Resize
[2024/08/01 20:34:41] ppocr INFO:             EastRandomCropData : 
[2024/08/01 20:34:41] ppocr INFO:                 keep_ratio : True
[2024/08/01 20:34:41] ppocr INFO:                 max_tries : 50
[2024/08/01 20:34:41] ppocr INFO:                 size : [640, 640]
[2024/08/01 20:34:41] ppocr INFO:             MakeBorderMap : 
[2024/08/01 20:34:41] ppocr INFO:                 shrink_ratio : 0.4
[2024/08/01 20:34:41] ppocr INFO:                 thresh_max : 0.7
[2024/08/01 20:34:41] ppocr INFO:                 thresh_min : 0.3
[2024/08/01 20:34:41] ppocr INFO:             MakeShrinkMap : 
[2024/08/01 20:34:41] ppocr INFO:                 min_text_size : 8
[2024/08/01 20:34:41] ppocr INFO:                 shrink_ratio : 0.4
[2024/08/01 20:34:41] ppocr INFO:             NormalizeImage : 
[2024/08/01 20:34:41] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/08/01 20:34:41] ppocr INFO:                 order : hwc
[2024/08/01 20:34:41] ppocr INFO:                 scale : 1./255.
[2024/08/01 20:34:41] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/08/01 20:34:41] ppocr INFO:             ToCHWImage : None
[2024/08/01 20:34:41] ppocr INFO:             KeepKeys : 
[2024/08/01 20:34:41] ppocr INFO:                 keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask']
[2024/08/01 20:34:41] ppocr INFO:     loader : 
[2024/08/01 20:34:41] ppocr INFO:         batch_size_per_card : 48
[2024/08/01 20:34:41] ppocr INFO:         drop_last : False
[2024/08/01 20:34:41] ppocr INFO:         num_workers : 8
[2024/08/01 20:34:41] ppocr INFO:         shuffle : True
[2024/08/01 20:34:41] ppocr INFO:         use_shared_memory : True
[2024/08/01 20:34:41] ppocr INFO: profiler_options : None
[2024/08/01 20:34:41] ppocr INFO: train with paddle 2.5.2 and device Place(gpu:0)
======================= Modified FLAGS detected =======================
FLAGS(name='FLAGS_selected_gpus', current_value='0', default_value='')
FLAGS(name='FLAGS_cudnn_batchnorm_spatial_persistent', current_value=True, default_value=False)
=======================================================================
I0801 20:34:41.828423 163924 tcp_utils.cc:181] The server starts to listen on IP_ANY:39872
I0801 20:34:41.828675 163924 tcp_utils.cc:130] Successfully connected to 10.8.145.246:39872
I0801 20:34:44.950347 163924 process_group_nccl.cc:120] ProcessGroupNCCL pg_timeout_ 1800000
[2024/08/01 20:34:44] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/08/01 20:34:44] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
W0801 20:34:44.961256 163924 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 90.2, Driver API Version: 50724.2, Runtime API Version: 50724.2
[2024/08/01 20:34:46] ppocr INFO: load pretrain successful from ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/08/01 20:34:46] ppocr INFO: train dataloader has 3 iters
[2024/08/01 20:34:46] ppocr INFO: valid dataloader has 500 iters
[2024/08/01 20:34:46] ppocr INFO: During the training process, after the 0th iteration, an evaluation is run every 60 iterations
[2024/08/01 20:35:08] ppocr INFO: epoch: [1/100], global_step: 3, lr: 0.001000, loss: 9.315265, loss_shrink_maps: 4.931784, loss_threshold_maps: 3.395502, loss_binary_maps: 0.987979, avg_reader_cost: 5.77630 s, avg_batch_cost: 6.50762 s, avg_samples: 12.5, ips: 1.92082 samples/s, eta: 1:47:22
[2024/08/01 20:35:09] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:35:18] ppocr INFO: epoch: [2/100], global_step: 6, lr: 0.001000, loss: 8.221654, loss_shrink_maps: 4.850829, loss_threshold_maps: 2.414495, loss_binary_maps: 0.975307, avg_reader_cost: 2.33003 s, avg_batch_cost: 2.57452 s, avg_samples: 12.5, ips: 4.85527 samples/s, eta: 1:14:10
[2024/08/01 20:35:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:35:28] ppocr INFO: epoch: [3/100], global_step: 9, lr: 0.001000, loss: 7.469735, loss_shrink_maps: 4.846783, loss_threshold_maps: 1.648593, loss_binary_maps: 0.974359, avg_reader_cost: 2.29233 s, avg_batch_cost: 2.53705 s, avg_samples: 12.5, ips: 4.92698 samples/s, eta: 1:02:36
[2024/08/01 20:35:29] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:35:36] ppocr INFO: epoch: [4/100], global_step: 10, lr: 0.001000, loss: 7.334120, loss_shrink_maps: 4.844826, loss_threshold_maps: 1.517886, loss_binary_maps: 0.973212, avg_reader_cost: 0.66885 s, avg_batch_cost: 0.75877 s, avg_samples: 4.8, ips: 6.32605 samples/s, eta: 0:59:49
[2024/08/01 20:35:38] ppocr INFO: epoch: [4/100], global_step: 12, lr: 0.001000, loss: 7.189727, loss_shrink_maps: 4.841861, loss_threshold_maps: 1.345324, loss_binary_maps: 0.971896, avg_reader_cost: 1.61461 s, avg_batch_cost: 1.76845 s, avg_samples: 7.7, ips: 4.35410 samples/s, eta: 0:56:35
[2024/08/01 20:35:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:35:47] ppocr INFO: epoch: [5/100], global_step: 15, lr: 0.001000, loss: 7.022568, loss_shrink_maps: 4.839262, loss_threshold_maps: 1.233652, loss_binary_maps: 0.971338, avg_reader_cost: 2.22912 s, avg_batch_cost: 2.47214 s, avg_samples: 12.5, ips: 5.05635 samples/s, eta: 0:52:37
[2024/08/01 20:35:48] ppocr INFO: save model in ./output/db_mv3/latest
Exception in thread Thread-6 (_thread_loop):
Traceback (most recent call last):
  File "/root/anaconda3/envs/dbnet/lib/python3.10/site-packages/paddle/io/dataloader/dataloader_iter.py", line 692, in _get_data
    data = self._data_queue.get(timeout=self._timeout)
  File "/root/anaconda3/envs/dbnet/lib/python3.10/multiprocessing/queues.py", line 114, in get
main proc 163924 exit, kill process group 163924    
[2024/08/01 20:36:50] ppocr INFO: Architecture : 
[2024/08/01 20:36:50] ppocr INFO:     Backbone : 
[2024/08/01 20:36:50] ppocr INFO:         model_name : large
[2024/08/01 20:36:50] ppocr INFO:         name : MobileNetV3
[2024/08/01 20:36:50] ppocr INFO:         scale : 0.5
[2024/08/01 20:36:50] ppocr INFO:     Head : 
[2024/08/01 20:36:50] ppocr INFO:         k : 50
[2024/08/01 20:36:50] ppocr INFO:         name : DBHead
[2024/08/01 20:36:50] ppocr INFO:     Neck : 
[2024/08/01 20:36:50] ppocr INFO:         name : DBFPN
[2024/08/01 20:36:50] ppocr INFO:         out_channels : 256
[2024/08/01 20:36:50] ppocr INFO:     Transform : None
[2024/08/01 20:36:50] ppocr INFO:     algorithm : DB
[2024/08/01 20:36:50] ppocr INFO:     model_type : det
[2024/08/01 20:36:50] ppocr INFO: Eval : 
[2024/08/01 20:36:50] ppocr INFO:     dataset : 
[2024/08/01 20:36:50] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/08/01 20:36:50] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
[2024/08/01 20:36:50] ppocr INFO:         name : SimpleDataSet
[2024/08/01 20:36:50] ppocr INFO:         transforms : 
[2024/08/01 20:36:50] ppocr INFO:             DecodeImage : 
[2024/08/01 20:36:50] ppocr INFO:                 channel_first : False
[2024/08/01 20:36:50] ppocr INFO:                 img_mode : BGR
[2024/08/01 20:36:50] ppocr INFO:             DetLabelEncode : None
[2024/08/01 20:36:50] ppocr INFO:             DetResizeForTest : 
[2024/08/01 20:36:50] ppocr INFO:                 image_shape : [736, 1280]
[2024/08/01 20:36:50] ppocr INFO:             NormalizeImage : 
[2024/08/01 20:36:50] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/08/01 20:36:50] ppocr INFO:                 order : hwc
[2024/08/01 20:36:50] ppocr INFO:                 scale : 1./255.
[2024/08/01 20:36:50] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/08/01 20:36:50] ppocr INFO:             ToCHWImage : None
[2024/08/01 20:36:50] ppocr INFO:             KeepKeys : 
[2024/08/01 20:36:50] ppocr INFO:                 keep_keys : ['image', 'shape', 'polys', 'ignore_tags']
[2024/08/01 20:36:50] ppocr INFO:     loader : 
[2024/08/01 20:36:50] ppocr INFO:         batch_size_per_card : 1
[2024/08/01 20:36:50] ppocr INFO:         drop_last : False
[2024/08/01 20:36:50] ppocr INFO:         num_workers : 0
[2024/08/01 20:36:50] ppocr INFO:         shuffle : False
[2024/08/01 20:36:50] ppocr INFO:         use_shared_memory : True
[2024/08/01 20:36:50] ppocr INFO: Global : 
[2024/08/01 20:36:50] ppocr INFO:     cal_metric_during_train : False
[2024/08/01 20:36:50] ppocr INFO:     checkpoints : None
[2024/08/01 20:36:50] ppocr INFO:     distributed : True
[2024/08/01 20:36:50] ppocr INFO:     epoch_num : 100
[2024/08/01 20:36:50] ppocr INFO:     eval_batch_step : [0, 60]
[2024/08/01 20:36:50] ppocr INFO:     infer_img : doc/imgs_en/img_10.jpg
[2024/08/01 20:36:50] ppocr INFO:     log_smooth_window : 20
[2024/08/01 20:36:50] ppocr INFO:     pretrained_model : ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/08/01 20:36:50] ppocr INFO:     print_batch_step : 10
[2024/08/01 20:36:50] ppocr INFO:     save_epoch_step : 1200
[2024/08/01 20:36:50] ppocr INFO:     save_inference_dir : None
[2024/08/01 20:36:50] ppocr INFO:     save_model_dir : ./output/db_mv3/
[2024/08/01 20:36:50] ppocr INFO:     save_res_path : ./output/det_db/predicts_db.txt
[2024/08/01 20:36:50] ppocr INFO:     use_gpu : True
[2024/08/01 20:36:50] ppocr INFO:     use_visualdl : False
[2024/08/01 20:36:50] ppocr INFO:     use_xpu : False
[2024/08/01 20:36:50] ppocr INFO: Loss : 
[2024/08/01 20:36:50] ppocr INFO:     alpha : 5
[2024/08/01 20:36:50] ppocr INFO:     balance_loss : True
[2024/08/01 20:36:50] ppocr INFO:     beta : 10
[2024/08/01 20:36:50] ppocr INFO:     main_loss_type : DiceLoss
[2024/08/01 20:36:50] ppocr INFO:     name : DBLoss
[2024/08/01 20:36:50] ppocr INFO:     ohem_ratio : 3
[2024/08/01 20:36:50] ppocr INFO: Metric : 
[2024/08/01 20:36:50] ppocr INFO:     main_indicator : hmean
[2024/08/01 20:36:50] ppocr INFO:     name : DetMetric
[2024/08/01 20:36:50] ppocr INFO: Optimizer : 
[2024/08/01 20:36:50] ppocr INFO:     beta1 : 0.9
[2024/08/01 20:36:50] ppocr INFO:     beta2 : 0.999
[2024/08/01 20:36:50] ppocr INFO:     lr : 
[2024/08/01 20:36:50] ppocr INFO:         learning_rate : 0.001
[2024/08/01 20:36:50] ppocr INFO:     name : Adam
[2024/08/01 20:36:50] ppocr INFO:     regularizer : 
[2024/08/01 20:36:50] ppocr INFO:         factor : 0
[2024/08/01 20:36:50] ppocr INFO:         name : L2
[2024/08/01 20:36:50] ppocr INFO: PostProcess : 
[2024/08/01 20:36:50] ppocr INFO:     box_thresh : 0.6
[2024/08/01 20:36:50] ppocr INFO:     max_candidates : 1000
[2024/08/01 20:36:50] ppocr INFO:     name : DBPostProcess
[2024/08/01 20:36:50] ppocr INFO:     thresh : 0.3
[2024/08/01 20:36:50] ppocr INFO:     unclip_ratio : 1.5
[2024/08/01 20:36:50] ppocr INFO: Train : 
[2024/08/01 20:36:50] ppocr INFO:     dataset : 
[2024/08/01 20:36:50] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/08/01 20:36:50] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/08/01 20:36:50] ppocr INFO:         name : SimpleDataSet
[2024/08/01 20:36:50] ppocr INFO:         ratio_list : [1.0]
[2024/08/01 20:36:50] ppocr INFO:         transforms : 
[2024/08/01 20:36:50] ppocr INFO:             DecodeImage : 
[2024/08/01 20:36:50] ppocr INFO:                 channel_first : False
[2024/08/01 20:36:50] ppocr INFO:                 img_mode : BGR
[2024/08/01 20:36:50] ppocr INFO:             DetLabelEncode : None
[2024/08/01 20:36:50] ppocr INFO:             IaaAugment : 
[2024/08/01 20:36:50] ppocr INFO:                 augmenter_args : 
[2024/08/01 20:36:50] ppocr INFO:                     args : 
[2024/08/01 20:36:50] ppocr INFO:                         p : 0.5
[2024/08/01 20:36:50] ppocr INFO:                     type : Fliplr
[2024/08/01 20:36:50] ppocr INFO:                     args : 
[2024/08/01 20:36:50] ppocr INFO:                         rotate : [-10, 10]
[2024/08/01 20:36:50] ppocr INFO:                     type : Affine
[2024/08/01 20:36:50] ppocr INFO:                     args : 
[2024/08/01 20:36:50] ppocr INFO:                         size : [0.5, 3]
[2024/08/01 20:36:50] ppocr INFO:                     type : Resize
[2024/08/01 20:36:50] ppocr INFO:             EastRandomCropData : 
[2024/08/01 20:36:50] ppocr INFO:                 keep_ratio : True
[2024/08/01 20:36:50] ppocr INFO:                 max_tries : 50
[2024/08/01 20:36:50] ppocr INFO:                 size : [640, 640]
[2024/08/01 20:36:50] ppocr INFO:             MakeBorderMap : 
[2024/08/01 20:36:50] ppocr INFO:                 shrink_ratio : 0.4
[2024/08/01 20:36:50] ppocr INFO:                 thresh_max : 0.7
[2024/08/01 20:36:50] ppocr INFO:                 thresh_min : 0.3
[2024/08/01 20:36:50] ppocr INFO:             MakeShrinkMap : 
[2024/08/01 20:36:50] ppocr INFO:                 min_text_size : 8
[2024/08/01 20:36:50] ppocr INFO:                 shrink_ratio : 0.4
[2024/08/01 20:36:50] ppocr INFO:             NormalizeImage : 
[2024/08/01 20:36:50] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/08/01 20:36:50] ppocr INFO:                 order : hwc
[2024/08/01 20:36:50] ppocr INFO:                 scale : 1./255.
[2024/08/01 20:36:50] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/08/01 20:36:50] ppocr INFO:             ToCHWImage : None
[2024/08/01 20:36:50] ppocr INFO:             KeepKeys : 
[2024/08/01 20:36:50] ppocr INFO:                 keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask']
[2024/08/01 20:36:50] ppocr INFO:     loader : 
[2024/08/01 20:36:50] ppocr INFO:         batch_size_per_card : 48
[2024/08/01 20:36:50] ppocr INFO:         drop_last : False
[2024/08/01 20:36:50] ppocr INFO:         num_workers : 8
[2024/08/01 20:36:50] ppocr INFO:         shuffle : True
[2024/08/01 20:36:50] ppocr INFO:         use_shared_memory : True
[2024/08/01 20:36:50] ppocr INFO: profiler_options : None
[2024/08/01 20:36:50] ppocr INFO: train with paddle 2.5.2 and device Place(gpu:0)
======================= Modified FLAGS detected =======================
FLAGS(name='FLAGS_selected_gpus', current_value='0', default_value='')
FLAGS(name='FLAGS_cudnn_batchnorm_spatial_persistent', current_value=True, default_value=False)
=======================================================================
I0801 20:36:50.837141 173852 tcp_utils.cc:181] The server starts to listen on IP_ANY:61895
I0801 20:36:50.837360 173852 tcp_utils.cc:130] Successfully connected to 10.8.145.246:61895
I0801 20:36:53.955340 173852 process_group_nccl.cc:120] ProcessGroupNCCL pg_timeout_ 1800000
[2024/08/01 20:36:53] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/08/01 20:36:53] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
W0801 20:36:53.965442 173852 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 90.2, Driver API Version: 50724.2, Runtime API Version: 50724.2
[2024/08/01 20:36:55] ppocr INFO: load pretrain successful from ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/08/01 20:36:55] ppocr INFO: train dataloader has 3 iters
[2024/08/01 20:36:55] ppocr INFO: valid dataloader has 500 iters
[2024/08/01 20:36:55] ppocr INFO: During the training process, after the 0th iteration, an evaluation is run every 60 iterations
[2024/08/01 20:37:18] ppocr INFO: epoch: [1/100], global_step: 3, lr: 0.001000, loss: 9.167787, loss_shrink_maps: 4.884733, loss_threshold_maps: 3.347504, loss_binary_maps: 0.981010, avg_reader_cost: 5.99907 s, avg_batch_cost: 6.56030 s, avg_samples: 12.5, ips: 1.90540 samples/s, eta: 1:48:14
[2024/08/01 20:37:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:37:27] ppocr INFO: epoch: [2/100], global_step: 6, lr: 0.001000, loss: 8.517704, loss_shrink_maps: 4.878246, loss_threshold_maps: 2.653457, loss_binary_maps: 0.979768, avg_reader_cost: 2.29476 s, avg_batch_cost: 2.54081 s, avg_samples: 12.5, ips: 4.91970 samples/s, eta: 1:14:19
[2024/08/01 20:37:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:37:37] ppocr INFO: epoch: [3/100], global_step: 9, lr: 0.001000, loss: 7.579160, loss_shrink_maps: 4.872590, loss_threshold_maps: 1.725883, loss_binary_maps: 0.977559, avg_reader_cost: 2.27011 s, avg_batch_cost: 2.51415 s, avg_samples: 12.5, ips: 4.97186 samples/s, eta: 1:02:35
[2024/08/01 20:37:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:37:45] ppocr INFO: epoch: [4/100], global_step: 10, lr: 0.001000, loss: 7.434976, loss_shrink_maps: 4.859844, loss_threshold_maps: 1.583262, loss_binary_maps: 0.975371, avg_reader_cost: 0.64691 s, avg_batch_cost: 0.74264 s, avg_samples: 4.8, ips: 6.46346 samples/s, eta: 0:59:43
[2024/08/01 20:37:47] ppocr INFO: epoch: [4/100], global_step: 12, lr: 0.001000, loss: 7.187126, loss_shrink_maps: 4.848718, loss_threshold_maps: 1.362642, loss_binary_maps: 0.971547, avg_reader_cost: 1.58232 s, avg_batch_cost: 1.73701 s, avg_samples: 7.7, ips: 4.43291 samples/s, eta: 0:56:22
[2024/08/01 20:37:47] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:37:56] ppocr INFO: epoch: [5/100], global_step: 15, lr: 0.001000, loss: 6.994406, loss_shrink_maps: 4.840827, loss_threshold_maps: 1.217628, loss_binary_maps: 0.969463, avg_reader_cost: 2.25643 s, avg_batch_cost: 2.50182 s, avg_samples: 12.5, ips: 4.99636 samples/s, eta: 0:52:33
[2024/08/01 20:37:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:38:06] ppocr INFO: epoch: [6/100], global_step: 18, lr: 0.001000, loss: 6.983128, loss_shrink_maps: 4.819912, loss_threshold_maps: 1.179187, loss_binary_maps: 0.967620, avg_reader_cost: 2.30910 s, avg_batch_cost: 2.57889 s, avg_samples: 12.5, ips: 4.84705 samples/s, eta: 0:50:04
[2024/08/01 20:38:07] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:38:15] ppocr INFO: epoch: [7/100], global_step: 20, lr: 0.001000, loss: 6.945792, loss_shrink_maps: 4.806562, loss_threshold_maps: 1.169578, loss_binary_maps: 0.963184, avg_reader_cost: 1.44255 s, avg_batch_cost: 1.63703 s, avg_samples: 9.6, ips: 5.86429 samples/s, eta: 0:48:33
[2024/08/01 20:38:16] ppocr INFO: epoch: [7/100], global_step: 21, lr: 0.001000, loss: 6.898098, loss_shrink_maps: 4.782396, loss_threshold_maps: 1.160690, loss_binary_maps: 0.955012, avg_reader_cost: 0.86733 s, avg_batch_cost: 0.92529 s, avg_samples: 2.9, ips: 3.13415 samples/s, eta: 0:48:08
[2024/08/01 20:38:17] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:38:26] ppocr INFO: epoch: [8/100], global_step: 24, lr: 0.001000, loss: 6.781187, loss_shrink_maps: 4.707208, loss_threshold_maps: 1.150966, loss_binary_maps: 0.932010, avg_reader_cost: 2.25489 s, avg_batch_cost: 2.50059 s, avg_samples: 12.5, ips: 4.99882 samples/s, eta: 0:46:27
[2024/08/01 20:38:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:38:35] ppocr INFO: epoch: [9/100], global_step: 27, lr: 0.001000, loss: 6.695997, loss_shrink_maps: 4.648070, loss_threshold_maps: 1.131002, loss_binary_maps: 0.914181, avg_reader_cost: 2.23444 s, avg_batch_cost: 2.47998 s, avg_samples: 12.5, ips: 5.04037 samples/s, eta: 0:45:01
[2024/08/01 20:38:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:38:45] ppocr INFO: epoch: [10/100], global_step: 30, lr: 0.001000, loss: 6.547852, loss_shrink_maps: 4.577702, loss_threshold_maps: 1.099947, loss_binary_maps: 0.887578, avg_reader_cost: 2.21700 s, avg_batch_cost: 2.52558 s, avg_samples: 12.5, ips: 4.94935 samples/s, eta: 0:43:51
[2024/08/01 20:38:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:38:55] ppocr INFO: epoch: [11/100], global_step: 33, lr: 0.001000, loss: 6.415780, loss_shrink_maps: 4.506867, loss_threshold_maps: 1.078970, loss_binary_maps: 0.860101, avg_reader_cost: 2.24669 s, avg_batch_cost: 2.51724 s, avg_samples: 12.5, ips: 4.96576 samples/s, eta: 0:42:49
[2024/08/01 20:38:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:39:04] ppocr INFO: epoch: [12/100], global_step: 36, lr: 0.001000, loss: 6.306919, loss_shrink_maps: 4.391849, loss_threshold_maps: 1.020907, loss_binary_maps: 0.829758, avg_reader_cost: 2.23096 s, avg_batch_cost: 2.51112 s, avg_samples: 12.5, ips: 4.97785 samples/s, eta: 0:41:53
[2024/08/01 20:39:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:39:14] ppocr INFO: epoch: [13/100], global_step: 39, lr: 0.001000, loss: 6.061879, loss_shrink_maps: 4.266415, loss_threshold_maps: 0.982377, loss_binary_maps: 0.818155, avg_reader_cost: 2.27600 s, avg_batch_cost: 2.55858 s, avg_samples: 12.5, ips: 4.88553 samples/s, eta: 0:41:04
[2024/08/01 20:39:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:39:23] ppocr INFO: epoch: [14/100], global_step: 40, lr: 0.001000, loss: 6.034086, loss_shrink_maps: 4.249962, loss_threshold_maps: 0.975071, loss_binary_maps: 0.814069, avg_reader_cost: 0.65931 s, avg_batch_cost: 0.74786 s, avg_samples: 4.8, ips: 6.41831 samples/s, eta: 0:40:42
[2024/08/01 20:39:24] ppocr INFO: epoch: [14/100], global_step: 42, lr: 0.001000, loss: 5.948371, loss_shrink_maps: 4.203692, loss_threshold_maps: 0.959928, loss_binary_maps: 0.803920, avg_reader_cost: 1.59297 s, avg_batch_cost: 1.74787 s, avg_samples: 7.7, ips: 4.40535 samples/s, eta: 0:40:15
[2024/08/01 20:39:25] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:39:34] ppocr INFO: epoch: [15/100], global_step: 45, lr: 0.001000, loss: 5.842136, loss_shrink_maps: 4.137426, loss_threshold_maps: 0.945386, loss_binary_maps: 0.786568, avg_reader_cost: 2.24691 s, avg_batch_cost: 2.59015 s, avg_samples: 12.5, ips: 4.82598 samples/s, eta: 0:39:35
[2024/08/01 20:39:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:39:44] ppocr INFO: epoch: [16/100], global_step: 48, lr: 0.001000, loss: 5.817528, loss_shrink_maps: 4.099481, loss_threshold_maps: 0.940276, loss_binary_maps: 0.778121, avg_reader_cost: 2.26891 s, avg_batch_cost: 2.51310 s, avg_samples: 12.5, ips: 4.97394 samples/s, eta: 0:38:52
[2024/08/01 20:39:44] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:39:53] ppocr INFO: epoch: [17/100], global_step: 50, lr: 0.001000, loss: 5.757682, loss_shrink_maps: 4.024876, loss_threshold_maps: 0.919434, loss_binary_maps: 0.774164, avg_reader_cost: 1.42779 s, avg_batch_cost: 1.65022 s, avg_samples: 9.6, ips: 5.81742 samples/s, eta: 0:38:24
[2024/08/01 20:39:54] ppocr INFO: epoch: [17/100], global_step: 51, lr: 0.001000, loss: 5.715228, loss_shrink_maps: 3.990124, loss_threshold_maps: 0.915702, loss_binary_maps: 0.766707, avg_reader_cost: 0.87391 s, avg_batch_cost: 0.93205 s, avg_samples: 2.9, ips: 3.11143 samples/s, eta: 0:38:15
[2024/08/01 20:39:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:40:04] ppocr INFO: epoch: [18/100], global_step: 54, lr: 0.001000, loss: 5.548355, loss_shrink_maps: 3.902008, loss_threshold_maps: 0.906152, loss_binary_maps: 0.745593, avg_reader_cost: 2.37035 s, avg_batch_cost: 2.61335 s, avg_samples: 12.5, ips: 4.78313 samples/s, eta: 0:37:40
[2024/08/01 20:40:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:40:13] ppocr INFO: epoch: [19/100], global_step: 57, lr: 0.001000, loss: 5.420370, loss_shrink_maps: 3.835642, loss_threshold_maps: 0.901369, loss_binary_maps: 0.729300, avg_reader_cost: 2.28187 s, avg_batch_cost: 2.53887 s, avg_samples: 12.5, ips: 4.92345 samples/s, eta: 0:37:03
[2024/08/01 20:40:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:40:23] ppocr INFO: epoch: [20/100], global_step: 60, lr: 0.001000, loss: 5.396798, loss_shrink_maps: 3.779905, loss_threshold_maps: 0.888486, loss_binary_maps: 0.723600, avg_reader_cost: 2.29904 s, avg_batch_cost: 2.56823 s, avg_samples: 12.5, ips: 4.86716 samples/s, eta: 0:36:29

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<01:05,  7.65it/s]
eval model::   1%|          | 4/500 [00:00<00:30, 16.19it/s]
eval model::   1%|▏         | 7/500 [00:00<00:24, 19.95it/s]
eval model::   2%|▏         | 10/500 [00:00<00:22, 21.91it/s]
eval model::   3%|▎         | 13/500 [00:00<00:23, 21.01it/s]
eval model::   3%|▎         | 16/500 [00:00<00:20, 23.23it/s]
eval model::   4%|▍         | 19/500 [00:00<00:22, 21.38it/s]
eval model::   4%|▍         | 22/500 [00:01<00:21, 22.13it/s]
eval model::   5%|▌         | 25/500 [00:01<00:21, 22.04it/s]
eval model::   6%|▌         | 28/500 [00:01<00:21, 22.41it/s]
eval model::   6%|▌         | 31/500 [00:01<00:23, 20.35it/s]
eval model::   7%|▋         | 34/500 [00:01<00:22, 20.34it/s]
eval model::   7%|▋         | 37/500 [00:01<00:21, 21.75it/s]
eval model::   8%|▊         | 40/500 [00:01<00:23, 19.93it/s]
eval model::   9%|▊         | 43/500 [00:02<00:23, 19.29it/s]
eval model::   9%|▉         | 45/500 [00:02<00:23, 19.32it/s]
eval model::   9%|▉         | 47/500 [00:02<00:23, 19.06it/s]
eval model::  10%|█         | 50/500 [00:02<00:21, 20.77it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 21.19it/s]
eval model::  11%|█         | 56/500 [00:02<00:21, 20.41it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:23, 19.02it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:25, 17.37it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:24, 17.72it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:21, 20.25it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:19, 22.42it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:17, 23.83it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:18, 23.51it/s]
eval model::  16%|█▌        | 78/500 [00:03<00:18, 23.30it/s]
eval model::  16%|█▌        | 81/500 [00:03<00:17, 23.76it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:17, 24.23it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 20.35it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:20, 19.73it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:20, 19.54it/s]
eval model::  19%|█▉        | 97/500 [00:04<00:20, 19.71it/s]
eval model::  20%|██        | 100/500 [00:04<00:20, 19.23it/s]
eval model::  20%|██        | 102/500 [00:04<00:20, 19.17it/s]
eval model::  21%|██        | 105/500 [00:05<00:19, 19.89it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:20, 18.97it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:21, 18.44it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:19, 19.42it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:19, 19.29it/s]
eval model::  24%|██▍       | 119/500 [00:05<00:18, 20.69it/s]
eval model::  24%|██▍       | 122/500 [00:05<00:17, 21.01it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:16, 22.35it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:16, 22.58it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:17, 20.85it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:17, 20.88it/s]
eval model::  27%|██▋       | 137/500 [00:06<00:17, 20.77it/s]
eval model::  28%|██▊       | 140/500 [00:06<00:17, 20.02it/s]
eval model::  29%|██▊       | 143/500 [00:06<00:18, 19.81it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 19.65it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 19.51it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 19.62it/s]
eval model::  30%|███       | 152/500 [00:07<00:18, 18.90it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 20.69it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:18, 18.95it/s]
eval model::  32%|███▏      | 160/500 [00:07<00:19, 17.70it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:17, 18.80it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:16, 20.34it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 19.95it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:16, 20.02it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:18, 17.27it/s]
eval model::  36%|███▌      | 178/500 [00:08<00:17, 18.90it/s]
eval model::  36%|███▌      | 180/500 [00:08<00:17, 18.32it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:16, 19.52it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:14, 21.90it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:14, 21.96it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:14, 21.85it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:17, 17.33it/s]
eval model::  40%|███▉      | 198/500 [00:09<00:17, 17.16it/s]
eval model::  40%|████      | 200/500 [00:09<00:16, 17.76it/s]
eval model::  40%|████      | 202/500 [00:10<00:16, 17.59it/s]
eval model::  41%|████      | 205/500 [00:10<00:21, 13.94it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:18, 16.12it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:16, 17.75it/s]
eval model::  43%|████▎     | 215/500 [00:10<00:14, 19.05it/s]
eval model::  44%|████▎     | 218/500 [00:11<00:16, 17.57it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:14, 18.68it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:13, 19.90it/s]
eval model::  45%|████▌     | 227/500 [00:11<00:13, 20.72it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:12, 22.19it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:11, 23.66it/s]
eval model::  47%|████▋     | 236/500 [00:11<00:12, 21.80it/s]
eval model::  48%|████▊     | 239/500 [00:11<00:11, 21.87it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 20.28it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:11, 22.02it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 21.52it/s]
eval model::  50%|█████     | 251/500 [00:12<00:11, 20.84it/s]
eval model::  51%|█████     | 254/500 [00:12<00:11, 20.53it/s]
eval model::  51%|█████▏    | 257/500 [00:12<00:11, 21.81it/s]
eval model::  52%|█████▏    | 260/500 [00:12<00:10, 22.08it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 21.68it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:11, 20.92it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:12, 18.54it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:12, 17.99it/s]
eval model::  55%|█████▍    | 274/500 [00:13<00:11, 19.12it/s]
eval model::  55%|█████▌    | 276/500 [00:13<00:13, 16.52it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:12, 17.75it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:11, 19.27it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:13, 16.13it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:12, 16.59it/s]
eval model::  58%|█████▊    | 290/500 [00:14<00:11, 18.40it/s]
eval model::  59%|█████▊    | 293/500 [00:14<00:10, 19.16it/s]
eval model::  59%|█████▉    | 295/500 [00:14<00:11, 18.25it/s]
eval model::  60%|█████▉    | 298/500 [00:15<00:10, 19.75it/s]
eval model::  60%|██████    | 301/500 [00:15<00:10, 19.27it/s]
eval model::  61%|██████    | 304/500 [00:15<00:10, 19.53it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:09, 19.53it/s]
eval model::  62%|██████▏   | 310/500 [00:15<00:09, 21.04it/s]
eval model::  63%|██████▎   | 313/500 [00:15<00:09, 20.47it/s]
eval model::  63%|██████▎   | 316/500 [00:15<00:08, 20.46it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:09, 19.61it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:08, 19.88it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:09, 19.38it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:09, 18.85it/s]
eval model::  66%|██████▌   | 329/500 [00:16<00:09, 18.32it/s]
eval model::  66%|██████▌   | 331/500 [00:16<00:09, 18.38it/s]
eval model::  67%|██████▋   | 334/500 [00:16<00:08, 19.37it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:08, 20.26it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:08, 18.98it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:07, 20.28it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:08, 17.80it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:08, 17.76it/s]
eval model::  70%|███████   | 351/500 [00:17<00:08, 18.05it/s]
eval model::  71%|███████   | 354/500 [00:17<00:07, 18.96it/s]
eval model::  71%|███████   | 356/500 [00:18<00:07, 18.01it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:08, 17.55it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:07, 17.99it/s]
eval model::  72%|███████▏  | 362/500 [00:18<00:07, 17.72it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:08, 16.43it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:07, 17.00it/s]
eval model::  74%|███████▍  | 369/500 [00:18<00:07, 17.94it/s]
eval model::  74%|███████▍  | 372/500 [00:18<00:06, 18.95it/s]
eval model::  75%|███████▌  | 375/500 [00:19<00:06, 19.78it/s]
eval model::  75%|███████▌  | 377/500 [00:19<00:06, 19.53it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:06, 18.26it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:05, 20.14it/s]
eval model::  77%|███████▋  | 386/500 [00:19<00:07, 16.17it/s]
eval model::  78%|███████▊  | 389/500 [00:19<00:06, 17.95it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:05, 19.45it/s]
eval model::  79%|███████▉  | 395/500 [00:20<00:05, 19.86it/s]
eval model::  80%|███████▉  | 398/500 [00:20<00:04, 20.54it/s]
eval model::  80%|████████  | 401/500 [00:20<00:05, 19.22it/s]
eval model::  81%|████████  | 404/500 [00:20<00:04, 19.20it/s]
eval model::  81%|████████  | 406/500 [00:20<00:04, 19.07it/s]
eval model::  82%|████████▏ | 409/500 [00:20<00:04, 20.30it/s]
eval model::  82%|████████▏ | 412/500 [00:21<00:04, 20.26it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:04, 20.19it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 18.52it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 16.99it/s]
eval model::  85%|████████▍ | 423/500 [00:21<00:04, 18.02it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:03, 19.03it/s]
eval model::  86%|████████▌ | 429/500 [00:21<00:03, 21.21it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 20.07it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 19.79it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 21.32it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:03, 19.27it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 20.76it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 20.12it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 19.26it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 19.03it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:02, 19.94it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:02, 19.79it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:01, 20.30it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:01, 18.01it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 19.89it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 19.17it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.01it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 19.77it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:01, 19.67it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 20.82it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 18.78it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 20.06it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 21.18it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 22.90it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 22.15it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.65it/s]
[2024/08/01 20:40:49] ppocr INFO: cur metric, precision: 0.2540322580645161, recall: 0.09099662975445354, hmean: 0.13399503722084366, fps: 42.71231614973914
[2024/08/01 20:40:49] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 20:40:49] ppocr INFO: best metric, hmean: 0.13399503722084366, precision: 0.2540322580645161, recall: 0.09099662975445354, fps: 42.71231614973914, best_epoch: 20
[2024/08/01 20:40:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:40:56] ppocr INFO: epoch: [21/100], global_step: 63, lr: 0.001000, loss: 5.356770, loss_shrink_maps: 3.767826, loss_threshold_maps: 0.888486, loss_binary_maps: 0.718283, avg_reader_cost: 1.65713 s, avg_batch_cost: 1.95343 s, avg_samples: 12.5, ips: 6.39899 samples/s, eta: 0:35:32
[2024/08/01 20:40:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:41:04] ppocr INFO: epoch: [22/100], global_step: 66, lr: 0.001000, loss: 5.220206, loss_shrink_maps: 3.682492, loss_threshold_maps: 0.865904, loss_binary_maps: 0.696989, avg_reader_cost: 1.52186 s, avg_batch_cost: 1.78401 s, avg_samples: 12.5, ips: 7.00669 samples/s, eta: 0:34:33
[2024/08/01 20:41:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:41:11] ppocr INFO: epoch: [23/100], global_step: 69, lr: 0.001000, loss: 4.714974, loss_shrink_maps: 3.318467, loss_threshold_maps: 0.875278, loss_binary_maps: 0.576473, avg_reader_cost: 1.53847 s, avg_batch_cost: 1.78187 s, avg_samples: 12.5, ips: 7.01511 samples/s, eta: 0:33:37
[2024/08/01 20:41:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:41:17] ppocr INFO: epoch: [24/100], global_step: 70, lr: 0.001000, loss: 4.706698, loss_shrink_maps: 3.283479, loss_threshold_maps: 0.882831, loss_binary_maps: 0.532320, avg_reader_cost: 0.43486 s, avg_batch_cost: 0.54496 s, avg_samples: 4.8, ips: 8.80805 samples/s, eta: 0:33:17
[2024/08/01 20:41:18] ppocr INFO: epoch: [24/100], global_step: 72, lr: 0.001000, loss: 4.494270, loss_shrink_maps: 3.141477, loss_threshold_maps: 0.882831, loss_binary_maps: 0.456164, avg_reader_cost: 1.18739 s, avg_batch_cost: 1.34246 s, avg_samples: 7.7, ips: 5.73572 samples/s, eta: 0:32:47
[2024/08/01 20:41:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:41:26] ppocr INFO: epoch: [25/100], global_step: 75, lr: 0.001000, loss: 4.048507, loss_shrink_maps: 2.767840, loss_threshold_maps: 0.874006, loss_binary_maps: 0.429532, avg_reader_cost: 1.52084 s, avg_batch_cost: 1.80214 s, avg_samples: 12.5, ips: 6.93618 samples/s, eta: 0:31:58
[2024/08/01 20:41:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:41:33] ppocr INFO: epoch: [26/100], global_step: 78, lr: 0.001000, loss: 3.948027, loss_shrink_maps: 2.651274, loss_threshold_maps: 0.870399, loss_binary_maps: 0.416558, avg_reader_cost: 1.56310 s, avg_batch_cost: 1.83148 s, avg_samples: 12.5, ips: 6.82508 samples/s, eta: 0:31:12
[2024/08/01 20:41:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:41:40] ppocr INFO: epoch: [27/100], global_step: 80, lr: 0.001000, loss: 3.898684, loss_shrink_maps: 2.620234, loss_threshold_maps: 0.875516, loss_binary_maps: 0.399731, avg_reader_cost: 0.93753 s, avg_batch_cost: 1.12413 s, avg_samples: 9.6, ips: 8.53991 samples/s, eta: 0:30:39
[2024/08/01 20:41:40] ppocr INFO: epoch: [27/100], global_step: 81, lr: 0.001000, loss: 3.892091, loss_shrink_maps: 2.594418, loss_threshold_maps: 0.866305, loss_binary_maps: 0.399731, avg_reader_cost: 0.61140 s, avg_batch_cost: 0.66923 s, avg_samples: 2.9, ips: 4.33336 samples/s, eta: 0:30:26
[2024/08/01 20:41:41] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:41:48] ppocr INFO: epoch: [28/100], global_step: 84, lr: 0.001000, loss: 3.733869, loss_shrink_maps: 2.431300, loss_threshold_maps: 0.858584, loss_binary_maps: 0.399731, avg_reader_cost: 1.62641 s, avg_batch_cost: 1.90622 s, avg_samples: 12.5, ips: 6.55749 samples/s, eta: 0:29:46
[2024/08/01 20:41:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:41:55] ppocr INFO: epoch: [29/100], global_step: 87, lr: 0.001000, loss: 3.568292, loss_shrink_maps: 2.341320, loss_threshold_maps: 0.851385, loss_binary_maps: 0.369127, avg_reader_cost: 1.55274 s, avg_batch_cost: 1.82403 s, avg_samples: 12.5, ips: 6.85294 samples/s, eta: 0:29:05
[2024/08/01 20:41:56] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:42:03] ppocr INFO: epoch: [30/100], global_step: 90, lr: 0.001000, loss: 3.275011, loss_shrink_maps: 2.119010, loss_threshold_maps: 0.842222, loss_binary_maps: 0.343508, avg_reader_cost: 1.53285 s, avg_batch_cost: 1.78708 s, avg_samples: 12.5, ips: 6.99466 samples/s, eta: 0:28:25
[2024/08/01 20:42:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:42:10] ppocr INFO: epoch: [31/100], global_step: 93, lr: 0.001000, loss: 3.181884, loss_shrink_maps: 2.018446, loss_threshold_maps: 0.842631, loss_binary_maps: 0.340795, avg_reader_cost: 1.58458 s, avg_batch_cost: 1.82994 s, avg_samples: 12.5, ips: 6.83084 samples/s, eta: 0:27:47
[2024/08/01 20:42:11] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:42:17] ppocr INFO: epoch: [32/100], global_step: 96, lr: 0.001000, loss: 3.135776, loss_shrink_maps: 1.934820, loss_threshold_maps: 0.842631, loss_binary_maps: 0.338562, avg_reader_cost: 1.60243 s, avg_batch_cost: 1.84715 s, avg_samples: 12.5, ips: 6.76719 samples/s, eta: 0:27:11
[2024/08/01 20:42:18] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:42:25] ppocr INFO: epoch: [33/100], global_step: 99, lr: 0.001000, loss: 3.129548, loss_shrink_maps: 1.934820, loss_threshold_maps: 0.842631, loss_binary_maps: 0.335990, avg_reader_cost: 1.57711 s, avg_batch_cost: 1.88426 s, avg_samples: 12.5, ips: 6.63392 samples/s, eta: 0:26:36
[2024/08/01 20:42:26] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:42:31] ppocr INFO: epoch: [34/100], global_step: 100, lr: 0.001000, loss: 3.099138, loss_shrink_maps: 1.920573, loss_threshold_maps: 0.842631, loss_binary_maps: 0.333742, avg_reader_cost: 0.41876 s, avg_batch_cost: 0.51803 s, avg_samples: 4.8, ips: 9.26596 samples/s, eta: 0:26:23
[2024/08/01 20:42:32] ppocr INFO: epoch: [34/100], global_step: 102, lr: 0.001000, loss: 3.070569, loss_shrink_maps: 1.905585, loss_threshold_maps: 0.847389, loss_binary_maps: 0.330582, avg_reader_cost: 1.13419 s, avg_batch_cost: 1.28982 s, avg_samples: 7.7, ips: 5.96980 samples/s, eta: 0:26:01
[2024/08/01 20:42:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:42:40] ppocr INFO: epoch: [35/100], global_step: 105, lr: 0.001000, loss: 3.057051, loss_shrink_maps: 1.886383, loss_threshold_maps: 0.847389, loss_binary_maps: 0.318210, avg_reader_cost: 1.53227 s, avg_batch_cost: 1.77748 s, avg_samples: 12.5, ips: 7.03243 samples/s, eta: 0:25:27
[2024/08/01 20:42:40] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:42:47] ppocr INFO: epoch: [36/100], global_step: 108, lr: 0.001000, loss: 2.912682, loss_shrink_maps: 1.747611, loss_threshold_maps: 0.836401, loss_binary_maps: 0.311897, avg_reader_cost: 1.57505 s, avg_batch_cost: 1.81993 s, avg_samples: 12.5, ips: 6.86840 samples/s, eta: 0:24:54
[2024/08/01 20:42:48] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:42:54] ppocr INFO: epoch: [37/100], global_step: 110, lr: 0.001000, loss: 2.912682, loss_shrink_maps: 1.756642, loss_threshold_maps: 0.836401, loss_binary_maps: 0.316587, avg_reader_cost: 0.95339 s, avg_batch_cost: 1.14480 s, avg_samples: 9.6, ips: 8.38573 samples/s, eta: 0:24:31
[2024/08/01 20:42:54] ppocr INFO: epoch: [37/100], global_step: 111, lr: 0.001000, loss: 2.912682, loss_shrink_maps: 1.756642, loss_threshold_maps: 0.836401, loss_binary_maps: 0.316587, avg_reader_cost: 0.62113 s, avg_batch_cost: 0.67932 s, avg_samples: 2.9, ips: 4.26901 samples/s, eta: 0:24:22
[2024/08/01 20:42:55] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:43:02] ppocr INFO: epoch: [38/100], global_step: 114, lr: 0.001000, loss: 2.902322, loss_shrink_maps: 1.747611, loss_threshold_maps: 0.834390, loss_binary_maps: 0.311266, avg_reader_cost: 1.55722 s, avg_batch_cost: 1.84691 s, avg_samples: 12.5, ips: 6.76806 samples/s, eta: 0:23:51
[2024/08/01 20:43:03] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:43:09] ppocr INFO: epoch: [39/100], global_step: 117, lr: 0.001000, loss: 2.847789, loss_shrink_maps: 1.705968, loss_threshold_maps: 0.828564, loss_binary_maps: 0.302142, avg_reader_cost: 1.54726 s, avg_batch_cost: 1.79328 s, avg_samples: 12.5, ips: 6.97048 samples/s, eta: 0:23:20
[2024/08/01 20:43:10] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:43:16] ppocr INFO: epoch: [40/100], global_step: 120, lr: 0.001000, loss: 2.783246, loss_shrink_maps: 1.661209, loss_threshold_maps: 0.827214, loss_binary_maps: 0.299705, avg_reader_cost: 1.56264 s, avg_batch_cost: 1.80875 s, avg_samples: 12.5, ips: 6.91086 samples/s, eta: 0:22:49

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 2/500 [00:00<00:35, 14.04it/s]
eval model::   1%|          | 5/500 [00:00<00:27, 17.96it/s]
eval model::   2%|▏         | 8/500 [00:00<00:27, 17.71it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.53it/s]
eval model::   3%|▎         | 14/500 [00:00<00:25, 19.29it/s]
eval model::   3%|▎         | 17/500 [00:00<00:22, 21.15it/s]
eval model::   4%|▍         | 20/500 [00:01<00:22, 21.18it/s]
eval model::   5%|▍         | 23/500 [00:01<00:21, 21.81it/s]
eval model::   5%|▌         | 26/500 [00:01<00:22, 20.82it/s]
eval model::   6%|▌         | 29/500 [00:01<00:22, 20.78it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 20.13it/s]
eval model::   7%|▋         | 35/500 [00:01<00:23, 19.91it/s]
eval model::   8%|▊         | 38/500 [00:01<00:22, 20.46it/s]
eval model::   8%|▊         | 41/500 [00:02<00:22, 20.74it/s]
eval model::   9%|▉         | 44/500 [00:02<00:22, 20.22it/s]
eval model::   9%|▉         | 47/500 [00:02<00:23, 19.16it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 19.42it/s]
eval model::  10%|█         | 52/500 [00:02<00:24, 18.36it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 19.92it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:23, 18.96it/s]
eval model::  12%|█▏        | 61/500 [00:03<00:25, 17.38it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:27, 16.06it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:23, 18.53it/s]
eval model::  14%|█▍        | 70/500 [00:03<00:19, 22.22it/s]
eval model::  15%|█▍        | 73/500 [00:03<00:20, 21.01it/s]
eval model::  15%|█▌        | 76/500 [00:03<00:19, 21.55it/s]
eval model::  16%|█▌        | 79/500 [00:03<00:19, 22.09it/s]
eval model::  16%|█▋        | 82/500 [00:04<00:19, 22.00it/s]
eval model::  17%|█▋        | 85/500 [00:04<00:21, 19.74it/s]
eval model::  18%|█▊        | 88/500 [00:04<00:20, 20.01it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 18.46it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:21, 19.04it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:21, 19.07it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.49it/s]
eval model::  20%|██        | 101/500 [00:05<00:21, 18.41it/s]
eval model::  21%|██        | 105/500 [00:05<00:18, 21.78it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:18, 21.66it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 19.60it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:18, 21.10it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:18, 21.08it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 20.34it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:16, 22.30it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:17, 20.94it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 20.74it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 19.21it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:19, 19.16it/s]
eval model::  28%|██▊       | 138/500 [00:06<00:17, 20.41it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:18, 19.76it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:16, 21.41it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:17, 19.92it/s]
eval model::  30%|███       | 150/500 [00:07<00:17, 20.24it/s]
eval model::  31%|███       | 153/500 [00:07<00:17, 19.32it/s]
eval model::  31%|███▏      | 157/500 [00:07<00:16, 21.12it/s]
eval model::  32%|███▏      | 160/500 [00:07<00:16, 20.79it/s]
eval model::  33%|███▎      | 163/500 [00:08<00:16, 20.88it/s]
eval model::  33%|███▎      | 166/500 [00:08<00:15, 21.56it/s]
eval model::  34%|███▍      | 169/500 [00:08<00:16, 19.97it/s]
eval model::  34%|███▍      | 172/500 [00:08<00:15, 21.29it/s]
eval model::  35%|███▌      | 175/500 [00:08<00:15, 21.59it/s]
eval model::  36%|███▌      | 178/500 [00:08<00:14, 22.28it/s]
eval model::  36%|███▌      | 181/500 [00:09<00:16, 19.05it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:15, 20.30it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 20.82it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:15, 20.53it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:15, 19.64it/s]
eval model::  39%|███▉      | 196/500 [00:09<00:17, 17.80it/s]
eval model::  40%|███▉      | 199/500 [00:09<00:16, 18.45it/s]
eval model::  40%|████      | 201/500 [00:10<00:16, 18.25it/s]
eval model::  41%|████      | 204/500 [00:10<00:15, 19.65it/s]
eval model::  41%|████▏     | 207/500 [00:10<00:19, 15.17it/s]
eval model::  42%|████▏     | 209/500 [00:10<00:18, 15.89it/s]
eval model::  42%|████▏     | 211/500 [00:10<00:17, 16.44it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:16, 17.06it/s]
eval model::  43%|████▎     | 215/500 [00:10<00:18, 15.75it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:19, 14.77it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 18.01it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:15, 18.27it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:13, 19.89it/s]
eval model::  46%|████▌     | 228/500 [00:11<00:13, 19.83it/s]
eval model::  46%|████▌     | 231/500 [00:11<00:12, 22.23it/s]
eval model::  47%|████▋     | 234/500 [00:11<00:11, 22.77it/s]
eval model::  47%|████▋     | 237/500 [00:11<00:11, 23.22it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:10, 23.73it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:11, 22.88it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:11, 23.07it/s]
eval model::  50%|████▉     | 249/500 [00:12<00:12, 19.50it/s]
eval model::  50%|█████     | 252/500 [00:12<00:12, 20.55it/s]
eval model::  51%|█████     | 255/500 [00:12<00:11, 20.91it/s]
eval model::  52%|█████▏    | 258/500 [00:12<00:11, 20.33it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:12, 19.39it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 18.58it/s]
eval model::  53%|█████▎    | 267/500 [00:13<00:11, 19.54it/s]
eval model::  54%|█████▍    | 270/500 [00:13<00:11, 20.44it/s]
eval model::  55%|█████▍    | 273/500 [00:13<00:11, 20.63it/s]
eval model::  55%|█████▌    | 276/500 [00:13<00:11, 18.90it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 19.20it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 19.92it/s]
eval model::  57%|█████▋    | 285/500 [00:14<00:13, 16.00it/s]
eval model::  58%|█████▊    | 289/500 [00:14<00:11, 19.01it/s]
eval model::  59%|█████▊    | 293/500 [00:14<00:09, 22.02it/s]
eval model::  59%|█████▉    | 296/500 [00:14<00:10, 19.95it/s]
eval model::  60%|██████    | 300/500 [00:15<00:09, 21.38it/s]
eval model::  61%|██████    | 303/500 [00:15<00:09, 21.72it/s]
eval model::  61%|██████    | 306/500 [00:15<00:08, 21.95it/s]
eval model::  62%|██████▏   | 309/500 [00:15<00:08, 21.29it/s]
eval model::  62%|██████▏   | 312/500 [00:15<00:08, 21.86it/s]
eval model::  63%|██████▎   | 315/500 [00:15<00:09, 19.82it/s]
eval model::  64%|██████▎   | 318/500 [00:15<00:08, 20.94it/s]
eval model::  64%|██████▍   | 321/500 [00:16<00:08, 20.83it/s]
eval model::  65%|██████▍   | 324/500 [00:16<00:09, 18.82it/s]
eval model::  65%|██████▌   | 327/500 [00:16<00:08, 19.85it/s]
eval model::  66%|██████▌   | 330/500 [00:16<00:09, 17.78it/s]
eval model::  67%|██████▋   | 333/500 [00:16<00:08, 19.39it/s]
eval model::  67%|██████▋   | 336/500 [00:16<00:08, 19.83it/s]
eval model::  68%|██████▊   | 339/500 [00:17<00:08, 19.96it/s]
eval model::  68%|██████▊   | 342/500 [00:17<00:08, 18.01it/s]
eval model::  69%|██████▉   | 345/500 [00:17<00:07, 19.69it/s]
eval model::  70%|██████▉   | 348/500 [00:17<00:07, 20.42it/s]
eval model::  70%|███████   | 351/500 [00:17<00:07, 19.07it/s]
eval model::  71%|███████   | 354/500 [00:17<00:07, 20.73it/s]
eval model::  71%|███████▏  | 357/500 [00:17<00:07, 19.25it/s]
eval model::  72%|███████▏  | 360/500 [00:18<00:06, 20.93it/s]
eval model::  73%|███████▎  | 363/500 [00:18<00:06, 20.79it/s]
eval model::  73%|███████▎  | 366/500 [00:18<00:07, 19.13it/s]
eval model::  74%|███████▍  | 369/500 [00:18<00:06, 20.37it/s]
eval model::  74%|███████▍  | 372/500 [00:18<00:06, 21.16it/s]
eval model::  75%|███████▌  | 375/500 [00:18<00:05, 22.21it/s]
eval model::  76%|███████▌  | 378/500 [00:18<00:05, 21.79it/s]
eval model::  76%|███████▌  | 381/500 [00:19<00:05, 22.38it/s]
eval model::  77%|███████▋  | 384/500 [00:19<00:05, 19.61it/s]
eval model::  77%|███████▋  | 387/500 [00:19<00:07, 14.50it/s]
eval model::  78%|███████▊  | 390/500 [00:19<00:06, 16.61it/s]
eval model::  79%|███████▊  | 393/500 [00:19<00:05, 18.12it/s]
eval model::  79%|███████▉  | 396/500 [00:19<00:05, 19.78it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:04, 21.54it/s]
eval model::  80%|████████  | 402/500 [00:20<00:04, 21.10it/s]
eval model::  81%|████████  | 405/500 [00:20<00:04, 22.38it/s]
eval model::  82%|████████▏ | 408/500 [00:20<00:04, 20.85it/s]
eval model::  82%|████████▏ | 411/500 [00:20<00:04, 20.51it/s]
eval model::  83%|████████▎ | 414/500 [00:20<00:04, 19.51it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:04, 18.78it/s]
eval model::  84%|████████▍ | 419/500 [00:21<00:04, 18.52it/s]
eval model::  84%|████████▍ | 421/500 [00:21<00:05, 15.78it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:04, 16.93it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:04, 17.57it/s]
eval model::  86%|████████▌ | 429/500 [00:21<00:03, 19.45it/s]
eval model::  86%|████████▋ | 432/500 [00:21<00:03, 20.91it/s]
eval model::  87%|████████▋ | 435/500 [00:21<00:02, 21.81it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:02, 21.52it/s]
eval model::  88%|████████▊ | 441/500 [00:22<00:02, 20.07it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 20.77it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 19.88it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 19.22it/s]
eval model::  91%|█████████ | 453/500 [00:22<00:02, 20.21it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:02, 20.16it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:02, 20.35it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:01, 19.79it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 19.43it/s]
eval model::  93%|█████████▎| 466/500 [00:23<00:01, 18.41it/s]
eval model::  94%|█████████▍| 469/500 [00:23<00:01, 20.84it/s]
eval model::  94%|█████████▍| 472/500 [00:23<00:01, 21.37it/s]
eval model::  95%|█████████▌| 475/500 [00:24<00:01, 18.35it/s]
eval model::  96%|█████████▌| 478/500 [00:24<00:01, 19.49it/s]
eval model::  96%|█████████▌| 481/500 [00:24<00:00, 19.33it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 20.07it/s]
eval model::  97%|█████████▋| 487/500 [00:24<00:00, 18.92it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 18.41it/s]
eval model::  98%|█████████▊| 491/500 [00:24<00:00, 18.06it/s]
eval model::  99%|█████████▉| 494/500 [00:24<00:00, 19.29it/s]
eval model::  99%|█████████▉| 497/500 [00:25<00:00, 20.86it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 22.67it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.84it/s]
[2024/08/01 20:43:42] ppocr INFO: cur metric, precision: 0.5422365245374094, recall: 0.32450649975926815, hmean: 0.4060240963855421, fps: 43.44324831682561
[2024/08/01 20:43:42] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 20:43:42] ppocr INFO: best metric, hmean: 0.4060240963855421, precision: 0.5422365245374094, recall: 0.32450649975926815, fps: 43.44324831682561, best_epoch: 40
[2024/08/01 20:43:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:43:49] ppocr INFO: epoch: [41/100], global_step: 123, lr: 0.001000, loss: 2.783246, loss_shrink_maps: 1.659631, loss_threshold_maps: 0.826001, loss_binary_maps: 0.299705, avg_reader_cost: 1.62283 s, avg_batch_cost: 1.92650 s, avg_samples: 12.5, ips: 6.48844 samples/s, eta: 0:22:21
[2024/08/01 20:43:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:43:57] ppocr INFO: epoch: [42/100], global_step: 126, lr: 0.001000, loss: 2.724448, loss_shrink_maps: 1.604308, loss_threshold_maps: 0.825066, loss_binary_maps: 0.297598, avg_reader_cost: 1.56961 s, avg_batch_cost: 1.82470 s, avg_samples: 12.5, ips: 6.85045 samples/s, eta: 0:21:52
[2024/08/01 20:43:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:44:04] ppocr INFO: epoch: [43/100], global_step: 129, lr: 0.001000, loss: 2.724448, loss_shrink_maps: 1.617960, loss_threshold_maps: 0.821252, loss_binary_maps: 0.297598, avg_reader_cost: 1.52608 s, avg_batch_cost: 1.78059 s, avg_samples: 12.5, ips: 7.02016 samples/s, eta: 0:21:23
[2024/08/01 20:44:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:44:10] ppocr INFO: epoch: [44/100], global_step: 130, lr: 0.001000, loss: 2.724448, loss_shrink_maps: 1.617960, loss_threshold_maps: 0.825066, loss_binary_maps: 0.297598, avg_reader_cost: 0.41998 s, avg_batch_cost: 0.51207 s, avg_samples: 4.8, ips: 9.37365 samples/s, eta: 0:21:13
[2024/08/01 20:44:11] ppocr INFO: epoch: [44/100], global_step: 132, lr: 0.001000, loss: 2.691556, loss_shrink_maps: 1.578372, loss_threshold_maps: 0.821252, loss_binary_maps: 0.295709, avg_reader_cost: 1.12152 s, avg_batch_cost: 1.27632 s, avg_samples: 7.7, ips: 6.03295 samples/s, eta: 0:20:55
[2024/08/01 20:44:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:44:19] ppocr INFO: epoch: [45/100], global_step: 135, lr: 0.001000, loss: 2.671915, loss_shrink_maps: 1.572094, loss_threshold_maps: 0.820098, loss_binary_maps: 0.295709, avg_reader_cost: 1.51533 s, avg_batch_cost: 1.77187 s, avg_samples: 12.5, ips: 7.05470 samples/s, eta: 0:20:27
[2024/08/01 20:44:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:44:26] ppocr INFO: epoch: [46/100], global_step: 138, lr: 0.001000, loss: 2.646094, loss_shrink_maps: 1.562257, loss_threshold_maps: 0.794736, loss_binary_maps: 0.293802, avg_reader_cost: 1.57409 s, avg_batch_cost: 1.81942 s, avg_samples: 12.5, ips: 6.87031 samples/s, eta: 0:20:00
[2024/08/01 20:44:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:44:33] ppocr INFO: epoch: [47/100], global_step: 140, lr: 0.001000, loss: 2.637463, loss_shrink_maps: 1.554586, loss_threshold_maps: 0.789567, loss_binary_maps: 0.292961, avg_reader_cost: 0.95502 s, avg_batch_cost: 1.19001 s, avg_samples: 9.6, ips: 8.06717 samples/s, eta: 0:19:41
[2024/08/01 20:44:34] ppocr INFO: epoch: [47/100], global_step: 141, lr: 0.001000, loss: 2.623577, loss_shrink_maps: 1.551246, loss_threshold_maps: 0.789567, loss_binary_maps: 0.291383, avg_reader_cost: 0.64371 s, avg_batch_cost: 0.70164 s, avg_samples: 2.9, ips: 4.13316 samples/s, eta: 0:19:34
[2024/08/01 20:44:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:44:41] ppocr INFO: epoch: [48/100], global_step: 144, lr: 0.001000, loss: 2.632892, loss_shrink_maps: 1.554586, loss_threshold_maps: 0.786388, loss_binary_maps: 0.292961, avg_reader_cost: 1.58798 s, avg_batch_cost: 1.83293 s, avg_samples: 12.5, ips: 6.81970 samples/s, eta: 0:19:07
[2024/08/01 20:44:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:44:49] ppocr INFO: epoch: [49/100], global_step: 147, lr: 0.001000, loss: 2.592569, loss_shrink_maps: 1.536291, loss_threshold_maps: 0.783840, loss_binary_maps: 0.285392, avg_reader_cost: 1.56364 s, avg_batch_cost: 1.81345 s, avg_samples: 12.5, ips: 6.89293 samples/s, eta: 0:18:41
[2024/08/01 20:44:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:44:56] ppocr INFO: epoch: [50/100], global_step: 150, lr: 0.001000, loss: 2.566654, loss_shrink_maps: 1.516177, loss_threshold_maps: 0.778304, loss_binary_maps: 0.284495, avg_reader_cost: 1.54188 s, avg_batch_cost: 1.80278 s, avg_samples: 12.5, ips: 6.93375 samples/s, eta: 0:18:15
[2024/08/01 20:44:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:45:04] ppocr INFO: epoch: [51/100], global_step: 153, lr: 0.001000, loss: 2.569175, loss_shrink_maps: 1.505052, loss_threshold_maps: 0.778945, loss_binary_maps: 0.281441, avg_reader_cost: 1.55684 s, avg_batch_cost: 1.80260 s, avg_samples: 12.5, ips: 6.93443 samples/s, eta: 0:17:50
[2024/08/01 20:45:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:45:11] ppocr INFO: epoch: [52/100], global_step: 156, lr: 0.001000, loss: 2.569175, loss_shrink_maps: 1.505052, loss_threshold_maps: 0.779307, loss_binary_maps: 0.281441, avg_reader_cost: 1.54383 s, avg_batch_cost: 1.83553 s, avg_samples: 12.5, ips: 6.81004 samples/s, eta: 0:17:25
[2024/08/01 20:45:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:45:18] ppocr INFO: epoch: [53/100], global_step: 159, lr: 0.001000, loss: 2.566143, loss_shrink_maps: 1.499647, loss_threshold_maps: 0.788902, loss_binary_maps: 0.278595, avg_reader_cost: 1.52118 s, avg_batch_cost: 1.76964 s, avg_samples: 12.5, ips: 7.06359 samples/s, eta: 0:16:59
[2024/08/01 20:45:19] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:45:24] ppocr INFO: epoch: [54/100], global_step: 160, lr: 0.001000, loss: 2.566143, loss_shrink_maps: 1.499647, loss_threshold_maps: 0.788902, loss_binary_maps: 0.278595, avg_reader_cost: 0.40304 s, avg_batch_cost: 0.52002 s, avg_samples: 4.8, ips: 9.23037 samples/s, eta: 0:16:50
[2024/08/01 20:45:26] ppocr INFO: epoch: [54/100], global_step: 162, lr: 0.001000, loss: 2.566143, loss_shrink_maps: 1.499647, loss_threshold_maps: 0.786143, loss_binary_maps: 0.278595, avg_reader_cost: 1.13790 s, avg_batch_cost: 1.29345 s, avg_samples: 7.7, ips: 5.95306 samples/s, eta: 0:16:34
[2024/08/01 20:45:27] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:45:33] ppocr INFO: epoch: [55/100], global_step: 165, lr: 0.001000, loss: 2.571414, loss_shrink_maps: 1.505488, loss_threshold_maps: 0.789632, loss_binary_maps: 0.281441, avg_reader_cost: 1.55802 s, avg_batch_cost: 1.81638 s, avg_samples: 12.5, ips: 6.88183 samples/s, eta: 0:16:10
[2024/08/01 20:45:34] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:45:41] ppocr INFO: epoch: [56/100], global_step: 168, lr: 0.001000, loss: 2.575551, loss_shrink_maps: 1.505488, loss_threshold_maps: 0.791204, loss_binary_maps: 0.284019, avg_reader_cost: 1.57602 s, avg_batch_cost: 1.88392 s, avg_samples: 12.5, ips: 6.63512 samples/s, eta: 0:15:46
[2024/08/01 20:45:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:45:48] ppocr INFO: epoch: [57/100], global_step: 170, lr: 0.001000, loss: 2.597019, loss_shrink_maps: 1.519064, loss_threshold_maps: 0.794226, loss_binary_maps: 0.284019, avg_reader_cost: 0.97628 s, avg_batch_cost: 1.18943 s, avg_samples: 9.6, ips: 8.07106 samples/s, eta: 0:15:30
[2024/08/01 20:45:48] ppocr INFO: epoch: [57/100], global_step: 171, lr: 0.001000, loss: 2.664136, loss_shrink_maps: 1.574778, loss_threshold_maps: 0.793856, loss_binary_maps: 0.290017, avg_reader_cost: 0.64351 s, avg_batch_cost: 0.70215 s, avg_samples: 2.9, ips: 4.13016 samples/s, eta: 0:15:23
[2024/08/01 20:45:49] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:45:56] ppocr INFO: epoch: [58/100], global_step: 174, lr: 0.001000, loss: 2.849042, loss_shrink_maps: 1.687109, loss_threshold_maps: 0.799458, loss_binary_maps: 0.310994, avg_reader_cost: 1.56601 s, avg_batch_cost: 1.85099 s, avg_samples: 12.5, ips: 6.75315 samples/s, eta: 0:14:59
[2024/08/01 20:45:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:46:03] ppocr INFO: epoch: [59/100], global_step: 177, lr: 0.001000, loss: 2.752444, loss_shrink_maps: 1.647073, loss_threshold_maps: 0.798242, loss_binary_maps: 0.309962, avg_reader_cost: 1.56931 s, avg_batch_cost: 1.81561 s, avg_samples: 12.5, ips: 6.88475 samples/s, eta: 0:14:35
[2024/08/01 20:46:04] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:46:11] ppocr INFO: epoch: [60/100], global_step: 180, lr: 0.001000, loss: 2.727903, loss_shrink_maps: 1.626835, loss_threshold_maps: 0.797147, loss_binary_maps: 0.305596, avg_reader_cost: 1.57890 s, avg_batch_cost: 1.82352 s, avg_samples: 12.5, ips: 6.85487 samples/s, eta: 0:14:12

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:51,  9.71it/s]
eval model::   1%|          | 3/500 [00:00<00:34, 14.41it/s]
eval model::   1%|          | 6/500 [00:00<00:24, 19.96it/s]
eval model::   2%|▏         | 8/500 [00:00<00:26, 18.76it/s]
eval model::   2%|▏         | 11/500 [00:00<00:24, 19.99it/s]
eval model::   3%|▎         | 13/500 [00:00<00:24, 19.98it/s]
eval model::   3%|▎         | 15/500 [00:00<00:24, 19.78it/s]
eval model::   4%|▍         | 19/500 [00:00<00:22, 21.22it/s]
eval model::   4%|▍         | 22/500 [00:01<00:22, 21.44it/s]
eval model::   5%|▌         | 25/500 [00:01<00:21, 21.92it/s]
eval model::   6%|▌         | 28/500 [00:01<00:22, 20.83it/s]
eval model::   6%|▌         | 31/500 [00:01<00:24, 19.08it/s]
eval model::   7%|▋         | 34/500 [00:01<00:24, 19.07it/s]
eval model::   7%|▋         | 37/500 [00:01<00:22, 20.75it/s]
eval model::   8%|▊         | 40/500 [00:02<00:23, 19.66it/s]
eval model::   9%|▊         | 43/500 [00:02<00:22, 20.10it/s]
eval model::   9%|▉         | 46/500 [00:02<00:23, 19.26it/s]
eval model::  10%|█         | 50/500 [00:02<00:20, 21.74it/s]
eval model::  11%|█         | 53/500 [00:02<00:21, 21.11it/s]
eval model::  11%|█         | 56/500 [00:02<00:21, 20.84it/s]
eval model::  12%|█▏        | 59/500 [00:02<00:20, 21.26it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:22, 19.29it/s]
eval model::  13%|█▎        | 64/500 [00:03<00:23, 18.62it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:19, 22.05it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:19, 21.50it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:20, 20.69it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:20, 20.15it/s]
eval model::  16%|█▌        | 80/500 [00:03<00:18, 22.18it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:19, 21.25it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:19, 20.94it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:20, 20.33it/s]
eval model::  18%|█▊        | 92/500 [00:04<00:20, 19.90it/s]
eval model::  19%|█▉        | 95/500 [00:04<00:20, 20.05it/s]
eval model::  20%|█▉        | 98/500 [00:04<00:19, 20.92it/s]
eval model::  20%|██        | 101/500 [00:04<00:20, 19.54it/s]
eval model::  21%|██        | 104/500 [00:05<00:18, 21.37it/s]
eval model::  21%|██▏       | 107/500 [00:05<00:18, 21.70it/s]
eval model::  22%|██▏       | 110/500 [00:05<00:17, 21.72it/s]
eval model::  23%|██▎       | 113/500 [00:05<00:17, 21.88it/s]
eval model::  23%|██▎       | 116/500 [00:05<00:18, 20.86it/s]
eval model::  24%|██▍       | 119/500 [00:05<00:17, 21.17it/s]
eval model::  24%|██▍       | 122/500 [00:05<00:18, 20.06it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:18, 20.58it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:17, 21.37it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:18, 20.24it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:17, 20.65it/s]
eval model::  27%|██▋       | 137/500 [00:06<00:18, 19.33it/s]
eval model::  28%|██▊       | 140/500 [00:06<00:18, 19.30it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:17, 19.99it/s]
eval model::  29%|██▉       | 146/500 [00:07<00:17, 20.27it/s]
eval model::  30%|██▉       | 149/500 [00:07<00:17, 20.05it/s]
eval model::  30%|███       | 152/500 [00:07<00:18, 18.75it/s]
eval model::  31%|███       | 155/500 [00:07<00:16, 20.45it/s]
eval model::  32%|███▏      | 158/500 [00:07<00:15, 22.22it/s]
eval model::  32%|███▏      | 161/500 [00:07<00:15, 21.77it/s]
eval model::  33%|███▎      | 164/500 [00:07<00:15, 22.22it/s]
eval model::  33%|███▎      | 167/500 [00:08<00:15, 20.98it/s]
eval model::  34%|███▍      | 170/500 [00:08<00:16, 20.18it/s]
eval model::  35%|███▍      | 173/500 [00:08<00:16, 19.80it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:17, 18.10it/s]
eval model::  36%|███▌      | 179/500 [00:08<00:17, 18.55it/s]
eval model::  36%|███▌      | 181/500 [00:08<00:18, 17.47it/s]
eval model::  37%|███▋      | 184/500 [00:09<00:15, 19.77it/s]
eval model::  37%|███▋      | 187/500 [00:09<00:15, 20.19it/s]
eval model::  38%|███▊      | 190/500 [00:09<00:16, 19.13it/s]
eval model::  39%|███▊      | 193/500 [00:09<00:15, 19.52it/s]
eval model::  39%|███▉      | 195/500 [00:09<00:17, 17.82it/s]
eval model::  39%|███▉      | 197/500 [00:09<00:16, 18.13it/s]
eval model::  40%|████      | 200/500 [00:09<00:14, 20.70it/s]
eval model::  41%|████      | 203/500 [00:10<00:14, 20.93it/s]
eval model::  41%|████      | 206/500 [00:10<00:21, 13.46it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:20, 14.01it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:19, 15.00it/s]
eval model::  43%|████▎     | 213/500 [00:10<00:16, 17.56it/s]
eval model::  43%|████▎     | 216/500 [00:11<00:18, 15.47it/s]
eval model::  44%|████▎     | 218/500 [00:11<00:18, 14.96it/s]
eval model::  44%|████▍     | 221/500 [00:11<00:16, 16.85it/s]
eval model::  45%|████▍     | 224/500 [00:11<00:14, 19.31it/s]
eval model::  45%|████▌     | 227/500 [00:11<00:13, 19.90it/s]
eval model::  46%|████▌     | 230/500 [00:11<00:12, 21.49it/s]
eval model::  47%|████▋     | 233/500 [00:11<00:11, 22.32it/s]
eval model::  47%|████▋     | 236/500 [00:11<00:11, 22.19it/s]
eval model::  48%|████▊     | 239/500 [00:12<00:11, 22.69it/s]
eval model::  48%|████▊     | 242/500 [00:12<00:12, 21.36it/s]
eval model::  49%|████▉     | 245/500 [00:12<00:11, 21.38it/s]
eval model::  50%|████▉     | 248/500 [00:12<00:11, 22.75it/s]
eval model::  50%|█████     | 251/500 [00:12<00:12, 19.86it/s]
eval model::  51%|█████     | 254/500 [00:12<00:11, 21.70it/s]
eval model::  51%|█████▏    | 257/500 [00:12<00:11, 21.79it/s]
eval model::  52%|█████▏    | 260/500 [00:12<00:10, 23.66it/s]
eval model::  53%|█████▎    | 263/500 [00:13<00:10, 22.55it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:11, 20.04it/s]
eval model::  54%|█████▍    | 269/500 [00:13<00:10, 21.66it/s]
eval model::  54%|█████▍    | 272/500 [00:13<00:10, 20.95it/s]
eval model::  55%|█████▌    | 275/500 [00:13<00:12, 18.72it/s]
eval model::  56%|█████▌    | 278/500 [00:13<00:11, 19.87it/s]
eval model::  56%|█████▌    | 281/500 [00:14<00:10, 20.24it/s]
eval model::  57%|█████▋    | 284/500 [00:14<00:10, 21.05it/s]
eval model::  57%|█████▋    | 287/500 [00:14<00:13, 15.96it/s]
eval model::  58%|█████▊    | 289/500 [00:14<00:12, 16.68it/s]
eval model::  58%|█████▊    | 292/500 [00:14<00:11, 18.90it/s]
eval model::  59%|█████▉    | 295/500 [00:14<00:11, 17.68it/s]
eval model::  60%|█████▉    | 298/500 [00:15<00:10, 19.75it/s]
eval model::  60%|██████    | 301/500 [00:15<00:10, 18.48it/s]
eval model::  61%|██████    | 304/500 [00:15<00:09, 19.83it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:09, 21.20it/s]
eval model::  62%|██████▏   | 310/500 [00:15<00:08, 21.84it/s]
eval model::  63%|██████▎   | 313/500 [00:15<00:08, 21.81it/s]
eval model::  63%|██████▎   | 316/500 [00:15<00:08, 21.59it/s]
eval model::  64%|██████▍   | 320/500 [00:15<00:07, 24.05it/s]
eval model::  65%|██████▍   | 323/500 [00:16<00:07, 23.17it/s]
eval model::  65%|██████▌   | 326/500 [00:16<00:07, 23.73it/s]
eval model::  66%|██████▌   | 329/500 [00:16<00:07, 21.68it/s]
eval model::  66%|██████▋   | 332/500 [00:16<00:07, 21.58it/s]
eval model::  67%|██████▋   | 335/500 [00:16<00:07, 21.99it/s]
eval model::  68%|██████▊   | 338/500 [00:16<00:08, 18.76it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 16.24it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:08, 18.75it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 20.05it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:07, 20.88it/s]
eval model::  70%|███████   | 352/500 [00:17<00:07, 20.43it/s]
eval model::  71%|███████   | 355/500 [00:17<00:06, 20.92it/s]
eval model::  72%|███████▏  | 358/500 [00:17<00:06, 20.57it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:06, 20.69it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.95it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:07, 18.96it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:06, 19.69it/s]
eval model::  75%|███████▍  | 373/500 [00:18<00:06, 20.65it/s]
eval model::  75%|███████▌  | 376/500 [00:18<00:05, 22.30it/s]
eval model::  76%|███████▌  | 379/500 [00:18<00:05, 23.03it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 21.32it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 21.48it/s]
eval model::  78%|███████▊  | 388/500 [00:19<00:07, 14.28it/s]
eval model::  78%|███████▊  | 391/500 [00:19<00:06, 16.35it/s]
eval model::  79%|███████▉  | 394/500 [00:19<00:06, 17.65it/s]
eval model::  79%|███████▉  | 397/500 [00:19<00:05, 19.19it/s]
eval model::  80%|████████  | 400/500 [00:20<00:04, 20.28it/s]
eval model::  81%|████████  | 404/500 [00:20<00:04, 22.86it/s]
eval model::  81%|████████▏ | 407/500 [00:20<00:04, 22.59it/s]
eval model::  82%|████████▏ | 410/500 [00:20<00:03, 23.35it/s]
eval model::  83%|████████▎ | 413/500 [00:20<00:03, 22.45it/s]
eval model::  83%|████████▎ | 416/500 [00:20<00:03, 22.29it/s]
eval model::  84%|████████▍ | 419/500 [00:20<00:03, 21.75it/s]
eval model::  84%|████████▍ | 422/500 [00:21<00:04, 19.05it/s]
eval model::  85%|████████▍ | 424/500 [00:21<00:03, 19.19it/s]
eval model::  85%|████████▌ | 426/500 [00:21<00:04, 17.86it/s]
eval model::  86%|████████▌ | 429/500 [00:21<00:03, 18.95it/s]
eval model::  86%|████████▋ | 432/500 [00:21<00:03, 20.78it/s]
eval model::  87%|████████▋ | 435/500 [00:21<00:02, 22.87it/s]
eval model::  88%|████████▊ | 438/500 [00:21<00:02, 23.11it/s]
eval model::  88%|████████▊ | 441/500 [00:21<00:02, 21.66it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 23.14it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 22.29it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 21.28it/s]
eval model::  91%|█████████ | 453/500 [00:22<00:02, 22.11it/s]
eval model::  91%|█████████ | 456/500 [00:22<00:02, 20.94it/s]
eval model::  92%|█████████▏| 459/500 [00:22<00:01, 21.15it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:02, 18.46it/s]
eval model::  93%|█████████▎| 464/500 [00:23<00:01, 18.37it/s]
eval model::  93%|█████████▎| 466/500 [00:23<00:02, 16.82it/s]
eval model::  94%|█████████▍| 469/500 [00:23<00:01, 19.19it/s]
eval model::  94%|█████████▍| 472/500 [00:23<00:01, 19.56it/s]
eval model::  95%|█████████▌| 475/500 [00:23<00:01, 20.98it/s]
eval model::  96%|█████████▌| 478/500 [00:23<00:00, 22.08it/s]
eval model::  96%|█████████▌| 481/500 [00:23<00:00, 21.75it/s]
eval model::  97%|█████████▋| 484/500 [00:24<00:00, 22.42it/s]
eval model::  97%|█████████▋| 487/500 [00:24<00:00, 21.82it/s]
eval model::  98%|█████████▊| 490/500 [00:24<00:00, 21.24it/s]
eval model::  99%|█████████▊| 493/500 [00:24<00:00, 21.02it/s]
eval model::  99%|█████████▉| 496/500 [00:24<00:00, 20.48it/s]
eval model:: 100%|█████████▉| 499/500 [00:24<00:00, 22.58it/s]
eval model:: 100%|██████████| 500/500 [00:24<00:00, 20.17it/s]
[2024/08/01 20:46:36] ppocr INFO: cur metric, precision: 0.581670362158167, recall: 0.3789118921521425, hmean: 0.45889212827988335, fps: 43.358808284451044
[2024/08/01 20:46:36] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 20:46:36] ppocr INFO: best metric, hmean: 0.45889212827988335, precision: 0.581670362158167, recall: 0.3789118921521425, fps: 43.358808284451044, best_epoch: 60
[2024/08/01 20:46:36] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:46:44] ppocr INFO: epoch: [61/100], global_step: 183, lr: 0.001000, loss: 2.727903, loss_shrink_maps: 1.626835, loss_threshold_maps: 0.797147, loss_binary_maps: 0.305596, avg_reader_cost: 1.76529 s, avg_batch_cost: 2.11084 s, avg_samples: 12.5, ips: 5.92182 samples/s, eta: 0:13:51
[2024/08/01 20:46:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:46:51] ppocr INFO: epoch: [62/100], global_step: 186, lr: 0.001000, loss: 2.653604, loss_shrink_maps: 1.587689, loss_threshold_maps: 0.791640, loss_binary_maps: 0.295319, avg_reader_cost: 1.61684 s, avg_batch_cost: 1.86155 s, avg_samples: 12.5, ips: 6.71484 samples/s, eta: 0:13:28
[2024/08/01 20:46:52] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:46:59] ppocr INFO: epoch: [63/100], global_step: 189, lr: 0.001000, loss: 2.604734, loss_shrink_maps: 1.534816, loss_threshold_maps: 0.783087, loss_binary_maps: 0.292783, avg_reader_cost: 1.56857 s, avg_batch_cost: 1.81687 s, avg_samples: 12.5, ips: 6.87996 samples/s, eta: 0:13:05
[2024/08/01 20:47:00] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:47:05] ppocr INFO: epoch: [64/100], global_step: 190, lr: 0.001000, loss: 2.604734, loss_shrink_maps: 1.534816, loss_threshold_maps: 0.783087, loss_binary_maps: 0.292783, avg_reader_cost: 0.43771 s, avg_batch_cost: 0.52786 s, avg_samples: 4.8, ips: 9.09327 samples/s, eta: 0:12:56
[2024/08/01 20:47:07] ppocr INFO: epoch: [64/100], global_step: 192, lr: 0.001000, loss: 2.547395, loss_shrink_maps: 1.500137, loss_threshold_maps: 0.777121, loss_binary_maps: 0.283142, avg_reader_cost: 1.15394 s, avg_batch_cost: 1.30954 s, avg_samples: 7.7, ips: 5.87993 samples/s, eta: 0:12:42
[2024/08/01 20:47:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:47:14] ppocr INFO: epoch: [65/100], global_step: 195, lr: 0.001000, loss: 2.488178, loss_shrink_maps: 1.454525, loss_threshold_maps: 0.763219, loss_binary_maps: 0.272721, avg_reader_cost: 1.52187 s, avg_batch_cost: 1.76736 s, avg_samples: 12.5, ips: 7.07270 samples/s, eta: 0:12:19
[2024/08/01 20:47:15] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:47:21] ppocr INFO: epoch: [66/100], global_step: 198, lr: 0.001000, loss: 2.518005, loss_shrink_maps: 1.475866, loss_threshold_maps: 0.767357, loss_binary_maps: 0.280372, avg_reader_cost: 1.54674 s, avg_batch_cost: 1.79196 s, avg_samples: 12.5, ips: 6.97562 samples/s, eta: 0:11:56
[2024/08/01 20:47:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:47:28] ppocr INFO: epoch: [67/100], global_step: 200, lr: 0.001000, loss: 2.541465, loss_shrink_maps: 1.500137, loss_threshold_maps: 0.769918, loss_binary_maps: 0.285934, avg_reader_cost: 0.95519 s, avg_batch_cost: 1.14914 s, avg_samples: 9.6, ips: 8.35410 samples/s, eta: 0:11:41
[2024/08/01 20:47:29] ppocr INFO: epoch: [67/100], global_step: 201, lr: 0.001000, loss: 2.518005, loss_shrink_maps: 1.475866, loss_threshold_maps: 0.769918, loss_binary_maps: 0.280372, avg_reader_cost: 0.62362 s, avg_batch_cost: 0.68173 s, avg_samples: 2.9, ips: 4.25389 samples/s, eta: 0:11:33
[2024/08/01 20:47:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:47:37] ppocr INFO: epoch: [68/100], global_step: 204, lr: 0.001000, loss: 2.433255, loss_shrink_maps: 1.403903, loss_threshold_maps: 0.760605, loss_binary_maps: 0.264343, avg_reader_cost: 1.54981 s, avg_batch_cost: 1.84099 s, avg_samples: 12.5, ips: 6.78982 samples/s, eta: 0:11:11
[2024/08/01 20:47:37] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:47:45] ppocr INFO: epoch: [69/100], global_step: 207, lr: 0.001000, loss: 2.475808, loss_shrink_maps: 1.427868, loss_threshold_maps: 0.762031, loss_binary_maps: 0.268611, avg_reader_cost: 1.64020 s, avg_batch_cost: 1.94210 s, avg_samples: 12.5, ips: 6.43633 samples/s, eta: 0:10:50
[2024/08/01 20:47:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:47:52] ppocr INFO: epoch: [70/100], global_step: 210, lr: 0.001000, loss: 2.430012, loss_shrink_maps: 1.407200, loss_threshold_maps: 0.760605, loss_binary_maps: 0.268170, avg_reader_cost: 1.56204 s, avg_batch_cost: 1.82756 s, avg_samples: 12.5, ips: 6.83972 samples/s, eta: 0:10:27
[2024/08/01 20:47:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:48:00] ppocr INFO: epoch: [71/100], global_step: 213, lr: 0.001000, loss: 2.475808, loss_shrink_maps: 1.427868, loss_threshold_maps: 0.756791, loss_binary_maps: 0.270736, avg_reader_cost: 1.60055 s, avg_batch_cost: 1.86241 s, avg_samples: 12.5, ips: 6.71172 samples/s, eta: 0:10:06
[2024/08/01 20:48:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:48:07] ppocr INFO: epoch: [72/100], global_step: 216, lr: 0.001000, loss: 2.505378, loss_shrink_maps: 1.438713, loss_threshold_maps: 0.765093, loss_binary_maps: 0.275271, avg_reader_cost: 1.54199 s, avg_batch_cost: 1.78683 s, avg_samples: 12.5, ips: 6.99563 samples/s, eta: 0:09:43
[2024/08/01 20:48:08] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:48:15] ppocr INFO: epoch: [73/100], global_step: 219, lr: 0.001000, loss: 2.421255, loss_shrink_maps: 1.396981, loss_threshold_maps: 0.764118, loss_binary_maps: 0.268848, avg_reader_cost: 1.62814 s, avg_batch_cost: 1.95705 s, avg_samples: 12.5, ips: 6.38716 samples/s, eta: 0:09:22
[2024/08/01 20:48:16] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:48:21] ppocr INFO: epoch: [74/100], global_step: 220, lr: 0.001000, loss: 2.399778, loss_shrink_maps: 1.382538, loss_threshold_maps: 0.764118, loss_binary_maps: 0.264474, avg_reader_cost: 0.42929 s, avg_batch_cost: 0.53496 s, avg_samples: 4.8, ips: 8.97265 samples/s, eta: 0:09:15
[2024/08/01 20:48:23] ppocr INFO: epoch: [74/100], global_step: 222, lr: 0.001000, loss: 2.431408, loss_shrink_maps: 1.405608, loss_threshold_maps: 0.771490, loss_binary_maps: 0.269414, avg_reader_cost: 1.16761 s, avg_batch_cost: 1.32330 s, avg_samples: 7.7, ips: 5.81877 samples/s, eta: 0:09:00
[2024/08/01 20:48:24] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:48:30] ppocr INFO: epoch: [75/100], global_step: 225, lr: 0.001000, loss: 2.442910, loss_shrink_maps: 1.407722, loss_threshold_maps: 0.771490, loss_binary_maps: 0.270123, avg_reader_cost: 1.55600 s, avg_batch_cost: 1.81452 s, avg_samples: 12.5, ips: 6.88889 samples/s, eta: 0:08:39
[2024/08/01 20:48:31] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:48:38] ppocr INFO: epoch: [76/100], global_step: 228, lr: 0.001000, loss: 2.431408, loss_shrink_maps: 1.405608, loss_threshold_maps: 0.768721, loss_binary_maps: 0.269414, avg_reader_cost: 1.65663 s, avg_batch_cost: 1.91094 s, avg_samples: 12.5, ips: 6.54128 samples/s, eta: 0:08:17
[2024/08/01 20:48:39] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:48:45] ppocr INFO: epoch: [77/100], global_step: 230, lr: 0.001000, loss: 2.453468, loss_shrink_maps: 1.409376, loss_threshold_maps: 0.771676, loss_binary_maps: 0.272417, avg_reader_cost: 0.92286 s, avg_batch_cost: 1.10983 s, avg_samples: 9.6, ips: 8.64994 samples/s, eta: 0:08:03
[2024/08/01 20:48:45] ppocr INFO: epoch: [77/100], global_step: 231, lr: 0.001000, loss: 2.453468, loss_shrink_maps: 1.409376, loss_threshold_maps: 0.774981, loss_binary_maps: 0.272417, avg_reader_cost: 0.60426 s, avg_batch_cost: 0.66224 s, avg_samples: 2.9, ips: 4.37909 samples/s, eta: 0:07:56
[2024/08/01 20:48:46] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:48:53] ppocr INFO: epoch: [78/100], global_step: 234, lr: 0.001000, loss: 2.497799, loss_shrink_maps: 1.450939, loss_threshold_maps: 0.777236, loss_binary_maps: 0.280438, avg_reader_cost: 1.59808 s, avg_batch_cost: 1.84174 s, avg_samples: 12.5, ips: 6.78706 samples/s, eta: 0:07:34
[2024/08/01 20:48:54] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:49:01] ppocr INFO: epoch: [79/100], global_step: 237, lr: 0.001000, loss: 2.453468, loss_shrink_maps: 1.409376, loss_threshold_maps: 0.780754, loss_binary_maps: 0.273164, avg_reader_cost: 1.59169 s, avg_batch_cost: 1.84730 s, avg_samples: 12.5, ips: 6.76663 samples/s, eta: 0:07:13
[2024/08/01 20:49:02] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:49:08] ppocr INFO: epoch: [80/100], global_step: 240, lr: 0.001000, loss: 2.461310, loss_shrink_maps: 1.430295, loss_threshold_maps: 0.773340, loss_binary_maps: 0.278059, avg_reader_cost: 1.54411 s, avg_batch_cost: 1.81454 s, avg_samples: 12.5, ips: 6.88878 samples/s, eta: 0:06:52

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:50,  9.90it/s]
eval model::   1%|          | 4/500 [00:00<00:27, 18.35it/s]
eval model::   1%|▏         | 7/500 [00:00<00:24, 20.50it/s]
eval model::   2%|▏         | 9/500 [00:00<00:24, 20.08it/s]
eval model::   2%|▏         | 11/500 [00:00<00:25, 19.20it/s]
eval model::   3%|▎         | 14/500 [00:00<00:24, 19.47it/s]
eval model::   3%|▎         | 17/500 [00:00<00:23, 21.00it/s]
eval model::   4%|▍         | 20/500 [00:01<00:24, 19.30it/s]
eval model::   5%|▍         | 23/500 [00:01<00:22, 20.87it/s]
eval model::   5%|▌         | 26/500 [00:01<00:25, 18.30it/s]
eval model::   6%|▌         | 29/500 [00:01<00:24, 18.89it/s]
eval model::   6%|▌         | 31/500 [00:01<00:25, 18.40it/s]
eval model::   7%|▋         | 34/500 [00:01<00:25, 18.31it/s]
eval model::   7%|▋         | 37/500 [00:01<00:24, 18.95it/s]
eval model::   8%|▊         | 39/500 [00:02<00:27, 16.76it/s]
eval model::   8%|▊         | 42/500 [00:02<00:25, 18.24it/s]
eval model::   9%|▉         | 45/500 [00:02<00:24, 18.51it/s]
eval model::   9%|▉         | 47/500 [00:02<00:25, 17.78it/s]
eval model::  10%|█         | 50/500 [00:02<00:23, 19.19it/s]
eval model::  11%|█         | 53/500 [00:02<00:22, 19.72it/s]
eval model::  11%|█         | 55/500 [00:02<00:22, 19.49it/s]
eval model::  11%|█▏        | 57/500 [00:03<00:23, 18.94it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:20, 21.06it/s]
eval model::  13%|█▎        | 63/500 [00:03<00:23, 18.69it/s]
eval model::  13%|█▎        | 66/500 [00:03<00:20, 21.05it/s]
eval model::  14%|█▍        | 69/500 [00:03<00:20, 21.38it/s]
eval model::  14%|█▍        | 72/500 [00:03<00:20, 21.11it/s]
eval model::  15%|█▌        | 75/500 [00:03<00:19, 21.55it/s]
eval model::  16%|█▌        | 78/500 [00:04<00:21, 19.45it/s]
eval model::  16%|█▌        | 81/500 [00:04<00:20, 20.09it/s]
eval model::  17%|█▋        | 84/500 [00:04<00:21, 19.57it/s]
eval model::  17%|█▋        | 87/500 [00:04<00:22, 18.49it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.15it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:22, 17.82it/s]
eval model::  19%|█▉        | 94/500 [00:04<00:21, 19.04it/s]
eval model::  19%|█▉        | 97/500 [00:05<00:19, 21.01it/s]
eval model::  20%|██        | 100/500 [00:05<00:21, 18.97it/s]
eval model::  21%|██        | 103/500 [00:05<00:20, 19.50it/s]
eval model::  21%|██        | 106/500 [00:05<00:20, 19.62it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:20, 19.09it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 19.46it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:19, 19.57it/s]
eval model::  23%|██▎       | 117/500 [00:06<00:20, 18.89it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:20, 18.14it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:19, 19.27it/s]
eval model::  25%|██▌       | 125/500 [00:06<00:20, 18.10it/s]
eval model::  26%|██▌       | 128/500 [00:06<00:18, 19.91it/s]
eval model::  26%|██▌       | 131/500 [00:06<00:18, 19.51it/s]
eval model::  27%|██▋       | 134/500 [00:06<00:17, 20.38it/s]
eval model::  27%|██▋       | 137/500 [00:07<00:19, 18.48it/s]
eval model::  28%|██▊       | 139/500 [00:07<00:19, 18.64it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:19, 18.34it/s]
eval model::  29%|██▊       | 143/500 [00:07<00:19, 18.51it/s]
eval model::  29%|██▉       | 145/500 [00:07<00:18, 18.74it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:20, 17.08it/s]
eval model::  30%|███       | 150/500 [00:07<00:18, 18.58it/s]
eval model::  30%|███       | 152/500 [00:07<00:19, 17.41it/s]
eval model::  31%|███       | 156/500 [00:08<00:16, 21.02it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:14, 23.14it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:17, 19.52it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:17, 19.44it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:17, 18.81it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:17, 18.88it/s]
eval model::  35%|███▍      | 173/500 [00:09<00:17, 18.98it/s]
eval model::  35%|███▌      | 175/500 [00:09<00:20, 15.87it/s]
eval model::  36%|███▌      | 178/500 [00:09<00:17, 18.33it/s]
eval model::  36%|███▌      | 180/500 [00:09<00:19, 16.27it/s]
eval model::  37%|███▋      | 183/500 [00:09<00:17, 18.16it/s]
eval model::  37%|███▋      | 186/500 [00:09<00:16, 18.96it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:16, 19.15it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:14, 20.60it/s]
eval model::  39%|███▉      | 194/500 [00:10<00:13, 22.10it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:15, 19.91it/s]
eval model::  40%|████      | 200/500 [00:10<00:13, 22.03it/s]
eval model::  41%|████      | 203/500 [00:10<00:12, 23.51it/s]
eval model::  41%|████      | 206/500 [00:10<00:19, 14.85it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:19, 14.76it/s]
eval model::  42%|████▏     | 210/500 [00:11<00:18, 15.42it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:16, 17.35it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:17, 16.35it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 15.65it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 17.62it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:16, 17.34it/s]
eval model::  45%|████▌     | 225/500 [00:11<00:14, 18.90it/s]
eval model::  46%|████▌     | 228/500 [00:12<00:13, 19.54it/s]
eval model::  46%|████▌     | 231/500 [00:12<00:12, 20.91it/s]
eval model::  47%|████▋     | 234/500 [00:12<00:13, 20.30it/s]
eval model::  47%|████▋     | 237/500 [00:12<00:12, 21.36it/s]
eval model::  48%|████▊     | 240/500 [00:12<00:11, 21.88it/s]
eval model::  49%|████▊     | 243/500 [00:12<00:12, 20.13it/s]
eval model::  49%|████▉     | 246/500 [00:12<00:11, 21.89it/s]
eval model::  50%|████▉     | 249/500 [00:13<00:12, 20.47it/s]
eval model::  50%|█████     | 252/500 [00:13<00:12, 20.36it/s]
eval model::  51%|█████     | 255/500 [00:13<00:11, 21.09it/s]
eval model::  52%|█████▏    | 258/500 [00:13<00:11, 21.44it/s]
eval model::  52%|█████▏    | 261/500 [00:13<00:12, 19.67it/s]
eval model::  53%|█████▎    | 264/500 [00:13<00:12, 18.37it/s]
eval model::  53%|█████▎    | 266/500 [00:13<00:12, 18.28it/s]
eval model::  54%|█████▍    | 269/500 [00:14<00:11, 19.84it/s]
eval model::  54%|█████▍    | 272/500 [00:14<00:11, 19.85it/s]
eval model::  55%|█████▌    | 275/500 [00:14<00:11, 19.54it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 19.56it/s]
eval model::  56%|█████▌    | 279/500 [00:14<00:11, 18.97it/s]
eval model::  56%|█████▋    | 282/500 [00:14<00:10, 21.66it/s]
eval model::  57%|█████▋    | 285/500 [00:15<00:16, 13.36it/s]
eval model::  58%|█████▊    | 288/500 [00:15<00:13, 15.49it/s]
eval model::  58%|█████▊    | 291/500 [00:15<00:11, 17.69it/s]
eval model::  59%|█████▉    | 294/500 [00:15<00:11, 18.47it/s]
eval model::  59%|█████▉    | 297/500 [00:15<00:11, 17.81it/s]
eval model::  60%|██████    | 300/500 [00:15<00:11, 17.18it/s]
eval model::  60%|██████    | 302/500 [00:16<00:12, 16.10it/s]
eval model::  61%|██████    | 304/500 [00:16<00:11, 16.74it/s]
eval model::  61%|██████▏   | 307/500 [00:16<00:10, 19.00it/s]
eval model::  62%|██████▏   | 310/500 [00:16<00:09, 20.17it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:09, 20.74it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:09, 20.39it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 20.32it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:08, 20.63it/s]
eval model::  65%|██████▌   | 325/500 [00:17<00:07, 22.64it/s]
eval model::  66%|██████▌   | 328/500 [00:17<00:08, 21.25it/s]
eval model::  66%|██████▌   | 331/500 [00:17<00:07, 21.38it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 23.27it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:07, 22.46it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:08, 18.81it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:07, 20.05it/s]
eval model::  69%|██████▉   | 346/500 [00:18<00:07, 21.52it/s]
eval model::  70%|██████▉   | 349/500 [00:18<00:06, 23.47it/s]
eval model::  70%|███████   | 352/500 [00:18<00:06, 22.77it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.88it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 22.21it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:07, 18.95it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.54it/s]
eval model::  73%|███████▎  | 366/500 [00:19<00:07, 18.82it/s]
eval model::  74%|███████▍  | 369/500 [00:19<00:06, 20.27it/s]
eval model::  74%|███████▍  | 372/500 [00:19<00:06, 18.85it/s]
eval model::  75%|███████▍  | 374/500 [00:19<00:06, 18.91it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:06, 17.76it/s]
eval model::  76%|███████▌  | 378/500 [00:19<00:06, 17.93it/s]
eval model::  76%|███████▌  | 380/500 [00:19<00:06, 17.45it/s]
eval model::  77%|███████▋  | 383/500 [00:19<00:05, 20.40it/s]
eval model::  77%|███████▋  | 386/500 [00:20<00:08, 13.04it/s]
eval model::  78%|███████▊  | 389/500 [00:20<00:07, 14.97it/s]
eval model::  78%|███████▊  | 392/500 [00:20<00:06, 16.52it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:06, 16.75it/s]
eval model::  79%|███████▉  | 396/500 [00:20<00:06, 17.24it/s]
eval model::  80%|███████▉  | 399/500 [00:20<00:05, 19.09it/s]
eval model::  80%|████████  | 402/500 [00:21<00:05, 18.52it/s]
eval model::  81%|████████  | 405/500 [00:21<00:04, 19.75it/s]
eval model::  82%|████████▏ | 408/500 [00:21<00:04, 21.03it/s]
eval model::  82%|████████▏ | 411/500 [00:21<00:04, 21.77it/s]
eval model::  83%|████████▎ | 414/500 [00:21<00:03, 22.95it/s]
eval model::  83%|████████▎ | 417/500 [00:21<00:03, 21.48it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 18.74it/s]
eval model::  85%|████████▍ | 423/500 [00:22<00:03, 19.91it/s]
eval model::  85%|████████▌ | 426/500 [00:22<00:03, 18.62it/s]
eval model::  86%|████████▌ | 429/500 [00:22<00:03, 19.55it/s]
eval model::  86%|████████▋ | 432/500 [00:22<00:03, 18.90it/s]
eval model::  87%|████████▋ | 435/500 [00:22<00:03, 20.43it/s]
eval model::  88%|████████▊ | 438/500 [00:22<00:03, 20.34it/s]
eval model::  88%|████████▊ | 441/500 [00:23<00:02, 19.69it/s]
eval model::  89%|████████▉ | 444/500 [00:23<00:02, 21.43it/s]
eval model::  89%|████████▉ | 447/500 [00:23<00:02, 21.85it/s]
eval model::  90%|█████████ | 450/500 [00:23<00:02, 21.52it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 20.96it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:02, 21.99it/s]
eval model::  92%|█████████▏| 459/500 [00:23<00:01, 21.61it/s]
eval model::  92%|█████████▏| 462/500 [00:24<00:01, 19.45it/s]
eval model::  93%|█████████▎| 465/500 [00:24<00:02, 17.15it/s]
eval model::  94%|█████████▎| 468/500 [00:24<00:01, 18.60it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 19.72it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 18.87it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 20.40it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 21.25it/s]
eval model::  97%|█████████▋| 483/500 [00:25<00:00, 22.28it/s]
eval model::  97%|█████████▋| 486/500 [00:25<00:00, 20.47it/s]
eval model::  98%|█████████▊| 489/500 [00:25<00:00, 20.38it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 22.09it/s]
eval model::  99%|█████████▉| 495/500 [00:25<00:00, 22.78it/s]
eval model:: 100%|█████████▉| 498/500 [00:25<00:00, 23.45it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.38it/s]
[2024/08/01 20:49:34] ppocr INFO: cur metric, precision: 0.5636743215031316, recall: 0.38998555609051516, hmean: 0.4610130904951622, fps: 42.94456567617779
[2024/08/01 20:49:34] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 20:49:34] ppocr INFO: best metric, hmean: 0.4610130904951622, precision: 0.5636743215031316, recall: 0.38998555609051516, fps: 42.94456567617779, best_epoch: 80
[2024/08/01 20:49:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:49:41] ppocr INFO: epoch: [81/100], global_step: 243, lr: 0.001000, loss: 2.438268, loss_shrink_maps: 1.406485, loss_threshold_maps: 0.771731, loss_binary_maps: 0.273164, avg_reader_cost: 1.50712 s, avg_batch_cost: 1.75234 s, avg_samples: 12.5, ips: 7.13331 samples/s, eta: 0:06:31
[2024/08/01 20:49:42] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:49:49] ppocr INFO: epoch: [82/100], global_step: 246, lr: 0.001000, loss: 2.420612, loss_shrink_maps: 1.395546, loss_threshold_maps: 0.767611, loss_binary_maps: 0.269850, avg_reader_cost: 1.66567 s, avg_batch_cost: 1.95431 s, avg_samples: 12.5, ips: 6.39611 samples/s, eta: 0:06:10
[2024/08/01 20:49:50] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:49:56] ppocr INFO: epoch: [83/100], global_step: 249, lr: 0.001000, loss: 2.414640, loss_shrink_maps: 1.369166, loss_threshold_maps: 0.759276, loss_binary_maps: 0.265700, avg_reader_cost: 1.53040 s, avg_batch_cost: 1.77482 s, avg_samples: 12.5, ips: 7.04296 samples/s, eta: 0:05:49
[2024/08/01 20:49:57] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:50:03] ppocr INFO: epoch: [84/100], global_step: 250, lr: 0.001000, loss: 2.414640, loss_shrink_maps: 1.369166, loss_threshold_maps: 0.745490, loss_binary_maps: 0.265700, avg_reader_cost: 0.42916 s, avg_batch_cost: 0.52838 s, avg_samples: 4.8, ips: 9.08444 samples/s, eta: 0:05:41
[2024/08/01 20:50:04] ppocr INFO: epoch: [84/100], global_step: 252, lr: 0.001000, loss: 2.415588, loss_shrink_maps: 1.395546, loss_threshold_maps: 0.745490, loss_binary_maps: 0.269850, avg_reader_cost: 1.15417 s, avg_batch_cost: 1.30932 s, avg_samples: 7.7, ips: 5.88091 samples/s, eta: 0:05:28
[2024/08/01 20:50:05] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:50:12] ppocr INFO: epoch: [85/100], global_step: 255, lr: 0.001000, loss: 2.459939, loss_shrink_maps: 1.426752, loss_threshold_maps: 0.757676, loss_binary_maps: 0.276341, avg_reader_cost: 1.52448 s, avg_batch_cost: 1.77864 s, avg_samples: 12.5, ips: 7.02784 samples/s, eta: 0:05:07
[2024/08/01 20:50:12] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:50:19] ppocr INFO: epoch: [86/100], global_step: 258, lr: 0.001000, loss: 2.459939, loss_shrink_maps: 1.404818, loss_threshold_maps: 0.745490, loss_binary_maps: 0.272018, avg_reader_cost: 1.60046 s, avg_batch_cost: 1.87842 s, avg_samples: 12.5, ips: 6.65452 samples/s, eta: 0:04:46
[2024/08/01 20:50:20] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:50:26] ppocr INFO: epoch: [87/100], global_step: 260, lr: 0.001000, loss: 2.466542, loss_shrink_maps: 1.404818, loss_threshold_maps: 0.763679, loss_binary_maps: 0.272018, avg_reader_cost: 0.92273 s, avg_batch_cost: 1.11271 s, avg_samples: 9.6, ips: 8.62761 samples/s, eta: 0:04:32
[2024/08/01 20:50:27] ppocr INFO: epoch: [87/100], global_step: 261, lr: 0.001000, loss: 2.466542, loss_shrink_maps: 1.404818, loss_threshold_maps: 0.766895, loss_binary_maps: 0.272018, avg_reader_cost: 0.60545 s, avg_batch_cost: 0.66356 s, avg_samples: 2.9, ips: 4.37037 samples/s, eta: 0:04:25
[2024/08/01 20:50:28] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:50:34] ppocr INFO: epoch: [88/100], global_step: 264, lr: 0.001000, loss: 2.463130, loss_shrink_maps: 1.404818, loss_threshold_maps: 0.766895, loss_binary_maps: 0.272018, avg_reader_cost: 1.57404 s, avg_batch_cost: 1.82020 s, avg_samples: 12.5, ips: 6.86739 samples/s, eta: 0:04:04
[2024/08/01 20:50:35] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:50:42] ppocr INFO: epoch: [89/100], global_step: 267, lr: 0.001000, loss: 2.473382, loss_shrink_maps: 1.427150, loss_threshold_maps: 0.769132, loss_binary_maps: 0.276879, avg_reader_cost: 1.65844 s, avg_batch_cost: 1.90183 s, avg_samples: 12.5, ips: 6.57262 samples/s, eta: 0:03:44
[2024/08/01 20:50:43] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:50:50] ppocr INFO: epoch: [90/100], global_step: 270, lr: 0.001000, loss: 2.473382, loss_shrink_maps: 1.427150, loss_threshold_maps: 0.769132, loss_binary_maps: 0.276879, avg_reader_cost: 1.58836 s, avg_batch_cost: 1.85405 s, avg_samples: 12.5, ips: 6.74200 samples/s, eta: 0:03:23
[2024/08/01 20:50:51] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:50:58] ppocr INFO: epoch: [91/100], global_step: 273, lr: 0.001000, loss: 2.473382, loss_shrink_maps: 1.417273, loss_threshold_maps: 0.769132, loss_binary_maps: 0.273429, avg_reader_cost: 1.56582 s, avg_batch_cost: 1.81649 s, avg_samples: 12.5, ips: 6.88139 samples/s, eta: 0:03:03
[2024/08/01 20:50:59] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:51:05] ppocr INFO: epoch: [92/100], global_step: 276, lr: 0.001000, loss: 2.424851, loss_shrink_maps: 1.381032, loss_threshold_maps: 0.772215, loss_binary_maps: 0.267047, avg_reader_cost: 1.52217 s, avg_batch_cost: 1.77300 s, avg_samples: 12.5, ips: 7.05018 samples/s, eta: 0:02:42
[2024/08/01 20:51:06] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:51:13] ppocr INFO: epoch: [93/100], global_step: 279, lr: 0.001000, loss: 2.452345, loss_shrink_maps: 1.410766, loss_threshold_maps: 0.772215, loss_binary_maps: 0.271741, avg_reader_cost: 1.56874 s, avg_batch_cost: 1.84708 s, avg_samples: 12.5, ips: 6.76745 samples/s, eta: 0:02:22
[2024/08/01 20:51:14] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:51:20] ppocr INFO: epoch: [94/100], global_step: 280, lr: 0.001000, loss: 2.464357, loss_shrink_maps: 1.414756, loss_threshold_maps: 0.772215, loss_binary_maps: 0.273429, avg_reader_cost: 0.45853 s, avg_batch_cost: 0.57801 s, avg_samples: 4.8, ips: 8.30436 samples/s, eta: 0:02:15
[2024/08/01 20:51:21] ppocr INFO: epoch: [94/100], global_step: 282, lr: 0.001000, loss: 2.464357, loss_shrink_maps: 1.414756, loss_threshold_maps: 0.768259, loss_binary_maps: 0.273429, avg_reader_cost: 1.25570 s, avg_batch_cost: 1.41299 s, avg_samples: 7.7, ips: 5.44944 samples/s, eta: 0:02:01
[2024/08/01 20:51:22] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:51:29] ppocr INFO: epoch: [95/100], global_step: 285, lr: 0.001000, loss: 2.454833, loss_shrink_maps: 1.415065, loss_threshold_maps: 0.770599, loss_binary_maps: 0.273573, avg_reader_cost: 1.56922 s, avg_batch_cost: 1.84096 s, avg_samples: 12.5, ips: 6.78992 samples/s, eta: 0:01:41
[2024/08/01 20:51:30] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:51:37] ppocr INFO: epoch: [96/100], global_step: 288, lr: 0.001000, loss: 2.454833, loss_shrink_maps: 1.417728, loss_threshold_maps: 0.770599, loss_binary_maps: 0.275876, avg_reader_cost: 1.57027 s, avg_batch_cost: 1.82948 s, avg_samples: 12.5, ips: 6.83254 samples/s, eta: 0:01:20
[2024/08/01 20:51:38] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:51:44] ppocr INFO: epoch: [97/100], global_step: 290, lr: 0.001000, loss: 2.427815, loss_shrink_maps: 1.388478, loss_threshold_maps: 0.775569, loss_binary_maps: 0.269373, avg_reader_cost: 0.96548 s, avg_batch_cost: 1.18847 s, avg_samples: 9.6, ips: 8.07763 samples/s, eta: 0:01:07
[2024/08/01 20:51:44] ppocr INFO: epoch: [97/100], global_step: 291, lr: 0.001000, loss: 2.388184, loss_shrink_maps: 1.340367, loss_threshold_maps: 0.776688, loss_binary_maps: 0.260988, avg_reader_cost: 0.64337 s, avg_batch_cost: 0.70148 s, avg_samples: 2.9, ips: 4.13409 samples/s, eta: 0:01:00
[2024/08/01 20:51:45] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:51:52] ppocr INFO: epoch: [98/100], global_step: 294, lr: 0.001000, loss: 2.356419, loss_shrink_maps: 1.305153, loss_threshold_maps: 0.762152, loss_binary_maps: 0.253402, avg_reader_cost: 1.54520 s, avg_batch_cost: 1.80006 s, avg_samples: 12.5, ips: 6.94423 samples/s, eta: 0:00:40
[2024/08/01 20:51:53] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:51:59] ppocr INFO: epoch: [99/100], global_step: 297, lr: 0.001000, loss: 2.326334, loss_shrink_maps: 1.302818, loss_threshold_maps: 0.753306, loss_binary_maps: 0.253402, avg_reader_cost: 1.53852 s, avg_batch_cost: 1.79637 s, avg_samples: 12.5, ips: 6.95846 samples/s, eta: 0:00:20
[2024/08/01 20:52:01] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:52:07] ppocr INFO: epoch: [100/100], global_step: 300, lr: 0.001000, loss: 2.273604, loss_shrink_maps: 1.267670, loss_threshold_maps: 0.744179, loss_binary_maps: 0.246552, avg_reader_cost: 1.54928 s, avg_batch_cost: 1.81224 s, avg_samples: 12.5, ips: 6.89753 samples/s, eta: 0:00:00

eval model::   0%|          | 0/500 [00:00<?, ?it/s]
eval model::   0%|          | 1/500 [00:00<00:53,  9.36it/s]
eval model::   1%|          | 3/500 [00:00<00:32, 15.25it/s]
eval model::   1%|          | 6/500 [00:00<00:27, 18.05it/s]
eval model::   2%|▏         | 8/500 [00:00<00:28, 17.55it/s]
eval model::   2%|▏         | 11/500 [00:00<00:26, 18.64it/s]
eval model::   3%|▎         | 14/500 [00:00<00:24, 20.18it/s]
eval model::   3%|▎         | 17/500 [00:00<00:22, 21.17it/s]
eval model::   4%|▍         | 20/500 [00:01<00:25, 18.46it/s]
eval model::   5%|▍         | 23/500 [00:01<00:23, 20.72it/s]
eval model::   5%|▌         | 26/500 [00:01<00:23, 19.95it/s]
eval model::   6%|▌         | 29/500 [00:01<00:23, 20.34it/s]
eval model::   6%|▋         | 32/500 [00:01<00:23, 19.91it/s]
eval model::   7%|▋         | 35/500 [00:01<00:24, 18.81it/s]
eval model::   8%|▊         | 38/500 [00:01<00:23, 20.01it/s]
eval model::   8%|▊         | 41/500 [00:02<00:23, 19.83it/s]
eval model::   9%|▉         | 44/500 [00:02<00:23, 19.48it/s]
eval model::   9%|▉         | 46/500 [00:02<00:24, 18.82it/s]
eval model::  10%|▉         | 49/500 [00:02<00:21, 21.04it/s]
eval model::  10%|█         | 52/500 [00:02<00:21, 21.08it/s]
eval model::  11%|█         | 55/500 [00:02<00:21, 20.56it/s]
eval model::  12%|█▏        | 58/500 [00:02<00:22, 19.66it/s]
eval model::  12%|█▏        | 60/500 [00:03<00:23, 19.00it/s]
eval model::  12%|█▏        | 62/500 [00:03<00:26, 16.66it/s]
eval model::  13%|█▎        | 65/500 [00:03<00:23, 18.83it/s]
eval model::  14%|█▎        | 68/500 [00:03<00:20, 20.94it/s]
eval model::  14%|█▍        | 71/500 [00:03<00:20, 21.24it/s]
eval model::  15%|█▍        | 74/500 [00:03<00:21, 19.94it/s]
eval model::  15%|█▌        | 77/500 [00:03<00:22, 18.47it/s]
eval model::  16%|█▌        | 80/500 [00:04<00:20, 20.00it/s]
eval model::  17%|█▋        | 83/500 [00:04<00:19, 20.91it/s]
eval model::  17%|█▋        | 86/500 [00:04<00:20, 19.83it/s]
eval model::  18%|█▊        | 89/500 [00:04<00:22, 18.14it/s]
eval model::  18%|█▊        | 91/500 [00:04<00:23, 17.54it/s]
eval model::  19%|█▊        | 93/500 [00:04<00:23, 16.97it/s]
eval model::  19%|█▉        | 96/500 [00:04<00:21, 18.98it/s]
eval model::  20%|█▉        | 99/500 [00:05<00:20, 19.58it/s]
eval model::  20%|██        | 101/500 [00:05<00:21, 18.16it/s]
eval model::  21%|██        | 104/500 [00:05<00:19, 19.98it/s]
eval model::  22%|██▏       | 108/500 [00:05<00:17, 21.80it/s]
eval model::  22%|██▏       | 111/500 [00:05<00:19, 20.37it/s]
eval model::  23%|██▎       | 114/500 [00:05<00:17, 22.48it/s]
eval model::  23%|██▎       | 117/500 [00:05<00:18, 20.61it/s]
eval model::  24%|██▍       | 120/500 [00:06<00:18, 20.13it/s]
eval model::  25%|██▍       | 123/500 [00:06<00:17, 21.83it/s]
eval model::  25%|██▌       | 126/500 [00:06<00:18, 20.73it/s]
eval model::  26%|██▌       | 129/500 [00:06<00:17, 21.55it/s]
eval model::  26%|██▋       | 132/500 [00:06<00:19, 18.97it/s]
eval model::  27%|██▋       | 135/500 [00:06<00:18, 19.47it/s]
eval model::  28%|██▊       | 138/500 [00:07<00:17, 20.35it/s]
eval model::  28%|██▊       | 141/500 [00:07<00:17, 20.35it/s]
eval model::  29%|██▉       | 144/500 [00:07<00:17, 20.87it/s]
eval model::  29%|██▉       | 147/500 [00:07<00:18, 19.48it/s]
eval model::  30%|███       | 150/500 [00:07<00:16, 21.05it/s]
eval model::  31%|███       | 153/500 [00:07<00:18, 19.21it/s]
eval model::  31%|███       | 156/500 [00:07<00:16, 20.95it/s]
eval model::  32%|███▏      | 159/500 [00:08<00:15, 22.19it/s]
eval model::  32%|███▏      | 162/500 [00:08<00:17, 18.80it/s]
eval model::  33%|███▎      | 165/500 [00:08<00:16, 19.88it/s]
eval model::  34%|███▎      | 168/500 [00:08<00:17, 19.09it/s]
eval model::  34%|███▍      | 171/500 [00:08<00:16, 19.62it/s]
eval model::  35%|███▍      | 174/500 [00:08<00:17, 18.75it/s]
eval model::  35%|███▌      | 176/500 [00:08<00:17, 18.94it/s]
eval model::  36%|███▌      | 179/500 [00:09<00:16, 19.79it/s]
eval model::  36%|███▋      | 182/500 [00:09<00:17, 18.55it/s]
eval model::  37%|███▋      | 185/500 [00:09<00:15, 21.00it/s]
eval model::  38%|███▊      | 188/500 [00:09<00:15, 19.68it/s]
eval model::  38%|███▊      | 191/500 [00:09<00:17, 17.76it/s]
eval model::  39%|███▉      | 194/500 [00:09<00:15, 19.90it/s]
eval model::  39%|███▉      | 197/500 [00:10<00:16, 18.12it/s]
eval model::  40%|████      | 200/500 [00:10<00:16, 18.70it/s]
eval model::  41%|████      | 203/500 [00:10<00:15, 19.45it/s]
eval model::  41%|████      | 206/500 [00:10<00:19, 14.90it/s]
eval model::  42%|████▏     | 208/500 [00:10<00:18, 15.41it/s]
eval model::  42%|████▏     | 210/500 [00:10<00:18, 15.31it/s]
eval model::  43%|████▎     | 213/500 [00:11<00:17, 16.27it/s]
eval model::  43%|████▎     | 215/500 [00:11<00:18, 15.56it/s]
eval model::  43%|████▎     | 217/500 [00:11<00:18, 14.96it/s]
eval model::  44%|████▍     | 220/500 [00:11<00:15, 17.57it/s]
eval model::  44%|████▍     | 222/500 [00:11<00:16, 16.58it/s]
eval model::  45%|████▌     | 226/500 [00:11<00:14, 18.51it/s]
eval model::  46%|████▌     | 229/500 [00:11<00:13, 20.51it/s]
eval model::  46%|████▋     | 232/500 [00:12<00:12, 21.54it/s]
eval model::  47%|████▋     | 235/500 [00:12<00:12, 21.55it/s]
eval model::  48%|████▊     | 238/500 [00:12<00:12, 21.38it/s]
eval model::  48%|████▊     | 241/500 [00:12<00:11, 21.67it/s]
eval model::  49%|████▉     | 244/500 [00:12<00:12, 20.40it/s]
eval model::  49%|████▉     | 247/500 [00:12<00:11, 21.09it/s]
eval model::  50%|█████     | 250/500 [00:12<00:13, 18.99it/s]
eval model::  51%|█████     | 253/500 [00:13<00:12, 20.56it/s]
eval model::  51%|█████     | 256/500 [00:13<00:11, 21.04it/s]
eval model::  52%|█████▏    | 259/500 [00:13<00:10, 22.41it/s]
eval model::  52%|█████▏    | 262/500 [00:13<00:10, 22.52it/s]
eval model::  53%|█████▎    | 265/500 [00:13<00:11, 19.66it/s]
eval model::  54%|█████▎    | 268/500 [00:13<00:11, 20.95it/s]
eval model::  54%|█████▍    | 271/500 [00:13<00:10, 21.36it/s]
eval model::  55%|█████▍    | 274/500 [00:14<00:10, 21.40it/s]
eval model::  55%|█████▌    | 277/500 [00:14<00:11, 20.12it/s]
eval model::  56%|█████▌    | 280/500 [00:14<00:10, 20.48it/s]
eval model::  57%|█████▋    | 283/500 [00:14<00:10, 21.34it/s]
eval model::  57%|█████▋    | 286/500 [00:14<00:14, 14.87it/s]
eval model::  58%|█████▊    | 289/500 [00:14<00:12, 16.49it/s]
eval model::  58%|█████▊    | 292/500 [00:15<00:11, 18.43it/s]
eval model::  59%|█████▉    | 295/500 [00:15<00:11, 17.41it/s]
eval model::  60%|█████▉    | 298/500 [00:15<00:10, 19.56it/s]
eval model::  60%|██████    | 301/500 [00:15<00:10, 19.48it/s]
eval model::  61%|██████    | 304/500 [00:15<00:09, 21.21it/s]
eval model::  61%|██████▏   | 307/500 [00:15<00:08, 22.61it/s]
eval model::  62%|██████▏   | 310/500 [00:15<00:08, 22.56it/s]
eval model::  63%|██████▎   | 313/500 [00:16<00:08, 21.93it/s]
eval model::  63%|██████▎   | 316/500 [00:16<00:09, 20.41it/s]
eval model::  64%|██████▍   | 319/500 [00:16<00:08, 20.40it/s]
eval model::  64%|██████▍   | 322/500 [00:16<00:09, 19.64it/s]
eval model::  65%|██████▌   | 325/500 [00:16<00:08, 21.45it/s]
eval model::  66%|██████▌   | 328/500 [00:16<00:08, 19.69it/s]
eval model::  66%|██████▌   | 331/500 [00:16<00:08, 19.48it/s]
eval model::  67%|██████▋   | 334/500 [00:17<00:07, 21.35it/s]
eval model::  67%|██████▋   | 337/500 [00:17<00:07, 21.00it/s]
eval model::  68%|██████▊   | 340/500 [00:17<00:09, 16.27it/s]
eval model::  69%|██████▊   | 343/500 [00:17<00:08, 18.69it/s]
eval model::  69%|██████▉   | 346/500 [00:17<00:07, 20.09it/s]
eval model::  70%|██████▉   | 349/500 [00:17<00:06, 21.72it/s]
eval model::  70%|███████   | 352/500 [00:17<00:06, 22.33it/s]
eval model::  71%|███████   | 355/500 [00:18<00:06, 21.75it/s]
eval model::  72%|███████▏  | 358/500 [00:18<00:06, 21.29it/s]
eval model::  72%|███████▏  | 361/500 [00:18<00:06, 20.11it/s]
eval model::  73%|███████▎  | 364/500 [00:18<00:07, 18.82it/s]
eval model::  73%|███████▎  | 367/500 [00:18<00:06, 19.41it/s]
eval model::  74%|███████▍  | 370/500 [00:18<00:06, 20.05it/s]
eval model::  75%|███████▍  | 373/500 [00:19<00:06, 20.53it/s]
eval model::  75%|███████▌  | 376/500 [00:19<00:05, 22.20it/s]
eval model::  76%|███████▌  | 379/500 [00:19<00:05, 20.24it/s]
eval model::  76%|███████▋  | 382/500 [00:19<00:05, 20.37it/s]
eval model::  77%|███████▋  | 385/500 [00:19<00:05, 19.60it/s]
eval model::  78%|███████▊  | 388/500 [00:19<00:07, 15.70it/s]
eval model::  78%|███████▊  | 391/500 [00:20<00:05, 18.18it/s]
eval model::  79%|███████▉  | 394/500 [00:20<00:05, 19.40it/s]
eval model::  79%|███████▉  | 397/500 [00:20<00:05, 20.14it/s]
eval model::  80%|████████  | 400/500 [00:20<00:04, 20.93it/s]
eval model::  81%|████████  | 403/500 [00:20<00:04, 21.31it/s]
eval model::  81%|████████  | 406/500 [00:20<00:04, 21.15it/s]
eval model::  82%|████████▏ | 409/500 [00:20<00:04, 22.16it/s]
eval model::  82%|████████▏ | 412/500 [00:20<00:04, 20.64it/s]
eval model::  83%|████████▎ | 415/500 [00:21<00:04, 19.84it/s]
eval model::  84%|████████▎ | 418/500 [00:21<00:04, 18.55it/s]
eval model::  84%|████████▍ | 420/500 [00:21<00:04, 17.11it/s]
eval model::  85%|████████▍ | 423/500 [00:21<00:04, 18.73it/s]
eval model::  85%|████████▌ | 425/500 [00:21<00:04, 17.51it/s]
eval model::  86%|████████▌ | 428/500 [00:21<00:03, 19.34it/s]
eval model::  86%|████████▌ | 431/500 [00:22<00:03, 19.97it/s]
eval model::  87%|████████▋ | 434/500 [00:22<00:03, 21.49it/s]
eval model::  87%|████████▋ | 437/500 [00:22<00:02, 22.49it/s]
eval model::  88%|████████▊ | 440/500 [00:22<00:02, 21.00it/s]
eval model::  89%|████████▉ | 444/500 [00:22<00:02, 22.56it/s]
eval model::  89%|████████▉ | 447/500 [00:22<00:02, 23.20it/s]
eval model::  90%|█████████ | 450/500 [00:22<00:02, 20.78it/s]
eval model::  91%|█████████ | 453/500 [00:23<00:02, 20.93it/s]
eval model::  91%|█████████ | 456/500 [00:23<00:02, 19.49it/s]
eval model::  92%|█████████▏| 458/500 [00:23<00:02, 18.24it/s]
eval model::  92%|█████████▏| 460/500 [00:23<00:02, 18.57it/s]
eval model::  92%|█████████▏| 462/500 [00:23<00:02, 17.77it/s]
eval model::  93%|█████████▎| 465/500 [00:23<00:02, 16.89it/s]
eval model::  94%|█████████▎| 468/500 [00:23<00:01, 18.61it/s]
eval model::  94%|█████████▍| 471/500 [00:24<00:01, 19.92it/s]
eval model::  95%|█████████▍| 474/500 [00:24<00:01, 19.25it/s]
eval model::  95%|█████████▌| 477/500 [00:24<00:01, 21.15it/s]
eval model::  96%|█████████▌| 480/500 [00:24<00:00, 23.08it/s]
eval model::  97%|█████████▋| 483/500 [00:24<00:00, 22.46it/s]
eval model::  97%|█████████▋| 486/500 [00:24<00:00, 20.27it/s]
eval model::  98%|█████████▊| 489/500 [00:24<00:00, 20.15it/s]
eval model::  98%|█████████▊| 492/500 [00:25<00:00, 19.40it/s]
eval model::  99%|█████████▉| 494/500 [00:25<00:00, 19.19it/s]
eval model::  99%|█████████▉| 496/500 [00:25<00:00, 19.32it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 23.12it/s]
eval model:: 100%|██████████| 500/500 [00:25<00:00, 19.72it/s]
[2024/08/01 20:52:33] ppocr INFO: cur metric, precision: 0.6058032554847842, recall: 0.4121328839672605, hmean: 0.4905444126074499, fps: 42.257599655773625
[2024/08/01 20:52:33] ppocr INFO: save best model is to ./output/db_mv3/best_accuracy
[2024/08/01 20:52:33] ppocr INFO: best metric, hmean: 0.4905444126074499, precision: 0.6058032554847842, recall: 0.4121328839672605, fps: 42.257599655773625, best_epoch: 100
[2024/08/01 20:52:33] ppocr INFO: save model in ./output/db_mv3/latest
[2024/08/01 20:52:33] ppocr INFO: best metric, hmean: 0.4905444126074499, precision: 0.6058032554847842, recall: 0.4121328839672605, fps: 42.257599655773625, best_epoch: 100
I0801 20:52:34.822872 174058 tcp_store.cc:289] receive shutdown event and so quit from MasterDaemon run loop
[2024/08/01 21:50:46] ppocr INFO: Architecture : 
[2024/08/01 21:50:46] ppocr INFO:     Backbone : 
[2024/08/01 21:50:46] ppocr INFO:         model_name : large
[2024/08/01 21:50:46] ppocr INFO:         name : MobileNetV3
[2024/08/01 21:50:46] ppocr INFO:         scale : 0.5
[2024/08/01 21:50:46] ppocr INFO:     Head : 
[2024/08/01 21:50:46] ppocr INFO:         k : 50
[2024/08/01 21:50:46] ppocr INFO:         name : DBHead
[2024/08/01 21:50:46] ppocr INFO:     Neck : 
[2024/08/01 21:50:46] ppocr INFO:         name : DBFPN
[2024/08/01 21:50:46] ppocr INFO:         out_channels : 256
[2024/08/01 21:50:46] ppocr INFO:     Transform : None
[2024/08/01 21:50:46] ppocr INFO:     algorithm : DB
[2024/08/01 21:50:46] ppocr INFO:     model_type : det
[2024/08/01 21:50:46] ppocr INFO: Eval : 
[2024/08/01 21:50:46] ppocr INFO:     dataset : 
[2024/08/01 21:50:46] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/08/01 21:50:46] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
[2024/08/01 21:50:46] ppocr INFO:         name : SimpleDataSet
[2024/08/01 21:50:46] ppocr INFO:         transforms : 
[2024/08/01 21:50:46] ppocr INFO:             DecodeImage : 
[2024/08/01 21:50:46] ppocr INFO:                 channel_first : False
[2024/08/01 21:50:46] ppocr INFO:                 img_mode : BGR
[2024/08/01 21:50:46] ppocr INFO:             DetLabelEncode : None
[2024/08/01 21:50:46] ppocr INFO:             DetResizeForTest : 
[2024/08/01 21:50:46] ppocr INFO:                 image_shape : [736, 1280]
[2024/08/01 21:50:46] ppocr INFO:             NormalizeImage : 
[2024/08/01 21:50:46] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/08/01 21:50:46] ppocr INFO:                 order : hwc
[2024/08/01 21:50:46] ppocr INFO:                 scale : 1./255.
[2024/08/01 21:50:46] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/08/01 21:50:46] ppocr INFO:             ToCHWImage : None
[2024/08/01 21:50:46] ppocr INFO:             KeepKeys : 
[2024/08/01 21:50:46] ppocr INFO:                 keep_keys : ['image', 'shape', 'polys', 'ignore_tags']
[2024/08/01 21:50:46] ppocr INFO:     loader : 
[2024/08/01 21:50:46] ppocr INFO:         batch_size_per_card : 1
[2024/08/01 21:50:46] ppocr INFO:         drop_last : False
[2024/08/01 21:50:46] ppocr INFO:         num_workers : 0
[2024/08/01 21:50:46] ppocr INFO:         shuffle : False
[2024/08/01 21:50:46] ppocr INFO:         use_shared_memory : True
[2024/08/01 21:50:46] ppocr INFO: Global : 
[2024/08/01 21:50:46] ppocr INFO:     cal_metric_during_train : False
[2024/08/01 21:50:46] ppocr INFO:     checkpoints : None
[2024/08/01 21:50:46] ppocr INFO:     distributed : True
[2024/08/01 21:50:46] ppocr INFO:     epoch_num : 100
[2024/08/01 21:50:46] ppocr INFO:     eval_batch_step : [0, 60]
[2024/08/01 21:50:46] ppocr INFO:     infer_img : doc/imgs_en/img_10.jpg
[2024/08/01 21:50:46] ppocr INFO:     log_smooth_window : 20
[2024/08/01 21:50:46] ppocr INFO:     pretrained_model : ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/08/01 21:50:46] ppocr INFO:     print_batch_step : 10
[2024/08/01 21:50:46] ppocr INFO:     save_epoch_step : 1200
[2024/08/01 21:50:46] ppocr INFO:     save_inference_dir : None
[2024/08/01 21:50:46] ppocr INFO:     save_model_dir : ./output/db_mv3/
[2024/08/01 21:50:46] ppocr INFO:     save_res_path : ./output/det_db/predicts_db.txt
[2024/08/01 21:50:46] ppocr INFO:     use_gpu : True
[2024/08/01 21:50:46] ppocr INFO:     use_visualdl : False
[2024/08/01 21:50:46] ppocr INFO:     use_xpu : False
[2024/08/01 21:50:46] ppocr INFO: Loss : 
[2024/08/01 21:50:46] ppocr INFO:     alpha : 5
[2024/08/01 21:50:46] ppocr INFO:     balance_loss : True
[2024/08/01 21:50:46] ppocr INFO:     beta : 10
[2024/08/01 21:50:46] ppocr INFO:     main_loss_type : DiceLoss
[2024/08/01 21:50:46] ppocr INFO:     name : DBLoss
[2024/08/01 21:50:46] ppocr INFO:     ohem_ratio : 3
[2024/08/01 21:50:46] ppocr INFO: Metric : 
[2024/08/01 21:50:46] ppocr INFO:     main_indicator : hmean
[2024/08/01 21:50:46] ppocr INFO:     name : DetMetric
[2024/08/01 21:50:46] ppocr INFO: Optimizer : 
[2024/08/01 21:50:46] ppocr INFO:     beta1 : 0.9
[2024/08/01 21:50:46] ppocr INFO:     beta2 : 0.999
[2024/08/01 21:50:46] ppocr INFO:     lr : 
[2024/08/01 21:50:46] ppocr INFO:         learning_rate : 0.001
[2024/08/01 21:50:46] ppocr INFO:     name : Adam
[2024/08/01 21:50:46] ppocr INFO:     regularizer : 
[2024/08/01 21:50:46] ppocr INFO:         factor : 0
[2024/08/01 21:50:46] ppocr INFO:         name : L2
[2024/08/01 21:50:46] ppocr INFO: PostProcess : 
[2024/08/01 21:50:46] ppocr INFO:     box_thresh : 0.6
[2024/08/01 21:50:46] ppocr INFO:     max_candidates : 1000
[2024/08/01 21:50:46] ppocr INFO:     name : DBPostProcess
[2024/08/01 21:50:46] ppocr INFO:     thresh : 0.3
[2024/08/01 21:50:46] ppocr INFO:     unclip_ratio : 1.5
[2024/08/01 21:50:46] ppocr INFO: Train : 
[2024/08/01 21:50:46] ppocr INFO:     dataset : 
[2024/08/01 21:50:46] ppocr INFO:         data_dir : /datasets/icdar2015/text_localization/
[2024/08/01 21:50:46] ppocr INFO:         label_file_list : ['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/08/01 21:50:46] ppocr INFO:         name : SimpleDataSet
[2024/08/01 21:50:46] ppocr INFO:         ratio_list : [1.0]
[2024/08/01 21:50:46] ppocr INFO:         transforms : 
[2024/08/01 21:50:46] ppocr INFO:             DecodeImage : 
[2024/08/01 21:50:46] ppocr INFO:                 channel_first : False
[2024/08/01 21:50:46] ppocr INFO:                 img_mode : BGR
[2024/08/01 21:50:46] ppocr INFO:             DetLabelEncode : None
[2024/08/01 21:50:46] ppocr INFO:             IaaAugment : 
[2024/08/01 21:50:46] ppocr INFO:                 augmenter_args : 
[2024/08/01 21:50:46] ppocr INFO:                     args : 
[2024/08/01 21:50:46] ppocr INFO:                         p : 0.5
[2024/08/01 21:50:46] ppocr INFO:                     type : Fliplr
[2024/08/01 21:50:46] ppocr INFO:                     args : 
[2024/08/01 21:50:46] ppocr INFO:                         rotate : [-10, 10]
[2024/08/01 21:50:46] ppocr INFO:                     type : Affine
[2024/08/01 21:50:46] ppocr INFO:                     args : 
[2024/08/01 21:50:46] ppocr INFO:                         size : [0.5, 3]
[2024/08/01 21:50:46] ppocr INFO:                     type : Resize
[2024/08/01 21:50:46] ppocr INFO:             EastRandomCropData : 
[2024/08/01 21:50:46] ppocr INFO:                 keep_ratio : True
[2024/08/01 21:50:46] ppocr INFO:                 max_tries : 50
[2024/08/01 21:50:46] ppocr INFO:                 size : [640, 640]
[2024/08/01 21:50:46] ppocr INFO:             MakeBorderMap : 
[2024/08/01 21:50:46] ppocr INFO:                 shrink_ratio : 0.4
[2024/08/01 21:50:46] ppocr INFO:                 thresh_max : 0.7
[2024/08/01 21:50:46] ppocr INFO:                 thresh_min : 0.3
[2024/08/01 21:50:46] ppocr INFO:             MakeShrinkMap : 
[2024/08/01 21:50:46] ppocr INFO:                 min_text_size : 8
[2024/08/01 21:50:46] ppocr INFO:                 shrink_ratio : 0.4
[2024/08/01 21:50:46] ppocr INFO:             NormalizeImage : 
[2024/08/01 21:50:46] ppocr INFO:                 mean : [0.485, 0.456, 0.406]
[2024/08/01 21:50:46] ppocr INFO:                 order : hwc
[2024/08/01 21:50:46] ppocr INFO:                 scale : 1./255.
[2024/08/01 21:50:46] ppocr INFO:                 std : [0.229, 0.224, 0.225]
[2024/08/01 21:50:46] ppocr INFO:             ToCHWImage : None
[2024/08/01 21:50:46] ppocr INFO:             KeepKeys : 
[2024/08/01 21:50:46] ppocr INFO:                 keep_keys : ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask']
[2024/08/01 21:50:46] ppocr INFO:     loader : 
[2024/08/01 21:50:46] ppocr INFO:         batch_size_per_card : 48
[2024/08/01 21:50:46] ppocr INFO:         drop_last : False
[2024/08/01 21:50:46] ppocr INFO:         num_workers : 8
[2024/08/01 21:50:46] ppocr INFO:         shuffle : True
[2024/08/01 21:50:46] ppocr INFO:         use_shared_memory : True
[2024/08/01 21:50:46] ppocr INFO: profiler_options : None
[2024/08/01 21:50:46] ppocr INFO: train with paddle 2.5.2 and device Place(gpu:0)
======================= Modified FLAGS detected =======================
FLAGS(name='FLAGS_selected_gpus', current_value='0', default_value='')
FLAGS(name='FLAGS_cudnn_batchnorm_spatial_persistent', current_value=True, default_value=False)
=======================================================================
I0801 21:50:46.192216 227381 tcp_utils.cc:181] The server starts to listen on IP_ANY:55563
I0801 21:50:46.192469 227381 tcp_utils.cc:130] Successfully connected to 10.8.145.246:55563
I0801 21:50:49.315490 227381 process_group_nccl.cc:120] ProcessGroupNCCL pg_timeout_ 1800000
[2024/08/01 21:50:49] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/train_icdar2015_label.txt']
[2024/08/01 21:50:49] ppocr INFO: Initialize indexs of datasets:['/datasets/icdar2015/text_localization/test_icdar2015_label.txt']
W0801 21:50:49.327540 227381 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 90.2, Driver API Version: 50724.2, Runtime API Version: 50724.2
[2024/08/01 21:50:50] ppocr INFO: load pretrain successful from ./pretrain_models/MobileNetV3_large_x0_5_pretrained
[2024/08/01 21:50:50] ppocr INFO: train dataloader has 3 iters
[2024/08/01 21:50:50] ppocr INFO: valid dataloader has 500 iters
[2024/08/01 21:50:50] ppocr INFO: During the training process, after the 0th iteration, an evaluation is run every 60 iterations
[2024/08/01 21:51:13] ppocr INFO: epoch: [1/100], global_step: 3, lr: 0.001000, loss: 9.386772, loss_shrink_maps: 4.923290, loss_threshold_maps: 3.477082, loss_binary_maps: 0.985261, avg_reader_cost: 5.99922 s, avg_batch_cost: 6.59723 s, avg_samples: 12.5, ips: 1.89474 samples/s, eta: 1:48:51
[2024/08/01 21:51:14] ppocr INFO: save model in ./output/db_mv3/latest