Merge remote-tracking branch 'upstream/dygraph' into dy3

configs/det/bak/det_r50_vd_db.yml

-Global:
-  use_gpu: true
-  epoch_num: 1200
-  log_smooth_window: 20
-  print_batch_step: 2
-  save_model_dir: ./output/det_r50_vd/
-  save_epoch_step: 1200
-  # evaluation is run every 5000 iterations after the 4000th iteration
-  eval_batch_step: 8
-  # if pretrained_model is saved in static mode, load_static_weights must set to True
-  load_static_weights: True
-  cal_metric_during_train: False
-  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/
-  checkpoints:
-  save_inference_dir:
-  use_visualdl: True
-  infer_img: doc/imgs_en/img_10.jpg
-  save_res_path: ./output/det_db/predicts_db.txt
-
-Optimizer:
-  name: Adam
-  beta1: 0.9
-  beta2: 0.999
-  learning_rate:
-    lr: 0.001
-  regularizer:
-    name: 'L2'
-    factor: 0
-
-Architecture:
-  type: det
-  algorithm: DB
-  Transform:
-  Backbone:
-    name: ResNet
-    layers: 50
-  Neck:
-    name: FPN
-    out_channels: 256
-  Head:
-    name: DBHead
-    k: 50
-
-Loss:
-  name: DBLoss
-  balance_loss: true
-  main_loss_type: DiceLoss
-  alpha: 5
-  beta: 10
-  ohem_ratio: 3
-
-PostProcess:
-  name: DBPostProcess
-  thresh: 0.3
-  box_thresh: 0.6
-  max_candidates: 1000
-  unclip_ratio: 1.5
-
-Metric:
-  name: DetMetric
-  main_indicator: hmean
-
-TRAIN:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./detection/
-    file_list:
-      - ./detection/train_icdar2015_label.txt # dataset1
-    ratio_list: [1.0]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - DetLabelEncode: # Class handling label
-      - IaaAugment:
-          augmenter_args:
-            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
-            - { 'type': Affine, 'args': { 'rotate': [ -10,10 ] } }
-            - { 'type': Resize,'args': { 'size': [ 0.5,3 ] } }
-      - EastRandomCropData:
-          size: [ 640,640 ]
-          max_tries: 50
-          keep_ratio: true
-      - MakeBorderMap:
-          shrink_ratio: 0.4
-          thresh_min: 0.3
-          thresh_max: 0.7
-      - MakeShrinkMap:
-          shrink_ratio: 0.4
-          min_text_size: 8
-      - NormalizeImage:
-          scale: 1./255.
-          mean: [ 0.485, 0.456, 0.406 ]
-          std: [ 0.229, 0.224, 0.225 ]
-          order: 'hwc'
-      - ToCHWImage:
-      - keepKeys:
-          keep_keys: ['image','threshold_map','threshold_mask','shrink_map','shrink_mask'] # dataloader will return list in this order
-  loader:
-    shuffle: True
-    drop_last: False
-    batch_size: 16
-    num_workers: 8
-
-EVAL:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./detection/
-    file_list:
-      - ./detection/test_icdar2015_label.txt
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - DetLabelEncode: # Class handling label
-      - DetResizeForTest:
-          image_shape: [736,1280]
-      - NormalizeImage:
-          scale: 1./255.
-          mean: [ 0.485, 0.456, 0.406 ]
-          std: [ 0.229, 0.224, 0.225 ]
-          order: 'hwc'
-      - ToCHWImage:
-      - keepKeys:
-          keep_keys: ['image','shape','polys','ignore_tags']
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size: 1 # must be 1
-    num_workers: 8
\ No newline at end of file

configs/rec/bak/rec_mv3_none_bilstm_ctc_simple.yml

-Global:
-  use_gpu: false
-  epoch_num: 500
-  log_smooth_window: 20
-  print_batch_step: 10
-  save_model_dir: ./output/rec/mv3_none_bilstm_ctc/
-  save_epoch_step: 500
-  # evaluation is run every 5000 iterations after the 4000th iteration
-  eval_batch_step: 127
-  # if pretrained_model is saved in static mode, load_static_weights must set to True
-  load_static_weights: True
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  save_inference_dir:
-  use_visualdl: False
-  infer_img: doc/imgs_words/ch/word_1.jpg
-  # for data or label process
-  max_text_length: 80
-  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
-  character_type: 'ch'
-  use_space_char: False
-  infer_mode: False
-  use_tps: False
-
-
-Optimizer:
-  name: Adam
-  beta1: 0.9
-  beta2: 0.999
-  learning_rate:
-    lr: 0.001
-  regularizer:
-    name: 'L2'
-    factor: 0.00001
-
-Architecture:
-  type: rec
-  algorithm: CRNN
-  Transform:
-  Backbone:
-    name: MobileNetV3
-    scale: 0.5
-    model_name: small
-    small_stride: [ 1, 2, 2, 2 ]
-  Neck:
-    name: SequenceEncoder
-    encoder_type: fc
-    hidden_size: 96
-  Head:
-    name: CTC
-    fc_decay: 0.00001
-
-Loss:
-  name: CTCLoss
-
-PostProcess:
-  name: CTCLabelDecode
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-
-TRAIN:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./rec
-    file_list:
-      - ./rec/train.txt # dataset1
-    ratio_list: [ 0.4,0.6 ]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - CTCLabelEncode: # Class handling label
-      - RecAug:
-      - RecResizeImg:
-          image_shape: [ 3,32,320 ]
-      - keepKeys:
-          keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
-  loader:
-    batch_size: 256
-    shuffle: True
-    drop_last: True
-    num_workers: 8
-
-EVAL:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./rec
-    file_list:
-      - ./rec/val.txt
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - CTCLabelEncode: # Class handling label
-      - RecResizeImg:
-          image_shape: [ 3,32,320 ]
-      - keepKeys:
-          keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size: 256
-    num_workers: 8

configs/rec/bak/rec_r34_vd_none_bilstm_ctc.yml

-Global:
-  use_gpu: false
-  epoch_num: 500
-  log_smooth_window: 20
-  print_batch_step: 10
-  save_model_dir: ./output/rec/res34_none_bilstm_ctc/
-  save_epoch_step: 500
-  # evaluation is run every 5000 iterations after the 4000th iteration
-  eval_batch_step: 127
-  # if pretrained_model is saved in static mode, load_static_weights must set to True
-  load_static_weights: True
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  save_inference_dir:
-  use_visualdl: False
-  infer_img: doc/imgs_words/ch/word_1.jpg
-  # for data or label process
-  max_text_length: 80
-  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
-  character_type: 'ch'
-  use_space_char: False
-  infer_mode: False
-  use_tps: False
-
-
-Optimizer:
-  name: Adam
-  beta1: 0.9
-  beta2: 0.999
-  learning_rate:
-    lr: 0.001
-  regularizer:
-    name: 'L2'
-    factor: 0.00001
-
-Architecture:
-  type: rec
-  algorithm: CRNN
-  Transform:
-  Backbone:
-    name: ResNet
-    layers: 34
-  Neck:
-    name: SequenceEncoder
-    encoder_type: fc
-    hidden_size: 96
-  Head:
-    name: CTC
-    fc_decay: 0.00001
-
-Loss:
-  name: CTCLoss
-
-PostProcess:
-  name: CTCLabelDecode
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-
-TRAIN:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./rec
-    file_list:
-      - ./rec/train.txt # dataset1
-    ratio_list: [ 0.4,0.6 ]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - CTCLabelEncode: # Class handling label
-      - RecAug:
-      - RecResizeImg:
-          image_shape: [ 3,32,320 ]
-      - keepKeys:
-          keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
-  loader:
-    batch_size: 256
-    shuffle: True
-    drop_last: True
-    num_workers: 8
-
-EVAL:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./rec
-    file_list:
-      - ./rec/val.txt
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - CTCLabelEncode: # Class handling label
-      - RecResizeImg:
-          image_shape: [ 3,32,320 ]
-      - keepKeys:
-          keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size: 256
-    num_workers: 8

configs/rec/bak/rec_r34_vd_none_none_ctc.yml

-Global:
-  use_gpu: false
-  epoch_num: 500
-  log_smooth_window: 20
-  print_batch_step: 10
-  save_model_dir: ./output/rec/res34_none_none_ctc/
-  save_epoch_step: 500
-  # evaluation is run every 5000 iterations after the 4000th iteration
-  eval_batch_step: 127
-  # if pretrained_model is saved in static mode, load_static_weights must set to True
-  load_static_weights: True
-  cal_metric_during_train: True
-  pretrained_model:
-  checkpoints:
-  save_inference_dir:
-  use_visualdl: False
-  infer_img: doc/imgs_words/ch/word_1.jpg
-  # for data or label process
-  max_text_length: 80
-  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
-  character_type: 'ch'
-  use_space_char: False
-  infer_mode: False
-  use_tps: False
-
-
-Optimizer:
-  name: Adam
-  beta1: 0.9
-  beta2: 0.999
-  learning_rate:
-    lr: 0.001
-  regularizer:
-    name: 'L2'
-    factor: 0.00001
-
-Architecture:
-  type: rec
-  algorithm: CRNN
-  Transform:
-  Backbone:
-    name: ResNet
-    layers: 34
-  Neck:
-    name: SequenceEncoder
-    encoder_type: reshape
-  Head:
-    name: CTC
-    fc_decay: 0.00001
-
-Loss:
-  name: CTCLoss
-
-PostProcess:
-  name: CTCLabelDecode
-
-Metric:
-  name: RecMetric
-  main_indicator: acc
-
-TRAIN:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./rec
-    file_list:
-      - ./rec/train.txt # dataset1
-    ratio_list: [ 0.4,0.6 ]
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - CTCLabelEncode: # Class handling label
-      - RecAug:
-      - RecResizeImg:
-          image_shape: [ 3,32,320 ]
-      - keepKeys:
-          keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
-  loader:
-    batch_size: 256
-    shuffle: True
-    drop_last: True
-    num_workers: 8
-
-EVAL:
-  dataset:
-    name: SimpleDataSet
-    data_dir: ./rec
-    file_list:
-      - ./rec/val.txt
-    transforms:
-      - DecodeImage: # load image
-          img_mode: BGR
-          channel_first: False
-      - CTCLabelEncode: # Class handling label
-      - RecResizeImg:
-          image_shape: [ 3,32,320 ]
-      - keepKeys:
-          keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
-  loader:
-    shuffle: False
-    drop_last: False
-    batch_size: 256
-    num_workers: 8

doc/doc_ch/inference.md

@@ -41,7 +41,7 @@ inference 模型（`paddle.jit.save`保存的模型）
 
 下载超轻量级中文检测模型：
 ```
-wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_det_train.tar -C ./ch_lite/
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_det_train.tar -C ./ch_lite/
 ```
 上述模型是以MobileNetV3为backbone训练的DB算法，将训练好的模型转换成inference模型只需要运行如下命令：
 ```
@@ -51,9 +51,9 @@ wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_det_train.tar
 # Global.load_static_weights 参数需要设置为 False。
 # Global.save_inference_dir参数设置转换的模型将保存的地址。
 
-python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db/
+python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db/
 ```
-转inference模型时，使用的配置文件和训练时使用的配置文件相同。另外，还需要设置配置文件中的`Global.checkpoints`参数，其指向训练中保存的模型参数文件。
+转inference模型时，使用的配置文件和训练时使用的配置文件相同。另外，还需要设置配置文件中的`Global.pretrained_model`参数，其指向训练中保存的模型参数文件。
 转换成功后，在模型保存目录下有三个文件：
 ```
 inference/det_db/
@@ -67,7 +67,7 @@ inference/det_db/
 
 下载超轻量中文识别模型：
 ```
-wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_rec_train.tar -C ./ch_lite/
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_rec_train.tar -C ./ch_lite/
 ```
 
 识别模型转inference模型与检测的方式相同，如下：
@@ -78,7 +78,7 @@ wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_rec_train.tar
 # Global.load_static_weights 参数需要设置为 False。
 # Global.save_inference_dir参数设置转换的模型将保存的地址。
 
-python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn/
+python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn/
 ```
 
 **注意：**如果您是在自己的数据集上训练的模型，并且调整了中文字符的字典文件，请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。
@@ -96,7 +96,7 @@ python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_trai
 
 下载方向分类模型：
 ```
-wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/
 ```
 
 方向分类模型转inference模型与检测的方式相同，如下：
@@ -107,7 +107,7 @@ wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar
 # Global.load_static_weights 参数需要设置为 False。
 # Global.save_inference_dir参数设置转换的模型将保存的地址。
 
-python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/cls/
+python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/cls/
 ```
 
 转换成功后，在目录下有三个文件：
@@ -152,10 +152,10 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_di
 <a name="DB文本检测模型推理"></a>
 ### 2. DB文本检测模型推理
 
-首先将DB文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（[模型下载地址](link))，可以使用如下命令进行转换：
+首先将DB文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar) )，可以使用如下命令进行转换：
 
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.checkpoints=./det_r50_vd_db_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db
+python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db
 ```
 
 DB文本检测模型推理，可以执行如下命令：
@@ -173,10 +173,10 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs_en/img_10.jpg" --det_
 <a name="EAST文本检测模型推理"></a>
 ### 3. EAST文本检测模型推理
 
-首先将EAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（[模型下载地址](link))，可以使用如下命令进行转换：
+首先将EAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例（ [模型下载地址 (coming soon)](link) )，可以使用如下命令进行转换：
 
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.checkpoints=./det_r50_vd_east_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east
+python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east
 ```
 
 **EAST文本检测模型推理，需要设置参数`--det_algorithm="EAST"`**，可以执行如下命令：
@@ -194,9 +194,9 @@ python3 tools/infer/predict_det.py --det_algorithm="EAST" --image_dir="./doc/img
 <a name="SAST文本检测模型推理"></a>
 ### 4. SAST文本检测模型推理
 #### (1). 四边形文本检测模型（ICDAR2015）  
-首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例([模型下载地址](link))，可以使用如下命令进行转换：
+首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在ICDAR2015英文数据集训练的模型为例([模型下载地址(coming soon)](link))，可以使用如下命令进行转换：
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.checkpoints=./det_r50_vd_sast_icdar15_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15
+python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15
 
 ```
 **SAST文本检测模型推理，需要设置参数`--det_algorithm="SAST"`**，可以执行如下命令：
@@ -208,10 +208,10 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
 ![](../imgs_results/det_res_img_10_sast.jpg)
 
 #### (2). 弯曲文本检测模型（Total-Text）  
-首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在Total-Text英文数据集训练的模型为例（[模型下载地址](link))，可以使用如下命令进行转换：
+首先将SAST文本检测训练过程中保存的模型，转换成inference model。以基于Resnet50_vd骨干网络，在Total-Text英文数据集训练的模型为例（[模型下载地址(coming soon)](link))，可以使用如下命令进行转换：
 
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.checkpoints=./det_r50_vd_sast_totaltext_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt
+python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt
 
 ```
 
@@ -254,10 +254,10 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['实力活力', 0.89552695]
 我们以 CRNN 为例，介绍基于CTC损失的识别模型推理。 Rosetta 使用方式类似，不用设置识别算法参数rec_algorithm。
 
 首先将 Rosetta 文本识别训练过程中保存的模型，转换成inference model。以基于Resnet34_vd骨干网络，使用MJSynth和SynthText两个英文文本识别合成数据集训练
-的模型为例（[模型下载地址](link))，可以使用如下命令进行转换：
+的模型为例（ [模型下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar) )，可以使用如下命令进行转换：
 
 ```
-python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o Global.checkpoints=./rec_r34_vd_none_bilstm_ctc_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn
+python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn
 
 ```
 
@@ -313,9 +313,9 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" -
 
 执行命令后，上图的预测结果为：
 ``` text
-2020-09-19 16:15:05,076-INFO: 	 index: [205 206  38  39]
-2020-09-19 16:15:05,077-INFO: 	 word : 바탕으로
-2020-09-19 16:15:05,077-INFO: 	 score: 0.9171358942985535
+2020-09-19 16:15:05,076-INFO:      index: [205 206  38  39]
+2020-09-19 16:15:05,077-INFO:      word : 바탕으로
+2020-09-19 16:15:05,077-INFO:      score: 0.9171358942985535
 ```
 
 <a name="方向分类模型推理"></a>
@@ -337,8 +337,7 @@ python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg"
 执行命令后，上面图像的预测结果（分类的方向和得分）会打印到屏幕上，示例如下：
 
 ```
-infer_img: doc/imgs_words/ch/word_1.jpg
-     result: ('0', 0.9998784)
+Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982]
 ```
 
 <a name="文本检测、方向分类和文字识别串联推理"></a>

doc/doc_ch/quickstart.md

@@ -9,12 +9,12 @@
 
 ## 2.inference模型下载
 
-* 移动端和服务器端的检测与识别模型如下，更多模型下载（包括多语言），可以参考[PP-OCR v1.1 系列模型下载](../doc_ch/models_list.md)
+* 移动端和服务器端的检测与识别模型如下，更多模型下载（包括多语言），可以参考[PP-OCR v2.0 系列模型下载](../doc_ch/models_list.md)
 
 | 模型简介     | 模型名称     |推荐场景          | 检测模型 | 方向分类器 | 识别模型 |
 | ------------ | --------------- | ----------------|---- | ---------- | -------- |
-| 中英文超轻量OCR模型（xM） |  |移动端&服务器端|[推理模型](link) / [预训练模型](link)|[推理模型]({}) / [预训练模型]({}) |[推理模型]({}) / [预训练模型]({})      |
-| 中英文通用OCR模型（xM）   | |服务器端 |[推理模型]({}) / [预训练模型]({})          |[推理模型]({}) / [预训练模型]({})    |[推理模型]({}) / [预训练模型]({}})  |
+| 中英文超轻量OCR模型（8.1M） | ch_ppocr_mobile_v2.0_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar)      |
+| 中英文通用OCR模型（155.1M）   | ch_ppocr_server_v2.0_xx |服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)          |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar)    |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar)  |
 
 
 * windows 环境下如果没有安装wget,下载模型时可将链接复制到浏览器中下载，并解压放置在相应目录下
@@ -37,28 +37,29 @@ cd ..
 ```
 mkdir inference && cd inference
 # 下载超轻量级中文OCR模型的检测模型并解压
-wget {} && tar xf ch_ppocr_mobile_v1.1_det_infer.tar
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
 # 下载超轻量级中文OCR模型的识别模型并解压
-wget {} && tar xf ch_ppocr_mobile_v1.1_rec_infer.tar
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
 # 下载超轻量级中文OCR模型的文本方向分类器模型并解压
-wget {} && tar xf ch_ppocr_mobile_v1.1_cls_infer.tar
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
 cd ..
 ```
 
 解压完毕后应有如下文件结构：
 
 ```
-|-inference
-    |-ch_ppocr_mobile_v1.1_det_infer
-        |- model
-        |- params
-    |-ch_ppocr_mobile_v1.1_rec_infer
-        |- model
-        |- params
-    |-ch_ppocr_mobile-v1.1_cls_infer
-        |- model
-        |- params
-    ...
+├── ch_ppocr_mobile_v2.0_cls_infer
+│   ├── inference.pdiparams
+│   ├── inference.pdiparams.info
+│   └── inference.pdmodel
+├── ch_ppocr_mobile_v2.0_det_infer
+│   ├── inference.pdiparams
+│   ├── inference.pdiparams.info
+│   └── inference.pdmodel
+├── ch_ppocr_mobile_v2.0_rec_infer
+    ├── inference.pdiparams
+    ├── inference.pdiparams.info
+    └── inference.pdmodel
 ```
 
 ## 3.单张图像或者图像集合预测
@@ -68,13 +69,13 @@ cd ..
 ```bash
 
 # 预测image_dir指定的单张图像
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
 
 # 预测image_dir指定的图像集合
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
 
 # 如果想使用CPU进行预测，需设置use_gpu参数为False
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True --use_gpu=False
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True --use_gpu=False
 ```
 
 - 通用中文OCR模型
@@ -83,7 +84,7 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_mode
 
 ```bash
 # 预测image_dir指定的单张图像
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_server_v1.1_det_infer/"  --rec_model_dir="./inference/ch_ppocr_server_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_server_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_server_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
 ```
 
 * 注意：

doc/doc_en/inference_en.md

@@ -43,21 +43,21 @@ Next, we first introduce how to convert a trained model into an inference model,
 
 Download the lightweight Chinese detection model:
 ```
-wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_det_train.tar -C ./ch_lite/
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_det_train.tar -C ./ch_lite/
 ```
 
 The above model is a DB algorithm trained with MobileNetV3 as the backbone. To convert the trained model into an inference model, just run the following command:
 ```
 # -c Set the training algorithm yml configuration file
 # -o Set optional parameters
-# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
+# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
 # Global.load_static_weights needs to be set to False
 # Global.save_inference_dir Set the address where the converted model will be saved.
 
-python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db/
+python3 tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_det_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db/
 ```
 
-When converting to an inference model, the configuration file used is the same as the configuration file used during training. In addition, you also need to set the `Global.checkpoints` parameter in the configuration file.
+When converting to an inference model, the configuration file used is the same as the configuration file used during training. In addition, you also need to set the `Global.pretrained_model` parameter in the configuration file.
 After the conversion is successful, there are three files in the model save directory:
 ```
 inference/det_db/
@@ -71,18 +71,18 @@ inference/det_db/
 
 Download the lightweight Chinese recognition model:
 ```
-wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_rec_train.tar -C ./ch_lite/
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_rec_train.tar -C ./ch_lite/
 ```
 
 The recognition model is converted to the inference model in the same way as the detection, as follows:
 ```
 # -c Set the training algorithm yml configuration file
 # -o Set optional parameters
-# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
+# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
 # Global.load_static_weights needs to be set to False
 # Global.save_inference_dir Set the address where the converted model will be saved.
 
-python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn/
+python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn/
 ```
 
 If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path.
@@ -100,18 +100,18 @@ inference/det_db/
 
 Download the angle classification model:
 ```
-wget -P ./ch_lite/ {link} && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/
+wget -P ./ch_lite/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar && tar xf ./ch_lite/ch_ppocr_mobile_v2.0_cls_train.tar -C ./ch_lite/
 ```
 
 The angle classification model is converted to the inference model in the same way as the detection, as follows:
 ```
 # -c Set the training algorithm yml configuration file
 # -o Set optional parameters
-# Global.checkpoints parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
+# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
 # Global.load_static_weights needs to be set to False
 # Global.save_inference_dir Set the address where the converted model will be saved.
 
-python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.checkpoints=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/cls/
+python3 tools/export_model.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_cls_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/cls/
 ```
 
 After the conversion is successful, there are two files in the directory:
@@ -158,10 +158,10 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/2.jpg" --det_model_di
 <a name="DB_DETECTION"></a>
 ### 2. DB TEXT DETECTION MODEL INFERENCE
 
-First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](link)), you can use the following command to convert:
+First, convert the model saved in the DB text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)), you can use the following command to convert:
 
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.checkpoints=./det_r50_vd_db_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db
+python3 tools/export_model.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=./det_r50_vd_db_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_db
 ```
 
 DB text detection model inference, you can execute the following command:
@@ -179,10 +179,10 @@ The visualized text detection results are saved to the `./inference_results` fol
 <a name="EAST_DETECTION"></a>
 ### 3. EAST TEXT DETECTION MODEL INFERENCE
 
-First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](link)), you can use the following command to convert:
+First, convert the model saved in the EAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link (coming soon)](link)), you can use the following command to convert:
 
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.checkpoints=./det_r50_vd_east_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east
+python3 tools/export_model.py -c configs/det/det_r50_vd_east.yml -o Global.pretrained_model=./det_r50_vd_east_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_east
 ```
 **For EAST text detection model inference, you need to set the parameter ``--det_algorithm="EAST"``**, run the following command:
 
@@ -200,10 +200,10 @@ The visualized text detection results are saved to the `./inference_results` fol
 <a name="SAST_DETECTION"></a>
 ### 4. SAST TEXT DETECTION MODEL INFERENCE
 #### (1). Quadrangle text detection model (ICDAR2015)  
-First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link](link)), you can use the following command to convert:
+First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the ICDAR2015 English dataset as an example ([model download link (coming soon)](link)), you can use the following command to convert:
 
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.checkpoints=./det_r50_vd_sast_icdar15_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15
+python3 tools/export_model.py -c configs/det/det_r50_vd_sast_icdar15.yml -o Global.pretrained_model=./det_r50_vd_sast_icdar15_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_ic15
 ```
 
 **For SAST quadrangle text detection model inference, you need to set the parameter `--det_algorithm="SAST"`**, run the following command:
@@ -217,10 +217,10 @@ The visualized text detection results are saved to the `./inference_results` fol
 ![](../imgs_results/det_res_img_10_sast.jpg)
 
 #### (2). Curved text detection model (Total-Text)  
-First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)), you can use the following command to convert:
+First, convert the model saved in the SAST text detection training process into an inference model. Taking the model based on the Resnet50_vd backbone network and trained on the Total-Text English dataset as an example ([model download link (coming soon)](https://paddleocr.bj.bcebos.com/SAST/sast_r50_vd_total_text.tar)), you can use the following command to convert:
 
 ```
-python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.checkpoints=./det_r50_vd_sast_totaltext_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt
+python3 tools/export_model.py -c configs/det/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./det_r50_vd_sast_totaltext_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/det_sast_tt
 ```
 
 **For SAST curved text detection model inference, you need to set the parameter `--det_algorithm="SAST"` and `--det_sast_polygon=True`**, run the following command:
@@ -262,10 +262,10 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['实力活力', 0.89552695]
 
 Taking CRNN as an example, we introduce the recognition model inference based on CTC loss. Rosetta and Star-Net are used in a similar way, No need to set the recognition algorithm parameter rec_algorithm.
 
-First, convert the model saved in the CRNN text recognition training process into an inference model. Taking the model based on Resnet34_vd backbone network, using MJSynth and SynthText (two English text recognition synthetic datasets) for training, as an example ([model download address](link)). It can be converted as follow:
+First, convert the model saved in the CRNN text recognition training process into an inference model. Taking the model based on Resnet34_vd backbone network, using MJSynth and SynthText (two English text recognition synthetic datasets) for training, as an example ([model download address](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)). It can be converted as follow:
 
 ```
-python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o Global.checkpoints=./rec_r34_vd_none_bilstm_ctc_v2.0.train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn
+python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn
 ```
 
 For CRNN text recognition model inference, execute the following commands:
@@ -335,7 +335,7 @@ The following will introduce the angle classification model inference.
 For angle classification model inference, you can execute the following commands:
 
 ```
-python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/"
+python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words_en/word_10.png" --cls_model_dir="./inference/cls/"
 ```
 
 ![](../imgs_words_en/word_10.png)
@@ -343,8 +343,7 @@ python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg"
 After executing the command, the prediction results (classification angle and score) of the above image will be printed on the screen.
 
 ```
-infer_img: doc/imgs_words_en/word_10.png
-     result: ('0', 0.9999995)
+ Predicts of ./doc/imgs_words_en/word_10.png:['0', 0.9999995]
 ```
 
 <a name="CONCATENATION"></a>

doc/doc_en/quickstart_en.md

@@ -9,13 +9,13 @@ Please refer to [quick installation](./installation_en.md) to configure the Padd
 
 ## 2.inference models
 
-The detection and recognition models on the mobile and server sides are as follows. For more models  (including multiple languages), please refer to [PP-OCR v1.1 series model list](../doc_ch/models_list.md)
+The detection and recognition models on the mobile and server sides are as follows. For more models  (including multiple languages), please refer to [PP-OCR v2.0 series model list](../doc_ch/models_list.md)
 
-
-| Model introduction    | Model name     |  Recommended scene      | Detection model | Direction Classifier | Recognition model |
+| Model introduction     | Model name      | Recommended scene          | Detection model | Direction Classifier | Recognition model |
 | ------------ | --------------- | ----------------|---- | ---------- | -------- |
-| Ultra-lightweight Chinese OCR model（xM） | ch_ppocr_mobile_v1.1_xx |Mobile-side/Server-side|[inference model](link) / [pretrained model](link)|[inference model](link) / [pretrained model](link) |[inference model](link) / [pretrained model](link)      |
-| Universal Chinese OCR model（xM）   |ch_ppocr_server_v1.1_xx|Server-side |[inference model](link) / [pretrained model](link)          |[inference model](link) / [pretrained model](link)    |[inference model](link) / [pretrained model](link)  |
+| Ultra-lightweight Chinese OCR model（8.1M） | ch_ppocr_mobile_v2.0_xx |Mobile-side/Server-side|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar)      |
+| Universal Chinese OCR model（155.1M）   | ch_ppocr_server_v2.0_xx |Server-side |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)          |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar)    |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [pretrained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar)  |
+
 
 * If `wget` is not installed in the windows environment, you can copy the link to the browser to download when downloading the model, then uncompress it and place it in the corresponding directory.
 
@@ -37,28 +37,29 @@ Take the ultra-lightweight model as an example:
 ```
 mkdir inference && cd inference
 # Download the detection model of the ultra-lightweight Chinese OCR model and uncompress it
-wget link && tar xf ch_ppocr_mobile_v1.1_det_infer.tar
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar
 # Download the recognition model of the ultra-lightweight Chinese OCR model and uncompress it
-wget link && tar xf ch_ppocr_mobile_v1.1_rec_infer.tar
-# Download the direction classifier model of the ultra-lightweight Chinese OCR model and uncompress it
-wget link && tar xf ch_ppocr_mobile_v1.1_cls_infer.tar
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
+# Download the angle classifier model of the ultra-lightweight Chinese OCR model and uncompress it
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar && tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
 cd ..
 ```
 
 After decompression, the file structure should be as follows:
 
 ```
-|-inference
-    |-ch_ppocr_mobile_v1.1_det_infer
-        |- model
-        |- params
-    |-ch_ppocr_mobile_v1.1_rec_infer
-        |- model
-        |- params
-    |-ch_ppocr_mobile_v1.1_cls_infer
-        |- model
-        |- params
-    ...
+├── ch_ppocr_mobile_v2.0_cls_infer
+│   ├── inference.pdiparams
+│   ├── inference.pdiparams.info
+│   └── inference.pdmodel
+├── ch_ppocr_mobile_v2.0_det_infer
+│   ├── inference.pdiparams
+│   ├── inference.pdiparams.info
+│   └── inference.pdmodel
+├── ch_ppocr_mobile_v2.0_rec_infer
+    ├── inference.pdiparams
+    ├── inference.pdiparams.info
+    └── inference.pdmodel
 ```
 
 ## 3. Single image or image set prediction
@@ -70,13 +71,13 @@ After decompression, the file structure should be as follows:
 ```bash
 
 # Predict a single image specified by image_dir
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
 
 # Predict imageset specified by image_dir
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
 
 # If you want to use the CPU for prediction, you need to set the use_gpu parameter to False
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v1.1_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True --use_gpu=False
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_mobile_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_mobile_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True --use_gpu=False
 ```
 
 - Universal Chinese OCR model
@@ -85,7 +86,7 @@ Please follow the above steps to download the corresponding models and update th
 
 ```
 # Predict a single image specified by image_dir
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_server_v1.1_det_infer/"  --rec_model_dir="./inference/ch_ppocr_server_v1.1_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v1.1_cls_infer/" --use_angle_cls=True --use_space_char=True
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/11.jpg" --det_model_dir="./inference/ch_ppocr_server_v2.0_det_infer/"  --rec_model_dir="./inference/ch_ppocr_server_v2.0_rec_infer/" --cls_model_dir="./inference/ch_ppocr_mobile_v2.0_cls_infer/" --use_angle_cls=True --use_space_char=True
 ```
 
 * Note

ppocr/data/__init__.py

@@ -67,6 +67,7 @@ def build_dataloader(config, mode, device, logger):
     drop_last = loader_config['drop_last']
     num_workers = loader_config['num_workers']
 
+    use_shared_memory = False
     if mode == "Train":
         #Distribute data to multiple cards
         batch_sampler = DistributedBatchSampler(
@@ -74,6 +75,7 @@ def build_dataloader(config, mode, device, logger):
             batch_size=batch_size,
             shuffle=False,
             drop_last=drop_last)
+        use_shared_memory = True
     else:
         #Distribute data to single card
         batch_sampler = BatchSampler(
@@ -87,6 +89,7 @@ def build_dataloader(config, mode, device, logger):
         batch_sampler=batch_sampler,
         places=device,
         num_workers=num_workers,
-        return_list=True)
+        return_list=True,
+        use_shared_memory=use_shared_memory)
 
     return data_loader

tools/export_model.py

@@ -28,7 +28,7 @@ from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
 from ppocr.utils.save_load import init_model
 from ppocr.utils.logging import get_logger
-from tools.program import load_config, merge_config,ArgsParser
+from tools.program import load_config, merge_config, ArgsParser
 
 
 def main():
@@ -36,7 +36,6 @@ def main():
     config = load_config(FLAGS.config)
     merge_config(FLAGS.opt)
     logger = get_logger()
-    print(config)
     # build post process
 
     post_process_class = build_post_process(config['PostProcess'],

tools/program.py

@@ -113,7 +113,6 @@ def merge_config(config):
                 global_config.keys(), sub_keys[0])
             cur = global_config[sub_keys[0]]
             for idx, sub_key in enumerate(sub_keys[1:]):
-                assert (sub_key in cur)
                 if idx == len(sub_keys) - 2:
                     cur[sub_key] = value
                 else: