rec_efficientb3_fpn_pren.yml

Global:
  use_gpu: True
  epoch_num: 8
  log_smooth_window: 20
  print_batch_step: 5
  save_model_dir: ./output/rec/pren_new
  save_epoch_step: 3
  # evaluation is run every 2000 iterations after the 4000th iteration
  eval_batch_step: [4000, 2000]
  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
  character_dict_path:
  max_text_length: &max_text_length 25
  infer_mode: False
  use_space_char: False
  save_res_path: ./output/rec/predicts_pren.txt

Optimizer:
  name: Adadelta
  lr:
    name: Piecewise
    decay_epochs: [2, 5, 7]
    values: [0.5, 0.1, 0.01, 0.001]

Architecture:
  model_type: rec
  algorithm: PREN
  in_channels: 3
  Backbone:
    name: EfficientNetb3_PREN
  Neck:
    name: PRENFPN
    n_r: 5
    d_model: 384
    max_len: *max_text_length
    dropout: 0.1
  Head:
    name: PRENHead

Loss:
  name: PRENLoss

PostProcess:
  name: PRENLabelDecode

Metric:
  name: RecMetric
  main_indicator: acc

Train:
  dataset:
    name: LMDBDataSet
    data_dir: ./train_data/data_lmdb_release/training/
    transforms:
      - DecodeImage:
          img_mode: BGR
          channel_first: False
      - PRENLabelEncode:
      - RecAug:
      - PRENResizeImg: 
          image_shape: [64, 256]  # h,w
      - KeepKeys:
          keep_keys: ['image', 'label']
  loader:
    shuffle: True
    batch_size_per_card: 128
    drop_last: True
    num_workers: 8

Eval:
  dataset:
    name: LMDBDataSet
    data_dir: ./train_data/data_lmdb_release/validation/
    transforms:
      - DecodeImage:
          img_mode: BGR
          channel_first: False
      - PRENLabelEncode:
      - PRENResizeImg: 
          image_shape: [64, 256]  # h,w
      - KeepKeys:
          keep_keys: ['image', 'label']
  loader:
    shuffle: False
    drop_last: False
    batch_size_per_card: 64
    num_workers: 8