Global:
  use_gpu: True
  epoch_num: 20
  log_smooth_window: 20
  print_batch_step: 10
  save_model_dir: ./output/rec/vitstr_none_ce/
  save_epoch_step: 1
  # evaluation is run every 2000 iterations after the 0th iteration#
  eval_batch_step: [0, 50]
  cal_metric_during_train: True
  pretrained_model:
  checkpoints:
  save_inference_dir:
  use_visualdl: False
  infer_img: doc/imgs_words_en/word_10.png
  # for data or label process
  character_dict_path: ppocr/utils/EN_symbol_dict.txt
  max_text_length: 25
  infer_mode: False
  use_space_char: False
  save_res_path: ./output/rec/predicts_vitstr.txt


Optimizer:
  name: Adadelta
  epsilon: 0.00000001
  rho: 0.95
  clip_norm: 5.0
  lr:
    learning_rate: 1.0

Architecture:
  model_type: rec
  algorithm: ViTSTR
  in_channels: 1
  Transform:
  Backbone:
    name: ViTSTR
    scale: tiny
  Neck:
    name: SequenceEncoder
    encoder_type: reshape
  Head:
    name: CTCHead

Loss:
  name: CELoss
  smoothing: False
  with_all: True

PostProcess:
  name: ViTSTRLabelDecode

Metric:
  name: RecMetric
  main_indicator: acc

Train:
  dataset:
    name: LMDBDataSet
    data_dir: ./train_data/data_lmdb_release/training
    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - ViTSTRLabelEncode: # Class handling label
      - GrayRecResizeImg:
          image_shape: [224, 224] # W H
          resize_type: PIL # PIL or OpenCV
          inter_type: 'Image.BICUBIC'
          scale: false
      - KeepKeys:
          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
  loader:
    shuffle: True
    batch_size_per_card: 48
    drop_last: True
    num_workers: 8

Eval:
  dataset:
    name: LMDBDataSet
    data_dir: ./train_data/data_lmdb_release/validation
    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - ViTSTRLabelEncode: # Class handling label
      - GrayRecResizeImg:
          image_shape: [224, 224] # W H
          resize_type: PIL # PIL or OpenCV
          inter_type: 'Image.BICUBIC'
          scale: false
      - KeepKeys:
          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
  loader:
    shuffle: False
    drop_last: False
    batch_size_per_card: 256
    num_workers: 2