OpenOCR/configs/rec/smtr/focalsvtr_smtr.yml

Global:
  device: gpu
  epoch_num: 20
  log_smooth_window: 20
  print_batch_step: 10
  output_dir: ./output/rec/u14m_filter/focalsvtr_smtr_maxratio12
  save_epoch_step: [15, 1]
  # evaluation is run every 2000 iterations
  eval_batch_step: [0, 500]
  eval_epoch_step: [0, 1]
  cal_metric_during_train: True
  pretrained_model: ./output/rec/focalsvtr_smtr/best.pth
  # ./output/focalnet_subs_nocmff_20ep_u14m_k8_max_ratio12_h8_norand1_h2_padrand_doub_96/best.pth
  # ./output/rec/focalsvtr_smtr/best.pth
  checkpoints:
  use_tensorboard: false
  infer_img: ../ltb/img
  # for data or label process
  character_dict_path: &character_dict_path ./tools/utils/EN_symbol_dict.txt # 96en
  # ./tools/utils/ppocr_keys_v1.txt  # ch
  max_text_length: &max_text_length 25
  use_space_char: &use_space_char False
  save_res_path: ./output/rec/u14m_filter/predicts_focalsvtr_smtr_maxratio12.txt
  use_amp: True

Optimizer:
  name: AdamW
  lr: 0.00065 # for 4gpus bs256/gpu
  weight_decay: 0.05
  filter_bias_and_bn: True

LRScheduler:
  name: OneCycleLR
  warmup_epoch: 1.5 # pct_start 0.075*20 = 1.5ep
  cycle_momentum: False

Architecture:
  model_type: rec
  algorithm: SMTR
  in_channels: 3
  Transform:
  Encoder:
    name: FocalSVTR
    img_size: [32, 128]
    depths: [6, 6, 6]
    embed_dim: 96
    sub_k: [[1, 1], [2, 1], [1, 1]]
    focal_levels: [3, 3, 3]
    last_stage: False
  Decoder:
    name: SMTRDecoder
    num_layer: 1
    ds: True
    max_len: *max_text_length
    next_mode: &next True
    sub_str_len: &subsl 5

Loss:
  name: SMTRLoss

PostProcess:
  name: SMTRLabelDecode
  next_mode: *next
  character_dict_path: *character_dict_path
  use_space_char: *use_space_char

Metric:
  name: RecMetric
  main_indicator: acc
  is_filter: True

Train:
  dataset:
    name: RatioDataSet
    ds_width: True
    padding: &padding True
    padding_rand: True
    padding_doub: True
    data_dir_list: ['../Union14M-L-LMDB-Filtered/filter_train_challenging',
    '../Union14M-L-LMDB-Filtered/filter_train_hard',
    '../Union14M-L-LMDB-Filtered/filter_train_medium',
    '../Union14M-L-LMDB-Filtered/filter_train_normal',
    '../Union14M-L-LMDB-Filtered/filter_train_easy',
    ]
    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - PARSeqAug:
      - SMTRLabelEncode: # Class handling label
          sub_str_len: *subsl
          character_dict_path: *character_dict_path
          use_space_char: *use_space_char
          max_text_length: *max_text_length
      - KeepKeys:
          keep_keys: ['image', 'label', 'label_subs', 'label_next', 'length_subs',
          'label_subs_pre', 'label_next_pre', 'length_subs_pre', 'length'] # dataloader will return list in this order
  sampler:
    name: RatioSampler
    scales: [[128, 32]] # w, h
    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
    first_bs: &bs 256
    fix_bs: false
    divided_factor: [4, 16] # w, h
    is_training: True
  loader:
    shuffle: True
    batch_size_per_card: *bs
    drop_last: True
    max_ratio: &max_ratio 12
    num_workers: 4

Eval:
  dataset:
    name: RatioDataSet
    ds_width: True
    padding: False
    padding_rand: False
    data_dir_list: [
      '../evaluation/CUTE80',
      '../evaluation/IC13_857',
      '../evaluation/IC15_1811',
      '../evaluation/IIIT5k',
      '../evaluation/SVT',
      '../evaluation/SVTP',
      ]
    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - ARLabelEncode: # Class handling label
          character_dict_path: *character_dict_path
          use_space_char: *use_space_char
          max_text_length: *max_text_length
      - KeepKeys:
          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
  sampler:
    name: RatioSampler
    scales: [[128, 32]] # w, h
    # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
    first_bs: 128
    fix_bs: false
    divided_factor: [4, 16] # w, h
    is_training: False
  loader:
    shuffle: False
    drop_last: False
    max_ratio: *max_ratio
    batch_size_per_card: 128
    num_workers: 4