configs/det/dbnet/repvit_db.yml

Global:
  device: gpu
  epoch_num: &epoch_num 500
  log_smooth_window: 20
  print_batch_step: 100
  save_model_dir: ./output/det_repsvtr_db
  save_epoch_step: 10
  eval_batch_step:
  - 0
  - 1000
  cal_metric_during_train: false
  checkpoints:
  pretrained_model: openocr_det_repvit_ch.pth
  save_inference_dir: null
  use_visualdl: false
  infer_img: ./testA
  save_res_path: ./checkpoints/det_db/predicts_db.txt
  distributed: true
  model_type: det

Architecture:
  algorithm: DB
  Backbone:
    name: RepSVTR_det
  Neck:
    name: RSEFPN
    out_channels: 96
    shortcut: True
  Head:
    name: DBHead
    k: 50

# Loss:
#   name: DBLoss
#   balance_loss: true
#   main_loss_type: DiceLoss
#   alpha: 5
#   beta: 10
#   ohem_ratio: 3

# Optimizer:
#   name: Adam
#   beta1: 0.9
#   beta2: 0.999
#   lr:
#     name: Cosine
#     learning_rate: 0.001 #(8*8c)
#     warmup_epoch: 2
#   regularizer:
#     name: L2
#     factor: 5.0e-05

PostProcess:
  name: DBPostProcess
  thresh: 0.3
  box_thresh: 0.6
  max_candidates: 1000
  unclip_ratio: 1.5
  score_mode: 'slow'

# Metric:
#   name: DetMetric
#   main_indicator: hmean

# Train:
#   dataset:
#     name: SimpleDataSet
#     data_dir: ./train_data/icdar2015/text_localization/
#     label_file_list:
#       - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
#     ratio_list: [1.0]
#     transforms:
#     - DecodeImage:
#         img_mode: BGR
#         channel_first: false
#     - DetLabelEncode: null
#     - CopyPaste: null
#     - 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
#         total_epoch: *epoch_num
#     - MakeShrinkMap:
#         shrink_ratio: 0.4
#         min_text_size: 8
#         total_epoch: *epoch_num
#     - NormalizeImage:
#         scale: 1./255.
#         mean:
#         - 0.485
#         - 0.456
#         - 0.406
#         std:
#         - 0.229
#         - 0.224
#         - 0.225
#         order: hwc
#     - ToCHWImage: null
#     - KeepKeys:
#         keep_keys:
#         - image
#         - threshold_map
#         - threshold_mask
#         - shrink_map
#         - shrink_mask
#   loader:
#     shuffle: true
#     drop_last: false
#     batch_size_per_card: 8
#     num_workers: 8

Eval:
  dataset:
    name: SimpleDataSet
    data_dir: ./train_data/icdar2015/text_localization/
    label_file_list:
      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
    transforms:
    - DecodeImage:
        img_mode: BGR
        channel_first: false
    - DetLabelEncode: null
    - DetResizeForTest:
        # image_shape: [1280, 1280]
        # keep_ratio: True
        # padding: True
        limit_side_len: 960
        limit_type: max
    - NormalizeImage:
        scale: 1./255.
        mean:
        - 0.485
        - 0.456
        - 0.406
        std:
        - 0.229
        - 0.224
        - 0.225
        order: hwc
    - ToCHWImage: null
    - KeepKeys:
        keep_keys:
        - image
        - shape
        - polys
        - ignore_tags
  loader:
    shuffle: false
    drop_last: false
    batch_size_per_card: 1
    num_workers: 2
profiler_options: null