det_mv3_db.yml 3.3 KB
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Global:
  use_gpu: true
  epoch_num: 1200
  log_smooth_window: 20
  print_batch_step: 2
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  save_model_dir: ./output/db_mv3/
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  save_epoch_step: 1200
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  # evaluation is run every 5000 iterations after the 4000th iteration
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  eval_batch_step: [4000, 5000]
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  # if pretrained_model is saved in static mode, load_static_weights must set to True
  load_static_weights: True
  cal_metric_during_train: False
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  pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
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  checkpoints: #./output/det_db_0.001_DiceLoss_256_pp_config_2.0b_4gpu/best_accuracy
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  save_inference_dir:
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  use_visualdl: False
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  infer_img: doc/imgs_en/img_10.jpg
  save_res_path: ./output/det_db/predicts_db.txt
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Architecture:
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  model_type: det
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  algorithm: DB
  Transform:
  Backbone:
    name: MobileNetV3
    scale: 0.5
    model_name: large
  Neck:
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    name: DBFPN
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    out_channels: 256
  Head:
    name: DBHead
    k: 50
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Loss:
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  name: DBLoss
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  balance_loss: true
  main_loss_type: DiceLoss
  alpha: 5
  beta: 10
  ohem_ratio: 3
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Optimizer:
  name: Adam
  beta1: 0.9
  beta2: 0.999
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  lr:
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#    name: Cosine
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    learning_rate: 0.001
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#    warmup_epoch: 0
  regularizer:
    name: 'L2'
    factor: 0
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PostProcess:
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  name: DBPostProcess
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  thresh: 0.3
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  box_thresh: 0.6
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  max_candidates: 1000
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  unclip_ratio: 1.5

Metric:
  name: DetMetric
  main_indicator: hmean

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Train:
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  dataset:
    name: SimpleDataSet
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    data_dir: ./train_data/icdar2015/text_localization/
    label_file_list:
      - ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
    ratio_list: [0.5]
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    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - DetLabelEncode: # Class handling label
      - IaaAugment:
          augmenter_args:
            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
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            - { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
            - { 'type': Resize, 'args': { 'size': [0.5, 3] } }
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      - EastRandomCropData:
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          size: [640, 640]
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          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.
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          mean: [0.485, 0.456, 0.406]
          std: [0.229, 0.224, 0.225]
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          order: 'hwc'
      - ToCHWImage:
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      - KeepKeys:
          keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
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  loader:
    drop_last: False
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    batch_size_per_card: 4
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    num_workers: 8
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Eval:
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  dataset:
    name: SimpleDataSet
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    data_dir: ./train_data/icdar2015/text_localization/
    label_file_list:
      - ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
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    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - DetLabelEncode: # Class handling label
      - DetResizeForTest:
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          image_shape: [736, 1280]
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      - NormalizeImage:
          scale: 1./255.
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          mean: [0.485, 0.456, 0.406]
          std: [0.229, 0.224, 0.225]
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          order: 'hwc'
      - ToCHWImage:
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      - KeepKeys:
          keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
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  loader:
    drop_last: False
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    batch_size_per_card: 1 # must be 1
    num_workers: 2