det_mv3_db.yml 1.2 KB
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Global:
  algorithm: DB
  use_gpu: true
  epoch_num: 1200
  log_smooth_window: 20
  print_batch_step: 2
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  save_model_dir: ./output/det_db/
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  save_epoch_step: 200
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  # evaluation is run every 5000 iterations after the 4000th iteration
  eval_batch_step: [4000, 5000]
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  train_batch_size_per_card: 16
  test_batch_size_per_card: 16
  image_shape: [3, 640, 640]
  reader_yml: ./configs/det/det_db_icdar15_reader.yml
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  pretrain_weights: ./pretrain_models/MobileNetV3_large_x0_5_pretrained/
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  checkpoints:
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  save_res_path: ./output/det_db/predicts_db.txt
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  save_inference_dir:
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Architecture:
  function: ppocr.modeling.architectures.det_model,DetModel

Backbone:
  function: ppocr.modeling.backbones.det_mobilenet_v3,MobileNetV3
  scale: 0.5
  model_name: large

Head:
  function: ppocr.modeling.heads.det_db_head,DBHead
  model_name: large
  k: 50
  inner_channels: 96
  out_channels: 2

Loss:
  function: ppocr.modeling.losses.det_db_loss,DBLoss
  balance_loss: true
  main_loss_type: DiceLoss
  alpha: 5
  beta: 10
  ohem_ratio: 3

Optimizer:
  function: ppocr.optimizer,AdamDecay
  base_lr: 0.001
  beta1: 0.9
  beta2: 0.999

PostProcess:
  function: ppocr.postprocess.db_postprocess,DBPostProcess
  thresh: 0.3
  box_thresh: 0.7
  max_candidates: 1000
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  unclip_ratio: 2.0