det_r18_vd_db.yml

Global:
  algorithm: DB
  use_gpu: true
  epoch_num: 1200
  log_smooth_window: 20
  print_batch_step: 2
  save_model_dir: ./output/det_r_18_vd_db/
  save_epoch_step: 200
  eval_batch_step: [3000, 2000]
  train_batch_size_per_card: 8
  test_batch_size_per_card: 1
  image_shape: [3, 640, 640]
  reader_yml: ./configs/det/det_db_icdar15_reader.yml
  pretrain_weights: ./pretrain_models/ResNet18_vd_pretrained/
  save_res_path: ./output/det_r18_vd_db/predicts_db.txt
  checkpoints:
  save_inference_dir:

Architecture:
  function: ppocr.modeling.architectures.det_model,DetModel

Backbone:
  function: ppocr.modeling.backbones.det_resnet_vd,ResNet
  layers: 18

Head:
  function: ppocr.modeling.heads.det_db_head,DBHead
  model_name: large
  k: 50
  inner_channels: 256
  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: 0.6
  max_candidates: 1000
  unclip_ratio: 1.5