det_r50_vd_sast_icdar15.yml

Global:
  algorithm: SAST
  use_gpu: true
  epoch_num: 2000
  log_smooth_window: 20
  print_batch_step: 2
  save_model_dir: ./output/det_sast/
  save_epoch_step: 20
  eval_batch_step: 5000
  train_batch_size_per_card: 8
  test_batch_size_per_card: 8
  image_shape: [3, 512, 512]
  reader_yml: ./configs/det/det_sast_icdar15_reader.yml
  pretrain_weights: ./pretrain_models/ResNet50_vd_ssld_pretrained/
  save_res_path: ./output/det_sast/predicts_sast.txt
  checkpoints: 
  save_inference_dir:

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

Backbone:
  function: ppocr.modeling.backbones.det_resnet_vd_sast,ResNet
  layers: 50

Head:
  function: ppocr.modeling.heads.det_sast_head,SASTHead
  model_name: large
  only_fpn_up: False
#   with_cab: False
  with_cab: True

Loss:
  function: ppocr.modeling.losses.det_sast_loss,SASTLoss

Optimizer:
  function: ppocr.optimizer,RMSProp
  base_lr: 0.001
  decay:
    function: piecewise_decay
    boundaries: [30000, 50000, 80000, 100000, 150000]
    decay_rate: 0.3

PostProcess:
  function: ppocr.postprocess.sast_postprocess,SASTPostProcess
  score_thresh: 0.5
  sample_pts_num: 2
  nms_thresh: 0.2
  expand_scale: 1.0
  shrink_ratio_of_width: 0.3