cls_mv3.yml

Global:
  use_gpu: true
  epoch_num: 100
  log_smooth_window: 20
  print_batch_step: 10
  save_model_dir: ./output/cls/mv3/
  save_epoch_step: 3
  # evaluation is run every 5000 iterations after the 4000th iteration
  eval_batch_step: [0, 1000]
  # if pretrained_model is saved in static mode, load_static_weights must set to True
  cal_metric_during_train: True
  pretrained_model:
  checkpoints:
  save_inference_dir:
  use_visualdl: False
  infer_img: doc/imgs_words_en/word_10.png
  label_list: ['0','180']

Architecture:
  model_type: cls
  algorithm: CLS
  Transform:
  Backbone:
    name: MobileNetV3
    scale: 0.35
    model_name: small
  Neck:
  Head:
    name: ClsHead
    class_dim: 2

Loss:
  name: ClsLoss

Optimizer:
  name: Adam
  beta1: 0.9
  beta2: 0.999
  lr:
    name: Cosine
    learning_rate: 0.001
  regularizer:
    name: 'L2'
    factor: 0

PostProcess:
  name: ClsPostProcess

Metric:
  name: ClsMetric
  main_indicator: acc

Train:
  dataset:
    name: SimpleDataSet
    data_dir: ./train_data/cls
    label_file_list:
      - ./train_data/cls/train.txt
    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - ClsLabelEncode: # Class handling label
      - RecAug:
          use_tia: False
      - RandAugment:
      - ClsResizeImg:
          image_shape: [3, 48, 192]
      - KeepKeys:
          keep_keys: ['image', 'label'] # dataloader will return list in this order
  loader:
    shuffle: True
    batch_size_per_card: 512
    drop_last: True
    num_workers: 8

Eval:
  dataset:
    name: SimpleDataSet
    data_dir: ./train_data/cls
    label_file_list:
      - ./train_data/cls/test.txt
    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - ClsLabelEncode: # Class handling label
      - ClsResizeImg:
          image_shape: [3, 48, 192]
      - KeepKeys:
          keep_keys: ['image', 'label'] # dataloader will return list in this order
  loader:
    shuffle: False
    drop_last: False
    batch_size_per_card: 512
    num_workers: 4