rec_mv3_none_bilstm_ctc.yml 2.1 KB
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Global:
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  use_gpu: true
  epoch_num: 72
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  log_smooth_window: 20
  print_batch_step: 10
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  save_model_dir: ./output/rec/mv3_none_bilstm_ctc/
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  save_epoch_step: 3
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  # evaluation is run every 2000 iterations
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  eval_batch_step: [0, 2000]
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  # if pretrained_model is saved in static mode, load_static_weights must set to True
  cal_metric_during_train: True
  pretrained_model:
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  checkpoints:
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  save_inference_dir:
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  use_visualdl: False
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  infer_img: doc/imgs_words_en/word_10.png
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  # for data or label process
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  character_dict_path: 
  character_type: en
  max_text_length: 25
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  infer_mode: False
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  use_space_char: False
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Optimizer:
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  name: Adam
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  beta1: 0.9
  beta2: 0.999
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  lr:
    learning_rate: 0.0005
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  regularizer:
    name: 'L2'
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    factor: 0
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Architecture:
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  model_type: rec
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  algorithm: CRNN
  Transform:
  Backbone:
    name: MobileNetV3
    scale: 0.5
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    model_name: large
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  Neck:
    name: SequenceEncoder
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    encoder_type: rnn
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    hidden_size: 96
  Head:
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    name: CTCHead
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    fc_decay: 0
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Loss:
  name: CTCLoss

PostProcess:
  name: CTCLabelDecode

Metric:
  name: RecMetric
  main_indicator: acc

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Train:
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  dataset:
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    name: LMDBDateSet
    data_dir: ./train_data/data_lmdb_release/training/
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    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - CTCLabelEncode: # Class handling label
      - RecResizeImg:
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          image_shape: [3, 32, 100]
      - KeepKeys:
          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
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  loader:
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    shuffle: False
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    batch_size_per_card: 256
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    drop_last: True
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    num_workers: 8
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Eval:
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  dataset:
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    name: LMDBDateSet
    data_dir: ./train_data/data_lmdb_release/validation/
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    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - CTCLabelEncode: # Class handling label
      - RecResizeImg:
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          image_shape: [3, 32, 100]
      - KeepKeys:
          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
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  loader:
    shuffle: False
    drop_last: False
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    batch_size_per_card: 256
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    num_workers: 4