rec_blue_plate_res45_train_v2.0.yml

Global:
  use_gpu: true
  epoch_num: 500
  log_smooth_window: 20
  print_batch_step: 10
  save_model_dir: ./output/rec_res45_blue_plate_125
  save_epoch_step: 10
  # evaluation is run every 5000 iterations after the 4000th iteration
  eval_batch_step: [0, 100]
  cal_metric_during_train: True
  pretrained_model: ./output/rec_res45_blue_plate_125/best_accuracy
  checkpoints: 
  save_inference_dir: ./output/rec_res45_blue_plate_125
  use_visualdl: False
  infer_img: test_data/data_img/
  # for data or label process
  character_dict_path: ppocr/utils/plate.txt
  max_text_length: 8
  infer_mode: False
  use_space_char: False
  save_res_path: ./output/rec_res45_blue_plate_125.txt


Optimizer:
  name: Adam
  beta1: 0.9
  beta2: 0.999
  lr:
    name: Cosine
    learning_rate: 0.001
    warmup_epoch: 5
  regularizer:
    name: 'L2'
    factor: 0.00001

Architecture:
  model_type: rec
  algorithm: CRNN
  Transform:
  Backbone:
    name: ResNet45
  Neck:
    name: SequenceEncoder
    encoder_type: rnn
    hidden_size: 96
  Head:
    name: CTCHead
    fc_decay: 0.00001

Loss:
  name: CTCLoss

PostProcess:
  name: CTCLabelDecode

Metric:
  name: RecMetric
  main_indicator: acc

Train:
  dataset:
    name: SimpleDataSet
    data_dir: ./train_data/
    label_file_list: ["./train_data/train.txt"]
    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - RecAug:
      - CTCLabelEncode: # Class handling label
      - RecResizeImg:
          image_shape: [3, 32, 100]
      - KeepKeys:
          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
  loader:
    shuffle: True
    batch_size_per_card: 256
    drop_last: True
    num_workers: 0

Eval:
  dataset:
    name: SimpleDataSet
    data_dir: ./test_data
    label_file_list: ["./test_data/train.txt"]
    transforms:
      - DecodeImage: # load image
          img_mode: BGR
          channel_first: False
      - CTCLabelEncode: # Class handling label
      - RecResizeImg:
          image_shape: [3, 32, 100]
      - KeepKeys:
          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
  loader:
    shuffle: False
    drop_last: False
    batch_size_per_card: 256
    num_workers: 0