PaddleOCR/configs/rec/rec_mv3_none_bilstm_ctc.yml

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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:
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