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Global:
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algorithm: CRNN
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use_gpu: true
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epoch_num: 1000
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log_smooth_window: 20
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print_batch_step: 10
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save_model_dir: ./output/rec_CRNN
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save_epoch_step: 300
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eval_batch_step: 500
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train_batch_size_per_card: 256
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test_batch_size_per_card: 256
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image_shape: [3, 32, 100]
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max_text_length: 25
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character_type: en
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loss_type: ctc
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distort: true
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debug: false
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reader_yml: ./configs/rec/rec_icdar15_reader.yml
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pretrain_weights: ./pretrain_models/rec_mv3_none_bilstm_ctc/best_accuracy
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checkpoints:
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save_inference_dir:
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infer_img:
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Architecture:
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function: ppocr.modeling.architectures.rec_model,RecModel
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Backbone:
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function: ppocr.modeling.backbones.rec_mobilenet_v3,MobileNetV3
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scale: 0.5
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model_name: large
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Head:
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function: ppocr.modeling.heads.rec_ctc_head,CTCPredict
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encoder_type: rnn
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SeqRNN:
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hidden_size: 96
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Loss:
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function: ppocr.modeling.losses.rec_ctc_loss,CTCLoss
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Optimizer:
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function: ppocr.optimizer,AdamDecay
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base_lr: 0.0005
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beta1: 0.9
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beta2: 0.999
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decay:
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function: cosine_decay
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step_each_epoch: 20
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total_epoch: 1000
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