2020-05-10 16:26:57 +08:00
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Global:
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algorithm: DB
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use_gpu: true
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epoch_num: 1200
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log_smooth_window: 20
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print_batch_step: 2
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2020-05-13 16:05:00 +08:00
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save_model_dir: ./output/det_db/
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2020-05-10 16:26:57 +08:00
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save_epoch_step: 200
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2020-07-07 19:34:10 +08:00
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# evaluation is run every 5000 iterations after the 4000th iteration
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eval_batch_step: [4000, 5000]
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2020-05-10 16:26:57 +08:00
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train_batch_size_per_card: 16
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test_batch_size_per_card: 16
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image_shape: [3, 640, 640]
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reader_yml: ./configs/det/det_db_icdar15_reader.yml
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2020-05-15 14:22:57 +08:00
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pretrain_weights: ./pretrain_models/MobileNetV3_large_x0_5_pretrained/
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2020-05-11 15:27:52 +08:00
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checkpoints:
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2020-05-13 16:05:00 +08:00
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save_res_path: ./output/det_db/predicts_db.txt
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2020-05-12 21:12:52 +08:00
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save_inference_dir:
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2020-05-10 16:26:57 +08:00
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Architecture:
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function: ppocr.modeling.architectures.det_model,DetModel
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Backbone:
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function: ppocr.modeling.backbones.det_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.det_db_head,DBHead
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model_name: large
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k: 50
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inner_channels: 96
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out_channels: 2
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Loss:
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function: ppocr.modeling.losses.det_db_loss,DBLoss
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balance_loss: true
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main_loss_type: DiceLoss
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alpha: 5
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beta: 10
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ohem_ratio: 3
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Optimizer:
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function: ppocr.optimizer,AdamDecay
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base_lr: 0.001
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beta1: 0.9
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beta2: 0.999
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PostProcess:
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function: ppocr.postprocess.db_postprocess,DBPostProcess
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thresh: 0.3
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box_thresh: 0.7
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max_candidates: 1000
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2020-07-07 14:46:42 +08:00
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unclip_ratio: 2.0
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