109 lines
2.5 KiB
YAML
109 lines
2.5 KiB
YAML
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Global:
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use_gpu: true
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epoch_num: 10000
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log_smooth_window: 20
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print_batch_step: 2
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save_model_dir: ./output/east_mv3/
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save_epoch_step: 1000
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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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cal_metric_during_train: False
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pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
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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:
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save_res_path: ./output/det_east/predicts_east.txt
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Architecture:
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model_type: det
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algorithm: EAST
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Transform:
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Backbone:
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name: MobileNetV3
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scale: 0.5
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model_name: large
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Neck:
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name: EASTFPN
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model_name: small
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Head:
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name: EASTHead
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model_name: small
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Loss:
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name: EASTLoss
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Optimizer:
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name: Adam
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beta1: 0.9
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beta2: 0.999
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lr:
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# name: Cosine
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learning_rate: 0.001
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# warmup_epoch: 0
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regularizer:
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name: 'L2'
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factor: 0
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PostProcess:
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name: EASTPostProcess
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score_thresh: 0.8
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cover_thresh: 0.1
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nms_thresh: 0.2
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Metric:
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name: DetMetric
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main_indicator: hmean
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Train:
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dataset:
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name: SimpleDataSet
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data_dir: ./train_data/icdar2015/text_localization/
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label_file_list:
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- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
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ratio_list: [1.0]
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- DetLabelEncode: # Class handling label
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- EASTProcessTrain:
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image_shape: [512, 512]
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background_ratio: 0.125
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min_crop_side_ratio: 0.1
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min_text_size: 10
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- KeepKeys:
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keep_keys: ['image', 'score_map', 'geo_map', 'training_mask'] # dataloader will return list in this order
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loader:
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shuffle: True
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drop_last: False
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batch_size_per_card: 16
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num_workers: 8
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Eval:
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dataset:
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name: SimpleDataSet
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data_dir: ./train_data/icdar2015/text_localization/
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label_file_list:
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- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
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transforms:
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- DecodeImage: # load image
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img_mode: BGR
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channel_first: False
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- DetLabelEncode: # Class handling label
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- DetResizeForTest:
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limit_side_len: 2400
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limit_type: max
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- NormalizeImage:
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scale: 1./255.
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: 'hwc'
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- ToCHWImage:
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- KeepKeys:
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keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
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loader:
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shuffle: False
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drop_last: False
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batch_size_per_card: 1 # must be 1
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num_workers: 2
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