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99 lines
2.0 KiB
YAML
99 lines
2.0 KiB
YAML
8 months ago
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Global:
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debug: false
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use_gpu: true
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epoch_num: 100
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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_ppocr_v3_rotnet
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save_epoch_step: 3
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eval_batch_step: [0, 2000]
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cal_metric_during_train: true
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pretrained_model: null
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checkpoints: null
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save_inference_dir: null
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use_visualdl: false
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infer_img: doc/imgs_words/ch/word_1.jpg
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character_dict_path: ppocr/utils/ppocr_keys_v1.txt
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max_text_length: 25
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infer_mode: false
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use_space_char: true
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save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
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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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regularizer:
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name: L2
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factor: 1.0e-05
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Architecture:
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model_type: cls
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algorithm: CLS
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Transform: null
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Backbone:
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name: MobileNetV1Enhance
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scale: 0.5
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last_conv_stride: [1, 2]
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last_pool_type: avg
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Neck:
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Head:
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name: ClsHead
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class_dim: 4
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Loss:
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name: ClsLoss
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main_indicator: acc
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PostProcess:
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name: ClsPostProcess
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Metric:
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name: ClsMetric
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main_indicator: acc
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Train:
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dataset:
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name: SimpleDataSet
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data_dir: ./train_data
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label_file_list:
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- ./train_data/train_list.txt
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transforms:
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- DecodeImage:
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img_mode: BGR
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channel_first: false
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- BaseDataAugmentation:
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- RandAugment:
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- SSLRotateResize:
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image_shape: [3, 48, 320]
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- KeepKeys:
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keep_keys: ["image", "label"]
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loader:
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collate_fn: "SSLRotateCollate"
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shuffle: true
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batch_size_per_card: 32
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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: SimpleDataSet
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data_dir: ./train_data
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label_file_list:
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- ./train_data/val_list.txt
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transforms:
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- DecodeImage:
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img_mode: BGR
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channel_first: false
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- SSLRotateResize:
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image_shape: [3, 48, 320]
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- KeepKeys:
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keep_keys: ["image", "label"]
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loader:
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collate_fn: "SSLRotateCollate"
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shuffle: false
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drop_last: false
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batch_size_per_card: 64
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num_workers: 8
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profiler_options: null
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