106 lines
2.3 KiB
YAML
106 lines
2.3 KiB
YAML
Global:
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use_gpu: false
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epoch_num: 500
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log_smooth_window: 20
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print_batch_step: 1
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save_model_dir: ./output/rec/test/
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save_epoch_step: 500
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# evaluation is run every 5000 iterations after the 4000th iteration
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eval_batch_step: 1016
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# if pretrained_model is saved in static mode, load_static_weights must set to True
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load_static_weights: True
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cal_metric_during_train: True
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pretrained_model:
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checkpoints: #output/rec/rec_crnn/best_accuracy
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save_inference_dir:
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use_visualdl: True
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infer_img: doc/imgs_words/ch/word_1.jpg
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# for data or label process
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max_text_length: 80
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character_dict_path: /home/zhoujun20/rec/lmdb/dict.txt
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character_type: 'en'
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use_space_char: True
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infer_mode: False
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use_tps: False
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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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learning_rate:
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name: Cosine
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lr: 0.0005
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warmup_epoch: 1
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regularizer:
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name: 'L2'
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factor: 0.00001
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Architecture:
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type: rec
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algorithm: CRNN
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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: small
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small_stride: [ 1, 2, 2, 2 ]
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Neck:
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name: SequenceEncoder
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encoder_type: reshape
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Head:
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name: CTC
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fc_decay: 0.00001
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Loss:
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name: CTCLoss
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PostProcess:
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name: CTCLabelDecode
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Metric:
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name: RecMetric
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main_indicator: acc
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TRAIN:
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dataset:
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name: LMDBDateSet
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file_list:
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- /Users/zhoujun20/Downloads/evaluation_new # dataset1
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ratio_list: [ 0.4,0.6 ]
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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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- CTCLabelEncode: # Class handling label
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- RecAug:
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- RecResizeImg:
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image_shape: [ 3,32,320 ]
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- keepKeys:
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keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
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loader:
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batch_size: 256
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shuffle: True
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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
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file_list:
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- /home/zhoujun20/rec/lmdb/val
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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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- CTCLabelEncode: # Class handling label
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- RecResizeImg:
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image_shape: [ 3,32,320 ]
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- keepKeys:
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keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
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loader:
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shuffle: False
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drop_last: False
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batch_size: 256
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num_workers: 8
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