96 lines
2.0 KiB
YAML
96 lines
2.0 KiB
YAML
Global:
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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/cls/mv3/
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save_epoch_step: 3
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# evaluation is run every 5000 iterations after the 4000th iteration
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eval_batch_step: [0, 1000]
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cal_metric_during_train: True
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pretrained_model:
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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: doc/imgs_words_en/word_10.png
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label_list: ['0','180']
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Architecture:
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model_type: cls
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algorithm: CLS
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Transform:
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Backbone:
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name: MobileNetV3
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scale: 0.35
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model_name: small
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Neck:
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Head:
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name: ClsHead
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class_dim: 2
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Loss:
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name: ClsLoss
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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: 0
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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/cls
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label_file_list:
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- ./train_data/cls/train.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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- ClsLabelEncode: # Class handling label
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- RecAug:
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use_tia: False
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- RandAugment:
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- ClsResizeImg:
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image_shape: [3, 48, 192]
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- KeepKeys:
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keep_keys: ['image', 'label'] # dataloader will return list in this order
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loader:
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shuffle: True
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batch_size_per_card: 512
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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/cls
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label_file_list:
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- ./train_data/cls/test.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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- ClsLabelEncode: # Class handling label
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- ClsResizeImg:
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image_shape: [3, 48, 192]
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- KeepKeys:
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keep_keys: ['image', 'label'] # dataloader will return list in this order
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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: 512
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num_workers: 4
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