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|
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dataset_type = 'CUB' |
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data_preprocessor = dict( |
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num_classes=200, |
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mean=[123.675, 116.28, 103.53], |
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std=[58.395, 57.12, 57.375], |
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|
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to_rgb=True, |
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) |
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|
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train_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='Resize', scale=600), |
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dict(type='RandomCrop', crop_size=448), |
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dict(type='RandomFlip', prob=0.5, direction='horizontal'), |
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dict(type='PackInputs'), |
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] |
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|
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test_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='Resize', scale=600), |
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dict(type='CenterCrop', crop_size=448), |
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dict(type='PackInputs'), |
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] |
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|
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train_dataloader = dict( |
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batch_size=8, |
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num_workers=2, |
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dataset=dict( |
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type=dataset_type, |
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data_root='data/CUB_200_2011', |
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test_mode=False, |
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pipeline=train_pipeline), |
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sampler=dict(type='DefaultSampler', shuffle=True), |
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) |
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|
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val_dataloader = dict( |
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batch_size=8, |
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|
num_workers=2, |
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dataset=dict( |
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type=dataset_type, |
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data_root='data/CUB_200_2011', |
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test_mode=True, |
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pipeline=test_pipeline), |
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sampler=dict(type='DefaultSampler', shuffle=False), |
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) |
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val_evaluator = dict(type='Accuracy', topk=(1, )) |
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|
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test_dataloader = val_dataloader |
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test_evaluator = val_evaluator |
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|
