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bc3c4a35ee
* [Feature]: Add caption * [Feature]: Update scienceqa * [CI] Add test mim CI. (#879) * refactor imagenet dataset * refactor imagenet dataset * refactor imagenet dataset * update imagenet21k * update configs * update mnist * update dataset_prepare.md * fix sun397 url and update user_guides/dataset_prepare.md * update dataset_prepare.md * fix sun397 dataset * fix sun397 * update chinese dataset_prepare.md * update dataset_prepare.md * [Refactor] update voc dataset * [Refactor] update voc dataset * refactor imagenet * refactor imagenet * use mmengine.fileio --------- Co-authored-by: liuyuan <3463423099@qq.com> Co-authored-by: Ma Zerun <mzr1996@163.com> Co-authored-by: Ezra-Yu <18586273+Ezra-Yu@users.noreply.github.com>
67 lines
1.7 KiB
Python
67 lines
1.7 KiB
Python
# dataset settings
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dataset_type = 'ImageNet'
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data_preprocessor = dict(
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num_classes=1000,
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# RGB format normalization parameters
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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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# convert image from BGR to RGB
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to_rgb=True,
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)
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bgr_mean = data_preprocessor['mean'][::-1]
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bgr_std = data_preprocessor['std'][::-1]
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='RandomResizedCrop', scale=224, backend='pillow'),
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dict(type='RandomFlip', prob=0.5, direction='horizontal'),
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dict(
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type='AutoAugment',
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policies='imagenet',
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hparams=dict(pad_val=[round(x) for x in bgr_mean])),
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dict(
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type='RandomErasing',
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erase_prob=0.2,
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mode='rand',
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min_area_ratio=0.02,
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max_area_ratio=1 / 3,
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fill_color=bgr_mean,
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fill_std=bgr_std),
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dict(type='PackInputs'),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
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dict(type='CenterCrop', crop_size=224),
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dict(type='PackInputs'),
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]
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train_dataloader = dict(
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batch_size=128,
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num_workers=5,
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dataset=dict(
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type=dataset_type,
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data_root='data/imagenet',
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split='train',
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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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val_dataloader = dict(
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batch_size=128,
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num_workers=5,
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dataset=dict(
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type=dataset_type,
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data_root='data/imagenet',
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split='val',
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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, 5))
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# If you want standard test, please manually configure the test dataset
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test_dataloader = val_dataloader
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test_evaluator = val_evaluator
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