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# dataset settings
dataset_type = 'ImageNet'
data_preprocessor = dict(
num_classes=1000,
# RGB format normalization parameters
mean=[0.48145466 * 255, 0.4578275 * 255, 0.40821073 * 255],
std=[0.26862954 * 255, 0.26130258 * 255, 0.27577711 * 255],
# convert image from BGR to RGB
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=336,
backend='pillow',
interpolation='bicubic'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackClsInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='ResizeEdge',
scale=336,
edge='short',
backend='pillow',
interpolation='bicubic'),
dict(type='CenterCrop', crop_size=336),
dict(type='PackClsInputs'),
]
train_dataloader = dict(
batch_size=16,
num_workers=5,
dataset=dict(
type=dataset_type,
data_root='data/imagenet',
ann_file='meta/train.txt',
data_prefix='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
)
val_dataloader = dict(
batch_size=16,
num_workers=5,
dataset=dict(
type=dataset_type,
data_root='data/imagenet',
ann_file='meta/val.txt',
data_prefix='val',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
)
val_evaluator = dict(type='Accuracy', topk=(1, 5))
# If you want standard test, please manually configure the test dataset
test_dataloader = val_dataloader
test_evaluator = val_evaluator
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