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efficientnet-l2_8xb8_in1k-800px.py 770 Bytes
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_base_ = [
    '../_base_/models/efficientnet_l2.py',
    '../_base_/datasets/imagenet_bs32.py',
    '../_base_/schedules/imagenet_bs256.py',
    '../_base_/default_runtime.py',
]

# dataset settings
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='EfficientNetRandomCrop', scale=800),
    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
    dict(type='PackInputs'),
]

test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='EfficientNetCenterCrop', crop_size=800),
    dict(type='PackInputs'),
]

train_dataloader = dict(batch_size=8, dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(batch_size=8, dataset=dict(pipeline=test_pipeline))
test_dataloader = dict(batch_size=8, dataset=dict(pipeline=test_pipeline))