_base_ = ['./efficientnetv2-b0_8xb32_in1k.py']
# model setting
model = dict(backbone=dict(arch='b3'), head=dict(in_channels=1536, ))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=240),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='EfficientNetCenterCrop', crop_size=300, crop_padding=0),
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = dict(dataset=dict(pipeline=test_pipeline))