efficientnetv2-b3_8xb32_in1k.py

_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='LoadImageFromFile'),
    dict(type='EfficientNetCenterCrop', crop_size=300, crop_padding=0),
    dict(type='PackInputs'),
]

train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = dict(dataset=dict(pipeline=test_pipeline))