mmselfsup/configs/benchmarks/classification/imagenet/resnet50_mhead_8xb32-steplr...

49 lines
1.2 KiB
Python

_base_ = [
'../_base_/models/resnet50_multihead.py',
'../_base_/datasets/imagenet.py',
'../_base_/schedules/sgd_steplr-100e.py',
'../_base_/default_runtime.py',
]
model = dict(backbone=dict(frozen_stages=4))
# dataset settings
img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_pipeline = [
dict(type='RandomResizedCrop', size=224),
dict(type='RandomHorizontalFlip'),
dict(
type='ColorJitter',
brightness=0.4,
contrast=0.4,
saturation=0.4,
hue=0.),
dict(type='ToTensor'),
dict(type='Lighting'),
dict(type='Normalize', **img_norm_cfg),
]
test_pipeline = [
dict(type='Resize', size=256),
dict(type='CenterCrop', size=224),
dict(type='ToTensor'),
dict(type='Normalize', **img_norm_cfg),
]
data = dict(
train=dict(pipeline=train_pipeline), val=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(
type='SGD',
lr=0.01,
momentum=0.9,
weight_decay=1e-4,
paramwise_options=dict(norm_decay_mult=0.),
nesterov=True)
# learning policy
lr_config = dict(policy='step', step=[30, 60, 90])
# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=90)
checkpoint_config = dict(interval=10)