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Commit dff2c686 authored by renzhc's avatar renzhc
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configs/_base_/models/replknet-XL_in1k.py 0 → 100644
View file @ dff2c686
model = dict(
type='ImageClassifier',
backbone=dict(
type='RepLKNet',
arch='XL',
out_indices=(3, ),
),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/repmlp-base_224.py 0 → 100644
View file @ dff2c686
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='RepMLPNet',
arch='B',
img_size=224,
out_indices=(3, ),
reparam_conv_kernels=(1, 3),
deploy=False),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=768,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/repvgg-A0_in1k.py 0 → 100644
View file @ dff2c686
model = dict(
type='ImageClassifier',
backbone=dict(
type='RepVGG',
arch='A0',
out_indices=(3, ),
),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1280,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/repvgg-B3_lbs-mixup_in1k.py 0 → 100644
View file @ dff2c686
model = dict(
type='ImageClassifier',
backbone=dict(
type='RepVGG',
arch='B3',
out_indices=(3, ),
),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2560,
loss=dict(
type='LabelSmoothLoss',
loss_weight=1.0,
label_smooth_val=0.1,
mode='classy_vision',
num_classes=1000),
topk=(1, 5),
),
train_cfg=dict(augments=dict(type='Mixup', alpha=0.2)),
)
configs/_base_/models/res2net101-w26-s4.py 0 → 100644
View file @ dff2c686
model = dict(
type='ImageClassifier',
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
deep_stem=False,
avg_down=False,
),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/res2net50-w14-s8.py 0 → 100644
View file @ dff2c686
model = dict(
type='ImageClassifier',
backbone=dict(
type='Res2Net',
depth=50,
scales=8,
base_width=14,
deep_stem=False,
avg_down=False,
),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/res2net50-w26-s4.py 0 → 100644
View file @ dff2c686
model = dict(
type='ImageClassifier',
backbone=dict(
type='Res2Net',
depth=50,
scales=4,
base_width=26,
deep_stem=False,
avg_down=False,
),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/res2net50-w26-s6.py 0 → 100644
View file @ dff2c686
model = dict(
type='ImageClassifier',
backbone=dict(
type='Res2Net',
depth=50,
scales=6,
base_width=26,
deep_stem=False,
avg_down=False,
),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/res2net50-w26-s8.py 0 → 100644
View file @ dff2c686
model = dict(
type='ImageClassifier',
backbone=dict(
type='Res2Net',
depth=50,
scales=8,
base_width=26,
deep_stem=False,
avg_down=False,
),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/res2net50-w48-s2.py 0 → 100644
View file @ dff2c686
model = dict(
type='ImageClassifier',
backbone=dict(
type='Res2Net',
depth=50,
scales=2,
base_width=48,
deep_stem=False,
avg_down=False,
),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/resnest101.py 0 → 100644
View file @ dff2c686
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNeSt',
depth=101,
num_stages=4,
stem_channels=128,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(
type='LabelSmoothLoss',
label_smooth_val=0.1,
num_classes=1000,
reduction='mean',
loss_weight=1.0),
topk=(1, 5),
cal_acc=False),
train_cfg=dict(augments=dict(type='Mixup', alpha=0.2)),
)
configs/_base_/models/resnest200.py 0 → 100644
View file @ dff2c686
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNeSt',
depth=200,
num_stages=4,
stem_channels=128,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(
type='LabelSmoothLoss',
label_smooth_val=0.1,
num_classes=1000,
reduction='mean',
loss_weight=1.0),
topk=(1, 5),
cal_acc=False),
train_cfg=dict(augments=dict(type='Mixup', alpha=0.2)),
)
configs/_base_/models/resnest269.py 0 → 100644
View file @ dff2c686
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNeSt',
depth=269,
num_stages=4,
stem_channels=128,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(
type='LabelSmoothLoss',
label_smooth_val=0.1,
num_classes=1000,
reduction='mean',
loss_weight=1.0),
topk=(1, 5),
cal_acc=False),
train_cfg=dict(augments=dict(type='Mixup', alpha=0.2)),
)
configs/_base_/models/resnest50.py 0 → 100644
View file @ dff2c686
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNeSt',
depth=50,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(
type='LabelSmoothLoss',
label_smooth_val=0.1,
num_classes=1000,
reduction='mean',
loss_weight=1.0),
topk=(1, 5),
cal_acc=False),
train_cfg=dict(augments=dict(type='Mixup', alpha=0.2)),
)
configs/_base_/models/resnet101.py 0 → 100644
View file @ dff2c686
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/resnet101_cifar.py 0 → 100644
View file @ dff2c686
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=101,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=10,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
))
configs/_base_/models/resnet152.py 0 → 100644
View file @ dff2c686
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=152,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/resnet152_cifar.py 0 → 100644
View file @ dff2c686
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=152,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=10,
in_channels=2048,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
))
configs/_base_/models/resnet18.py 0 → 100644
View file @ dff2c686
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet',
depth=18,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=512,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/resnet18_cifar.py 0 → 100644
View file @ dff2c686
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ResNet_CIFAR',
depth=18,
num_stages=4,
out_indices=(3, ),
style='pytorch'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=10,
in_channels=512,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
))
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