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Commit d476eeba authored by renzhc's avatar renzhc
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upload mmpretrain

parent 62b8498e
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configs/_base_/models/conformer/base-p16.py 0 → 100644
View file @ d476eeba
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='Conformer', arch='base', drop_path_rate=0.1, init_cfg=None),
neck=None,
head=dict(
type='ConformerHead',
num_classes=1000,
in_channels=[1536, 576],
init_cfg=None,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
cal_acc=False),
init_cfg=[
dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
],
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0)
]),
)
configs/_base_/models/conformer/small-p16.py 0 → 100644
View file @ d476eeba
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='Conformer', arch='small', drop_path_rate=0.1, init_cfg=None),
neck=None,
head=dict(
type='ConformerHead',
num_classes=1000,
in_channels=[1024, 384],
init_cfg=None,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
cal_acc=False),
init_cfg=[
dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
],
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0)
]),
)
configs/_base_/models/conformer/small-p32.py 0 → 100644
View file @ d476eeba
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='Conformer',
arch='small',
patch_size=32,
drop_path_rate=0.1,
init_cfg=None),
neck=None,
head=dict(
type='ConformerHead',
num_classes=1000,
in_channels=[1024, 384],
init_cfg=None,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
cal_acc=False),
init_cfg=[
dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
],
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0)
]),
)
configs/_base_/models/conformer/tiny-p16.py 0 → 100644
View file @ d476eeba
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='Conformer', arch='tiny', drop_path_rate=0.1, init_cfg=None),
neck=None,
head=dict(
type='ConformerHead',
num_classes=1000,
in_channels=[256, 384],
init_cfg=None,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
cal_acc=False),
init_cfg=[
dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),
dict(type='Constant', layer='LayerNorm', val=1., bias=0.)
],
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0)
]),
)
configs/_base_/models/convmixer/convmixer-1024-20.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='ConvMixer', arch='1024/20'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1024,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
))
configs/_base_/models/convmixer/convmixer-1536-20.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='ConvMixer', arch='1536/20'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1536,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
))
configs/_base_/models/convmixer/convmixer-768-32.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='ConvMixer', arch='768/32', act_cfg=dict(type='ReLU')),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=768,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
))
configs/_base_/models/convnext/convnext-base.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='ConvNeXt', arch='base', drop_path_rate=0.5),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1024,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
init_cfg=None,
),
init_cfg=dict(
type='TruncNormal', layer=['Conv2d', 'Linear'], std=.02, bias=0.),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0),
]),
)
configs/_base_/models/convnext/convnext-large.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='ConvNeXt', arch='large', drop_path_rate=0.5),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1536,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
init_cfg=None,
),
init_cfg=dict(
type='TruncNormal', layer=['Conv2d', 'Linear'], std=.02, bias=0.),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0),
]),
)
configs/_base_/models/convnext/convnext-small.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='ConvNeXt', arch='small', drop_path_rate=0.4),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=768,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
init_cfg=None,
),
init_cfg=dict(
type='TruncNormal', layer=['Conv2d', 'Linear'], std=.02, bias=0.),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0),
]),
)
configs/_base_/models/convnext/convnext-tiny.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='ConvNeXt', arch='tiny', drop_path_rate=0.1),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=768,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
init_cfg=None,
),
init_cfg=dict(
type='TruncNormal', layer=['Conv2d', 'Linear'], std=.02, bias=0.),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0),
]),
)
configs/_base_/models/convnext/convnext-xlarge.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='ConvNeXt', arch='xlarge', drop_path_rate=0.5),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2048,
loss=dict(
type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
init_cfg=None,
),
init_cfg=dict(
type='TruncNormal', layer=['Conv2d', 'Linear'], std=.02, bias=0.),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0),
]),
)
configs/_base_/models/convnext_v2/atto.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ConvNeXt',
arch='atto',
drop_path_rate=0.1,
layer_scale_init_value=0.,
use_grn=True,
),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=320,
loss=dict(type='LabelSmoothLoss', label_smooth_val=0.2),
init_cfg=None,
),
init_cfg=dict(
type='TruncNormal', layer=['Conv2d', 'Linear'], std=.02, bias=0.),
)
configs/_base_/models/convnext_v2/base.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ConvNeXt',
arch='base',
drop_path_rate=0.1,
layer_scale_init_value=0.,
use_grn=True,
),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1024,
loss=dict(type='LabelSmoothLoss', label_smooth_val=0.1),
init_cfg=None,
),
init_cfg=dict(
type='TruncNormal', layer=['Conv2d', 'Linear'], std=.02, bias=0.),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0),
]),
)
configs/_base_/models/convnext_v2/femto.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ConvNeXt',
arch='femto',
drop_path_rate=0.1,
layer_scale_init_value=0.,
use_grn=True,
),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=384,
loss=dict(type='LabelSmoothLoss', label_smooth_val=0.1),
init_cfg=None,
),
init_cfg=dict(
type='TruncNormal', layer=['Conv2d', 'Linear'], std=.02, bias=0.),
)
configs/_base_/models/convnext_v2/huge.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ConvNeXt',
arch='huge',
drop_path_rate=0.1,
layer_scale_init_value=0.,
use_grn=True,
),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2816,
loss=dict(type='LabelSmoothLoss', label_smooth_val=0.1),
init_cfg=None,
),
init_cfg=dict(
type='TruncNormal', layer=['Conv2d', 'Linear'], std=.02, bias=0.),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0),
]),
)
configs/_base_/models/convnext_v2/large.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ConvNeXt',
arch='large',
drop_path_rate=0.1,
layer_scale_init_value=0.,
use_grn=True,
),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1536,
loss=dict(type='LabelSmoothLoss', label_smooth_val=0.1),
init_cfg=None,
),
init_cfg=dict(
type='TruncNormal', layer=['Conv2d', 'Linear'], std=.02, bias=0.),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0),
]),
)
configs/_base_/models/convnext_v2/nano.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ConvNeXt',
arch='nano',
drop_path_rate=0.1,
layer_scale_init_value=0.,
use_grn=True,
),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=640,
loss=dict(type='LabelSmoothLoss', label_smooth_val=0.2),
init_cfg=None,
),
init_cfg=dict(
type='TruncNormal', layer=['Conv2d', 'Linear'], std=.02, bias=0.),
)
configs/_base_/models/convnext_v2/pico.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ConvNeXt',
arch='pico',
drop_path_rate=0.1,
layer_scale_init_value=0.,
use_grn=True,
),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=512,
loss=dict(type='LabelSmoothLoss', label_smooth_val=0.1),
init_cfg=None,
),
init_cfg=dict(
type='TruncNormal', layer=['Conv2d', 'Linear'], std=.02, bias=0.),
)
configs/_base_/models/convnext_v2/tiny.py 0 → 100644
View file @ d476eeba
# Model settings
model = dict(
type='ImageClassifier',
backbone=dict(
type='ConvNeXt',
arch='tiny',
drop_path_rate=0.2,
layer_scale_init_value=0.,
use_grn=True,
),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=768,
loss=dict(type='LabelSmoothLoss', label_smooth_val=0.2),
init_cfg=None,
),
init_cfg=dict(
type='TruncNormal', layer=['Conv2d', 'Linear'], std=.02, bias=0.),
train_cfg=dict(augments=[
dict(type='Mixup', alpha=0.8),
dict(type='CutMix', alpha=1.0),
]),
)
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