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Commit 1b56329e authored by renzhc's avatar renzhc
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upload mmpretrain

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configs/_base_/models/efficientnet_b0.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNet', arch='b0'),
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/efficientnet_b1.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNet', arch='b1'),
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/efficientnet_b2.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNet', arch='b2'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1408,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/efficientnet_b3.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNet', arch='b3'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1536,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/efficientnet_b4.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNet', arch='b4'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1792,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/efficientnet_b5.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNet', arch='b5'),
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/efficientnet_b6.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNet', arch='b6'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2304,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/efficientnet_b7.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNet', arch='b7'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2560,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/efficientnet_b8.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNet', arch='b8'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=2816,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/efficientnet_em.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
# `em` means EfficientNet-EdgeTPU-M arch
backbone=dict(type='EfficientNet', arch='em', act_cfg=dict(type='ReLU')),
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/efficientnet_es.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
# `es` means EfficientNet-EdgeTPU-S arch
backbone=dict(type='EfficientNet', arch='es', act_cfg=dict(type='ReLU')),
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/efficientnet_l2.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNet', arch='l2'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=5504,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/efficientnet_v2/efficientnetv2_b0.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNetV2', arch='b0'),
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/efficientnet_v2/efficientnetv2_b1.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNetV2', arch='b1'),
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/efficientnet_v2/efficientnetv2_b2.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNetV2', arch='b2'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1408,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/efficientnet_v2/efficientnetv2_b3.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNetV2', arch='b3'),
neck=dict(type='GlobalAveragePooling'),
head=dict(
type='LinearClsHead',
num_classes=1000,
in_channels=1536,
loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
topk=(1, 5),
))
configs/_base_/models/efficientnet_v2/efficientnetv2_l.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNetV2', arch='l'),
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/efficientnet_v2/efficientnetv2_m.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNetV2', arch='m'),
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/efficientnet_v2/efficientnetv2_s.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNetV2', arch='s'),
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/efficientnet_v2/efficientnetv2_xl.py 0 → 100644
View file @ 1b56329e
# model settings
model = dict(
type='ImageClassifier',
backbone=dict(type='EfficientNetV2', arch='xl'),
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),
))
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