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

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configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py 0 → 100644
View file @ 1b56329e
_base_ = [
'../_base_/models/efficientnet_b4.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
data_preprocessor = dict(
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
# convert image from BGR to RGB
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=380),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=380),
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))
configs/efficientnet/efficientnet-b4_8xb32_in1k.py 0 → 100644
View file @ 1b56329e
_base_ = [
'../_base_/models/efficientnet_b4.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=380),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=380),
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))
configs/efficientnet/efficientnet-b5_8xb32-01norm_in1k.py 0 → 100644
View file @ 1b56329e
_base_ = [
'../_base_/models/efficientnet_b5.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
data_preprocessor = dict(
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
# convert image from BGR to RGB
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=456),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=456),
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))
configs/efficientnet/efficientnet-b5_8xb32_in1k.py 0 → 100644
View file @ 1b56329e
_base_ = [
'../_base_/models/efficientnet_b5.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=456),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=456),
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))
configs/efficientnet/efficientnet-b6_8xb32-01norm_in1k.py 0 → 100644
View file @ 1b56329e
_base_ = [
'../_base_/models/efficientnet_b6.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
data_preprocessor = dict(
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
# convert image from BGR to RGB
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=528),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=528),
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))
configs/efficientnet/efficientnet-b6_8xb32_in1k.py 0 → 100644
View file @ 1b56329e
_base_ = [
'../_base_/models/efficientnet_b6.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=528),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=528),
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))
configs/efficientnet/efficientnet-b7_8xb32-01norm_in1k.py 0 → 100644
View file @ 1b56329e
_base_ = [
'../_base_/models/efficientnet_b7.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
data_preprocessor = dict(
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
# convert image from BGR to RGB
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=600),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=600),
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))
configs/efficientnet/efficientnet-b7_8xb32_in1k.py 0 → 100644
View file @ 1b56329e
_base_ = [
'../_base_/models/efficientnet_b7.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=600),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=600),
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))
configs/efficientnet/efficientnet-b8_8xb32-01norm_in1k.py 0 → 100644
View file @ 1b56329e
_base_ = [
'../_base_/models/efficientnet_b8.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
data_preprocessor = dict(
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
# convert image from BGR to RGB
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=672),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=672),
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))
configs/efficientnet/efficientnet-b8_8xb32_in1k.py 0 → 100644
View file @ 1b56329e
_base_ = [
'../_base_/models/efficientnet_b8.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=672),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=672),
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))
configs/efficientnet/efficientnet-em_8xb32-01norm_in1k.py 0 → 100644
View file @ 1b56329e
_base_ = [
'../_base_/models/efficientnet_em.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
data_preprocessor = dict(
mean=[127.5, 127.5, 127.5],
std=[127.5, 127.5, 127.5],
# convert image from BGR to RGB
to_rgb=True,
)
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=240),
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))
configs/efficientnet/efficientnet-es_8xb32-01norm_in1k.py 0 → 100644
View file @ 1b56329e
_base_ = [
'../_base_/models/efficientnet_es.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=224),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=224),
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))
configs/efficientnet/efficientnet-l2_8xb32_in1k-475px.py 0 → 100644
View file @ 1b56329e
_base_ = [
'../_base_/models/efficientnet_l2.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=475),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=475),
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))
configs/efficientnet/efficientnet-l2_8xb8_in1k-800px.py 0 → 100644
View file @ 1b56329e
_base_ = [
'../_base_/models/efficientnet_l2.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=800),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=800),
dict(type='PackInputs'),
]
train_dataloader = dict(batch_size=8, dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(batch_size=8, dataset=dict(pipeline=test_pipeline))
test_dataloader = dict(batch_size=8, dataset=dict(pipeline=test_pipeline))
configs/efficientnet/metafile.yml 0 → 100644
View file @ 1b56329e
Collections:
- Name: EfficientNet
Metadata:
Training Data: ImageNet-1k
Architecture:
- 1x1 Convolution
- Average Pooling
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- RMSProp
- Squeeze-and-Excitation Block
- Swish
Paper:
URL: https://arxiv.org/abs/1905.11946v5
Title: "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks"
README: configs/efficientnet/README.md
Code:
Version: v0.20.1
URL: https://github.com/open-mmlab/mmpretrain/blob/v0.20.1/mmcls/models/backbones/efficientnet.py
Models:
- Name: efficientnet-b0_3rdparty_8xb32_in1k
Metadata:
FLOPs: 420592480
Parameters: 5288548
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 76.74
Top 5 Accuracy: 93.17
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32_in1k_20220119-a7e2a0b1.pth
Config: configs/efficientnet/efficientnet-b0_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckpts/efficientnet-b0.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b0_3rdparty_8xb32-aa_in1k
Metadata:
FLOPs: 420592480
Parameters: 5288548
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 77.26
Top 5 Accuracy: 93.41
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa_in1k_20220119-8d939117.pth
Config: configs/efficientnet/efficientnet-b0_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b0.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b0_3rdparty_8xb32-aa-advprop_in1k
Metadata:
FLOPs: 420592480
Parameters: 5288548
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 77.53
Top 5 Accuracy: 93.61
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa-advprop_in1k_20220119-26434485.pth
Config: configs/efficientnet/efficientnet-b0_8xb32-01norm_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b0.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b0_3rdparty-ra-noisystudent_in1k
Metadata:
FLOPs: 420592480
Parameters: 5288548
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 77.63
Top 5 Accuracy: 94.00
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty-ra-noisystudent_in1k_20221103-75cd08d3.pth
Config: configs/efficientnet/efficientnet-b0_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/noisystudent/noisy_student_efficientnet-b0.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b1_3rdparty_8xb32_in1k
Metadata:
FLOPs: 744059920
Parameters: 7794184
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 78.68
Top 5 Accuracy: 94.28
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32_in1k_20220119-002556d9.pth
Config: configs/efficientnet/efficientnet-b1_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckpts/efficientnet-b1.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b1_3rdparty_8xb32-aa_in1k
Metadata:
FLOPs: 744059920
Parameters: 7794184
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 79.20
Top 5 Accuracy: 94.42
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa_in1k_20220119-619d8ae3.pth
Config: configs/efficientnet/efficientnet-b1_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b1.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b1_3rdparty_8xb32-aa-advprop_in1k
Metadata:
FLOPs: 744059920
Parameters: 7794184
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 79.52
Top 5 Accuracy: 94.43
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa-advprop_in1k_20220119-5715267d.pth
Config: configs/efficientnet/efficientnet-b1_8xb32-01norm_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b1.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b1_3rdparty-ra-noisystudent_in1k
Metadata:
FLOPs: 744059920
Parameters: 7794184
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 81.44
Top 5 Accuracy: 95.83
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty-ra-noisystudent_in1k_20221103-756bcbc0.pth
Config: configs/efficientnet/efficientnet-b1_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/noisystudent/noisy_student_efficientnet-b1.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b2_3rdparty_8xb32_in1k
Metadata:
FLOPs: 1066620392
Parameters: 9109994
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 79.64
Top 5 Accuracy: 94.80
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32_in1k_20220119-ea374a30.pth
Config: configs/efficientnet/efficientnet-b2_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckpts/efficientnet-b2.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b2_3rdparty_8xb32-aa_in1k
Metadata:
FLOPs: 1066620392
Parameters: 9109994
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 80.21
Top 5 Accuracy: 94.96
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa_in1k_20220119-dd61e80b.pth
Config: configs/efficientnet/efficientnet-b2_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b2.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b2_3rdparty_8xb32-aa-advprop_in1k
Metadata:
FLOPs: 1066620392
Parameters: 9109994
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 80.45
Top 5 Accuracy: 95.07
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa-advprop_in1k_20220119-1655338a.pth
Config: configs/efficientnet/efficientnet-b2_8xb32-01norm_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b2.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b2_3rdparty-ra-noisystudent_in1k
Metadata:
FLOPs: 1066620392
Parameters: 9109994
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 82.47
Top 5 Accuracy: 96.23
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty-ra-noisystudent_in1k_20221103-301ed299.pth
Config: configs/efficientnet/efficientnet-b2_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/noisystudent/noisy_student_efficientnet-b2.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b3_3rdparty_8xb32_in1k
Metadata:
FLOPs: 1953798216
Parameters: 12233232
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 81.01
Top 5 Accuracy: 95.34
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32_in1k_20220119-4b4d7487.pth
Config: configs/efficientnet/efficientnet-b3_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckpts/efficientnet-b3.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b3_3rdparty_8xb32-aa_in1k
Metadata:
FLOPs: 1953798216
Parameters: 12233232
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 81.58
Top 5 Accuracy: 95.67
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa_in1k_20220119-5b4887a0.pth
Config: configs/efficientnet/efficientnet-b3_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b3.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b3_3rdparty_8xb32-aa-advprop_in1k
Metadata:
FLOPs: 1953798216
Parameters: 12233232
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 81.81
Top 5 Accuracy: 95.69
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa-advprop_in1k_20220119-53b41118.pth
Config: configs/efficientnet/efficientnet-b3_8xb32-01norm_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b3.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b3_3rdparty-ra-noisystudent_in1k
Metadata:
FLOPs: 1953798216
Parameters: 12233232
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 84.02
Top 5 Accuracy: 96.89
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty-ra-noisystudent_in1k_20221103-a4ab5fd6.pth
Config: configs/efficientnet/efficientnet-b3_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/noisystudent/noisy_student_efficientnet-b3.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b4_3rdparty_8xb32_in1k
Metadata:
FLOPs: 4659080176
Parameters: 19341616
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 82.57
Top 5 Accuracy: 96.09
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32_in1k_20220119-81fd4077.pth
Config: configs/efficientnet/efficientnet-b4_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckpts/efficientnet-b4.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b4_3rdparty_8xb32-aa_in1k
Metadata:
FLOPs: 4659080176
Parameters: 19341616
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 82.95
Top 5 Accuracy: 96.26
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa_in1k_20220119-45b8bd2b.pth
Config: configs/efficientnet/efficientnet-b4_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b4.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b4_3rdparty_8xb32-aa-advprop_in1k
Metadata:
FLOPs: 4659080176
Parameters: 19341616
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 83.25
Top 5 Accuracy: 96.44
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa-advprop_in1k_20220119-38c2238c.pth
Config: configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b4.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b4_3rdparty-ra-noisystudent_in1k
Metadata:
FLOPs: 4659080176
Parameters: 19341616
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 85.25
Top 5 Accuracy: 97.52
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty-ra-noisystudent_in1k_20221103-16ba8a2d.pth
Config: configs/efficientnet/efficientnet-b4_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/noisystudent/noisy_student_efficientnet-b4.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b5_3rdparty_8xb32_in1k
Metadata:
FLOPs: 10799472560
Parameters: 30389784
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 83.18
Top 5 Accuracy: 96.47
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32_in1k_20220119-e9814430.pth
Config: configs/efficientnet/efficientnet-b5_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckpts/efficientnet-b5.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b5_3rdparty_8xb32-aa_in1k
Metadata:
FLOPs: 10799472560
Parameters: 30389784
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 83.82
Top 5 Accuracy: 96.76
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa_in1k_20220119-2cab8b78.pth
Config: configs/efficientnet/efficientnet-b5_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b5.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b5_3rdparty_8xb32-aa-advprop_in1k
Metadata:
FLOPs: 10799472560
Parameters: 30389784
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 84.21
Top 5 Accuracy: 96.98
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa-advprop_in1k_20220119-f57a895a.pth
Config: configs/efficientnet/efficientnet-b5_8xb32-01norm_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b5.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b5_3rdparty-ra-noisystudent_in1k
Metadata:
FLOPs: 10799472560
Parameters: 30389784
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 86.08
Top 5 Accuracy: 97.75
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty-ra-noisystudent_in1k_20221103-111a185f.pth
Config: configs/efficientnet/efficientnet-b5_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/noisystudent/noisy_student_efficientnet-b5.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b6_3rdparty_8xb32-aa_in1k
Metadata:
FLOPs: 19971777560
Parameters: 43040704
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 84.05
Top 5 Accuracy: 96.82
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa_in1k_20220119-45b03310.pth
Config: configs/efficientnet/efficientnet-b6_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b6.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b6_3rdparty_8xb32-aa-advprop_in1k
Metadata:
FLOPs: 19971777560
Parameters: 43040704
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 84.74
Top 5 Accuracy: 97.14
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa-advprop_in1k_20220119-bfe3485e.pth
Config: configs/efficientnet/efficientnet-b6_8xb32-01norm_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b6.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b6_3rdparty-ra-noisystudent_in1k
Metadata:
FLOPs: 19971777560
Parameters: 43040704
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 86.47
Top 5 Accuracy: 97.87
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty-ra-noisystudent_in1k_20221103-7de7d2cc.pth
Config: configs/efficientnet/efficientnet-b6_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/noisystudent/noisy_student_efficientnet-b6.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b7_3rdparty_8xb32-aa_in1k
Metadata:
FLOPs: 39316473392
Parameters: 66347960
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 84.38
Top 5 Accuracy: 96.88
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa_in1k_20220119-bf03951c.pth
Config: configs/efficientnet/efficientnet-b7_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b7.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b7_3rdparty_8xb32-aa-advprop_in1k
Metadata:
FLOPs: 39316473392
Parameters: 66347960
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 85.14
Top 5 Accuracy: 97.23
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa-advprop_in1k_20220119-c6dbff10.pth
Config: configs/efficientnet/efficientnet-b7_8xb32-01norm_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b7.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b7_3rdparty-ra-noisystudent_in1k
Metadata:
FLOPs: 39316473392
Parameters: 66347960
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 86.83
Top 5 Accuracy: 98.08
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty-ra-noisystudent_in1k_20221103-a82894bc.pth
Config: configs/efficientnet/efficientnet-b7_8xb32_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/noisystudent/noisy_student_efficientnet-b7.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-b8_3rdparty_8xb32-aa-advprop_in1k
Metadata:
FLOPs: 64999827816
Parameters: 87413142
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 85.38
Top 5 Accuracy: 97.28
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b8_3rdparty_8xb32-aa-advprop_in1k_20220119-297ce1b7.pth
Config: configs/efficientnet/efficientnet-b8_8xb32-01norm_in1k.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b8.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-l2_3rdparty-ra-noisystudent_in1k-800px
Metadata:
FLOPs: 174203533416
Parameters: 480309308
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 88.33
Top 5 Accuracy: 98.65
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-l2_3rdparty-ra-noisystudent_in1k_20221103-be73be13.pth
Config: configs/efficientnet/efficientnet-l2_8xb8_in1k-800px.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/noisystudent/noisy_student_efficientnet-l2.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
- Name: efficientnet-l2_3rdparty-ra-noisystudent_in1k-475px
Metadata:
FLOPs: 484984099280
Parameters: 480309308
In Collection: EfficientNet
Results:
- Dataset: ImageNet-1k
Metrics:
Top 1 Accuracy: 88.18
Top 5 Accuracy: 98.55
Task: Image Classification
Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-l2_3rdparty-ra-noisystudent_in1k-475px_20221103-5a0d8058.pth
Config: configs/efficientnet/efficientnet-l2_8xb32_in1k-475px.py
Converted From:
Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/noisystudent/noisy_student_efficientnet-l2_475.tar.gz
Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
configs/efficientnet_v2/README.md 0 → 100644
View file @ 1b56329e
# EfficientNetV2
> [EfficientNetV2: Smaller Models and Faster Training](https://arxiv.org/abs/2104.00298)
<!-- [ALGORITHM] -->
## Abstract
This paper introduces EfficientNetV2, a new family of convolutional networks that have faster training speed and better parameter efficiency than previous models. To develop this family of models, we use a combination of training-aware neural architecture search and scaling, to jointly optimize training speed and parameter efficiency. The models were searched from the search space enriched with new ops such as Fused-MBConv. Our experiments show that EfficientNetV2 models train much faster than state-of-the-art models while being up to 6.8x smaller. Our training can be further sped up by progressively increasing the image size during training, but it often causes a drop in accuracy. To compensate for this accuracy drop, we propose to adaptively adjust regularization (e.g., dropout and data augmentation) as well, such that we can achieve both fast training and good accuracy. With progressive learning, our EfficientNetV2 significantly outperforms previous models on ImageNet and CIFAR/Cars/Flowers datasets. By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87.3% top-1 accuracy on ImageNet ILSVRC2012, outperforming the recent ViT by 2.0% accuracy while training 5x-11x faster using the same computing resources. Code will be available at https://github.com/google/automl/tree/master/efficientnetv2.
<div align=center>
<img src="https://user-images.githubusercontent.com/18586273/208616931-0c5107f1-f08c-48d3-8694-7a6eaf227dc2.png" width="50%"/>
</div>
## How to use it?
<!-- [TABS-BEGIN] -->
**Predict image**
```python
from mmpretrain import inference_model
predict = inference_model('efficientnetv2-b0_3rdparty_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])
```
**Use the model**
```python
import torch
from mmpretrain import get_model
model = get_model('efficientnetv2-b0_3rdparty_in1k', pretrained=True)
inputs = torch.rand(1, 3, 224, 224)
out = model(inputs)
print(type(out))
# To extract features.
feats = model.extract_feat(inputs)
print(type(feats))
```
**Test Command**
Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
Test:
```shell
python tools/test.py configs/efficientnet_v2/efficientnetv2-b0_8xb32_in1k.py https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-b0_3rdparty_in1k_20221221-9ef6e736.pth
```
<!-- [TABS-END] -->
## Models and results
### Pretrained models
| Model | Params (M) | Flops (G) | Config | Download |
| :----------------------------------- | :--------: | :-------: | :----------------------------------------: | :-----------------------------------------------------------------------------------------------------: |
| `efficientnetv2-s_3rdparty_in21k`\* | 48.16 | 3.31 | [config](efficientnetv2-s_8xb32_in21k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-s_3rdparty_in21k_20221220-c0572b56.pth) |
| `efficientnetv2-m_3rdparty_in21k`\* | 80.84 | 5.86 | [config](efficientnetv2-m_8xb32_in21k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-m_3rdparty_in21k_20221220-073e944c.pth) |
| `efficientnetv2-l_3rdparty_in21k`\* | 145.22 | 13.11 | [config](efficientnetv2-l_8xb32_in21k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-l_3rdparty_in21k_20221220-f28f91e1.pth) |
| `efficientnetv2-xl_3rdparty_in21k`\* | 234.82 | 18.86 | [config](efficientnetv2-xl_8xb32_in21k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-xl_3rdparty_in21k_20221220-b2c9329c.pth) |
*Models with * are converted from the [timm](https://github.com/rwightman/pytorch-image-models/blob/main/timm/models/efficientnet.py). The config files of these models are only for inference. We haven't reproduce the training results.*
### Image Classification on ImageNet-1k
| Model | Pretrain | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) | Config | Download |
| :-------------------------------------------- | :----------: | :--------: | :-------: | :-------: | :-------: | :---------------------------------------------: | :---------------------------------------------------------: |
| `efficientnetv2-b0_3rdparty_in1k`\* | From scratch | 7.14 | 0.92 | 78.52 | 94.44 | [config](efficientnetv2-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-b0_3rdparty_in1k_20221221-9ef6e736.pth) |
| `efficientnetv2-b1_3rdparty_in1k`\* | From scratch | 8.14 | 1.44 | 79.80 | 94.89 | [config](efficientnetv2-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-b1_3rdparty_in1k_20221221-6955d9ce.pth) |
| `efficientnetv2-b2_3rdparty_in1k`\* | From scratch | 10.10 | 1.99 | 80.63 | 95.30 | [config](efficientnetv2-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-b2_3rdparty_in1k_20221221-74f7d493.pth) |
| `efficientnetv2-b3_3rdparty_in1k`\* | From scratch | 14.36 | 3.50 | 82.03 | 95.88 | [config](efficientnetv2-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-b3_3rdparty_in1k_20221221-b6f07a36.pth) |
| `efficientnetv2-s_3rdparty_in1k`\* | From scratch | 21.46 | 9.72 | 83.82 | 96.67 | [config](efficientnetv2-s_8xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-s_3rdparty_in1k_20221220-f0eaff9d.pth) |
| `efficientnetv2-m_3rdparty_in1k`\* | From scratch | 54.14 | 26.88 | 85.01 | 97.26 | [config](efficientnetv2-m_8xb32_in1k-480px.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-m_3rdparty_in1k_20221220-9dc0c729.pth) |
| `efficientnetv2-l_3rdparty_in1k`\* | From scratch | 118.52 | 60.14 | 85.43 | 97.31 | [config](efficientnetv2-l_8xb32_in1k-480px.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-l_3rdparty_in1k_20221220-5c3bac0f.pth) |
| `efficientnetv2-s_in21k-pre_3rdparty_in1k`\* | ImageNet-21k | 21.46 | 9.72 | 84.29 | 97.26 | [config](efficientnetv2-s_8xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-s_in21k-pre-3rdparty_in1k_20221220-7a7c8475.pth) |
| `efficientnetv2-m_in21k-pre_3rdparty_in1k`\* | ImageNet-21k | 54.14 | 26.88 | 85.47 | 97.76 | [config](efficientnetv2-m_8xb32_in1k-480px.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-m_in21k-pre-3rdparty_in1k_20221220-a1013a04.pth) |
| `efficientnetv2-l_in21k-pre_3rdparty_in1k`\* | ImageNet-21k | 118.52 | 60.14 | 86.31 | 97.99 | [config](efficientnetv2-l_8xb32_in1k-480px.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-l_in21k-pre-3rdparty_in1k_20221220-63df0efd.pth) |
| `efficientnetv2-xl_in21k-pre_3rdparty_in1k`\* | ImageNet-21k | 208.12 | 98.34 | 86.39 | 97.83 | [config](efficientnetv2-xl_8xb32_in1k-512px.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnetv2/efficientnetv2-xl_in21k-pre-3rdparty_in1k_20221220-583ac18b.pth) |
*Models with * are converted from the [timm](https://github.com/rwightman/pytorch-image-models/blob/main/timm/models/efficientnet.py). The config files of these models are only for inference. We haven't reproduce the training results.*
## Citation
```bibtex
@inproceedings{tan2021efficientnetv2,
title={Efficientnetv2: Smaller models and faster training},
author={Tan, Mingxing and Le, Quoc},
booktitle={International Conference on Machine Learning},
pages={10096--10106},
year={2021},
organization={PMLR}
}
```
configs/efficientnet_v2/efficientnetv2-b0_8xb32_in1k.py 0 → 100644
View file @ 1b56329e
_base_ = [
'../_base_/models/efficientnet_v2/efficientnetv2_b0.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
dataset_type = 'ImageNet'
data_preprocessor = dict(
num_classes=1000,
# RGB format normalization parameters
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
# convert image from BGR to RGB
to_rgb=True,
)
bgr_mean = data_preprocessor['mean'][::-1]
bgr_std = data_preprocessor['std'][::-1]
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=192,
backend='pillow',
interpolation='bicubic'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='RandAugment',
policies='timm_increasing',
num_policies=2,
total_level=10,
magnitude_level=9,
magnitude_std=0.5,
hparams=dict(
pad_val=[round(x) for x in bgr_mean], interpolation='bicubic')),
dict(
type='RandomErasing',
erase_prob=0.25,
mode='rand',
min_area_ratio=0.02,
max_area_ratio=1 / 3,
fill_color=bgr_mean,
fill_std=bgr_std),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=224, 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))
configs/efficientnet_v2/efficientnetv2-b1_8xb32_in1k.py 0 → 100644
View file @ 1b56329e
_base_ = ['./efficientnetv2-b0_8xb32_in1k.py']
# model setting
model = dict(backbone=dict(arch='b1'), head=dict(in_channels=1280, ))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=192),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=240, 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))
configs/efficientnet_v2/efficientnetv2-b2_8xb32_in1k.py 0 → 100644
View file @ 1b56329e
_base_ = ['./efficientnetv2-b0_8xb32_in1k.py']
# model setting
model = dict(backbone=dict(arch='b2'), head=dict(in_channels=1408, ))
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=208),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=260, 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))
configs/efficientnet_v2/efficientnetv2-b3_8xb32_in1k.py 0 → 100644
View file @ 1b56329e
_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))
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