You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
360 lines
8.9 KiB
360 lines
8.9 KiB
# ResNeSt
|
|
|
|
A **ResNeSt** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks [Split-Attention blocks](https://paperswithcode.com/method/split-attention). The cardinal group representations are then concatenated along the channel dimension: $V = \text{Concat}${$V^{1},V^{2},\cdots{V}^{K}$}. As in standard residual blocks, the final output $Y$ of otheur Split-Attention block is produced using a shortcut connection: $Y=V+X$, if the input and output feature-map share the same shape. For blocks with a stride, an appropriate transformation $\mathcal{T}$ is applied to the shortcut connection to align the output shapes: $Y=V+\mathcal{T}(X)$. For example, $\mathcal{T}$ can be strided convolution or combined convolution-with-pooling.
|
|
|
|
{% include 'code_snippets.md' %}
|
|
|
|
## How do I train this model?
|
|
|
|
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
|
|
|
|
## Citation
|
|
|
|
```BibTeX
|
|
@misc{zhang2020resnest,
|
|
title={ResNeSt: Split-Attention Networks},
|
|
author={Hang Zhang and Chongruo Wu and Zhongyue Zhang and Yi Zhu and Haibin Lin and Zhi Zhang and Yue Sun and Tong He and Jonas Mueller and R. Manmatha and Mu Li and Alexander Smola},
|
|
year={2020},
|
|
eprint={2004.08955},
|
|
archivePrefix={arXiv},
|
|
primaryClass={cs.CV}
|
|
}
|
|
```
|
|
|
|
<!--
|
|
Models:
|
|
- Name: resnest50d_4s2x40d
|
|
Metadata:
|
|
FLOPs: 5657064720
|
|
Epochs: 270
|
|
Batch Size: 8192
|
|
Training Data:
|
|
- ImageNet
|
|
Training Techniques:
|
|
- AutoAugment
|
|
- DropBlock
|
|
- Label Smoothing
|
|
- Mixup
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Resources: 64x NVIDIA V100 GPUs
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Convolution
|
|
- Dense Connections
|
|
- Global Average Pooling
|
|
- Max Pooling
|
|
- ReLU
|
|
- Residual Connection
|
|
- Softmax
|
|
- Split Attention
|
|
File Size: 122133282
|
|
Tasks:
|
|
- Image Classification
|
|
Training Time: ''
|
|
ID: resnest50d_4s2x40d
|
|
LR: 0.1
|
|
Layers: 50
|
|
Dropout: 0.2
|
|
Crop Pct: '0.875'
|
|
Momentum: 0.9
|
|
Image Size: '224'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bicubic
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L218
|
|
Config: ''
|
|
In Collection: ResNeSt
|
|
- Name: resnest200e
|
|
Metadata:
|
|
FLOPs: 45954387872
|
|
Epochs: 270
|
|
Batch Size: 2048
|
|
Training Data:
|
|
- ImageNet
|
|
Training Techniques:
|
|
- AutoAugment
|
|
- DropBlock
|
|
- Label Smoothing
|
|
- Mixup
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Resources: 64x NVIDIA V100 GPUs
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Convolution
|
|
- Dense Connections
|
|
- Global Average Pooling
|
|
- Max Pooling
|
|
- ReLU
|
|
- Residual Connection
|
|
- Softmax
|
|
- Split Attention
|
|
File Size: 193782911
|
|
Tasks:
|
|
- Image Classification
|
|
Training Time: ''
|
|
ID: resnest200e
|
|
LR: 0.1
|
|
Layers: 200
|
|
Dropout: 0.2
|
|
Crop Pct: '0.909'
|
|
Momentum: 0.9
|
|
Image Size: '320'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bicubic
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L194
|
|
Config: ''
|
|
In Collection: ResNeSt
|
|
- Name: resnest14d
|
|
Metadata:
|
|
FLOPs: 3548594464
|
|
Epochs: 270
|
|
Batch Size: 8192
|
|
Training Data:
|
|
- ImageNet
|
|
Training Techniques:
|
|
- AutoAugment
|
|
- DropBlock
|
|
- Label Smoothing
|
|
- Mixup
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Resources: 64x NVIDIA V100 GPUs
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Convolution
|
|
- Dense Connections
|
|
- Global Average Pooling
|
|
- Max Pooling
|
|
- ReLU
|
|
- Residual Connection
|
|
- Softmax
|
|
- Split Attention
|
|
File Size: 42562639
|
|
Tasks:
|
|
- Image Classification
|
|
Training Time: ''
|
|
ID: resnest14d
|
|
LR: 0.1
|
|
Layers: 14
|
|
Dropout: 0.2
|
|
Crop Pct: '0.875'
|
|
Momentum: 0.9
|
|
Image Size: '224'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bilinear
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L148
|
|
Config: ''
|
|
In Collection: ResNeSt
|
|
- Name: resnest101e
|
|
Metadata:
|
|
FLOPs: 17423183648
|
|
Epochs: 270
|
|
Batch Size: 4096
|
|
Training Data:
|
|
- ImageNet
|
|
Training Techniques:
|
|
- AutoAugment
|
|
- DropBlock
|
|
- Label Smoothing
|
|
- Mixup
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Resources: 64x NVIDIA V100 GPUs
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Convolution
|
|
- Dense Connections
|
|
- Global Average Pooling
|
|
- Max Pooling
|
|
- ReLU
|
|
- Residual Connection
|
|
- Softmax
|
|
- Split Attention
|
|
File Size: 193782911
|
|
Tasks:
|
|
- Image Classification
|
|
Training Time: ''
|
|
ID: resnest101e
|
|
LR: 0.1
|
|
Layers: 101
|
|
Dropout: 0.2
|
|
Crop Pct: '0.875'
|
|
Momentum: 0.9
|
|
Image Size: '256'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bilinear
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L182
|
|
Config: ''
|
|
In Collection: ResNeSt
|
|
- Name: resnest269e
|
|
Metadata:
|
|
FLOPs: 100830307104
|
|
Epochs: 270
|
|
Batch Size: 2048
|
|
Training Data:
|
|
- ImageNet
|
|
Training Techniques:
|
|
- AutoAugment
|
|
- DropBlock
|
|
- Label Smoothing
|
|
- Mixup
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Resources: 64x NVIDIA V100 GPUs
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Convolution
|
|
- Dense Connections
|
|
- Global Average Pooling
|
|
- Max Pooling
|
|
- ReLU
|
|
- Residual Connection
|
|
- Softmax
|
|
- Split Attention
|
|
File Size: 445402691
|
|
Tasks:
|
|
- Image Classification
|
|
Training Time: ''
|
|
ID: resnest269e
|
|
LR: 0.1
|
|
Layers: 269
|
|
Dropout: 0.2
|
|
Crop Pct: '0.928'
|
|
Momentum: 0.9
|
|
Image Size: '416'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bicubic
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L206
|
|
Config: ''
|
|
In Collection: ResNeSt
|
|
- Name: resnest26d
|
|
Metadata:
|
|
FLOPs: 4678918720
|
|
Epochs: 270
|
|
Batch Size: 8192
|
|
Training Data:
|
|
- ImageNet
|
|
Training Techniques:
|
|
- AutoAugment
|
|
- DropBlock
|
|
- Label Smoothing
|
|
- Mixup
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Resources: 64x NVIDIA V100 GPUs
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Convolution
|
|
- Dense Connections
|
|
- Global Average Pooling
|
|
- Max Pooling
|
|
- ReLU
|
|
- Residual Connection
|
|
- Softmax
|
|
- Split Attention
|
|
File Size: 68470242
|
|
Tasks:
|
|
- Image Classification
|
|
Training Time: ''
|
|
ID: resnest26d
|
|
LR: 0.1
|
|
Layers: 26
|
|
Dropout: 0.2
|
|
Crop Pct: '0.875'
|
|
Momentum: 0.9
|
|
Image Size: '224'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bilinear
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L159
|
|
Config: ''
|
|
In Collection: ResNeSt
|
|
- Name: resnest50d
|
|
Metadata:
|
|
FLOPs: 6937106336
|
|
Epochs: 270
|
|
Batch Size: 8192
|
|
Training Data:
|
|
- ImageNet
|
|
Training Techniques:
|
|
- AutoAugment
|
|
- DropBlock
|
|
- Label Smoothing
|
|
- Mixup
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Resources: 64x NVIDIA V100 GPUs
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Convolution
|
|
- Dense Connections
|
|
- Global Average Pooling
|
|
- Max Pooling
|
|
- ReLU
|
|
- Residual Connection
|
|
- Softmax
|
|
- Split Attention
|
|
File Size: 110273258
|
|
Tasks:
|
|
- Image Classification
|
|
Training Time: ''
|
|
ID: resnest50d
|
|
LR: 0.1
|
|
Layers: 50
|
|
Dropout: 0.2
|
|
Crop Pct: '0.875'
|
|
Momentum: 0.9
|
|
Image Size: '224'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bilinear
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L170
|
|
Config: ''
|
|
In Collection: ResNeSt
|
|
- Name: resnest50d_1s4x24d
|
|
Metadata:
|
|
FLOPs: 5686764544
|
|
Epochs: 270
|
|
Batch Size: 8192
|
|
Training Data:
|
|
- ImageNet
|
|
Training Techniques:
|
|
- AutoAugment
|
|
- DropBlock
|
|
- Label Smoothing
|
|
- Mixup
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Resources: 64x NVIDIA V100 GPUs
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Convolution
|
|
- Dense Connections
|
|
- Global Average Pooling
|
|
- Max Pooling
|
|
- ReLU
|
|
- Residual Connection
|
|
- Softmax
|
|
- Split Attention
|
|
File Size: 103045531
|
|
Tasks:
|
|
- Image Classification
|
|
ID: resnest50d_1s4x24d
|
|
LR: 0.1
|
|
Layers: 50
|
|
Dropout: 0.2
|
|
Crop Pct: '0.875'
|
|
Momentum: 0.9
|
|
Image Size: '224'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bicubic
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L229
|
|
In Collection: ResNeSt
|
|
Collections:
|
|
- Name: ResNeSt
|
|
Paper:
|
|
title: 'ResNeSt: Split-Attention Networks'
|
|
url: https://paperswithcode.com//paper/resnest-split-attention-networks
|
|
type: model-index
|
|
Type: model-index
|
|
-->
|