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.
184 lines
5.2 KiB
184 lines
5.2 KiB
# ResNeXt
|
|
|
|
A **ResNeXt** repeats a [building block](https://paperswithcode.com/method/resnext-block) that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension, *cardinality* (the size of the set of transformations) $C$, as an essential factor in addition to the dimensions of depth and width.
|
|
|
|
{% 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
|
|
@article{DBLP:journals/corr/XieGDTH16,
|
|
author = {Saining Xie and
|
|
Ross B. Girshick and
|
|
Piotr Doll{\'{a}}r and
|
|
Zhuowen Tu and
|
|
Kaiming He},
|
|
title = {Aggregated Residual Transformations for Deep Neural Networks},
|
|
journal = {CoRR},
|
|
volume = {abs/1611.05431},
|
|
year = {2016},
|
|
url = {http://arxiv.org/abs/1611.05431},
|
|
archivePrefix = {arXiv},
|
|
eprint = {1611.05431},
|
|
timestamp = {Mon, 13 Aug 2018 16:45:58 +0200},
|
|
biburl = {https://dblp.org/rec/journals/corr/XieGDTH16.bib},
|
|
bibsource = {dblp computer science bibliography, https://dblp.org}
|
|
}
|
|
```
|
|
|
|
<!--
|
|
Type: model-index
|
|
Collections:
|
|
- Name: ResNeXt
|
|
Paper:
|
|
Title: Aggregated Residual Transformations for Deep Neural Networks
|
|
URL: https://paperswithcode.com/paper/aggregated-residual-transformations-for-deep
|
|
Models:
|
|
- Name: resnext101_32x8d
|
|
In Collection: ResNeXt
|
|
Metadata:
|
|
FLOPs: 21180417024
|
|
Parameters: 88790000
|
|
File Size: 356082095
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Batch Normalization
|
|
- Convolution
|
|
- Global Average Pooling
|
|
- Grouped Convolution
|
|
- Max Pooling
|
|
- ReLU
|
|
- ResNeXt Block
|
|
- Residual Connection
|
|
- Softmax
|
|
Tasks:
|
|
- Image Classification
|
|
Training Data:
|
|
- ImageNet
|
|
ID: resnext101_32x8d
|
|
Crop Pct: '0.875'
|
|
Image Size: '224'
|
|
Interpolation: bilinear
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnet.py#L877
|
|
Weights: https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 79.3%
|
|
Top 5 Accuracy: 94.53%
|
|
- Name: resnext50_32x4d
|
|
In Collection: ResNeXt
|
|
Metadata:
|
|
FLOPs: 5472648192
|
|
Parameters: 25030000
|
|
File Size: 100435887
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Batch Normalization
|
|
- Convolution
|
|
- Global Average Pooling
|
|
- Grouped Convolution
|
|
- Max Pooling
|
|
- ReLU
|
|
- ResNeXt Block
|
|
- Residual Connection
|
|
- Softmax
|
|
Tasks:
|
|
- Image Classification
|
|
Training Data:
|
|
- ImageNet
|
|
ID: resnext50_32x4d
|
|
Crop Pct: '0.875'
|
|
Image Size: '224'
|
|
Interpolation: bicubic
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnet.py#L851
|
|
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50_32x4d_ra-d733960d.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 79.79%
|
|
Top 5 Accuracy: 94.61%
|
|
- Name: resnext50d_32x4d
|
|
In Collection: ResNeXt
|
|
Metadata:
|
|
FLOPs: 5781119488
|
|
Parameters: 25050000
|
|
File Size: 100515304
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Batch Normalization
|
|
- Convolution
|
|
- Global Average Pooling
|
|
- Grouped Convolution
|
|
- Max Pooling
|
|
- ReLU
|
|
- ResNeXt Block
|
|
- Residual Connection
|
|
- Softmax
|
|
Tasks:
|
|
- Image Classification
|
|
Training Data:
|
|
- ImageNet
|
|
ID: resnext50d_32x4d
|
|
Crop Pct: '0.875'
|
|
Image Size: '224'
|
|
Interpolation: bicubic
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnet.py#L869
|
|
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50d_32x4d-103e99f8.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 79.67%
|
|
Top 5 Accuracy: 94.87%
|
|
- Name: tv_resnext50_32x4d
|
|
In Collection: ResNeXt
|
|
Metadata:
|
|
FLOPs: 5472648192
|
|
Parameters: 25030000
|
|
File Size: 100441675
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Batch Normalization
|
|
- Convolution
|
|
- Global Average Pooling
|
|
- Grouped Convolution
|
|
- Max Pooling
|
|
- ReLU
|
|
- ResNeXt Block
|
|
- Residual Connection
|
|
- Softmax
|
|
Tasks:
|
|
- Image Classification
|
|
Training Techniques:
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Data:
|
|
- ImageNet
|
|
ID: tv_resnext50_32x4d
|
|
LR: 0.1
|
|
Epochs: 90
|
|
Crop Pct: '0.875'
|
|
LR Gamma: 0.1
|
|
Momentum: 0.9
|
|
Batch Size: 32
|
|
Image Size: '224'
|
|
LR Step Size: 30
|
|
Weight Decay: 0.0001
|
|
Interpolation: bilinear
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L842
|
|
Weights: https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth
|
|
Results:
|
|
- Task: Image Classification
|
|
Dataset: ImageNet
|
|
Metrics:
|
|
Top 1 Accuracy: 77.61%
|
|
Top 5 Accuracy: 93.68%
|
|
-->
|