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.
187 lines
5.2 KiB
187 lines
5.2 KiB
# Summary
|
|
|
|
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.
|
|
|
|
The model in this collection utilises semi-supervised learning to improve the performance of the model. The approach brings important gains to standard architectures for image, video and fine-grained classification.
|
|
|
|
Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
|
|
|
|
{% 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/abs-1905-00546,
|
|
author = {I. Zeki Yalniz and
|
|
Herv{\'{e}} J{\'{e}}gou and
|
|
Kan Chen and
|
|
Manohar Paluri and
|
|
Dhruv Mahajan},
|
|
title = {Billion-scale semi-supervised learning for image classification},
|
|
journal = {CoRR},
|
|
volume = {abs/1905.00546},
|
|
year = {2019},
|
|
url = {http://arxiv.org/abs/1905.00546},
|
|
archivePrefix = {arXiv},
|
|
eprint = {1905.00546},
|
|
timestamp = {Mon, 28 Sep 2020 08:19:37 +0200},
|
|
biburl = {https://dblp.org/rec/journals/corr/abs-1905-00546.bib},
|
|
bibsource = {dblp computer science bibliography, https://dblp.org}
|
|
}
|
|
```
|
|
|
|
<!--
|
|
Models:
|
|
- Name: ssl_resnext101_32x16d
|
|
Metadata:
|
|
FLOPs: 46623691776
|
|
Epochs: 30
|
|
Batch Size: 1536
|
|
Training Data:
|
|
- ImageNet
|
|
- YFCC-100M
|
|
Training Techniques:
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Resources: 64x GPUs
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Batch Normalization
|
|
- Convolution
|
|
- Global Average Pooling
|
|
- Grouped Convolution
|
|
- Max Pooling
|
|
- ReLU
|
|
- ResNeXt Block
|
|
- Residual Connection
|
|
- Softmax
|
|
File Size: 777518664
|
|
Tasks:
|
|
- Image Classification
|
|
ID: ssl_resnext101_32x16d
|
|
LR: 0.0015
|
|
Layers: 101
|
|
Crop Pct: '0.875'
|
|
Image Size: '224'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bilinear
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L944
|
|
In Collection: SSL ResNext
|
|
- Name: ssl_resnext50_32x4d
|
|
Metadata:
|
|
FLOPs: 5472648192
|
|
Epochs: 30
|
|
Batch Size: 1536
|
|
Training Data:
|
|
- ImageNet
|
|
- YFCC-100M
|
|
Training Techniques:
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Resources: 64x GPUs
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Batch Normalization
|
|
- Convolution
|
|
- Global Average Pooling
|
|
- Grouped Convolution
|
|
- Max Pooling
|
|
- ReLU
|
|
- ResNeXt Block
|
|
- Residual Connection
|
|
- Softmax
|
|
File Size: 100428550
|
|
Tasks:
|
|
- Image Classification
|
|
ID: ssl_resnext50_32x4d
|
|
LR: 0.0015
|
|
Layers: 50
|
|
Crop Pct: '0.875'
|
|
Image Size: '224'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bilinear
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L914
|
|
In Collection: SSL ResNext
|
|
- Name: ssl_resnext101_32x4d
|
|
Metadata:
|
|
FLOPs: 10298145792
|
|
Epochs: 30
|
|
Batch Size: 1536
|
|
Training Data:
|
|
- ImageNet
|
|
- YFCC-100M
|
|
Training Techniques:
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Resources: 64x GPUs
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Batch Normalization
|
|
- Convolution
|
|
- Global Average Pooling
|
|
- Grouped Convolution
|
|
- Max Pooling
|
|
- ReLU
|
|
- ResNeXt Block
|
|
- Residual Connection
|
|
- Softmax
|
|
File Size: 177341913
|
|
Tasks:
|
|
- Image Classification
|
|
ID: ssl_resnext101_32x4d
|
|
LR: 0.0015
|
|
Layers: 101
|
|
Crop Pct: '0.875'
|
|
Image Size: '224'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bilinear
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L924
|
|
In Collection: SSL ResNext
|
|
- Name: ssl_resnext101_32x8d
|
|
Metadata:
|
|
FLOPs: 21180417024
|
|
Epochs: 30
|
|
Batch Size: 1536
|
|
Training Data:
|
|
- ImageNet
|
|
- YFCC-100M
|
|
Training Techniques:
|
|
- SGD with Momentum
|
|
- Weight Decay
|
|
Training Resources: 64x GPUs
|
|
Architecture:
|
|
- 1x1 Convolution
|
|
- Batch Normalization
|
|
- Convolution
|
|
- Global Average Pooling
|
|
- Grouped Convolution
|
|
- Max Pooling
|
|
- ReLU
|
|
- ResNeXt Block
|
|
- Residual Connection
|
|
- Softmax
|
|
File Size: 356056638
|
|
Tasks:
|
|
- Image Classification
|
|
ID: ssl_resnext101_32x8d
|
|
LR: 0.0015
|
|
Layers: 101
|
|
Crop Pct: '0.875'
|
|
Image Size: '224'
|
|
Weight Decay: 0.0001
|
|
Interpolation: bilinear
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L934
|
|
In Collection: SSL ResNext
|
|
Collections:
|
|
- Name: SSL ResNext
|
|
Paper:
|
|
title: Billion-scale semi-supervised learning for image classification
|
|
url: https://papperswithcode.com//paper/billion-scale-semi-supervised-learning-for
|
|
type: model-index
|
|
Type: model-index
|
|
-->
|