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218 lines
6.2 KiB
218 lines
6.2 KiB
# SSL ResNeXT
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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.
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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.
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Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
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{% include 'code_snippets.md' %}
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## How do I train this model?
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You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
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## Citation
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```BibTeX
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@article{DBLP:journals/corr/abs-1905-00546,
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author = {I. Zeki Yalniz and
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Herv{\'{e}} J{\'{e}}gou and
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Kan Chen and
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Manohar Paluri and
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Dhruv Mahajan},
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title = {Billion-scale semi-supervised learning for image classification},
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journal = {CoRR},
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volume = {abs/1905.00546},
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year = {2019},
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url = {http://arxiv.org/abs/1905.00546},
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archivePrefix = {arXiv},
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eprint = {1905.00546},
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timestamp = {Mon, 28 Sep 2020 08:19:37 +0200},
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biburl = {https://dblp.org/rec/journals/corr/abs-1905-00546.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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```
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<!--
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Type: model-index
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Collections:
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- Name: SSL ResNext
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Paper:
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Title: Billion-scale semi-supervised learning for image classification
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URL: https://paperswithcode.com/paper/billion-scale-semi-supervised-learning-for
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Models:
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- Name: ssl_resnext101_32x16d
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In Collection: SSL ResNext
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Metadata:
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FLOPs: 46623691776
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Parameters: 194030000
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File Size: 777518664
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Global Average Pooling
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- Grouped Convolution
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- Max Pooling
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- ReLU
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- ResNeXt Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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- YFCC-100M
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Training Resources: 64x GPUs
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ID: ssl_resnext101_32x16d
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LR: 0.0015
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Epochs: 30
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Layers: 101
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Crop Pct: '0.875'
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Batch Size: 1536
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L944
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Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x16-15fffa57.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 81.84%
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Top 5 Accuracy: 96.09%
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- Name: ssl_resnext101_32x4d
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In Collection: SSL ResNext
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Metadata:
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FLOPs: 10298145792
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Parameters: 44180000
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File Size: 177341913
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Global Average Pooling
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- Grouped Convolution
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- Max Pooling
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- ReLU
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- ResNeXt Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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- YFCC-100M
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Training Resources: 64x GPUs
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ID: ssl_resnext101_32x4d
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LR: 0.0015
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Epochs: 30
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Layers: 101
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Crop Pct: '0.875'
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Batch Size: 1536
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L924
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Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x4-dc43570a.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 80.91%
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Top 5 Accuracy: 95.73%
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- Name: ssl_resnext101_32x8d
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In Collection: SSL ResNext
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Metadata:
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FLOPs: 21180417024
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Parameters: 88790000
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File Size: 356056638
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Global Average Pooling
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- Grouped Convolution
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- Max Pooling
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- ReLU
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- ResNeXt Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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- YFCC-100M
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Training Resources: 64x GPUs
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ID: ssl_resnext101_32x8d
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LR: 0.0015
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Epochs: 30
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Layers: 101
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Crop Pct: '0.875'
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Batch Size: 1536
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L934
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Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x8-2cfe2f8b.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 81.61%
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Top 5 Accuracy: 96.04%
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- Name: ssl_resnext50_32x4d
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In Collection: SSL ResNext
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Metadata:
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FLOPs: 5472648192
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Parameters: 25030000
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File Size: 100428550
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Convolution
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- Global Average Pooling
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- Grouped Convolution
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- Max Pooling
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- ReLU
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- ResNeXt Block
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Data:
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- ImageNet
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- YFCC-100M
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Training Resources: 64x GPUs
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ID: ssl_resnext50_32x4d
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LR: 0.0015
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Epochs: 30
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Layers: 50
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Crop Pct: '0.875'
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Batch Size: 1536
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Image Size: '224'
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Weight Decay: 0.0001
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/resnet.py#L914
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Weights: https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext50_32x4-ddb3e555.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 80.3%
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Top 5 Accuracy: 95.41%
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-->
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