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117 lines
3.6 KiB
117 lines
3.6 KiB
# SWSL ResNet
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**Residual Networks**, or **ResNets**, learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. Instead of hoping each few stacked layers directly fit a desired underlying mapping, residual nets let these layers fit a residual mapping. They stack [residual blocks](https://paperswithcode.com/method/residual-block) ontop of each other to form network: e.g. a ResNet-50 has fifty layers using these blocks.
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The models in this collection utilise semi-weakly 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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Models:
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- Name: swsl_resnet18
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Metadata:
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FLOPs: 2337073152
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Epochs: 30
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Batch Size: 1536
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Training Data:
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- IG-1B-Targeted
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- ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 64x GPUs
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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File Size: 46811375
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Tasks:
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- Image Classification
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ID: swsl_resnet18
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LR: 0.0015
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Layers: 18
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Crop Pct: '0.875'
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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#L954
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In Collection: SWSL ResNet
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- Name: swsl_resnet50
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Metadata:
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FLOPs: 5282531328
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Epochs: 30
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Batch Size: 1536
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Training Data:
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- IG-1B-Targeted
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- ImageNet
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Training Techniques:
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- SGD with Momentum
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- Weight Decay
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Training Resources: 64x GPUs
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Bottleneck Residual Block
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- Convolution
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- Global Average Pooling
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- Max Pooling
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- ReLU
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- Residual Block
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- Residual Connection
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- Softmax
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File Size: 102480594
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Tasks:
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- Image Classification
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ID: swsl_resnet50
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LR: 0.0015
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Layers: 50
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Crop Pct: '0.875'
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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#L965
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In Collection: SWSL ResNet
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Collections:
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- Name: SWSL ResNet
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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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type: model-index
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Type: model-index
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-->
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