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103 lines
2.9 KiB
103 lines
2.9 KiB
# Wide ResNet
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**Wide Residual Networks** are a variant on [ResNets](https://paperswithcode.com/method/resnet) where we decrease depth and increase the width of residual networks. This is achieved through the use of [wide residual blocks](https://paperswithcode.com/method/wide-residual-block).
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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/ZagoruykoK16,
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author = {Sergey Zagoruyko and
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Nikos Komodakis},
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title = {Wide Residual Networks},
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journal = {CoRR},
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volume = {abs/1605.07146},
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year = {2016},
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url = {http://arxiv.org/abs/1605.07146},
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archivePrefix = {arXiv},
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eprint = {1605.07146},
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timestamp = {Mon, 13 Aug 2018 16:46:42 +0200},
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biburl = {https://dblp.org/rec/journals/corr/ZagoruykoK16.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: Wide ResNet
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Paper:
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Title: Wide Residual Networks
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URL: https://paperswithcode.com/paper/wide-residual-networks
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Models:
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- Name: wide_resnet101_2
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In Collection: Wide ResNet
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Metadata:
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FLOPs: 29304929280
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Parameters: 126890000
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File Size: 254695146
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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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- Max Pooling
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- ReLU
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- Residual Connection
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- Softmax
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- Wide Residual Block
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Tasks:
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- Image Classification
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Training Data:
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- ImageNet
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ID: wide_resnet101_2
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/5f9aff395c224492e9e44248b15f44b5cc095d9c/timm/models/resnet.py#L802
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Weights: https://download.pytorch.org/models/wide_resnet101_2-32ee1156.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: 78.85%
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Top 5 Accuracy: 94.28%
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- Name: wide_resnet50_2
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In Collection: Wide ResNet
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Metadata:
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FLOPs: 14688058368
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Parameters: 68880000
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File Size: 275853271
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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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- Max Pooling
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- ReLU
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- Residual Connection
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- Softmax
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- Wide Residual Block
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Tasks:
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- Image Classification
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Training Data:
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- ImageNet
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ID: wide_resnet50_2
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/5f9aff395c224492e9e44248b15f44b5cc095d9c/timm/models/resnet.py#L790
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/wide_resnet50_racm-8234f177.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: 81.45%
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Top 5 Accuracy: 95.52%
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-->
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