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308 lines
7.8 KiB
308 lines
7.8 KiB
4 years ago
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# Summary
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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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{% 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/HeZRS15,
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author = {Kaiming He and
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Xiangyu Zhang and
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Shaoqing Ren and
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Jian Sun},
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title = {Deep Residual Learning for Image Recognition},
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journal = {CoRR},
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volume = {abs/1512.03385},
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year = {2015},
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url = {http://arxiv.org/abs/1512.03385},
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archivePrefix = {arXiv},
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eprint = {1512.03385},
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timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
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biburl = {https://dblp.org/rec/journals/corr/HeZRS15.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: resnet26
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Metadata:
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FLOPs: 3026804736
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Training Data:
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- ImageNet
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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: 64129972
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Tasks:
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- Image Classification
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ID: resnet26
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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/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L675
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In Collection: ResNet
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- Name: tv_resnet152
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Metadata:
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FLOPs: 14857660416
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Epochs: 90
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Batch Size: 32
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Training Data:
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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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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: 241530880
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Tasks:
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- Image Classification
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ID: tv_resnet152
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LR: 0.1
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Crop Pct: '0.875'
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LR Gamma: 0.1
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Momentum: 0.9
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Image Size: '224'
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LR Step Size: 30
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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#L769
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In Collection: ResNet
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- Name: resnet18
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Metadata:
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FLOPs: 2337073152
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Training Data:
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- ImageNet
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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: 46827520
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Tasks:
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- Image Classification
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ID: resnet18
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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/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L641
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In Collection: ResNet
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- Name: resnet50
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Metadata:
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FLOPs: 5282531328
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Training Data:
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- ImageNet
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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: 102488165
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Tasks:
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- Image Classification
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ID: resnet50
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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/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L691
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In Collection: ResNet
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- Name: resnet34
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Metadata:
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FLOPs: 4718469120
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Training Data:
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- ImageNet
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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: 87290831
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Tasks:
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- Image Classification
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ID: resnet34
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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/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L658
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In Collection: ResNet
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- Name: resnetblur50
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Metadata:
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FLOPs: 6621606912
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Training Data:
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- ImageNet
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Architecture:
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- 1x1 Convolution
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- Batch Normalization
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- Blur Pooling
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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: 102488165
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Tasks:
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- Image Classification
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ID: resnetblur50
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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/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnet.py#L1160
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In Collection: ResNet
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- Name: tv_resnet34
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Metadata:
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FLOPs: 4718469120
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Epochs: 90
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Batch Size: 32
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Training Data:
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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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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: 87306240
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Tasks:
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- Image Classification
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ID: tv_resnet34
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LR: 0.1
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Crop Pct: '0.875'
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LR Gamma: 0.1
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Momentum: 0.9
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Image Size: '224'
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LR Step Size: 30
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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#L745
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In Collection: ResNet
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- Name: tv_resnet101
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Metadata:
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FLOPs: 10068547584
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Epochs: 90
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Batch Size: 32
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Training Data:
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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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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: 178728960
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Tasks:
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- Image Classification
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ID: tv_resnet101
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LR: 0.1
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Crop Pct: '0.875'
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LR Gamma: 0.1
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Momentum: 0.9
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Image Size: '224'
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LR Step Size: 30
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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#L761
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In Collection: ResNet
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- Name: tv_resnet50
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Metadata:
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FLOPs: 5282531328
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Epochs: 90
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Batch Size: 32
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Training Data:
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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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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: 102502400
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Tasks:
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- Image Classification
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ID: tv_resnet50
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LR: 0.1
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Crop Pct: '0.875'
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LR Gamma: 0.1
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Momentum: 0.9
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Image Size: '224'
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LR Step Size: 30
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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#L753
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In Collection: ResNet
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Collections:
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- Name: ResNet
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Paper:
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title: Deep Residual Learning for Image Recognition
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url: https://papperswithcode.com//paper/deep-residual-learning-for-image-recognition
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type: model-index
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Type: model-index
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-->
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