# Glu on ResNet

**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. 

The weights from this model were ported from [Gluon](https://cv.gluon.ai/model_zoo/classification.html).

{% 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/HeZRS15,
  author    = {Kaiming He and
               Xiangyu Zhang and
               Shaoqing Ren and
               Jian Sun},
  title     = {Deep Residual Learning for Image Recognition},
  journal   = {CoRR},
  volume    = {abs/1512.03385},
  year      = {2015},
  url       = {http://arxiv.org/abs/1512.03385},
  archivePrefix = {arXiv},
  eprint    = {1512.03385},
  timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```

<!--
Models:
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  Metadata:
    FLOPs: 10068547584
    Training Data:
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    Architecture:
    - 1x1 Convolution
    - Batch Normalization
    - Bottleneck Residual Block
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    - Global Average Pooling
    - Max Pooling
    - ReLU
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    - Softmax
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    Tasks:
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    ID: gluon_resnet101_v1b
    Crop Pct: '0.875'
    Image Size: '224'
    Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L89
  In Collection: Gloun ResNet
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    Training Data:
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    Architecture:
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    - Batch Normalization
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    Tasks:
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    ID: gluon_resnet101_v1s
    Crop Pct: '0.875'
    Image Size: '224'
    Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L166
  In Collection: Gloun ResNet
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    Training Data:
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    Architecture:
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  In Collection: Gloun ResNet
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  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L97
  In Collection: Gloun ResNet
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  Metadata:
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    Training Data:
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  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L138
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    Training Data:
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    Architecture:
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    Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L147
  In Collection: Gloun ResNet
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  Metadata:
    FLOPs: 16594624512
    Training Data:
    - ImageNet
    Architecture:
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    - Batch Normalization
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    Image Size: '224'
    Interpolation: bicubic
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  In Collection: Gloun ResNet
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  Metadata:
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    Training Data:
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    Architecture:
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    - Batch Normalization
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  In Collection: Gloun ResNet
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  Metadata:
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    Training Data:
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    Architecture:
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  In Collection: Gloun ResNet
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  Metadata:
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    Training Data:
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    Architecture:
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    Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L73
  In Collection: Gloun ResNet
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  Metadata:
    FLOPs: 5590551040
    Training Data:
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    Architecture:
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    - Batch Normalization
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    Image Size: '224'
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  In Collection: Gloun ResNet
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    Training Data:
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    Architecture:
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    - Batch Normalization
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  Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/gluon_resnet.py#L129
  In Collection: Gloun ResNet
- Name: gluon_resnet50_v1s
  Metadata:
    FLOPs: 7019495424
    Training Data:
    - ImageNet
    Architecture:
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    - Batch Normalization
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    ID: gluon_resnet50_v1s
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  In Collection: Gloun ResNet
Collections:
- Name: Gloun ResNet
  Paper:
    title: Deep Residual Learning for Image Recognition
    url: https://paperswithcode.com//paper/deep-residual-learning-for-image-recognition
  type: model-index
Type: model-index
-->