new ResNet variant.  ![](/assets/images/resnest.jpg)
](/hub/pytorch_vision_resnest/)

[

  * ResNet 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Deep residual networks pre-trained on ImageNet  ![](/assets/images/resnet.png)
](/hub/pytorch_vision_resnet/)

[

  * ResNext 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Next generation ResNets, more efficient and accurate
![](/assets/images/resnext.png) ](/hub/pytorch_vision_resnext/)

[

  * ShuffleNet v2 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
An efficient ConvNet optimized for speed and memory, pre-trained on ImageNet
![](/assets/images/shufflenet_v2_1.png) ](/hub/pytorch_vision_shufflenet_v2/)

[

  * SNNMLP 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Brain-inspired Multilayer Perceptron with Spiking Neurons
![](/assets/images/snnmlp.png) ](/hub/pytorch_vision_snnmlp/)

[

  * SqueezeNet 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Alexnet-level accuracy with 50x fewer parameters.
![](/assets/images/squeezenet.png) ](/hub/pytorch_vision_squeezenet/)

[

  * vgg-nets 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Award winning ConvNets from 2014 ImageNet ILSVRC challenge
![](/assets/images/vgg.png) ](/hub/pytorch_vision_vgg/)

[

  * Wide ResNet 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Wide Residual Networks  ![](/assets/images/wide_resnet.png)
](/hub/pytorch_vision_wide_resnet/)

[

  * SimpleNet 
  *   * ![](/assets/images/logo-github.svg)
  *   * 
Lets Keep it simple, Using simple architectures to outperform deeper and more
complex architectures  ![](/assets/images/simplenet.jpg) ](/hub/simplenet/)

[ All Research Models (52) ](/hub/research-models)

###  How it works —

###  Publishing Models

PyTorch Hub supports publishing pre-trained models (model definitions and pre-
trained weights) to a GitHub repository by adding a simple ` hubconf.py `
file.

###  Loading models

Users can load pre-trained models using ` torch.hub.load() ` API.

Here’s an example showing how to load the ` resnet18 ` entrypoint from the `
pytorch/vision ` repo.

` model = torch.hub.load('pytorch/vision', 'resnet18', pretrained=True) `

[ See Full Documentation ](https://pytorch.org/docs/stable/hub.html)

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[ View Tutorials ](https://pytorch.org/tutorials)

##  Resources

Find development resources and get your questions answered

[ View Resources ](/resources)

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