Our goal is to curate high-quality, easily-reproducible, maximally-beneficial models for research reproducibility.
Hence, we may work with you to refine your pull request and in some cases reject some low-quality models to be published.
Once we accept your pull request, your model will soon appear on Pytorch hub webpage for all users to explore.
[User] Workflow
As a user, PyTorch Hub allows you to follow a few simple steps and do things like: 1) explore available models; 2) load a model; and 3) understand what methods are available for any given model. Let's walk through some examples of each.
Explore available entrypoints.
Users can list all available entrypoints in a repo using the torch.hub.list() API.
```python



torch.hub.list('pytorch/vision')
['alexnet',
'deeplabv3_resnet101',
'densenet121',
...
'vgg16',
'vgg16_bn',
'vgg19',
 'vgg19_bn']
 ```


