How to use PyTorch Hub to share and discover pre-trained models.

PyTorch Hub is a platform for sharing and discovering pre-trained models, where users can find various types of pre-trained models and download them locally for use. Here are the general steps for using PyTorch Hub:

1. Install PyTorch: First, make sure you have PyTorch library installed. Installation guides can be found on the official PyTorch website.
2. Import PyTorch Hub: To import the PyTorch Hub library in Python code, you can use the following code.

import torch
import torch.hub as hub

1. Find models using PyTorch Hub: You can use the torch.hub.list() function to list all available models in PyTorch Hub. You can select and download models based on their name and relevant information.
2. Download models: You can download and load the selected model using the torch.hub.load() function. For example, the following code will download and load a model named “pytorch/vision:v0.6.0”:

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

1. Using the model for inference: Once the model has been downloaded and loaded, it can be used for inference. Input data can be passed to the model and the output of the model can be obtained. For example, the following code will use the model for inference on an image.

input = torch.randn(1, 3, 224, 224)
output = model(input)

By following these steps, you can utilize PyTorch Hub to share and discover various pre-trained models, and apply them in your applications. PyTorch Hub offers users a convenient way to access and use a wide range of models, from computer vision to natural language processing and other fields of pre-trained models.