Discovering pre-trained models on PyTorch Hub

PyTorch Hub is a repository of pre-trained models that can be easily accessed and used in your own projects. In this tutorial, we will explore how to use PyTorch Hub to find pre-trained models, download them, and use them in your own code.

Step 1: Install PyTorch
Before you can use PyTorch Hub, you need to have PyTorch installed on your machine. You can install PyTorch by following the instructions on the PyTorch website https://pytorch.org/.

Step 2: Import the necessary libraries
Once you have PyTorch installed, you can start by importing the necessary libraries:

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models

Step 3: Explore pre-trained models on PyTorch Hub
To explore the pre-trained models available on PyTorch Hub, you can use the torch.hub.list function. This function takes a namespace (such as pytorch/vision for computer vision models) and returns a list of all the available models in that namespace.

torch.hub.list('pytorch/vision')

This will display a list of pre-trained models that are available in the pytorch/vision namespace. You can scroll through the list to find the model you are interested in.

Step 4: Download a pre-trained model
Once you have found a pre-trained model that you want to use, you can download it using the torch.hub.load function. This function takes a namespace, model name, and optionally a pretrained argument to specify if you want to use the pre-trained weights.

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

This will download the pre-trained ResNet-18 model and load it into a variable called model.

Step 5: Use the pre-trained model
Now that you have downloaded the pre-trained model, you can use it to make predictions on your own data. First, you need to preprocess your input data to match the input format expected by the model.

# Preprocess input data
input_data = torch.randn(1, 3, 224, 224)  # Example input data

Next, you can pass the input data through the pre-trained model to make predictions.

output = model(input_data)

The output variable will contain the predicted output of the model. You can use this output for further analysis or post-processing.

Step 6: Fine-tune the pre-trained model
If you want to fine-tune the pre-trained model on your own dataset, you can do so by retraining the final layers of the model. First, you need to freeze the weights of the pre-trained layers and only train the final layers.

# Freeze pretrained layers
for param in model.parameters():
    param.requires_grad = False

# Modify the final layer for fine-tuning
model.fc = nn.Linear(model.fc.in_features, num_classes)

# Define optimizer and loss function
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

# Train the model
for epoch in range(num_epochs):
    for i, data in enumerate(train_loader):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

By following these steps, you can explore pre-trained models on PyTorch Hub, download them, use them for making predictions, and fine-tune them on your own dataset. PyTorch Hub provides a convenient way to access state-of-the-art models and speed up your development process.