Implement and Train ViT From Scratch for Image Recognition – PyTorch

ViT (Vision Transformer) is a deep learning model that has gained popularity for its effectiveness in image recognition tasks. In this article, we will discuss how to implement and train ViT from scratch using PyTorch.

Setting Up the Environment

Before we begin, make sure you have PyTorch installed in your environment. If not, you can easily install it using pip:

    pip install torch torchvision
  

Implementing ViT

We will start by implementing the ViT model from scratch. You can use the following code as a starting point:

    
      import torch
      import torch.nn as nn

      class VisionTransformer(nn.Module):
          def __init__(self, num_classes, patch_size, dim, depth, heads, mlp_dim):
              super(VisionTransformer, self).__init__()
              ...
              # Implement the ViT architecture here
              ...
          def forward(self, x):
              ...
              # Define the forward pass here
              ...
    
  

Training ViT

Once the ViT model is implemented, you can train it on a dataset of your choice. You can use the following code as a reference:

    
      import torch.optim as optim
      from torch.utils.data import DataLoader

      # Define your dataset and dataloader here
      dataset = ...
      dataloader = ...

      # Instantiate the ViT model
      model = VisionTransformer(num_classes, patch_size, dim, depth, heads, mlp_dim)

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

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

Conclusion

Implementing and training ViT from scratch for image recognition in PyTorch can be a challenging but rewarding task. By following the steps outlined in this article, you can gain a better understanding of how ViT works and how it can be applied to real-world problems.