I’ve always been fascinated by how computers learn to see. Recently, I’ve been exploring how transformers, which started in language processing, are now changing image classification. This shift from traditional convolutional neural networks to Vision Transformers caught my attention. I want to share my journey in building and training these models with PyTorch. If you’re curious about modern computer vision, stick around—I’ll make it simple and practical.

Let’s start with the basics. Vision Transformers treat images as sequences of patches rather than grids of pixels. Each patch gets embedded into a vector, and the model learns relationships between them. Have you ever wondered how a model can understand an entire image at once? That’s the power of self-attention in ViTs.

First, we need to set up our environment. I recommend using PyTorch and some helper libraries. Here’s how I do it:

import torch
import torch.nn as nn
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import timm
import numpy as np

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

The core of a ViT is patch embedding. We split the image into small pieces and convert them into vectors. Why patches? Because it lets the model focus on local features before building global understanding.

class PatchEmbedding(nn.Module):
    def __init__(self, img_size=224, patch_size=16, in_channels=3, embed_dim=768):
        super().__init__()
        self.projection = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
    
    def forward(self, x):
        x = self.projection(x)
        x = x.flatten(2).transpose(1, 2)
        return x

After embedding, we add positional information. Images have spatial relationships, and we need to preserve that. How does the model know where each patch is located? Through positional encodings added to the patch embeddings.

Next comes the transformer encoder with multi-head self-attention. This allows the model to weigh the importance of different patches. Think of it as the model deciding which parts of the image to focus on.

class MultiHeadAttention(nn.Module):
    def __init__(self, embed_dim=768, num_heads=12):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = embed_dim // num_heads
        self.qkv = nn.Linear(embed_dim, embed_dim * 3)
        self.proj = nn.Linear(embed_dim, embed_dim)
    
    def forward(self, x):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]
        attn = (q @ k.transpose(-2, -1)) * (self.head_dim ** -0.5)
        attn = attn.softmax(dim=-1)
        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        return x

Training a ViT requires a good dataset and careful hyperparameter tuning. I often use CIFAR-10 or ImageNet for practice. The key is to start with a small learning rate and use data augmentation to prevent overfitting.

What if you don’t want to build from scratch? Pre-trained models are a great starting point. Libraries like timm offer ready-to-use ViTs.

model = timm.create_model('vit_base_patch16_224', pretrained=True)
model.head = nn.Linear(model.head.in_features, num_classes)  # Adjust for your task

Fine-tuning on custom datasets is straightforward. Freeze the early layers and train the classification head. This way, you leverage pre-learned features without starting from zero.

Evaluation involves checking accuracy and visualizing attention maps. I like to plot which image regions the model focuses on. It’s revealing to see how the model makes decisions.

Performance optimization includes using mixed precision and gradient checkpointing. These techniques reduce memory usage and speed up training.

Compared to CNNs, ViTs excel with large datasets but might need more data to shine. They capture global context better, while CNNs are great at local features. Have you considered which approach suits your project?

Common issues include overfitting on small datasets or slow convergence. Using regularization and learning rate schedules helps mitigate these problems.

I hope this guide helps you dive into Vision Transformers. They’re a powerful tool in computer vision, and with PyTorch, implementing them is accessible. If you found this useful, please like, share, and comment with your experiences. Let’s keep the conversation going!