I’ve been watching the world of computer vision change in front of me. For years, convolutional neural networks, or CNNs, were the undisputed champions for understanding images. Then, something unexpected happened. Researchers successfully applied the transformer—a model famous for its work with language—to pictures. The result is the Vision Transformer, or ViT. This approach challenges old ideas by showing that an architecture built on attention can not only compete with but often surpass traditional methods, especially when you have enough data. Today, I want to show you how these models are built from the ground up. I’ll walk you through creating your own Vision Transformer using PyTorch, piece by piece. Ready to see how it works?

Let’s start with the core idea. A standard transformer processes a sequence of words. To make it work with an image, we first need to create a sequence from the picture. We do this by cutting the image into small, fixed-size squares called patches. Think of it like dividing a large poster into a grid of smaller sticky notes. Each patch is then flattened and passed through a linear layer to create a patch embedding. This transforms the raw pixel values of the patch into a vector the transformer can understand.

But here’s a question: if we just feed these patch embeddings to the model, how does it know the original spatial arrangement? A sentence has a clear order, but patches from an image could get mixed up. The solution is to add positional embeddings. We create a set of learnable vectors, one for each patch position, and add them to the patch embeddings. This gives the model a clue about where each patch came from in the original image.

Now we have a sequence of patch tokens, plus one extra token. We prepend a special classification token to the sequence. This token, often just called the [CLS] token, travels through the transformer. By the end, its final state is used to make the image classification prediction. Why use a special token instead of averaging all the patch outputs? It’s a design choice that allows the model to collect global information from all patches into one dedicated vector.

The heart of the transformer is the multi-head self-attention mechanism. It allows each patch to “look” at every other patch and decide how much focus to put on each one. This is how the model builds relationships. It might learn that a patch containing a dog’s eye is very relevant to a patch containing its nose. We calculate attention scores, which tell us the importance of other patches for understanding the current one. This happens in parallel across multiple “heads,” letting the model capture different types of relationships simultaneously.

After attention, we need to process that information. This is handled by a simple feed-forward neural network within each transformer block. But to train these deep networks effectively, we need normalization and shortcuts. Each transformer block uses layer normalization to stabilize the learning process and residual connections to help gradients flow. A standard block looks like this: normalization, attention, a residual add, another normalization, the feed-forward network, and another residual add. This pattern is repeated multiple times.

Let’s look at some code to make this concrete. First, we define a basic multi-head self-attention module. This is the engine of our transformer.

import torch
import torch.nn as nn
import math

class MultiHeadAttention(nn.Module):
    def __init__(self, embedding_dim=768, num_heads=12, dropout=0.1):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = embedding_dim // num_heads
        
        self.query = nn.Linear(embedding_dim, embedding_dim)
        self.key = nn.Linear(embedding_dim, embedding_dim)
        self.value = nn.Linear(embedding_dim, embedding_dim)
        self.out = nn.Linear(embedding_dim, embedding_dim)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x):
        batch_size, seq_len, embed_dim = x.shape
        # Project to query, key, value
        q = self.query(x)
        k = self.key(x)
        v = self.value(x)
        
        # Reshape for multi-head attention
        q = q.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        k = k.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        v = v.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        
        # Scaled dot-product attention
        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
        attention_weights = torch.softmax(scores, dim=-1)
        attention_weights = self.dropout(attention_weights)
        
        context = torch.matmul(attention_weights, v)
        context = context.transpose(1, 2).contiguous().view(batch_size, seq_len, embed_dim)
        
        return self.out(context)

Following attention, we have the feed-forward network. It’s surprisingly straightforward—just two linear layers with a non-linearity in between, often GELU. This block allows for further processing of the information gathered by attention.

The complete Vision Transformer architecture strings these components together. We start with patch embedding and positional embedding. We then pass the sequence through a stack of identical transformer blocks. Finally, we take the state of the classification token and run it through a small classification head, which is usually just a single linear layer.

Training a ViT has its own quirks. They typically need more data than CNNs to perform well from scratch. This is because the self-attention mechanism is less biased towards local patterns than a convolution is. To help with this, strong data augmentation is crucial. Techniques like RandAugment, MixUp, and CutMix are not just helpful; they are often essential for good performance. Can you guess what happens if you skip them? The model might struggle to generalize.

Let’s write a small part of the training loop to see how we handle a batch of data. Notice how we calculate loss for both the primary prediction and any augmentation mix.

def train_step(model, batch, criterion, device='cuda'):
    images, labels = batch
    images, labels = images.to(device), labels.to(device)
    
    # Forward pass
    logits = model(images)
    
    # Calculate standard loss
    loss = criterion(logits, labels)
    
    # For demonstration: If using MixUp, you'd have mixed images and paired labels
    # The loss calculation would be adjusted accordingly.
    return loss

Finally, evaluating your model isn’t just about the final accuracy number. Because of the attention mechanism, we can visualize which patches the model focused on when making a decision. We can extract the attention weights from the last layer and project them back onto the original image. This creates a heatmap showing what the model “looked at.” It’s a powerful way to build trust and understand potential failures.

Building a Vision Transformer from scratch teaches you how attention works in a visual domain. You see how a sequence model can interpret spatial information. It’s a clear example of how ideas from one field, like natural language processing, can cross over and spark progress in another. I hope building one helps you see the connections between different areas of machine learning.

What was the biggest surprise for you when learning about transformers for vision? Was it the effectiveness of patches, or the need for positional information? Share your thoughts below. If this guide helped you piece together how ViTs work, please consider liking it and sharing it with others who might be curious. I’d love to hear about your own projects in the comments