Recently, while sorting through thousands of personal photos, a thought struck me: how does a machine actually understand what’s in these pictures? For years, convolutional neural networks (CNNs) have been the default answer. But a new architecture, the Vision Transformer, is challenging that dominance by looking at images in a completely different way. Today, I want to show you how to build one from the ground up. If you’ve ever felt curious about how these models work beyond just importing a pre-trained version, follow along. Let’s get our hands dirty with some code.

Let’s start with the fundamental shift in thinking. Instead of using convolutional filters that slide across an image, Vision Transformers treat an image like a sentence. They split the picture into fixed-size patches—imagine a 16x16 grid—and flatten each patch into a sequence of vectors. This sequence is then processed by the same type of attention mechanism that revolutionized natural language processing.

Why would this work for images? Doesn’t it ignore the spatial relationships that convolutions capture so well? The clever part is the addition of positional embeddings. Since the transformer itself has no innate concept of order, we explicitly add information about where each patch is located in the original image. This allows the model to learn the spatial context.

The core of this architecture is the multi-head self-attention mechanism. Let’s look at how to implement it. This code allows the model to focus on different parts of the image simultaneously, building a rich understanding of context.

import torch
import torch.nn as nn

class MultiHeadSelfAttention(nn.Module):
    def __init__(self, embed_dim=768, num_heads=12, dropout=0.0):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = embed_dim // num_heads
        self.scale = self.head_dim ** -0.5

        self.qkv = nn.Linear(embed_dim, embed_dim * 3)
        self.projection = nn.Linear(embed_dim, embed_dim)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        batch_size, seq_len, embed_dim = x.shape
        qkv = self.qkv(x).reshape(batch_size, seq_len, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]

        attn_scores = (q @ k.transpose(-2, -1)) * self.scale
        attn_probs = torch.softmax(attn_scores, dim=-1)
        attn_probs = self.dropout(attn_probs)

        context = (attn_probs @ v).transpose(1, 2).reshape(batch_size, seq_len, embed_dim)
        output = self.projection(context)
        return output

This block calculates attention scores between all patches. It’s the step that lets a patch representing a dog’s ear relate to a patch containing its tail, no matter the distance between them. Next, we need to build a Transformer Block that uses this attention.

A full Vision Transformer stacks these attention blocks. But a transformer block isn’t just attention; it also has a feed-forward network and layer normalization. This creates a stable and powerful processing unit.

class TransformerBlock(nn.Module):
    def __init__(self, embed_dim=768, num_heads=12, mlp_ratio=4.0, dropout=0.0):
        super().__init__()
        self.norm1 = nn.LayerNorm(embed_dim)
        self.attn = MultiHeadSelfAttention(embed_dim, num_heads, dropout)
        self.norm2 = nn.LayerNorm(embed_dim)
        
        mlp_hidden_dim = int(embed_dim * mlp_ratio)
        self.mlp = nn.Sequential(
            nn.Linear(embed_dim, mlp_hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(mlp_hidden_dim, embed_dim),
            nn.Dropout(dropout)
        )
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        # Attention with residual connection
        x = x + self.dropout(self.attn(self.norm1(x)))
        # MLP with residual connection
        x = x + self.mlp(self.norm2(x))
        return x

Notice the residual connections—they help gradients flow during training. Now, how do we bring all these pieces together into a complete classifier? The final piece is the classification head. We prepend a special [CLS] token to our sequence of patch embeddings. After processing through all the transformer blocks, the state of this token is used to make the final prediction.

Training this model requires some specific strategies. Vision Transformers are known to be data-hungry. If you’re not using a massive dataset like ImageNet, you might need to apply strong data augmentation to prevent overfitting. Techniques like RandAugment or MixUp can artificially expand your dataset and improve generalization.

Another critical aspect is the learning rate schedule. A warm-up phase, where the learning rate gradually increases from a very low value, is often essential for stable training. This helps the model settle into a good parameter space early on.

So, what does a simple training loop look like? Here’s a skeleton to give you an idea.

def train_epoch(model, loader, criterion, optimizer, device):
    model.train()
    total_loss = 0
    for images, labels in loader:
        images, labels = images.to(device), labels.to(device)
        
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        total_loss += loss.item()
    return total_loss / len(loader)

It looks deceptively simple, doesn’t it? The real magic is in the model’s forward pass, where all the patches interact. Once you have a trained model, you can explore its attention maps. Visualizing which patches the [CLS] token attends to can give you surprising insights into what the model deems important in an image, sometimes highlighting objects in ways we wouldn’t expect.

Building this from scratch teaches you more than just how to call torchvision.models.vit_b_16(). You gain an intuition for the flow of data, the purpose of each component, and the impact of hyperparameters like patch size and embedding dimension. Start with a small dataset like CIFAR-10, use a tiny version of the model, and watch it learn. The moment you see it correctly classify an image based on a global understanding, rather than just local features, is incredibly rewarding.

I hope this guide demystifies Vision Transformers for you. Building one piece by piece is the best way to truly grasp their power and elegance. Did you find this walk-through helpful? Do you have your own insights or tips on training ViTs? I’d love to hear from you—share your thoughts in the comments below, and if this was useful, please pass it on to others in your network who might be on a similar learning journey.