I’ve been thinking about Vision Transformers a lot lately. They represent such a fundamental shift in how we approach computer vision. While convolutional networks have served us well for years, the transformer architecture brings something different to the table - a way to understand images through global relationships rather than local features. That’s why I decided to build one from scratch.

Have you ever wondered what makes Vision Transformers so effective? It’s their ability to treat images as sequences of patches, much like how we process sentences as sequences of words. This approach allows the model to capture relationships between distant parts of an image that convolutional networks might miss.

Let me show you how we can implement this step by step. We’ll start with the patch embedding layer, which breaks down images into manageable pieces. Here’s a clean implementation:

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

The magic really happens in the multi-head attention mechanism. This is where the model learns which patches to focus on and how they relate to each other. Did you know that this attention mechanism allows the model to simultaneously process information from multiple representation subspaces?

Here’s how we implement the core attention mechanism:

class MultiHeadAttention(nn.Module):
    def __init__(self, dim, heads=8, dropout=0.1):
        super().__init__()
        self.heads = heads
        self.head_dim = dim // heads
        self.scale = self.head_dim ** -0.5
        
        self.qkv = nn.Linear(dim, dim * 3)
        self.projection = nn.Linear(dim, dim)
        self.dropout = nn.Dropout(dropout)
        
    def forward(self, x):
        batch_size, seq_len, dim = x.shape
        qkv = self.qkv(x).chunk(3, dim=-1)
        q, k, v = map(lambda t: t.view(batch_size, seq_len, self.heads, self.head_dim).transpose(1, 2), qkv)
        
        attention = (q @ k.transpose(-2, -1)) * self.scale
        attention = attention.softmax(dim=-1)
        attention = self.dropout(attention)
        
        out = (attention @ v).transpose(1, 2).contiguous()
        out = out.view(batch_size, seq_len, dim)
        return self.projection(out)

Training these models requires some special considerations. Have you thought about how we handle the positional information? Since transformers don’t have inherent notion of sequence order, we need to add positional encodings to help the model understand spatial relationships between patches.

The training process itself involves several important techniques. Mixup and cutmix help with regularization, while label smoothing prevents the model from becoming overconfident in its predictions. These techniques might seem small, but they can make a significant difference in final performance.

When it comes to optimization, I’ve found that using AdamW with cosine learning rate decay works particularly well. The warmup phase is crucial - it helps stabilize training in the early epochs when gradients can be quite large.

What really excites me about building from scratch is the deep understanding you gain. You’re not just calling a pre-built function; you’re creating each component and seeing how they interact. This knowledge becomes invaluable when you need to debug issues or customize the architecture for specific tasks.

The results can be quite impressive. With proper training, even a relatively small Vision Transformer can achieve competitive performance on standard benchmarks. The key is patience and careful attention to the training details.

I hope this journey through building a Vision Transformer has been as exciting for you as it has been for me. Building complex models from scratch gives you insights that simply using pre-built libraries can’t provide. If you found this helpful, I’d love to hear your thoughts - feel free to share your experiences or questions in the comments below.