Understanding Vision Transformers by Building One from Scratch

How Vision Transformers Differ from Convolutional Neural Networks

Vision Transformers represent a significant shift in computer vision architecture by applying transformer models traditionally used in natural language processing to image data. Unlike convolutional neural networks that process images through local filters and gradually build up to global understanding, Vision Transformers take a fundamentally different approach by treating images as sequences of patches and analyzing global context from the very beginning.

The core architecture involves splitting an input image into fixed-size patches that are then flattened into vector sequences. Each patch becomes a token in the transformer sequence, similar to how words are treated in language models. A special classification token is added to the sequence specifically for the final classification task. These patches then pass through multiple transformer encoder blocks that process the entire image context simultaneously rather than progressively.

• Images are divided into fixed-size patches that become sequence tokens
• A special classification token is added for final prediction tasks
• Transformer encoder blocks process global context simultaneously
• Position embeddings preserve spatial information about patch locations
• Self-attention mechanisms allow patches to interact globally

Key Architectural Components of Vision Transformers

The transformer encoder blocks consist of multi-head self-attention layers and feed-forward networks with layer normalization and residual connections. The self-attention mechanism enables each patch to attend to all other patches in the image, creating a rich understanding of global relationships and dependencies. This global receptive field from the first layer contrasts sharply with CNNs that start with small local receptive fields and gradually expand through deeper layers.

CNNs examine images with a magnifying glass focusing on local details first while Vision Transformers see the entire canvas at once processing global context from the beginning

Building Vision Transformers from scratch provides valuable insights into their architectural advantages and data requirements. The global attention mechanism offers powerful representation capabilities but comes with increased computational demands and the need for extensive training data. Understanding these trade-offs helps in selecting the appropriate architecture for specific computer vision tasks and appreciating why Vision Transformers have become increasingly important in modern AI research.