Building Vision Transformers from Scratch: A Step-by-Step Guide to Patch Tokenization and Self-Attention in Computer Vision

A Vision Transformer (ViT) is a deep learning architecture that adapts the Transformer model—originally developed for natural language processing—to solve computer vision tasks. Unlike traditional convolutional neural networks (CNNs), which rely on local spatial patterns and hierarchical feature extraction, ViTs process images by breaking them into a sequence of smaller, fixed-size patches. These patches are then treated as tokens, much like words in a sentence, allowing the model to leverage the powerful self-attention mechanism to capture global relationships across the image. The core idea behind ViTs begins with image patch tokenization. An input image, such as a 224×224 pixel image, is divided into non-overlapping patches—typically 16×16 pixels each. This results in a grid of 14×14 = 196 patches. Each patch is flattened into a one-dimensional vector and linearly projected into a fixed-dimensional embedding space. This creates a sequence of patch embeddings, which serve as the input tokens for the Transformer encoder. To preserve positional information—since the Transformer architecture does not inherently understand spatial order—positional embeddings are added to each patch embedding. These embeddings are learnable parameters that encode the location of each patch within the image grid. The combined patch and positional embeddings form the final input sequence to the Transformer. The Transformer encoder then processes this sequence through multiple layers. Each layer consists of two main components: a multi-head self-attention mechanism and a feed-forward network. The self-attention mechanism allows every patch to attend to all other patches, enabling the model to capture long-range dependencies and global context across the image. This is a key advantage over CNNs, which are limited by local receptive fields and require deep stacks of layers to achieve global awareness. After the self-attention layer, a residual connection and layer normalization are applied, followed by a feed-forward network that further processes the features. This pattern repeats across multiple Transformer blocks, progressively refining the representation of each patch. Finally, the output from the first patch (often referred to as the [CLS] token, borrowed from NLP) is used for classification. This special token is initialized with a learnable embedding and is updated throughout the network. At the end of the process, the [CLS] token’s final representation is passed through a classifier head—typically a simple linear layer—to produce the final prediction, such as the class label of the image. The ViT architecture, introduced in the research paper arXiv:2010.11929, demonstrated that with sufficient training data, ViTs could match or even surpass the performance of state-of-the-art CNNs on image classification benchmarks like ImageNet. This marked a significant shift in computer vision, showing that Transformers could be effectively applied to visual data when trained on large-scale datasets. While ViTs are powerful, they typically require large amounts of data to perform well—more than traditional CNNs—due to their fewer inductive biases. However, their ability to model long-range dependencies and global context makes them particularly effective for complex vision tasks, including object detection, segmentation, and multimodal learning. To fully grasp the structure and flow of a Vision Transformer, it is helpful to refer to a visual diagram that illustrates the entire pipeline: image input, patching, embedding, positional encoding, Transformer blocks, and final classification. Keeping such a visual aid alongside the explanation enhances understanding and clarifies how each component interacts within the model.