“Building Vision Transformers from Scratch: A Comprehensive Guide”

Author(s): Ajay Kumar mahto

Originally published on Towards AI.

Building Vision Transformers from Scratch: A Comprehensive Guide

A Vision Transformer (ViT) is a deep learning model architecture that applies the Transformer framework, originally designed for natural language processing (NLP), to computer vision tasks. Instead of using traditional convolutional neural networks (CNNs), ViTs treat images as sequences of smaller patches, similar to how words are processed in text, and use self-attention mechanisms to learn spatial relationships between these patches.

This article provides an in-depth exploration of Vision Transformers (ViT), covering their architecture, including image patch tokenization, positional encoding, self-attention, and multi-head attention mechanisms. It breaks down the classification pipeline, detailing how embeddings are generated, the role of the class token, and the function of the MLP head in producing classification outputs. Furthermore, it discusses key techniques like layer normalization and the importance of positional information in retaining spatial relationships in images, ultimately providing a comprehensive guide for understanding and implementing Vision Transformers in machine learning tasks.

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Published via Towards AI