“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.

The article provides a detailed overview of Vision Transformers (ViT), explaining how they process images by dividing them into patches and applying the Transformer architecture. It covers essential components like image patch tokenization, positional encoding, and the multi-head attention mechanism, illustrating the importance of each step. The final sections detail the classification task managed by ViTs, emphasizing the use of MLP heads for output generation, and highlight the advantages of ViT over traditional methods.

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Published via Towards AI