Building a Vision Transformer Model From Scratch
by Matt Nguyen
The self-attention-based transformer model was first introduced by Vaswani et al. in their paper Attention Is All You Need in 2017 and has been widely used in natural language processing. A transformer model is what is used by OpenAI to create ChatGPT. Transformers not only work on text, but also on images, and essentially any sequential data. In 2021, Dosovitsky et al. introduced the idea of using the transformers for computer vision tasks such as image classification in their paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In their paper, they were able to achieve excellent results with their vision transformer model compared to convolutional networks and required a lot less resources to train.
In this tutorial, we are going to build a vision transformer model from scratch and test is on the MNIST dataset, a collection of handwritten digits that have become a standard benchmark in machine learning. Notebook with the code from tutorial can be found here.