Vision Transformer Paper Introduction

The Vision Transformer (ViT) paper (opens in a new tab) introduces a groundbreaking approach to computer vision tasks by leveraging the power of transformers, originally designed for natural language processing. Authored by Alexey Dosovitskiy et al., the paper challenges traditional convolutional neural networks (CNNs) in image classification and demonstrates the effectiveness of transformer architectures in capturing long-range dependencies within images.

ViT marks a departure from the conventional CNN-based methodologies that dominate the field of computer vision. By casting image processing as a sequence-to-sequence task, ViT transforms images into sequences of tokens, allowing transformers to be applied directly to the spatial information present in the data. This novel approach not only simplifies the architectural design but also showcases the versatility of transformers beyond text-based tasks.

In this introduction, we will delve into the key motivations behind the development of Vision Transformer, the core architectural components, and the notable experimental results that position ViT as a pioneering model in the realm of image understanding. As we navigate through the paper, we will uncover the unique contributions that ViT brings to the table, shedding light on its potential implications for the future of computer vision.

Why am I doing this

In order to be a good machine learning engineer one of a good path is implementing papers. This make one to be exposed to different ideas on how researcher solve problems and a place to pratice your skills. As said by George Hortz

Without any more introductino lets start implementing the paper