The Vision Transformer, introduced in the paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" by Dosovitskiy et al., presents a novel approach to image understanding by leveraging the Transformer architecture, which has achieved significant success in natural language processing tasks.
In this repository, we delve into the implementation and experimentation of Vision Transformer models for image classification. The Vision Transformer breaks from the traditional Convolutional Neural Network (CNN) paradigm by casting image classification as a sequence-to-sequence problem. Instead of using convolutions, it processes image patches as input tokens and applies self-attention mechanisms to capture global and local dependencies within the image.
Vision Transformer Architecture: Explore the inner workings of the Vision Transformer model, including its self-attention mechanism and multi-head attention layers.
Training Pipelines: Dive into training pipelines tailored for Vision Transformer models, including data preprocessing, augmentation, and fine-tuning strategies.
Evaluation Metrics: Evaluate model performance using standard image classification metrics such as accuracy, precision, recall, and F1-score.