Overview
The Vision Transformer (ViT) is a deep learning model architecture based on the Transformer, originally designed for natural language processing, adapted for computer vision tasks. ViT models break down an image into patches, treat these patches as tokens, and input them into a Transformer encoder. This architecture allows the model to capture global relationships between image regions, enabling it to achieve state-of-the-art performance on image classification tasks. The repository provides JAX/Flax implementations of ViT and MLP-Mixer models, pre-trained on ImageNet and ImageNet-21k datasets. It includes code for fine-tuning these models, allowing users to adapt them to specific datasets and tasks. The models were originally trained in the Big Vision codebase, offering advanced features like multi-host training.