vit-pytorch is a PyTorch library providing implementations of the Vision Transformer for image classification, along with many follow-up ViT variant architectures, installable via pip. It also includes wrappers for masked image modeling pretraining schemes such as SimMIM and MAE.
Project overview
It ships many ViT research variants together in a single pip package, reducing the implementation effort normally required to reproduce Vision Transformer architectures in PyTorch.
Project type
Image & Vision
Deployment
Refer to project documentation
License
MIT
Best for
AI engineers who want ready-to-use PyTorch implementations of Vision Transformer and follow-up ViT variants for their own training and experimentation.
Key capabilities
PyTorch implementation of Vision Transformer for image classification with configurable parameters (image size, patch size, depth, heads).
Includes follow-up variants such as SimpleViT and NaViT (native resolution ViT handling multiple image resolutions in one batch).
Wrappers for masked image modeling pretraining such as SimMIM and MAE, returning a loss for training a ViT.
Limitations and risks
The library does not provide pretrained model weights; users needing them are directed by the README to Ross Wightman's repository.
The project is a library, so users interact with it through code; coding is required to use it.
Getting started
Install with pip install vit-pytorch, then import ViT and run a forward pass on an image tensor. Setup difficulty is documented as easy.
Evidence and sources
GitHub project description: Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
README: For a Pytorch implementation with pretrained models, please see Ross Wightman's repository