vit-pytorch

Why we included this project

Most people know this repo as the PyTorch reference for the original Vision Transformer, the architecture that showed image classification could be done with a single transformer encoder and no convolutional backbone. Its real value is the breadth: clean, dependency-light implementations of the base ViT and a long list of follow-ups, including SimpleViT, CaiT, CvT, MobileViT, MaxViT, XCiT, and masked autoencoder pretraining, so you can swap architectures and compare them without reimplementing each paper yourself. Each variant is a small, readable class with a short usage snippet, which makes the codebase a good place to start when you want to prototype or understand how these models are actually wired. It is a research-oriented library rather than a model zoo, so you bring your own weights and training loop; for pretrained checkpoints, pair it with something like timm. If you are exploring transformer-based vision or building a custom classification pipeline, this is a fast way to test ideas.