vit-pytorch is a PyTorch library providing ready-to-use implementations of the Vision Transformer and numerous related architectures for image classification. It consolidates a wide variety of transformer variants and pre-training tasks into a single package to accelerate experimentation with attention mechanisms in computer vision.
Project overview
By offering a broad collection of vision transformer architectures alongside self-supervised pre-training tasks and knowledge distillation in one installable package, vit-pytorch removes the need to assemble disparate implementations, reducing the overhead of adopting and iterating on attention-based vision models.
Project type
Model Runtime · Data Processing · Image & Vision
Deployment
Refer to project documentation
License
MIT
Best for
AI engineers, researchers, and developers who need a reusable PyTorch library to rapidly experiment with various vision transformer architectures.
Key capabilities
Provides numerous implementations of vision transformer architectures including ViT, SimpleViT, NaViT, DeepViT, CaiT, and others.
Offers built-in support for distilling knowledge from convolutional networks to vision transformers.
Includes tasks for training vision transformers using self-supervised methods like SimMIM, MAE, Masked Patch Prediction, and Masked Position Prediction.
Provides access to attention maps via forward pass modifications in specific variants.
Limitations and risks
The library does not offer pretrained models out of the box. Users seeking pretrained weights are redirected to another repository to obtain them.
Getting started
Install the library using pip install vit-pytorch. After installation, import ViT into a Python script, instantiate the desired model, and pass an image tensor to begin processing.
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: $ pip install vit-pytorch
README: import torch from vit_pytorch import ViT
README: You can use it by importing the `SimpleViT` as shown below