Practical overview
When it fits
Use it when a PyTorch computer-vision project needs implementations of multiple transformer-based vision architectures or a starting point for image-classification experiments.
Who it is for
- PyTorch developers building image-classification models
- Researchers and students comparing vision transformer architectures
- Practitioners prototyping distilled or mobile-oriented vision models
Documented uses
- Implement Vision Transformer-based image-classification models
- Compare transformer architectures in computer-vision experiments
- Explore distilled vision models
- Prototype mobile-oriented vision transformer designs
Requirements & limits
The repository is public, active and MIT-licensed. It is a Python library for the PyTorch ecosystem and supports Linux, Windows and macOS environments, including desktop and server use.
- It includes multiple architectures rather than only a basic ViT implementation.
- Its primary focus is computer vision and image classification.
- The MIT license permits use under that license's terms.
- The implementations are provided in Python for the PyTorch ecosystem.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install and use a basic ViT classifier
Set up vit-pytorch and obtain class predictions from a Vision Transformer using a 256×256 image tensor.
Before you start
- Python with PyTorch available
- An input tensor shaped like
(1, 3, 256, 256) - An image size divisible by the patch size
- More than 16 image patches
Steps
Install the package with the command below.
pip install vit-pytorchImport
torchandViTfromvit_pytorch.Create a
ViTmodel withimage_size=256,patch_size=32,num_classes=1000,dim=1024,depth=6,heads=16,mlp_dim=2048,dropout=0.1, andemb_dropout=0.1.Provide an input tensor shaped
(1, 3, 256, 256)and pass it to the model.Use the returned tensor as the class predictions; the documented output shape is
(1, 1000).
Expected outcome
A configured ViT model returns predictions for the supplied image tensor.
Guide limitations
- The documented example uses randomly generated input rather than a real image.
image_sizemust be divisible bypatch_size.- The number of patches must be greater than 16.
- For rectangular images, the documented guidance is to use the maximum width or height as the image size.
- The documentation does not describe training, pretrained weights, or production deployment.
Install and use NaViT with variable-resolution images
Set up NaViT and obtain predictions for multiple images with different resolutions.
Before you start
- Python with PyTorch available
- The installed
vit-pytorchpackage - Image tensors with three channels
- Variable-resolution image tensors grouped as documented, or a list of images when automatic grouping is enabled
Steps
Install the package with the command below.
pip install vit-pytorchImport
torchandNaViTfromvit_pytorch.na_vit.Create a
NaViTmodel with the documented parameters, includingimage_size=256,patch_size=32,num_classes=1000,dim=1024,depth=6,heads=16,mlp_dim=2048,dropout=0.1,emb_dropout=0.1, andtoken_dropout_prob=0.1.Either provide images as grouped lists matching the documented batch structure, or provide a flat image list with
group_images=Trueandgroup_max_seq_len=64.Pass the images to the model and use the returned
(5, 1000)tensor for the five documented example images.
Expected outcome
NaViT returns one 1,000-class prediction row for each of the five variable-resolution images.
Guide limitations
- The documented grouped-input example requires images to be placed so the maximum allowed self-attention sequence length is not exceeded.
- The examples use randomly generated tensors rather than real images.
- The documentation does not describe training or deployment.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 30 September 2026
- Original website title
- lucidrains/vit-pytorch · Official homepage title · retained in its source language