vit-pytorch
Vision Transformer (ViT) - Pytorch
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has low install friction, carries permissive MIT licensing, and offers a comprehensive suite of transformer vision models backed by recent research. It is well-suited for both research prototyping and production image classification systems. No known vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires torch and torchvision to be installed; GPU recommended for practical training but not required for inference on small batches.
- Low install friction with a pure Python wheel.
- Actively maintained with a recent release.
License · maintenance · safety
permissive license (permissive) — MIT License permits free use, modification, and distribution with minimal restrictions—suitable for both research and commercial projects.
last release 2026-08-02 (12 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 184,548 downloads/mo, #10,033 on PyPI
Alternatives
Verify before relying
pip install vit-pytorch
import torch
from vit_pytorch import ViT
v = ViT(
image_size=256,
patch_size=32,
num_classes=1000,
dim=1024,
depth=6,
heads=16,
mlp_dim=2048
)
img = torch.randn(1, 3, 256, 256)
preds = v(img)- Whether pretrained model weights are included or must be sourced separately
- GPU memory requirements for different model variants and batch sizes
- Training time and convergence characteristics compared to CNN baselines
What it is and what it does
vit-pytorch is a collection of PyTorch implementations of Vision Transformer architectures and related vision models. It provides the core ViT model alongside numerous variants—SimpleViT, NaViT, CaiT, Token-to-Token ViT, LeViT, MobileViT, and others—each addressing different efficiency or accuracy trade-offs. The package also includes support for self-supervised learning techniques like masked autoencoders and masked image modeling, as well as knowledge distillation from convolutional networks.
The library is designed for researchers and practitioners building image classification systems with transformers. It depends on torch for computation, torchvision for standard vision utilities, and einops for flexible tensor reshaping. Models are instantiated with configurable parameters (patch size, embedding dimension, depth, attention heads, dropout rates) and accept batches of images as input, returning class predictions or intermediate representations.
Use it for
- Train a vision transformer from scratch on a custom image classification dataset using the base ViT or SimpleViT variant
- Distill knowledge from a pretrained ResNet or other CNN teacher into a smaller, faster ViT student model
- Implement masked image modeling or masked autoencoder pretraining for self-supervised representation learning
- Experiment with variable-resolution image batching using NaViT for faster training on mixed-size datasets
- Deploy efficient vision models on resource-constrained devices using MobileViT or LeViT variants
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has low install friction, carries permissive MIT licensing, and offers a comprehensive suite of transformer vision models backed by recent research. It is well-suited for both research prototyping and production image classification systems. No known vulnerabilities.
Install
vit-pytorch on PyPI
Before you install
Low install friction with a pure Python wheel. Actively maintained with a recent release. Depends on torch and torchvision, which are standard deep learning libraries, plus einops for tensor operations.
Requires torch and torchvision to be installed; GPU recommended for practical training but not required for inference on small batches.
License in practice
MIT License permits free use, modification, and distribution with minimal restrictions—suitable for both research and commercial projects.
Quickstart
pip install vit-pytorch
import torch
from vit_pytorch import ViT
v = ViT(
image_size=256,
patch_size=32,
num_classes=1000,
dim=1024,
depth=6,
heads=16,
mlp_dim=2048
)
img = torch.randn(1, 3, 256, 256)
preds = v(img)Verify before relying
- Whether pretrained model weights are included or must be sourced separately
- GPU memory requirements for different model variants and batch sizes
- Training time and convergence characteristics compared to CNN baselines
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packageseinopstorchtorchvision |
| Maintenance | Actively maintained 12 days since the last release |
| First released | |
| Downloads | 184,548 / month, #10,033 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: vit_pytorch-1.24.2-py3-none-any.whl
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