# ViT-PyTorch — Vision Transformer Implementations in Pure PyTorch

> A comprehensive collection of Vision Transformer (ViT) variants implemented in clean PyTorch, covering dozens of architectures from the original ViT to modern variants like CaiT, CrossViT, and Swin.

## Install

Save as a script file and run:

# ViT-PyTorch — Vision Transformer Implementations in Pure PyTorch

## Quick Use
```bash
pip install vit-pytorch
```
```python
from vit_pytorch import ViT

model = 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 = model(img)  # (1, 1000)
```

## Introduction
ViT-PyTorch provides clean, readable PyTorch implementations of Vision Transformer architectures. Rather than optimizing for production speed, it focuses on clarity and correctness, making it a go-to resource for researchers who want to understand, modify, or build upon transformer-based vision models.

## What ViT-PyTorch Does
- Implements 40+ Vision Transformer variants as self-contained PyTorch modules
- Provides drop-in models for image classification, segmentation, and detection tasks
- Includes the original ViT, DeiT, Swin Transformer, CaiT, CrossViT, and many more
- Offers configurable architectures with clear parameter interfaces for experimentation
- Supports self-supervised approaches like DINO and MAE for pretraining

## Architecture Overview
Each model is implemented as an independent PyTorch nn.Module with minimal dependencies. The common pattern splits images into patches, projects them into token embeddings, applies a stack of transformer encoder layers with multi-head self-attention, and outputs class predictions via a classification head. Variants introduce hierarchical stages, shifted windows, cross-attention, or register tokens.

## Self-Hosting & Configuration
- Install via pip with no additional dependencies beyond PyTorch
- Import specific architectures directly from the vit_pytorch namespace
- Configure model dimensions, depth, number of heads, and patch size via constructor arguments
- Combine with any PyTorch training loop or framework like Lightning or Hugging Face Trainer
- Use pretrained weights from timm or train from scratch on custom datasets

## Key Features
- 40+ architectures in a single cohesive package with consistent API design
- Pure PyTorch with no custom CUDA kernels or framework lock-in
- Each implementation is self-contained and easy to copy into your own codebase
- Active maintenance with new architectures added as papers are published
- Clear code prioritizing readability over micro-optimization

## Comparison with Similar Tools
- **timm (PyTorch Image Models)** — production-focused with pretrained weights; vit-pytorch prioritizes code clarity
- **Hugging Face Transformers** — broader NLP+vision library; vit-pytorch is vision-only and lighter weight
- **torchvision** — official PyTorch library with fewer transformer variants; vit-pytorch covers more architectures
- **MMDetection** — object detection focused; vit-pytorch provides backbone models as building blocks
- **DINO (official)** — single architecture; vit-pytorch implements many variants including DINO

## FAQ
**Q: Are pretrained weights available?**
A: The package focuses on architecture implementations. For pretrained weights, use the timm library which shares compatible model definitions.

**Q: Can I use these models for tasks beyond classification?**
A: Yes. The backbone can be used as a feature extractor for detection, segmentation, or any downstream task by removing the classification head.

**Q: How do I choose which ViT variant to use?**
A: For general use, start with the standard ViT or Swin Transformer. For efficiency, try MobileViT. For self-supervised pretraining, use the MAE implementation.

**Q: Is it suitable for production deployment?**
A: The code prioritizes clarity. For production inference, consider converting to ONNX or using optimized implementations from timm.

## Sources
- https://github.com/lucidrains/vit-pytorch
- https://arxiv.org/abs/2010.11929

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