---
license: mit
base_model:
- timm/deit_small_patch16_224.fb_in1k
- timm/deit_tiny_patch16_224.fb_in1k
- timm/cait_xxs24_224.fb_dist_in1k
metrics:
- accuracy
tags:
- Interpretability
- ViT
- Classification
- XAI
---

# ProtoViT: Interpretable Vision Transformer with Adaptive Prototype Learning

This repository contains pretrained ProtoViT models for interpretable image classification, as described in our paper "Interpretable Image Classification with Adaptive Prototype-based Vision Transformers".

## Model Description

[ProtoViT](https://github.com/Henrymachiyu/ProtoViT) combines Vision Transformers with prototype-based learning to create models that are both highly accurate and interpretable. Rather than functioning as a black box, ProtoViT learns interpretable prototypes that explain its classification decisions through visual similarities.

### Supported Architectures

We provide three variants of ProtoViT:

- **ProtoViT-T**: Built on DeiT-Tiny backbone
- **ProtoViT-S**: Built on DeiT-Small backbone
- **ProtoViT-CaiT**: Built on CaiT-XXS24 backbone

## Performance

All models were trained and evaluated on the CUB-200-2011 fine-grained bird species classification dataset.

| Model Version | Backbone | Resolution | Top-1 Accuracy | Checkpoint |
|--------------|----------|------------|----------------|------------|
| ProtoViT-T | DeiT-Tiny | 224×224 | 83.36% | [Download](https://huggingface.co/chiyum609/ProtoViT/blob/main/DeiT_Tiny_finetuned0.8336.pth) |
| ProtoViT-S | DeiT-Small | 224×224 | 85.30% | [Download](https://huggingface.co/chiyum609/ProtoViT/blob/main/DeiT_Small_finetuned0.8530.pth) |
| ProtoViT-CaiT | CaiT_xxs24 | 224×224 | 86.02% | [Download](https://huggingface.co/chiyum609/ProtoViT/blob/main/CaiT_xxs24_224_finetuned0.8602.pth) |

## Features

- 🔍 **Interpretable Decisions**: The model performs classification with self-explainatory reasoning based on the input’s similarity to learned prototypes, the key features for each classes.
- 🎯 **High Accuracy**: Achieves competitive performance on fine-grained classification tasks
- 🚀 **Multiple Architectures**: Supports various Vision Transformer backbones
- 📊 **Analysis Tools**: Comes with tools for both local and global prototype analysis

## Requirements

- Python 3.8+
- PyTorch 1.8+
- timm==0.4.12
- torchvision
- numpy
- pillow

## Limitations and Bias
- Data Bias: These models are trained on CUB-200-2011, which may not generalize well to images outside this dataset.
- Resolution Constraints: The models are trained at a resolution of 224×224; higher or lower resolutions may impact performance.
- Location Misalignment: Same as the CNN based models, these models are not perfectly immune to location misalignment under adversarial attack. 

## Citation

If you use this model in your research, please cite:

```bibtex
@article{ma2024interpretable,
  title={Interpretable Image Classification with Adaptive Prototype-based Vision Transformers},
  author={Ma, Chiyu and Donnelly, Jon and Liu, Wenjun and Vosoughi, Soroush and Rudin, Cynthia and Chen, Chaofan},
  journal={arXiv preprint arXiv:2410.20722},
  year={2024}
}
```

## Acknowledgements

This implementation builds upon the following excellent repositories:
- [DeiT](https://github.com/facebookresearch/deit)
- [CaiT](https://github.com/facebookresearch/deit)
- [ProtoPNet](https://github.com/cfchen-duke/ProtoPNet)

## License

This project is released under [MIT] license.

## Contact

For any questions or feedback, please:
1. Open an issue in the GitHub repository
2. Contact [Chiyu.ma.gr@dartmouth.edu]