Update README.md

README.md

@@ -4,4 +4,83 @@ 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 Prototypical 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 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 help 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
+
+## 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 [Your Contact Information]