Add metadata and improve model card (#1)

- Add metadata and improve model card (556fcba28f1d2df5b854024260563ec2ae39ac7b)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,31 +1,49 @@
 ---
+license: cc-by-nc-4.0
+pipeline_tag: image-segmentation
+library_name: pytorch
 tags:
-- pytorch
+- medical
+- retina
+- fundus
+- medical-imaging
 ---
-# Model card for FunduSegmenter
-This is the official model card for sharing the pre-trained weights and datasets for [FunduSegmenter](https://github.com/JusticeZzy/FunduSegmenter).
 
-Our paper link is [here]().
+# FunduSegmenter
 
-License: Creative Commons Attribution-NonCommercial 4.0 International Public License (CC BY-NC 4.0)
+This is the official repository for **FunduSegmenter**, the first adaptation of the [RETFound](https://github.com/rmaphoh/RETFound) foundation model for joint optic disc (OD) and optic cup (OC) segmentation in retinal fundus images.
 
-### Contents
+- **Paper:** [FunduSegmenter: Leveraging the RETFound Foundation Model for Joint Optic Disc and Optic Cup Segmentation in Retinal Fundus Images](https://huggingface.co/papers/2508.11354)
+- **Code:** [GitHub - JusticeZzy/FunduSegmenter](https://github.com/JusticeZzy/FunduSegmenter)
+- **License:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (Non-commercial use only)
 
-1. Datasets.zip. Our processed datasets.
+## Introduction
+FunduSegmenter integrates a series of novel modules with RETFound, including a Pre-adapter, a Decoder, a Post-adapter, skip connections with Convolutional Block Attention Module (CBAM), and a Vision Transformer block adapter. The model demonstrates strong stability and generalization on both in-distribution and out-of-distribution data, providing stable OD and OC segmentation across multiple fundus camera datasets (IDRiD, Drishti-GS, RIM-ONE-r3, and REFUGE).
 
-2. DUNet_OD_CentreCrop_pretrained.pth. The pre-trained DUNet for OD center cropping.
+## Repository Contents
+This repository hosts the pre-trained weights and datasets associated with the paper:
 
-3. FunduSegmenter_CroppedImage.pth. The pre-trained FunduSegmenter trained on cropped training images.
+1. **Datasets.zip**: Processed datasets used in the study.
+2. **DUNet_OD_CentreCrop_pretrained.pth**: Pre-trained DUNet used for OD center cropping pre-processing.
+3. **FunduSegmenter_CroppedImage.pth**: Pre-trained FunduSegmenter trained on cropped ROI images.
+4. **FunduSegmenter_OriginalImage.pth**: Pre-trained FunduSegmenter trained on original fundus images.
+5. **Weights.zip**: All trained weights reported in the paper's experiments.
+6. **mobilenet_v2-6a65762b.pth**: Required pre-trained weight for the DoFE baseline.
 
-4. FunduSegmenter_OriginalImage.pth. The pre-trained FunduSegmenter trained on original training images.
+## License Note
+While the code in the GitHub repository is under the MIT License, all the weights (including the original RETFound weights and the derived FunduSegmenter weights) are under **CC BY-NC 4.0** (Creative Commons Attribution-NonCommercial 4.0 International). This means the weights are for **non-commercial use only**.
 
-5. Weights.zip. All trained weights (our models) reported in our paper.
+## Citation
+If you use our code or weights, please cite the following paper:
 
-6. mobilenet_v2-6a65762b.pth. Required pre-trained weight by DoFE.
-
-### Citation
-```
+```bibtex
+@article{zhao2025fundusegmenter,
+  title={FunduSegmenter: Leveraging the RETFound Foundation Model for Joint Optic Disc and Optic Cup Segmentation in Retinal Fundus Images},
+  author={Zhao, Zhenyi and Mookiah, M R K and Trucco, Emanuele},
+  journal={arXiv preprint arXiv:2508.11354},
+  year={2025}
+}
 ```
 
-### Contact
-Zhenyi Zhao, zhenyi.zhao@hotmail.com or 2578745@dundee.ac.uk
+## Contact
+Zhenyi Zhao: zhenyi.zhao@hotmail.com or 2578745@dundee.ac.uk