Improve model card: Add pipeline tag, paper link, GitHub link, and experimental results

This PR enhances the model card for the "Full-scale Representation Guided Network for Retinal Vessel Segmentation" model by:

* Adding the `pipeline_tag: image-segmentation` to improve discoverability on the Hugging Face Hub.
* Updating the `repository` metadata to point to the main GitHub repository (`https://github.com/ZombaSY/FSG-Net-pytorch`) and removing the redundant `github-link` tag.
* Including links to both the official Hugging Face paper page and the arXiv preprint for comprehensive paper access.
* Adding a clear link to the GitHub repository within the content section for code and usage instructions.
* Providing a concise summary of the paper's contribution.
* Including the experimental results table for quick reference on model performance.
* Adding the official BibTeX citation.
* Directing users to the GitHub repository for detailed setup and usage instructions, rather than duplicating extensive content.

Please review and merge if these updates are satisfactory.

README.md

@@ -1,11 +1,41 @@
 ---
 license: mit
+pipeline_tag: image-segmentation
+repository: https://github.com/ZombaSY/FSG-Net-pytorch
+---
 
-tags:
-- github-link
-repository: https://github.com/ZombaSY/FSG-Net-pytorch/releases/tag/1.1.0
+# Full-scale Representation Guided Network for Retinal Vessel Segmentation
 
----
-    
-**arXiv:2501.18921**  
-https://arxiv.org/abs/2501.18921
+This repository contains the Full-Scale Guided Network (FSG-Net), a novel approach for retinal vessel segmentation. FSG-Net introduces a feature representation module that effectively captures full-scale structural information using modernized convolution blocks. A guided convolution block then refines this information, leveraging an attention-guided filter similar to unsharp masking to enhance fine vascular structures. This architecture delivers competitive performance for retinal vessel segmentation across multiple public datasets.
+
+The model was presented in the paper [Full-scale Representation Guided Network for Retinal Vessel Segmentation](https://huggingface.co/papers/2501.18921) ([arXiv:2501.18921](https://arxiv.org/abs/2501.18921)).
+
+Code: [https://github.com/ZombaSY/FSG-Net-pytorch](https://github.com/ZombaSY/FSG-Net-pytorch)
+
+## Experimental Results
+
+The model achieves competitive performance across several public datasets:
+
+| Dataset     | mIoU   | F1 score | Acc    | AUC    | Sen    | MCC    |
+| :---------- | :----- | :------- | :----- | :----- | :----- | :----- |
+| DRIVE       | 84.068 | 83.229   | 97.042 | 98.235 | 84.207 | 81.731 |
+| STARE       | 86.118 | 85.100   | 97.746 | 98.967 | 86.608 | 83.958 |
+| CHASE\_DB1  | 82.680 | 81.019   | 97.515 | 99.378 | 85.995 | 79.889 |
+| HRF         | 83.088 | 81.567   | 97.106 | 98.744 | 83.616 | 80.121 |
+
+## Usage
+
+For detailed instructions on environment setup, dataset preparation, training, and inference, please refer to the [official GitHub repository](https://github.com/ZombaSY/FSG-Net-pytorch). Pre-trained models for each dataset can also be found on the [GitHub releases page](https://github.com/ZombaSY/FSG-Net-pytorch/releases/tag/1.1.0).
+
+## Citation
+
+If you find this work helpful or inspiring, please consider citing our paper:
+
+```bibtex
+@article{seo2025fullscalerepresentationguidednetwork,
+  title   = {Full-scale Representation Guided Network for Retinal Vessel Segmentation},
+  author  = {Sunyong Seo, Huisu Yoon, Semin Kim, Jongha Lee},
+  journal = {arXiv preprint arXiv:2501.18921},
+  year    = {2025}
+}
+```