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  <div align="center">
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  <a href="https://arxiv.org/abs/2508.14475"><img src="https://img.shields.io/badge/Arxiv-preprint-red"></a>
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  <a href="https://pxf0429.github.io/FGResQ/"><img src="https://img.shields.io/badge/Homepage-green"></a>
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- <a href='https://github.com/sxfly99/FGRestore/stargazers'><img src='https://img.shields.io/github/stars/sxfly99/FGRestore.svg?style=social'></a>
 
 
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  </div>
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  <h1 align="center">Fine-grained Image Quality Assessment for Perceptual Image Restoration</h1>
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  <div style="font-family: sans-serif; margin-bottom: 2em;">
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  <h2 style="border-bottom: 1px solid #eaecef; padding-bottom: 0.3em; margin-bottom: 1em;">πŸ“° News</h2>
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  <ul style="list-style-type: none; padding-left: 0;">
 
 
 
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  <li style="margin-bottom: 0.8em;">
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  <strong>[2025-11-08]</strong> πŸŽ‰πŸŽ‰πŸŽ‰Our paper, "Fine-grained Image Quality Assessment for Perceptual Image Restoration", has been accepted to appear at AAAI 2026!
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  </li>
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  ### 2. Download Pre-trained Weights
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  Place the downloaded files in the `weights` directory.
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  # Path to the main model weights
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  model_path = "weights/FGResQ.pth"
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  # Initialize the inference engine
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  model = FGResQ(model_path=model_path)
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  ```
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  author={Sheng, Xiangfei and Pan, Xiaofeng and Yang, Zhichao and Chen, Pengfei and Li, Leida},
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  journal={arXiv preprint arXiv:2508.14475},
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  year={2025}
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- }
 
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  <div align="center">
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  <a href="https://arxiv.org/abs/2508.14475"><img src="https://img.shields.io/badge/Arxiv-preprint-red"></a>
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  <a href="https://pxf0429.github.io/FGResQ/"><img src="https://img.shields.io/badge/Homepage-green"></a>
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+ <a href="https://huggingface.co/spaces/orpheus0429/FGResQ"><img src="https://img.shields.io/badge/πŸ€—%20Hugging%20Face-Spaces-blue"></a>
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+ <a href='https://github.com/sxfly99/FGResQ/stargazers'><img src='https://img.shields.io/github/stars/sxfly99/FGResQ.svg?style=social'></a>
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+
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  </div>
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  <h1 align="center">Fine-grained Image Quality Assessment for Perceptual Image Restoration</h1>
 
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  <div style="font-family: sans-serif; margin-bottom: 2em;">
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  <h2 style="border-bottom: 1px solid #eaecef; padding-bottom: 0.3em; margin-bottom: 1em;">πŸ“° News</h2>
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  <ul style="list-style-type: none; padding-left: 0;">
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+ <li style="margin-bottom: 0.8em;">
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+ <strong>[2025-11-19]</strong> The model is now available on the <a href="https://huggingface.co/orpheus0429/FGResQ">HuggingFace Hub</a>. A live demo is also available on <a href="https://huggingface.co/spaces/orpheus0429/FGResQ">HuggingFace Spaces</a> for you to try it out directly in your browser.
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+ </li>
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  <li style="margin-bottom: 0.8em;">
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  <strong>[2025-11-08]</strong> πŸŽ‰πŸŽ‰πŸŽ‰Our paper, "Fine-grained Image Quality Assessment for Perceptual Image Restoration", has been accepted to appear at AAAI 2026!
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  </li>
 
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  ### 2. Download Pre-trained Weights
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+ You can download the pre-trained model weights from the following link:
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+ [**Download Weights (Google Drive)**](https://drive.google.com/drive/folders/10MVnAoEIDZ08Rek4qkStGDY0qLiWUahJ?usp=drive_link), [**(Baidu Netdisk)**](https://pan.baidu.com/s/1a2IZbr_PrgZYCbUbjKLykA?pwd=9ivu) or [**(HuggingFace)**](https://huggingface.co/orpheus0429/FGResQ)
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  Place the downloaded files in the `weights` directory.
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  # Path to the main model weights
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  model_path = "weights/FGResQ.pth"
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+ # or use HuggingFace Model
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+ # from huggingface_hub import hf_hub_download
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+ # model_path = hf_hub_download(
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+ # repo_id="orpheus0429/FGResQ",
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+ # filename="weights/FGResQ.pth"
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+ # )
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+
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  # Initialize the inference engine
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  model = FGResQ(model_path=model_path)
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  ```
 
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  author={Sheng, Xiangfei and Pan, Xiaofeng and Yang, Zhichao and Chen, Pengfei and Li, Leida},
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  journal={arXiv preprint arXiv:2508.14475},
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  year={2025}
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+ }