This guide will help you get started with the FGResQ inference code.

First, clone the repository and install the required dependencies.

git clone https://github.com/sxfly99/FGRestore.git
cd FGRestore
pip install -r requirements.txt

You can download the pre-trained model weights from the following link: Download Weights (Google Drive) or (Baidu Netdisk)

Place the downloaded files in the weights directory.

• FGResQ.pth: The main model for quality scoring and ranking.
• Degradation.pth: The weights for the degradation-aware task branch.

Create the weights directory if it doesn't exist and place the files inside.

FGRestore/
|-- weights/
|   |-- FGResQ.pth
|   |-- Degradation.pth
|-- model/
|   |-- FGResQ.py
|-- requirements.txt
|-- README.md

The FGResQ provides two main functionalities: scoring a single image and comparing a pair of images.

First, import and initialize the FGResQ.

from model.FGResQ import FGResQ

# Path to the main model weights
model_path = "weights/FGResQ.pth"

# Initialize the inference engine
model = FGResQ(model_path=model_path)

You can get a quality score for a single image. The score typically ranges from 0 to 1, where a higher score indicates better quality.

image_path = "path/to/your/image.jpg"
quality_score = model.predict_single(image_path)
print(f"The quality score for the image is: {quality_score:.4f}")

You can also compare two images to determine which one has better quality.

image_path1 = "path/to/image1.jpg"
image_path2 = "path/to/image2.jpg"

comparison_result = model.predict_pair(image_path1, image_path2)

# The result includes a human-readable comparison and raw probabilities
print(f"Comparison: {comparison_result['comparison']}")
# Example output: "Comparison: Image 1 is better"

print(f"Raw output probabilities: {comparison_result['comparison_raw']}")
# Example output: "[0.8, 0.1, 0.1]" (Probabilities for Image1 > Image2, Image2 > Image1, Image1 ≈ Image2)

If you find this work is useful, pleaes cite our paper!

@article{sheng2025fg,
  title={Fine-grained Image Quality Assessment for Perceptual Image Restoration},
  author={Sheng, Xiangfei and Pan, Xiaofeng and Yang, Zhichao and Chen, Pengfei and Li, Leida},
  journal={arXiv preprint arXiv:2508.14475},
  year={2025}
}