FGAesQ

<div align="center"> <a href="https://arxiv.org/abs/2603.03907"><img src="https://img.shields.io/badge/Arxiv-preprint-red"></a> <a href="https://yzc-ippl.github.io/FG-IAA/"><img src="https://img.shields.io/badge/Homepage-green"></a> <a href='https://github.com/yzc-ippl/FG-IAA/stargazers'><img src='https://img.shields.io/github/stars/yzc-ippl/FG-IAA.svg?style=social'></a> </div> <h1 align="center">Fine-grained Image Aesthetic Assessment: Learning Discriminative Scores from Relative Ranks</h1> <div align="center"> Zhichao Yang^1†, Jianjie Wang^1†, Zhixianhe Zhang¹, Pangu Xie¹, Xiangfei Sheng¹, Pengfei Chen¹, Leida Li^1,2* </div> <div align="center"> ¹School of Artificial Intelligence, ²State Key Laboratory of EMIM, Xidian University </div> <div align="center"> ^†Equal contribution &nbsp;&nbsp; ^*Corresponding author </div> <br> <div align="center"> <img src="FGAesthetics+Q.png" width="900"/> </div> <div style="font-family: sans-serif; margin-bottom: 2em;"> <h2 style="border-bottom: 1px solid #eaecef; padding-bottom: 0.3em; margin-bottom: 1em;">News</h2> <ul style="list-style-type: none; padding-left: 0;"> <li style="margin-bottom: 0.8em;"> <strong>[2026-04-10]</strong> ✨</span>✨</span> The <strong>Inference Code</strong> and <strong>Pre-trained Weights</strong>, are now publicly available. A demo video demonstrating FGAesQ's application in <strong>LivePhoto Cover Recommendation</strong> is also provided. </li> <li style="margin-bottom: 0.8em;"> <strong> [2026-04-09]</strong> 🎉</span>🎉</span> Congratulations! Our paper has been accepted for an <strong>Oral Presentation</strong> at CVPR 2026. </li> <li style="margin-bottom: 0.8em;"> <strong>[2026-02-21]</strong> 🎉</span>🎉</span> Our paper, "Fine-grained Image Aesthetic Assessment: Learning Discriminative Scores from Relative Ranks", has been accepted to <strong>CVPR 2026</strong>! </li> </ul> </div>

Applicatons (More scenarios will be uncovered)

<div align="center"> <video src="https://github.com/yzc-ippl/FG-IAA/releases/download/v1.0/demo_2.mp4" width="900" controls></video> </div>

Quick Start

This guide will help you get started with FGAesQ inference in minutes.

1. Installation

Clone the repository and install the required dependencies:

git clone https://github.com/yzc-ippl/FG-IAA.git
cd FG-IAA
pip install -r requirements.txt

Note: The CLIP dependency is installed directly from the official OpenAI repository and will be fetched automatically via pip install -r requirements.txt.

2. Download Pre-trained Weights

Download the pre-trained model weights from: (Hugging Face)  |  (Baidu Netdisk)

Place the downloaded weight file at a path of your choice and set MODEL_PATH accordingly in the inference scripts.

The expected project structure is as follows:

FG-IAA/
FGAesQ_Inference/
   ├──utils/
        ├── FGAesQ.py               # Model definition
        ├── DiffToken.py            # Differential token preprocessing
        ├── data_utils.py
        └── clip_vit_base_16_224.pt
   ├── inference_series.py         # Series-mode inference
   ├── inference_single.py         # Single-image inference
   ├── requirements.txt
 README.md

3. Run Inference

FGAesQ supports two inference modes: Series Mode for photo series ranking, and Single Mode for individual image scoring.

---

🖼️ Mode 1 — Single Image / Folder Scoring

Use inference_single.py to score a single image or all images within a folder.

Configuration (edit the main() function in inference_single.py):

MODEL_PATH = "path/to/your/model.pt"   # Path to the pre-trained weights
INPUT_PATH = "path/to/image_or_folder" # Single image file or folder of images
OUTPUT_TXT = "path/to/output.txt"      # Output txt path (folder mode only; set None to auto-generate)
DEVICE     = "cuda"
BATCH_SIZE = 128

Run:

python inference_single.py

Output format (single_result.txt):

Total: 3
============================================================

  1. photo_A.jpg                                      0.872314
  2. photo_B.jpg                                      0.751203
  3. photo_C.jpg                                      0.634891

• Single image: the predicted aesthetic score is printed directly to the terminal.
• Folder: a ranked list of all images with scores is saved to OUTPUT_TXT.

---

📂 Mode 2 — Photo Series Ranking

Use inference_series.py to rank images within multiple photo series simultaneously.

The input folder should contain one sub-folder per series, with image files named in the format {series_id}-{index}.jpg (e.g., 000009-01.jpg, 000009-02.jpg).

input_folder/
 000009/
   ├── 000009-01.jpg
   ├── 000009-02.jpg
   └── 000009-03.jpg
 000010/
   ├── 000010-01.jpg
   └── 000010-02.jpg
 ...

Configuration (edit the main() function in inference_series.py):

MODEL_PATH    = "path/to/your/model.pt"   # Path to the pre-trained weights
INPUT_FOLDER  = "path/to/series_folder"   # Root folder containing all series sub-folders
OUTPUT_FOLDER = "path/to/series_result"   # Output directory for per-series result txt files
DEVICE        = "cuda:0"
BATCH_SIZE    = 64
MAX_SIZE      = 2048  # Max image resolution (long edge). Use None for no limit.
                      # Recommended: 2048 if many images exceed this resolution.

Run:

python inference_series.py

Output format (one {series_id}_result.txt per series in OUTPUT_FOLDER):

Series: 9
Count: 3
============================================================

Ranking: 000009-02.jpg  000009-01.jpg  000009-03.jpg

Scores:  0.8812  0.7654  0.6231

Order: 000009-02.jpg > 000009-01.jpg > 000009-03.jpg

Each output file contains the predicted ranking and aesthetic scores for all images in that series, sorted from best to worst.

---

Citation

If you find this work useful, please cite our paper!

@article{yang2026fine,
  title={Fine-grained Image Aesthetic Assessment: Learning Discriminative Scores from Relative Ranks},
  author={Yang, Zhichao and Wang, Jianjie and Zhang, Zhixianhe and Xie, Pangu and Sheng, Xiangfei and Chen, Pengfei and Li, Leida},
  journal={arXiv preprint arXiv:2603.03907},
  year={2026}
}