Fine-grained Image Aesthetic Assessment: Learning Discriminative Scores from Relative Ranks
Zhichao Yang1†,
Jianjie Wang1†,
Zhixianhe Zhang1,
Pangu Xie1,
Xiangfei Sheng1,
Pengfei Chen1,
Leida Li1,2*
1School of Artificial Intelligence,
2State Key Laboratory of EMIM, Xidian University
†Equal contribution *Corresponding author
News
-
[2026-04-10] ✨✨ The Inference Code and Pre-trained Weights, are now publicly available. A demo video demonstrating FGAesQ's application in LivePhoto Cover Recommendation is also provided.
-
[2026-04-09] 🎉🎉 Congratulations! Our paper has been accepted for an Oral Presentation at CVPR 2026.
-
[2026-02-21] 🎉🎉 Our paper, "Fine-grained Image Aesthetic Assessment: Learning Discriminative Scores from Relative Ranks", has been accepted to CVPR 2026!
## Applicatons (More scenarios will be uncovered)
## Quick Start
This guide will help you get started with FGAesQ inference in minutes.
### 1. Installation
Clone the repository and install the required dependencies:
```bash
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)**](https://huggingface.co/yzc002/FGAesQ) | [**(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`):
```python
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:**
```bash
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`):
```python
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:**
```bash
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!
```bibtex
@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}
}
```
