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README.md

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+<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<sup>1†</sup>,
+    Jianjie Wang<sup>1†</sup>,
+    Zhixianhe Zhang<sup>1</sup>,
+    Pangu Xie<sup>1</sup>,
+    Xiangfei Sheng<sup>1</sup>,
+    Pengfei Chen<sup>1</sup>,
+    Leida Li<sup>1,2*</sup>
+</div>
+
+<div align="center">
+  <sup>1</sup>School of Artificial Intelligence,
+  <sup>2</sup>State Key Laboratory of EMIM, Xidian University
+</div>
+
+<div align="center">
+<sup>†</sup>Equal contribution &nbsp;&nbsp; <sup>*</sup>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:
+
+```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) &nbsp;|&nbsp; [**(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}
+}
+```