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README.md

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+<h1 align="center">Multi-Level Transitional Contrast Learning for Personalized Image Aesthetics Assessment</h1>
+
+<div align="center">
+    <a href="https://github.com/yzc-ippl/" target="_blank">Zhichao Yang</a><sup>1</sup>,
+    <a href="https://web.xidian.edu.cn/ldli/" target="_blank">Leida Li</a><sup>1*</sup>,
+    <a href="#" target="_blank">Yuzhe Yang</a><sup>2</sup>,
+    <a href="#" target="_blank">Yaqian Li</a><sup>2</sup>,
+    <a href="#" target="_blank">Weisi Lin</a><sup>3</sup>,
+</div>
+
+<div align="center">
+  <sup>1</sup>School of Artificial Intelligence, Xidian University
+  <br>
+  <sup>2</sup>OPPO Research Institute,
+  <sup>3</sup> School of Computer Science and Engineering, Nanyang Technological University
+</div>
+
+<div align="center">
+<sup>*</sup>Corresponding author
+</div>
+
+<div align="center">
+  <img src="MTCL.png" width="800"/>
+</div>
+
+
+
+
+## Introduction： 
+### PyTorch implementation for the [paper](https://ieeexplore.ieee.org/abstract/document/10168279)  
+
+### Model weight：[**(Hugging Face)**](https://huggingface.co/yzc002/MTCL) [**(Baidu Netdisk)**](https://pan.baidu.com/s/1wsb249NwjgaoPCBlHNRM1Q?pwd=0981)
+
+## Inference Guide：
+
+### 1. Overview
+This guide will help you get started with the MTCL inference code.
+
+### 2. Model Architecture
+
+MTCL consists of three main components:
+```
+**GIAA Model**: General Image Aesthetic Assessment backbone (ResNet-50 based)
+**Contrast Model**: Contrastive learning encoder for personalized features
+**PIAA Model**: Fusion of GIAA and Contrast features with personalized regression head
+```
+
+### 3. Directory Structure
+```
+project_root/
+├── code/
+│   ├── GIAA/
+│   │   └── train_GIAA_model.py       # GIAA model definition
+│   ├── MTCL/
+│   │   └── Contrast_Database         # Contrast data for training
+│   │   └── FlickrAES_TrainUser       # Train user of FlickrAES
+│   │   └── train_Contrast_model.py   # Contrast model definition
+│   └── PIAA/
+│       └── ├── FlickrAES_PIAA/
+            │       └── image/        # Flickr-AES images
+            │       └── label/
+            │           ├── test_worker.csv                   # Test Worker information
+            │           └── image_labeled_by_each_worker.csv  # Image ratings by workers
+
+            └── test_PIAA_model.py          # This inference script
+```
+
+### 4. Download Model Weight
+Pre-trained PIAA Model: Place at 
+```
+./model/ResNet50/ResNet50-FlickrAes-PIAA.pt
+./model/ResNext101/ResNext101-FlickrAes-PIAA.pt
+```
+Flickr-AES Dataset:  
+```
+Images: ./FlickerAes_PIAA/image/
+Labels: ./FlickerAes_PIAA/label/
+```
+
+### 5. Running Inference
+```
+python test_PIAA_model.py
+```
+
+## Citation
+If you find our work is useful, pleaes cite the paper: 
+```bibtex
+@article{yang2023multi,
+  title={Multi-level transitional contrast learning for personalized image aesthetics assessment},
+  author={Yang, Zhichao and Li, Leida and Yang, Yuzhe and Li, Yaqian and Lin, Weisi},
+  journal={IEEE Transactions on Multimedia},
+  volume={26},
+  pages={1944--1956},
+  year={2023},
+  publisher={IEEE}
+}
+```