Champion-Solution-for-CVPR-NTIRE-2024-Quality-Assessment-on-AIGC Quality Assessment for AI-Generated Content - Track 1: Image

Beijing University of Posts and Telecommunications.
Beijing Xiaomi Mobile Software Co., Ltd.

Environment Installation

• torch 1.8+
• torchvision
• Python 3
• pip install ftfy regex tqdm
• pip install git+https://github.com/openai/CLIP.git

Data Preparation

Download the competition test dataset from the specified website and unzip it into the "./data/AIGCQA-30K-Image/test" directory.

Trained Weights

Download weights from: google drive.

Evaluation on AIGCQA-30k-Image test-set

After preparing the code environment and downloading the data and model weights, run the following code to obtain the output.txt file.

python AIGC_DB_prompt_final.py

If you find our work is useful, pleaes cite our paper:

Paper will be released.

Our other works:

• "Thinking Image Color Aesthetics Assessment: Models, Datasets and Benchmarks.", [pdf] [code], ICCV 2023.
• "EAT: An Enhancer for Aesthetics-Oriented Transformers.", [pdf] [code] ACMMM 2023.
• "Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks.", [pdf] [code] IJCAI 2022.