[国内的小伙伴请看更详细的中文说明]This repo contains the official implementation and the new IAA dataset TAD66K of the IJCAI 2022 paper. Our new work on ICCV2023：Link

Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks

Shuai He, Yongchang Zhang, Rui Xie, Dongxiang Jiang, Anlong Ming

Beijing University of Posts and Telecommunications

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TAD66K  

Introduction

• We build a large-scale dataset called the Theme and Aesthetics Dataset with 66K images (TAD66K), which is specifically designed for IAA. Specifically, (1) it is a theme-oriented dataset containing 66K images covering 47 popular themes. All images were carefully selected by hand based on the theme. (2) In addition to common aesthetic criteria, we provide 47 criteria for the 47 themes. Images of each theme are annotated independently, and each image contains at least 1200 effective annotations (so far the richest annotations). These high-quality annotations could help to provide deeper insight into the performance of models.

Download Dataset

• Download from here google, it contains images with the largest side scaled to 800, and labels categorized by different themes.
• or here baidu, code: 8888

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TANet  

Introduction

We propose a baseline model, called the Theme and Aesthetics Network (TANet), which can maintain a constant perception of aesthetics to effectively deal with the problem of attention dispersion. Moreover, TANet can adaptively learn the rules for predicting aesthetics according to a recognized theme. By comparing 17 methods with TANet on three representative datasets: AVA, FLICKR-AES and the proposed TAD66K, TANet achieves state-of-the-art performance on all three datasets.

Environment Installation

• pandas==0.22.0
• nni==1.8
• requests==2.18.4
• torchvision==0.8.2+cu101
• numpy==1.13.3
• scipy==0.19.1
• tqdm==4.43.0
• torch==1.7.1+cu101
• scikit_learn==1.0.2
• tensorboardX==2.5

How to Run the Code

• We used the hyperparameter tuning tool nni, maybe you should know how to use this tool first (it will only take a few minutes of your time), because our training and testing will be in this tool.
• Train or test, please run: nnictl create --config config.yml -p 8999
• The Web UI urls are: http://127.0.0.1:8999 or http://172.17.0.3:8999
• Note: nni is not necessary, if you don't want to use this tool, just make simple modifications to our code, such as changing param_group['lr'] to param_group.lr, etc.
• PS: The work of train on the FLICKR-AES dataset may not be made public, because we are currently cooperating with a company, and the relevant model has been embedded into the system, and there are some confidentiality requirements.

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

@article{herethinking,
  title={Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks},
  author={He, Shuai and Zhang, Yongchang and Xie, Rui and Jiang, Dongxiang and Ming, Anlong},
  journal={IJCAI},
  year={2022},
}

Try!

TANet.real-time.inference.video.1.mp4

TANet.real-time.inference.video.2.mp4

TANet.real-time.inference.video.3.mp4

Our other work

🎁 Projects	📚 Publication	🌈 Content	⭐ Stars
Pixel-level image exposure assessment【首个像素级曝光评估】	NIPS 2024	Code, Dataset
Long-tail solution for image aesthetics assessment【美学评估数据不平衡解决方案】	ICML 2024	Code
CLIP-based image aesthetics assessment【基于CLIP多因素色彩美学评估】	Information Fusion 2024	Code, Dataset
Compare-based image aesthetics assessment【基于对比学习的多因素美学评估】	ACMMM 2024	Code
Image color aesthetics assessment【首个色彩美学评估】	ICCV 2023	Code, Dataset
Image aesthetics assessment【通用美学评估】	ACMMM 2023	Code
Theme-oriented image aesthetics assessment【首个多主题美学评估】	IJCAI 2022	Code, Dataset
Select prompt based on image aesthetics assessment【基于美学评估的提示词筛选】	IJCAI 2024	Code
Motion rhythm synchronization with beats【动作与韵律对齐】	IJCAI 2024	Code, Dataset
Champion Solution for AIGC Image Quality Assessment【NTIRE AIGC图像质量评估赛道冠军】	CVPRW NTIRE 2024	Code