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| license: apache-2.0 |
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| # ERNIE-Image-Aes: Robust Image Aesthetics Scoring with Balanced Category Generalization |
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| <img src="https://cdn-uploads.huggingface.co/production/uploads/5f8d780e5d083370c711f575/EVsGxYPd7kVWWIKlBj-d9.png" width="100%"> |
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| [📄 Paper] |
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| ## 🌟 Highlights |
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| ERNIE-Image-Aes is a 8B vision-language model for image aesthetic scoring, initialized from [ArtiMuse](https://github.com/thunderbolt215/ArtiMuse) and fine-tuned on a diverse, professionally annotated dataset. It substantially outperforms existing aesthetic predictors (LAION-AES, ArtiMuse, UniPercept) in generalization across diverse image categories. |
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| **Key advantages:** |
| - Balanced predictions across photography, anime, design, everyday snapshots, and film photography |
| - No systematic bias toward specific image types (e.g., AI-generated content or black-and-white photos) |
| - Swiss-tournament based pairwise annotation for high-quality training labels |
| - Achieves **0.7445 SRCC** and **0.7598 PLCC** on ERIA-1K benchmark |
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| ## 🔍 Motivation |
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| Off-the-shelf aesthetic predictors exhibit systematic biases: |
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| | Model | Bias | |
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| | LAION-Aesthetic | Disproportionately high scores for AI-generated/anime content | |
| | ArtiMuse | Overscores black-and-white photography and casual everyday snapshots | |
| | UniPercept | Strong preference for monochrome images; overscores casual snapshots | |
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| ERNIE-Image-Aes addresses these failure modes through a purpose-built annotation pipeline with explicit category balance. |
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| ## 📊 Results on ERIA-1K Benchmark |
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| | Model | SRCC | PLCC | |
| |-------|------|------| |
| | LAION AES | 0.2944 | 0.3138 | |
| | ArtiMuse | 0.4277 | 0.4704 | |
| | UniPercept | 0.4533 | 0.4748 | |
| | **ERNIE-Image-Aes** | **0.7445** | **0.7598** | |
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| **Annotation Protocol:** |
| - Pairwise Swiss-system tournament for stable and reproducible rankings |
| - Tier labels from 1 to 10 |
| - Annotators recruited from professional backgrounds (Central Academy of Fine Arts, Sichuan Fine Arts Institute, Communication University of China, etc.) |
| - All annotators passed aesthetic calibration screening prior to participation |
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| ## ⚙️ Setup |
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| Please follow the setup instructions in the [ArtiMuse repository](https://huggingface.co/Thunderbolt215215/ArtiMuse). |
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| ## 🙏 Acknowledgements |
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| Our work builds upon [ArtiMuse](https://github.com/thunderbolt215/ArtiMuse) and [InternVL-3](https://github.com/OpenGVLab/InternVL). We sincerely thank the authors for their excellent contributions to the community. |
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| ## ✒️ Citation |
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| If you find this work useful, please consider citing: |
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| ```bibtex |
| ``` |
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