| # ERIA-1K: ERNIE-Image-Aes-1K, A Deployment-Oriented Image Aesthetics Benchmark with Realistic Distribution |
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| [[🤗 Dataset](https://huggingface.co/datasets/baidu/ERIA-1K-Benchmark)] [[🧩 ERNIE-Image-Aes Model](https://huggingface.co/baidu/ERNIE-Image-Aes)] |
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| ## 🔍 Overview |
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| Existing aesthetic benchmarks are predominantly constructed from curated, high-production-value image collections, often sourced from platforms such as Flickr and DPChallenge. These datasets tend to skew toward professional or semi-professional photography communities, Western photographic traditions, and visually polished content, and therefore do not fully reflect the diversity of images encountered in real-world deployment. |
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| We introduce **ERIA-1K** (**ER**NIE-**I**mage-**A**es-1K), an open-source human-annotated aesthetic benchmark designed to reflect realistic image distributions and provide a more deployment-oriented evaluation protocol. |
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| ## 🌟 Highlights |
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| - **1,000 images** spanning 6 categories with proportions calibrated to approximate real-world distribution |
| - **Pairwise Swiss-system tournament** annotation for stable and reproducible rankings |
| - **Professional annotators** from fine arts, design, and photography backgrounds |
| - **Tier labels from 1 to 10** produced by calibrated annotators |
| - Designed to expose systematic biases of existing aesthetic predictors |
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| ## 📊 Benchmark Results |
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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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| ## 📂 Data Format |
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| ``` |
| ERIA-1K-Benchmark/ |
| └── test/ |
| ├── test.json |
| ├── 000001.jpg |
| ├── 000002.jpg |
| └── ... |
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| - `images/`: contains the 1,000 benchmark images |
| - `test.json`: provides the ground-truth aesthetic tier scores for evaluation |
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| ## 📊 Evaluation |
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| To evaluate your model on ERIA-1K, compute **SRCC** (Spearman's Rank Correlation Coefficient) and **PLCC** (Pearson's Linear Correlation Coefficient) between your model's predicted scores and the ground-truth tier labels in `test.json`. |
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| ## ✒️ Citation |
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| If you find this benchmark useful, please consider citing: |
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| ```bibtex |
| @misc{ernie_image_aes_2025, |
| title={ERNIE-Image}, |
| year={2025}, |
| } |
| ``` |
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