# ERIA-1K: ERNIE-Image-Aes-1K, A Deployment-Oriented Image Aesthetics Benchmark with Realistic Distribution

[[🤗 Dataset](https://huggingface.co/datasets/baidu/ERIA-1K-Benchmark)] [[🧩 ERNIE-Image-Aes Model](https://huggingface.co/baidu/ERNIE-Image-Aes)]

## 🔍 Overview

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.

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.

## 🌟 Highlights

- **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


## 📊 Benchmark Results

| 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** |

## 📂 Data Format

```
ERIA-1K-Benchmark/
└── test/
    ├── test.json
    ├── 000001.jpg
    ├── 000002.jpg
    └── ...



- `images/`: contains the 1,000 benchmark images
- `test.json`: provides the ground-truth aesthetic tier scores for evaluation

## 📊 Evaluation

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`.

## ✒️ Citation

If you find this benchmark useful, please consider citing:

```bibtex
@misc{ernie_image_aes_2025,
      title={ERNIE-Image},
      year={2025},
}
```