---
title: VisionRewardDB-Image
canonical_url: "https://www.modelscope.cn/datasets/ZhipuAI/VisionRewardDB-Image"
md_url: "https://www.modelscope.cn/datasets/ZhipuAI/VisionRewardDB-Image.md"
repository: ZhipuAI/VisionRewardDB-Image
chinese_name: VisionRewardDB-Image
last_updated: 2025-07-30
license: apache-2.0
storage_size: "8.8 GB"
downloads: 389
stars: 1
---

# VisionRewardDB-Image

> VisionRewardDB-Image - ZhipuAI 在 ModelScope 开源的数据集。VisionRewardDB-Image

ZhipuAI/VisionRewardDB-Image 是 ModelScope 魔搭社区上的数据集，存储大小 8.8 GB，采用 apache-2.0 许可。

- **Repository**: ZhipuAI/VisionRewardDB-Image
- **License**: apache-2.0
- **Storage size**: 8.8 GB
- **Downloads**: 389
- **Stars**: 1
- **Last updated**: 2025-07-30

Source: https://www.modelscope.cn/datasets/ZhipuAI/VisionRewardDB-Image

---

# VisionRewardDB-Image

## Introduction

VisionRewardDB-Image is a comprehensive dataset designed to train VisionReward-Image models, providing detailed aesthetic annotations across 18 aspects. The dataset aims to enhance the assessment and understanding of visual aesthetics and quality. 🌟✨

For more detail, please refer to the [**Github Repository**](https://github.com/THUDM/VisionReward). 🔍📚


## Annotation Detail

Each image in the dataset is annotated with the following attributes:

<table border="1" style="border-collapse: collapse; width: 100%;">
    <tr>
        <th style="padding: 8px; width: 30%;">Dimension</th>
        <th style="padding: 8px; width: 70%;">Attributes</th>
    </tr>
    <tr>
        <td style="padding: 8px;">Composition</td>
        <td style="padding: 8px;">Symmetry; Object pairing; Main object; Richness; Background</td>
    </tr>
    <tr>
        <td style="padding: 8px;">Quality</td>
        <td style="padding: 8px;">Clarity; Color Brightness; Color Aesthetic; Lighting Distinction; Lighting Aesthetic</td>
    </tr>
    <tr>
        <td style="padding: 8px;">Fidelity</td>
        <td style="padding: 8px;">Detail realism; Detail refinement; Body; Face; Hands</td>
    </tr>
    <tr>
        <td style="padding: 8px;">Safety & Emotion</td>
        <td style="padding: 8px;">Emotion; Safety</td>
    </tr>
</table>

### Example: Scene Richness (richness)
- **2:** Very rich  
- **1:** Rich  
- **0:** Normal  
- **-1:** Monotonous  
- **-2:** Empty  

For more detailed annotation guidelines(such as the meanings of different scores and annotation rules), please refer to:
- [annotation_deatil](https://flame-spaghetti-eb9.notion.site/VisionReward-Image-Annotation-Detail-196a0162280e80ef8359c38e9e41247e)
- [annotation_deatil_zh](https://flame-spaghetti-eb9.notion.site/VisionReward-Image-195a0162280e8044bcb4ec48d000409c)


## Additional Feature Detail
The dataset includes two special features: `annotation` and `meta_result`.

### Annotation
The `annotation` feature contains scores across 18 different dimensions of image assessment, with each dimension having its own scoring criteria as detailed above.

### Meta Result
The `meta_result` feature transforms multi-choice questions into a series of binary judgments. For example, for the `richness` dimension:

| Score | Is the image very rich? | Is the image rich? | Is the image not monotonous? | Is the image not empty? |
|-------|------------------------|-------------------|---------------------------|----------------------|
| 2     | 1                      | 1                 | 1                         | 1                    |
| 1     | 0                      | 1                 | 1                         | 1                    |
| 0     | 0                      | 0                 | 1                         | 1                    |
| -1    | 0                      | 0                 | 0                         | 1                    |
| -2    | 0                      | 0                 | 0                         | 0                    |

Each element in the binary array represents a yes/no answer to a specific aspect of the assessment. For detailed questions corresponding to these binary judgments, please refer to the `meta_qa_en.txt` file.

### Meta Mask
The `meta_mask` feature is used for balanced sampling during model training:
- Elements with value 1 indicate that the corresponding binary judgment was used in training
- Elements with value 0 indicate that the corresponding binary judgment was ignored during training

## Data Processing

We provide `extract.py` for processing the dataset into JSONL format. The script can optionally extract the balanced positive/negative QA pairs used in VisionReward training by processing `meta_result` and `meta_mask` fields.

```bash
python extract.py [--save_imgs] [--process_qa]
```

## Citation Information
```
@misc{xu2024visionrewardfinegrainedmultidimensionalhuman,
      title={VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation}, 
      author={Jiazheng Xu and Yu Huang and Jiale Cheng and Yuanming Yang and Jiajun Xu and Yuan Wang and Wenbo Duan and Shen Yang and Qunlin Jin and Shurun Li and Jiayan Teng and Zhuoyi Yang and Wendi Zheng and Xiao Liu and Ming Ding and Xiaohan Zhang and Xiaotao Gu and Shiyu Huang and Minlie Huang and Jie Tang and Yuxiao Dong},
      year={2024},
      eprint={2412.21059},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.21059}, 
}
```