---
title: EditReward-Data
canonical_url: "https://www.modelscope.cn/datasets/TIGER-Lab/EditReward-Data"
md_url: "https://www.modelscope.cn/datasets/TIGER-Lab/EditReward-Data.md"
repository: TIGER-Lab/EditReward-Data
last_updated: 2025-10-12
license: cc-by-nc-4.0
storage_size: "81 GB"
downloads: 784
stars: 0
---

# EditReward-Data

> EditReward-Data - TIGER-Lab 在 ModelScope 开源的数据集。This repository contains EditReward-Data, a large-scale, high-fidelity human preference dataset for instruction-guided image editing. It was introduced in the paper EditReward: A Human-Aligned Reward Model for…

TIGER-Lab/EditReward-Data 是 ModelScope 魔搭社区上的数据集，存储大小 81 GB，采用 cc-by-nc-4.0 许可。

- **Repository**: TIGER-Lab/EditReward-Data
- **License**: cc-by-nc-4.0
- **Storage size**: 81 GB
- **Downloads**: 784
- **Stars**: 0
- **Last updated**: 2025-10-12

Source: https://www.modelscope.cn/datasets/TIGER-Lab/EditReward-Data

---

# EditReward-Data

This repository contains **EditReward-Data**, a large-scale, high-fidelity human preference dataset for instruction-guided image editing. It was introduced in the paper [EditReward: A Human-Aligned Reward Model for Instruction-Guided Image Editing](https://huggingface.co/papers/2509.26346).

`EditReward-Data` comprises over 200K manually annotated preference pairs. These annotations were meticulously curated by trained experts following a rigorous and standardized protocol, ensuring high alignment with considered human judgment and minimizing label noise. The dataset covers a diverse range of edits produced by seven state-of-the-art models across twelve distinct sources. It serves as crucial training data for reward models like EditReward, designed to score instruction-guided image edits.

- **Paper:** [EditReward: A Human-Aligned Reward Model for Instruction-Guided Image Editing](https://huggingface.co/papers/2509.26346)
- **Project Page:** [https://tiger-ai-lab.github.io/EditReward](https://tiger-ai-lab.github.io/EditReward)
- **Code Repository:** [https://github.com/TIGER-AI-Lab/EditReward](https://github.com/TIGER-AI-Lab/EditReward)

<p align="center">
  <img src="https://github.com/TIGER-AI-Lab/EditReward/blob/main/assets/pipeline.png?raw=true" alt="EditReward Pipeline" width="900"/>
</p>

## Dataset Overview

EditReward-Data is designed to enable the training of reward models that can score instruction-guided image edits. The dataset facilitates assessing and improving the alignment of image editing models with human preferences. The dataset statistics are shown below:

<p align="left">
  <img src="https://github.com/TIGER-AI-Lab/EditReward/blob/main/assets/dataset_stat.png?raw=true" alt="Dataset Statistics" width="900"/>
</p>

## Sample Usage

To download the `EditReward-Data` dataset to your local machine, use the `huggingface-cli` command:

```bash
huggingface-cli download --repo-type dataset TIGER-Lab/EditReward-Data --local-dir /your-local-dataset-path
```

## Citation

Please kindly cite our paper if you use our code, data, models, or results:

```bibtex
@article{wu2025editreward,
  title={EditReward: A Human-Aligned Reward Model for Instruction-Guided Image Editing},
  author={Wu, Keming and Jiang, Sicong and Ku, Max and Nie, Ping and Liu, Minghao and Chen, Wenhu},
  journal={arXiv preprint arXiv:2509.26346},
  year={2025}
}
```