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}
}
```

# EditReward-Data

本仓库包含**EditReward-Data**，一款面向指令引导图像编辑（instruction-guided image editing）的大规模高保真人类偏好数据集。该数据集源自论文《EditReward：面向指令引导图像编辑的人类对齐奖励模型》（https://huggingface.co/papers/2509.26346）。

`EditReward-Data` 包含超过20万条人工标注的偏好样本对。这些标注由经过专业培训的专家依据严格标准化的流程精心整理，确保与人类深思熟虑后的判断高度对齐，同时最大限度降低标注噪声（label noise）。该数据集涵盖了来自12种不同来源、由7款业界顶尖（state-of-the-art）图像编辑模型生成的多样化编辑结果，可作为EditReward等面向指令引导图像编辑评分的奖励模型的关键训练数据。

- **论文链接：** [《EditReward：面向指令引导图像编辑的人类对齐奖励模型》](https://huggingface.co/papers/2509.26346)
- **项目主页：** [https://tiger-ai-lab.github.io/EditReward](https://tiger-ai-lab.github.io/EditReward)
- **代码仓库：** [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 流程框架" width="900"/>
</p>

## 数据集概览

EditReward-Data 旨在支持可对指令引导图像编辑进行评分的奖励模型训练，该数据集有助于评估并提升图像编辑模型与人类偏好的对齐程度。数据集统计信息如下：

<p align="left">
<img src="https://github.com/TIGER-AI-Lab/EditReward/blob/main/assets/dataset_stat.png?raw=true" alt="数据集统计信息" width="900"/>
</p>

## 示例用法

若需将`EditReward-Data`数据集下载至本地，请使用以下`huggingface-cli`命令：

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

## 引用声明

若您使用本代码、数据集、模型或实验结果，请引用我们的论文：

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}
}