human-style-preferences-images

# Rapidata Image Generation Preference Dataset

<a href="https://www.rapidata.ai">
<img src="https://cdn-uploads.huggingface.co/production/uploads/66f5624c42b853e73e0738eb/jfxR79bOztqaC6_yNNnGU.jpeg" width="400" alt="Dataset visualization">
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This dataset was collected in ~4 Days using the [Rapidata Python API](https://docs.rapidata.ai), accessible to anyone and ideal for large scale data annotation.

Explore our latest model rankings on our [website](https://www.rapidata.ai/benchmark).

If you get value from this dataset and would like to see more in the future, please consider liking it.

## Overview
One of the largest human preference datasets for text-to-image models, this release contains over 1,200,000 human preference votes. This preference dataset builds on the already published [Preference Dataset](https://huggingface.co/datasets/Rapidata/700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3) and shows Rapidata's ability to consistently rank new image generation models at unprecedented speeds.

## Key Features
- **Massive Scale**: 1,200,000+ individual human preference votes collected in under 100 hours
- **Global Representation**: Collected from participants across the globe
- **Diverse Prompts**: Carefully curated prompts testing various aspects of image generation
- **Leading Models**: Comparisons between state-of-the-art image generation models

<img src="https://cdn-uploads.huggingface.co/production/uploads/66f5624c42b853e73e0738eb/1LVQj_G5bFio7w4WXPxsC.png" alt="Image description" width="650">
**Figure:** Overview of the distribution of annotators by continent (left) compared to the world population distribution (right)

## Applications
This dataset is invaluable for:
- Benchmarking new image generation models
- Developing better evaluation metrics for generative models
- Understanding global preferences in AI-generated imagery
- Training and fine-tuning image generation models
- Researching cross-cultural aesthetic preferences

## Data Collection Powered by Rapidata
What traditionally would take weeks or months of data collection was accomplished in under 100 hours through Rapidata's innovative annotation platform. Our technology enables:
- Lightning-fast data collection at massive scale
- Global reach across 145+ countries
- Built-in quality assurance mechanisms
- Comprehensive demographic representation
- Cost-effective large-scale annotation

## About Rapidata
Rapidata's technology makes collecting human feedback at scale faster and more accessible than ever before. Visit [rapidata.ai](https://www.rapidata.ai/) to learn more about how we're revolutionizing human feedback collection for AI development.

# Rapidata 图像生成偏好数据集

<a href="https://www.rapidata.ai">
<img src="https://cdn-uploads.huggingface.co/production/uploads/66f5624c42b853e73e0738eb/jfxR79bOztqaC6_yNNnGU.jpeg" width="400" alt="数据集可视化">
</a>

本数据集依托[Rapidata Python API](https://docs.rapidata.ai)采集，耗时约4天，面向所有用户开放，是大规模数据标注的理想选择。

欢迎访问我们的[官网](https://www.rapidata.ai/benchmark)查看最新的模型排名。

若本数据集对您有所助益并希望后续推出更多同类资源，欢迎为其点赞。

## 数据集概览

本数据集是目前规模最大的文本到图像模型人类偏好数据集之一，共包含超120万条人类偏好投票。本偏好数据集基于已发布的[偏好数据集](https://huggingface.co/datasets/Rapidata/700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3)构建，展现了Rapidata以空前速度持续对新型图像生成模型进行排名的能力。

## 核心特性

- **超大规模**：在100小时内采集超120万条独立人类偏好投票
- **全球覆盖**：参与者来自全球各地
- **提示词多样化**：经过精心筛选的提示词，覆盖图像生成的各类评测维度
- **前沿模型对比**：涵盖当前最先进的图像生成模型之间的对比

<img src="https://cdn-uploads.huggingface.co/production/uploads/66f5624c42b853e73e0738eb/1LVQj_G5bFio7w4WXPxsC.png" alt="图像说明" width="650">
**图**：按大洲划分的标注者分布概况（左图）与全球人口分布情况（右图）对比

## 应用场景

本数据集可广泛应用于：
- 新型图像生成模型的基准测试
- 为生成式模型开发更优质的评测指标
- 探究人工智能生成图像的全球偏好差异
- 图像生成模型的训练与微调
- 跨文化审美偏好研究

## 基于Rapidata的数据采集流程

依托Rapidata创新的标注平台，原本需要数周乃至数月的数据采集工作可在100小时内完成。我们的技术支持以下能力：
- 超高速大规模数据采集
- 覆盖全球145个以上国家的全球触达能力
- 内置质量保障机制
- 全面的人口统计学代表性
- 高性价比的大规模标注服务

## 关于Rapidata

Rapidata的技术让大规模人类反馈采集比以往任何时候都更加快捷、易用。请访问[rapidata.ai](https://www.rapidata.ai/)了解更多关于我们如何革新人工智能开发领域的人类反馈采集技术的信息。