117k_human_preferences_flux1.0_V_flux1.1Blueberry

# Rapidata Image Generation Alignment Dataset

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This Dataset is a 1/3 of a 340k human annotation dataset that was split into three modalities: Preference, Coherence, Text-to-Image Alignment.
- Link to the Text-2-Image Alignment dataset: https://huggingface.co/datasets/Rapidata/117k_human_alignment_flux1.0_V_flux1.1Blueberry
- Link to the Coherence dataset: https://huggingface.co/datasets/Rapidata/117k_human_coherence_flux1.0_V_flux1.1Blueberry

It was collected in ~2 Days using the Rapidata Python API https://docs.rapidata.ai

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

## Overview
This dataset focuses on human comparative evaluations of AI-generated images. Participants were shown two images—one generated by Flux 1.0 and the other by Flux 1.1Blueberry—and asked, "Which image do you prefer?" Each pair of images was reviewed by at least 26 participants, generating a robust set of 117,000+ individual votes.

## Key Features
- **Massive Scale**: 117,000+ individual human preference votes from all over the world
- **Diverse Prompts**: 281 carefully curated prompts testing various aspects of image generation
- **Leading Models**: Comparisons between two state-of-the-art image generation models
- **Rigorous Methodology**: Uses pairwise comparisons with built-in quality controls
- **Rich Demographic Data**: Includes annotator information about age, gender, and geographic location

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

## Data Collection Powered by Rapidata
What traditionally would take weeks or months of data collection was accomplished in just 24 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.

We created the dataset using our in-house developed [API](https://docs.rapidata.ai/), which you can access to gain near-instant human intelligence at your fingertips.

# Rapidata 图像生成对齐数据集

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本数据集为34万人类标注数据集的三分之一，该原始数据集被划分为三个模态：偏好性、一致性、文本到图像对齐。
- 文本到图像对齐数据集链接：https://huggingface.co/datasets/Rapidata/117k_human_alignment_flux1.0_V_flux1.1Blueberry
- 一致性数据集链接：https://huggingface.co/datasets/Rapidata/117k_human_coherence_flux1.0_V_flux1.1Blueberry

本数据集通过Rapidata Python API（https://docs.rapidata.ai）耗时约2天完成采集。

若本数据集为您带来价值并期望未来看到更多相关资源，不妨为其点赞。

## 概述
本数据集聚焦于AI生成图像的人类对比评估。参与者将看到两张图像——一张由Flux 1.0生成，另一张由Flux 1.1Blueberry生成——并被问及：“您更偏好哪张图像？” 每一组图像均由至少26名参与者进行评审，累计产生超过11.7万份有效个体投票，构建了可靠的标注数据集。

## 核心特性
- **海量规模**：来自全球的11.7万+份人类偏好投票数据
- **多样提示词**：281个精心筛选的提示词，覆盖图像生成的各类测试维度
- **顶尖模型对比**：两款当前最先进的图像生成模型之间的性能对比
- **严谨的研究方法**：采用成对比较范式，并内置质量管控机制
- **丰富的人口统计数据**：包含标注者的年龄、性别及地理位置信息

## 应用场景
本数据集具有极高应用价值，可用于：
- 图像生成模型的训练与微调
- 探究AI生成图像的全球偏好特征
- 研发更优质的生成模型评估指标
- 跨文化审美偏好相关研究
- 新型图像生成模型的性能基准测试

## 基于Rapidata的数据采集
传统上需要数周乃至数月完成的数据采集工作，借助Rapidata的创新标注平台仅用24小时即可完成。我们的技术具备以下优势：
- 极速海量数据采集能力
- 覆盖145+国家的全球触达范围
- 内置质量保障机制
- 全面的人口统计样本代表性
- 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变得前所未有的快速与便捷。请访问[rapidata.ai](https://www.rapidata.ai/)了解我们如何革新AI开发领域的人类反馈采集流程。

我们通过自研的[API](https://docs.rapidata.ai/)构建了本数据集，您可通过该接口轻松获取近乎实时的人类智能服务。

搜集汇总

背景与挑战

背景概述

该数据集是Rapidata图像生成对齐数据集的一部分，专注于人类对AI生成图像的偏好比较。它包含超过117,000个投票，基于281个提示，对比Flux 1.0和Flux 1.1Blueberry模型，每个图像对由至少26名全球参与者评估，数据在约2天内通过Rapidata平台收集完成。