--- title: 117k_human_coherence_flux1.0_V_flux1.1Blueberry canonical_url: "https://www.modelscope.cn/datasets/Rapidata/117k_human_coherence_flux1.0_V_flux1.1Blueberry" md_url: "https://www.modelscope.cn/datasets/Rapidata/117k_human_coherence_flux1.0_V_flux1.1Blueberry.md" repository: Rapidata/117k_human_coherence_flux1.0_V_flux1.1Blueberry last_updated: 2025-01-25 license: "Apache License 2.0" storage_size: "819 MB" downloads: 1326 stars: 0 --- # 117k_human_coherence_flux1.0_V_flux1.1Blueberry > 117k_human_coherence_flux1.0_V_flux1.1Blueberry - Rapidata 在 ModelScope 开源的数据集。Rapidata Image Generation Alignment Dataset Rapidata/117k_human_coherence_flux1.0_V_flux1.1Blueberry 是 ModelScope 魔搭社区上的数据集，存储大小 819 MB，采用 Apache License 2.0 许可。 - **Repository**: Rapidata/117k_human_coherence_flux1.0_V_flux1.1Blueberry - **License**: Apache License 2.0 - **Storage size**: 819 MB - **Downloads**: 1326 - **Stars**: 0 - **Last updated**: 2025-01-25 Source: https://www.modelscope.cn/datasets/Rapidata/117k_human_coherence_flux1.0_V_flux1.1Blueberry --- # Rapidata Image Generation Alignment Dataset Dataset visualization 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 Preference dataset: https://huggingface.co/datasets/Rapidata/117k_human_preferences_flux1.0_V_flux1.1Blueberry - Link to the Text-2-Image Alignment dataset: https://huggingface.co/datasets/Rapidata/117k_human_alignment_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 is more plausible to exist and has fewer odd or impossible-looking things?" 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.