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Building upon Google's research [Rich Human Feedback for Text-to-Image Generation](https://arxiv.org/abs/2312.10240), and the
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[smaller, previous version of this dataset](https://huggingface.co/datasets/Rapidata/text-2-image-Rich-Human-Feedback), we have collected over 3.7 million responses from 307'415 individual humans using Rapidata via the [Python API](https://docs.rapidata.ai/). Collection took less than 2 weeks.
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If you get value from this dataset and would like to see more in the future, please consider liking it
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# Overview
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We asked humans to evaluate AI-generated images in style, coherence and prompt alignment. For images that contained flaws, participants were asked to identify specific problematic areas. Additionally, for all images, participants identified words from the prompts that were not accurately represented in the generated images.
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Building upon Google's research [Rich Human Feedback for Text-to-Image Generation](https://arxiv.org/abs/2312.10240), and the
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[smaller, previous version of this dataset](https://huggingface.co/datasets/Rapidata/text-2-image-Rich-Human-Feedback), we have collected over 3.7 million responses from 307'415 individual humans using Rapidata via the [Python API](https://docs.rapidata.ai/). Collection took less than 2 weeks.
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If you get value from this dataset and would like to see more in the future, please consider liking it ♥️
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# Overview
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We asked humans to evaluate AI-generated images in style, coherence and prompt alignment. For images that contained flaws, participants were asked to identify specific problematic areas. Additionally, for all images, participants identified words from the prompts that were not accurately represented in the generated images.
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