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Beyond Image Preferences - Rich Human Feedback for Text-to-Image Generation
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13Lino Giger
LinoGiger
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Integrating human feedback is vital for evolving AI models. Boost quality, scalability, and cost-effectiveness with our crowdsourcing tool!
..Or run A/B tests and gather thousands of responses in minutes. Upload two images, ask a question, and watch the insights roll in!
Check it out here and let us know your feedback: https://app.rapidata.ai/compare
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This dataset was collected in roughly 4 hours using the Rapidata Python API, showcasing how quickly large-scale annotations can be performed with the right tooling!
All that at less than the cost of a single hour of a typical ML engineer in Zurich!
The new dataset of ~22,000 human annotations evaluating AI-generated videos based on different dimensions, such as Prompt-Video Alignment, Word for Word Prompt Alignment, Style, Speed of Time flow and Quality of Physics.
https://huggingface.co/datasets/Rapidata/text-2-video-Rich-Human-Feedback
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Runway Gen-3 Alpha: The Style and Coherence Champion
Runway's latest video generation model, Gen-3 Alpha, is something special. It ranks #3 overall on our text-to-video human preference benchmark, but in terms of style and coherence, it outperforms even OpenAI Sora.
However, it struggles with alignment, making it less predictable for controlled outputs.
We've released a new dataset with human evaluations of Runway Gen-3 Alpha: Rapidata's text-2-video human preferences dataset. If you're working on video generation and want to see how your model compares to the biggest players, we can benchmark it for you.
🚀 DM us if you’re interested!
Dataset: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-runway-alpha
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Article
Crowd-sourced Open Preference Dataset for Text-to-Image Generation
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