C. Emre Karataş

emredeveloper

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liked a model about 5 hours ago

nvidia/DAM-3B

liked a Space about 5 hours ago

nvidia/describe-anything-model-demo

reacted to jasoncorkill's post with 🚀 about 11 hours ago

🚀 Building Better Evaluations: 32K Image Annotations Now Available Today, we're releasing an expanded version: 32K images annotated with 3.7M responses from over 300K individuals which was completed in under two weeks using the Rapidata Python API. https://huggingface.co./datasets/Rapidata/text-2-image-Rich-Human-Feedback-32k A few months ago, we published one of our most liked dataset with 13K images based on the @data-is-better-together's dataset, following Google's research on "Rich Human Feedback for Text-to-Image Generation" (https://arxiv.org/abs/2312.10240). It collected over 1.5M responses from 150K+ participants. https://huggingface.co./datasets/Rapidata/text-2-image-Rich-Human-Feedback In the examples below, users highlighted words from prompts that were not correctly depicted in the generated images. Higher word scores indicate more frequent issues. If an image captured the prompt accurately, users could select [No_mistakes]. We're continuing to work on large-scale human feedback and model evaluation. If you're working on related research and need large, high-quality annotations, feel free to get in touch: [email protected].

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emredeveloper's activity

liked a model about 5 hours ago

nvidia/DAM-3B

Image-Text-to-Text • Updated 4 days ago • 1.42k • 84

liked a Space about 5 hours ago

139

Describe Anything

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Describe parts of images using text prompts

reacted to jasoncorkill's post with 🚀 about 11 hours ago

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🚀 Building Better Evaluations: 32K Image Annotations Now Available

Today, we're releasing an expanded version: 32K images annotated with 3.7M responses from over 300K individuals which was completed in under two weeks using the Rapidata Python API.

Rapidata/text-2-image-Rich-Human-Feedback-32k

A few months ago, we published one of our most liked dataset with 13K images based on the @data-is-better-together 's dataset, following Google's research on "Rich Human Feedback for Text-to-Image Generation" (https://arxiv.org/abs/2312.10240). It collected over 1.5M responses from 150K+ participants.

Rapidata/text-2-image-Rich-Human-Feedback

In the examples below, users highlighted words from prompts that were not correctly depicted in the generated images. Higher word scores indicate more frequent issues. If an image captured the prompt accurately, users could select [No_mistakes].

We're continuing to work on large-scale human feedback and model evaluation. If you're working on related research and need large, high-quality annotations, feel free to get in touch: [email protected].