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Jason Corkill

jasoncorkill
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https://rapidata.ai

AI & ML interests

Human data annotation

Recent Activity

reacted to their post with ❤️ 10 days ago
🔥 Hidream I1 is online! 🔥 We just added Hidream I1 to our T2I leaderboard (https://www.rapidata.ai/leaderboard/image-models) benchmarked using 195k+ human responses from 38k+ annotators, all collected in under 24 hours. It landed #3 overall, right behind: - @openai 4o - @black-forest-labs Flux 1 Pro ...and just ahead of @black-forest-labs Flux 1.1 Pro, @xai-org Aurora and @google Imagen3. Want to dig into the data? Check out our dataset here: https://huggingface.co/datasets/Rapidata/Hidream_t2i_human_preference What model should we benchmark next?
reacted to their post with 🚀 10 days ago
🔥 Hidream I1 is online! 🔥 We just added Hidream I1 to our T2I leaderboard (https://www.rapidata.ai/leaderboard/image-models) benchmarked using 195k+ human responses from 38k+ annotators, all collected in under 24 hours. It landed #3 overall, right behind: - @openai 4o - @black-forest-labs Flux 1 Pro ...and just ahead of @black-forest-labs Flux 1.1 Pro, @xai-org Aurora and @google Imagen3. Want to dig into the data? Check out our dataset here: https://huggingface.co/datasets/Rapidata/Hidream_t2i_human_preference What model should we benchmark next?
reacted to their post with 🔥 10 days ago
🔥 Hidream I1 is online! 🔥 We just added Hidream I1 to our T2I leaderboard (https://www.rapidata.ai/leaderboard/image-models) benchmarked using 195k+ human responses from 38k+ annotators, all collected in under 24 hours. It landed #3 overall, right behind: - @openai 4o - @black-forest-labs Flux 1 Pro ...and just ahead of @black-forest-labs Flux 1.1 Pro, @xai-org Aurora and @google Imagen3. Want to dig into the data? Check out our dataset here: https://huggingface.co/datasets/Rapidata/Hidream_t2i_human_preference What model should we benchmark next?
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jasoncorkill's activity

reacted to their post with ❤️🚀🔥 10 days ago
view post
Post
2855
🔥 Hidream I1 is online! 🔥

We just added Hidream I1 to our T2I leaderboard (https://www.rapidata.ai/leaderboard/image-models) benchmarked using 195k+ human responses from 38k+ annotators, all collected in under 24 hours.

It landed #3 overall, right behind:
- @openai 4o
- @black-forest-labs Flux 1 Pro
...and just ahead of @black-forest-labs Flux 1.1 Pro, @xai-org Aurora and @google Imagen3.

Want to dig into the data? Check out our dataset here:
Rapidata/Hidream_t2i_human_preference

What model should we benchmark next?
posted an update 10 days ago
view post
Post
2855
🔥 Hidream I1 is online! 🔥

We just added Hidream I1 to our T2I leaderboard (https://www.rapidata.ai/leaderboard/image-models) benchmarked using 195k+ human responses from 38k+ annotators, all collected in under 24 hours.

It landed #3 overall, right behind:
- @openai 4o
- @black-forest-labs Flux 1 Pro
...and just ahead of @black-forest-labs Flux 1.1 Pro, @xai-org Aurora and @google Imagen3.

Want to dig into the data? Check out our dataset here:
Rapidata/Hidream_t2i_human_preference

What model should we benchmark next?
liked a dataset 10 days ago
reacted to their post with ❤️🔥🚀 26 days ago
view post
Post
5528
🚀 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].
posted an update 26 days ago
view post
Post
5528
🚀 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].
updated a dataset 26 days ago
liked a dataset 26 days ago
replied to their post about 1 month ago
liked a dataset about 1 month ago
reacted to their post with ❤️🔥🚀 about 1 month ago
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3282
🚀 We tried something new!

We just published a dataset using a new (for us) preference modality: direct ranking based on aesthetic preference. We ranked a couple of thousand images from most to least preferred, all sampled from the Open Image Preferences v1 dataset by the amazing @data-is-better-together team.

📊 Check it out here:
Rapidata/2k-ranked-images-open-image-preferences-v1

We're really curious to hear your thoughts!
Is this kind of ranking interesting or useful to you? Let us know! 💬

If it is, please consider leaving a ❤️ and if we hit 30 ❤️s, we’ll go ahead and rank the full 17k image dataset!
·
replied to their post about 1 month ago
posted an update about 1 month ago
view post
Post
3282
🚀 We tried something new!

We just published a dataset using a new (for us) preference modality: direct ranking based on aesthetic preference. We ranked a couple of thousand images from most to least preferred, all sampled from the Open Image Preferences v1 dataset by the amazing @data-is-better-together team.

📊 Check it out here:
Rapidata/2k-ranked-images-open-image-preferences-v1

We're really curious to hear your thoughts!
Is this kind of ranking interesting or useful to you? Let us know! 💬

If it is, please consider leaving a ❤️ and if we hit 30 ❤️s, we’ll go ahead and rank the full 17k image dataset!
·
updated a dataset about 1 month ago
reacted to their post with 🔥 about 2 months ago
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Post
3062
🔥 Yesterday was a fire day!
We dropped two brand-new datasets capturing Human Preferences for text-to-video and text-to-image generations powered by our own crowdsourcing tool!

Whether you're working on model evaluation, alignment, or fine-tuning, this is for you.

1. Text-to-Video Dataset (Pika 2.2 model):
Rapidata/text-2-video-human-preferences-pika2.2

2. Text-to-Image Dataset (Reve-AI Halfmoon):
Rapidata/Reve-AI-Halfmoon_t2i_human_preference

Let’s train AI on AI-generated content with humans in the loop.
Let’s make generative models that actually get us.