Rapidata/Imagen4_t2i_human_preference
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Rapidata/text-2-video-human-preferences-veo3
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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.
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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🚀 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].
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].
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🚀 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!
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!
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🚀 Rapidata: Setting the Standard for Model Evaluation
Rapidata is proud to announce our first independent appearance in academic research, featured in the Lumina-Image 2.0 paper. This marks the beginning of our journey to become the standard for testing text-to-image and generative models. Our expertise in large-scale human annotations allows researchers to refine their models with accurate, real-world feedback.
As we continue to establish ourselves as a key player in model evaluation, we’re here to support researchers with high-quality annotations at scale. Reach out to [email protected] to see how we can help.
Lumina-Image 2.0: A Unified and Efficient Image Generative Framework (2503.21758)
Rapidata is proud to announce our first independent appearance in academic research, featured in the Lumina-Image 2.0 paper. This marks the beginning of our journey to become the standard for testing text-to-image and generative models. Our expertise in large-scale human annotations allows researchers to refine their models with accurate, real-world feedback.
As we continue to establish ourselves as a key player in model evaluation, we’re here to support researchers with high-quality annotations at scale. Reach out to [email protected] to see how we can help.
Lumina-Image 2.0: A Unified and Efficient Image Generative Framework (2503.21758)
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At Rapidata, we compared DeepL with LLMs like DeepSeek-R1, Llama, and Mixtral for translation quality using feedback from over 51,000 native speakers. Despite the costs, the performance makes it a valuable investment, especially in critical applications where translation quality is paramount. Now we can say that Europe is more than imposing regulations.
Our dataset, based on these comparisons, is now available on Hugging Face. This might be useful for anyone working on AI translation or language model evaluation.
Rapidata/Translation-deepseek-llama-mixtral-v-deepl
Our dataset, based on these comparisons, is now available on Hugging Face. This might be useful for anyone working on AI translation or language model evaluation.
Rapidata/Translation-deepseek-llama-mixtral-v-deepl
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Benchmarking Google's Veo2: How Does It Compare?
The results did not meet expectations. Veo2 struggled with style consistency and temporal coherence, falling behind competitors like Runway, Pika, Tencent, and even Alibaba. While the model shows promise, its alignment and quality are not yet there.
Google recently launched Veo2, its latest text-to-video model, through select partners like fal.ai. As part of our ongoing evaluation of state-of-the-art generative video models, we rigorously benchmarked Veo2 against industry leaders.
We generated a large set of Veo2 videos spending hundreds of dollars in the process and systematically evaluated them using our Python-based API for human and automated labeling.
Check out the ranking here: https://www.rapidata.ai/leaderboard/video-models
Rapidata/text-2-video-human-preferences-veo2
The results did not meet expectations. Veo2 struggled with style consistency and temporal coherence, falling behind competitors like Runway, Pika, Tencent, and even Alibaba. While the model shows promise, its alignment and quality are not yet there.
Google recently launched Veo2, its latest text-to-video model, through select partners like fal.ai. As part of our ongoing evaluation of state-of-the-art generative video models, we rigorously benchmarked Veo2 against industry leaders.
We generated a large set of Veo2 videos spending hundreds of dollars in the process and systematically evaluated them using our Python-based API for human and automated labeling.
Check out the ranking here: https://www.rapidata.ai/leaderboard/video-models
Rapidata/text-2-video-human-preferences-veo2
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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.
Rapidata/text-2-video-Rich-Human-Feedback
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.
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: Rapidata/text-2-video-human-preferences-runway-alpha
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: Rapidata/text-2-video-human-preferences-runway-alpha
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