Rapidata/1k-ranked-videos-coherence
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Rapidata/text-2-video-human-preferences-veo3
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Benchmark Update:
@google
Veo3 (Text-to-Video)
Two months ago, we benchmarked @google ’s Veo2 model. It fell short, struggling with style consistency and temporal coherence, trailing behind Runway, Pika, @tencent , and even @alibaba-pai .
That’s changed.
We just wrapped up benchmarking Veo3, and the improvements are substantial. It outperformed every other model by a wide margin across all key metrics. Not just better, dominating across style, coherence, and prompt adherence. It's rare to see such a clear lead in today’s hyper-competitive T2V landscape.
Dataset coming soon. Stay tuned.
Two months ago, we benchmarked @google ’s Veo2 model. It fell short, struggling with style consistency and temporal coherence, trailing behind Runway, Pika, @tencent , and even @alibaba-pai .
That’s changed.
We just wrapped up benchmarking Veo3, and the improvements are substantial. It outperformed every other model by a wide margin across all key metrics. Not just better, dominating across style, coherence, and prompt adherence. It's rare to see such a clear lead in today’s hyper-competitive T2V landscape.
Dataset coming soon. Stay tuned.
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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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🔥 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.
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.
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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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🔥 It's out! We published the dataset for our evaluation of
@OpenAI
's new 4o image generation model.
Rapidata/OpenAI-4o_t2i_human_preference
Yesterday we published the first large evaluation of the new model, showing that it absolutely leaves the competition in the dust. We have now made the results and data available here! Please check it out and ❤️ !
Rapidata/OpenAI-4o_t2i_human_preference
Yesterday we published the first large evaluation of the new model, showing that it absolutely leaves the competition in the dust. We have now made the results and data available here! Please check it out and ❤️ !
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🚀 First Benchmark of
@OpenAI
's 4o Image Generation Model!
We've just completed the first-ever (to our knowledge) benchmarking of the new OpenAI 4o image generation model, and the results are impressive!
In our tests, OpenAI 4o image generation absolutely crushed leading competitors, including @black-forest-labs , @google , @xai-org , Ideogram, Recraft, and @deepseek-ai , in prompt alignment and coherence! They hold a gap of more than 20% to the nearest competitor in terms of Bradley-Terry score, the biggest we have seen since the beginning of the benchmark!
The benchmarks are based on 200k human responses collected through our API. However, the most challenging part wasn't the benchmarking itself, but generating and downloading the images:
- 5 hours to generate 1000 images (no API available yet)
- Just 10 minutes to set up and launch the benchmark
- Over 200,000 responses rapidly collected
While generating the images, we faced some hurdles that meant that we had to leave out certain parts of our prompt set. Particularly we observed that the OpenAI 4o model proactively refused to generate certain images:
🚫 Styles of living artists: completely blocked
🚫 Copyrighted characters (e.g., Darth Vader, Pokémon): initially generated but subsequently blocked
Overall, OpenAI 4o stands out significantly in alignment and coherence, especially excelling in certain unusual prompts that have historically caused issues such as: 'A chair on a cat.' See the images for more examples!
We've just completed the first-ever (to our knowledge) benchmarking of the new OpenAI 4o image generation model, and the results are impressive!
In our tests, OpenAI 4o image generation absolutely crushed leading competitors, including @black-forest-labs , @google , @xai-org , Ideogram, Recraft, and @deepseek-ai , in prompt alignment and coherence! They hold a gap of more than 20% to the nearest competitor in terms of Bradley-Terry score, the biggest we have seen since the beginning of the benchmark!
The benchmarks are based on 200k human responses collected through our API. However, the most challenging part wasn't the benchmarking itself, but generating and downloading the images:
- 5 hours to generate 1000 images (no API available yet)
- Just 10 minutes to set up and launch the benchmark
- Over 200,000 responses rapidly collected
While generating the images, we faced some hurdles that meant that we had to leave out certain parts of our prompt set. Particularly we observed that the OpenAI 4o model proactively refused to generate certain images:
🚫 Styles of living artists: completely blocked
🚫 Copyrighted characters (e.g., Darth Vader, Pokémon): initially generated but subsequently blocked
Overall, OpenAI 4o stands out significantly in alignment and coherence, especially excelling in certain unusual prompts that have historically caused issues such as: 'A chair on a cat.' See the images for more examples!
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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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