datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Omni-Preferenceopen-image-preferences-v1
Open Image Preferences
Prompt: Anime-style concept art of a Mayan Quetzalcoatl biomutant, dystopian world, vibrant colors, 4K.
Image 1
Image 2
Prompt: 8-bit pixel art of a blue knight, green car, and glacier landscape in Norway, fantasy style, colorful and detailed.
Image 1… See the full description on the dataset page: https://huggingface.co/datasets/data-is-better-together/open-image-preferences-v1.gdpval_preference_rubricslora-fusing-preferencesAA_preference_cosiAA_preference_CherryAA_preference_l0text-2-video-human-preferences
Rapidata Video Generation Preference Dataset
This dataset was collected in ~12 hours using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
The data collected in this dataset informs our text-2-video model benchmark. We just started so currently only two models are represented in this set:
Sora
Hunyouan
Pika 2.0
Runway ML Alpha
Luma Ray 2
Explore our latest model rankings on our website.
If you get value from this dataset and would… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences.AA_preference_cocourcamera-movement-human-preference-324k
Rapidata Camera Movement Benchmark
Built by Rapidata.
This dataset contains 324,044 human responses, collected with the
Rapidata Python SDK, comparing how well 15 image-to-video models and world models
execute a described camera movement from a single still image. Each row is a head-to-head comparison between
two models' clips generated from the same still and the same instruction, judged by human annotators who
watched a reference animation of the requested movement.
The task… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/camera-movement-human-preference-324k.text-2-video-human-preferences-seedance-1-pro
Rapidata Video Generation Seedance 1 Pro Human Preference
In this dataset, ~60k human responses from ~20k human annotators were collected to evaluate Seedance 1 Pro video generation model on our benchmark. This dataset was collected in roughtly 30 min using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-seedance-1-pro.trajectory-preference-v1human-coherence-preferences-images
Rapidata Image Generation Coherence Dataset
This dataset was collected in ~4 Days using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
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If you get value from this dataset and would like to see more in the future, please consider liking it.
Overview
One of the largest human annotated coherence datasets for text-to-image models, this release contains over 1,200,000 human… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/human-coherence-preferences-images.text-2-video-human-preferences-veo3
Rapidata Video Generation Veo 3 Human Preference
In this dataset, ~46k human responses from ~20k human annotators were collected to evaluate Veo3 video generation model on our benchmark. This dataset was collected in roughly 35 minutes using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-veo3.700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3
NOTE: A newer version of this dataset is available Imagen3_Flux1.1_Flux1_SD3_MJ_Dalle_Human_Preference_Dataset
Rapidata Image Generation Preference Dataset
This Dataset is a 1/3 of a 2M+ human annotation dataset that was split into three modalities: Preference, Coherence, Text-to-Image Alignment.
Link to the Coherence dataset: https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Coherence_Dataset
Link to the Text-2-Image Alignment dataset:… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3.human-style-preferences-images
Rapidata Image Generation Preference Dataset
This dataset was collected in ~4 Days using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking it.
Overview
One of the largest human preference datasets for text-to-image models, this release contains over 1,200,000 human preference… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/human-style-preferences-images.Seedream-3_t2i_human_preference
Rapidata Seedream 3 Preference
This T2I dataset contains over ~400'000 human responses from over ~30'000 individual annotators, collected in less than 7h using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating OpenAI 4o (version from 26.3.2025) across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Seedream-3_t2i_human_preference.human-alignment-preferences-images
Rapidata Image Generation Alignment Dataset
This dataset was collected in ~4 Days using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking it.
Overview
One of the largest human annotated alignment datasets for text-to-image models, this release contains over 1,200,000 human… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/human-alignment-preferences-images.Flux-2-pro_t2i_human_preference
Rapidata Flux 2 Pro Preference
This T2I dataset contains over ~400'000 human responses from over ~50'000 individual annotators, collected in less than 7h using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Flux 2 Pro (version from 25.11.25) across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Flux-2-pro_t2i_human_preference.OpenAI-4o_t2i_human_preference
Rapidata OpenAI 4o Preference
This T2I dataset contains over 200'000 human responses from over ~45,000 individual annotators, collected in less than half a day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating OpenAI 4o (version from 26.3.2025) across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/OpenAI-4o_t2i_human_preference.site-scansHunyuanImage-2.1_t2i_human_preference
Rapidata Hunyuan Image 2.1 Preference
This T2I dataset contains over ~400'000 human responses from over ~50'000 individual annotators, collected in less than 7h using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Hunyuan Image 2.1 (version from 19.9.2025) across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/HunyuanImage-2.1_t2i_human_preference.xAI_Aurora_t2i_human_preferences
Rapidata Aurora Preference
This T2I dataset contains over 400k human responses from over 86k individual annotators, collected in just ~2 Days using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Aurora across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking it.… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/xAI_Aurora_t2i_human_preferences.Runway_Frames_t2i_human_preferences
Rapidata Frames Preference
This T2I dataset contains roughly 400k human responses from over 82k individual annotators, collected in just ~2 Days using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Frames across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking it.… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Runway_Frames_t2i_human_preferences.Ideogram-V2_t2i_human_preference
Rapidata Ideogram-V2 Preference
This T2I dataset contains over 195k human responses from over 42k individual annotators, collected in just ~1 Day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Ideogram-V2 across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking it.… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Ideogram-V2_t2i_human_preference.OpenGVLab_Lumina_t2i_human_preference
Rapidata Lumina Preference
This T2I dataset contains over 400k human responses from over 86k individual annotators, collected in just ~2 Days using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Lumina across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking it.… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/OpenGVLab_Lumina_t2i_human_preference.text-2-video-human-preferences-moonvalley-marey
Rapidata Video Generation Marey Pro Human Preference
In this dataset, ~75k human responses from ~15k human annotators were collected to evaluate Marey video generation model on our benchmark. This dataset was collected in roughtly 30 min using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-moonvalley-marey.text-2-video-human-preferences-veo3.1
Rapidata Video Generation Veo 3.1 Human Preference
In this dataset, ~74k human responses from ~23k human annotators were collected to evaluate the Veo 3.1 video generation model on our benchmark. This dataset was collected using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking it ❤️… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-veo3.1.relative-camera-movement-human-preference-900k
Rapidata Relative Camera Movement Benchmark
Built by Rapidata.
This dataset contains 900,318 human responses, collected with the
Rapidata Python SDK, comparing how well 15 image-to-video models and world models
move the camera relative to what is in the scene. Each row is a head-to-head comparison between two models' clips
generated from the same still and the same instruction, judged by human annotators.
Our Camera Movement Benchmark asks for scene-agnostic
moves — "tilt thirty… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/relative-camera-movement-human-preference-900k.Imagen4_t2i_human_preference
Rapidata Imagen 4 Preference
This T2I dataset contains over 195k human responses from over 70k individual annotators, collected in just ~1 Day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Imagen 4 (imagen-4.0-ultra-generate-exp-05-20) across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Imagen4_t2i_human_preference.