datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MPII_Human_Pose_Dataset
Dataset Card for MPII Human Pose
MPII Human Pose dataset is a state of the art benchmark for evaluation of articulated human pose estimation.
The dataset includes around 25K images containing over 40K people with annotated body joints.
The images were systematically collected using an established taxonomy of every day human activities.
Overall the dataset covers 410 human activities and each image is provided with an activity label.
Each image was extracted from a YouTube… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/MPII_Human_Pose_Dataset.text-2-image-Rich-Human-Feedback
Building upon Google's research Rich Human Feedback for Text-to-Image Generation we have collected over 1.5 million responses from 152'684 individual humans using Rapidata via the Python API. Collection took roughly 5 days.
If you get value from this dataset and would like to see more in the future, please consider liking it.
Overview
We asked humans to evaluate AI-generated images in style, coherence and prompt alignment. For images that contained flaws, participants were… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-image-Rich-Human-Feedback.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.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NOTE: A newer version of this dataset is available Imagen3_Flux1.1_Flux1_SD3_MJ_Dalle_Human_Preference_Dataset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.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.Flux_SD3_MJ_Dalle_Human_Alignment_Dataset
NOTE: A newer version of this dataset is available Imagen3_Flux1.1_Flux1_SD3_MJ_Dalle_Human_Alignment_Dataset
Rapidata Image Generation Alignment 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 Preference dataset:… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Alignment_Dataset.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.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.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.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.human_behavior_atlas_tar
Human Behavior Atlas (HBA)
Human Behavior Atlas (HBA) is a unified benchmark for multimodal behavioral understanding.It aggregates and standardizes multiple behavioral datasets into a single training and evaluation framework, enabling consistent training and evaluation of foundation models on psychological and social behavior tasks (e.g., emotion, intent, sarcasm, mental health signals, nonverbal behavior).
Dataset on Hugging Face:… See the full description on the dataset page: https://huggingface.co/datasets/HumanBehaviorAtlas/human_behavior_atlas_tar.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.Flux_SD3_MJ_Dalle_Human_Coherence_Dataset
NOTE: A newer version of this dataset is available: Imagen3_Flux1.1_Flux1_SD3_MJ_Dalle_Human_Coherence_Dataset
Rapidata Image Generation Coherence 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 Preference dataset: https://huggingface.co/datasets/Rapidata/700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3
Link to the Text-2-Image Alignment dataset:… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Coherence_Dataset.text-2-image-human-preferences-2m
Text-to-image human preferences: 2M votes across 30 models
This dataset contains the complete voting record behind the
Datapoint Image Bench
leaderboard: 2,161,160 validated pairwise votes — exactly 10 for each of
216,116 image pairs. The votes compare 30 text-to-image models in a complete
round-robin on 500 prompts, judged by annotators from over 200 countries.
Every vote includes the annotator's trust score at the time the vote was
cast.
Built on the Datapoint annotation… See the full description on the dataset page: https://huggingface.co/datasets/datapointai/text-2-image-human-preferences-2m.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.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.Recraft-V2_t2i_human_preference
Rapidata Recraft-V2 Preference
This T2I dataset contains over 195k human responses from over 47k individual annotators, collected in just ~1 Day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Recraft-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/Recraft-V2_t2i_human_preference.Imagen-4-ultra-24-7-25_t2i_human_preference
Rapidata Imagen 4 Ultra 24.7.25 Preference
This T2I dataset contains over ~400'000 human responses from over ~83'000 individual annotators, collected in less than 7h using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Imagen 4 Ultra (version from 24.7.2025) 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… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Imagen-4-ultra-24-7-25_t2i_human_preference.HunyuanImage-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.Human_Action_Recognition
Dataset Summary
A dataset from kaggle. origin: https://dphi.tech/challenges/data-sprint-76-human-activity-recognition/233/data
Introduction
The dataset features 15 different classes of Human Activities.
The dataset contains about 12k+ labelled images including the validation images.
Each image has only one human activity category and are saved in separate folders of the labelled classes
PROBLEM STATEMENT
Human Action Recognition (HAR) aims to understand… See the full description on the dataset page: https://huggingface.co/datasets/Bingsu/Human_Action_Recognition.human-nonhuman-face-classification
Human vs Non-Human Face Dataset
A robust dataset for binary classification between real human faces and non-human face-like objects (statues, art, gaming, anime).
📊 Dataset Statistics
Split
Human
Non-Human
Total
Train
3,024
2,949
5,973
Validation
864
842
1,706
Test
433
422
855
Total
8,534
📁 Format
Labels: 0: human, 1: non_human.
🚀 Quick Start
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/LakoreAI/human-nonhuman-face-classification.MM-Food-100K
Overview
This project aims to introduce and release a comprehensive food image dataset designed specifically for computer vision tasks, particularly food recognition, classification, and nutritional analysis. We hope this dataset will provide a reliable resource for researchers and developers to advance the field of food AI. By publishing on Hugging Face, we expect to foster community collaboration and accelerate innovation in applications such as smart recipe recommendations… See the full description on the dataset page: https://huggingface.co/datasets/Humanbased-AI/MM-Food-100K.Reve-AI-Halfmoon_t2i_human_preference
Rapidata Reve AI Halfmoon Preference
This T2I dataset contains over 195k human responses from over 51k individual annotators, collected in just ~1 Day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Reve AI Halfmoon 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… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Reve-AI-Halfmoon_t2i_human_preference.synthetic-human-expressions-poses-3d
3D Synthetic Human Poses and FACS Expressions Dataset
This is a high-fidelity synthetic dataset consisting of 10,075 pairs of 3D human character renders and detailed natural language annotations.
Dataset Structure & Generation
To ensure consistency, the dataset is generated using a single base 3D human model. The diversity of the dataset is achieved through a wide range of body poses, facial expressions, and camera angles:
Character: 1 base human model.
Camera… See the full description on the dataset page: https://huggingface.co/datasets/nadizik/synthetic-human-expressions-poses-3d.Hidream_t2i_human_preference
Rapidata Hidream I1 full Preference
This T2I dataset contains over 195k human responses from over 38k individual annotators, collected in just ~1 Day using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Hidream I1 full 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/Hidream_t2i_human_preference.Recraft-v3-24-7-25_t2i_human_preference
Rapidata Recraft v3 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 Recraft v3 (version from 24.7.2025) 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/Recraft-v3-24-7-25_t2i_human_preference.human_behavior_atlas
Human Behavior Atlas (HBA)
Human Behavior Atlas (HBA) is a unified benchmark for multimodal behavioral understanding.It aggregates and standardizes multiple behavioral datasets into a single training and evaluation framework, enabling consistent training and evaluation of foundation models on psychological and social behavior tasks (e.g., emotion, intent, sarcasm, mental health signals, nonverbal behavior).
Dataset on Hugging Face:… See the full description on the dataset page: https://huggingface.co/datasets/droiden/human_behavior_atlas.humancentric-scenes-ai
HumanCentric-Scenes-AI
A multimodal benchmark of 296 AI-generated human-centric scenes across four domains:
CCTV / surveillance imagery (Set 2, 85 images). Midjourney-generated stills that mimic low-resolution security-camera footage — parking lots, building interiors, outdoor public spaces — designed to test whether detection cues survive heavy compression and low-light noise.
Occupation × gender portraits (Set 3, 128 images). A balanced 64-occupation × 2-gender paired design… See the full description on the dataset page: https://huggingface.co/datasets/Nima0Kamali/humancentric-scenes-ai.humancentric-scenes-ai
HumanCentric-Scenes-AI
A multimodal benchmark of 296 AI-generated human-centric scenes across four domains:
CCTV / surveillance imagery (Set 2, 85 images). Midjourney-generated stills that mimic low-resolution security-camera footage — parking lots, building interiors, outdoor public spaces — designed to test whether detection cues survive heavy compression and low-light noise.
Occupation × gender portraits (Set 3, 128 images). A balanced 64-occupation × 2-gender paired design… See the full description on the dataset page: https://huggingface.co/datasets/rjmaftv33/humancentric-scenes-ai.BBBC021-Human-MCF7-Cells
BBBC021: Human MCF7 cells – compound-profiling experiment
Link
This dataset contains images of MCF-7 breast cancer cells treated with 113 small molecules across eight concentrations, labeled for DNA, F-actin, and B-tubulin.
Citation and Copyright
As requested by the original authors:
We used image set BBBC021v1 [Caie et al., Molecular Cancer Therapeutics, 2010], available from the Broad Bioimage Benchmark Collection [Ljosa et al., Nature Methods, 2012].
Copyright: The… See the full description on the dataset page: https://huggingface.co/datasets/roslu/BBBC021-Human-MCF7-Cells.