datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
PhysicalAI-SimReady-Warehouse-01
NVIDIA Physical AI SimReady Warehouse OpenUSD Dataset
Dataset Version: 1.1.0
Date: May 18, 2025
Author: NVIDIA, Corporation
License: CC-BY-4.0 (Creative Commons Attribution 4.0 International)
Contents
This dataset includes the following:
This README file
A CSV catalog that enumerates all of the OpenUSD assets that are part of this dataset including a sub-folder of images that showcase each 3D asset (physical_ai_simready_warehouse_01.csv). The CSV file is organized in… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-SimReady-Warehouse-01.GroMo25
GroMo25: Multiview Time-Series Plant Image Dataset for Age Estimation and Leaf Counting
Dataset Summary
GroMo25 is a multiview, time-series plant image dataset designed for plant age estimation (in days) and leaf counting tasks in precision agriculture. It contains high-quality images of four crop species — Wheat, Okra, Radish, and Mustard — captured over multiple days under controlled conditions. Each plant is photographed from 24 angles across 5 vertical levels per day… See the full description on the dataset page: https://huggingface.co/datasets/MrigLabIITRopar/GroMo25.fish-vista
Dataset Card for Fish-Visual Trait Analysis (Fish-Vista)
Note that the '</Use this dataset>' option will only load the CSV files. To download the entire dataset, including all processed images and segmentation annotations, refer to Instructions for downloading dataset and images.
See Example Code to Use the Segmentation Dataset
Figure 1. A schematic representation of the different tasks in Fish-Vista Dataset.
Instructions for downloading dataset… See the full description on the dataset page: https://huggingface.co/datasets/imageomics/fish-vista.multimodal-ct-radiology-reports
Perle AI Multi-phase CECT and CT with Radiology Reports
Summary
A de-identified CT dataset from Perle AI, paired with the original radiology reports. It supports work on multi-modal medical imaging: phase or pathology classification, report generation from images, and visual question answering.
The release has three configurations:
Config
Modality
Subjects
Pairing
cect_3phase
3-phase contrast-enhanced abdominal CT (DICOM)
5
per-subject text report +… See the full description on the dataset page: https://huggingface.co/datasets/Perle-ai/multimodal-ct-radiology-reports.webvid-10MREPID
REPID: Rendering Evaluation of Photographic Image Dataset
REPID (officially introduced as the Rendering Evaluation of Photographic Image Dataset) is a large-scale benchmark designed for Image Rendering Quality Assessment (IRQA) in paper Beyond distortions: a benchmark for subjective evaluation of image rendering quality.
Unlike traditional Image Quality Assessment (IQA) which focuses on technical degradations like noise or blur, REPID aims to model subjective human aesthetic… See the full description on the dataset page: https://huggingface.co/datasets/vsevolodpl/REPID.SwissCrop25
SwissCrop25
A national benchmark dataset for operational crop mapping in Switzerland, providing Sentinel-2
time series, daily temperature data, and parcel-level crop type labels across seven growing
seasons (2019–2025).
Introduced in: SwissCrop25: A National Multi-Year Benchmark for Operational Crop Mapping
(TerraBytes II Workshop, ECCV 2026) — [Paper] [Code] [Team]
Highlights
Nationwide coverage of Switzerland (41,285 km²)
Seven growing seasons (2019–2025)
73… See the full description on the dataset page: https://huggingface.co/datasets/EOA-team/SwissCrop25.llbench-dataset
LL-Bench: Rethinking Low-Level Vision Evaluation in the Era of Large-Scale Generative Models via Human Preferences
Anonymous release prepared for NeurIPS 2026 review. Please do not redistribute.
LL-Bench is a large-scale, human-preference benchmark for evaluating low-level
vision restoration in the era of large generative models (LGMs). It compares
10 LGMs with 16 specilist and 5 all-in-one models across 16 low-level vision tasks, paired with dense human annotations:pairwise… See the full description on the dataset page: https://huggingface.co/datasets/anonymousllbench/llbench-dataset.questFish2024
Dataset Card for QUEST Fish 2024
Images collected by teachers during a QUEST workshop. In 2024, the images were of fish collected from bodies of water near Princeton University.
Dataset Details
Dataset Structure
/dataset/
<folder>/
<img_id 1>.png
<img_id 2>.png
...
<img_id n>.png
...
<img_id 1>.png
<img_id 2>.png
...
<img_id n>.png
fieldData2024.csv
Data Instances… See the full description on the dataset page: https://huggingface.co/datasets/imageomics/questFish2024.MethaneUnion
MethaneUnion
This dataset product is organized as:
datasets/
temporal_split/
original_scale/{train,test}.csv
120m_GSD/{train,test}.csv
360m_GSD/{train,test}.csv
480m_GSD/{train,test}.csv
960m_GSD/{train,test}.csv
geo_split/
original_scale/{train,test}.csv
120m_GSD/{train,test}.csv
360m_GSD/{train,test}.csv
480m_GSD/{train,test}.csv
960m_GSD/{train,test}.csv
Each CSV contains exactly these columns: id, label, latitude, longitude… See the full description on the dataset page: https://huggingface.co/datasets/yuyao42/MethaneUnion.localizer
Dartbrains Localizer Dataset
A subset of the Brainomics/Localizer functional MRI dataset, prepared for the Dartbrains neuroimaging course at Dartmouth College.
Quick Start
Load beta maps (recommended for most exercises)
from datasets import load_dataset
ds = load_dataset("dartbrains/localizer", "betas")
img = ds[0]["nifti"] # nibabel.Nifti1Image
subject = ds[0]["subject"] # "S01"
condition = ds[0]["condition"] # "audio_computation"… See the full description on the dataset page: https://huggingface.co/datasets/dartbrains/localizer.FairVision
Dataset Card: Harvard-FairVision
Dataset Summary
Harvard-FairVision is the first large-scale medical fairness dataset with both 2D and 3D imaging data, covering three major eye diseases affecting approximately 380 million people worldwide. It contains 30,000 subjects (10,000 per disease) across Age-Related Macular Degeneration (AMD), Diabetic Retinopathy (DR), and glaucoma, each with paired SLO fundus photos and 3D OCT B-scans and six demographic identity attributes.
This… See the full description on the dataset page: https://huggingface.co/datasets/harvardairobotics/FairVision.Spatial-DISE
Spatial-DISE: A Unified Benchmark for Evaluating Spatial Reasoning in Vision-Language Models
📋 Overview
Spatial-DISE is a comprehensive benchmark dataset designed to evaluate spatial reasoning capabilities in vision-language models. The dataset focuses on various aspects of spatial intelligence including 3D perception, spatial transformation, and geometric reasoning across multiple difficulty levels.
🧪 Evaluation Support
Supported:… See the full description on the dataset page: https://huggingface.co/datasets/TACPS-liv/Spatial-DISE.retina-age-analysis
Retina Age Analysis Dataset
Dataset Description
This dataset contains 9,857 retinal fundus images from 5,393 patients for age prediction tasks.
Dataset Summary
Task: Age prediction from retinal fundus images
Images: 9,857 high-quality retinal images
Patients: 5,393 unique patients
Age Range: 5-97 years
Image Format: JPEG
Average Image Size: ~1 MB
Supported Tasks
Regression: Predict continuous age (5-97 years)
Classification: Predict age group (5… See the full description on the dataset page: https://huggingface.co/datasets/ramankamran/retina-age-analysis.SA-BENCH
SA-BENCH
SA-BENCH is the benchmark dataset released with “Beyond Pixels: Benchmarking and Reward-Based Assessing Framework for Visual Spatial Aesthetics.”
It evaluates the spatial aesthetics of interior images along four dimensions:
distortion
harmony
layout
lighting
The dataset contains 17,768 annotated examples across four spatial-aesthetic dimensions, with image assets and human annotations for training and evaluation.
Dataset Details
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/AliHome3D/SA-BENCH.pcabiop
PCaBiop
PCaBiop is a whole-slide-image benchmark for measuring the robustness of slide-level pathology foundation models to non-biological variation. It pairs a controlled two-centre prostate-biopsy cohort, in which the contributing centre acts as a labelled confounder, with an external out-of-domain cohort for downstream shortcut-learning evaluation.
The benchmark accompanies the paper A distributional robustness margin for pathology foundation models (citation below… See the full description on the dataset page: https://huggingface.co/datasets/waticlems/pcabiop.cnn-based-drowsiness-detection-data
CNN-Based Drowsiness Detection - Dataset
Preprocessed, auto-labeled face-crop images used to train the model in
notgoodkeeper/cnn-based-drowsiness-detection.
Code: https://github.com/not-good-keeper/cnn-based-drowsiness-detection
Collection
Frames were captured from a webcam, then run through:
Haar Cascade face detection -> crop + pad + resize to 412x412
MediaPipe Selfie Segmentation -> background replaced with white
CLAHE contrast normalization -> grayscale… See the full description on the dataset page: https://huggingface.co/datasets/notgoodkeeper/cnn-based-drowsiness-detection-data.bigearthnet
BigEarthNet - HDF5 version
This repository contains an export of the existing BigEarthNet dataset in HDF5 format. All Sentinel-2 acquisitions are exported according to TorchGeo's dataset (120x120 pixels resolution).
Sentinel-1 is not contained in this repository for the moment.
CSV files contain for each satellite acquisition the corresponding HDF5 file and the index.
A PyTorch dataset class which can be used to iterate over this dataset can be found here, as well as the script used… See the full description on the dataset page: https://huggingface.co/datasets/lc-col/bigearthnet.PlantInquiryVQA
PlantInquiryVQA — Thinking Like a Botanist
Benchmark and framework for multi-turn, intent-driven visual question answering in plant pathology.
Accepted at ACL 2026 Findings.
Overview
PlantInquiryVQA formalises diagnostic reasoning in plant pathology as a Chain-of-Inquiry (CoI) — an ordered sequence of visually-grounded questions that adapts to the plant's severity and the expert's epistemic intent (Diagnosis / Prognosis / Management).
The benchmark evaluates… See the full description on the dataset page: https://huggingface.co/datasets/SyedNazmusSakib/PlantInquiryVQA.SNAP
SNAP Benchmark
Code and annotations: [https://github.com/ykotseruba/SNAP]
SNAP (stands for Shutter speed, ISO seNsitivity, and APerture) is a new benchmark consisting of images of objects taken under controlled lighting conditions and with densely sampled camera settings.
This benchmark allows testing the effects of capture bias, which includes camera settings and illumination, on performance of vision algorithms.
SNAP contains 37,558 images of 100 scenes (10 scenes per 10 object… See the full description on the dataset page: https://huggingface.co/datasets/ykotseruba/SNAP.Heliconius-Collection_Cambridge-Butterfly
Dataset Card for Heliconius Collection (Cambridge Butterfly)
Dataset Description
Dataset Summary
Subset of the collection records from Chris Jiggins' research group at the University of Cambridge, collection covers nearly 20 years of field studies.
This subset contains approximately 36,189 RGB images of 11,962 specimens (29,134 images of 10,086 specimens across all Heliconius). Many records have both images and locality data.
Most images were… See the full description on the dataset page: https://huggingface.co/datasets/imageomics/Heliconius-Collection_Cambridge-Butterfly.handwritten-digit-dataset
Handwritten Digit Dataset
This dataset contains a collection of handwritten digits (0-9) contributed by users through an interactive web-based drawing application. The dataset is continuously updated, reflecting real-world human handwriting variability.
Dataset Details
The images are pre-processed to match the standard machine learning format for digit recognition:
Dimensions: 28x28 pixels.
Format: Grayscale (single channel).
Processing: Each digit is cropped to… See the full description on the dataset page: https://huggingface.co/datasets/zentardev/handwritten-digit-dataset.Harvard-GF
Dataset Card: Harvard-GF
Dataset Summary
Harvard-GF (Harvard Glaucoma Fairness) is a retinal nerve disease dataset for fairness learning in glaucoma detection, featuring both 2D and 3D OCT imaging data with balanced racial groups. It contains 3,300 samples from 3,300 patients with equal representation across Asian, Black, and White racial groups — a unique design addressing the doubled glaucoma prevalence observed in Black patients compared to other races.
This dataset… See the full description on the dataset page: https://huggingface.co/datasets/harvardairobotics/Harvard-GF.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.cardiac_cine_acdc
ACDC (Cardiac Cine-MRI)
ACDC (Automatic Cardiac Diagnosis Challenge, MICCAI 2017) is a cine‑MRI dataset for cardiac segmentation.This repository contains processed NIfTI files in Data/processed_output/acdc format.
Dataset Summary
Modality: Cardiac cine‑MRI (NIfTI)
Task: Segmentation of LV, RV, and myocardium
Frames: ED/ES + full SAX time series (sax_t)
Labels: LV/RV cavities + myocardium
Splits: train, test (as provided in processed output)
Data Structure (per… See the full description on the dataset page: https://huggingface.co/datasets/viennh2012/cardiac_cine_acdc.CrossViewUrbanTrafficDataset
Cross-View Urban Traffic Dataset
Dataset Summary
The Cross-View Urban Traffic Dataset (CVUTD) is a benchmark for cross-view urban traffic perception built from synchronized ego-centric bicycle videos and aerial drone videos recorded at real urban intersections in Regensburg, Germany.
The dataset is designed to support two linked tasks:
Cross-view identity matching between street-view and drone-view object tracks
Ego-to-BEV prediction using aerial supervision… See the full description on the dataset page: https://huggingface.co/datasets/prakharbh/CrossViewUrbanTrafficDataset.SA-BENCH
SA-BENCH
SA-BENCH is the benchmark dataset released with “Beyond Pixels: Benchmarking and Reward-Based Assessing Framework for Visual Spatial Aesthetics.”
Accepted to CVPRW 2026.
GitHub | CVF Open Access | arXiv | Model
It evaluates the spatial aesthetics of interior images along four dimensions:
distortion
harmony
layout
lighting
SA-BENCH contains 17,768 annotated examples across four spatial-aesthetic dimensions, with image assets and human annotations for training and… See the full description on the dataset page: https://huggingface.co/datasets/gaoyuan-ai/SA-BENCH.PhysicalAI-SimReady-Warehouse-01
NVIDIA Physical AI SimReady Warehouse OpenUSD Dataset
Dataset Version: 1.1.0
Date: May 18, 2025
Author: NVIDIA, Corporation
License: CC-BY-4.0 (Creative Commons Attribution 4.0 International)
Contents
This dataset includes the following:
This README file
A CSV catalog that enumerates all of the OpenUSD assets that are part of this dataset including a sub-folder of images that showcase each 3D asset (physical_ai_simready_warehouse_01.csv). The CSV file is… See the full description on the dataset page: https://huggingface.co/datasets/yzhllm/PhysicalAI-SimReady-Warehouse-01.3D-DefectBench
3D-DefectBench
A controlled benchmark for evaluating vision-language models (VLMs) as fine-grained judges of
defects in text-to-3D generation.
3D-DefectBench is a VLM-as-a-judge benchmark for detecting fine-grained defects in textured 3D
meshes. It lets you measure how well any VLM judge aligns with human judgment: run your judge over the
assets and score its predictions against the human defect labels provided here.
Each example pairs a text prompt with a generated, textured 3D… See the full description on the dataset page: https://huggingface.co/datasets/zzhao0500/3D-DefectBench.FairVLMed
Dataset Card: Harvard-FairVLMed
Dataset Summary
Harvard-FairVLMed is the first fair vision-language medical dataset designed for studying fairness in medical vision-language (VL) foundation models. It contains 10,000 SLO fundus images paired with de-identified clinical notes and comprehensive demographic annotations, enabling in-depth fairness analysis across four protected attributes: race, gender, ethnicity, and preferred language.
This dataset was introduced at CVPR… See the full description on the dataset page: https://huggingface.co/datasets/harvardairobotics/FairVLMed.