datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
lgg-mri-segmentation-research
LGG Brain MRI Segmentation with Genomic Clusters
This repository provides a Patient-Centric version of the Lower-Grade Glioma (LGG) Segmentation dataset. While other versions of this data exist, they often treat slices as independent images. This version preserves the 3D patient volume and integrates all genomic/clinical labels directly into a multimodal-ready format.
🌟 Why This Version?
Developed for Multimodal AI Research, this dataset addresses several limitations… See the full description on the dataset page: https://huggingface.co/datasets/Ehsan-rmz/lgg-mri-segmentation-research.BDD100K-Semantic-SegmentationBDD100K: Semantic Segmentation Dataset
Unofficial redistribution of BDD100K's semantic segmentation task (10K-image subset), under BDD100K's own data license (non-commercial/educational/research redistribution, with notice, explicitly permitted).
Disclaimer
This repository is not an official release of BDD100K.
BDD100K was created by Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darrell at UC Berkeley… See the full description on the dataset page: https://huggingface.co/datasets/dronefreak/BDD100K-Semantic-Segmentation.table_spill_cleanup_bimanual_rgbd_segmentation_poses
Exylos Bimanual Table Spill Cleanup Rich-Modality Sample
A compact, rich-modality bimanual robot manipulation dataset for tabletop spill cleanup.
Each episode combines synchronized dual-arm Panda state/action trajectories, 7 RGB camera streams, per-frame depth maps, per-frame segmentation masks, object pose streams, phase annotations, and an objective cleanup success metric based on the remaining spill fraction.
This dataset is a rich-modality inspection sample for the Exylos… See the full description on the dataset page: https://huggingface.co/datasets/ExylosAi/table_spill_cleanup_bimanual_rgbd_segmentation_poses.wmh-segmentation
WMH Segmentation Challenge Dataset
Dataset Description
The WMH Segmentation Challenge dataset for white matter hyperintensities segmentation. This dataset contains MRI FLAIR scans with dense segmentation annotations.
Dataset Details
Modality: MRI FLAIR
Target: white matter hyperintensities
Format: NIfTI (.nii.gz)
Dataset Structure
Each sample in the JSONL file contains:
{
"image": "path/to/image.nii.gz",
"mask":… See the full description on the dataset page: https://huggingface.co/datasets/MedOtter/wmh-segmentation.fire-segmentation-dataset
Fire Segmentation Dataset (YOLO-seg format)
Instance-segmentation dataset for fire detection: 1,348 images with
polygon mask labels in Ultralytics YOLO segmentation format. Built to train
sreeharivp23/fire-segmentation-yolo11n.
Contents
Split
Images
train
1,146
val
202
1,098 fire images with one or more fire polygon instances
250 negatives (no fire) with empty label files
fire_seg/
├── data.yaml # Ultralytics dataset config (1 class:… See the full description on the dataset page: https://huggingface.co/datasets/sreeharivp23/fire-segmentation-dataset.vhr-building-segmentation
HOT Building Segmentation Dataset
Dataset Description
A semantic segmentation dataset for building footprint extraction from aerial imagery, built from validated Humanitarian OpenStreetMap Team (HOT) Tasking Manager projects that use OpenAerialMap (OAM) imagery.
Dataset Summary
This dataset pairs 256x256 aerial image tiles (zoom level 19) from OpenAerialMap with building footprint labels from OpenStreetMap. All source projects have been fully… See the full description on the dataset page: https://huggingface.co/datasets/hotosm/vhr-building-segmentation.pothole-segmentation
Dataset Labels
['pothole']
Number of Images
{'test': 5, 'train': 80, 'valid': 5}
How to Use
Install datasets:
pip install datasets
Load the dataset:
from datasets import load_dataset
ds = load_dataset("keremberke/pothole-segmentation", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/imacs-pothole-detection-wo8mu/pothole-detection-irkz9/dataset/4
Citation
@misc{… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/pothole-segmentation.pcb-defect-segmentation
Dataset Labels
['dry_joint', 'incorrect_installation', 'pcb_damage', 'short_circuit']
Number of Images
{'valid': 25, 'train': 128, 'test': 36}
How to Use
Install datasets:
pip install datasets
Load the dataset:
from datasets import load_dataset
ds = load_dataset("keremberke/pcb-defect-segmentation", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/diplom-qz7q6/defects-2q87r/dataset/8… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/pcb-defect-segmentation.power-plant-olmoearth-segmentation
OlmoEarth v1.2-ready power energy dataset
Derived cook 20260830T191500Z from pinned source 35da3d0549b16e15114ded1eb7df3f4a378e9b4f.
The branch uses no train/validation/test split. Every accepted row is tagged all.
Accepted imagery rows: 12453
Quality-unavailable rows: 1
Release mode: partial with documented quality-unavailable exclusions
Clearest-available quality fallbacks: 10
Sentinel-2 L2A: uint16 [12,1,128,128]
Band order: B02,B03,B04,B08,B05,B06,B07,B8A,B11,B12,B01,B09… See the full description on the dataset page: https://huggingface.co/datasets/sarkarghya/power-plant-olmoearth-segmentation.wind-and-solar-candidate-olmoearth-segmentation
OlmoEarth v1.2-ready renewable energy dataset
Derived cook 20260830T191500Z from pinned source 2c551f58998cd25554ea679148b21a9c701b51db.
The branch uses no train/validation/test split. Every accepted row is tagged all.
Accepted imagery rows: 12649
Quality-unavailable rows: 4
Release mode: partial with documented quality-unavailable exclusions
Clearest-available quality fallbacks: 4
Sentinel-2 L2A: uint16 [12,1,128,128]
Band order: B02,B03,B04,B08,B05,B06,B07,B8A,B11,B12,B01,B09… See the full description on the dataset page: https://huggingface.co/datasets/sarkarghya/wind-and-solar-candidate-olmoearth-segmentation.GenSC-6G-Segmentation
GenSC-6G - Scalable Semantic Communication Framework and Dataset
This repository contains the first semantic communication dataset and playground, designed to be scalable, reproducible, and adaptable for a wide range of applications. The dataset and framework are tailored for semantic decoding, classification, and localization tasks in 6G applications, integrating generative AI and semantic communication. Implementation of GenSC-6G: A Prototype Testbed for Integrated Generative AI… See the full description on the dataset page: https://huggingface.co/datasets/CQILAB/GenSC-6G-Segmentation.medieval-segmentation
Dataset Card for CATMuS Medieval (Segmentation Version)
Join our Discord to ask questions about the dataset:
Dataset Details
CATMuS Medieval Segmentation (Consistent Approaches to Transcribing Manuscripts) is a specialized dataset designed for layout analysis of medieval manuscripts using the SegmOnto vocabulary for region and line classification. This dataset addresses the challenges associated with establishing consistent ground truth in layout analysis tasks… See the full description on the dataset page: https://huggingface.co/datasets/CATMuS/medieval-segmentation.shapenet_segmentationsatellite-building-segmentation
Dataset Labels
['building']
Number of Images
{'train': 6764, 'valid': 1934, 'test': 967}
How to Use
Install datasets:
pip install datasets
Load the dataset:
from datasets import load_dataset
ds = load_dataset("keremberke/satellite-building-segmentation", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/roboflow-universe-projects/buildings-instance-segmentation/dataset/1
Citation… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/satellite-building-segmentation.Sombench-IMP-Segmentation
SomBench Benchmark: Irregular Mare Patch (IMP) Segmentation
Science theme: Volcanic history
Task: Binary semantic segmentation
Dataset Summary
A binary semantic-segmentation benchmark for irregular mare patches
(IMPs): rare, morphologically subtle features interpreted as unusually young
volcanic landforms. Each sample is an LROC NAC image tile paired with a
binary IMP mask (IMP vs. background). The set is derived from published IMP
polygon annotations, framed as a… See the full description on the dataset page: https://huggingface.co/datasets/nasa-ibm-ai4science/Sombench-IMP-Segmentation.droid_dataset_segmentation_mask
DROID SAM 3.1 Segmentation Masks
This dataset is a mask-only sidecar generated from the original
droid_101/0.0.1 RLDS release. It does not redistribute DROID images or
actions. Its episode_index follows the RLDS episode order.
The same episodes appear in lerobot/droid_1.0.1, but LeRobot stores them in a
different episode order. Therefore, mask episode_index and LeRobot
episode_index must not be joined directly. Use the mapping file described
below to associate these masks with… See the full description on the dataset page: https://huggingface.co/datasets/EpicPinkPenguin/droid_dataset_segmentation_mask.basil-segmentation-plantcv
maximilian-franz/basil-segmentation-plantcv
Per-instance segmented basil crops produced by the plantcv backend. This is a Hugging Face ImageFolder dataset: file_name points to the black-background masked crop used for downstream image analysis and bbox_file_name points to the corresponding unmasked rectangular crop. Empty masks are omitted. Bounding boxes use native source-frame coordinates.
Load it with:
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/maximilian-franz/basil-segmentation-plantcv.basil-segmentation-sam3
maximilian-franz/basil-segmentation-sam3
Per-instance segmented basil crops produced by the sam3 backend. This is a Hugging Face ImageFolder dataset: file_name points to the black-background masked crop used for downstream image analysis and bbox_file_name points to the corresponding unmasked rectangular crop. Empty masks are omitted. Bounding boxes use native source-frame coordinates.
Load it with:
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/maximilian-franz/basil-segmentation-sam3.Segmentationtcga-tissue-segmentation
Overview
This dataset consists of 242 images from The Cancer Genome Atlas (TCGA) pathology dataset manually annotated for segmentation of tissue (i.e. pixel-level annotation of presence or absence of tissue).
Each image is a full TCGA slide (mostly H&E) downsampled to 10 microns per pixel (MPP) and saved as a PNG.
Each image has a corresponding mask, which is also saved as a PNG where each pixel corresponds to the pixel at the same position in the 10 MPP image.
The pixel values of… See the full description on the dataset page: https://huggingface.co/datasets/conflux-xyz/tcga-tissue-segmentation.segmentation_training_data
Build Change augmented segmentation dataset
Paired images and segmentation masks, including augmented (rotated, flipped,
transformed) copies of each original photo, paired by filename. Every row links
to the original photo it was made from whenever that original is in the dataset.
Load it
No Hugging Face account or token is needed:
from datasets import load_dataset
ds = load_dataset("buildchange/segmentation_training_data", split="train")
row = ds[0]
row["image"]… See the full description on the dataset page: https://huggingface.co/datasets/buildchange/segmentation_training_data.basil-segmentation-hybrid
maximilian-franz/basil-segmentation-hybrid
Per-instance segmented basil crops produced by the hybrid backend. This is a Hugging Face ImageFolder dataset: file_name points to the black-background masked crop used for downstream image analysis and bbox_file_name points to the corresponding unmasked rectangular crop. Empty masks are omitted. Bounding boxes use native source-frame coordinates.
Load it with:
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/maximilian-franz/basil-segmentation-hybrid.awesome-degraded-segmentation
A Survey on Degraded Image Segmentation
A comprehensive survey on robust image segmentation under various degradation conditions
Paper | Paper List | Project Page
Abstract
Segmentation is the core of visual understanding — the foundation of Physical AI and World Model.
Image segmentation is a fundamental task in computer vision with wide-ranging applications. While deep learning models have achieved remarkable success under ideal conditions… See the full description on the dataset page: https://huggingface.co/datasets/Linwei-Chen/awesome-degraded-segmentation.car-parts-segmentation-yolo
AutoInspect - Car Parts Dataset (Ultralytics YOLO segmentation)
YOLO-версия датасета с сегментацией деталей авто Car Parts Dataset.
Часть проекта AutoInspect (pipeline: view classification → car parts segmentation → damage segmentation).
Основан на датасете от HITL. Для парных деталей были прставлены тэги side (left/right) при помощи Supervisely App. Список таких деталей:
Headlight
Tail-light
Mirror
Front-window
Back-window
Front-door
Back-door
Front-wheel
Back-wheel
Fender… See the full description on the dataset page: https://huggingface.co/datasets/mitbersh/car-parts-segmentation-yolo.lgg-mri-segmentation-research
LGG Brain MRI Segmentation with Genomic Clusters
This repository provides a Patient-Centric version of the Lower-Grade Glioma (LGG) Segmentation dataset. While other versions of this data exist, they often treat slices as independent images. This version preserves the 3D patient volume and integrates all genomic/clinical labels directly into a multimodal-ready format.
🌟 Why This Version?
Developed for Multimodal AI Research, this dataset addresses several limitations… See the full description on the dataset page: https://huggingface.co/datasets/vpasx/lgg-mri-segmentation-research.synthetic_kidney_stone_segmentation_datapothole-segmentation
Dataset Labels
['potholes', 'object', 'pothole', 'potholes']
Number of Images
{'valid': 157, 'test': 80, 'train': 582}
How to Use
Install datasets:
pip install datasets
Load the dataset:
from datasets import load_dataset
ds = load_dataset("manot/pothole-segmentation", name="full")
example = ds['train'][0]
Roboflow Dataset Page
https://universe.roboflow.com/abdulmohsen-fahad-f7pdw/road-damage-xvt2d/dataset/3
Citation… See the full description on the dataset page: https://huggingface.co/datasets/manot/pothole-segmentation.hot-building-segmentation
HOT Building Segmentation Dataset
Dataset Description
A semantic segmentation dataset for building footprint extraction from aerial imagery, built from validated Humanitarian OpenStreetMap Team (HOT) Tasking Manager projects that use OpenAerialMap (OAM) imagery.
Dataset Summary
This dataset pairs 256x256 aerial image tiles (zoom level 19) from OpenAerialMap with building footprint labels from OpenStreetMap. All source projects have been fully validated through… See the full description on the dataset page: https://huggingface.co/datasets/kshitijrajsharma/hot-building-segmentation.racelinecalc-track-segmentation
RaceLineCalc Track Segmentation Dataset
A small top-down racetrack segmentation dataset created for the automatic boundary detection feature in RaceLineCalc.
The companion model repository is RedRatInHat/racelinecalc-track-mask.
Contents
raw/ - 120 RGB racetrack images.
labels/<sample_id>/target_track_mask.png - binary track-surface masks.
labels/<sample_id>/overlay.png - visual QA overlays.
labels/<sample_id>/label.json - full freeform labeler annotation… See the full description on the dataset page: https://huggingface.co/datasets/RedRatInHat/racelinecalc-track-segmentation.Lung_Nodule_Segmentation
Lung Nodule Segmentation Dataset
Introduction
Welcome to the Lung Nodule Segmentation Dataset repository! This project aims to provide a comprehensive dataset for researchers and developers to build and evaluate machine learning models for lung nodule segmentation. Accurate detection and segmentation of lung nodules are crucial steps in the early diagnosis and treatment of lung cancer.
Dataset Overview
The dataset consists of high-resolution CT scans with… See the full description on the dataset page: https://huggingface.co/datasets/basilshaji/Lung_Nodule_Segmentation.