datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
image-segmentation-toy-datauniversal-lesion-segmentation
Universal Lesion Segmentation Datasets
A collection of public medical imaging datasets for lesion segmentation in CT scans. These are the datasets exactly as downloaded from their original sources.
Datasets
This repository contains the following datasets:
CECT - Liver (primary). Luo J, Wang X, Zhang Y, et al. Comprehensive multi-phase three-dimensional contrast-enhanced CT imaging dataset for primary liver cancer. Scientific Data. 2025;12(1):768.… See the full description on the dataset page: https://huggingface.co/datasets/nielsRocholl/universal-lesion-segmentation.Imbalanced_Segmentation_Datasetsmask-for-image-segmentation-testslgg-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.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.object-segmentationFootball-Player-Segmentation
Dataset Card for football-player-segmentation
This dataset is specifically designed for computer vision tasks related to player detection and segmentation in foot goalkeeperders, and forwards, captured from various angles and distances.
This is a FiftyOne dataset with 512 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
#… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/Football-Player-Segmentation.synthetic_kidney_stone_segmentation_dataAVM_Segmentation_train
Dataset Card for AVM (Around View Monitoring) Semantic Segmentation Dataset
This repository provides a FiftyOne-compatible version of the AVM semantic segmentation dataset for autonomous parking systems, with enhanced metadata and visualization capabilities.
This is a FiftyOne dataset with 6763 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/AVM_Segmentation_train.rfdetr-segmentation-leibniz-dataset
Dataset Card for Leibniz's Manuscripts (Instance Segmentation Dataset)
This dataset comprises instance segmentation annotations in raw COCO format, used to train an RF-DETR-Seg-nano model for the automatic recognition of textual, graphical, and mathematical expression zones within the manuscripts of the philosopher and mathematician Gottfried Wilhelm Leibniz (17th-early 18th c.).
Dataset Details
Uses
Direct Use
This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/DenisaBumba/rfdetr-segmentation-leibniz-dataset.dam-segmentation
Dam Segmentation Dataset
Multispectral UAV Remote Sensing Data for Embankment Dam Segmentation
Dataset Summary
This dataset contains a series of multispectral image slices captured at the embankment dams and dikes
of the Belo Monte Hydroelectric Complex, located in the state of Pará, northern Brazil. Each image is
paired with its respective NDRE vegetation index values, binary segmentation mask and multiclass
segmentation mask.
The multispectral images were… See the full description on the dataset page: https://huggingface.co/datasets/andrematte/dam-segmentation.SpectralWaste-Segmentation
SpectralWaste Segmentation → FiftyOne (Grouped RGB + Hyperspectral)
The labeled split of SpectralWaste, rebuilt as a grouped FiftyOne dataset pairing each colour frame with its hyperspectral cube.
The recordings come from a working waste-sorting plant, looking down at the conveyor as material passes. Each frame is captured twice over, once in colour and once by a shortwave infrared camera reading 224 bands from about 900 to 1700 nm. Material that looks identical in colour… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/SpectralWaste-Segmentation.robotic-instrument-segmentation-miccai-2017tunnel-crack-segmentation-dataset
Complex Tunnel Crack Dataset (CTCD)
292 tunnel lining images (256 × 256) with pixel-level crack masks, collected from
52 highway tunnels in Yunnan and Zhejiang, China, using a high-speed tunnel
inspection vehicle. Crack pixels were manually
annotated in Photoshop with a 1–2 px brush sized by crack width.
This dataset was introduced in the CraSAM paper (Structural Control and Health
Monitoring, 2025), where it is used to evaluate cross-scenario generalization of… See the full description on the dataset page: https://huggingface.co/datasets/shiweiluo99/tunnel-crack-segmentation-dataset.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.ROI-1555_Rebar_Detection_and_Instance_Segmentation_DatasetROI-1555: Rebar Detection and Instance Segmentation Dataset
ROI-1555 for rebar object detection and instance segmentation contains 1555 rebar images and their fine-labeled bounding boxes and pixel-wise masks.
Diverse rebar specifications, layouts, application scenarios, and environmental conditions.
Usage
Here is an example to convert the annotations to MSCOCO 2017 format
python
cp -r 1260/img_label tools/data_annotated/train2017
cd tools
python labelme2coco_instance.py… See the full description on the dataset page: https://huggingface.co/datasets/tsrobcvai/ROI-1555_Rebar_Detection_and_Instance_Segmentation_Dataset.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.robotic-scene-segmentation-miccai-2018face-segmentation-image-dataset
Image Dataset of Face Segmentation for recognition tasks
Dataset comprises 87,800+ images annotated with 100+ landmarks, providing a comprehensive foundation for research in face recognition, segmentation tasks, and object recognition. It is designed to support the development of learning models, recognition algorithms, and segmentation techniques.
By utilizing this dataset, researchers and developers can advance their understanding and capabilities in facial recognition, face… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/face-segmentation-image-dataset.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.satellite-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.sorghum_weed_segmentation
Sorghum Weed Segmentation
A dataset for semantic segmentation of weeds in a sorghum plot. The dataset contains 252 images with pixel-level mask annotations. In the masks, 0 is the background, 1 is sorghum, 2 is grass, and 3 is broadleaf weed.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{justina2024sorghumweeddataset_classification,
title={SorghumWeedDataset\_Classification and… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/sorghum_weed_segmentation.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.semantic-segmentation-test-sampleThis dataset contains 10 examples of the segments/sidewalk-semantic dataset (i.e. 10 images with corresponding ground-truth segmentation maps).
cityscapes_segmentationLung_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.SoccerNet_Field_SegmentationProcessed data from the Soccernet 2023 dataset. Processing notebook is included in this repo.
To see an example:
def show_item(item):
fig, axs = plt.subplots(nrows = 1, ncols = 4, figsize = (20, 4))
axs[0].imshow(item['image'])
axs[0].set_title("Image")
axs[0].axis('off')
axs[1].imshow(overlay_mask(item['image'], item['outlines']))
axs[1].set_title("Outlines")
axs[1].axis('off')
axs[2].imshow(show_segments(item['segments']))
axs[2].set_title("Segments")… See the full description on the dataset page: https://huggingface.co/datasets/nreHieW/SoccerNet_Field_Segmentation.RS_Image_Segmentation_Vaihingenbasil-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.