In 1204 CT images we segmented 104 anatomical structures (27 organs, 59 bones, 10 muscles, 8 vessels) covering a majority of relevant classes for most use cases. The CT images were randomly sampled from clinical routine, thus representing a real world dataset which generalizes to clinical application. The dataset contains a wide range of different pathologies, scanners, sequences and institutions.

You can find a segmentation model trained on this dataset here.

More details about the dataset can be found in the corresponding paper. Please cite this paper if you use the dataset.

This dataset was created by the department of Research and Analysis at University Hospital Basel.

In 1204 CT images we segmented 104 anatomical structures (27 organs, 59 bones, 10 muscles, 8 vessels) covering a majority of relevant classes for most use cases. The CT images were randomly sampled from clinical routine, thus representing a real world dataset which generalizes to clinical application. The dataset contains a wide range of different pathologies, scanners, sequences and institutions. s0720/segmentations/portal_vein_and_splenic_vein.nii.gz 187.74kB s0720/segmentations/pancreas.nii.gz 45.25kB s0720/segmentations/lung_upper_lobe_right.nii.gz 218.92kB s0720/segmentations/lung_upper_lobe_left.nii.gz 230.82kB s0720/segmentations/lung_middle_lobe_right.nii.gz 201.18kB s0720/segmentations/lung_lower_lobe_right.nii.gz 240.63kB s0720/segmentations/lung_lower_lobe_left.nii.gz 239.49kB s0720/segmentations/liver.nii.gz 273.08kB s0720/segmentations/kidney_right.nii.gz 198.91kB s0720/segmentations/kidney_left.nii.gz 197.82kB s0720/segmentations/inferi

The Total Segmentator dataset contains 1204 CT images with labels of 104 anatomical structures.

Info: This is version 2 of the TotalSegmentator dataset.

In 1228 CT images we segmented 117 anatomical structures covering a majority of relevant classes for most use cases. The CT images were randomly sampled from clinical routine, thus representing a real world dataset which generalizes to clinical application. The dataset contains a wide range of different pathologies, scanners, sequences and institutions.

Link to a copy of this dataset on Dropbox for much quicker download: Dropbox Link

Overview of differences to v1 of this dataset: here

A small subset of this dataset with only 102 subjects for quick download+exploration can be found here: here

You can find a segmentation model trained on this dataset here.

More details about the dataset can be found in the corresponding paper (the paper describes v1 of the dataset). Please cite this paper if you use the dataset.

This dataset was created by the department of Research and Analysis at University Hospital Basel.

Dataset Card for TotalSegmentator

This is a FiftyOne dataset with 50 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

Note: other available arguments include 'max_samples', etc

dataset = fouh.load_from_hub("dgural/Total-Segmentator-50")

Launch the App

session = fo.launch_app(dataset)

  Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/dgural/Total-Segmentator-50.

About

This is a derivative of the TotalSegmentator dataset.

1228 CT images and corresponding segmentation mask of 117 structures We combined multiple segmentation masks into a single nii.gz file under the folder Masks, and moved all CT images to the folder Images. All images and masks are renamed according to case IDs.

This dataset is released under the CC-BY-4.0 license.

  News 🔥

[10 Oct, 2025] This dataset is integrated into 🔥MedVision🔥

  Official… See the full description on the dataset page: https://huggingface.co/datasets/YongchengYAO/TotalSegmentator-CT-Lite.

Chest ST Segmentation of a pathology and anatomical regions

Dataset comprises 1,000+ studies, featuring 7 pathologies and covering 8 anatomical regions. It includes a variety of CT scans that facilitate research in lung segmentation and disease detection. Researchers can leverage this dataset for clinical practice, studying imaging data for better early detection methods and computer-aided screening.

The data is provided in nii format and includes both volumetric data and the corresponding masks for each study, facilitating comprehensive analysis and segmentation tasks. - Get the data

Example of data

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F6edb5c13d2dfea3b64a305f889ccec07%2FFrame%20170%20(2).png?generation=1732242366229473&alt=media" alt="">

Researchers can leverage this dataset to explore automated segmentation techniques, utilizing deep learning and machine learning models for improved image analysis.The dataset is ideal for medical research, disease detection, and classification tasks, particularly for developing computer-aided diagnosis and machine learning models

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

About data

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F2954f658cf5d63becab24b81739d686d%2FFrame%20169%20(1).png?generation=1732241020560152&alt=media" alt=""> By utilizing this dataset, researchers can contribute to the development of more accurate and efficient diagnosis systems, ultimately improving patient outcomes in clinical practice.

🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects

A high-quality full-body segmentation dataset containing 1192 annotated images designed for computer vision tasks such as human segmentation, pose analysis, body detection, and AI model training.

KSSD2025 – CT Kidney Stone Segmentation Dataset

A High-Quality Annotated Dataset for Deep Learning-Based Kidney Stone Segmentation

📌 Overview

KSSD2025 is a dataset of axial CT images with expert-annotated kidney stone segmentation masks, created to support deep learning research in medical image segmentation. It is derived from the public dataset by Islam et al. (2022), which contains CT images with different kidney conditions. KSSD2025 focuses exclusively on kidney stone cases, offering precise ground-truth masks for developing and benchmarking AI-based segmentation models.

🎈 Description

This dataset presents a carefully refined subset of the original "CT Kidney Dataset: Normal-Cyst-Tumor and Stone" by Islam et al., comprising only axial CT images that exhibit kidney stones. Out of 12,446 images in the original collection, 838 images were selected for manual annotation based on the presence of stones and the axial orientation, which offers better anatomical context for segmentation tasks.

To ensure high-quality ground-truth segmentation, a three-step preprocessing pipeline was applied:

1) Thresholding: Pixel intensity thresholding at 150 was used to isolate high-density structures, which often correspond to kidney stones.

2) Connected Component Filtering: Regions larger than 300 pixels were discarded to remove bones and other non-stone structures.

3) Manual Refinement: Remaining artifacts were removed and stone regions refined in collaboration with specialists in urology and radiology.

Each image in the dataset is paired with a binary mask that precisely delineates kidney stone regions, making it ideal for training and evaluating deep learning models in tasks like medical image segmentation and object detection.

📊 Dataset Details Total Annotated Images: 838 View: Axial Annotations: Binary segmentation masks (kidney stone regions) Image Format: TIF Size: 305.38 MB Source Dataset: CT KIDNEY DATASET: Normal-Cyst-Tumor and Stone Annotation Method: Semi-automatic (thresholding + connected components) followed by expert manual refinement

🔍 Use Cases ✔️ Deep Learning-Based Kidney Stone Segmentation ✔️ AI-Powered Medical Imaging Tools ✔️ Benchmarking Medical Image Segmentation Models ✔️ Educational Applications in Radiology and Urology

🔬 Research Potential

KSSD2025 addresses the scarcity of annotated kidney stone segmentation datasets. By offering pixel-level annotations, it opens new opportunities for developing robust segmentation models and AI-assisted diagnostic systems in urology.

⚖️ License

Datafiles © Nazmul Islam

🏫 Institutions Involved

• Centro Universitário FEI
• Hospital Universitário da Universidade de São Paulo Based on original dataset by: Islam MN, Hasan M, Hossain M, Alam M, Rabiul G, Uddin MZ, Soylu A. Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography. Scientific Reports. 2022.

📢 Citation

If you use this dataset in your research, please cite:

Islam MN, Hasan M, Hossain M, Alam M, Rabiul G, Uddin MZ, Soylu A. Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography. Scientific Reports. 2022.

M. F. Bouzon et al., "KSSD2025: A New Annotated Dataset for Automatic Kidney Stone Segmentation and Evaluation with Modified U-Net Based Deep Learning Models," in IEEE Access, doi: 10.1109/ACCESS.2025.3610027

🙏 If you find this dataset helpful, please give it an upvote and share your feedback. Thank you! 😊

Total-Text is a dataset tailored for instance segmentation, semantic segmentation, and object detection tasks, containing 1555 images with 11165 labeled objects belonging to a single class — text with text label tag. Its primary aim is to open new research avenues in the scene text domain. Unlike traditional text datasets, Total-Text uniquely includes curved-oriented text in addition to horizontal and multi-oriented text, offering diverse text orientations in more than half of its images. This variety makes it a crucial resource for advancing text-related studies in computer vision and natural language processing.

Spine MRI Dataset, Anomaly Detection & Segmentation

The dataset consists of .dcm files containing MRI scans of the spine of the person with several dystrophic changes, such as osteophytes, dorsal disc extrusions, dorsal disc protrusions and spondyloarthrosis. The images are labeled by the doctors and accompanied by report in PDF-format. The dataset includes 5 studies, made from the different angles which provide a comprehensive understanding of a several dystrophic changes and… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/spine-segmentation-dataset.

Humans in the Loop is excited to publish a new open access dataset for Teeth segmentation on dental radiology scans. The segmentation is done manually by 12 Humans in the Loop trainees in the Democratic Republic of Congo as part of their trainings, using the Panoramic radiography database published by Lopez et al. The dataset consists of 598 images with a total of 15,318 polygons, where each tooth is segmented with a different class.

This Teeth segmentation dataset is dedicated to the public domain by Humans in the Loop under CC0 1.0 license.

Brain MR images and FLAIR abnormality segmentation masks created by hand are part of this dataset. These pictures came from TCIA, or The Cancer Imaging Archive. Their genetic cluster data and fluid-attenuated inversion recovery (FLAIR) sequences are from 110 patients with lower-grade glioma who are part of the Cancer Genome Atlas (TCGA) collection. You can find patient information and genomic clusters of tumours in the data.csv file. Deep Learning Projects for Final Year FYI: It is not my… See the full description on the dataset page: https://huggingface.co/datasets/gymprathap/Brain-MRI-LGG-Segmentation.

Deep learning has emerged as the preeminent technique for semantic segmentation of brain MRI tumors. However, existing methods often rely on hierarchical downsampling to generate multi-scale feature maps, effectively capturing fine-grained global features but struggling with large-scale local features due to insufficient network depth. This limitation is particularly detrimental for segmenting diminutive targets such as brain tumors, where local feature extraction is crucial. Augmenting network depth to address this issue leads to excessive parameter counts, incompatible with resource-constrained devices. To tackle this challenge, we propose that object recognition should exhibit scale invariance, so we introduce a shared CNN network architecture for image encoding. The input MRI image is directly downsampled into three scales, with a shared 10-layer convolutional network employed across all scales to extract features. This approach enhances the network’s ability to capture large-scale local features without increasing the total parameter count. Further, we utilize a Transformer on the smallest scale to extract global features. The decoding stage follows the UNet structure, incorporating incremental upsampling and feature fusion from previous scales. Comparative experiments on the LGG Segmentation Dataset and BraTS21 dataset demonstrate that our proposed LiteMRINet achieves higher segmentation accuracy while significantly reducing parameter count. This makes our approach particularly advantageous for devices with limited memory resources. Our code is available at https://github.com/chinaericy/MRINet.

Brain CT Segmentation for medical diagnostics

The dataset contains over 1,000 studies encompassing 10 pathologies, providing a comprehensive resource for advancing research in brain imaging techniques. It comprises a wide variety of CT scans aimed at facilitating segmentation tasks related to brain tumors, lesions, and other brain structures.

The data is provided in nii format and includes both volumetric data and the corresponding masks for each study, facilitating comprehensive analysis and segmentation tasks. - Get the data

Example of data

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2Fee1c47ab109dafcd75a65eade894bee8%2FFrame%20170%20(3).png?generation=1732665779862401&alt=media" alt="">

Researchers can leverage this dataset for clinical practice, studying imaging data for better early detection methods and computer-aided screening. The CT head images are particularly relevant for studies focusing on brain hemorrhages and other intracranial conditions, ensuring that the dataset addresses a broad range of clinical needs.

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

About data

Pathologies: - Acute ischemia - Chronic ischemia - 5 classes of intracranial hemorrhagic stroke (intraventricular, intraparenchymal, epidural, subdural, subarachnoid) - Subgaleal hematoma - Fracture - Tumor

By leveraging this dataset, researchers can enhance segmentation performances and improve segmentation accuracy in identifying and classifying different brain tissues and pathologies.

🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects

The dataset used in the paper for whole heart segmentation.

7 Types of Human Segmentation

The dataset includes 7 different types of image segmentation of people in underwear. For women, 4 types of labeling are provided, and for men, 3 types of labeling are provided. The dataset solves tasks in the field of recommendation systems and e-commerce.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset - Full dataset

Types of labeling

Women I - distinctively detailed labeling of women. Special emphasis is placed on distinguishing the internal, external side, and lower breast depending on the type of underwear. The labeling also includes the face and hair, hands, forearms, shoulders, armpits, thighs, shins, underwear, accessories, and smartphones.

![https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fe157d0b7db89497f85c9b2d79d301086%2Fgirls_1_227.png?generation=1681741881080579&alt=media" alt="">

Women II - labeling of images of women with attention to the side abs area (highlighted in gray on the labeling). The labeling also includes the face and hair, hands, forearms, thighs, underwear, accessories, and smartphones.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F901d120c0273ea9a5a328fff15e26583%2Fgirls_2_-1087839647-1867563540.png?generation=1681741958025976&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F75fb0412edf631adce5f42ab6b9e8052%2Fwomen_fat_image_56570.png?generation=1681742864993159&alt=media" alt="">

Women III - primarily labeling of underwear. In addition to the underwear itself, the labeling includes the face and hair, abdomen, and arms and legs.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F6f32a06f0754a5a116fc994feae8c6f1%2Fgirls_5_111.png?generation=1681742011331681&alt=media" alt="">

Women IV - labeling of both underwear and body parts. It includes labeling of underwear, face and hair, hands, forearms, body, legs, as well as smartphones and tattoos.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F0dc22fcfd8b6e4fad3aa1806d14223ef%2Fgirls_6_image_4534.png?generation=1681742073295272&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F4a398547e13c555fdad142f521e62a5f%2Fsports_girls_IadpBSd3mI%20(1).png?generation=1681742947264828&alt=media" alt="">

Men I - labeling of the upper part of men's bodies. It includes labeling of hands and wrists, shoulders, body, neck, face and hair, as well as phones and accessories.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F3dae9889adb2b1415353769ccdd9c01b%2Fman_regular_1532667_38709335.png?generation=1681742128995529&alt=media" alt="">

Men II - more detailed labeling of men's bodies. The labeling includes hands and wrists, shoulders, body and neck, head and hair, underwear, tattoos and accessories, nipple and navel area.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fa57123e41066aa277bfeac140f4457da%2Fmen_1_3046.png?generation=1681742173310957&alt=media" alt="">

Men Neuro - labeling produced by a neural network for subsequent correction by annotators.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F5281cd644bc3f5949aaa9c40fb1cafd4%2F4595%20(1).png?generation=1681742187215164&alt=media" alt="">

🧩 This is just an example of the data. Leave a request here to learn more

🚀 You can learn more about our high-quality unique datasets here

keywords: body segmentation dataset, human part segmentation dataset, human semantic part segmentation, human body segmentation data, human body segmentation deep learning, computer vision dataset, people images dataset, biometric data dataset, biometric dataset, images database, image-to-image, people segmentation, machine learning

Hepatic Vessel Skeletonization Dataset (Task08_HepaticVessel)

Overview

This dataset is derived from the hepatic vessel task of the Medical Segmentation Decathlon (MSD) Task 8. It comprises manually revised vessel skeletons and modified vessel segmentations that are initially generated via automatic 3D thinning and subsequently refined through manual revision. Both the skeletons and the labels have been refined to provide a high-quality ground truth for skeletonization algorithm evaluation.

Dataset Description

Modifications from Original MSD Task 8

Label modifications: Using 3D Slicer, vessel segmentations were refined to remove vessels not located within the liver parenchyma, large segmentations of the inferior vena cava and aorta that were inconsistent across the dataset, and anatomical structures not relevant for hepatic vessel skeletonization analysis.

Skeleton revision: The manual revision process addressed broken and missing branches, incorrect or ambiguous vessel representations, and redundant skeleton points generated by the automatic thinning algorithm.

Dataset Characteristics

The dataset covers various anatomical aspects including vessel representation up to the third level of ramification, anatomically diverse hepatic vessel structures, and consistent spatial resolution and coordinate systems.

Level of ramification is defined as the level of branching in a vessel tree where branches extend at least three levels deep from the main trunk following anatomical hierarchy.

File Structure

Task08_HepaticVessel/├── 0_README.md # This documentation file├── labelsTr/ # Modified vessel segmentations (NIfTI format)│ ├── hepaticvessel_001_mod.nii.gz│ ├── hepaticvessel_002_mod.nii.gz│ ├── hepaticvessel_004_mod.nii.gz│ ├── hepaticvessel_005_mod.nii.gz│ ├── hepaticvessel_007_mod.nii.gz│ ├── hepaticvessel_008_mod.nii.gz│ ├── hepaticvessel_010_mod.nii.gz│ ├── hepaticvessel_011_mod.nii.gz│ ├── hepaticvessel_013_mod.nii.gz│ ├── hepaticvessel_016_mod.nii.gz│ └── hepaticvessel_018_mod.nii.gz└── skeletons/ # Manually revised skeletons (JSON format) ├── hepaticvessel_001_LNC.json ├── hepaticvessel_001_NVO.json ├── hepaticvessel_001_ROF.json ├── hepaticvessel_002_LNC.json ├── hepaticvessel_002_NVO.json ├── hepaticvessel_004_LNC.json ├── hepaticvessel_004_NVO.json ├── hepaticvessel_005_LNC.json ├── hepaticvessel_005_NVO.json ├── hepaticvessel_007_LNC.json ├── hepaticvessel_007_NVO.json ├── hepaticvessel_008_NVO.json ├── hepaticvessel_010_NVO.json ├── hepaticvessel_011_NVO.json ├── hepaticvessel_013_NVO.json ├── hepaticvessel_016_NVO.json └── hepaticvessel_018_LNC.json

Naming Convention

Vessel Segmentations (labelsTr/)

Pattern: hepaticvessel_[3-digit-number]_mod.nii.gzExample: hepaticvessel_008_mod.nii.gz

Skeletons (skeletons/)

Pattern: hepaticvessel_[3-digit-number]_[ANNOTATOR_INITIALS].jsonExample: hepaticvessel_008_LNC.json

Annotator Codes

LNC: Lois Nodar Corral**NVO**: Noelia Velo Outumuro**ROF**: Roque Otero Freiría

Data Format

Vessel Segmentations

Format: NIfTI (.nii.gz)Binary masks (0 = background, 1 = vessel)Coordinate system: RAS (Right-Anterior-Superior)Spatial resolution: Variable (inherited from original MSD dataset)

Skeletons

Format: JSON arrays containing 3D coordinatesCoordinate system: Voxel coordinates (matching corresponding segmentation)Structure:json[ [x1, y1, z1], [x2, y2, z2], [x3, y3, z3], ...]

Each coordinate triplet [x, y, z] represents a voxel position in the 3D volume where the skeleton passes through.

Dataset Statistics

• Total cases: 11 vessel segmentations- Total skeletons: 17 manually revised skeletons- Multiple annotations: Some cases have multiple annotator versions for validation- Coverage: Cases 001, 002, 004, 005, 007, 008, 010, 011, 013, 016, 018

File Correspondence

| Case | Segmentation File | Available Skeletons ||------|-------------------|-------------------|| 001 | hepaticvessel_001_mod.nii.gz | LNC, NVO, ROF || 002 | hepaticvessel_002_mod.nii.gz | LNC, NVO || 004 | hepaticvessel_004_mod.nii.gz | LNC, NVO || 005 | hepaticvessel_005_mod.nii.gz | LNC, NVO || 007 | hepaticvessel_007_mod.nii.gz | LNC, NVO || 008 | hepaticvessel_008_mod.nii.gz | NVO || 010 | hepaticvessel_010_mod.nii.gz | NVO || 011 | hepaticvessel_011_mod.nii.gz | NVO || 013 | hepaticvessel_013_mod.nii.gz | NVO || 016 | hepaticvessel_016_mod.nii.gz | NVO || 018 | hepaticvessel_018_mod.nii.gz | LNC |

Usage Guidelines

1. Correspondence: Each skeleton file corresponds to a vessel segmentation with the same case number2. Coordinate system: Skeleton coordinates are in voxel space of the corresponding NIfTI volume3. Validation: Cases with multiple annotators can be used for inter-observer variability studies4. Citation: Please cite the original Medical Segmentation Decathlon when using this dataset

Software Tools

The manual revision was performed using custom Python tools for skeleton visualization and editing. The annotation software used for skeleton revision is available in a separate GitHub repository (link to be provided upon publication). This tool provides an interactive 3D visualization environment that allows for precise skeleton editing, branch correction, and quality validation.

The Python-based annotation tool, available at https://github.com/Removirt/skeleton-viewer features interactive 3D visualization of vessel segmentations and skeletons, point-by-point skeleton editing capabilities, branch connection and disconnection tools, real-time validation of topological correctness, and multi-platform compatibility (Windows, macOS, Linux).

Dataset Access

This dataset is publicly available through Zenodo. The complete dataset including all vessel segmentations, manually revised skeletons, and documentation can be downloaded from: https://doi.org/10.5281/zenodo.15729285

Citation

This DatasetNodar-Corral, L., Fdez-Gonzalez, M., Fdez-Vidal, X. R., Otero Freiría, R., Velo Outumuro, N., & Comesaña Figueroa, E. (2025). Refined 3D Hepatic Vessel Skeleton Dataset from the Medical Segmentation Decathlon (Task08_HepaticVessel) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15729285

Original Medical Segmentation DecathlonSimpson, A. L., Antonelli, M., Bakas, S., et al. (2019). A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063.

BibTeX Format```bibtex@dataset{hepatic_vessel_skeletons_2025, author = {Nodar-Corral, L., Fdez-Gonzalez, M., Fdez-Vidal, X. R., Otero Freiría, R., Velo Outumuro, N., & Comesaña Figueroa, E.}, title = {Refined 3D Hepatic Vessel Skeleton Dataset from the Medical Segmentation Decathlon (Task08_HepaticVessel)}, year = {2025}, publisher = {Zenodo}, doi = {10.5281/zenodo.15729285}, url =

@article{simpson2019large, title={A large annotated medical image dataset for the development and evaluation of segmentation algorithms}, author={Simpson, Amber L and Antonelli, Michela and Bakas, Spyridon and others}, journal={arXiv preprint arXiv:1902.09063}, year={2019}}```

Contact

For questions about this dataset or annotation methodology, please contact the first author on lois.nodar.corral@usc.es or loisnodar@gmail.com, or any of the other authors on their ORCID correspondence.

Links

Paper: https://arxiv.org/pdf/1908.09101v2 Repository: https://github.com/Mhaiyang/ICCV2019_MirrorNet Project page: https://mhaiyang.github.io/ICCV2019_MirrorNet/index.html We got our data from: https://github.com/Charmve/Mirror-Glass-Detection

  Split info

We split the train to train and validation with the ratio 80% and 20% respectively. If you want to use the original split, you can just combine train and validation.

  License info

Refer to the… See the full description on the dataset page: https://huggingface.co/datasets/rdyzakya/Mirror-Segmentation-Dataset.

We established a large-scale plant disease segmentation dataset named PlantSeg. PlantSeg comprises more than 11,400 images of 115 different plant diseases from various environments, each annotated with its corresponding segmentation label for diseased parts. To the best of our knowledge, PlantSeg is the largest plant disease segmentation dataset containing in-the-wild images. Our dataset enables researchers to evaluate their models and provides a valid foundation for the development and benchmarking of plant disease segmentation algorithms.

Please note that due to the image limitations of Roboflow, the dataset provided here is not complete.

Project page: https://github.com/tqwei05/PlantSeg

Paper: https://arxiv.org/abs/2409.04038

Complete dataset download: https://zenodo.org/records/13958858

Reference: @article{wei2024plantseg, title={PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation}, author={Wei, Tianqi and Chen, Zhi and Yu, Xin and Chapman, Scott and Melloy, Paul and Huang, Zi}, journal={arXiv preprint arXiv:2409.04038}, year={2024} }