Clothing Segmentation DataSet

## Overview

Clothing Segmentation DataSet is a dataset for instance segmentation tasks - it contains Ropa annotations for 1,084 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Discover the Remote Sensing Object Segmentation Dataset Perfect for GIS, AI driven environmental studies, and satellite image analysis.

Sidewalk Segmentation

## Overview

Sidewalk Segmentation is a dataset for instance segmentation tasks - it contains Sidewalks annotations for 1,928 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Retinal Layer Segmentation Dataset consists of optical coherence tomography (OCT) images used for segmenting different layers of the retina. It includes two primary files: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20817079%2Fa1281bd2b6d4d8444a69a9368ab30ed5%2FScreenshot%20from%202025-02-03%2002-59-44.png?generation=1738525806422198&alt=media" alt="">

resized_images.npy – A NumPy array containing preprocessed and resized OCT images, which serve as input for deep learning models.

resized_labeledimages.npy – A NumPy array containing corresponding labeled segmentation masks, where each pixel is annotated to represent different retinal layers. This dataset is commonly used for medical image analysis, particularly in ophthalmology, to develop automated segmentation models for diagnosing retinal diseases such as diabetic retinopathy and age-related macular degeneration.

Road Segmentation Dataset

This dataset comprises a collection of images captured through DVRs (Digital Video Recorders) showcasing roads. Each image is accompanied by segmentation masks demarcating different entities (road surface, cars, road signs, marking and background) within the scene.

  💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on TrainingData to buy the dataset

The dataset can be utilized… See the full description on the dataset page: https://huggingface.co/datasets/TrainingDataPro/roads-segmentation-dataset.

Delve into the Pupils Segmentation Dataset Essential for ophthalmology tech, AI driven vision studies, and advanced eye research.

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} }

Kidney Stones Segmentation

## Overview

Kidney Stones Segmentation is a dataset for semantic segmentation tasks - it contains Kidney Stones RcvI annotations for 1,299 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The HaN-Seg: Head and Neck Organ-at-Risk CT & MR Segmentation Dataset is a publicly available dataset of anonymized head and neck (HaN) images of 42 patients that underwent both CT and T1-weighted MR imaging for the purpose of image-guided radiotherapy planning. In addition, the dataset also contains reference segmentations of 30 organs-at-risk (OARs) for CT images in the form of binary segmentation masks, which were obtained by curating manual pixel-wise expert image annotations. A full description of the HaN-Seg dataset can be found in:

G. Podobnik, P. Strojan, P. Peterlin, B. Ibragimov, T. Vrtovec, "HaN-Seg: The head and neck organ-at-risk CT & MR segmentation dataset", Medical Physics, 2023. 10.1002/mp.16197,

and any research originating from its usage is required to cite this paper.

In parallel with the release of the dataset, the HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge is launched to promote the development of new and application of existing state-of-the-art fully automated techniques for OAR segmentation in the HaN region from CT images that exploit the information of multiple imaging modalities, in this case from CT and MR images. The task of the HaN-Seg challenge is to automatically segment up to 30 OARs in the HaN region from CT images in the devised test set, consisting of 14 CT and MR images of the same patients, given the availability of the training set (i.e. the herein publicly available HaN-Seg dataset), consisting of 42 CT and MR images of the same patients with reference 3D OAR binary segmentation masks for CT images.

Please find below a list of relevant publications that address: (1) the assessment of inter-observer and inter-modality variability in OAR contouring, (2) results of the HaN-Seg challenge, (3) development of our multimodal segmentation model, and (4) development of MR-to-CT image-to-image translation using diffusion models:

1. G. Podobnik, B. Ibragimov, P. Strojan, P. Peterlin, T. Vrtovec, "vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images", Medical Physics, 2024. 10.1002/mp.16924,
2. G. Podobnik, B. Ibragimov, E. Tappeiner, et al., "HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge", Radiotherapy and Oncology, 2024. 10.1016/j.radonc.2024.110410,
3. G. Podobnik, P. Strojan, P. Peterlin, B. Ibragimov, T. Vrtovec, "Multimodal CT and MR Segmentation of Head and Neck Organs-at-Risk", MICCAI 2023, 2023. 10.1007/978-3-031-43901-8_71,
4. R. M. Šter, G. Podobnik, T. Vrtovec, "Diffusion-based MR-to-CT translation of head and neck images", SPIE Medical Imaging 2025, 2025. 10.1117/12.3047458.

Sparsely Annotated Region and Organ Segmentation (SAROS) contributes a large heterogeneous semantic segmentation annotation dataset for existing CT imaging cases on TCIA. The goal of this dataset is to provide high-quality annotations for building body composition analysis tools (References: Koitka 2020 and Haubold 2023). Existing in-house segmentation models were employed to generate annotation candidates on randomly selected cases. All generated annotations were manually reviewed and corrected by medical residents and students on every fifth axial slice while other slices were set to an ignore label (numeric value 255). 900 CT series from 882 patients were randomly selected from the following TCIA collections (number of CTs per collection in parenthesis): ACRIN-FLT-Breast (32), ACRIN-HNSCC-FDG-PET/CT (48), ACRIN-NSCLC-FDG-PET (129), Anti-PD-1_Lung (12), Anti-PD-1_MELANOMA (2), C4KC-KiTS (175), COVID-19-NY-SBU (1), CPTAC-CM (1), CPTAC-LSCC (3), CPTAC-LUAD (1), CPTAC-PDA (8), CPTAC-UCEC (26), HNSCC (17), Head-Neck Cetuximab (12), LIDC-IDRI (133), Lung-PET-CT-Dx (17), NSCLC Radiogenomics (7), NSCLC-Radiomics (56), NSCLC-Radiomics-Genomics (20), Pancreas-CT (58), QIN-HEADNECK (94), Soft-tissue-Sarcoma (6), TCGA-HNSC (1), TCGA-LIHC (33), TCGA-LUAD (2), TCGA-LUSC (3), TCGA-STAD (2), TCGA-UCEC (1). A script to download and resample the images is provided in our GitHub repository: https://github.com/UMEssen/saros-dataset The annotations are provided in NIfTI format and were performed on 5mm slice thickness. The annotation files define foreground labels on the same axial slices and match pixel-perfect. In total, 13 semantic body regions and 6 body part labels were annotated with an index that corresponds to a numeric value in the segmentation file.

Body Regions

1. Subcutaneous Tissue
2. Muscle
3. Abdominal Cavity
4. Thoracic Cavity
5. Bones
6. Parotid Glands
7. Pericardium
8. Breast Implant
9. Mediastinum
10. Brain
11. Spinal Cord
12. Thyroid Glands
13. Submandibular Glands

Body Parts

1. Torso
2. Head
3. Right Leg
4. Left Leg
5. Right Arm
6. Left Arm

The labels which were modified or require further commentary are listed and explained below:

• Subcutaneous Adipose Tissue: The cutis was included into this label due to its limited differentiation in 5mm-CT.
• Muscle: All muscular tissue was segmented contiguously and not separated into single muscles. Thus, fascias and intermuscular fat were included into the label. Inter- and intramuscular fat is subtracted automatically in the process.
• Abdominal Cavity: This label includes the pelvis. The label does not separate between the positional relationships of the peritoneum.
• Mediastinum: The International Thymic Malignancy Group (ITMIG) scheme was used for the segmentation guidelines.
• Head + Neck: The neck is confined by the base of the trapezius muscle.
• Right + Left Leg: The legs are separated from the torso by the line between the two lowest points of the Rami ossa pubis.
• Right + Left Arm: The arms are separated from the torso by the diagonal between the most lateral point of the acromion and the tuberculum infraglenoidale.

For reproducibility on downstream tasks, five cross-validation folds and a test set were pre-defined and are described in the provided spreadsheet. Segmentation was conducted strictly in accordance with anatomical guidelines and only modified if required for the gain of segmentation efficiency.

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.

"Image.zip" contains 955 corrrosion images, 1480 crack images, 1269 free lime images, 873 water leakage images, and 1244 spalling images. These images are labeled with numbers from 0 to 6 including the background. The "Label.zip" file contains the labeled images, and the "Image.json" file contains the label information.

This dataset is composed of 81 pairs of correlated images. Each pair contains one image of an iron ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255).

The sample came from an itabiritic iron ore concentrate from Quadrilátero Ferrífero (Brazil) mainly composed of hematite and quartz, with little magnetite and goethite. It was classified by size and concentrated with a dense liquid. Then, the fraction -149+105 μm with density greater than 3.2 was cold mounted with epoxy resin and subsequently ground and polished.

Correlative microscopy was employed for image acquisition. Thus, 81 fields were imaged on a reflected light microscope with a 10× (NA 0.20) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 999×756 pixels with a resolution of 1.05 µm/pixel. Finally, the images from SEM were thresholded to generate the reference images.

Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012).

This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model that reached mean values of 91.43% and 93.13% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights.

For further questions and suggestions, please do not hesitate to contact us.

Contact email: ogomes@gmail.com

If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5014700

Please also cite this paper, which provides additional details about the dataset:

Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images. Minerals Engineering, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.

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Customer Personality Analysis involves a thorough examination of a company's optimal customer profiles. This analysis facilitates a deeper understanding of customers, enabling businesses to tailor products to meet the distinct needs, behaviors, and concerns of various customer types.

By conducting a Customer Personality Analysis, businesses can refine their products based on the preferences of specific customer segments. Rather than allocating resources to market a new product to the entire customer database, companies can identify the segments most likely to be interested in the product. Subsequently, targeted marketing efforts can be directed toward those particular segments, optimizing resource utilization and increasing the likelihood of successful product adoption.

Details of Features are as below:

• Id: Unique identifier for each individual in the dataset.
• Year_Birth: The birth year of the individual.
• Education: The highest level of education attained by the individual.
• Marital_Status: The marital status of the individual.
• Income: The annual income of the individual.
• Kidhome: The number of young children in the household.
• Teenhome: The number of teenagers in the household.
• Dt_Customer: The date when the customer was first enrolled or became a part of the company's database.
• Recency: The number of days since the last purchase or interaction.
• MntWines: The amount spent on wines.
• MntFruits: The amount spent on fruits.
• MntMeatProducts: The amount spent on meat products.
• MntFishProducts: The amount spent on fish products.
• MntSweetProducts: The amount spent on sweet products.
• MntGoldProds: The amount spent on gold products.
• NumDealsPurchases: The number of purchases made with a discount or as part of a deal.
• NumWebPurchases: The number of purchases made through the company's website.
• NumCatalogPurchases: The number of purchases made through catalogs.
• NumStorePurchases: The number of purchases made in physical stores.
• NumWebVisitsMonth: The number of visits to the company's website in a month.
• AcceptedCmp3: Binary indicator (1 or 0) whether the individual accepted the third marketing campaign.
• AcceptedCmp4: Binary indicator (1 or 0) whether the individual accepted the fourth marketing campaign.
• AcceptedCmp5: Binary indicator (1 or 0) whether the individual accepted the fifth marketing campaign.
• AcceptedCmp1: Binary indicator (1 or 0) whether the individual accepted the first marketing campaign.
• AcceptedCmp2: Binary indicator (1 or 0) whether the individual accepted the second marketing campaign.
• Complain: Binary indicator (1 or 0) whether the individual has made a complaint.
• Z_CostContact: A constant cost associated with contacting a customer.
• Z_Revenue: A constant revenue associated with a successful campaign response.
• Response: Binary indicator (1 or 0) whether the individual responded to the marketing campaign.

iloncka/mosquito-species-segmentation-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

A leaf disease segmentation dataset is typically used in the field of computer vision and machine learning for developing and evaluating models that can automatically detect and segment plant diseases from images of plant leaves..

Alabama Buildings Segmentation dataset is the combination of BingMap satellite images and masks from Microsoft Maps. It is almost from Alabama, US (99%). Others from Columbia. Dataset contains 10200 satellite images and 10200 masks with weight ~ 17Gb. The satellite images from this dataset have resolution 0.5m/pixel, image size 1024x1024, ~1.5Mb/image. Dataset only contains pictures that have the total area of builbuilding in mask >= 1% area of that pictures. It means there are no images that do not have any building in this dataset.

The dataset is composed of 490 images and their labelled ground truth, which consists of binary masks where zero is assigned to the background pixels and one to the water pixels. You can get the full dataset of 11900 images with their mask at the following link: https://drive.google.com/file/d/1Tm0p7XLzpLlXycSxxu2X7WENTYHh97qC/view?usp=sharing