The dataset includes 458 hi-res images together with their alpha maps (BW) indicating the crack presence. The ground truth for semantic segmentation has two classes to conduct binary pixelwise classification. The photos are captured in various buildings located in Middle East Technical University.

You can access a larger dataset containing images with 227x227 px dimensions for classification which are produced from this dataset from http://dx.doi.org/10.17632/5y9wdsg2zt.1 .

The dataset contains images of medical images and corresponding labels.

stevenbucaille/image-segmentation-models-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

All the nine datasets used in Hi-gMISnet paper with exact train, validation, and test splits. Paper link: https://iopscience.iop.org/article/10.1088/1361-6560/ad3cb3 Github Repo: https://github.com/tushartalukder/Hi-gMISnet.git

Cite as: @article{showrav2024hi, title={Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGAN}, author={Showrav, Tushar Talukder and Hasan, Md Kamrul}, journal={Physics in Medicine and Biology} }

View on bioimage.io # HPA Cell Image Segmentation Dataset

This dataset includes annotated cell images obtained from the Human Protein Atlas (http://www.proteinatlas.org), each image contains 4 channels (Microtubules, ER, Nuclei and Protein of Interest). The cells in each image are annotated with polygons and saved into GeoJSON format produced with Kaibu(https://kaibu.org) annotation tool.

hpa_cell_segmentation_dataset_v2_512x512_4train_159test.zip is an example dataset for running a deep learning-based interactive annotation tools in ImJoy (https://github.com/imjoy-team/imjoy-interactive-segmentation).

hpa_dataset_v2.zip is a full annotate image segmentation dataset

Utility functions in Python for reading the GeoJSON annotation can be found here: https://github.com/imjoy-team/kaibu-utils/blob/main/kaibu_utils/init.py

OPMD Image Segmentation

## Overview

OPMD Image Segmentation is a dataset for semantic segmentation tasks - it contains OPMD annotations for 359 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).

Unet++: A nested U-Net architecture for medical image segmentation.

This dataset was created to support the training and evaluation of semantic segmentation models for plant region extraction in digitised herbarium specimens. It was created in the context of the e-col+ project (ANR‐21‐ESRE‐005).ContentSegmentation dataset:This dataset consists of digitised herbarium specimen images and their corresponding segmented versions, where only plant regions are preserved and background elements are removed.The archive segmentation_dataset.zip contains two main components:train/: 2,952 images.unsegmented_images/: 1,476 original RGB herbarium images.segmented_images/: 1,476 segmented versions of the same images (plant-only regions).test/: 333 additional herbarium images, used for evaluating segmentation models on unseen data.Training and validation images were selected from the Herbarium Segmentation Dataset (https://doi.org/10.6084/m9.figshare.27685914.v1), while the test set consists of newly and manually annotated images.Out-of-Distribution evaluation dataset:The Out Of Distribution (OOD) dataset comprises 171 unannotated images, selected to represent visually challenging conditions, including:noisy or highly textured backgrounds,colored backdrops (e.g., yellow, pink, dark grey),intricate plant morphologies,and common digitisation artifacts like pins, overlapping components, and mosaic-like patterns.

Dataset

This dataset was created by Muhammad Navaid

Contents

Transform healthcare diagnostics with image segmentation. Dive into advanced techniques for detailed medical imaging, aiding patient care.

Dataset copy

It is copied from the YouTube video 208 - Multiclass semantic segmentation using U-Net. Channel name - DigitalSreeni

For easier implementation and catchup with the tutorial video the dataset is stored in here

nielsr/image-segmentation-toy-data dataset hosted on Hugging Face and contributed by the HF Datasets community

The Herbarium Image Segmentation Dataset originates from the MNHN (Muséum National d’Histoire Naturelle) in Paris and includes 11 diverse plant families and genera, offering a rich variety within dicotyledons. The dataset comprises 2,277 RGB images, each paired with a corresponding segmentation mask. These images cover a range of genera: Amborella (91 images), Castanea (161), Desmodium (164), Ulmus (352), Rubus (184), Litsea (199), Eugenia (219), Laurus (250), Convolvulaceae (177), Magnolia (162), and Monimiaceae (318), showcasing significant morphological diversity.This dataset was generated by removing non-plant backgrounds to enhance the clarity of plant features. It is suitable for segmentation tasks in botanical research and supports studies on plant morphology, biodiversity, and conservation. The segmented images can improve accuracy in classification tasks, particularly in identifying plant morphological traits, and are intended to facilitate research in plant science, biodiversity, and conservation.

The dataset consists of six collections of SEM images, three trained U-net AI models, and CSV files with image quality metrics and trained AI model accuracy metrics. Each SEM image collection contains images augmented with Poisson noise and contrast. This work was performed with funding from the CHIPS Metrology Program, part of CHIPS for America, National Institute of Standards and Technology, U.S. Department of Commerce.

The “FruitSeg30_Segmentation Dataset & Mask Annotations” is a comprehensive collection of high-resolution images of various fruits, accompanied by precise segmentation masks. We structured this dataset into 30 distinct classes, which containing 1969 images and their corresponding masks, with each measuring 512×512 pixels. Each class folder contains two subfolders: “Images” with high-quality JPG images captured under diverse conditions and “Mask” with PNG files representing the segmentation masks. We meticulously collected the dataset from various locations in Malaysia, Bangladesh, and Australia, ensuring a robust and diverse collection suitable for training and evaluating image segmentation models like U-Net. This resource is ideal for automated fruit recognition and classification applications, agricultural quality control, and computer vision and image processing research. By providing precise annotations and a wide range of fruit types, this dataset serves as a valuable asset for advancing research and development in these fields.

The global Semantic Image Segmentation Services market, valued at $7236 million in 2025, is projected to experience robust growth, driven by the increasing adoption of AI-powered image analysis across diverse sectors. A Compound Annual Growth Rate (CAGR) of 12.4% from 2025 to 2033 indicates a significant market expansion, reaching an estimated value exceeding $20,000 million by 2033. Key drivers include the burgeoning Film and Television industry's need for advanced visual effects, the rise of sophisticated photo editing studios demanding precise image manipulation, and the growing application of semantic segmentation in autonomous vehicles and medical imaging. The cloud-based segment dominates the market due to its scalability and cost-effectiveness, while the on-premise segment caters to organizations with stringent data security requirements. North America and Europe currently hold significant market share, but rapid technological advancements and increasing digitalization in the Asia-Pacific region promise substantial future growth. Challenges include the high cost of development and implementation, the need for extensive data annotation, and ensuring the accuracy and reliability of algorithms. However, continuous innovation in deep learning techniques and the growing availability of labeled datasets are mitigating these challenges, fueling market expansion. The competitive landscape is characterized by a mix of established players and emerging startups. Companies like iMerit, Keymakr, and Appen are significant players offering diverse services, ranging from data annotation to complete image segmentation solutions. The presence of smaller players such as Fiverr and TalentMatch reflects the opportunity for specialized services and niche applications. The market's growth is further supported by increasing collaborations between technology providers and end-users, resulting in customized solutions tailored to specific industry needs. This trend points towards a future where semantic image segmentation is integrated into everyday applications, driving further market expansion and reinforcing its importance in various sectors.

1. Convolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications, and are particularly suited for image data. Image segmentation (the classification of all pixels in images) is one such application and can for example be used to assess forest structural metrics. While CNN-based image segmentation methods for such applications have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists.
2. Here, we present R package imageseg which implements a CNN-based workflow for general-purpose image segmentation using the U-Net and U-Net++ architectures in R. The workflow covers data (pre)processing, model training, and predictions. We illustrate the utility of the package with image recognition models for two forest structural metrics: tree canopy density and understory vegetation density. We trained the models using large and dive...

Pothole Image Segmentation

## Overview

Pothole Image Segmentation is a dataset for instance segmentation tasks - it contains Potholes annotations for 2,586 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).

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other)

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other)

Description

4088 images and 4088 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue bands only

These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.

Two data sources have been combined

Dataset 1

• 1018 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7335647
• Labels have been reclassified from 4 classes to 2 classes.
• Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.
• These images and labels have been made using the Doodleverse software package, Doodler*.

Dataset 2

• 3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******
• A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water

File descriptions

• classes.txt, a file containing the class names
• images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents
• labels.zip, a zipped folder containing the 1-band label images
• overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, bllue=0=other)
• resized_images.zip, RGB images resized to 512x512x3 pixels
• resized_labels.zip, label images resized to 512x512x1 pixels

References

*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.

**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information

****Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647

*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/

******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.

The U-net is a deep convolutional neural network for biomedical image segmentation.