Image Segmentation

## Overview

Image Segmentation is a dataset for instance segmentation tasks - it contains Chicken annotations for 426 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).

Rambutan Image Segmentation

## Overview

Rambutan Image Segmentation is a dataset for semantic segmentation tasks - it contains Fruit annotations for 627 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).

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

The Multiclass Weeds Dataset for Image Segmentation comprises two species of weeds: Soliva Sessilis (Field Burrweed) and Thlaspi Arvense L. (Field Pennycress). Weed images were acquired during the early growth stage under field conditions in a brinjal farm located in Gorakhpur, Uttar Pradesh, India. The dataset contains 7872 augmented images and corresponding masks. Images were captured using various smartphone cameras and stored in RGB color format in JPEG format. The captured images were labeled using the labelme tool to generate segmented masks. Subsequently, the dataset was augmented to generate the final dataset.

Drone Image Segmentation

## Overview

Drone Image Segmentation is a dataset for object detection tasks - it contains Plants Null annotations for 292 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

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:The dataset consists of original digitised herbarium specimen images and their corresponding segmented versions, in which only the plant regions are preserved while background elements (labels, barcodes, colour charts) are masked. Images are split into two main categories:unsegmented_images/: original RGB imagessegmented_images/: processed images with background removed manually. Each of these directories contains three subfolders corresponding to the standard data splits:train/: 1,180 imagesval/: 296 imagestest/: 333 imagesTraining 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.

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

Image Segmentation Project

## Overview

Image Segmentation Project is a dataset for instance segmentation tasks - it contains Tank annotations for 578 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).

Image Segmentation Test 1

## Overview

Image Segmentation Test 1 is a dataset for instance segmentation tasks - it contains Damage Fruit annotations for 458 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).

Road Image Segmentation

## Overview

Road Image Segmentation is a dataset for object detection tasks - it contains Roads annotations for 433 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).

Tumor Detection Dataset: Instance Segmentation

The Tumor Detection Dataset is a specialized dataset intended for a Computer Vision Project that focuses on Instance Segmentation. The project's primary objective is to detect and delineate tumor instances within medical images. This dataset provides valuable insights for diagnostic purposes. It was last updated around 9 months ago, indicating its relevance and currency in the field of computer vision.

Details of the Dataset:

Project Type: Instance Segmentation Subject: Tumor Classes: - Tumor_Good_Chance - Tumor_Less_Chance - Tumor_Moderate_Chance

Project Overview:

The project relating to this dataset aims to use advanced instance segmentation techniques to address the detection of tumors in medical imagery. Instance segmentation not only involves identifying the presence of tumors but also accurately outlining their borders within the images. This detailed level of analysis is crucial for medical professionals to assess the nature and severity of tumors.

Classes:

The dataset is classified into three groups, each representing a different probability or severity of tumor presence. These classes, namely Tumor_Good_Chance, Tumor_Less_Chance, and Tumor_Moderate_Chance, allow a nuanced understanding of the detected tumors.

Please when using this dataset for your research cite me by this DOI: 10.13140/RG.2.2.19014.08003

In ResearchGate : https://www.researchgate.net/publication/382268347_Brain_Tumor_Image_DataSet_Instance_Segmentation

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F14850461%2F85f006dd4752e6776b4f8a28f7e000fd%2FUntitled.png?generation=1705680442021663&alt=media" alt="">

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.

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.

cloud coverage

open-source-metrics/image-segmentation-checkpoint-downloads dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

Context

Example segmentation data-set for my image segmentation articles.

• Detectron2 Train a Instance Segmentation Model
• Getting started with Mask R-CNN in Keras
• Train a Mask R-CNN model with the Tensorflow Object Detection API

YOLOv8 Image segmentation dataset: PELLET Casimir Marius

This dataset includes 100 images from the PELLET Casimir Marius story on Europeana. It is available in YOLOv8 format, to train a model to segment text lines and illustrations from page images. The ground truth was generated using Teklia's open-source annotation interface Callico. This work is marked with CC0 1.0. To view a copy of this license, visit https://creativecommons.org/publicdomain/zero/1.0/.

hf-internal-testing/mask-for-image-segmentation-tests dataset hosted on Hugging Face and contributed by the HF Datasets community

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