Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

Test Dataset for 3D semantic image segmentation of the Breast, Fibrograndular Tissue, and Breast Carcinoma

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opencc-by-4.0Oct 2024View details →

Bugzz lightyears: To Semantic Segmentation and Bug-yond!

Dataset Title:  Bugzz lightyears: To Semantic Segmentation and Bug-yond! <h3>Description:</h3> This dataset comprises a collection of real and robotic toy bugs designed for a small-scale semantic segmentation project. Each bug has been captured six times from various angles, ensuring comprehensive coverage of their features and details. The dataset serves as a valuable resource for exploring semantic segmentation techniques and evaluating machine learning models. <h3>Dataset Details:</h3> <ul> <li>Images: Each bug is represented by six images taken from different perspectives, facilitating robust segmentation and analysis.</li> <li>Segmentation: The dataset has been meticulously segmented using Label Studio in conjunction with the SAM (Segment Anything Model), enabling precise delineation of each bug from the background.</li> <li>Diversity: The collection includes a variety of bugs, both real and robotic, providing a unique blend for training and testing segmentation models.</li> </ul> <h3>Usage: This toy dataset is ideal for researchers and developers interested in:</h3> <ul> <li>Experimenting with semantic segmentation algorithms.</li> <li>Developing and refining computer vision models for object detection and segmentation.</li> <li>Educational purposes in machine learning and computer vision courses.</li> </ul> <h3>License: This dataset is made available under [specify license type, e.g., CC BY 4.0], allowing for both academic and commercial use, with proper attribution to the creator.</h3>

opencc-by-4.0Jul 2022View details →

Annotated dataset for the semantic segmentation of radishes

This folder contains pictures of radishes collected on the PMF experimental field during Spring 2017. There are two kinds of labeled images in the following folders: -  human annotations: human annotators draw polygons around each plant and those were then refined using an active contours algorithm. -  machine annotations: An SVM trained on the human annotations was used to produce labeled images. Images with bad segmentation were manually discarded. Each of these folder contains an images folder containing original pictures and a labels folder containing binary segmentation masks.

opencc-by-4.0May 2021View details →

Background-Foreground-Segmentation Labels for "Self-improving Semantic Perception on a Construction Robot"

Background-foreground-segmentation labels the paper "Self-improving Semantic Perception on a Construction Robot" of CoRL 2021

dryad36/100

Data from: Early detection of encroaching woody Juniperus virginiana and its classification in multi-species forest using UAS imagery and semantic segmentation algorithms

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publicJun 2021View details →

dryad36/100

U-net for automated thoracic CT semantic segmentation

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publicMay 2023View details →

opencc-by-4.0Jun 2020View details →

A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains

Content This repository contains pre-trained computer vision models, data labels, and images used in the pre-print publication "A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains": <ol> <li>ADPdevkit: a folder containing the 50 validation ("tuning") set and 50 evaluation ("segtest") set of images from the Atlas of Digital Pathology database formatted in the VOC2012 style--the full database of 17,668 images is available for download from the original website</li> <li>VOCdevkit: a folder containing the relevant files for the PASCAL VOC2012 Segmentation dataset, with both the trainaug and test sets</li> <li>DGdevkit: a folder containing the 803 test images of the DeepGlobe Land Cover challenge dataset formatted in the VOC2012 style</li> <li>cues: a folder containing the pre-generated weak cues for ADP, VOC2012, and DeepGlobe datasets, as required for the SEC and DSRG methods</li> <li>models_cnn: a folder containing the pre-trained CNN models</li> <li>models_wsss: a folder containing the pre-trained SEC, DSRG, and IRNet models, along with dense CRF settings</li> </ol> More information For more information, please refer to the following article. Please cite this article when using the data set. @misc{chan2019comprehensive,     title={A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains},     author={Lyndon Chan and Mahdi S. Hosseini and Konstantinos N. Plataniotis},     year={2019},     eprint={1912.11186},     archivePrefix={arXiv},     primaryClass={cs.CV} } For the full code released on GitHub, please visit the repository at: <a href="https://github.com/lyndonchan/wsss-analysis">https://github.com/lyndonchan/wsss-analysis</a> Contact For questions, please contact: Lyndon Chan lyndon.chan@mail.utoronto.ca http://orcid.org/0000-0002-1185-7961

opencc-by-4.0Apr 2024View details →

DepthMars Dataset for Semantic Segmentation of the Martian Surface from Rover Images

This dataset is resulted from a research article, "DepthFormer: Depth-Enhanced Transformer Network for Semantic Segmentation of the Martian Surface from Rover Images", which includes surface images on Mars collected by the Zhurong rover along its traverse, depth images generated from stereo images, and corresponding manually labeled images.

openmit-licenseAug 2017View details →

Materials in Vessels Dataset, Annotated images of materials in transparent vessels for semantic segmentation

Data set of materials in vessels The handling of materials in glassware vessels is the main task in chemistry laboratory research as well as a large number of other activities. Visual recognition of the physical phase of the materials is essential for many methods ranging from a simple task such as fill-level evaluation to the identification of more complex properties such as solvation, precipitation, crystallization and phase separation. To help train neural nets for this task, a new data set was created. The data set contains a thousand images of materials, in different phases and involved in different chemical processes, in a laboratory setting. Each pixel in each image is labeled according to several layers of classification, as given below: a. Vessel/Background: For each pixel assign value of one if it is part of the vessel and zero otherwise. This annotation was used as the ROI map for the valve filter method. b. Filled/Empty: This is similar to the above, but also distinguishes between the filled and empty regions of the vessel. For each pixel, one of the following three values is assigned:0 (background); 1 (empty vessel); or 2 (filled vessel). c. Phase type: This is similar to the above but distinguishes between liquid and solid regions of the filled vessel. For each pixel, one of the following four values: 0 (background); 1 (empty vessel); 2 (liquid); or 3 (solid). d. Fine-grained physical phase type: This is similar to the above but distinguishes between specific classes of physical phase. For each pixel, one of 15 values is assigned: 1 (background); 2 (empty vessel); 3 (liquid); 4 (liquid phase two, in the case where more than one phase of the liquid appears in the vessel); 5 (suspension); 6 (emulsion); 7 (foam); 8 (solid); 9 (gel); 10 (powder); 11 (granular); 12 (bulk); 13 (solid-liquid mixture); 14 (solid phase two, in the case where more than one phase of solid exists in the vessel): and 15 (vapor). The annotations are given as images of the size of the original image, where the pixel value is the class number. The annotation of the vessel region (a) is used in the ROI input for the valve filter net . 4.1. Validation/testing set The data set is divided into training and testing sets. The testing set is itself divided into two subsets; one contains images extracted from the same YouTube channels as the training set, and therefore was taken under similar conditions as the training images. The second subset contains images extracted from YouTube channels not included in the training set, and hence contains images taken under different conditions from those used to train the net. 4.2. Creating the data set The creation of a large number of images with a variety of chemical processes and settings could have been a daunting task. Luckily, several YouTube channels dedicated to chemical experiments exist which offer high-quality footage of chemistry experiments. Thanks to these channels, including NurdRage, NileRed, ChemPlayer, it was possible to collect a large number of high-quality images in a short time. Pixel-wise annotation of these images was another challenging task, and was performed by Alexandra Emanuel and Mor Bismuth. For more details see:  <a href="https://arxiv.org/pdf/1708.08711.pdf">Setting attention region for convolutional neural  networks using region selective features, for  recognition of materials within glass vessels</a> This dataset was first published in 2017.8 For newer and Bigger datasets see https://zenodo.org/record/4736111#.YbG-RrtyZH4 https://zenodo.org/record/3697452#.YbG-TLtyZH4

opencc-by-4.0Sep 2024View details →

Electronic Devices Dataset for 2-Class Semantic Segmentation

This dataset contains images used in the monograph titled Zastosowanie wybranych metod uczenia głębokiego w wizji komputerowej (Application of Selected Deep Learning Methods in Computer Vision) to build the U-Net model. The full collection consists of 600 image files of resolution 512x512 pixels showing small electronic devices and office accessories (<a title="Electronic Devices Dataset" href="https://drive.google.com/file/d/1CocbDdwcF9hpqniNpkqERjgVHR5O1iqW/view?usp=drive_link" target="_blank" rel="noopener">https://drive.google.com/file/d/1CocbDdwcF9hpqniNpkqERjgVHR5O1iqW/view?usp=drive_link</a>). The set was randomly divided into a training part (50% of the full set), validation and test part (each accounted for 25% of the full set). As a result, the training part contains 300 files, validation part – 150 and test part - 150. The collection was created by augmenting the original set of 100 images with vertical and horizontal flip, random rotation from -45 to 45 degrees, and a combination of both flips and random rotation. The images are labeled with masks representing 2 kind of objects – REMOTES and BATTERIES. Therefore, the dataset can be used to build models for multiclass semantic segmentation.

opencc-by-4.0Aug 2021View details →

U-TAE pre-trained weights on PASTIS for Semantic segmentation

Pre-trained weights of U-TAE for semantic segmentation. See <a href="https://github.com/VSainteuf/utae-paps">companion GitHub repository</a> and <a href="https://arxiv.org/abs/2107.07933">paper</a> for more information.

opencc-by-4.0Nov 2022View details →

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

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other) Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts (water, other) Description 3649 images and 3649 associated labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) 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, near-infrared, and short-wave infrared 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 * 579 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7344571 * 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 <ul> <li>3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******</li> <li>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</li> </ul> File descriptions <ul> <li>    classes.txt, a file containing the class names</li> <li>    images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents</li> <li>    labels.zip, a zipped folder containing the 1-band label images</li> <li>    nir.zip, a zipped folder containing the 1-band near-infrared (NIR) images</li> <li>    swir.zip, a zipped folder containing the 1-band shorttwave infrared (SWIR) images</li> <li>    overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, blue=0=other)</li> <li>    resized_images.zip, RGB images resized to 512x512x3 pixels</li> <li>    resized_labels.zip, label images resized to 512x512x1 pixels</li> <li>    resized_nir.zip, NIR images resized to 512x512x1 pixels</li> <li>    resized_swir.zip, SWIR images resized to 512x512x1 pixels</li> </ul> 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. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571 *****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.

ClinicalTrials.gov32/100

The Construction and Effect Verification of a Deep Learning-based Automated Semantic Segmentation Model for Medical Imaging

ClinicalTrials.gov study NCT06864702. IPD Sharing: UNDECIDED. Countries: 1. Publications: 11.

restrictedIPD-UNDECIDEDFeb 2026View details →

opencc-by-4.0Feb 2020View details →

Nepal landslide dataset for semantic segmentation

This database contains images used for the semantic segmentation of landslide scars from a fully convolutional neural network U-Net. 1. Training dataset: it contains 230 GeoTIFF 8 bits images and associated PNG masks (scars indicated in white and background in black color). 2. Validation dataset: it contains 35 GeoTIFF 8 bits images and associated PNG masks used for U-Net validation step. 3. Test dataset: it contains 10 GeoTIFF 8 bits images and associated PNG masks for testing. Also, the "SHAPEFILES_LANDSLIDES.rar" file contains the vector layers of the masked images in .shp format.

opencc-by-4.0Apr 2020View details →

Japan landslide dataset for semantic segmentation

This database contains images used for the semantic segmentation of landslide scars from a fully convolutional neural network U-Net. 1. Training dataset: it contains 125 GeoTIFF 8 bits images and associated PNG masks (scars indicated in white and background in black color). 2. Validation dataset: it contains 10 GeoTIFF 8 bits images and associated PNG masks used for U-Net validation step. 3. Test dataset: it contains 10 GeoTIFF 8 bits images and associated PNG masks for testing. Also, the "SHAPEFILES_LANDSLIDES.rar" file contains the vector layers of the masked images in .shp format.

opencc-by-4.0Aug 2020View details →

Amazon Rainforest dataset for semantic segmentation V2

This database contains images used for training a fully convolutional neural network for the semantic segmentation of forested areas in images from the Sentinel-2 Level 2A Satellite. The images refer to the RGB composition (bands 4, 3 and 2). The histogram for each image was selected as follows: <ul> <li>Band 4 (red): values ranging from 103 to 2724;</li> <li>Band 3 (green): values ranging from 194 to 2888;</li> <li>Band 2 (blue): values ranging from 99 to 2798.</li> </ul> After that, each band was converted to a byte type (0-255). The images are still divided into three main sets: training, validation and testing: <ol> <li>Training dataset: it contains 1.123 GeoTIFF images with 512x512 pixels and associated PNG masks (clouds indicated in white and background in black color).</li> <li>Validation dataset: it contains 100 GeoTIFF images with 512x512 pixels and associated PNG masks used for validation step.</li> <li>Test dataset: it contains 100 GeoTIFF images 512x512 pixels for testing.</li> </ol>

opencc-by-4.0Aug 2023View details →

Consumer-grade UAV imagery facilitates semantic segmentation of species-rich savanna tree layers

This data set was sampled and used for the following publication: Popp, M.R., Kalwij, J.M. Consumer-grade UAV imagery facilitates semantic segmentation of species-rich savanna tree layers. Sci Rep 13, 13892 (2023). http://dx.doi.org/10.1038/s41598-023-40989-7. The data set contains RGB orthomosaics sampled via a DJI Phantom 4 Pro at approx. 1.2 cm GSD in the folder /out. Folder /shp contains subfolders that hold shapefiles delineating tree crowns by species as polygons. Species names are given in SpeciesList.csv. Encoding of the values for species/groups of species used in the study to consecutive integers can be found in class_encoding.csv.

opencc-by-4.0May 2024View details →

Boston & Corniolo Datasets - road segmentation - described in "An Enhanced Loss Function for Semantic Road Segmentation in Remote Sensing Images""

In Corniolo.rar the masks (values {0,1}) are saved in the png files

opencc-by-4.0Sep 2024View details →

Test Dataset for 3D semantic image segmentation of the Liver and Tumor

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