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328 results for “segmentation dataset”
An urban traffic dataset composed of visible images and their semantic segmentation generated by the CARLA simulator
<p><strong>If you use this dataset please cite this paper: Rosende, S.B.; Gavilán, D.S.J.; Fernández-Andrés, J.; Sánchez-Soriano, J. An Urban Traffic Dataset Composed of Visible Images and Their Semantic Segmentation Generated by the CARLA Simulator. <em>Data</em> 2024, <em>9</em>, 4. <a href="https://doi.org/10.3390/data9010004">https://doi.org/10.3390/data9010004</a></strong></p> <p>A dataset of aerial urban traffic images and their semantic segmentation is presented to be used to train computer vision algorithms, among which those based on convolutional neural networks stand out. The images have been generated using the CARLA simulator (but would be like those that could be obtained with fixed aerial cameras or by using AUVs) in the field of intelligent transportation management. The presented dataset is available and accessible to improve the performance of vision and road traffic management systems, especially for the detection of incorrect or dangerous maneuvers.</p>
SPIDER - Lumbar spine segmentation in MR images: a dataset and a public benchmark
<p>This is a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 studies of 218 patients with a history of low back pain. The data was collected from four different hospitals. There is an additional hidden test set, not available here, used in the accompanying SPIDER challenge on spider.grand-challenge.org. We share this data to encourage wider participation and collaboration in the field of spine segmentation, and ultimately improve the diagnostic value of lumbar spine MRI.</p> <p>Which MRI studies are assigned to the training and validation sets can be found in the overview file. This file also provides the biological sex for all patients and the age for the patients for which this was available. It also includes a number of scanner and acquisition parameters for each individual MRI study. The dataset also comes with radiological gradings found in a separate file for the following degenerative changes:</p> <p>1.    Modic changes (type I, II or III)</p> <p>2.    Upper and lower endplate changes / Schmorl nodes (binary)</p> <p>3.    Spondylolisthesis (binary)</p> <p>4.    Disc herniation (binary)</p> <p>5.    Disc narrowing (binary)</p> <p>6.    Disc bulging (binary)</p> <p>7.    Pfirrman grade (grade 1 to 5). </p> <p>All radiological gradings are provided per IVD level.</p> <div>This dataset, and the associated public benchmark, are described in this paper: <a href="https://www.nature.com/articles/s41597-024-03090-w" target="_blank" rel="noopener">https://www.nature.com/articles/s41597-024-03090-w</a></div> <div>The public segmenation challenge can be found here: <a href="https://spider.grand-challenge.org/" target="_blank" rel="noopener">https://spider.grand-challenge.org/</a></div> <div> </div> <div>When using this dataset, please cite this dataset with the correct DOI, and also cite the afformentioned paper.</div>
Large Spots DeepMIB project, synthetic dataset for testing 2D semantic segmentation
<p>A complete DeepMIB project with a synthetic dataset generated for quick tests of semantic segmentation approaches.<br>The dataset includes a trained DeepLabV3-Resnet18 network for detection of large spots on a black background. </p><p>The network can be opened by loading "2D_LargeSpots_2cl_DeepLabV3.mibCfg" file by</p><ul><li><i>MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load </i></li><li>Drag and drop of the config file into DeepMIB window</li></ul><p>Microscopy Image Browser: <a href="https://mib.helsinki.fi">https://mib.helsinki.fi</a></p>
RafanoSet: Dataset of raw, manual and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation in Heterogenous Agriculture Environment
<p>This dataset is a collection of raw and annotated Multispectral (MS) images acquired in a heterogenous agricultural environment with MicaSense RedEdge-M camera. The spectra particularly Green, Blue, Red, Red Edge and Near Infrared (NIR) were acquired at sub-metre level.. <br><br>The MS images were labelled manually using VIA and automatically using Grounding DINO in combination with Segment Anything Model. The segmentation masks obtained using these two annotation techniqes over as well as the source code to perform necessary image processing operations are provided in the repository. The images are focussed over Horseradish (Raphanus Raphanistrum) infestations in Triticum Aestivum (wheat) crops.</p> <p>The nomenclature of sequecncing and naming images and annotations has been in this format: IMG_<scene number>_<spectral channel number><br><strong>_1</strong>: Blue<br><strong>_2</strong>: Green<br><strong>_3</strong>: Red<br><strong>_4</strong>: Near Infrared<br><strong>_5</strong>: RedEdge<br><br>Example: An image name <strong>IMG_0200_3 </strong>represents the scene number<strong> 200</strong> in <strong>Red channel</strong></p> <p>This dataset 'RafanoSet'is categorized in 6 directories namely 'Raw Images', 'Manual Annotations', 'Automated Annotations', 'Binary Masks - Manual', 'Binary Masks - Automated' and 'Codes'. The sub-directory 'Raw Images' consists of manually acquired 85 images in .PNG format. over 17 different scenes. The sub-directory 'Manual Annotations' consists of annotation file 'region_data' in COCO segmentation format. The sub-directory 'Automated Annotations' consists of 80 automatically annotated images in .JPG format and 80 .XML files in Pascal VOC annotation format.</p> <p>The scientific framework of image acquisition and annotations are explained in the Data in Brief paper which is the course of peer review. This is just a prerequisite to the data article. <br><br>Field experimentation roles:</p> <p>The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy.</p> <p>Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'. </p> <p>Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data validation, annotation management and litmus testing of the datasets.</p>
MatSeg DataSet and Benchmark For Zero-Shot Material States Segmentation From images
<h2>This is an old version for the new version see <a href="../records/11331618">https://zenodo.org/records/11331618</a></h2> <p> </p> <p>A Dataset and Benchmark for zero-shot segmentation of materials states described in: “Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data” Described in <strong><a href="https://arxiv.org/pdf/2403.03309.pdf">https://arxiv.org/pdf/2403.03309.pdf</a> </strong></p> <p>See ReadMe in the zip file for technical details.</p> <p> </p> <h2><strong>MatSeg Benchmark </strong></h2> <p>A benchmark for zero-shot material state segmentation. The benchmark contains 820 real-world images with a wide range of material states and settings. For example: food states (cooked/burned..), plants (infected/dry.), to rocks/soil (minerals/sediment), construction/metals (rusted, worn), liquids (foam/sediment), and many other states in a class-agnostic manner. The goal is to evaluate the segmentation of material materials without knowledge or pretraining on the material or setting. The focus is on materials with complex scattered boundaries, and gradual transition (like the level of wetness of the surface). The annotation of the benchmark is point-based and similarity-based. Hence, for each image, we select several points and regions (Figure 4). We group the points of the same materials into the same label, we also define a group of points that have partial similarity. For example points in group A are more similar to points in group B than to points in group C (In case materials A and B are similar to each other but not identical). This approach allows us to capture the complexity of gradual transition and partial similarities in the world. While also enabling dealing with complex scattered and blurry shapes without needing to annotate the full shape which in many cases is unclear or very hard.</p> <p>Files <a href="../api/records/10801191/draft/files/MatSegBenchmarkPart1of3.zip/content" target="_blank" rel="noopener noreferrer">MatSegBenchmark</a>*.zip</p> <h2><strong>MatSeg synthetic Dataset Samples </strong></h2> <p>Synthethic dataset of images of materials spread on object surfaces and their segmentation map.</p> <p>The synthetic dataset is a very big, sample of the dataset as been uploaded.</p> <p>Files: MatSegSynthehticDataSample*.zip</p> <p>The full dataset can be found in this URLS:</p> <p><a href="https://e.pcloud.link/publink/show?code=kZHCcnZOfzqInb3anSl7xzFBoqCDmkr2JKV">https://e.pcloud.link/publink/show?code=kZHCcnZOfzqInb3anSl7xzFBoqCDmkr2JKV</a></p> <p><a href="https://icedrive.net/s/SBb3g9WzQ5wZuxX9892Z3R4bW8jw">https://icedrive.net/s/SBb3g9WzQ5wZuxX9892Z3R4bW8jw</a></p> <p> </p> <p>Generation Script for the synthetic data:</p> <p><a href="https://github.com/sagieppel/MatSeg-Synthethic-Dataset-Generation-Script">https://github.com/sagieppel/MatSeg-Synthethic-Dataset-Generation-Script</a></p> <p><a href="../records/10822596/files/sagieppel/MatSeg-Synthethic-Dataset-Generation-Script-3.zip?download=1">https://zenodo.org/records/10822596</a></p> <p> </p> <p> </p> <p> </p>
ExpertSegmentation: Sample datasets for segmentation of microscopy with domain-informed targets via custom loss
<p>Sample datasets for submission to NeurIPS 2024 titled ExpertSegmentation: Segmentation for microscopy with domain-informed targets via custom loss. Hand-labels were generated using Ilastik. Files include:</p> <ol> <li><strong>NMC_3D</strong>: 3D, 2-phase, microCT image of a Lithium-ion battery LiNiMnCoO2 electrode</li> <li><strong>NMC_3D_Labels: </strong>Hand labels for NMC_3D volume.</li> <li><strong>NMC_2D</strong>: 2D, 4-phase, SEM image of a Lithium-ion battery LiNiMnCoO2 electrode cross-section</li> <li><strong>NMC_2D_Labels: </strong>Hand labels for NMC_2D image.</li> <li><strong>Graphite_3D</strong>: 3D, 3-phase, microCT image of a Laser-ablated lithium-ion battery graphite electrode</li> <li><strong>Graphite_3D_Labels: </strong>Hand labels for Graphite_3D volume.</li> <li><strong>PEMFC_3D</strong>: 3D, 5-phase, microCT image of a Polymer Electrolyte Membrane Fuel Cell (PEMFC)</li> <li><strong>PEMFC_3D_Labels: </strong>Hand labels for PEMFC_3D volume.</li> </ol> <p>See references for dataset sources.</p>
DeepBacs – Escherichia coli bright field segmentation dataset
<p>Training and test images of live <em>E. coli</em> cells imaged under bright field for the task of segmentation.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>The example shows a bright field image of live <em>E. coli </em>cells and the manually annotated segmentation mask.</p> <p> </p> <p><strong>Data type</strong>: Paired bright field and segmented mask images </p> <p><strong>Microscopy data type</strong>: 2D bright field images recorded at 1 min interval</p> <p><strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective</p> <p><strong>Cell type</strong>: <em>E. coli</em> MG1655 wild type strain (CGSC #6300).</p> <p><strong>File format</strong>: .tif (8-bit)</p> <p><strong>Image size</strong>: 1024 x 1024 px² (79 nm / pixel), 19/15 individual frames (training/test dataset)</p> <p>1024 x 1024 px² (79 nm / pixel), 9 regions of interest with 80 frames @ 1 min time interval (live-cell time series)</p> <p><strong>Image preprocessing</strong>: Raw images were recorded in 16-bit mode (image size 512 x 512 px² @ 158 nm/px). Images were upscaled with a factor of 2 (no interpolation) to enable generation of higher-quality segmentation masks. Two sets of mask images are provided: RoiMaps for instance segmentation using e.g. StarDist or binary images for CARE or U-Net.</p> <p><br> <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p> </p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578</p>
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
<p>The benchmark code is available at: <a href="https://github.com/Junjue-Wang/LoveDA">https://github.com/Junjue-Wang/LoveDA</a></p> <p><strong>Highlights: </strong></p> <ol> <li>5987 high spatial resolution (0.3 m) remote sensing images from Nanjing, Changzhou, and Wuhan</li> <li>Focus on different geographical environments between Urban and Rural</li> <li>Advance both semantic segmentation and domain adaptation tasks</li> <li>Three considerable challenges: multi-scale objects, complex background samples, and inconsistent class distributions</li> </ol> <p><strong>Reference:</strong></p> <pre><code>@inproceedings{wang2021loveda, title={Love{DA}: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation}, author={Junjue Wang and Zhuo Zheng and Ailong Ma and Xiaoyan Lu and Yanfei Zhong}, booktitle={Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks}, editor = {J. Vanschoren and S. Yeung}, year={2021}, volume = {1}, pages = {}, url={https://datasets-benchmarks proceedings.neurips.cc/paper/2021/file/4e732ced3463d06de0ca9a15b6153677-Paper-round2.pdf} }</code></pre> <p><strong>License:</strong></p> <p>The owners of the data and of the copyright on the data are RSIDEA, Wuhan University. Use of the Google Earth images must respect the "Google Earth" terms of use. All images and their associated annotations in LoveDA can be used for academic purposes only, <strong>but any commercial use is prohibited. (CC BY-NC-SA 4.0)</strong></p>
An Image Dataset for Training Deep Learning Segmentation Models to Identify Karst Sinkholes
<p>The image dataset was prepared for training deep learning image segmentation models to identify karst sinkholes. Information about the work can be found at (https://github.com/mvrl/sink-seg/). The dataset consists of a DEM image, an aerial image, and a binary sinkhole label image in an area in central Kentucky, USA. It also includes four images derived from the DEM image. The image dataset is sourced from publicly available data from Kentucky's Elevation Data & Aerial Photography Program (https://kyfromabove.ky.gov/) and Kentucky LiDAR-derived sinkholes (https://kgs.uky.edu/geomap).</p> <p> </p>
Cell Instance Segmentation Dataset
<p>This project contains the data for the paper:</p> <p>A. Bouyssoux, R. Fezzani and J. -C. Olivo-Marin, "<em>Cell Instance Segmentation Using Z-Stacks In Digital Cytopathology,</em>" 2022 IEEE International Symposium on Biomedical Imaging (ISBI), 2022.</p> <p>The code associated with this project is available at: <a href="https://gitlab.com/vitadx/articles/zstacks_cell_instance_segmentation">https://gitlab.com/vitadx/articles/zstacks_cell_instance_segmentation</a></p> <p>A new large Cell Instance Segmentation Dataset (CISD) is introduced. It comprises 3911 samples containing at least two touching or overlapping urothelial cells. Cell instances were manually annotated by trained cytotechnicians. All samples are extracted from 30 digital cytology slides stained with nine variations of Papanicolaou staining. The cytology slides are prepared from urine samples from healthy patients, using a Hologic ThinPrep®5000 processor, and routinely stained with the Agilent Dako CoverStainer®. The slides are finally digitized using a Hamamatsu NanoZoomer®S360 with 21 focal planes and centered on the best focus plane determined by the scanner autofocus.</p> <p>Note that cell instances with a bounding box width/height smaller than 10% of the sample width/height, as well as red blood cells and neutrophil cells were automatically filtered out because under-represented in the available data.</p> <p>Each sample is considered in three different manners in the CISD, allowing experimentation with different methods<br> for handling Z-stack data and comparison with simple 2D acquisition:</p> <ul> <li>Center slice: the best focus plane only as determined by the scanner, which is equivalent to a 2D slide acquisition.</li> <li>Extended Depth of Field (EDF): the 21 planes merged in an image where all textured parts appear in focus.</li> <li>Raw Z-stack: the volume composed of the 21 focal planes.</li> </ul> <p>Once extracted, the dataset folder contains three subfolders, one for each of the three types of samples described here above, containing the images and stack of images. A JSON file contains the instance masks, encoded in RLE format.</p>
DoPose: dataset for object segmentation and 6D pose estimation
<p>DoPose (Dortmund Pose)is a dataset of highly cluttered and closely stacked objects. The dataset is saved in the <a href="https://github.com/thodan/bop_toolkit/blob/master/docs/bop_datasets_format.md">BOP format</a>. The dataset includes RGB images, Depth images, 6D Pose of objects, segmentation mask (all and visible), COCO Json annotation, camera transformations, and 3D model of all objects. The dataset contains 2 different types of scenes (table and bin). Each scene contains different view angles. For the bin scenes, the data contains 183 scenes with 2150 image views. In those 183 scenes 35 scenes contain 2 views, 20 contains 3 views and 128 contains 16 views. And for table scenes, the data contains 118 scenes with 1175 image views. in Those 118 scenes, 20 scenes contain 3 views, 50 scenes with 6 images, and 48 scenes with 17 images. So in total, our data contains 301 scenes and 3325 view images. Most of the scenes contain mixed objects. The dataset contains 19 objects in total.</p> <p>For more info about the dataset content and collection process please refer to our <a href="https://arxiv.org/abs/2204.13613">Arxiv preprint</a></p> <p>If you have any questions about the dataset, please contact <strong>anas.gouda@tu-dortmund.de</strong></p>
DeepBacs – Escherichia coli release from stationary phase - Bright field segmentation dataset and StarDist model
<p>Training and test images of live <em>E. coli</em> cells imaged under bright field for the task of segmentation.</p> <p>Additional information can be found on this<a href="https://github.com/HenriquesLab/DeepBacs/wiki"> github wiki</a>.</p> <p>The example shows a bright field image of live <em>E. coli </em>cells of an overnight culture and the manually annotated segmentation mask.</p> <p> </p> <p><strong>Data type</strong>: Paired bright field and segmented mask images </p> <p><strong>Microscopy data type</strong>: 2D bright field images recorded at 2 min interval</p> <p><strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective</p> <p><strong>Cell type</strong>: <em>E. coli</em> MG1655 wild type strain (CGSC #6300).</p> <p><strong>File format</strong>: .tif (8-bit)</p> <p><strong>Image size</strong>: 512 x 512 px² (106 nm / pixel), 19/15 individual frames (training/test dataset)</p> <p>512 x 512 px² (106 nm / pixel), 7 regions of interest with 20 frames @ 2 min time interval (live-cell time series)</p> <p><strong>Data annotation</strong>: Images were annotated using the Fiji freehand selection tool.</p> <p><strong>Image preprocessing</strong>: Time series were stabilized using the Fiji plugin StackReg and the 480 x 480 px center region was cropped</p> <p><strong>StarDist model</strong></p> <p>The StarDist 2D model was trained from scratch for 200 epochs on 33 paired image patches (image dimensions: (512, 512 px²), patch size: (512 x 512 px²)) with a batch size of 2, 80 rays, grid size 1, 4-fold data augmentation and a mae loss function, using the StarDist 2D ZeroCostDL4Mic notebook (v 1.13) (von Chamier & Laine et al., 2020). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.3), numpy (v 1.21.5), cuda (v 11.1.105). The training was accelerated using a Tesla K80 GPU.</p> <p>Model weights can be used with the ZeroCostDL4Mic StarDist 2D notebook or the Fiji StarDist plugin.</p> <p><br> <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578 </p>
ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes
<p>Less than 35% of recyclable waste is being actually recycled in the US, which leads to increased soil and sea pollution and is one of the major concerns of environmental researchers as well as the common public. At the heart of the problem are the inefficiencies of the waste sorting process (separating paper, plastic, metal, glass, etc.) due to the extremely complex and cluttered nature of the waste stream. Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets. This challenging computer vision task currently lacks suitable datasets or methods in the available literature. In this paper, we take a step towards computer-aided waste detection and present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. We believe that ZeroWaste will catalyze research in object detection and semantic segmentation in extreme clutter as well as applications in the recycling domain.</p> <p>Our project page can be found at <a href="http://ai.bu.edu/zerowaste/">http://ai.bu.edu/zerowaste/</a></p> <p>Please use the following password to extract the zip files: UP#1VuX409z4</p>
SROADEX: Dataset for binary recognition and semantic segmentation of road surface areas from high resolution Aerial Orthoimages Covering Approximately 8,650 km2 of the Spanish Territory Tagged with Road Information
<p>The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography representing the axes of the different types of roads (urban, interurban and rural). This cartography has been obtained from different Spanish official sources (National Geographic Institute and autonomic cartographic agencies) that we have revised and edited in a meticulous and systematic way to verify that the roads are represented on the cartography according to the orthoimages, available on January 1, 2021 in the download center of the National Center of Geographic Information (CNIG), on 16 rectangular areas (28,5 km * 18,5 km) of the Spanish territory (insular and peninsular).</p> <p>The dataset consists of 777599 images in png format of 256x256 pixels, organized in folders for the different trainings, separating those corresponding to training, testing and validation.</p> <p>The structure of the data is as follows:<br> 1-Road-Ortho and 1-Road-Mask contain the images and ground true for training the semantic segmentation networks.<br> 1-Road-Ortho and 2-NoRoad-Ortho contain aerial images containing or not containing vials, for the training of binary tessellation networks identifying tessellations with vials.<br> Moreover, in each folder the structure is the same: train, test, validation containing 90%, 5% and 5% of the total images and masks of each type.</p> <p>1-Road-Ortho</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p>1-Road-Mask</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p>2-NoRoad-Ortho</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p> </p>
Jewelry segmentation masks for the 11k Hands dataset
<p>We provide an additional set of segmentation masks for jewelry in the 11K Hands dataset [1]. We filtered out a total of 3179 hands <br> with jewelry and were manually annotated using CVAT. For ease of use, the maks have the same size and filename as the original images and are exported in png format. The pixel value represents whether jewelry exists, being 0 background and 1 jewelry.</p> <p>The 11k Hands [1] dataset is a collection of 11,076 hand photos (1600 × 1200 pixels) from 190 people aged 18 to 75 years old. Each hand was shot from both the dorsal and palmar sides, on a uniform white background, at roughly the same distance from the camera. Each image has a metadata record that includes the following information: the subject ID, gender, age, skin color, and a set of information about the captured hand, such as right- or left-hand, hand side (dorsal or palmar), and logical indicators indicating whether the hand image contains accessories, nail polish, or irregularities. You can download <a href="https://drive.google.com/open?id=1KcMYcNJgtK1zZvfl_9sTqnyBUTri2aP2">here</a> the original 11K Hands dataset and the <a href="https://drive.google.com/file/d/1RC86-rVOR8c93XAfM9b9R45L7C2B0FdA/view?usp=sharing">metadata</a>.</p> <p>In the future, we will add our paper if accepted. In the meantime, if you use the masks provided on this webpage, please cite our DOI: <em>10.5281/zenodo.6541286</em> and the original 11K Hands paper.</p> <p>[1] Mahmoud Afifi, "11K Hands: Gender recognition and biometric identification using a large dataset of hand images." Multimedia Tools and Applications, 2019.</p>
Dataset for generating LOD3 building models from structure-from-motion and semantic segmentation
<p>This repository contains the codes for computing geometrical digital twins as LOD3 models for buildings, using a structure from motion and semantic segmentation. The methodology hereby implements was presented in the paper [Generating LOD3 building models from structure-from-motion and semantic segmentation" by Pantoja-Rosero et., al. (2022)] (<a href="https://doi.org/10.1016/j.autcon.2022.104430">https://doi.org/10.1016/j.autcon.2022.104430</a>)</p>
Dataset of very-high-resolution satellite RGB images to train deep learning models to detect and segment high-mountain juniper shrubs in Sierra Nevada (Spain)
<p>This dataset provides annotated very-high-resolution satellite RGB images extracted from Google Earth to train deep learning models to perform instance segmentation of Juniperus communis L. and Juniperus sabina L. shrubs. All images are from the high mountain of Sierra Nevada in Spain. The dataset contains 810 images (.jpg) of size 224x224 pixels. We also provide partitioning of the data into Train (567 images), Test (162 images), and Validation (81 images) subsets. Their annotations are provided in three different .json files following the COCO annotation format.</p>
A Dataset of Synthetic Images of Outdoor Scenes Taken from Sidewalks, for Temporal Semantic Segmentation Applications
<p>This dataset has been generated using the CARLA simulator (release 0.9.11), an open-source 3D simulator for experiments in autonomous vehicle, based on the Unreal Engine game engine. It comes with pre-made city environment maps. CARLA is distributed with several integrated maps as well as parameters to increase the variety in the dataset. In the release that we have used, there are 13 semantic segmentation classes: None, Building, Fence, Other, Pedestrian, Pole, Lane-marking, Road, Sidewalk, Vegetation, Vehicle, Wall, and Traffic sign. The "None" category corresponds to textures that are not part of an object, such as lawns which are not part of "Vegetation", or sky. In the “Other” category are found objects that are not included in the other classes like plant and flower pots. For smart mobility applications, the “Sidewalks” and “Road” classes are of particular importance to find the way forward, as well as “Buildings” and “Poles” for obstacle avoidance. Sequences are made of 4 images. The dataset is composed of 46436 frames (11609 sequences) partitioned in 41024 frames (10256 sequences) for train, 2696 frames (674 sequences) for validation, and 2716 for test (679 sequences). The size of the images is 800 x 600 (resp. width x height).</p> <p>Additionaly, we have generated another smaller dataset with images taken from 2 different viewpoints: one located on the road and the other located on the sidewalk. The number of frames for train/validation/test is respectively 7288 (1822 sequences) partitioned in 6344 (1687 sequences) for train, 416 frames (104 sequences) for validation, and 424 for test (106 sequences). This smaller dataset is aimed at showing the importance of the viewpoint in the result of semantic segmentation. This can be done by cross-validation: learning on images taken from a viewpoint located on the road and test on images with a viewpoint located on the sidewalk, and vice versa.</p>
A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer
<p>Dataset access for the paper: A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer</p>
visuAAL Skin Segmentation Dataset
<p>The visuAAL Skin Segmentation Dataset contains 46,775 high quality images divided into a training set with 45,623 images, and a validation set with 1,152 images. Skin areas have been obtained automatically from the <a href="https://fashionpedia.github.io/home/index.html">FashionPedia garment dataset</a>. The process to extract the skin areas is explained in detail in the paper <a href="https://doi.org/10.1007/978-3-031-13321-3_6">'From Garment to Skin: The visuAAL Skin Segmentation Dataset'</a>.</p> <p>If you use the visuAAL Skin Segmentation Dataset, please, cite:</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.6973396">https://doi.org/10.5281/zenodo.6973396</a></li> <li><a href="https://doi.org/10.1007/978-3-031-13321-3_6">https://doi.org/10.1007/978-3-031-13321-3_6</a></li> </ul> <p>How to use:</p> <ol> <li>Download the FashionPedia dataset from <a href="https://fashionpedia.github.io/home/Fashionpedia_download.html">https://fashionpedia.github.io/home/Fashionpedia_download.html</a></li> <li>Download the visuAAL Skin Segmentation Dataset. The dataset consists of two folders, namely train_masks and val_masks. Each folder corresponds to the training and validation sets in the original FashionPedia dataset.</li> <li>After extracting the images from FashionPedia, for each image existing in the visuAAL skin segmentation dataset, the original image can be found with the same name (file_name in the annotations file).</li> </ol> <p>A sample of image data in the FashionPedia dataset is:</p> <p>{'id': 12305,</p> <p> 'width': 680,</p> <p> 'height': 1024,</p> <p> 'file_name': '064c8022b32931e787260d81ed5aafe8.jpg',</p> <p> 'license': 4,</p> <p> 'time_captured': 'March-August, 2018',</p> <p> 'original_url': 'https://farm2.staticflickr.com/1936/8607950470_9d9d76ced7_o.jpg',</p> <p> 'isstatic': 1,</p> <p> 'kaggle_id': '064c8022b32931e787260d81ed5aafe8'}</p> <p>NOTE: Not all the images in the FashionPedia dataset have the correponding skin mask in the visuAAL Skin Segmentation Dataset, as there are images in which only garment parts and not people are present in them. These images were removed when creating the visuAAL Skin Segmentation Dataset. However, all the instances in the visuAAL skin segmentation dataset have their corresponding match in the FashionPedia dataset.</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.