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86 results for “RGB images”
RGB images of aluminum recyclable waste
<div> </div> <div>A dataset of RGB images depicting multiple aluminum recyclables on a conveyor belt. The dataset is produced as part of the EU funded project RECLAIM: GA-101070524.</div> <div> <p>Please find the updated version of aluminum recyclable waste data at the following link: https://zenodo.org/records/11504630</p> </div>
RGB images of Tetra Pak recyclable waste
<p>A dataset of RGB images depicting multiple Tetra Pak recyclables on a conveyor belt. The dataset is produced as part of the EU funded project RECLAIM: GA-101070524.</p> <p><span>Please find the updated version of Tetra Pak recyclable waste data at the following link: </span>https://zenodo.org/records/11504685</p>
Satellite Images from ASTER TIR and Landsat 9 (RGB+TIR) for evaluation of unmixing methodologies
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DS-Fi3 nd2 RGB Image Import Issue
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RGB images of sorghum fields gathered by UAV flights
<p>A supplementary datase for the paper <span>Hybrid-AI and model ensembling to exploit UAV-based RGB imagery: Evaluation of sorghum crops nitrogen content.</span> The data set consist of RGB images of sorghum fields gathered by UAV flights and a CSV that contains laboratory measured ground-truth for Nitrogen content. </p>
Aerial RGB and Thermal Infrared (TIR) Images of Vineyards and Pseudo-coloring RGB Images of the Plant's Stressed Areas.
<p>This dataset consists of 375 high-resolution visible-spectrum (RGB) and 375 thermal infrared (TIR) images of a vineyard (Vitis vinifera L.) captured by a Unmanned Aerial Vehicle (UAV) carrying TIR and RGB sensors. Also, the dataset contains 375 RGB images with pseudo-coloring where plants' stressed areas exist, aligned, and cropped based on the TIR images' Field of View (FOV).</p>
Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)
<p><em><strong>Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)</strong></em></p> <p>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)</p> <p><strong>Description</strong></p> <p>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</p> <p>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**.</p> <p>Two data sources have been combined</p> <p><strong>Dataset 1</strong></p> <p>* 579 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7344571<br> * Labels have been reclassified from 4 classes to 2 classes.<br> * 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.<br> * These images and labels have been made using the Doodleverse software package, Doodler*.</p> <p><strong>Dataset 2</strong></p> <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> <p><strong>File descriptions</strong></p> <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> <p>References</p> <p>*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.</p> <p>**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</p> <p>***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</p> <p>****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</p> <p>*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/</p> <p>******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.</p>
Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.
<p><em><strong>Doodleverse/Segmentation Zoo Res-UNet models for 2-class (water, other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.</strong></em></p> <p>These Residual-UNet model data are based on RGB+NIR+SWIR (red, green, blue, near infrared and shortwave infrared) images of coasts and associated labels.</p> <p>Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263</a></p> <p>Classes: {0=other, 1=water}</p> <p><strong>File descriptions</strong></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. <strong>'.json' </strong>config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2.<strong> '.h5'</strong> weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3.<strong> '_modelcard.json'</strong> model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. <strong> '_model_history.npz'</strong> model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. <strong> '.png'</strong> model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p> </p> <p><strong>References</strong></p> <p>*Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a></p> <p>** Buscombe, Daniel. (2022). Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7384263">https://doi.org/10.5281/zenodo.7384263</a></p>
Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for CoastTrain/5-class segmentation of RGB 768x768 NAIP images
<p><em><strong>Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for CoastTrain 5-class segmentation of RGB 768x768 NAIP images</strong></em></p> <p>These Residual-UNet model data are based on Coast Train images and associated labels. https://coasttrain.github.io/CoastTrain/docs/Version%201:%20March%202022/data</p> <p>Models have been created using Segmentation Gym* using the following dataset**: https://doi.org/10.1038/s41597-023-01929-2</p> <p>Image size used by model: 768 x 768 x 3 pixels</p> <p><em>classes:</em></p> <ol> <li>water</li> <li>whitewater</li> <li>sediment</li> <li>other_bare_natural_terrain</li> <li>other_terrain</li> </ol> <p><em>File descriptions</em></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p><em>References</em><br> *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</p> <p>**Buscombe, D., Wernette, P., Fitzpatrick, S. <em>et al.</em> A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal Environments. <em>Sci Data</em> <strong>10</strong>, 46 (2023). https://doi.org/10.1038/s41597-023-01929-2</p>
Doodleverse/Segmentation Zoo Res-UNet models for v2 PCMSC/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images
<p>Doodleverse/Segmentation Zoo Res-UNet models for v2 PCMSC/planecam/2-class (water, nowater) segmentation of RGB 1024x768 high-res. images</p> <p>These Residual-UNet models have been created using Segmentation Gym*</p> <p>Image size used by model: 1024 x 768 x 3 pixels</p> <p>classes:</p> <ol> <li>water</li> <li>other</li> </ol> <p><br> <strong>File descriptions</strong></p> <p>For each model, there are 5 files with the same root name:</p> <p>1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse.</p> <p>2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function `seg_images_in_folder.py`. Models may be ensembled.</p> <p>3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model</p> <p>4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py`</p> <p>5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py`</p> <p>Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU</p> <p><strong>References</strong><br> *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</p>
OPTIMA - RGB colour images and multispectral images (including LabelImg annotations)
<p>The images and annotations (LabelImg) in this folder are acquired during the H2020 OPTIMA project. The images were acquired in orchards, vineyards and fields in three European countries (France, Italy, Spain). The images represent three diseases in three crops: apple scab in apple, alternaria in carrot, downy mildew in grape. The txt files contain the bounding box locations for the diseases (bounding box detection for YOLOv5 object detection). </p> <p>The folder contains three subfolders:</p> <ol> <li>apple_applescab <ol> <li>ms: multispectral images acquired on 10-05-2022 in Spain with the Silios multispectral camera</li> <li>rgb: rgb colour images acquired on 27-05-2021 and 10-06-2021 in Spain with the NEON-202B-JT2-X smart-camera</li> </ol> </li> <li>carrot_alternaria <ol> <li>ms: multispectral images acquired on 23-09-2021 and 24-09-2021 in France with the Silios multispectral camera</li> <li>rgb: rgb colour images acquired on 02-09-2021, 24-09-2021, and 04-11-2021 in France with the NEON-201B-JT2-X smart-camera</li> </ol> </li> <li>grape_downymildew <ol> <li>ms: multispectral images acquired on 06-06-2019 in Italy with the IMEC multispectral camera</li> <li>rgb: rgb colour images acquired on 23-07-2021 in Italy with the NEON-201B-JT2-X smart-camera</li> </ol> </li> </ol>
High-quality Image (NIR and RGB) Dataset Synchronized With Contact Vital Sings Recordings and Clinical Data of Stratified Healthy Population. Algorithms and AI Models to Obtain a Set of Vital Signs Im
ClinicalTrials.gov study NCT05947721. IPD Sharing: NO. Countries: 1. Publications: 2.
RGB(Red- Green-Blue) Measurement of the Nasal Mucosal Image
ClinicalTrials.gov study NCT03070288. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: LiDAR and RGB-image analysis to predict hairy vetch biomass in breeding nurseries
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Data from: An RGB colour image steganography scheme using overlapping block-based pixel-value differencing
This paper presents a steganographic scheme based on the RGB colour cover image. The secret message bits are embedded into each colour pixel sequentially by the pixel-value differencing (PVD) technique. PVD basically works on two consecutive non-overlapping components; as a result, the straightforward conventional PVD technique is not applicable to embed the secret message bits into a colour pixel, since a colour pixel consists of three colour components, i.e. red, green and blue. Hence, in the proposed scheme, initially the three colour components are represented into two overlapping blocks like the combination of red and green colour components, while another one is the combination of green and blue colour components, respectively. Later, the PVD technique is employed on each block independently to embed the secret data. The two overlapping blocks are readjusted to attain the modified three colour components. The notion of overlapping blocks has improved the embedding capacity of the cover image. The scheme has been tested on a set of colour images and satisfactory results have been achieved in terms of embedding capacity and upholding the acceptable visual quality of the stego-image.
Supplementary material 1 from: Thanayutsiri T, Charoenying T, Patrojanasophon P, Pamornpathomkul B, Opanasopit P, Ngawhirunpat T, Rojanarata T (2023) Facile, sensitive and reagent-saving smartphone-based digital image colorimetric assay of captopril tablets enabled by long-pathlength RGB acquisition. Pharmacia 70(4): 1511-1519. https://doi.org/10.3897/pharmacia.70.e114927
Supplementary data
Labeled RGB and depth images for cattle body condition score prediction
<p>This dataset contains a collection of preclassified Criollo cow RGB+depth videos, as well as processed depth, grayscale, and edge images. The dataset was previously used to train convolutional neural networks and vision transformers to estimate body condition scores of cattle, and will be useful to other researchers in need a high quaility visual dataset that incorporates depth for the purposes of three dimensional representations of cattle. The availability of compatible software and image processing packages makes this dataset very robust and applicable to other areas of both agricultural and machine learning research. This dataset has several features that make it a good test of machine learning algorithms such as low sample uniqueness and a slightly subjective metric.</p>
Data from: An RGB colour image steganography scheme using overlapping block-based pixel-value differencing
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Labeled RGB and depth images for cattle body condition score prediction
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High-Resolution RGB Images and Corresponding Masks of Agricultural Fields
<p>This dataset consists of <strong>325 high-resolution RGB images</strong> and their corresponding <strong>masks</strong>. The images were captured using a DJI AIR 2S drone from four distinct types of agricultural fields: <strong>orchards, olive groves, wheat (green), and vineyards</strong>.</p> <p>All images are provided in <strong>TIFF format</strong>, representing the raw outputs directly from the drone without any preprocessing or compression, thus preserving full sensor quality. The masks are also stored in <strong>TIFF format</strong>.</p> <p>The masks were generated using <strong>orthomosaic processing software</strong>.</p> <p>This dataset is intended for <strong>semantic segmentation tasks</strong> in the field of precision agriculture, supporting the development of models for <strong>automatic vegetation detection</strong> and <strong>crop analysis</strong>.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
Annotated Behaviour and Observability Dataset (ABODe)
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.