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166 results for “Image Classification”
Pixel-based forest classification of Sentinel-2 images using automatically generated datasets
<p>Contains six training datasets, composed of 800, 1600 and 3200 images. Each training dataset made up of OSM (<em>OpenStreetMap</em>) masks or HRL (<em>Copernicus pan-European High Resolution Layers</em>).</p> <p>Additional 2 evaluation datasets based on OSM and HRL. Composed of 200 evaluation images.</p> <p>For study area <em>lithuania_2018_06.tiff</em> is provided. This contains a fully preprocessed study area (removed clouds, composed mosaic).</p> <p>We provide additionally a merged mosaic of Lithuanian HRL in <em>lithuania_HRL.tiff </em>file.</p> <p><em>OpenStreetMap</em> database is not provided, it can be found at https://planet.openstreetmap.org.</p>
Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification - Features
<p>A release of the sources employed in the FRMIL work - "Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification", presented at MICCAI 2022.</p> <p>We release the Camelyon16 Whole Slide Image (WSI) extracted patch-level features for reproducibility.</p>
Zircon classification from cathodoluminescence images using deep learning
<p>The dataset contains a total of 4282 original and processed zircon CL images collected from published papers and unpublished personal collections. Igneous, metamorphic, and hydrothermal zircon were classified and labeled manually. The dataset was used for a deep learning classification task which was submitted to "Geoscience Frontiers", and was entitled as "Zircon classification from cathodoluminescence images using deep learning".</p>
Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset
<p>NXTensor extraction library, experiment code, tropical cyclone and background images and their metadata generated from the meterological reanalysis ERA5 and MERRA-2 according to the HURDAT2 cyclone tracks.</p> <p>Version specifications:</p> <ul> <li>NXTensor: v0.3.3.10</li> <li>Experiment code: v2.0.3</li> <li>Image sets: v1</li> </ul> <p> </p>
Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"
<p>Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"</p> <p> </p> <p>Please find below an explanation for the <strong>files </strong>in this repository:</p> <p><br> <br> <strong>DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z</strong></p> <p>Experimental data. To reproduce the analyses, unzip both files and put the content into a folder called "Dataset"</p> <p><strong>02_CNN_PhenotypeClassif.7z</strong></p> <p>CNN Phenotype classification. Model was trained using AIDeveloper. using manually labelled data. Labelled Data is contained in folder "03_GatedData". The AIDeveloper session file in "02_Model\M10_Nitta6l_32pix_8class_meta.xlsx" shows, which files correspond to which subpopulation. The final model "M10_Nitta6l_32pix_8class_448.model" and corresponding .pb files are also located in that folder.</p> <p><strong>03_ExampleMeasurement.zip</strong></p> <p>One measurement file and a corresponding scatterplot</p> <p><strong>04_Dataset_load.zip</strong></p> <p>The python script "03_ExtractFeatures.py" loads the list of available experiment files (01_Dataset_Table_v02.csv). The experiment files are contained in DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z. The scrip then evaluates each experiment file to obtain distribution parameters for Area and Solidity. These values are written to new "01_Dataset_Table_v03.csv".</p> <p><strong>05_RF_training</strong></p> <p>Scripts to train and evaluate the Random Forest model (using features contained in "01_Dataset_Table_v03.csv").</p> <p><strong>07_pytranskit</strong></p> <p>Scripts for training and evaluating CDT-PLDA classifier</p> <p> </p> <p> </p>
Classification of Eye Images by Personal Details With Transfer Learning Algorithms
<p>During the data collection phase of the research, first of all, a brief information was given to the participants about the study, and how the data would be used and what to do. Photographs of the eye area were collected from participants consisting of a total of 96 different people aged between 3-64. It has been clearly stated that there will be no situations that will define them during the photo shoot. Then, at least ten images of the right eye area of each person were taken. In addition, at least ten photographs of the left eye area were taken. Along with these photographs, no data other than the age and gender of the persons was recorded. Below are images of two people of different genders.</p> <p>A total of 1980 images were obtained from the participants, as in the figure above. More than ten images were obtained from some people. For this reason, there is a difference in the number of photos of people. Care has been taken to use different angles and lights so that each photograph does not form the same frame. Thus, photographs that were not, all the same, were collected. In order for each photograph not to be confused with another photograph, a naming rule has been developed to express the person, age, gender and the number of the photograph taken. An underscore ("_") character is inserted between each expression. Each expression used in the naming convention is given below in order.</p> <ul> <li>Person ID: It is a unique code value for each person photographed. This value ranges from 1 to 100.</li> <li>Age: The age is written directly as a number to express how old the person is. This value varies between 3-64.</li> <li>Gender ID: The value of 1 is expressed if the person photographed is male, and the value of 0 if it is a woman.</li> <li>Photo ID: Due to the fact that more than one photo was taken for each person, each photo was numbered sequentially from 1-10.</li> </ul> <p>If this dataset is used, reference should be made to the article below.</p> <ul> <li>Aktürk, C., Aydemir, E., Hama Rashid, Y. M. 2022. Classification of eye images according to person details with the transfer learning algorithms. Acta Informatica Pragensia, DOI: 10.18267/j.aip.190</li> </ul>
Downsized camera trap images for automated classification
<b>Description: </b><p>Downsized (256x256) camera trap images used for the analyses in "Can CNN-based species classification generalise across variation in habitat within a camera trap survey?", and the dataset composition for each analysis. Note that images tagged as 'human' have been removed from this dataset. Full-size images for the BorneoCam dataset will be made available at LILA.science. The full SAFE camera trap dataset metadata is available at DOI: 10.5281/zenodo.6627707.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://safeproject.net/projects/project_view/203"><b>Machine learning and image recognition to monitor spatio-temporal changes in the behaviour and dynamics of species interactions</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (NERC QMEE CDT Studentship, NE/P012345/1, <a href="http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A">http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://safeproject.net/datasets/xml_metadata?id=6627707">here</a></p><p><b>Files: </b>This dataset consists of 3 files: CT_image_data_info2.xlsx, DN_256x256_image_files.zip, DN_generalisability_code.zip</p><p><b>CT_image_data_info2.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Dataset Images</b> (described in worksheet Dataset_images)</p><p>Description: This worksheet details the composition of each dataset used in the analyses</p><p>Number of fields: 69</p><p>Number of data rows: 270287</p><p>Fields: </p><ul><li><b>filename</b>: Root ID (Field type: id)</li><li><b>camera_trap_site</b>: Site ID for the camera trap location (Field type: location)</li><li><b>taxon</b>: Taxon recorded by camera trap (Field type: taxa)</li><li><b>dist_level</b>: Level of disturbance at site (Field type: ordered categorical)</li><li><b>baseline</b>: Label as to whether image is included in the baseline training, validation (val) or test set, or not included (NA) (Field type: categorical)</li><li><b>increased_cap</b>: Label as to whether image is included in the 'increased cap' training, validation (val) or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_individ_event_level</b>: Label as to whether image is included in the 'individual disturbance level datasets split at event level' training, validation (val) or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_1</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 1' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_2</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 2' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 3' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 4' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 5' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_1_2</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 2 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_1_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 3 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_1_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_1_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_2_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 3 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_2_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_2_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 3 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 3 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_pair_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 4 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_2_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2 and 3 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_2_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_2_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_1_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_2_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_2_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_2_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_triple_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 3, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_quad_1_2_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 3 and 4 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_quad_1_2_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 3 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_quad_1_2_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_quad_1_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 3, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_quad_2_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 3, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_combined_event_level_all_1_2_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 3, 4 and 5 (all)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_individ_1</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance level 1' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_individ_2</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance level 2' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_individ_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance level 3' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_individ_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance level 4' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_individ_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance level 5' training or test set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_1_2</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1 and 2 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_1_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1 and 3 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_1_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_1_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_2_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2 and 3 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_2_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_2_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 3 and 4 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 3 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_pair_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 4 and 5 (pair)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_2_3</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2 and 3 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_2_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_2_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_1_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_2_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_2_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_2_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_triple_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 3, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_quad_1_2_3_4</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2, 3 and 4 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_quad_1_2_3_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2, 3 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_quad_1_2_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_quad_1_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 3, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_quad_2_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 2, 3, 4 and 5 (quad)' training set, or not included (NA) (Field type: categorical)</li><li><b>dist_camera_level_all_1_2_3_4_5</b>: Label as to whether image is included in the 'disturbance level combination analysis split at camera level: disturbance levels 1, 2, 3, 4 and 5 (all)' training set, or not included (NA) (Field type: categorical)</li></ul></li></ol><p><b>DN_256x256_image_files.zip</b></p><p>Description: Zip file containing all images used in the analyses</p><p><b>DN_generalisability_code.zip</b></p><p>Description: Zip file containing code for the analyses</p><p><b>Date range: </b>2011-03-19 to 2018-06-12</p><p><b>Latitudinal extent: </b>4.6350 to 4.7538</p><p><b>Longitudinal extent: </b>116.9472 to 117.6253</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Aves <br> -  -  -  -  Galliformes <br> -  -  -  -  Passeriformes <br> -  -  -  -  Cuculiformes <br> -  -  -  -  -  Cuculidae <br> -  -  -  -  Columbiformes <br> -  -  -  -  -  Columbidae <br> -  -  -  -  Gruiformes <br> -  -  -  -  -  Gruidae <br> -  -  -  Mammalia <br> -  -  -  -  Proboscidea <br> -  -  -  -  -  Elephantidae <br> -  -  -  -  -  -  <i>Elephas</i> <br> -  -  -  -  -  -  -  <i>Elephas maximus</i> <br> -  -  -  -  -  -  -  -  <i>Elephas maximus borneensis</i> <br> -  -  -  -  Erinaceomorpha <br> -  -  -  -  -  Erinaceidae <br> -  -  -  -  -  -  <i>Echinosorex</i> <br> -  -  -  -  -  -  -  <i>Echinosorex gymnura</i> <br> -  -  -  -  Pholidota <br> -  -  -  -  -  Manidae <br> -  -  -  -  -  -  <i>Manis</i> <br> -  -  -  -  -  -  -  <i>Manis javanica</i> <br> -  -  -  -  Scandentia <br> -  -  -  -  -  Tupaiidae <br> -  -  -  -  -  -  <i>Tupaia</i> <br> -  -  -  -  -  -  -  <i>Tupaia dorsalis</i> <br> -  -  -  -  -  -  -  <i>Tupaia gracilis</i> <br> -  -  -  -  -  -  -  <i>Tupaia longipes</i> <br> -  -  -  -  -  -  -  <i>Tupaia tana</i> <br> -  -  -  -  Artiodactyla <br> -  -  -  -  -  Cervidae <br> -  -  -  -  -  -  <i>Muntiacus</i> <br> -  -  -  -  -  -  -  <i>Muntiacus atherodes</i> <br> -  -  -  -  -  -  -  <i>Muntiacus muntjak</i> <br> -  -  -  -  -  -  <i>Rusa</i> <br> -  -  -  -  -  -  -  <i>Rusa unicolor</i> <br> -  -  -  -  -  Suidae <br> -  -  -  -  -  -  <i>Sus</i> <br> -  -  -  -  -  -  -  <i>Sus barbatus</i> <br> -  -  -  -  -  Bovidae <br> -  -  -  -  -  -  <i>Bos</i> <br> -  -  -  -  -  -  -  <i>Bos javanicus</i> <br> -  -  -  -  -  Tragulidae <br> -  -  -  -  -  -  <i>Tragulus</i> <br> -  -  -  -  -  -  -  <i>Tragulus kanchil</i> <br> -  -  -  -  -  -  -  <i>Tragulus napu</i> <br> -  -  -  -  Rodentia <br> -  -  -  -  -  Sciuridae <br> -  -  -  -  -  -  <i>Lariscus</i> <br> -  -  -  -  -  -  <i>Sundasciurus</i> <br> -  -  -  -  -  -  -  <i>Sundasciurus hippurus</i> <br> -  -  -  -  -  -  -  <i>Sundasciurus lowii</i> <br> -  -  -  -  -  Hystricidae <br> -  -  -  -  -  -  <i>Hystrix</i> <br> -  -  -  -  -  -  -  <i>Hystrix brachyura</i> <br> -  -  -  -  -  -  -  <i>Hystrix crassispinis</i> <br> -  -  -  -  -  -  <i>Trichys</i> <br> -  -  -  -  -  -  -  <i>Trichys fasciculata</i> <br> -  -  -  -  -  Muridae <br> -  -  -  -  -  -  <i>Sundamys</i> <br> -  -  -  -  -  -  -  <i>Sundamys muelleri</i> <br> -  -  -  -  -  -  <i>Leopoldamys</i> <br> -  -  -  -  -  -  -  <i>Leopoldamys sabanus</i> <br> -  -  -  -  Primates <br> -  -  -  -  -  Hominidae <br> -  -  -  -  -  -  <i>Homo</i> <br> -  -  -  -  -  -  -  <i>Homo sapiens</i> <br> -  -  -  -  -  -  <i>Pongo</i> <br> -  -  -  -  -  -  -  <i>Pongo pygmaeus</i> <br> -  -  -  -  -  Cercopithecidae <br> -  -  -  -  -  -  <i>Presbytis</i> <br> -  -  -  -  -  -  <i>Macaca</i> <br> -  -  -  -  -  -  -  <i>Macaca fascicularis</i> <br> -  -  -  -  -  -  -  <i>Macaca nemestrina</i> <br> -  -  -  -  -  Tarsiidae <br> -  -  -  -  -  -  <i>Cephalopachus</i> <br> -  -  -  -  -  -  -  <i>Cephalopachus bancanus</i> <br> -  -  -  -  Carnivora <br> -  -  -  -  -  Felidae <br> -  -  -  -  -  -  <i>Prionailurus</i> <br> -  -  -  -  -  -  -  <i>Prionailurus bengalensis</i> <br> -  -  -  -  -  -  <i>Pardofelis</i> <br> -  -  -  -  -  -  -  <i>Pardofelis marmorata</i> <br> -  -  -  -  -  -  <i>Neofelis</i> <br> -  -  -  -  -  -  -  <i>Neofelis nebulosa</i> <br> -  -  -  -  -  -  <i>Catopuma</i> <br> -  -  -  -  -  -  -  <i>Catopuma badia</i> <br> -  -  -  -  -  Mustelidae <br> -  -  -  -  -  -  <i>Amblonyx</i> <br> -  -  -  -  -  -  -  <i>Amblonyx cinereus</i> <br> -  -  -  -  -  -  <i>Martes</i> <br> -  -  -  -  -  -  -  <i>Martes flavigula</i> <br> -  -  -  -  -  Mephitidae <br> -  -  -  -  -  -  <i>Mydaus</i> <br> -  -  -  -  -  -  -  <i>Mydaus marchei</i> <br> -  -  -  -  -  Ursidae <br> -  -  -  -  -  -  <i>Helarctos</i> <br> -  -  -  -  -  -  -  <i>Helarctos malayanus</i> <br> -  -  -  -  -  Canidae <br> -  -  -  -  -  -  <i>Canis</i> <br> -  -  -  -  -  -  -  <i>Canis lupus</i> <br> -  -  -  -  -  -  -  -  <i>Canis lupus familiaris</i> <br> -  -  -  -  -  Viverridae <br> -  -  -  -  -  -  <i>Paguma</i> <br> -  -  -  -  -  -  -  <i>Paguma larvata</i> <br> -  -  -  -  -  -  <i>Prionodon</i> <br> -  -  -  -  -  -  -  <i>Prionodon linsang</i> <br> -  -  -  -  -  -  <i>Arctictis</i> <br> -  -  -  -  -  -  -  <i>Arctictis binturong</i> <br> -  -  -  -  -  -  <i>Paradoxurus</i> <br> -  -  -  -  -  -  -  <i>Paradoxurus hermaphroditus</i> <br> -  -  -  -  -  -  <i>Hemigalus</i> <br> -  -  -  -  -  -  -  <i>Hemigalus derbyanus</i> <br> -  -  -  -  -  -  <i>Viverra</i> <br> -  -  -  -  -  -  -  <i>Viverra tangalunga</i> <br> -  -  -  -  -  Herpestidae <br> -  -  -  -  -  -  <i>Herpestes</i> <br> -  -  -  -  -  -  -  <i>Herpestes brachyurus</i> <br> -  -  -  -  -  -  -  <i>Herpestes semitorquatus</i> <br></div><p></p>
Oral Cancer Image Classification
<p><span>The dataset containing healthy and cancer images was obtained from Kaggle. Following an 80/10/10 split, samples were then selected for the training set (approximately 131 images), validation set (approximately 19 images), and test set (approximately 18 entirely new images). The test images were obtained from trusted medical institutes and medical databases, including online sources. </span> </p>
Data, code, models for "Weakly Supervised Semantic Segmentation for Joint Key Local Structure Localization and Classification of Aurora Image"
<p>Data, code and models for https://ieeexplore.ieee.org/document/8410588/</p>
SeagrassFinder: An Underwater Eelgrass Image Classification Dataset
<p><span>This dataset is published as part of the publishing of the paper “SeagrassFinder: Deep Learning for Eelgrass Detection and Coverage Estimation in the Wild” in the Journal Ecological Informatics. The dataset is created as a machine learning dataset for training computer vision models to classify the presence of eelgrass. </span></p> <p><span>This dataset was created by the main author Jannik Els</span>äßer as part of his bachelor's thesis. The original video transect data in this dataset comes from DHI A/S work providing By og Havn a “Summer Status” report on the maritime environmental impacts of the Lynetteholm project. More information on the project and the report is available here: <span><a href="https://byoghavn.dk/mediebibliotek/lynetteholm-sommerstatus-2023/">https://byoghavn.dk/mediebibliotek/lynetteholm-sommerstatus-2023/</a></span><br><br>The dataset consists of underwater images taken on a sled, dragged through the water by a survey vessel. The camera used is a Subsea HD-Camera made by LH-Camera. Images were created by taking 5 video frames each second, and then randomly sampling. Each image is labeled True or False for eelgrass presence. In total, the dataset consists of 8500 images from 6 different transects, with 4482 images containing eelgrass, and 4042 images not containing eelgrass. All images have been annotated by a both domain-experts, and non-domain experts. Images were annotated using a uniform sampling process. In the occurrence of any disagreement between annotators, images have been removed from the dataset. For more information on the dataset creation, please refer to the corresponding paper.</p> <p>We recommend using one transect as a test dataset, and not using a random split of all images to create the test dataset. When using a random split of all images, a form of data leakage occurs, since some images can be very similar to other images.</p> <p>An unfortunate limitation, we believe caused by the compression of the videos in the camera system, is some frames contain an echo or form of motion trail. This can lead to ghost like eelgrass features in some frames. This should be taken into consideration when applying the dataset in future locations.</p>
Pretraining convolutional neural networks for mudstones petrographic thin section image classification
<p>This dataset was used in the paper "Pretraining convolutional neural networks for mudstones petrographic thin section image classification"</p>
Supporting figures for Intelligent Image Classification for Grading Egyptian Cotton Lint
<p>Supporting figures for Intelligent Image Classification for Grading Egyptian Cotton Lint manuscript sent for review in Sensors. </p>
Precomputation of Image Features for the Classification of Dynamic Properties in Waves
<p>Image data and H5 files from feature extraction too large for Github related to the project <a href="https://github.com/ryan597/Precomputation-of-features--classification">Precomputation of Image Features for the Classification of Dynamic Properties in Waves</a>.</p>
Trained network for classification of images from yeast cell lifespans - DetecDiv (id01)
<p>Trained network for classification of images from yeast cell lifespans.</p> <p>Related to the dataset: <a href="https://doi.org/10.5281/zenodo.5552642">doi.org/10.5281/zenodo.5552642</a></p> <p>Testset: <a href="https://doi.org/10.5281/zenodo.5866747">doi.org/10.5281/zenodo.5866747 </a></p> <p><strong>------------------------------------------</strong></p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p>
Data from: Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs
<p>Very shallow coral reefs (< 5 m deep) are naturally exposed to strong sea surface temperature variations, UV radiation and other stressors exacerbated by climate change, raising great concern over their future. As such, accurate and ecologically informative coral reef maps are fundamental for their management and conservation. Since traditional mapping and monitoring methods fall short in very shallow habitats, shallow reefs are increasingly mapped with Unmanned Aerial Vehicles (UAVs). UAV-imagery is commonly processed with Structure-from-Motion (SfM) to create orthomosaics and Digital Elevation Models (DEMs) spanning several hundred metres. Techniques to convert these SfM products to ecologically relevant habitat maps are still relatively underdeveloped. Here we demonstrate that incorporating geomorphometric variables (the DEM and its derivatives) in addition to spectral information (the orthomosaic) can greatly enhance the accuracy of automatic habitat classification. Therefore, we mapped three very shallow reef areas off KAUST on the Saudi Arabian Red Sea coast with an RTK-ready UAV. Imagery was processed with SfM, and classified through Object-Based Image Analysis (OBIA). Within our OBIA workflow, we observed overall accuracy increases of up to 11% when training a Random Forest classifier on both spectral and geomorphometric variables as opposed to traditional methods that only use spectral information. Our work highlights the potential of incorporating a UAV's DEM in OBIA for benthic habitat mapping, a promising but still scarcely exploited asset.</p>
Training and test data for: Not getting in too deep: A practical deep learning approach to routine crystallisation image classification
<p>These data were used to classify crystallisation experiments in Milne et al., (<a href="https://doi.org/10.1101/2022.09.28.509868">https://doi.org/10.1101/2022.09.28.509868</a>). Here, four of the most widely-used convolutional deep-learning network architectures that can be implemented without the need for extensive computational resources were compared. It was shown that the classifiers have different strengths that can be combined to provide an ensemble classifier achieving a classification accuracy comparable to that obtained by a large consortium initiative (Bruno et al. PLOS one, 13(6), 2018). Eight classes were used to rank the experimental outcomes, thereby providing detailed information that can be used with routine crystallography experiments to automatically identify crystal formation for drug discovery and pave the way for further exploration of the relationship between crystal formation and crystallisation conditions.</p>
Image dataset for training of an insect classification model
<p> </p><p><strong>This version is deprecated! Please use the updated </strong><a href="https://doi.org/10.5281/zenodo.8325383"><strong>Insect Detect - insect classification dataset v2</strong></a><strong> with more images and classes.</strong></p><p> </p><p>This dataset contains images of various insects and some other arthropods, sitting on or flying above an artificial flower platform. All images were automatically recorded with the <a href="https://maxsitt.github.io/insect-detect-docs/">Insect Detect DIY camera trap</a>, a hardware combination of the Luxonis OAK-1, Raspberry Pi Zero 2 W and PiJuice Zero pHAT for automated insect monitoring (<a href="https://doi.org/10.1101/2023.12.05.570242">bioRxiv preprint</a>).</p><p>This classification dataset contains the cropped bounding boxes, exported from the <a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_detection/dataset/6">Insect_Detect_detection</a> dataset together with 290 new images of <i>Episyrphus balteatus</i>.</p><h2>Classes</h2><p>The following classes were annotated in this dataset:</p><ul><li><strong>wasp</strong> (mostly <i>Vespula</i> sp.)</li><li><strong>hbee</strong> (<i>Apis mellifera</i>)</li><li><strong>fly</strong> (mostly Brachycera)</li><li><strong>hovfly</strong> (various Syrphidae, e.g. <i>Eupeodes corollae</i>,<i> Scaeva pyrastri</i>)</li><li><strong>episyr_balt</strong> (<i>Episyrphus balteatus</i>)</li><li><strong>other</strong> (all Arthropods with insufficient occurences, e.g. various Hymenoptera, true bugs, beetles)</li><li><strong>shadow</strong> (shadows of the recorded insects)</li></ul><p>View the <a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_classification/health">Health Check</a> for more info on class balance.</p><h2>Deployment</h2><p>You can use this dataset as starting point to train your own insect classification models. Check the <a href="https://maxsitt.github.io/insect-detect-docs/modeltraining/train_classification/">model training instructions</a> for more information.</p><p>To deploy the image classification model (ONNX format) on your PC for fast CPU inference, follow the provided <a href="https://maxsitt.github.io/insect-detect-docs/deployment/classification/">Step by Step instructions</a>. Open source Python scripts to deploy the trained model can be found at the <a href="https://github.com/maxsitt/insect-detect-ml">insect-detect-ml GitHub repo</a>.</p>
image classification dataset on carbon fiber reinforcement quality control
<p>Image classification dataset on carbon fiber quality control.</p> <p>To represent a practical quality control problem, a dataset was generated using carbon plain weave with a grammage of 200g/m². Pieces of the weave, measuring 300x300 mm², were cut using a CNC cutter table. Two such pieces were stacked, with a binder applied between them for shape stability after the forming process. The formed stacks were then scanned using a high-resolution camera mounted on a robotic arm, resulting in 500 images of the textiles' surfaces in three-dimensional shape. The images were then cropped to 341x384 pixels patches and transformed to grayscale.</p> <p>Each patch was classified into one of three classes: normal textile, gap, or fold. Images that were blurred, out of focus, or had bad contrast were sorted out. The dataset has not yet been released, and will be available upon the acceptance of the document.</p>
Labels for land cover classification for Sentinel 2 images in livestock farms
<p>Land cover data labeling for 2022 Sentinel 2 images in livestock farms, mostly in different agroclimatic regions of Spain. The data has a resolution of 10m and the label values correspond to:</p> <ul> <li>0: Unproductive.</li> <li>1: Woodland.</li> <li>2: Pasture.</li> </ul> <p>Dataset produced within the <a href="https://ai4copernicus-project.eu/">AI4Copernicus</a> European H2020 project.</p>
Detection and Classification of Diabetic Retinopathy From Posterior Pole Images With A Deep Learning Model
ClinicalTrials.gov study NCT04805541. IPD Sharing: NO. Countries: 1. Publications: 2.
ScienceDex guides
Understand access before you commit
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.