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166
datasets available to search
ShareScore release 0.9.0
Dataset results
166 results for “Image Classification”
Underwater Image Classification
<p>The Underwater Image Dataset is a comprehensive collection of images designed to facilitate marine biology research and underwater object detection tasks. It consists of five distinct classes: Clams, Dolphins, Jellyfish, Lobster, and Nudibranchs, each represented by 500 high-quality images. This dataset provides a diverse range of underwater scenes, capturing the unique characteristics and habitats of these marine species, and is an invaluable resource for training and testing machine learning models in underwater image classification and analysis.</p>
Blastoid images dataset for classification
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Fusarium image classification dataset
<p>Please visit our GitHub-Repository for more details: <a href="https://github.com/cvims/FHB_classification">FHB_classification</a></p> <p> </p> <p>This research was partly supported by funds of the Federal Ministry of Food and Agriculture (BMEL) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE) under the innovation support program for the project 2818407A18.</p>
Digital Humanities Multi-Task Image Classification Dataset (DHMTIC)
<p>This dataset was assembled to train a neural network to undertake a multi-task, multi-class classification challenge, as well as to facilitate parameterized image searches for research queries in the humanities. This is significant for several reasons. To illustrate, large quantities of textual and visual data can be processed more efficiently through specially trained neural networks. Additionally, parameterized image searches permit a detailed examination and analysis of visual data, enabling tasks such as identifying image copies or tracking the evolution and reuse of image motifs. The images hail from diverse research fields, including art and architecture, design, and life sciences. Each image has been classified for two tasks: media type classification and content type classification. The content type, which refers to the subject depicted in the image, is categorized into 14 classes, whereas the media type, denoting whether the image is a graphic, photograph, or drawing, is divided into 3 classes. The class distribution is skewed, with a single class housing a large number of entries and the remaining classes having fewer. For example, in the content type, the "object" class contains the bulk of the data, whereas classes like "musical notation" and "map" form the tail, each with fewer than 100 examples in the training and validation subsets. Similarly, in the media type, the "graphic" subset contains the majority of samples, with the "photography" and "drawing" subsets containing considerably fewer.</p>
Smaller is better? Unduly nice accuracy assessments in image classification due to spatial autocorrelation in identification of small sized objects
<p>Deriving the thematic accuracy of models is a fundamental part of image classification analyses. However, due to high spatial autocorrelation in remotely sensed imagery, accuracy assessments can be biased, which leads to relevant overestimation of accuracies. </p>
Dataset related to the article "A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images"
<p>This record contains raw data related to the article "A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images"</p> <p>A B S T R A C T<br> Background and objective: In patients with suspected Coronary Artery Disease (CAD), the severity of stenosis needs<br> to be assessed for precise clinical management. An automatic deep learning-based algorithm to classify coronary<br> stenosis lesions according to the Coronary Artery Disease Reporting and Data System (CAD-RADS) in multiplanar<br> reconstruction images acquired with Coronary Computed Tomography Angiography (CCTA) is proposed.<br> Methods: In this retrospective study, 288 patients with suspected CAD who underwent CCTA scans were included.<br> To model long-range semantic information, which is needed to identify and classify stenosis with challenging<br> appearance, we adopted a token-mixer architecture (ConvMixer), which can learn structural relationship over<br> the whole coronary artery. ConvMixer consists of a patch embedding layer followed by repeated convolutional<br> blocks to enable the algorithm to learn long-range dependences between pixels. To visually assess ConvMixer<br> performance, Gradient-Weighted Class Activation Mapping (Grad-CAM) analysis was used.<br> Results: Experimental results using 5-fold cross-validation showed that our ConvMixer can classify significant<br> coronary artery stenosis (i.e., stenosis with luminal narrowing ≥50%) with accuracy and sensitivity of 87% and<br> 90%, respectively. For CAD-RADS 0 vs. 1–2 vs. 3–4 vs. 5 classification, ConvMixer achieved accuracy and<br> sensitivity of 72% and 75%, respectively. Additional experiments showed that ConvMixer achieved a better<br> trade-off between performance and complexity compared to pyramid-shaped convolutional neural networks.<br> Conclusions: Our algorithm might provide clinicians</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.