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24
datasets available to search
ShareScore release 0.9.0
Dataset results
24 results for “instance segmentation”
Dataset from "Identifying zebrafish segmentation phenotype features using multiple instance learning"
<p>The dataset was used to produce the results from the "Identifying zebrafish segmentation phenotype features using multiple instance learning" manuscript. It contains images of zebrafish embryos obtained by in situ hybridization that were used to train and evaluate the performance of different neural network-based image classifiers. Images were split into 4 classes: unmodified (WT) zebrafish embryo, and 3 more phenotypes reflecting different segmentation clock defects.<br> Folder 'data' contains 3 different directories: 'training', the set used for training of the classifiers, 'validation', the set used for evaluation of the performance of the classifiers, with 20 images from each class, and 'fish_part_labels' that contains the annotation of zebrafish embryo parts (head, trunk, tail, yolk, and yolk extension) of the images from the 'validation' set.</p>
M3CropSeg: A Multi-platform, Multi-temporal, and Multi-resolution Remote Sensing Dataset for Crop Semantic to Instance and Dynamic Segmentation.
<p>Earth observation (EO) provides various multi-platform, multi-temporal, and multiresolution<br> remote sensing imagery for dynamic monitoring of planet Earth, with<br> a wide variety of uses. Crop monitoring is a typical application, which involves<br> timely gathering of the information of crop types, boundaries, and dynamic changes<br> during the whole crop growth period. However, most of the existing datasets and<br> benchmarks focus on medium-resolution (≥ 10 m) classification of the main crop<br> type by using satellite image time series (SITS), where the individual boundaries<br> (parcels) and the dynamic changes of the crop cannot be obtained, due to the<br> limited spatial resolution and the lack of multi-season annotation. In this paper,<br> a multi-platform, multi-temporal, and multi-resolution (M3) remote sensing crop<br> segmentation dataset (M3CropSeg) is introduced for very high resolution (VHR, 1<br> m) crop semantic segmentation to instance segmentation and dynamic segmentation.<br> Specifically, M3CropSeg contains 16311 pairs of airborne VHR (1 m) and SITS<br> (10 m) images, with 45 crop types and 101k instance annotations, covering a<br> 26,000 km<sup>2</sup> area of California in the U.S. M3CropSeg has various challenges,<br> including M3 data fusion, class imbalance, fine-grained classification, and multilabel<br> classification. Three tracks are designed for M3CropSeg, i.e., M3 semantic<br> segmentation, M3 instance segmentation, and M3 dynamic segmentation, to obtain<br> high-resolution pixel-level, parcel-level, and multi-season crop types, respectively.<br> The corresponding benchmarks are also provided to address the above challenges,<br> along with a variety of experimental analyses.</p>
LDD: A Grape Diseases Dataset Detection and Instance Segmentation
<p><span><span>The Instance Segmentation task, an extension of the well-known Object Detection task, is of great help in many areas, such as precision agriculture: being able to automatically identify plant organs and the possible diseases</span><br>associated with them, allows to effectively scale and automate crop monitoring and its diseases control. <br><br>To address the problem related to early disease detection and diagnosis on vines plants, a new dataset has been created with the goal of advancing the state-of-the-art of diseases recognition via instance segmentation approa<br>ches. <br><br>This was achieved by gathering images of leaves and clusters of grapes affected by diseases in their natural context. <br><br>The dataset contains photos of 10 object types which include leaves and grapes with and without symptoms of the eight more common grape diseases, with a total of 17,706 labeled instances in 1,092 images. <br><br>Multiple statistical measures are proposed in order to offer a complete view on the characteristics of the dataset. <br><br>Preliminary results for the object detection and instance segmentation tasks reached by the models Mask R-CNN and R^3-CNN are provided as baseline, demonstrating that the procedure is able to reach promising results about th<br>e objective of automatic diseases’ symptoms recognition.<br></span></p>
Self-Supervised Learning Cell Image Dataset of Master Thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs"
<p>This is the self-supervised learning cell image dataset of master thesis "Enhancing Cell Instance Segmentation in 3D Microscopy using Self-Supervised ViTs". We gather images from datasets such as the LIVECell dataset, the EVICAN dataset, as well as datasets available on Image Data Resource (https://idr.openmicroscopy.org/) and the Broad Bioimage Benchmark Collection (https://bbbc.broadinstitute.org/). Only images with sizes larger than 512x512 are collected. For datasets containing more than 1000 images, we randomly select 1000 images. Otherwise, we retain all images in the dataset. </p> <p> </p> <p>After download, please put all compressed folders of subdatasets in the "image" folder under the root directory.</p>
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.