Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3
datasets available to search
ShareScore release 0.9.0
Dataset results
3 results for “apple image dataset”
AGS_apple_detection - Apple fruit images dataset for full image object detection
<p>This dataset correspond to full apple tree images (623) annotated for the task of object detection with its corresponding annotations in yolo format saved as txt files. The dataset was divided into test, train and validation<br><br>The data was collected in 2017 on 4 different apple varieties using a Samsung sm-a510F cell phone at two different resolutions: 2448 x 3264 px and 3096 x 4128 px in the orchards of Agroscope located in Wallis, Switzerland. </p>
Astur Apple image Dataset
<p>Struture of file: 2022-v2-9classes-AsturApple-Balanced-SIZE224-train-dev-test.hdf5<br> -------------------------------------------------------------------------------------------<br> This file contains dataset of 6108 cider apple color images, 224x224 pixels to support the article "Transfer learning with convolutional neural networks<br> for classification of cider apple varieties" results.</p> <p>-The images belong to nine apple classes: 'BLANQUINA' 'CARRIO' 'FLORINA' 'FUENTES' 'PRIETA' 'RAXAO' 'REINETA ENCARNADA' 'REINETA PINTA' 'REINETA ROJA DEL CANADA'<br> -Full dataset is split in 4886 images for training, 611 for testing and 611 for validation.<br> -Class labels are codified as one-hot enconding binary labels. (e.g. [0 0 0 0 0 1 0] ---> Reineta Pinta)<br> -Training image set are store as tensor "trainX": (4168,224,224,3)<br> -Training class labels "trainY": (4886,7)<br> -Test image set tensor "testX": (611, 224,224,3)<br> -Test image binary class labels "testY": (611, 7)<br> -Validation image set tensor "devX": (611, 224,224,3)<br> -Validation binary class labels "devY": (611, 7)</p> <p>* file was created with h5py module in Python.</p> <p> </p>
Dataset with results of "Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit with internal disorders"
<p><strong>Summary</strong></p> <p>This dataset contains all results from the paper "Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit with internal disorders". Most notably, this dataset contains the corresponding CT slices for slice photographs of 1347 'Kanzi' apples. This dataset also contains data of the results section, metadata required to make the registration code run, and segmentation masks of the apple slice photographs. The "raw" data that was used to produce these results can be found in another Zenodo dataset: <a href="../record/8167285">https://zenodo.org/record/8167285</a>.</p> <p><br><strong>Description</strong></p> <ul> <li><strong>registered ct photo side-by-side view.zip </strong>is the easiest way to explore the registered CT photo image pairs. For every apple slice it contains a .png image consisting of the slice photo, registered CT slice and a combined view (photo=green, CT=purple) side-by-side. The resolution was reduced to reduce the file size.</li> <li><strong>registered ct slices.zip </strong>contains the full resolution CT slices as .tiff files. The matching slice photos can be found in <strong>slice_photos_crop.zip</strong> in <a href="../record/8167285">https://zenodo.org/record/8167285</a>.</li> <li><strong>photo metadata.zip </strong>contains all metadata files required to run the code on <a href="https://github.com/D1rk123/apple_photo_ct_workflow">https://github.com/D1rk123/apple_photo_ct_workflow</a>.</li> <li><strong>results.zip</strong> contains the IPCED annotations and per apple metrics that were used to calculate all the average metrics and tables in the results section of the paper.</li> <li><strong>subset experiment registered annotation slice.zip </strong>contains the full resolution CT slices of the annotation slice in the subset experiment as .tiff files.</li> <li><strong>segmentation masks.zip </strong>contains slice photo segmentation masks as .png images. There are subfolders for the training set, the test set and the masks used for the workflow in the paper.</li> </ul> <p><br><strong>Research group</strong><br>This dataset was produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a></p> <p><strong>Contact details</strong><br>dirk [dot] schut [at] cwi [dot] nl</p> <p><strong>Acknowledgments</strong><br>This work was funded by the Dutch Research Council (NWO) through the UTOPIA project (ENWSS.2018.003).</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.