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27
datasets available to search
ShareScore release 0.9.0
Dataset results
27 results for “imagenet”
Tiny-ImageNet-R
<p>Tiny-ImageNet-R, is a down-sampled subset of ImageNet-R(enditions) imagenet-r. It contains roughly 12,000 samples categorized in 64 classes (a subset of Tiny-ImageNet classes), spread across multiple visual domains such as art, cartoons, sculptures, origami, graffiti, and embroidery.</p>
ImageNet16: Small scale ImageNet Classification
<p>This is a subset of ImageNet called "ImageNet16" more suited for cases with limited computational budget and faster experimentation. </p> <p>Each class has 400 train images and 100 test images.</p> <p>* Credit also goes to original creators that constructed the dataset. Unfortunately, I was not able to relocated it online so I reupload it here.</p> <p> </p> <p>If used in your work please cite as follows:</p> <p>C. Kyrkou, "Toward Efficient Convolutional Neural Networks With Structured Ternary Patterns," in <em>IEEE Transactions on Neural Networks and Learning Systems</em>, doi: 10.1109/TNNLS.2024.3380827.</p> <p> </p> <p>The classes corresponding to imagenet1K:</p> <p>• n02009912 American_egret</p> <p>• n02113624 toy_poodle</p> <p>• n02123597 Siamese_cat</p> <p>• n02132136 brown_bear</p> <p>• n02504458 African_elephant</p> <p>• n02690373 airliner</p> <p>• n02835271 bicycle-built-for-two</p> <p>• n02951358 canoe</p> <p>• n03041632 cleaver</p> <p>• n03085013 computer_keyboard</p> <p>• n03196217 digital_clock</p> <p>• n03977966 police_van</p> <p>• n04099969 rocking_chair</p> <p>• n04111531 rotisserie</p> <p>• n04285008 sports_car</p> <p>• n04591713 wine_bottle</p> <p> </p> <p><strong>From original map.txt</strong></p> <p>knife = n03041632</p> <p>keyboard = n03085013</p> <p>elephant = n02504458</p> <p>bicycle = n02835271</p> <p>airplane = n02690373</p> <p>clock = n03196217</p> <p>oven = n04111531</p> <p>chair = n04099969</p> <p>bear = n02132136</p> <p>boat = n02951358</p> <p>cat = n02123597</p> <p>bottle = n04591713</p> <p>truck = n03977966</p> <p>car = n04285008</p> <p>bird = n02009912</p> <p>dog = n02113624</p> <p> </p> <p><strong>Folder Structure</strong></p> <p>-<train></p> <p> -- <class idx 1></p> <p> --- <filename1>.JPEG</p> <p> --- <filename2>.JPEG</p> <p> --- ....</p> <p> -- <class idx 1></p> <p> --...</p> <p> </p> <p>-<val></p> <p> -- <class idx 1></p> <p> --- <filename1>.JPEG</p> <p> --- <filename2>.JPEG</p> <p> --- ....</p> <p> -- <class idx 1></p> <p> --...</p> <p> </p> <p><strong>Some preliminary results:</strong></p> <table> <tbody> <tr> <td><strong>Model Name</strong></td> <td><strong>Accuracy (Top-1)</strong></td> </tr> <tr> <td>VGG16</td> <td>85.3</td> </tr> <tr> <td>ResNet50</td> <td>88.2</td> </tr> <tr> <td>MobileNetV2</td> <td>91.0</td> </tr> <tr> <td>EfficientNet B0</td> <td>85.6</td> </tr> </tbody> </table> <p> </p> <p><em>Massive Credit to original ImageNet authors</em><br>[1] Olga Russakovsky<em>, Jia Deng</em>, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei.ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015</p>
Imagenet Features Extracted with LBP
<p>This dataset contains features extracted from the Imagenet dataset using Local Binary Patterns Histograms. The LBP algorithm is configured with params <em>p=8</em>, <em>r=1</em>, <em>gridX=8</em> and <em>gridY=8</em>. Feature extraction was performed using the Python package <a href="https://github.com/jgoodman8/py-image-features-extractor" target="_blank" rel="nofollow noopener noreferrer">Py Image Feature Extractor</a>.</p>
SAMPLER representations of frozen and FFPE TCGA-LUAD WSIs using an InceptionV3 backbone pretrained on imagenet
Open the record for dataset details and reuse information.
SAMPLER representations of frozen and FFPE TCGA-KIRP WSIs using an InceptionV3 backbone pretrained on imagenet
Open the record for dataset details and reuse information.
Frost images for ImageNet-C
<p>Images used by Dan Hendrycks with unknown license.</p>
ImageNet Validation Images with Corrupted Patches
<p>ImageNet Validation Images with Corrupted Patches</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.