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9
datasets available to search
ShareScore release 0.9.0
Dataset results
9 results for “fine-grained analysis”
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Object Detection Dataset (Oxford-IIIT Pet)
<p>Preprocessed dataset for Oxford-IIIT Pet in YOLOv5 format.. Ground truth labels for head bounding boxes, body bounding boxes (derived from segmentation mask).</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Breed Classification Dataset (Oxford-IIIT Pet)
<p>Oxford-IIIT Pet Dataset with ground truth labels for breeds (from https://public.roboflow.com/object-detection/oxford-pets).</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - Kashtanka Pets (All Dev and Test Images, Single Folder)
<p>Kashtanka Pets images, with all Dev and Test images (total 66639 images). In a single folder, with filenames indicating path of file in original dataset distribution.</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Object Detection Dataset (Tsinghua Dogs)
<p>Preprocessed dataset for Tsinghua Dogs in YOLOv5 format.. Ground truth labels for head bounding boxes, body bounding boxes</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Breed Classification Dataset (Tsinghua Dogs)
<p>Tsinghua Dogs Dataset with ground truth labels for breeds in YOLOv5 format.</p>
Fine-grained automated visual analysis of herbarium specimens for phenological data extraction: an annotated dataset of reproductive organs in Strepanthus herbarium specimens
<p>This dataset contains annotations of 31 herbarium specimens of <em>Streptanhus tortuosus Kellogg</em> for which we have we carefully and manually drew and annotated the contours of four reproductive organs: “bud”, “flower”, “immature fruit” and “mature fruit”.</p> <p>The dataset can be used to assess the ability of automated methods to count and detect precisely the shapes of these reproductive organs, with a view to conducting phenological studies.</p> <p>The annotations are formatted in accordance with the COCO data format, a usual format for object detection tasks in the field of Computer Vision. The annotations are divided into two files:</p> <ul> <li>train_21_full_masks.json contains the mask coordinates and labels of 21 herbarium sheets that can be used for training models</li> <li>test_10_full_masks.json contains the mask coordinates and labels of 10 other herbarium that can be used as a groundtruth file for evaluating the predictions, typically with the COCO evaluation scripts (<a href="https://github.com/cocodataset/cocoapi">https://github.com/cocodataset/cocoapi</a>)</li> </ul> <p>Please refer to the following publication for a first assessment of this dataset with a Mask-RCNN approach:</p> <p><em>H. Goëau, A. Mora-Fallas, J. Champ, N. Love, S. Mazer, E. Mata-Montero, A. Joly, P. Bonnet. </em>2020. New fine-grained method for automated visual analysis of herbarium specimens: a case study for phenological data extraction. <em>Applications in Plant Sciences </em></p> <p> </p> <p> </p> <p> </p> <p> </p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - Kashtanka Pets (400 Hand-labelled Images - Cats & Dogs, Single Folder)
<p>400 images (200 cats, 200 dogs) hand-labelled by Maria E. with head and body bounding box labels in YOLOv5 format. Images are in a single folder, no separate folders for cats and dogs.</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - Kashtanka Pets (200 Hand-labelled Images, Cats and Dogs, Separate Folders)
<p>400 images (200 cats, 200 dogs) hand-labelled by Maria E. with head and body bounding box labels in YOLOv5 format. Images for cats, for dogs are in a separate folders.</p>
Multilingual fine-grained sentiment analysis corpus
<p>A sentiment annotated corpus based on Fallout New Vegas. The corpus has the following sentiments: <em>neutral, anger, disgust, fear, happy, pained, sad, surprised</em> in the following languages: <em>English, German, Italian, Spanish and French</em>.</p> <p>Please cite the following paper: Mika Hämäläinen, Khalid Alnajjar, and Thierry Poibeau. 2022. Video Games as a Corpus: Sentiment Analysis using Fallout New Vegas Dialog. In <em>FDG’22: Proceedings of the 17th International Conference on the Foundations of Digital Games (FDG ’22)</em></p> <p> </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.