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59
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ShareScore release 0.9.0
Dataset results
59 results for “Fine-grained”
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>
Lithofacies characteristics and controlling factors of fine-grained sedimentary rocks in the Lower 1st Member of the Shahejie Formation in northern Lixian Slope of Raoyang Sag, China
<p>The data used in the article</p>
Supplemental Materials to "A Fine-grained Taxonomy of Code Review Feedback in TypeScript Projects"
<p>Supplemental Materials to the paper "A Fine-grained Taxonomy of Code Review Feedback in TypeScript Projects".</p>
Contrastive Learning for Fine-Grained Ship Classification in Remote Sensing Images
<p>Dataset for Contrastive Learning for Fine-Grained Ship Classification in Remote Sensing Images from https://github.com/WindVChen/Push-and-Pull-Network?tab=readme-ov-file</p>
RealBiomFall: A Fine-grained Realistic Fall Dataset from the Perspective of Biomechanics
<p>This repository holds the video clips and annotations of "RealBiomFall: A Fine-grained Realistic Fall Dataset from the Perspective of Biomechanics".</p> <p>Currently, we include 100 video clips ("video_clips-trimmed_cropped_padded_resized-100.zip") and their corresponding temporal and semantical annotations ("label-100.zip"). We will release all the data after our paper is accepted.</p> <p>In "label-100.zip", there are five "*.pkl" files indicating the annotations of provided 100 video clips:</p> <ol> <li>labels_temporal_coarse.pkl: including coarse temporal annotations.</li> <li>labels_temporal_finegrained.pkl: including fine-grained temporal annotations.</li> <li>labels_smc_coarse.pkl: including coarse semantical annotations.</li> <li>labels_smc_midlevel.pkl: including mid-level semantical annotations.</li> <li>labels_smc_finegrained.pkl: including fine-grained semantical annotations.</li> </ol> <p>For more information about our label formatting, please check the "README.md".</p> <p>Besides video clips and annotations, we also provide two demo videos ("demo_1.mp4" and "demo_2.mp4") to help to quickly understand our data pattern.</p>
The code and data for paper: "Observing Fine-Grained Changes in Jupyter Notebooks During Development Time"
<div> <p>This package represents supplementary materials for the paper "Observing Fine-Grained Changes in Jupyter Notebooks During Development Time". Please refer to README in the archive for details.</p> </div>
Dataset of "Inferring Fine-grained Traceability Links between Javadoc Comments and JUnit Test Code"
<p>Dataset of "Inferring Fine-grained Traceability Links between Javadoc Comments and JUnit Test Code"</p>
Data from: Fine-grain, large-domain climate models based on climate station and comprehensive topographic information improve microrefugia detection
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Data from: Fine-grained adaptive divergence in an amphibian: genetic basis of phenotypic divergence and the role of non-random gene flow in restricting effective migration among wetlands
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Dataset of "Inferring Fine-grained Traceability Links between Javadoc Comments and JUnit Test Code"
<p>Dataset of Inferring Fine-grained Traceability Links between Javadoc Comments and JUnit Test Code</p>
Advanced Iterative Model for Lumpy Skin Disease Prediction Using Fine-grained Feature Fusion and Adaptive Transfer Learning
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Fine-grained Vocal Imitation Set
<p>This dataset includes 763 vocal imitations of 108 sound events. The sound event recordings were taken from a subset of Vocal Imitation Set (<a href="http://zenodo.org/record/1340763">zenodo.org/record/1340763</a>). While the original VocalImitationSet only contains vocal imitations of a single reference recording per class, this new dataset contains vocal imitations of multiple reference recordings per class. Class names and filenames in this dataset are matched with the VocalImitationSet. Read the following paper to get more detailed information about VocalImitationSet.</p> <p>[<a href="https://interactiveaudiolab.github.io/assets/papers/DCASE2018_Kim.pdf">pdf</a>] Bongjun Kim, Madhav Ghei, Bryan Pardo, and Zhiyao Duan, "Vocal Imitation Set: a dataset of vocally imitated sound events using the AudioSet ontology," *Proceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018)*, Nov. 2018.</p> <p>Contact Info:</p> <p>- Interactive Audio Lab: <a href="http://music.eecs.northwestern.edu/">http://music.eecs.northwestern.edu</a></p> <p>- Bongjun Kim <a href="mailto:bongjun@u.northwestern.edu">bongjun@u.northwestern.edu</a> | <a href="http://www.bongjunkim.com/">http://www.bongjunkim.com</a></p> <p>- Bryan Pardo <a href="mailto:pardo@northwestern.edu">pardo@northwestern.edu</a> | <a href="http://www.bryanpardo.com/">http://www.bryanpardo.com</a></p>
A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads
<p>The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets.</p>
A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads
<p>The previous fine-grained datasets mainly focus on classification and are often captured in a controlled setup, with the camera focusing on the objects. We introduce the first Fine-Grained Vehicle Detection (FGVD) dataset in the wild, captured from a moving camera mounted on a car. It contains 5502 scene images with 210 unique fine-grained labels of multiple vehicle types organized in a three-level hierarchy. While previous classification datasets also include makes for different kinds of cars, the FGVD dataset introduces new class labels for categorizing two-wheelers, autorickshaws, and trucks. The FGVD dataset is challenging as it has vehicles in complex traffic scenarios with intra-class and inter-class variations in types, scale, pose, occlusion, and lighting conditions. The current object detectors like yolov5 and faster RCNN perform poorly on our dataset due to a lack of hierarchical modeling. Along with providing baseline results for existing object detectors on FGVD Dataset, we also present the results of a combination of an existing detector and the recent Hierarchical Residual Network (HRN) classifier for the FGVD task. Finally, we show that FGVD vehicle images are the most challenging to classify among the fine-grained datasets.</p>
Oracle segmentation maps for fine-grained multitask CLIP
<p>Segmentation maps for attributes of CUB200 dataset using OFA and Unified-IO.</p>
OKG: A Knowledge Graph for Fine-grained Understanding of Social Media Discourse on Inequality
<p>The Observatory Knowledge Graph (OKG) is a knowledge graph with tweets on inequality in terms of the OBIO ontology (https://w3id.org/okg/obio-ontology/), which integrates social media metadata with various types of linguistic knowledge. The OKG can be used as the backbone of a social media observatory, to facilitate a deeper understanding of social media discourse on inequality.</p> <p>We retrieved tweets and retweets published from the end (30th) of May 2020 to the beginning (1st) of May 2023.</p> <p>In this version of the OKG, we use a sample of 85,247 tweets, published from May 30th to August 27th, 2020. To be compliant with Twitter's policies, we remove usernames and id's, as well as the tweet texts and sentences. We also replace user IRIs with skolem IRIs through skolemization. </p> <p>Access to the OKG as well as the SPARQL endpoint can be requested by sending a mail to the contact person (l.stork@uva.nl) with the following information: </p> <ol> <li>A description of the use case </li> <li>Affiliation of the researchers involved</li> <li>How their work is in line with Twitter's policies: https://developer.twitter.com/en/developer-terms/policy#4-d</li> </ol>
Supplementary Data for Manuscript "A relation of resistivity-hydraulic conductivity for fine-grained soil based on coupled electric double layer model and modified Kozeny-Carman model"
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FOCA:Fine-Grained OCTA dataset focuses on the complete microvascular structure
<p>These data are from paired Angiovue (RTVue XR Avanti, Optovue) instrument and Zeiss Cirrus 5000-HD-OCT Angioplex (Carl Zeiss Meditec) instrument devices, totaling 88 images(1:1).</p> <table> <tbody> <tr> <td>Dataset</td> <td>Range</td> <td>Detail</td> <td>Number</td> </tr> <tr> <td>FOCA(with annotation)</td> <td>3mm*3mm</td> <td>These 40 paired images were labeled using a complete fine annotation that was able to label each identifiable microvascular region. The labeling process is integrated according to the paired images as a way to avoid human errors in the labeling process.</td> <td>40</td> </tr> <tr> <td>FOCA(without annotation)</td> <td>3mm*3mm</td> <td>These 44 paired images are scans of the same eye from the same patient on different devices for which alignment has been completed.</td> <td>44</td> </tr> </tbody> </table> <p> </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.