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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45CTUK5
Repositorysid.inpe.br/sibgrapi/2021/09.06.17.20
Last Update2021:09.06.17.20.44 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.06.17.20.44
Metadata Last Update2022:06.14.00.00.29 (UTC) administrator
DOI10.1109/SIBGRAPI54419.2021.00039
Citation KeyKuhnMore:2021:DaFiCl
TitleBRCars: a Dataset for Fine-Grained Classification of Car Images
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size2622 KiB
2. Context
Author1 Kuhn, Daniel M.
2 Moreira, Viviane P.
Affiliation1 Institute of Informatics - UFRGS 
2 Institute of Informatics - UFRGS
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addressviviane@inf.ufrgs.br
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2021-09-06 17:20:44 :: viviane@inf.ufrgs.br -> administrator ::
2022-03-02 00:54:15 :: administrator -> menottid@gmail.com :: 2021
2022-03-02 13:27:36 :: menottid@gmail.com -> administrator :: 2021
2022-06-14 00:00:29 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsfine-grained computer vision
car model classification
AbstractFine-grained computer vision tasks refer to the ability of distinguishing objects that belong to the same parent class, differentiating themselves by subtle visual elements. Image classification in car models is considered a fine-grained classification task. In this work, we introduce BRCars, a dataset that seeks to replicate the main challenges inherent to the task of classifying car images in many practical applications. BRCars contains around 300K images collected from a Brazilian car advertising website. The images correspond to 52K car instances and are distributed among 427 different models. The images are both from the exterior and the interior of the cars and present an unbalanced distribution across the different models. In addition, they are characterized by a lack of standardization in terms of perspective. We adopted a semi-automated annotation pipeline with the help of the new CLIP neural network, which enabled distinguishing thousands of images among different perspectives using textual queries. Experiments with standard deep learning classifiers were performed to serve as baseline results for future work on this topic. BRCars dataset is available at https://github.com/danimtk/brcars-dataset.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2021 > BRCars: a Dataset...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > BRCars: a Dataset...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45CTUK5
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45CTUK5
Languageen
Target FileSIBGRAPI_2021_cars_classifiction.pdf
User Groupviviane@inf.ufrgs.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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