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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/4AQHJBE
Repositorysid.inpe.br/sibgrapi/2024/02.25.13.05
Last Update2024:02.25.13.05.52 (UTC) amarthur@usp.br
Metadata Repositorysid.inpe.br/sibgrapi/2024/02.25.13.05.52
Metadata Last Update2024:02.25.13.05.52 (UTC) amarthur@usp.br
Citation KeyMagalhãesHira:2023:SpSpCl
TitleSpider Species Classification Using Vision Transformers and Convolutional Neural Networks
FormatOn-line
Year2023
Access Date2024, May 01
Number of Files1
Size653 KiB
2. Context
Author1 Magalhães, Arthur Teixeira
2 Hirata, Nina Sumiko Tomita
Affiliation1 Instituto de Matemática e Estatística - Universidade de São Paulo
2 Instituto de Matemática e Estatística - Universidade de São Paulo
EditorClua, Esteban Walter Gonzalez
Körting, Thales Sehn
Paulovich, Fernando Vieira
Feris, Rogerio
e-Mail Addressamarthur@usp.br
Conference NameConference on Graphics, Patterns and Images, 36 (SIBGRAPI)
Conference LocationRio Grande, RS
DateNov. 06-09, 2023
Book TitleProceedings
Tertiary TypeUndergraduate Work
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsMachine Learning
Computer Vision
Image Classification
Deep Learning
Convolutional Neural Networks
Vision Transformers
AbstractSpiders often seek shelter in the heat and safety of homes and although most of them are harmless, some can represent a real danger. Since differentiating spider species can be a challenge for individuals without prior knowledge, having a method to identify them could be useful in order to avoid potentially venomous ones. To address this question, this project aimed to analyze and compare the performance of convolutional neural networks (CNN) and vision transformers (ViT) regarding the quantitative and qualitative performance in the task of classifying different species of spiders from their images. We utilized publicly available images consisting of 25 Brazilian spider species and around 25,000 images. We selected the models based on their metrics and generalization performance in this classification task. The preliminary results indicated that ConvNeXt emerged as the most proficient among the examined Convolutional Neural Networks, achieving a macro accuracy of 88.5%. As for the Vision Transformers, MaxViT surpassed its counterparts, registering a macro accuracy of 90.1%, and outperformed the models in a direct comparison of their performance metrics. These results may contribute to the development of applications aimed at identifying spiders and providing information of interest about the species.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/4AQHJBE
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/4AQHJBE
Languageen
Target FileSpider_Species_Classification.pdf
User Groupamarthur@usp.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
7. Description control
e-Mail (login)amarthur@usp.br
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