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DC poleHodnotaJazyk
dc.contributor.authorPicek, Lukáš
dc.contributor.authorHrúz, Marek
dc.contributor.authorDurso, Andrew M.
dc.contributor.authorBolon, Isabelle
dc.date.accessioned2023-02-13T11:00:21Z-
dc.date.available2023-02-13T11:00:21Z-
dc.date.issued2022
dc.identifier.citationPICEK, L. HRÚZ, M. DURSO, AM. BOLON, I. Overview of SnakeCLEF 2022: Automated Snake Species Identification on a Global Scale. In Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum. Bologna: CEUR-WS, 2022. s. 1957-1969. ISBN: neuvedeno , ISSN: 1613-0073cs
dc.identifier.isbnneuvedeno
dc.identifier.issn1613-0073
dc.identifier.uri2-s2.0-85136952391
dc.identifier.urihttp://hdl.handle.net/11025/51465
dc.format13 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherCEUR-WSen
dc.relation.ispartofseriesProceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forumen
dc.rights© authorsen
dc.titleOverview of SnakeCLEF 2022: Automated Snake Species Identification on a Global Scaleen
dc.typekonferenční příspěvekcs
dc.typeConferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe main goal of the third year of the SnakeCLEF challenge was to provide an evaluation platform that helps track the performance of AI-driven methods for snake species recognition systems on a global scale and allows direct comparison with human experts. We ran two challenges separately for humans — experts and novices — and AI methods in order to lay the groundwork for future comparison between human and machine-based snake species identification. We have provided 187,129 snake observations with 318,532 photographs — 270,251 for training and 48,281 for testing — of 1,572 snake species collected in 208 countries. The human performance evaluation was conducted on a tailored subset with 150 images derived from the full test set. We report (i) a description of the provided data, (ii) evaluation methodology and principles, (iii) an overview of the methods submitted by the participating teams, and (iv) a discussion of the obtained results. © 2022 Copyright for this paper by its authors.en
dc.subject.translatedbenchmarken
dc.subject.translatedbiodiversityen
dc.subject.translatedclassificationen
dc.subject.translatedcomputer visionen
dc.subject.translatedepidemiologyen
dc.subject.translatedfine grained visual categorizationen
dc.subject.translatedglobal healthen
dc.subject.translatedLifeCLEFen
dc.subject.translatedmachine learningen
dc.subject.translatedreptileen
dc.subject.translatedsnakeen
dc.subject.translatedsnake biteen
dc.subject.translatedSnakeCLEFen
dc.subject.translatedspecies identificationen
dc.type.statusPeer-revieweden
dc.identifier.obd43937113
dc.project.IDSS05010008/Detekce, identifikace a monitoring živočichů pokročilými metodami počítačového viděnícs
dc.project.IDSGS-2022-017/Inteligentní metody strojového vnímání a porozumění 5cs
dc.project.IDLM2018101/LINDAT/CLARIAH-CZ – Digitální výzkumná infrastruktura pro jazykové technologie, umění a humanitní vědycs
Vyskytuje se v kolekcích:Konferenční příspěvky / Conference papers (NTIS)
Konferenční příspěvky / Conference Papers (KKY)
OBD

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