Overview of FungiCLEF 2023: Fungi Recognition Beyond 1/0 Cost

dc.contributor.authorPicek, Lukáš
dc.contributor.authorŠulc, Milan
dc.contributor.authorChamidullin, Rail
dc.contributor.authorMatas, Jiří
dc.date.accessioned2025-06-20T08:44:04Z
dc.date.available2025-06-20T08:44:04Z
dc.date.issued2023
dc.date.updated2025-06-20T08:44:04Z
dc.description.abstractComputer vision systems for fungi recognition aid mycologists, researchers, and enthusiasts in the efficient identification of mushroom species. FungiCLEF 2023, the second edition of the fungi recognition challenge at LifeCLEF, builds upon the Danish Fungi 2020 dataset and upon its predecessor by presenting several recognition tasks differing in the cost functions corresponding to different practical scenarios, including poisonous/edible decision-making or discovering unseen species. With practical applications in mind, the 2023 challenge only accepted submissions with model size under 1GB. The competition received 16 final submissions from 3 teams. This overview paper provides a detailed description of the challenge data and tasks, a review of the submitted methods, and a discussion of the results.en
dc.format11
dc.identifier.isbnneuvedeno
dc.identifier.issn1613-0073
dc.identifier.obd43940700
dc.identifier.orcidPicek, Lukáš 0000-0002-6041-9722
dc.identifier.orcidChamidullin, Rail 0000-0003-1728-8939
dc.identifier.urihttp://hdl.handle.net/11025/60819
dc.language.isoen
dc.project.IDSGS-2022-017
dc.project.IDSS05010008
dc.publisherCEUR-WS
dc.relation.ispartofseries24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023
dc.subjectclassificationen
dc.subjectcomputer visionen
dc.subjectfine grained visual categorizationen
dc.subjectfungien
dc.subjectFungiCLEFen
dc.subjectLifeCLEFen
dc.subjectmachine learningen
dc.subjectmetadataen
dc.subjectopen-set recognitionen
dc.subjectspecies identificationen
dc.titleOverview of FungiCLEF 2023: Fungi Recognition Beyond 1/0 Costen
dc.typeStať ve sborníku (D)
dc.typeSTAŤ VE SBORNÍKU
dc.type.statusPublished Version
local.files.count1*
local.files.size7354136*
local.has.filesyes*
local.identifier.eid2-s2.0-85175659725

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