Overview of FungiCLEF 2024: Revisiting Fungi Species Recognition Beyond 0-1 Cost

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43972936" target="_blank" >RIV/49777513:23520/24:43972936 - isvavai.cz</a>

Výsledek na webu

<a href="https://ceur-ws.org/Vol-3740" target="_blank" >https://ceur-ws.org/Vol-3740</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Overview of FungiCLEF 2024: Revisiting Fungi Species Recognition Beyond 0-1 Cost

Popis výsledku v původním jazyce

The third edition of the fungi recognition challenge, FungiCLEF 2024, organized within LifeCLEF, advances the field of mushroom species identification using computer vision and machine learning. Building on the Danish Fungi 2020 dataset and incorporating new data from the CzechFungi app, FungiCLEF 2024 challenges participants to recognize fungi species from images and metadata, focusing on efficient inference and minimalization of edible and poisonous species confusion. The strict limits on computational complexity ensure that the resulting solutions are practical for use in real-world settings with limited computational resources. The competition attracted seven teams, with five outperforming the provided baseline, which was based on the pre-trained EfficientNet-B1 model. This overview paper provides (i) a comprehensive description of the challenge and provided baseline method, (ii) detailed characteristics of the dataset and task specifications, (iii) an examination of the methods employed by contestants, and (iv) a discussion of the competition outcomes. The results highlight incremental advancements in fungi recognition, showcasing innovative approaches and techniques that push the limits of previous work. © 2024 Copyright for this paper by its authors.

Název v anglickém jazyce

Overview of FungiCLEF 2024: Revisiting Fungi Species Recognition Beyond 0-1 Cost

Popis výsledku anglicky

The third edition of the fungi recognition challenge, FungiCLEF 2024, organized within LifeCLEF, advances the field of mushroom species identification using computer vision and machine learning. Building on the Danish Fungi 2020 dataset and incorporating new data from the CzechFungi app, FungiCLEF 2024 challenges participants to recognize fungi species from images and metadata, focusing on efficient inference and minimalization of edible and poisonous species confusion. The strict limits on computational complexity ensure that the resulting solutions are practical for use in real-world settings with limited computational resources. The competition attracted seven teams, with five outperforming the provided baseline, which was based on the pre-trained EfficientNet-B1 model. This overview paper provides (i) a comprehensive description of the challenge and provided baseline method, (ii) detailed characteristics of the dataset and task specifications, (iii) an examination of the methods employed by contestants, and (iv) a discussion of the competition outcomes. The results highlight incremental advancements in fungi recognition, showcasing innovative approaches and techniques that push the limits of previous work. © 2024 Copyright for this paper by its authors.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

CEUR Workshop Proceedings

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

8

Strana od-do

1958-1965

Název nakladatele

CEUR-WS

Místo vydání

neuveden

Místo konání akce

Grenoble, France

Datum konání akce

9. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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