Overview of FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problem
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F22%3A43966114" target="_blank" >RIV/49777513:23520/22:43966114 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/68407700:21230/22:00362993
Výsledek na webu
<a href="https://ceur-ws.org/Vol-3180/paper-157.pdf" target="_blank" >https://ceur-ws.org/Vol-3180/paper-157.pdf</a>
DOI - Digital Object Identifier
—
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Overview of FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problem
Popis výsledku v původním jazyce
The main goal of the new LifeCLEF challenge, FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problem, was to provide an evaluation ground for end-to-end fungi species recognition in an open class set scenario. An AI-based fungi species recognition system deployed in the Atlas of Danish Fungi helps mycologists to collect valuable data and allows users to learn about fungi species identification. Advances in fungi recognition from images and metadata will allow continuous improvement of the system deployed in this citizen science project. The training set is based on the Danish Fungi 2020 dataset and contains 295,938 photographs of 1,604 species. For testing, we provided a collection of 59,420 expert-approved observations collected in 2021. The test set includes 1,165 species from the training set and 1,969 unknown species, leading to an open-set recognition problem. This paper provides (i) a description of the challenge task and datasets, (ii) a summary of the evaluation methodology, (iii) a review of the systems submitted by the participating teams, and (iv) a discussion of the challenge results. © 2022 Copyright for this paper by its authors.
Název v anglickém jazyce
Overview of FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problem
Popis výsledku anglicky
The main goal of the new LifeCLEF challenge, FungiCLEF 2022: Fungi Recognition as an Open Set Classification Problem, was to provide an evaluation ground for end-to-end fungi species recognition in an open class set scenario. An AI-based fungi species recognition system deployed in the Atlas of Danish Fungi helps mycologists to collect valuable data and allows users to learn about fungi species identification. Advances in fungi recognition from images and metadata will allow continuous improvement of the system deployed in this citizen science project. The training set is based on the Danish Fungi 2020 dataset and contains 295,938 photographs of 1,604 species. For testing, we provided a collection of 59,420 expert-approved observations collected in 2021. The test set includes 1,165 species from the training set and 1,969 unknown species, leading to an open-set recognition problem. This paper provides (i) a description of the challenge task and datasets, (ii) a summary of the evaluation methodology, (iii) a review of the systems submitted by the participating teams, and (iv) a discussion of the challenge results. © 2022 Copyright for this paper by its authors.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
20205 - Automation and control systems
Návaznosti výsledku
Projekt
<a href="/cs/project/SS05010008" target="_blank" >SS05010008: Detekce, identifikace a monitoring živočichů pokročilými metodami počítačového vidění</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum
ISBN
—
ISSN
1613-0073
e-ISSN
—
Počet stran výsledku
12
Strana od-do
1970-1981
Název nakladatele
CEUR-WS
Místo vydání
Bologna
Místo konání akce
Bologna, Italy
Datum konání akce
5. 9. 2022
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—