Full metadata record
DC pole | Hodnota | Jazyk |
---|---|---|
dc.contributor.author | Picek, Lukáš | |
dc.contributor.author | Durso, Andrew M. | |
dc.contributor.author | Bolon, Isabelle | |
dc.contributor.author | de Castañeda, Rafael Ruiz | |
dc.date.accessioned | 2022-03-28T10:00:30Z | - |
dc.date.available | 2022-03-28T10:00:30Z | - |
dc.date.issued | 2021 | |
dc.identifier.citation | PICEK, L. DURSO, AM. BOLON, I. DE CASTAÑEDA, RR. Overview of SnakeCLEF 2021: Automatic snake species identification with country-level focus. In Proceedings of the Working Notes of CLEF 2021 - Conference and Labs of the Evaluation Forum. Bucharest: CEUR-WS, 2021. s. 1463-1476. ISBN: neuvedeno , ISSN: 1613-0073 | cs |
dc.identifier.isbn | neuvedeno | |
dc.identifier.issn | 1613-0073 | |
dc.identifier.uri | 2-s2.0-85113525880 | |
dc.identifier.uri | http://hdl.handle.net/11025/47274 | |
dc.format | 14 s. | cs |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en |
dc.publisher | CEUR-WS | en |
dc.relation.ispartofseries | Proceedings of the Working Notes of CLEF 2021 - Conference and Labs of the Evaluation Forum | en |
dc.rights | © authors | en |
dc.title | Overview of SnakeCLEF 2021: Automatic snake species identification with country-level focus | en |
dc.type | konferenční příspěvek | cs |
dc.type | ConferenceObject | en |
dc.rights.access | openAccess | en |
dc.type.version | publishedVersion | en |
dc.description.abstract-translated | A robust and accurate AI-driven system as an assistance tool for snake species identification has vast potential to help lower deaths and disabilities caused by snakebites. With that in mind, we prepared the SnakeCLEF 2021: Automatic Snake Species Identification Challenge with Country-Level Focus, designed to provide an evaluation platform that can help track the performance of end-to-end AI-driven snake species recognition systems with a focus on overall country-wise performance. We have provided 386,006 photographs of 772 snake species collected in 188 countries and country-species presence mapping for the challenge. In this paper, we report 1) a description of the provided data, 2) evaluation methodology and principles, 3) an overview of the systems submitted by the participating teams, and 4) a discussion of the obtained results. | en |
dc.subject.translated | Biodiversity | en |
dc.subject.translated | Birds | en |
dc.subject.translated | Conservation | en |
dc.subject.translated | Geographical distribution | en |
dc.subject.translated | Historic preservation | en |
dc.subject.translated | Machine learning | en |
dc.subject.translated | Remote sensing | en |
dc.type.status | Peer-reviewed | en |
dc.identifier.obd | 43933882 | |
dc.project.ID | SGS-2019-027/Inteligentní metody strojového vnímání a porozumění 4 | cs |
Vyskytuje se v kolekcích: | Konferenční příspěvky / Conference Papers (KKY) OBD |
Soubory připojené k záznamu:
Soubor | Velikost | Formát | |
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paper-125.pdf | 12,17 MB | Adobe PDF | Zobrazit/otevřít |
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http://hdl.handle.net/11025/47274
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