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dc.contributor.authorChamidullin, Rail
dc.contributor.authorŠulc, Milan
dc.contributor.authorMatas, Jiří
dc.contributor.authorPicek, Lukáš
dc.date.accessioned2022-03-28T10:00:30Z-
dc.date.available2022-03-28T10:00:30Z-
dc.date.issued2021
dc.identifier.citationCHAMIDULLIN, R. ŠULC, M. MATAS, J. PICEK, L. A deep learning method for visual recognition of snake species. In Proceedings of the Working Notes of CLEF 2021 - Conference and Labs of the Evaluation Forum. neuvedeno: CEUR-WS, 2021. s. 1512-1525. ISBN: neuvedeno , ISSN: 1613-0073cs
dc.identifier.isbnneuvedeno
dc.identifier.issn1613-0073
dc.identifier.uri2-s2.0-85113481716
dc.identifier.urihttp://hdl.handle.net/11025/47277
dc.format14 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherCEUR-WSen
dc.relation.ispartofseriesProceedings of the Working Notes of CLEF 2021 - Conference and Labs of the Evaluation Forumen
dc.rights© authorsen
dc.titleA deep learning method for visual recognition of snake speciesen
dc.typekonferenční příspěvekcs
dc.typeConferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe paper presents a method for image-based snake species identification. The proposed method is based on deep residual neural networks - ResNeSt, ResNeXt and ResNet - fine-tuned from ImageNet pre-trained checkpoints. We achieve performance improvements by: discarding predictions of species that do not occur in the country of the query; combining predictions from an ensemble of classifiers; and applying mixed precision training, which allows training neural networks with larger batch size. We experimented with loss functions inspired by the considered metrics: soft F1 loss and weighted cross entropy loss. However, the standard cross entropy loss achieved superior results both in accuracy and in F1 measures. The proposed method scored third in the SnakeCLEF 2021 challenge, achieving 91.6% classification accuracy, Country F1 Score of 0.860, and F1 Score of 0.830.en
dc.subject.translatedSnake species identificationen
dc.subject.translatedFine-grained classificationen
dc.subject.translatedComputer visionen
dc.subject.translatedConvolutional neural networksen
dc.subject.translatedDeep learningen
dc.type.statusPeer-revieweden
dc.identifier.obd43933885
dc.project.IDSGS-2019-027/Inteligentní metody strojového vnímání a porozumění 4cs
Vyskytuje se v kolekcích:Konferenční příspěvky / Conference Papers (KKY)
OBD

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