Szczegóły publikacji
Opis bibliograficzny
Canine age classification using Deep Learning as a step toward preventive medicine in animals / Szymon MAZUREK, Maciej WIELGOSZ, Jakub CAPUTA, Rafał FRĄCZEK, Michał KARWATOWSKI, Jakub GRZESZCZYK, Daria ŁUKASIK, Anna Śmiech, Paweł RUSSEK, Ernest JAMRO, Agnieszka DĄBROWSKA-BORUCH, Marcin PIETROŃ, Sebastian KORYCIAK, Kazimierz WIATR // W: FedCSIS 2022 [Dokument elektroniczny] : proceedings of the 17th conference on Computer Science and Intelligence Systems : September 4–7, 2022, Sofia, Bulgaria / eds. Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki, Dominik Ślęzak. — Wersja do Windows. — Dane tekstowe. — Warszawa : Polskie Towarzystwo Informatyczne ; [Piscataway] : IEEE, cop. 2022. — (Annals of Computer Science and Information Systems ; ISSN 2300-5963 ; vol. 30). — Dod. ISBN 978-83-965897-1-2 (ART), ISBN 978-83-965897-0-5 (USB). — e-ISBN: 978-83-962423-9-6. — S. 169–172. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://annals-csis.org/Volume_30/pliks/fedcsis.pdf [2022-10-06]. — Bibliogr. s. 172, Abstr. — Sz. Mazurek, M. Wielgosz, R. Frączek, M. Karwatowski, P. Russek, E. Jamro, A. Dąbrowska-Boruch, M. Pietroń, S. Koryciak, K. Wiatr - dod. afiliacja: CYFRONET AGH
Autorzy (14)
Dane bibliometryczne
| ID BaDAP | 142911 |
|---|---|
| Data dodania do BaDAP | 2022-10-27 |
| DOI | 10.15439/2022F226 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | 2022 17th Conference on Computer Science and Intelligence Systems |
| Czasopismo/seria | Annals of Computer Science and Information Systems |
Abstract
The main goal of this work was to implement a reliable machine learning algorithm that can classify the age of a dog, given only a photograph of its face. The problem, which seems simple for humans, presents itself as very difficult for the machine learning algorithms due to differences of facial features among the dogs population. As convolutional neural networks (CNNs) performed poorly in this problem, authors took other approach of creating novel architecture consisting of combination of CNN and vision transformer (ViT) and examining the age of the dogs separately for every breed. Authors were able to achieve better results than those in initial works covering the problem.