Open AccessBettaNet: A Deep Learning Architecture for Classification of Wild Siamese Betta Species
Author(s) -
Voravarun Pattana-Anake,
Pimsiri Danphitsanuparn,
Ferdin Joe John Joseph
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1055/1/012104
Subject(s) - artificial intelligence , architecture , fish <actinopterygii> , machine learning , residual neural network , computer science , deep learning , biology , fishery , geography , archaeology
Fish classification is a mix of animal sciences and artificial intelligence. With the advent of machine learning in artificial intelligence, classification has been done using computer vision algorithms and now deep learning is gaining prominence. Betta fish classification is not much explored. The wild species of Betta Splendens which are native to the Kingdom of Thailand are taken in the research reported in this paper. BettaNet architecture, a modified version of ResNet 152 is used to classify 6 species of wild species of betta. The experimental results show that the proposed BettaNet architecture holds better in performance in terms of accuracy and F1-scores. Two different datasets were used and the performance obtained by the proposed architecture reduced the cross-entropy loss over different experimental configurations.