Cat Breeds Classification Using Compound Model Scaling Convolutional Neural Networks.

DOI

10.2991/assehr.k.220301.150How to use a DOI?

Keywords

Deep Learning; Convolutional Neural Networks; Cat Breeds; EfficientNet-B0

Abstract

Cats are one of the most popular animals in the world. Many cat breeds in the world are only about 1%. Therefore, most are dominated by mixed cats or domestic cats. Nevertheless, there are so many different types of cat breeds in the world that it is sometimes difficult to identify them. Therefore, we need a system that can recognize and classify the types of cat breeds automatically. In this study, we used one of the deep learning methods that can recognize and classify an object, a Convolutional Neural Networks (CNN). The EfficientNet-B0 architecture was used as a model to extract image features automatically. The collection of nine different cat breeds containing 2700 images was used as a working dataset fed into the EfficientNet-B0 architecture. Based on the experiments, the system succeeds in classify cat breeds images, and the best model has achieved classification accuracy of 95%.

Copyright

© 2022 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Tita Karlita
AU  - Nadia Azahro Choirunisa
AU  - Rengga Asmara
AU  - Fitri Setyorini
PY  - 2022
DA  - 2022/03/04
TI  - Cat Breeds Classification Using Compound Model Scaling Convolutional Neural Networks.
BT  - Proceedings of the International Conference on Applied Science and Technology on Social Science 2021 (iCAST-SS 2021)
PB  - Atlantis Press
SP  - 909
EP  - 914
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.220301.150
DO  - 10.2991/assehr.k.220301.150
ID  - Karlita2022
ER  -