Cat Breeds Classification Using Compound Model Scaling Convolutional Neural Networks.
- 10.2991/assehr.k.220301.150How to use a DOI?
- Deep Learning; Convolutional Neural Networks; Cat Breeds; EfficientNet-B0
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%.
- © 2022 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
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 -