Comparative Analysis of VGG16 and EfficientNet for Image-Based Cat Breed Classification

Hao Xu

2024

Abstract

Nowadays, Convolutional Neural Network (CNN) architectures are widely used to distinguish animal species. For example, they are used to differentiate between various types of sheep, dogs, fish, and so on. This greatly assists people in identifying their species and assessing their value. After all, it is challenging for individuals to differentiate these animals' species without extensive relevant experience and expertise. Although Deoxyribonucleic Acid (DNA) testing can be used for identification, it is time-consuming and costly, making it impractical. Utilizing machine learning methods for differentiation saves a significant amount of time and effort. However, different CNN architectures have distinct focuses and functionalities. This study compares the differences between Visual Geometry Group (VGG)16 and EfficientNetB0 by classifying cat breeds. The primary method is to train models using these two CNNs and then compare their performance, focusing on their accuracy, computational efficiency, and generalization capabilities. This study reveals the strengths and weaknesses of these two models, enabling you to understand which neural network is more suitable for use.

Paper Citation

in Harvard Style

Xu H. (2024). Comparative Analysis of VGG16 and EfficientNet for Image-Based Cat Breed Classification. In Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM; ISBN 978-989-758-738-2, SciTePress, pages 238-242. DOI: 10.5220/0013297100004558

in Bibtex Style

@conference{mlscm24,
author={Hao Xu},
title={Comparative Analysis of VGG16 and EfficientNet for Image-Based Cat Breed Classification},
booktitle={Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM},
year={2024},
pages={238-242},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013297100004558},
isbn={978-989-758-738-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM
TI - Comparative Analysis of VGG16 and EfficientNet for Image-Based Cat Breed Classification
SN - 978-989-758-738-2
AU - Xu H.
PY - 2024
SP - 238
EP - 242
DO - 10.5220/0013297100004558
PB - SciTePress