Comparative Performance of MobileNet V1, MobileNet V2, and EfficientNet B0 for Endangered Species Classification

Congyuan Tan

2025

Abstract

In recent years, deep learning techniques have proven to be effective tools for identifying and classifying endangered species, providing essential data for conservation efforts. Convolutional neural networks (CNNs) have become a popular choice for such tasks due to their ability to automatically extract meaningful features from image data. This study compares the performance of three models—MobileNet V1, MobileNet V2, and EfficientNet B0—in classifying endangered species using a dataset of 250 images from five species: Jaguar, Black-faced Black Spider Monkey, Giant Otter, Blue-headed Macaw, and Hyacinth Macaw. These models were evaluated based on key metrics, including accuracy, precision, recall, and F1 score. The results showed that EfficientNet B0 outperformed both MobileNet V1 and MobileNet V2 across all metrics, demonstrating its suitability for tasks involving complex species classification. Additionally, this study highlights the impact of architectural differences on classification performance, providing insights into the practical application potential of these models in wildlife monitoring.

Paper Citation

in Harvard Style

Tan C. (2025). Comparative Performance of MobileNet V1, MobileNet V2, and EfficientNet B0 for Endangered Species Classification. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 67-71. DOI: 10.5220/0013678200004670

in Bibtex Style

@conference{icdse25,
author={Congyuan Tan},
title={Comparative Performance of MobileNet V1, MobileNet V2, and EfficientNet B0 for Endangered Species Classification},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={67-71},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013678200004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Comparative Performance of MobileNet V1, MobileNet V2, and EfficientNet B0 for Endangered Species Classification
SN - 978-989-758-765-8
AU - Tan C.
PY - 2025
SP - 67
EP - 71
DO - 10.5220/0013678200004670
PB - SciTePress