Fine-Grained Butterfly Image Classification Based on the ResNet-50 Model and Deep Learning

Yuchen Cao

2024

Abstract

Accurate identification of butterfly species is essential for biodiversity conservation and ecological research. Traditionally, classification methods depend on manual expertise, which can be both inefficient and subjective. Recent advancements in deep learning present an automated, image-based classification approach as a promising alternative. Despite this, fine-grained butterfly classification remains relatively underexplored, contending with challenges such as minimal inter-species differences and considerable intra-species variability. This study endeavors to establish a robust butterfly classification system utilizing ResNet-50, a convolutional neural network (CNN). The approach involved training and testing with an extensive dataset of butterfly images, complemented by data augmentation techniques to enhance generalization. The findings indicate substantial efficacy, with a validation accuracy of 0.9227 and a validation loss of 0.3213, underscoring the method's effectiveness in automated species identification.

Paper Citation

in Harvard Style

Cao Y. (2024). Fine-Grained Butterfly Image Classification Based on the ResNet-50 Model and Deep Learning. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 159-164. DOI: 10.5220/0013511900004619

in Bibtex Style

@conference{daml24,
author={Yuchen Cao},
title={Fine-Grained Butterfly Image Classification Based on the ResNet-50 Model and Deep Learning},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={159-164},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013511900004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Fine-Grained Butterfly Image Classification Based on the ResNet-50 Model and Deep Learning
SN - 978-989-758-754-2
AU - Cao Y.
PY - 2024
SP - 159
EP - 164
DO - 10.5220/0013511900004619
PB - SciTePress