Using Deep Convolutional Neural Networks with Transfer Learning for Reptile and Amphibian Classification

Abstract

This paper presents a new approach to amphibian and reptile automated classification utilizing deep convolutional
neural networks (CNNs) and supervised learning. Drawing on our knowledge of the ecological importance of these
two vertebrate groups and the limitations of traditional classification methods, we use deep learning to train a
MobileNetV2 model that can accurately identify species. We use transfer learning to optimize a pre-trained CNN on
a massive collection of reptile and amphibian pictures, allowing us to overcome the limitations of small-scale
datasets. Because of its excellent extraction efficiency, the model is able to generalize well to many other species. In
addition, the study delves into the significance of image augmentation techniques for enhancing model performance,
particularly in cases when labeled data is few.