SnakeFace: a transfer learning based app for snake classification

Abstract

Introduction: deep learning emerged in 2012 as one of the most important machine learning technologies, reducing image identification error from 25% to 5%. This article has two goals: 1) to demonstrate to the general public the ease of building state-of-the-art machine learning models without coding expertise; 2) to present a basic model adaptable to any biological image identification, such as species identification. Method: We present three test concept models that showcase distinct perspectives of the app. The models aim at separating images into classes such as genus, species, and subspecies, and the input image can be easily adapted for different cases. We have applied deep learning and transfer learning using Teachable Machine. Results: Our basic models demonstrate high accuracy in identifying different species based on images, highlighting the potential for this method to be applied in biology. Discussions: the presented models showcase the ease of using machine learning nowadays for image identification. Furthermore, the adaptability of this method to various species and genuses emphasizes its importance in the biological fields, as root for inspiring collaborations with computer science. Future collaborations could lead to increasingly accurate and efficient models in this arena using well-curated datasets.