SnakeFace: a transfer learning based app for snake classification

Abstract

Introduction: deep learning emerged in 2012 as one of themost important machine learning technologies, reducing image identification error from25% to 5%. This article has two goals: 1) to demonstrate to the general public the ease of building state-of-the-art machine learningmodels without coding expertise; 2) to present a basicmodel adaptable to any biological image identification, such as species identification. Method: We present three test-of-conceptmodels thatshowcase distinct perspectives of the app. Themodels aim at separating images into classes such as genus, species, and subspecies, and the input images can be easily adapted for different cases. We have applied deep learning and transfer learning using TeachableMachine. Results: Our basicmodels demonstrate high accuracy in identifying different speciesbased on images, highlighting the potential for thismethod to be applied in biology. Discussions: the presentedmodels 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. On our, future collaborations could lead to increasingly accurate and efficientmodels in this arena using well-curated datasets.