Snake Species Classification Using ConvNeXtXLarge

Abstract

In this paper we are studying present a novel approach to snake species classification, utilizing ConvNeXtXL, a cutting-edge convolutional neural network architecture, and integrating a Language Model like GPT-3.5 for detailed information retrieval. The methodology involves collecting a diverse dataset of snake images, preprocessing them for uniformity, and fine-tuning the ConvNeXtXL model to classify 80 different snake species efficiently. Additionally, GPT-3.5 generates informative textual descriptions about the classified images, enriching the dataset with contextual knowledge about snake behavior and ecology. To make the classification system accessible, a web application is developed using Streamlit, enabling users to upload snake images and receive both visual classification results and textual descriptions. This combined approach enhances species identification, facilitates research and conservation efforts, and promotes public engagement in snake biodiversity and conservation.