International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Snake Species Classification Using ConvNeXtXLarge

Author(s) Chandrashekar N, Jayavrinda Vrindavanam, Pradeep Kumar
Country India
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.
Keywords Deep learning, ConvNeXtXLarge, Image processing, LLM, transfer learning
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-03-10
Cite This Snake Species Classification Using ConvNeXtXLarge - Chandrashekar N, Jayavrinda Vrindavanam, Pradeep Kumar - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.14841
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.14841
Short DOI https://doi.org/gtmztf
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E-ISSN 2582-2160
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