Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers

• URL: http://arxiv.org/abs/2507.21364v1
• Date: Mon, 28 Jul 2025 22:18:13 GMT
• Title: Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers
• Authors: Lukman Jibril Aliyu, Umar Sani Muhammad, Bilqisu Ismail, Nasiru Muhammad, Almustapha A Wakili, Seid Muhie Yimam, Shamsuddeen Hassan Muhammad, Mustapha Abdullahi,
• Abstract summary: Wildlife populations in Africa face severe threats, with vertebrate numbers declining by over 65% in the past five decades.<n>In response, image classification using deep learning has emerged as a promising tool for biodiversity monitoring and conservation.<n>This paper presents a comparative study of deep learning models for automatically classifying African wildlife images.
• Score: 3.4801331938495705
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wildlife populations in Africa face severe threats, with vertebrate numbers declining by over 65% in the past five decades. In response, image classification using deep learning has emerged as a promising tool for biodiversity monitoring and conservation. This paper presents a comparative study of deep learning models for automatically classifying African wildlife images, focusing on transfer learning with frozen feature extractors. Using a public dataset of four species: buffalo, elephant, rhinoceros, and zebra; we evaluate the performance of DenseNet-201, ResNet-152, EfficientNet-B4, and Vision Transformer ViT-H/14. DenseNet-201 achieved the best performance among convolutional networks (67% accuracy), while ViT-H/14 achieved the highest overall accuracy (99%), but with significantly higher computational cost, raising deployment concerns. Our experiments highlight the trade-offs between accuracy, resource requirements, and deployability. The best-performing CNN (DenseNet-201) was integrated into a Hugging Face Gradio Space for real-time field use, demonstrating the feasibility of deploying lightweight models in conservation settings. This work contributes to African-grounded AI research by offering practical insights into model selection, dataset preparation, and responsible deployment of deep learning tools for wildlife conservation.

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