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

Lukman Jibril Aliyu; Umar Sani Muhammad; Bilqisu Ismail; Nasiru Muhammad; Almustapha A Wakili; Seid Muhie Yimam; Shamsuddeen Hassan Muhammad; Mustapha Abdullahi

arXiv · 2507.21364

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

Lukman Jibril Aliyu·Umar Sani Muhammad·Bilqisu Ismail·Nasiru Muhammad·Almustapha A Wakili·Seid Muhie Yimam·Shamsuddeen Hassan Muhammad·Mustapha Abdullahi

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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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Lukman Jibril Aliyu, Umar Sani Muhammad, Bilqisu Ismail, Nasiru Muhammad, Almustapha A Wakili, Seid Muhie Yimam, Shamsuddeen Hassan Muhammad, Mustapha Abdullahi. 2025-07-28. Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers. https://arxiv.org/abs/2507.21364

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