Evaluating Deep Learning Models for African Wildlife Image
  Classification: From DenseNet to Vision Transformers
Authors: name: Lukman Jibril Aliyu, name: Umar Sani Muhammad, name: Bilqisu Ismail, name: Nasiru Muhammad, name: Almustapha A Wakili, name: Seid Muhie Yimam, name: Shamsuddeen Hassan Muhammad, name: Mustapha Abdullahi

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.