Evaluating Deep Learning Models for African Wildlife Image
  Classification: From DenseNet to Vision Transformers
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.
6889808631e1218a089281a2
Lukman Jibril Aliyu
claimed_verified
2025-07-30T09:04:32.997Z
64e88f2ff2f5545edaff5083
https://cdn-avatars.huggingface.co/v1/production/uploads/64e88f2ff2f5545edaff5083/ckZTNArwRsH5RfJksL-YJ.jpeg
Lukman Jibril Aliyu
user
lukmanaj
6889808631e1218a089281a3
Umar Sani Muhammad
6889808631e1218a089281a4
Bilqisu Ismail
6889808631e1218a089281a5
Nasiru Muhammad
6889808631e1218a089281a6
Almustapha A Wakili
6889808631e1218a089281a7
Seid Muhie Yimam
6889808631e1218a089281a8
Shamsuddeen Hassan Muhammad
6889808631e1218a089281a9
Mustapha Abdullahi
Evaluating Deep Learning Models for African Wildlife Image
  Classification: From DenseNet to Vision Transformers
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.
A comparative study evaluates deep learning models for African wildlife image classification, highlighting trade-offs between accuracy, resource requirements, and deployability, with DenseNet-201 and ViT-H/14 performing best among convolutional networks and transformers, respectively.
transfer learning frozen feature extractors DenseNet-201 ResNet-152 EfficientNet-B4 Vision Transformer ViT-H/14 convolutional networks computational cost deployability Hugging Face Gradio Space