Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913964276842496 |
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| author | Aliyu, Lukman Jibril Muhammad, Umar Sani Ismail, Bilqisu Muhammad, Nasiru Wakili, Almustapha A Yimam, Seid Muhie Muhammad, Shamsuddeen Hassan Abdullahi, Mustapha |
| author_facet | Aliyu, Lukman Jibril Muhammad, Umar Sani Ismail, Bilqisu Muhammad, Nasiru Wakili, Almustapha A Yimam, Seid Muhie Muhammad, Shamsuddeen Hassan Abdullahi, Mustapha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21364 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers Aliyu, Lukman Jibril Muhammad, Umar Sani Ismail, Bilqisu Muhammad, Nasiru Wakili, Almustapha A Yimam, Seid Muhie Muhammad, Shamsuddeen Hassan Abdullahi, Mustapha Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2507.21364 |