mariam-madata/serengeti-wildlife-classifier

African wildlife species classification using EfficientNet transfer learning — 96.3% accuracy on buffalo, elephant, rhino, zebra # 🦁 Serengeti African Wildlife Classifier **Deep learning model for automated wildlife species identification — Serengeti National Park, Tanzania** ## 📊 Results | Metric | Score | |---|---| | Test Accuracy | **96.3%** | | Macro F1 Score | **0.96** | | Training Time | ~4 minutes | ## Per-Class Performance | Species | Precision | Recall | F1 | |---|---|---|---| | 🐃 Buffalo | 0.97 | 0.92 | 0.95 | | 🐘 Elephant | 0.94 | 0.99 | 0.97 | | 🦏 Rhino | 0.95 | 0.96 | 0.95 | | 🦓 Zebra | 1.00 | 0.98 | 0.99 | > Zebra achieved perfect precision (1.00) ## 🧠 Model - EfficientNet-B0 Transfer Learning - PyTorch framework - 1,504 camera trap images ## 🌍 Impact Tanzania generates $2.5B annually from wildlife tourism. This tool supports anti-poaching and conservation monitoring. ## 👤 Author **Mariam Khamis Madata** — Dar es Salaam, Tanzania Portfolio · GitHub · LinkedIn ## 🔗 Related Projects Diabetic Retinopathy Detection — Medical AI · 83% accuracy