Wildlife Detection
Author: Darshan Modi
This model is a high‑performance object detection system trained on a curated dataset of African wildlife, including:
- Buffalo
- Elephant
- Rhinoceros
- Zebra
The dataset contains diverse lighting conditions, camera angles, and natural environments, making the model suitable for wildlife monitoring, conservation research, anti‑poaching systems, and ecological analytics.
🧠 Model Overview
This ONNX model is optimized for real‑time inference on edge devices and cloud environments. It is built using the Ultralytics YOLO architecture and exported for ONNX runtime compatibility.
Key Features
- Detects multiple African wildlife species
- Lightweight and fast inference
- Suitable for Raspberry Pi, Jetson Nano, cloud servers, and desktop GPUs
- Ideal for conservation AI, camera‑trap automation, and wildlife analytics
📊 Performance Metrics
Evaluated on a held‑out validation set from the same dataset:
- Precision: 93%
- Recall: 88%
- mAP50: 94%
These metrics indicate strong detection accuracy with low false positives and reliable species identification.
📦 Intended Use
This model is designed for:
- Wildlife conservation projects
- Camera‑trap automation
- Animal population monitoring
- Anti‑poaching surveillance
- Ecological research
- Real‑time detection on edge devices
⚙️ Technical Details
- Format: ONNX
- Input size: 640×640
- Architecture: YOLO‑based detector
- Classes: Buffalo, Elephant, Rhino, Zebra
- Training epochs: 30
👤 Author
Created by: Darshan Modi
Focused on building practical AI systems for wildlife monitoring, safety applications, and real‑world deployment on edge devices.
