Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+language:
+- en
+metrics:
+- precision
+- recall
+pipeline_tag: object-detection
+tags:
+- wild
+- life
+- animals
+- object
+- detection
+---
+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.