WILDFIRE SMOKE DETECTION USING YOLOv8

Overview

This project leverages YOLOv8 (You Only Look Once) for detecting wildfire smoke in images. The model is trained on a custom dataset and can be used to identify smoke in new images.

Table of Contents

• Installation
• Usage
• Training
• Inference
• Results
• License

Training Configuration

The model training configuration includes the following parameters:

Data Path: data.yaml (path to the dataset configuration file)

• Epochs: 50
• Image Size: 640
• Batch Size: 16
• Optimizer: Adam
• Learning Rate: 0.001
• Weight Decay: 0.0005

To set up this project, follow these steps

1. Clone the Repository

git clone https://github.com/yourusername/Wildfire-Smoke-Detection-using-Yolov8.git
cd wildfire-smoke-detection

2. Set Up the Environment:

pip install ultralytics

3. Mount Google Drive (if using Google Colab):

from google.colab import drive
drive.mount('/content/drive')