YOLOv8 Object Detection

This project implements real-time object detection using the YOLOv8 model from Ultralytics and OpenCV. The script captures video from a webcam, processes frames using YOLOv8, and displays detected objects with bounding boxes and labels.

Features

• Uses YOLOv8m for object detection.
• Displays bounding boxes with class labels and confidence scores.
• Real-time FPS display.
• Supports CUDA (GPU) if available.
• Custom colors for different object classes.

Requirements

Make sure you have the following dependencies installed:

pip install torch numpy opencv-python ultralytics supervision

Usage

1. Clone the Repository (if applicable):

   git clone https://github.com/yourusername/yolo-object-detection.git
   cd yolo-object-detection

2. Run the Script:

   python detection.py

3. Press ESC to Exit.

File Structure

project-folder/
│── detection.py      # Main script for object detection
│── README.md         # Documentation file

How It Works

1. Initialize Model: Loads YOLOv8m and sets up device (CPU/GPU).
2. Capture Frames: Reads frames from the webcam.
3. Run Predictions: Detects objects using YOLO.
4. Draw Bounding Boxes: Uses OpenCV to annotate frames.
5. Show Output: Displays annotated video with FPS.

Troubleshooting

• Ensure your webcam is working properly.
• If running on GPU, make sure CUDA is installed and compatible.
• If supervision module causes errors, update it:

  pip install --upgrade supervision

Acknowledgments

• Ultralytics YOLOv8
• OpenCV

License

This project is open-source and available under the MIT License.