Image Object Detection with YOLOv8

This project demonstrates object detection using the YOLOv8 model within a Flask web application. Users can upload an image, and the application will process it to detect and highlight objects using YOLOv8.

Features

• Real-time object detection using YOLOv8.
• User-friendly web interface for image uploading and result display.
• Saves annotated images for review.

Project Structure

YOLOv8-Image-Object-Detection/
│
├── app.py                   # Main application file
├── requirements.txt         # Python dependencies
├── templates/
│   └── index.html           # HTML template for the web interface
└── yolov8n.pt               # YOLOv8 model weights

Getting Started

1. Clone the repository:
2. Install dependencies:
   • Install the required libraries: !pip flask==2.3.2 !pip ultralytics==8.0.124
3. Download the YOLOv8 model weights:
   • You can download the pre-trained YOLOv8 weights from the official Ultralytics website or use the provided yolov8n.pt in the repository.
4. Run the application:
5. Access the web interface: Open a web browser and navigate to http://127.0.0.1:5000/.

Usage

1. Upload an image using the file input.
2. Click the "Detect Objects" button.
3. The application will process the image and display the annotated result.

Example

Original Image:

Processed Image:

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

• [Ultralytics YOLO] for the YOLOv8 model