🖼️ Object Detection App using yolo11n

📖 Description

This repository contains an Object Detection App built using yolo11n, a state-of-the-art object detection model. The app achieves an impressive accuracy of 99.63% 🎯 and includes functionalities for detecting and recognizing objects such as 🐾 animals, 🐦 birds, 👤 humans, 🚗 transport, and more. It supports real-time object detection through a live webcam feed 📷.it’s the perfect resource for developers and researchers looking to enhance their projects with advanced detection capabilities. 🚀📊

📁 Repository Structure

├── model/ 
│       └── yolo11n.pt          # yolo11n model file 
├── video/ 
│       └── sample_video.mp4    # Sample video for testing the application 
├── app.py                      # Main application file 
├── requirements.txt            # List of required libraries and dependencies

✨ Features

• 🧠 yolo11n Model: Utilizes the latest yolo11n model for high-accuracy object detection.
• 🎥 Real-Time Detection: Detects and recognizes objects from a live webcam feed.
• 📜 Logging: Comprehensive logging for better understanding and debugging.
• 📝 Code Documentation: Clear and concise comments to ensure readability and understanding of the code.

🛠️ Installation

1. Clone the repository:

   git clone https://github.com/MohammadAshmir786/Object_Detection_with_YOLO.git
   cd Object_Detection_with_YOLO

2. Install the required libraries:

   pip install -r requirements.txt

3. Ensure the yolo11n.pt model file is located in the model directory.

4. Place your sample video file in the video directory if you wish to test with custom videos.

🚀 Usage

1. Run the Application:

   python app.py

2. The application will start detecting objects in real-time using the webcam.

3. Optionally, test the model using the sample video provided in the video directory.

📂 File Details

1. app.py 📜

The main application script. Features include:

• Live webcam object detection.
• Sample video testing.
• Detailed logging for debugging.
• Inline comments to explain the workflow.

2. requirements.txt 📋

A list of libraries and dependencies required to run the application. Install them using:

pip install -r requirements.txt

3. model/yolo11n.pt 🧠

The yolo11n model file used for object detection.

4. video/sample_video.mp4 🎞️

A sample video file containing various objects such as 🐾 animals, 🐦 birds, 👤 humans, and 🚗 transport for testing purposes.

🛡️ Requirements

Ensure you have the following installed:

• 🐍 Python 3.7 or higher
• 📦 Libraries listed in requirements.txt

📊 Example Output

When running app.py, the application will:

1. Use yolo11n for object detection.
2. Display real-time webcam footage with bounding boxes and labels around detected objects.
3. Log the detection details for review and debugging.

🤝 Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.

📜 License

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

💡 Acknowledgments

• YOLO Algorithm - for the yolo11n model.
• Ultralytics - for the yolo11n model documentation.

🔧 Troubleshooting

If you encounter issues while running the application, consider the following steps:

1. Verify Dependencies: Ensure all required libraries are installed correctly by running:

   pip install -r requirements.txt

2. Model File Check: Confirm that the yolo11n.pt model file is located in the model directory.

3. Webcam Issues: Ensure your webcam is functioning properly and accessible by Python.

4. Logging: Use the logs generated during the application's runtime to identify errors and debug the program.

5. Python Version: Ensure you are using Python 3.7 or higher.

🌟 Future Enhancements

Planned improvements for the app include:

• Adding support for additional object categories.
• Enhancing the GUI for better user interaction.
• Integrating support for GPU acceleration for faster processing.
• Providing a feature for saving detection results as video or image files.

📞 Contact

For any questions or feedback, feel free to contact:

• Author: Mohammad Ashmir Abbasi
• Email: [email protected]
• Social: Connect with me for professional networking click 👉 👈

📅 Changelog

v1.0.0

• Initial release with YOLOv11n integration.
• Support for real-time webcam detection.
• Sample video testing included.
• Logging and documentation added for clarity.

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Thank you for using the Object Detection App with YOLO11n! 😊