{"id":20492405,"url":"https://github.com/letsdoitbycode/objectify-yolov5","last_synced_at":"2025-06-27T18:04:12.130Z","repository":{"id":261227854,"uuid":"883644105","full_name":"letsdoitbycode/Objectify-YOLOv5","owner":"letsdoitbycode","description":"This project is a real-time object detection system that leverages the YOLOv5 model for detecting objects in a video stream from a webcam or other video input. 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The system is built using a Flask web application to serve the video feed, providing an interactive, real-time experience directly in a browser. YOLOv5's efficiency and speed make it ideal for real-time applications where both performance and accuracy are critical.\n\n\n### Features\n- Real-Time Object Detection: Detects and classifies objects in real-time using YOLOv5.\n- Web-Based Interface: Access video feed with detections via a browser-based interface built with HTML and CSS.\n- Responsive UI: An intuitive and modern UI design provides a user-friendly experience.\n\n### How It Works\n- Video Capture: Captures frames from a webcam or other video input.\n- Object Detection: Each frame is passed through the YOLOv5 model to detect objects.\n- Annotation: Detected objects are labeled and bounded in the frames.\n- Streaming to Web Interface: The Flask app streams these annotated frames in real-time to a web page, accessible from any browser.\n\n\n### Installation\n1. Clone the repository\n   ```sh\n   git clone https://github.com/letsdoitbycode/Objectify-YOLOv5\n   cd Objectify-YOLOv5\n   ```\n\n3. Create a virtual environment and activate it:\n   ```sh\n   python -m venv venv\n   source venv/bin/activate  # On Windows use `venv\\Scripts\\activate`\n   ```\n\n3. Install the required packages:\n   ```sh\n   pip install torch torchvision opencv-python matplotlib\n   pip install requirements.txt   #else you can do this directly\n   ```\n\n4. Set Up YOLOv5 in the Project:\n   ```sh\n   git clone https://github.com/ultralytics/yolov5\n   cd yolov5\n   pip install -r requirements.txt\n   ```\n\n5. Return to the main project folder:\n   ```sh\n   cd..\n   ```\n   \n6. Run the Flask app:\n    ```sh\n   python app.py\n    ```\n\n### Project Structure\n```plaintext\nObjectify-YOLOv5/\n│\n├── templates/\n│   └── index.html          # HTML file for UI \n├── static/\n│   └── style.css          # CSS file for styling\n├── venv                    # Virtual environment\n├── yolov5                  # Model YOLOv5 \n├── README.md               # This README file\n├── app.py                  # Main Flask application\n├── yolov5s.pt              # mirror of the YOLOv5 project\n└── requirements.txt        # requirement file\n\n\n```\n\n### Acknowledgements\n- Ultralytics for the YOLOv5 model: YOLOv5 GitHub Repository\n- OpenCV for video processing\n- Flask for powering the web interface\n\n## Contributing\nContributions are welcome! Please open an issue or submit a pull request for any changes or improvements.\n\n\n## DEMP APPLICATION\n----\n\n![Screenshot (14)](https://github.com/user-attachments/assets/cf39523f-4e55-4c19-ad66-d1fcce93251c)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fletsdoitbycode%2Fobjectify-yolov5","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fletsdoitbycode%2Fobjectify-yolov5","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fletsdoitbycode%2Fobjectify-yolov5/lists"}