Aron4557/Namibia-bank-note-live-detector

Created by Aron Dionisius # Namibia-bank-note-live-detector Created by Aron Dioniius Project Overview a Namibian note currency detection system using computer vision and deep learning. The development followed a pipeline starting with data collection of various NAD denominations under different conditions. The collected dataset underwent augmentation techniques including rotation, scaling, and brightness adjustments to enhance diversity and robustness. All images were meticulously annotated using Roboflow with bounding boxes around each currency denomination. The annotated data was then used to train a YOLOv8 model on Google Colab, leveraging GPU acceleration for efficient training. The final model was integrated into a user-friendly PyQt5 interface providing real-time detection capabilities through webcam feed and static image analysis. #Installation Requirements Python Version Python 3.8 or higher #Required Python Packages Run these commands in your terminal/command prompt: bash pip install ultralytics pip install opencv-python pip install PyQt5 Alternative: Install all at once bash pip install ultralytics opencv-python PyQt5 Specific Versions (Recommended) bash pip install ultralytics==8.0.0 pip install opencv-python==4.8.0 pip install PyQt5==5.15.0 Quick Start Guide #Zip file Download 1. Download and Extract Download the project ZIP file Extract the contents to your desired location If you see repetitive folders (like NAD_Detector/NAD_Detector/), delete the outer folder so you have only one NAD_Detector folder 2. File Structure After extraction, ensure your folder contains: text NAD_Detector/ ├── weights/ │ ├── best.pt │ └── last.pt ├── nad_live_gui.py └── README.md 3. Open with Visual Studio Code Right-click the NAD_Detector folder Select "Open with Code" or open VS Code and drag the folder into it Ensure you're in the correct project directory 4. Run the Application Open nad_live_gui.py in VS Code Press F5 or click the "Run" button Or use the terminal: pyt …