🔍 Project Overview:

Developed a computer vision solution to detect 19 types of cutleries on a fast-moving conveyor belt.
Fine-tuned a lightweight YOLOv8 nano model with carefully annotated datasets for precise detection.
⚙️ Technical Highlights:

Converted the trained model to a TensorRT engine for faster inference on Jetson Nano 4GB.
Built an interactive Tkinter application for user-friendly operation.
Integrated database connection for automated count updates.
Controlled conveyor operation via relay through the app.
🔗 App Workflow:

Enter a Job Number to organize client data.
Start the conveyor and detection system with real-time count tracking.
Stop the conveyor and submit the count to update the database.
🎥 Video Demo Included:

Highlights the app interface, Jetson Nano integration, and detection in action.
🔧 Ideal for Applications in:

Industrial automation and real-time object detection.