Real-time Object Detection and Neural Network Training Visualization

Overview

This project combines real-time object detection using YOLOv8 with concurrent neural network training visualization. It processes a video stream, detects objects, collects training data, and simultaneously displays object counts and neural network training progress.

Features

• Real-time object detection using YOLOv8
• Concurrent neural network training on detected objects
• Live visualization of object counts and training loss
• Multithreaded processing for smooth performance

Requirements

• Python 3.7+
• OpenCV
• NumPy
• Matplotlib
• Ultralytics YOLO
• TensorFlow/Keras

pip install -r requirements.txt