Skip to content

Latest commit

 

History

History
31 lines (24 loc) · 859 Bytes

README.md

File metadata and controls

31 lines (24 loc) · 859 Bytes
Raw

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