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YOLOv8 Object Tracking Tutorial

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Model Overview: YOLOv8 is Ultralytics's latest version of YOLO for real-time object detection
Model achieves high accuracy and speed in object detection and tracking
Custom annotated image dataset is essential for training
Key Architecture Features: Mosaic data augmentation mixes four images for better context
Anchor-free detection improves generalization by predicting object centers
C2f module replaces C3 module for faster training
Decoupled head separates classification and regression tasks
Implementation Details: Project uses Ultralytics YOLO API for model usage
Gradio interface enables easy testing of tracking functionality
Video processing includes tracking objects and annotating frames
Model supports both object detection and tracking on video streams
Technical Requirements: Ultralytics library installation required
Project structure includes main.py, demo.py, and yolo_tracking.py
Code runs on Windows, macOS, and Linux systems
Source code and example images available for download