Overview
In this 4 hr course, students will learn how to master YOLOv4 for AI-driven object detection. From creating datasets and training custom detectors to building real-world apps like mask detection and vehicle tracking, attendees will gain comprehensive skills to implement YOLOv4 techniques effectively both in code and GUIs.
What I will be able to do after this course
- Create, train, and deploy custom YOLOv4 object detection models using Python.
- Develop real-world computer vision applications, including a mask detection system.
- Implement YOLOv4 with real-time video processing for use cases like social distancing monitoring.
- Enhance datasets using advanced augmentation methods for improved training outcomes.
- Integrate YOLOv4 capabilities in user interfaces designed with PyQT.
Course Instructor(s)
Augmented AI, developers of this course, include industry experts on computer vision and AI with years of hands-on teaching and real-world problem-solving experience. They have dedicated their skills to imparting high-quality education to beginners and professionals alike while ensuring practical implementation of theories.
Who is it for?
This course is designed for software developers, machine learning enthusiasts, and researchers with a fundamental knowledge of programming, especially Python, and a basic understanding of AI. It addresses the needs of individuals aiming to enhance their skills in computer vision applications, professional development, or academic AI research.
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