This Computer Vision & YOLOv8 Object Detection Course is designed to help learners understand modern computer vision techniques used in artificial intelligence and deep learning projects. The course focuses on object detection, object tracking, and training AI models using custom datasets.
The course begins with an introduction to selecting the best computer vision model for different project requirements. Learners will understand how model performance, speed, and accuracy affect real-world AI applications.
Students will then explore YOLOv8 object detection, one of the most popular real-time computer vision models. The course provides step-by-step guidance on training YOLOv8 using custom datasets for detecting specific objects in images and videos.
In addition, learners will study object tracking techniques using ByteTrack and Supervision to track and count moving objects in real-time applications. The training also covers native YOLOv8 tracking methods using live webcam streams for practical AI deployment.
The course further introduces DETR Object Detection Transformers and explains how transformer-based detection models are trained and used in computer vision systems.
By the end of this course, learners will be able to train, track, and deploy computer vision models for object detection and AI automation projects using YOLOv8
