Annotate and Analyze Objects for Vision
By External Provider
About this course
This short course shows you how to build reliable vision datasets and configure detection models with confidence. You’ll learn how to run a quality-controlled annotation process, review bounding boxes, coach annotators, and check dataset consistency using IoU-based audits. You’ll also explore how to analyze object sizes with clustering to generate anchor box parameters for models like YOLOv8. Through compact videos, guided readings, and hands-on exercises, you’ll practice using tools such as CVAT and Python notebooks to complete tasks common in production vision teams. By the end, you’ll be able to create a clean bounding-box dataset and use real measurements to tune model anchors—skills that support robust, scalable computer-vision pipelines.