This short, hands-on course helps you evaluate and refine image segmentation results with confidence. You will learn how to measure performance using IoU, Dice, class-wise tables, and visual overlays—then turn these insights into practical improvements using simple, production-friendly post-processing techniques. Along the way, you’ll work with common tools used by ML and data science teams and practice interpreting segmentation behavior in real scenarios. You will build a refinement pipeline that includes CRF-based smoothing and morphological operations, test its impact, and document your results like an applied ML engineer. Whether you're debugging your first segmentation model or optimizing a mature one, this course gives you the evaluation and improvement skills that computer vision teams rely on daily.