Fine-Tuning and Evaluating Vision AI Models

Building high-performing computer vision systems requires more than training a model—it requires careful evaluation, reliable predictions, and continuous refinement. In this course, you'll learn how to fine-tune and evaluate computer vision models used in real-world AI systems. You'll begin by applying transfer learning techniques to improve model accuracy on domain-specific datasets and analyzing learning-rate schedules to understand training behavior. Next, you'll evaluate the calibration of classification models and apply post-hoc correction methods to improve prediction reliability. The …