PyTorch Image Segmentation: Train UNet and Foundation Models

Extracting precise boundaries from visual data is a cornerstone of modern computer vision, powering autonomous systems and medical diagnostics. This course guides you through the core concepts of semantic image segmentation using Python and PyTorch. You will transition from understanding basic pixel-level classification to implementing, training, and deploying sophisticated segmentation models. By studying clean code implementations and step-by-step explanations, you will gain the confidence to apply these techniques to your own custom image datasets. What you'll learn: - Understand the foundational concepts of semantic segmentation, loss functions, and evaluation metrics. - Build and train classic architectures like UNet from scratch using PyTorch. - Leverage modern foundation models, such as the Segment Anything Model (SAM), for zero-shot segmentation tasks. - Prepare, augment, and pipeline custom image datasets using modern Python libraries. - Optimize and deploy trained models for real-world inference environments. The course begins with essential theoretical definitions and dataset preparation techniques before guiding you through model architecture implementation, training loops, and modern deployment workflows. This course is designed for aspiring computer vision engineers, data scientists, and developers who have a basic familiarity with Python and want to learn image segmentation from the ground up without complex prerequisites. Start reading today to build your own computer vision segmentation pipeline.