Introduction to Semantic Segmentation and U-Net

Computer vision is more than just identifying objects in a photo; it is about understanding the exact boundaries and context of every pixel. Semantic segmentation enables machines to perceive the world in high definition, powering technologies from autonomous navigation to medical diagnostics. In this text-based course, you will transition from basic image classification to pixel-level scene understanding. You will learn the theoretical foundations of semantic segmentation, explore how neural networks process spatial information, and write clean, modern code to build, train, and evaluate your own segmentation models. What you'll learn: - Understand the fundamental concepts of semantic segmentation and how it differs from image classification and object detection. - Build a complete U-Net architecture from scratch using modern deep learning framework conventions. - Apply essential data preprocessing and augmentation techniques specifically designed for pixel-level masks. - Evaluate model performance using industry-standard metrics like Intersection over Union (IoU) and the Dice coefficient. - Implement transfer learning using pre-trained modern backbones to accelerate training and improve mask accuracy. - Write clean training and validation loops to monitor model convergence and prevent overfitting. The course starts with core definitions, image processing fundamentals, and the mathematics behind spatial downsampling and upsampling. You will then progress through step-by-step code explanations, analyzing how encoder-decoder architectures preserve critical spatial details. This course is designed for beginners in computer vision and machine learning who have a basic understanding of Python and neural networks. No prior experience with image segmentation is required. Start reading today to master the core techniques of pixel-level computer vision.