AI Engineering/Phase 04/Lesson 07

Semantic Segmentation — U-Net

Segmentation is classification at every pixel. U-Net makes it work by pairing a downsampling encoder with an upsampling decoder and wiring skip connections between them.

Build 1.3 hr Python

Prerequisites: Phase 4 Lesson 03 (CNNs), Phase 4 Lesson 04 (Image Classification)

#Learning Objectives

Distinguish semantic, instance, and panoptic segmentation and pick the right task for a given problem
Build a U-Net from scratch in PyTorch with encoder blocks, a bottleneck, a decoder with transposed convolutions, and skip connections
Implement pixel-wise cross-entropy, Dice loss, and the combined loss that is the current default for medical and industrial segmentation
Read IoU and Dice metrics per class and diagnose whether a bad score comes from small-object recall, boundary accuracy, or class imbalance

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.