Phase 1: Convolutional Neural Networks · ~75 minutes · Python

Instance Segmentation — Mask R-CNN

Add a tiny mask branch to a Faster R-CNN detector and you have instance segmentation. The hard part is RoIAlign, and it is harder than it looks.

Hiring signal: Understanding of instance segmentation — mask r-cnn internals

What you will learn

Trace the Mask R-CNN architecture end-to-end: backbone, FPN, RPN, RoIAlign, box head, mask head
Implement RoIAlign from scratch and explain why RoIPool is no longer used
Use the torchvision `maskrcnn_resnet50_fpn_v2` pretrained model for production-quality instance masks and read its output format correctly
Fine-tune Mask R-CNN on a small custom dataset by replacing the box and mask heads and keeping the backbone frozen

Introduction

Type: Build + Learn Languages: Python Prerequisites: Phase 4 Lesson 06 (YOLO), Phase 4 Lesson 07 (U-Net) Time: ~75 minutes

Learning Objectives

Trace the Mask R-CNN architecture end-to-end: backbone, FPN, RPN, RoIAlign, box head, mask head
Implement RoIAlign from scratch and explain why RoIPool is no longer used
Use the torchvision maskrcnn_resnet50_fpn_v2 pretrained model for production-quality instance masks and read its output format correctly
Fine-tune Mask R-CNN on a small custom dataset by replacing the box and mask heads and keeping the backbone frozen

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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