Object Detection — YOLO from Scratch

Detection is classification plus regression, run at every position in a feature map, then cleaned up with non-maximum suppression.

Build 1.3 hr Python

Prerequisites: Phase 4 Lesson 03 (CNNs), Phase 4 Lesson 04 (Image Classification), Phase 4 Lesson 05 (Transfer Learning)

#Learning Objectives

Explain the grid-and-anchor design that turns detection into a dense prediction problem and state what every number in the output tensor means
Compute Intersection-over-Union between boxes and implement non-maximum suppression from scratch
Build a minimal YOLO-style head on top of a pretrained backbone, including the classification, objectness, and box-regression losses
Read a detection metric row (precision@0.5, recall, mAP@0.5, mAP@0.5:0.95) and pick which knob to turn next

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