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

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.

What you will learn

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

Introduction

Type: Build
Languages: Python
Prerequisites: Image Classification
Time: ~75 minutes

Objective

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

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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