Day 15 · Pick the Right Tool

🏷️ The pro wordChoosing classify vs detect vs segment vs track (or no AI) is problem framing (task formulation). Sometimes plain classical computer vision (OpenCV) beats deep learning.

🎯 Model Match Arena

Which tool for the job?

Mission 1 of 7

What kind of AI should you use?

Correct: 0 / 7

🧭 Your Decision Cheat-Sheet

Ask: "what output do I actually need?"

🏷️

Classification

One label for the whole image. ("cat or dog?")

📦

Detection

Find & box each thing. ("count the cars")

🎨

Segmentation

Exact pixels of each thing. ("cut out the person")

🎯

Tracking

Follow it across a video. ("follow the ball")

⚙️

Just a rule

No AI needed! ("if temp > 100 → alarm")

💡 The pro move: don't reach for the fanciest AI. Reach for the simplest thing that does the job. Sometimes that's a rule, not a model.

🚀 Beyond the Basics

How the pros really do it

You matched tasks to the vision flavors. Pros have an even bigger toolbox:

🟢

Classify / detect / segment (what you learned)

✅ Covers most jobs.

🎯

More tools

Tracking (follow objects in video), pose estimation (body/hand points), OCR (read text) - each a whole field.

⚙️

Sometimes: no deep learning

Classical CV (color, shapes, thresholds in OpenCV) is faster and simpler for easy tasks.

🏷️ Pro names to look uptask formulation · tracking / pose estimation / OCR · classical CV (OpenCV).

🎉 Day 15 Complete!

You can now look at a real problem and pick the right kind of AI — or decide it needs no AI at all. That's how real engineers think.

Day 16: Fast, Accurate, Private →

Finale: even with the right tool, you must choose a good model — juggling speed, accuracy, size, privacy, and cost. The last mission of Module 1! 🛍️