Image Classification Basics (Lesson)
ReadyA five-step walkthrough of how AI classifies an image: input → feature extraction (grayscale + outline) → learn patterns (same-class examples averaged into a “prototype”) → classify (a transparent mini model next to a real model) → output (confidence and the top-two margin). Use the built-in dog / cat samples or upload a photo; the real model returns Top-5.
No image selected yet.
Classification always starts with an image. Pick a sample (say dog or cat), or drop in your own photo — it will travel through every step below.
Click or drag a file here
Common formats supported, auto-resized
Why is it important?
Lets computers recognise the world
Classification is the first step of most vision tasks: telling what is in the picture.
Turns chaos into order
Sorting huge image collections by category powers search, albums and inspection.
Auto-spots what is wrong
Things that do not fit any known class are often exactly what we should worry about.
Powers countless apps
From photo organising to medical diagnosis, classifiers live in everyone’s pocket.