How AI Learns to See: Image Labeling and Training Data Explained

Computer vision models need data to learn - and lots of it. Models are given thousands or even millions of images, each tagged with details about what's in the picture, so that it can learn.

Let’s say you want it to recognise cats. Before it can do that, you need to give it thousands of images labelled “cat,” but also just as many that are labelled “dog,” “car,” “tree,” or whatever else. Over time, the model starts to spot patterns, whiskers, ear shape, fur texture and it learns to tell what is a cat, and what’s not.

The more examples we provide to the AI, the better it gets at telling things apart. But it’s not just about dumping loads of images into a system, the way those images are labelled matters just as much.

Each picture needs a clear label that tells the AI what it’s actually looking at. Sometimes that’s as simple as “cat” or “car.” Other times, it needs to be given more detail, like highlighting a circle around a dog’s head or tagging exactly where the wheels are on a bike. These labels are like teacher notes, and essentially helps the AI connect the dots between what it sees and what it should recognise.

How Image Annotation Actually Works

Training data is often done manually by human annotators. For object detection, they might draw boxes around cars in traffic photos and tag each one: “car,” “truck,” “bike.”