A classifier takes an entire image and produces a single label, such as "dog" or "street scene." The label describes the dominant content of the whole frame. Detection changes the question. Instead of one answer for the whole image, the model must produce a set of answers, each naming an object and stating where that object sits. In a photo with two dogs and a bicycle, classification can only say "dog" or "bicycle" — it cannot report both, and it cannot say that one dog is on the left and the bicycle is in the middle. Detection can. The output of a detector is a list, and each entry in the list contains a class label, a location, and a confidence score. The location is what classification never produced, and producing it is the core new capability of this chapter.
Learn MotionHow AI Sees: A Conceptual Guide to Image and Video Understanding
Beyond Labels: Locating and Describing What Is Seen
One Label for the Whole Image Is Not Enough
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Look at the left side of the diagram: the whole image goes in, and one label comes out. That is classification, and it works fine when there is one obvious subject. Now look at the right side. The same image produces a list — each row names an object and points to where it is. The key difference is not accuracy; it is the shape of the answer. A single label cannot describe a scene with several objects, because there is no place to put the second object. Detection solves that by making the output a list instead of a single value.
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