Anchor box principle is a prior box design introduced in the middle and later versions of the YOLO series. A set of preset rectangular boxes (anchor boxes) with fixed aspect ratios are generated through clustering on the dataset. The model performs offset regression based on anchor boxes instead of directly predicting absolute coordinates, reducing regression difficulty and improving the model's detection effect on objects of different scales and shapes.
