anchor box

Anchor boxes (also known as default boxes) are a concept commonly used in object detection algorithms, particularly in models like Single Shot MultiBox Detector (SSD) and Faster R-CNN. They are predefined bounding boxes of various sizes and aspect ratios that serve as reference frames for detecting objects in an image.

The idea behind anchor boxes is to provide prior knowledge about the possible locations and scales of objects in an image. These boxes are placed at different positions and scales across the image. During the training process, the algorithm matches these anchor boxes with the ground truth bounding boxes of objects in the training dataset.

The match between anchor boxes and ground truth boxes is determined based on metrics such as intersection over union (IoU). The algorithm learns to predict the offsets and class probabilities for each anchor box, which are then used to classify and localize objects in the image during inference.

By utilizing multiple anchor boxes with different sizes and aspect ratios, object detection models can handle objects of various shapes and scales effectively. The anchor boxes act as a set of reference templates that help the model locate and recognize objects in an image.