…ts with different bounding boxes for every point. It’s a common trick used in Yolo and Faster RCNN. In SSD, multiple boxes for every feature point are called priors, while in Faster RCNN they are called anchors. I won’t draw them here. However you can check the visualisation of anchors in the Faster RCNN post. They bear the same concept. For every prior, we predict one bounding box for all the classes, so there are 4 values for very feature point. Beware it’s different from Faster RCNN. It may lead to worse bounding box prediction due to the confusion among different classes.