Yolo Anchor Boxes

[ai_curious]

I’m not sure I completely understand the question. But I think this slide and discussion is in the context of training. During training you do have all object shape(s) and center location(s). You use them in the loss computation to train the model on which grid cell and anchor box are correct for the ground truth objects. You then use the learned parameters to make better predictions at operational runtime when you don’t know the shape or center location. Does this make sense?

Here are a couple of related threads, but you can find many others using the search…

Applying YOLO anchor boxes

Applying YOLO Anchor Boxes A previous thread provides an introduction to extracting from labelled ground truth data a small set, on the order of 3 <= k <= 10 , of shapes for what is referred to in the YOLO 9000 paper as dimension clusters or priors, but in this class as anchor boxes . NOTE: Just to make things more confusing, the YOLO 9000 paper uses anchor boxes to refer instead to the hand-picked shapes described in the Faster R-CNN paper, not the ones generated by K-Means. The YOLO 9000 pap…

Detecting Multiple Objects using YOLO - Grid Cells plus Anchor Boxes

Detecting Multiple Objects in YOLO – Grid Cells plus Anchor Boxes One key feature of YOLO is its ability to detect (locate plus classify) multiple objects per image. Previous threads have introduced the concept of anchor boxes and how they are applied during training data setup. This thread will cover how grid cells and anchor boxes together enable predictions on multiple objects within one image. The motivation Think back to your first efforts to train a classifier for image based on a singl…