Yolo Anchor Boxes

[ai_curious]

@Stephano_Cotsoradis

are anchor boxes … pre determined shapes that are assigned to a specific class … or are they shapes that the network learns from the correct training example from the parameters (bx by bh bw)

Neither of these is precisely correct.

1. Anchor boxes shapes are predetermined, but they are not class-specific. Think it through; cars close up and cars far away are not the same size. Also, some models have been trained on data with thousands, even tens of thousands, of classes. Since anchor box number determines YOLO network output shape and thus number of predictions (and computations) you maybe can have 10 anchor boxes but you cannot have 10,000.

2. The anchor box shapes are learned from the training data, but not by the YOLO network and not including location (b_x, b_y). Rather, the shapes are selected through running unsupervised learning on the ground truth shapes only…location is irrelevant. This also has been previously discussed extensively in the forum.

Deriving YOLO anchor boxes

Deriving Anchor Boxes for YOLO One area of recurring questions about YOLO is anchor boxes, introduced with the second 2016 paper, YOLO9000 aka YOLO v2, as an optimization to help improve model stability during training. I’ll defer discussion of the role they play for now and just cover how they are created. Once you see what they are, I think it will be easier to understand how they are used. The Dataset I worked with the Berkeley DeepDrive 100K dataset (BDD). To keep the size manageable, I u…

Hope this helps