About the prediction of yolo boundary box prediction

Dear Mentor,

I found that in yolo_head() function, the prediction of bounding box on width and height is unscaled from log-scale to linear scale, and box_wh is unscaled relative to anchor boxes.

  1. box_wh = K.exp(feats[…, 2:4])
  2. box_wh = box_wh * anchors_tensor

Does it mean the width and height of a bounding box is scaled with respect to width and height of an anchor box , and applying log scaling?

log(width of bounding box / width of anchor box)
log(height of bounding box / height of anchor box)

Then, the log-scaled width and height are annotated in the truth label in the training set?

Thank you.

Maybe that will help?

Let us know