Week3 Lab1, YOLO, CNN

[ai_curious]

@picnic

Anchor boxes are defined by size AND position

In the absence of further examples, maybe let’s explore these one at a time. This might help us identify the source of some of the contradictory and incorrect information. In your understanding is position a pixel in an image? A 480x480 image contains 230,400 pixels; are there also 230,400 anchor boxes? If not, how many are there? Is it the same for every image? How is an anchor box position determined, assigned, and used in the code? If I recall correctly, the anchors.txt in the exercise contains 10 numbers read into a Python list. The code treats these as 5 pairs of 2; is one of those the position? The equations for predicted bounding box shape in the original YOLO papers (and in the code for this exercise) only describe and use two values for an anchor box. Is either one of those a position?

The equations show up in the research papers but also in several existing threads. Here is one:

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 pape…

Note: the paper refers to them as dimension priors with p_w and p_h for width and height, respectively. (Spoiler alert, no anchor box position)