Questions on Yolo Algorithm

[ecsca]

I really appreciate your detailed answer and also the thread that you shared.

Below block from your thread(Detecting Multiple Objects using YOLO - Grid Cells plus Anchor Boxes) helped a lot for me to understand YOLO.

@ai_curious

Here S represents the grid cell dimension, and B represents the number of anchor boxes. The idea is that by shaping the last layer of the CNN this way, it can output S * S * B values. In this example it is 3 * 3 * 5 = 453. A CNN with this shape could make 45 cat/non-cat predictions on the same input image from the same, single forward propagation. By adding a 4th dimension you can even produce more output values. Say C represents 4 bounding box location predictions. Now you have S * S * B * C values. 3 * 3 * 5 * 4 = 180 predictions. Note, you could think of the previous depiction as C = 1.

So, YOLO allows us to make S * S * B classification output in a single forward propagation by adding one more dimension(of course corresponding model change), and also we can add one more dimension C for bounding box prediction if we need, and it can be also done in a same forward propagation,

therefore we can do multiple classification and bounding box prediction in a single forward propagation which is the meaning of “You Look Only Once”.