Question / confusion about YOLO

[ai_curious]

Also see

Week 3: finding the correct cell in YOLO

In the example above there will be 2 grid cells with non-empty training data and 18 with empty. The grid cells with object centers contained within them have a ground truth bounding box that includes a perimeter around the entire object, not just those parts of the object within the grid cell that contains its center. The grid cells that don’t contain an object’s center will have no information about any objects. During training, incorrect grid cell center and bounding box shape predictions ar…

And

How does a cell detect a bounding box bigger than itself, YOLO?

This is correct, but it is important to see how that happens, which is described in one of the replies above in this thread. At training time you know the ground truth bounding box location, the training image dimensions, and the number of grid cells. From this, you can calculate which pixel is the ground truth bounding box center, and map that to one specific grid cell. That grid cell is the given a 1 for object presence, and the other grid cells 0. Grid cell center location / object prese…

Or even

https://www.coursera.org/learn/convolutional-neural-networks/lecture/fF3O0/yolo-algorithm

You can easily start with Y the correct shape and initialized to 0. Then, overwrite just the locations that have training labels. It is straightforward to determine in which grid cell the center of the object is located. First, determine how many pixels map to each grid cell. Then, from your bounding box label, determine the pixel coordinate of the object center. From those two numbers, determine how many grid cell units the object is offset from the origin. In the autonomous driving exerci…