# Solved – Yolo loss function for detecting 1 class

I'm trying to work on a Yolo implementation which searches a 19×19 grid to find a specific item. There is only a single class in all of these images I am looking to get bounding boxes for. I'm a little confused about the calculation of the loss function though. The output from my CNN is a 19x19x5 matrix (P , x , y , w , h), with P being the probability that an object is located in this frame.

The way I've interpreted this function is as follows:
– Add the sum of squares of the x and y coordinates from true and pred
– Multiply by 1 if the ground truth object is present, otherwise 0
– Multiply by 5 to increase the loss from bounding boxes

``-repeat this with the sum of squares of the square root of w and h ``

Here is the part that confuses me now. Since I do not have any classes, only a probability of object being in this frame, I'm not sure how to account for this.

Should I treat C as equal to P, and simply take the sum of squares as:
1obj (Pi – Ptruei) + 0.5* 1noobj (Pi-Ptruei)

My guess is that if the object is located in that cell, this loss function would penalize localization error to a greater extent. Whereas if there are no objects, it will penalize the probability of the object.

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