# Creating an Object Detection model with images and coordinates of bounding boxes

I am trying to build a custom Object Detection model which can detect guns from a given image. The dataset is from here.

The dataset has a good number of images and each image has 4 coordinates of bounding boxes with it. I recently read about YOLO and the structure of its labels is as follows:

I need to get this label for every grid cell of the input image. The dataset which I have contains coordinates for the object and not a grid cell in the image.

Also, I can manage to get bx, by, bh, bw and c1 , c2 , c3, but how do I extract pc for training from the dataset?

I will like to build a YOLO model from scratch but I don't know how to transform the dataset so that I can train a YOLO on it.

So, I found the answer myself. Basically, if the grid cell contains the centre of the bounding box then the grid cell will have a $$p_c$$ of 1 otherwise 0 if there is no object.