text: Title: Intuition behind box regression formula 
  Body: ### Search before asking
  
  - [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.
  
  
  ### Question
  
  Hi, 
  I am struggling to get an intuition for the formula for the bounding box regression in YOLOv5. I am aware of issues [#4373](https://github.com/ultralytics/yolov5/issues/4373) and #471, but I didn't quite understand the explanations there. More specifically, I don't understand the point of multiplying the output of the sigmoid by 2 and subtracting 0.5 in the below equation.
  <img width="277" alt="Screenshot 2024-04-05 at 18 16 27" src="https://github.com/ultralytics/yolov5/assets/143072547/1b6135d6-af89-4fe4-b65f-db37e6473c39">
  
   As I understand, applying the sigmoid to the network output is intended to force the predicted offsets for the center point of the bounding boxes to lie between 0 and 1 so that the prediction will lie within the grid cell. This way, the offsets can take on values between -0.5 and 1.5, and thus can fall outside of a grid cell. Could you elaborate on the logic behind this? Also, if the offsets are always added to the grid cell location (i.e.,` c_x`/`c_y`), this means that the latter must be defined as the top left corner of the cell, correct? If it was the center of the cell, this way one could only predict points in the bottom right quarter of the cell. 
  
  
  Thanks a lot!
  
  ### Additional
  
  _No response_