title: Yolo v3 model output clarification with keras
body: <p>I'm using yolo v3 model with keras and this network is giving me as output container with shape like this:</p>
  
  <pre><code>[(1, 13, 13, 255), (1, 26, 26, 255), (1, 52, 52, 255)]
  </code></pre>
  
  <p>So I found this <a href="https://www.cyberailab.com/home/a-closer-look-at-yolov3" rel="nofollow noreferrer">link</a> </p>
  
  <p>Then I understand the value 255 in each of the 3 containers, I also understand that there is 3 containers because there is 3 different image scaling for bounding boxes creation.</p>
  
  <p>But I did not understand why in the output vector there are 13 * 13 lists for the first scaling rate then 26 *26 lists for the second then 52 * 52 for the last.</p>
  
  <p>I can't manage to find some good explanations about that so I can't use this network.
  If someone knows where I can find some information about the output dimension I would be very greatful.</p>
  
  <p><strong>EDIT</strong></p>
  
  <p>Is it because if i cut the image in 13 by 13 sections i'm only able to detect 13*13 objects considering that each sections are the center of an object ?</p>