title: Understanding How YOLO is trained
body: <p>I'm trying to understand how YOLO (v2) is trained. To do so, I'm using this keras implementation <a href="https://github.com/experiencor/keras-yolo2" rel="nofollow noreferrer">https://github.com/experiencor/keras-yolo2</a> to train YOLO from scratch on VOC dataset (I'm open to other implementation, but I never worked with pytorch, so, keras implementation will be first choice).</p>
  
  <p>1- As I understood YOLO, it is first trained for classification on imageNet, then these trained weights (for classification) should be use somewhere when training yolo for regression (to detect bounding boxes). In most code I found on internet to train yolo from scratch (for regression), I don't see the part where these classification weight are loaded. When does this happen? when are the classification weights used in training yolo regression?</p>
  
  <p>2- Is my understanding as described in 1) correct? </p>
  
  <p>Thanks in advance</p>