Body: <p>SO the <a href="https://arxiv.org/pdf/1804.02767.pdf" rel="nofollow noreferrer">YOLO V3</a> and <a href="https://arxiv.org/abs/1708.02002" rel="nofollow noreferrer">RetinaNet</a> both uses the Feature pyramids which look something like this:<a href="https://miro.medium.com/max/2584/1*XOumTDx4QEZ6qd_z1Rgf5g.png" rel="nofollow noreferrer"><img src="https://miro.medium.com/max/2584/1*XOumTDx4QEZ6qd_z1Rgf5g.png" alt="enter image description here" /></a>
  (except <code>b</code> and <code>e</code> which have one output)</p>
  <p>I'm just confuse how the predictions and training is done?
  Do we have to give <strong>EACH</strong> feature map a different <code>Y label</code>? IF yes, how is that possible? We need to have <code>N</code> different ground truth in my opinion. (Also ther'll be 3 different losses I think?)</p>
  <p>If not, then how are these done at once?</p>
  <p>There is a lot of confusion on these networks because I am not able to get my head around <strong>How are y-labels provided, trained and predicted in YOLOv3 and RetinaNet</strong> . Everything will make sense about loss, multioutputs and all if I know this one thing.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-06T10:06:19.300Z
Comments.Text: great question man, in short they do a heck of a complicated things to map boxes to anchors and then to tensors. Moreover each of the different approaches use a different strategy to map anchors, so the answer to your question is not short.
Comments.UserDisplayName: JVGD
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-09T07:24:57.793Z
Comments.Text: Long would do too ;)
Comments.UserDisplayName: Deshwal
Tags: <deep-learning><computer-vision><object-detection><object-recognition><yolo>
Title: How are Ground truth provided to each Pyramid map in RetinaNet or YOLOv3 Paper? How is the mapping of Feature Pyramids done to Ground Truth
UserDisplayName: Deshwal
ThreadText: # How are Ground truth provided to each Pyramid map in RetinaNet or YOLOv3 Paper? How is the mapping of Feature Pyramids done to Ground Truth
  
  (Asked by: username_0 on 2021-01-31)
  
  SO the [YOLO V3](https://arxiv.org/pdf/1804.02767.pdf) and [RetinaNet](https://arxiv.org/abs/1708.02002) both uses the Feature pyramids which look something like this:[![enter image description here](https://miro.medium.com/max/2584/1*XOumTDx4QEZ6qd_z1Rgf5g.png)](https://miro.medium.com/max/2584/1*XOumTDx4QEZ6qd_z1Rgf5g.png)
  (except `b` and `e` which have one output)
  
  I'm just confuse how the predictions and training is done?
  Do we have to give **EACH** feature map a different `Y label`? IF yes, how is that possible? We need to have `N` different ground truth in my opinion. (Also ther'll be 3 different losses I think?)
  
  If not, then how are these done at once?
  
  There is a lot of confusion on these networks because I am not able to get my head around **How are y-labels provided, trained and predicted in YOLOv3 and RetinaNet** . Everything will make sense about loss, multioutputs and all if I know this one thing.
  
  **Tags:** deep-learning, computer-vision, object-detection, object-recognition, yolo
  
  > **Comments:**
  > **username_1**: great question man, in short they do a heck of a complicated things to map boxes to anchors and then to tensors. Moreover each of the different approaches use a different strategy to map anchors, so the answer to your question is not short.  
  > **username_0**: Long would do too ;)  
  
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  ## 0 answers
  
  