Answers.Body: <p>In the meantime I found out that indeed the <strong>training</strong> of DeepLabv3 is performed in a patch-based manner which means the usage of a patch with a given <strong>crop size</strong>, that is extracted randomly from an example pair (input, target).
  <br>
  <br>
  Regarding <strong>validation/testing</strong>, full-size example pairs (input, target) are used.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-05-10T13:19:32.190Z
Answers.Text: As it’s currently written, your answer is unclear. Please [edit] to add additional details that will help others understand how this addresses the question asked. You can find more information on how to write good answers [in the help center](/help/how-to-answer).
Answers.UserDisplayName: Community
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-05-03T09:29:29.353Z
Answers.LastActivityDate: 2023-05-03T09:29:29.353Z
Answers.UserDisplayName: Manuel Maior
Body: <p>In the paper in which DeepLabv3 is presented, the authors are mentioning that:
  <br></p>
  <p><em>&quot;For atrous convolution with large rates to be effective, large
  <strong>crop size</strong> is required; otherwise, the filter weights with large
  atrous rate are mostly applied to the padded zero region. We
  thus employ <strong>crop size</strong> to be <strong>513</strong> during both training and test
  on PASCAL VOC 2012 dataset&quot;</em></p>
  <p>What does this mean? <br>
  If for example I want to use the Cityscapes dataset for training, considering the idea mentioned by the authors, I should take <strong>513x513</strong> croppings for each example image that is originally <strong>2048x1024</strong>? <br>
  Does the <strong>crop size</strong> refer to something else? Or if not, does it involve other steps/operations too? <br></p>
  <p>I would be really grateful if somebody could clarify this concept and how it is applied during <strong>training</strong> and how it affects the <strong>inference</strong>.</p>
  <p>Paper link: <a href="https://arxiv.org/pdf/1706.05587.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1706.05587.pdf</a></p>
  
Tags: <deep-learning><papers><semantic-segmentation>
Title: What does crop size refer to in DeepLabv3 paper?
UserDisplayName: Manuel Maior
ThreadText: # What does crop size refer to in DeepLabv3 paper?
  
  (Asked by: username_0 on 2023-04-28)
  
  In the paper in which DeepLabv3 is presented, the authors are mentioning that:
  
  *"For atrous convolution with large rates to be effective, large
  **crop size** is required; otherwise, the filter weights with large
  atrous rate are mostly applied to the padded zero region. We
  thus employ **crop size** to be **513** during both training and test
  on PASCAL VOC 2012 dataset"*
  
  What does this mean?   
  If for example I want to use the Cityscapes dataset for training, considering the idea mentioned by the authors, I should take **513x513** croppings for each example image that is originally **2048x1024**?   
  Does the **crop size** refer to something else? Or if not, does it involve other steps/operations too?
  
  I would be really grateful if somebody could clarify this concept and how it is applied during **training** and how it affects the **inference**.
  
  Paper link: <https://arxiv.org/pdf/1706.05587.pdf>
  
  **Tags:** deep-learning, papers, semantic-segmentation
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_0 on 2023-05-03. Score: 1
  
  In the meantime I found out that indeed the **training** of DeepLabv3 is performed in a patch-based manner which means the usage of a patch with a given **crop size**, that is extracted randomly from an example pair (input, target).
    
    
  Regarding **validation/testing**, full-size example pairs (input, target) are used.
  
  > **Comments:**
  > **username_1**: As it’s currently written, your answer is unclear. Please [edit] to add additional details that will help others understand how this addresses the question asked. You can find more information on how to write good answers [in the help center](/help/how-to-answer).  
  
  ---
  
  