Answers.Body: <p>You can train a model with that dataset, but it will perform worse compared to a higher fidelity dataset. Training on images with unannotated buildings is essentially instructing the model to stochastically ignore buildings, which is confusing.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-10-13T20:29:11.403Z
Answers.LastActivityDate: 2023-10-13T20:29:11.403Z
Answers.UserDisplayName: Karl
Body: <p>I am wondering if it is required or not that the masks used for binary instance segmentation are complete.</p>
  <p>For instance, I want to find the buildings in aerial imagery. If my masks cover, let say between 25% and 75% of the training image buildings, can these samples be used for training the model? Does the masks should cover 100% of the buildings in the samples?</p>
  <p>I know it would be better to mask all buildings, but if the mask is not complete, can it be used for training or not?</p>
  <p>I am using a deeplabv3 model.</p>
  <p>Here an example of what I mean
  <a href="https://i.sstatic.net/MMMOg.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/MMMOg.png" alt="enter image description here" /></a></p>
  
Tags: <image-segmentation><instance-segmentation>
Title: Binary instance segmentation - does the masks have to be complete
UserDisplayName: Below the Radar
ThreadText: # Binary instance segmentation - does the masks have to be complete
  
  (Asked by: username_0 on 2023-10-12)
  
  I am wondering if it is required or not that the masks used for binary instance segmentation are complete.
  
  For instance, I want to find the buildings in aerial imagery. If my masks cover, let say between 25% and 75% of the training image buildings, can these samples be used for training the model? Does the masks should cover 100% of the buildings in the samples?
  
  I know it would be better to mask all buildings, but if the mask is not complete, can it be used for training or not?
  
  I am using a deeplabv3 model.
  
  Here an example of what I mean
  [![enter image description here](https://i.sstatic.net/MMMOg.png)](https://i.sstatic.net/MMMOg.png)
  
  **Tags:** image-segmentation, instance-segmentation
  
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  ## 1 answers
  
  ### Answer by username_1 on 2023-10-13. Score: 1
  
  You can train a model with that dataset, but it will perform worse compared to a higher fidelity dataset. Training on images with unannotated buildings is essentially instructing the model to stochastically ignore buildings, which is confusing.
  
  
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