Answers.Body: <p>You can always train your network with higher resolution images. There is nothing preventing you to do that if you don't have any restriction about inference time. </p>
  
  <p>Also, the paper is actually mentioning how to upsample. Check for the phrase "Upsample using deconvolutional layers". </p>
  
  <p>The most common ways to upsample are either using the deconvolutional layer or just by simple resize methods (like image resize). </p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-02-28T02:26:34.057Z
Answers.Text: It kinda fascinates me to know that people just employ simple bilinear-interpolation image resize to scale up and down. Do you have experiment how much artifact that would introduce, say image and annotation might be mis-align due to the scaling process?
Answers.UserDisplayName: AugLe
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-02-28T09:56:18.270Z
Answers.Text: Nope. As soon as you scale as floating point, it should be fine.
Answers.UserDisplayName: Deniz Beker
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Answers.CreationDate: 2018-02-27T02:07:20.013Z
Answers.LastActivityDate: 2018-02-27T02:07:20.013Z
Answers.UserDisplayName: Deniz Beker
Body: <p>Many of the architectures that do semantic segmentation like SegNet, DilatedNet (Yu and Koltun), DeepLab, etc. do not work on high resolution images. For such benchmarks like <a href="https://www.cityscapes-dataset.com/benchmarks/#pixel-level-results" rel="nofollow noreferrer">Cityscapes</a>, what is a standard/practical approach for such methods to perform on the benchmark?</p>
  
  <p>I've tried to look into the paper, but I couldn't find such details. There's an <a href="http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review" rel="nofollow noreferrer">article</a> mentioning that they output at 1/8 of input images than do interpolation (usually 2, 4 or 8 times) from their results, but the article does not specify which upsampling techniques are the most reasonable one.</p>
  
Tags: <deep-learning><autonomous-vehicles>
Title: Semantic Segmentation how to upsampling
UserDisplayName: AugLe
ThreadText: # Semantic Segmentation how to upsampling
  
  (Asked by: username_0 on 2018-01-17)
  
  Many of the architectures that do semantic segmentation like SegNet, DilatedNet (Yu and Koltun), DeepLab, etc. do not work on high resolution images. For such benchmarks like [Cityscapes](https://www.cityscapes-dataset.com/benchmarks/#pixel-level-results), what is a standard/practical approach for such methods to perform on the benchmark?
  
  I've tried to look into the paper, but I couldn't find such details. There's an [article](http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review) mentioning that they output at 1/8 of input images than do interpolation (usually 2, 4 or 8 times) from their results, but the article does not specify which upsampling techniques are the most reasonable one.
  
  **Tags:** deep-learning, autonomous-vehicles
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2018-02-27. Score: 2
  
  You can always train your network with higher resolution images. There is nothing preventing you to do that if you don't have any restriction about inference time.
  
  Also, the paper is actually mentioning how to upsample. Check for the phrase "Upsample using deconvolutional layers".
  
  The most common ways to upsample are either using the deconvolutional layer or just by simple resize methods (like image resize).
  
  > **Comments:**
  > **username_0**: It kinda fascinates me to know that people just employ simple bilinear-interpolation image resize to scale up and down. Do you have experiment how much artifact that would introduce, say image and annotation might be mis-align due to the scaling process?  
  > **username_1**: Nope. As soon as you scale as floating point, it should be fine.  
  
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