Answers.Body: <p>As it is told in <a href="https://pillow.readthedocs.io/en/stable/index.html" rel="nofollow noreferrer">PIL documentation</a></p>
  <p>It uses some filters to resize images.And those filters are explained <a href="https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-filters" rel="nofollow noreferrer">here</a>
  uses mostly numerical methods as I see. So it is <em>approximating</em> the image data input. Which means you are right about data loss. But here might be the question would it change the data so much If it is done after augmentation or before?</p>
  <p>Since in numerical methodic approaches the more values means more valid approximation in general.</p>
  <p>So it might be beneficial to augment and then resize.</p>
  <p>But one should look for its <em>real</em> answer. Mine is just a thought experiment. You will get better <em>proven</em> answers</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-09-07T20:36:20.267Z
Answers.Text: Awesome, thanks! I'm going to look around for similar studies done, and perhaps perform my own.
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Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-09-07T19:26:25.287Z
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Body: <p>For the purposes of training a Convolutional Neural Network Classifier, should image augmentation be done before or after resizing the training images?</p>
  <p>To reduce file size and speed up training time, developers often resize training images to a set height and width using something like PIL (Python Imaging Library).</p>
  <p>If the images are augmented (to increase training set size), should it be done before or after resizing the members of the set?</p>
  <p>For simplicity sake, it would probably be faster to augment the images after resizing, but I am wondering if any useful data is lost in this process. I assume it may depend on the method used to resize the images (cropping, scaling technique, etc.)</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-09-07T19:18:14.687Z
Comments.Text: It is beneficial to check resize function built-in PIL here: https://pillow.readthedocs.io/en/stable/_modules/PIL/Image.html#Image.resize
Comments.UserDisplayName: Nabla
Tags: <convolutional-neural-networks><python><image-processing>
Title: Should image augmentation be applied before or after image resizing?
UserDisplayName: justanotheruser
ThreadText: # Should image augmentation be applied before or after image resizing?
  
  (Asked by: username_0 on 2020-09-07)
  
  For the purposes of training a Convolutional Neural Network Classifier, should image augmentation be done before or after resizing the training images?
  
  To reduce file size and speed up training time, developers often resize training images to a set height and width using something like PIL (Python Imaging Library).
  
  If the images are augmented (to increase training set size), should it be done before or after resizing the members of the set?
  
  For simplicity sake, it would probably be faster to augment the images after resizing, but I am wondering if any useful data is lost in this process. I assume it may depend on the method used to resize the images (cropping, scaling technique, etc.)
  
  **Tags:** convolutional-neural-networks, python, image-processing
  > **Comments:**
  > **username_1**: It is beneficial to check resize function built-in PIL here: https://pillow.readthedocs.io/en/stable/_modules/PIL/Image.html#Image.resize  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-09-07. Score: 0 | Accepted answer
  
  As it is told in [PIL documentation](https://pillow.readthedocs.io/en/stable/index.html)
  
  It uses some filters to resize images.And those filters are explained [here](https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-filters)
  uses mostly numerical methods as I see. So it is *approximating* the image data input. Which means you are right about data loss. But here might be the question would it change the data so much If it is done after augmentation or before?
  
  Since in numerical methodic approaches the more values means more valid approximation in general.
  
  So it might be beneficial to augment and then resize.
  
  But one should look for its *real* answer. Mine is just a thought experiment. You will get better *proven* answers
  
  > **Comments:**
  > **username_0**: Awesome, thanks! I'm going to look around for similar studies done, and perhaps perform my own.  
  
  ---
  
  