Body: <p>I am wondering how much I should extend my training set with data augmentation. Is there somewhere a pre-defined number I can go with?</p>
  <p>Suppose I have 10000 images, can I go as far as 10x or 20x times, to get 100000 and 200000, respectively, images? I am wondering how will this impact model training. I am using a mask R-CNN.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-09-08T14:21:47.157Z
Comments.Text: If hardware and training time isn't a limitation, just try it and see when validation accuracy starts to decrease.
Comments.UserDisplayName: JobHunter69
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-09-09T10:34:13.480Z
Comments.Text: Maybe you should describe which model you are trying to use (I suppose it's a neural network) and how you are augmenting your training data. There are several ways you can augment the training data.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-09-09T12:29:51.513Z
Comments.Text: @nbro Mask RCNN, this is the model.
Comments.UserDisplayName: Ad Blu
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-09-09T23:46:33.177Z
Comments.Text: Literally augment as much as possible as long as a valid image is still produced. The gold standard for CV is human equivalent understanding. Now you dont need to preagument them, they can be augmented at sampling time.
Comments.UserDisplayName: FourierFlux
Tags: <machine-learning><data-preprocessing><data-augmentation><mask-rcnn>
Title: How much should we augment our training data?
UserDisplayName: Ad Blu
ThreadText: # How much should we augment our training data?
  
  (Asked by: username_0 on 2020-09-08)
  
  I am wondering how much I should extend my training set with data augmentation. Is there somewhere a pre-defined number I can go with?
  
  Suppose I have 10000 images, can I go as far as 10x or 20x times, to get 100000 and 200000, respectively, images? I am wondering how will this impact model training. I am using a mask R-CNN.
  
  **Tags:** machine-learning, data-preprocessing, data-augmentation, mask-rcnn
  
  > **Comments:**
  > **username_1**: If hardware and training time isn't a limitation, just try it and see when validation accuracy starts to decrease.  
  > **username_2**: Maybe you should describe which model you are trying to use (I suppose it's a neural network) and how you are augmenting your training data. There are several ways you can augment the training data.  
  > **username_0**: @username_2 Mask RCNN, this is the model.  
  > **username_3**: Literally augment as much as possible as long as a valid image is still produced. The gold standard for CV is human equivalent understanding. Now you dont need to preagument them, they can be augmented at sampling time.  
  
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  ## 0 answers
  
  