<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>

CC BY-SA 4.0
2020-09-08T14:21:47.157Z
If hardware and training time isn't a limitation, just try it and see when validation accuracy starts to decrease.
JobHunter69
CC BY-SA 4.0
2020-09-09T10:34:13.480Z
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.
nbro
CC BY-SA 4.0
2020-09-09T12:29:51.513Z
@nbro Mask RCNN, this is the model.
Ad Blu
CC BY-SA 4.0
2020-09-09T23:46:33.177Z
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.
FourierFlux
<machine-learning><data-preprocessing><data-augmentation><mask-rcnn>
How much should we augment our training data?
Ad Blu
# 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

