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Should validation images be augmented?
You should validate only on the original images. The augmentation is there so that it can help your model generalize better, but to evaluate your model you need actual images, not transformed ones.
Should you augment validation data?
Adding augmented data will not improve the accuracy of the validation. It will at best say something about how well your method responds to the data augmentation, and at worst ruin the validation results and interpretability. never do 3, as you will get leakage.
Should data augmentation be used on the validation set Why or why not How about on the test set?
Data augmentation is done only on training set as it helps the model become more generalize and robust. So there’s no point of augmenting the test set. In computer vision, you can use data augmentation during test time to obtain different views on the test image.
Is there a problem with data augmentation in validation?
There is no problem with augmenting validation data but it won’t increase the accuracy of the model. The goal of data augmentation is to generalize the model and make it learn more orientation of the images, such that the during testing the model is able to apprehend the test data well.
When is it OK to validate only with the original images?
Ideally, data augmentation is a step in your training pipeline, which comes after splitting your data into train/validation/test sets. Otherwise, you have the same data point in both training and testing, even if it a little rotated. So your training pipeline could be something like this:
Which is the best example of data augmentation?
This is, of course, an incredibly simplified example. You more than likely have more than a single image — you probably have 10s or 100s of images and now your goal is to turn that smaller set into 1000s of images for training. In those situations, dataset expansion and dataset generation may be worth exploring.
Why is data augmentation done only on training set?
Data augmentation is done only on training set as it helps the model become more generalize and robust. So there’s no point of augmenting the test set. Do it only on the training set. The reason why we use a training and a test set in the first place is that we want to estimate the error our system will have in reality.