# Data augmentation parameters

When I use data augmentation to increase the train dataset, should I use all augmentation techniques (parameters in keras)?

Which data augmentation parameters should use with flow_from_directory?

However, what if we for example were working on a model for self driving cars? Using the vertical_flip just doesn't make sense, because the car will (hopefully!) be er be driving along on its roof.
I would suggest starting with no augmentation and slowly adding one possibility at a time. For example, you record an accuracy of 80% with no augmentation. Then addfeaturewise_normalizatiom and featurewise_std_normalization giving you an accuracy of 85%. Then adding horizontal_flip gets you to 90%. Finally you try adding zca_whitening and that send you back down to 86%.