Random Erasing
Another data augmentation technique known to help the classification accuracy is Random Erasing [10], [11]. Often paired with Automatic Augmentation methods, it usually yields additional improvements in accuracy due to its regularization effect. In our experiments we tuned only the probability of applying the method via a grid search and found that it’s beneficial to keep its probability at low levels, typically around 10%. 
Here is the extra parameter introduced on top of the previous:
random_erase=0.1,

Applying Random Erasing increases our Acc@1 by further 0.190 points.
Label Smoothing