so no augmentation and make this augment it right that's that's how you can think of it the fact that there is weak and here strong augmentation I think is just a your classic trick to get more training data but in essence you can think of it as this is here the clean thing you just want to produce a label and then you want the that an Augmented version of the image has the same label now you can think of it shortly what does this model learn if you just have this you remember I think the important thing is always to remember that there