When using Data augmentation is it ok to validate only with the original images?
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.

To do this in keras you need to define two instances of the ImageDataGenerator, one for training and one for validating. To train the model you need to set both generators to the fit_generator function.

train_gen = ImageDataGenerator(aug_params).flow_from_directory(train_dir)
valid_gen = ImageDataGenerator().flow_from_directory(valid_dir)

model.fit_generator(train_gen, validation_data=valid_gen)


It is possible to achieve a higher validation accuracy than a train accuracy if you heavily augment the training data.