I'm trying to classify rooftop sky images orientations, whether it is horizontal or vertical. Knowing that the most obvious feature here is known: orientation. I can simply augment each class by rotating it 90° so it belongs to the other class. It is like dynamic augmentation to different classes.
Using keras image classification capabilities, obviously defeats the purpose:
datagen = ImageDataGenerator( zoom_range=0.2, # randomly zoom into images # rotation_range=90, # mistaken the model horizontal_flip=True, # randomly flip images vertical_flip=False) # randomly flip images
I am using keras for classification but open to other libraries, and techniques other than deep learning.