How to train a keras model on both original and augmented data from ImageDataGenerator? I have a dataset that contains about 87000 images in a directory, with each class in a separate subfolder. I've tried the class ImageDataGenerator() and the function flow_from_directory() for generating the images, it worked completely fine but I have a question..
Does flow_from_directory() only yield the augmented images? and if this is the case, how can I train my model &quot;which has overfit the training set&quot; on both original and augmented data?
Thanks
ImageDataGenerator do augmentation on the fly base on the setting you give , so it did not separate “original” or “augmented “ data , just the possibility of data been augmented.
so your question actually is “ how to tune model when overfitting happens ”? I think you can start from adding dropout or reduce number of parameters. Hope this helps, cheers.