How to train a keras model on both original and augmented data from ImageDataGenerator?

Question

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 "which has overfit the training set" on both original and augmented data?
Thanks

黃仲民 · Answer

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.

Harish Vutukuri · Answer

Below are the things can be done in order to reduce variance (overfitting):

Add more training data.
Normalization (BatchNorm, LayerNorm)
Data Augmentation
Regularization (Dropout, L2, WeightDecay)
Error Analysis
Tune Hyperparameters
Early Stopping
Use better state of the art model or transfer learning

How to train a keras model on both original and augmented data from ImageDataGenerator?

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