Building Complex Image Augmentation Pipelines with Tensorflow

If you want to train your models with Tensorflow in the most efficient way you probably should use TFRecords and the Tensorflow data module to build your pipelines, but depending on the requirements and constraints of your applications, using them might be necessary not and an option, the good news is that Tensorflow has made both of them pretty clean and easy to use. In this article, we will go through a simple yet efficient way of building pipelines with complex combinations of data augmentation using the Tensorflow data module. One of the options I mentioned that could improve your models' training, is to use TFRecords, TFRecord is a simple format provided by Tensorflow for storing data, I am not going into too many details about TFRecords because it is not the focus of this article but if you want to learn more check out this tutorial from Tensorflow. The information provided here can be applied to train models with Tensorflow in any hardware, I am going to use TPU as the target hardware because if you are using TPUs, probably you are already trying to make the most of your resources, and you would need to use the Tensorflow data module anyway. First, we will begin by taking a look at how data augmentation is done at the official data augmentation tutorial by Tensorflow.