After loading in the data from the train and test data folders and setting up our simple base model, we decided it would be worth the effort to figure out how to upload the data in the TFRecords format. TFRecords is a binary storage format specifically designed to expedite performance and training time of models built in Tensor Flow. In essence, data in the TFRecords format is optimized for use with Tensorflow in various aspects. Despite the previously mentioned advantages of using this data format, getting the data into a format that is ready to feed into a model is not straightforward. Doing so requires defining functions to read the files and decode the images contained in those files. It is also logical to augment the data (flip, randomly change brightness, add saturation, etc.) in this step since the images will eventually be reshaped into arrays.