When using image augmentation with the ImageDataGenerator what happens to your raw image data on disk?
ImageDataGenerator will NOT add new images to your data set in a sense that it will not make your epochs bigger. Instead, in each epoch it will provide slightly altered images (depending on your configuration). It will always generate new images, no matter how many epochs you have.
How do you use data augmentation in Tensorflow?
- Overview.
- Setup.
- Download a dataset.
- Use Keras preprocessing layers. Resizing and rescaling. Data augmentation. Two options to use the preprocessing layers. Apply the preprocessing layers to the datasets. Train a model.
- Using tf.image. Data augmentation. Random transformations. Apply augmentation to a dataset.
- Next steps.
How to make simple and efficient data augmentations?
With a simple function we can plot images from this dataset: Upsampled examples from the CIFAR dataset before any data augmentation was applied. To augment the dataset it can beneficial to make augmenter functions: a function that receives an image (a tf.Tensor) and returns a new augmented image.
How to use tf.cond for data augmentation?
To make sure that some part of our data retains it original dimension, a tf.cond call can be used. tf.cond expects three parameters: a predicate (or condition), a true function true_fn and a false function false_fn. The predicate should be an operation that evaluates to true or false, after which true_fn or false_fn is called respectively.
How to write your own TensorFlow data augmentation pipeline?
For finer control, you can write your own data augmentation pipelines or layers using tf.data and tf.image. You may also want to check out TensorFlow Addons Image: Operations and TensorFlow I/O: Color Space Conversions Since the flowers dataset was previously configured with data augmentation, let’s reimport it to start fresh.
How to augment a dataset using TensorFlow?
To augment the dataset it can beneficial to make augmenter functions: a function that receives an image (a tf.Tensor) and returns a new augmented image. By defining functions for each augmentation operation we can easily attach them to datasets and control when they are evaluated.