RandAugment layer

[source]

RandAugment class

keras_cv.layers.RandAugment(
    value_range,
    augmentations_per_image=3,
    magnitude=0.5,
    magnitude_stddev=0.15,
    rate=0.9090909090909091,
    geometric=True,
    seed=None,
    **kwargs
)

RandAugment performs the Rand Augment operation on input images.

This layer can be thought of as an all-in-one image augmentation layer. The policy implemented by this layer has been benchmarked extensively and is effective on a wide variety of datasets.

The policy operates as follows:

For each augmentation in the range [0, augmentations_per_image], the policy selects a random operation from a list of operations. It then samples a random number and if that number is less than rate applies it to the given image.

References

• RandAugment

Arguments

• value_range: the range of values the incoming images will have. Represented as a two number tuple written [low, high]. This is typically either [0, 1] or [0, 255] depending on how your preprocessing pipeline is set up.
• augmentations_per_image: the number of layers to use in the rand augment policy, defaults to 3.
• magnitude: magnitude is the mean of the normal distribution used to sample the magnitude used for each data augmentation. Magnitude should be a float in the range [0, 1]. A magnitude of 0 indicates that the augmentations are as weak as possible (not recommended), while a value of 1.0 implies use of the strongest possible augmentation. All magnitudes are clipped to the range [0, 1] after sampling. Defaults to 0.5.
• magnitude_stddev: the standard deviation to use when drawing values for the perturbations. Keep in mind magnitude will still be clipped to the range [0, 1] after samples are drawn from the normal distribution. Defaults to 0.15.
• rate: the rate at which to apply each augmentation. This parameter is applied on a per-distortion layer, per image. Should be in the range [0, 1]. To reproduce the original RandAugment paper results, set this to 10/11. The original RandAugment paper includes an Identity transform. By setting the rate to 10/11 in our implementation, the behavior is identical to sampling an Identity augmentation 10/11th of the time. Defaults to 1.0.
• geometric: whether to include geometric augmentations. This should be set to False when performing object detection. Defaults to True.

Example

(x_test, y_test), _ = keras.datasets.cifar10.load_data()
rand_augment = keras_cv.layers.RandAugment(
    value_range=(0, 255), augmentations_per_image=3, magnitude=0.5
)
x_test = rand_augment(x_test)