AugMix layer

[source]

AugMix class

keras_cv.layers.AugMix(
    value_range,
    severity=0.3,
    num_chains=3,
    chain_depth=[1, 3],
    alpha=1.0,
    seed=None,
    **kwargs
)

Performs the AugMix data augmentation technique.

AugMix aims to produce images with variety while preserving the image semantics and local statistics. During the augmentation process, each image is augmented num_chains different ways, each way consisting of chain_depth augmentations. Augmentations are sampled from the list: translation, shearing, rotation, posterization, histogram equalization, solarization and auto contrast. The results of each chain are then mixed together with the original image based on random samples from a Dirichlet distribution.

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.
• severity: A tuple of two floats, a single float or a keras_cv.FactorSampler. A value is sampled from the provided range. If a float is passed, the range is interpreted as (0, severity). This value represents the level of strength of augmentations and is in the range [0, 1]. Defaults to 0.3.
• num_chains: an integer representing the number of different chains to be mixed, defaults to 3.
• chain_depth: an integer or range representing the number of transformations in the chains. If a range is passed, a random chain_depth value sampled from a uniform distribution over the given range is called at the start of the chain. Defaults to [1,3].
• alpha: a float value used as the probability coefficients for the Beta and Dirichlet distributions, defaults to 1.0.
• seed: Integer. Used to create a random seed.

References

• AugMix paper - Official Code - Unofficial TF Code

Sample Usage:

(images, labels), _ = keras.datasets.cifar10.load_data()
augmix = keras_cv.layers.AugMix([0, 255])
augmented_images = augmix(images[:100])