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Showing content from https://keras.io/api/layers/preprocessing_layers/image_augmentation/aug_mix below:

AugMix layer

[source]

keras.layers.AugMix(
    value_range=(0, 255),
    num_chains=3,
    chain_depth=3,
    factor=0.3,
    alpha=1.0,
    all_ops=True,
    interpolation="bilinear",
    seed=None,
    data_format=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, the same augmentation is applied across all images in the batch in num_chains different ways, with each chain consisting of chain_depth augmentations.

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.
• num_chains: an integer representing the number of different chains to be mixed, defaults to 3.
• chain_depth: an integer representing the maximum number of transformations to be applied in each chain. The actual number of transformations in each chain will be sampled randomly from the range [0,chain_depth]. Defaults to 3.
• factor: The strength of the augmentation as a normalized value between 0 and 1. Default is 0.3.
• alpha: a float value used as the probability coefficients for the Beta and Dirichlet distributions, defaults to 1.0.
• all_ops: Use all operations (including random_brightness, random_color_degeneration, random_contrast and random_sharpness). Default is True.
• interpolation: The interpolation method to use for resizing operations. Options include "nearest", "bilinear". Default is "bilinear".
• seed: Integer. Used to create a random seed.

References

• AugMix paper - Official Code

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