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► Keras 3 API documentation / Layers API / Preprocessing layers / Image augmentation layers / RandomErasing layer

RandomErasing layer

[source]

RandomErasing class

keras.layers.RandomErasing(
    factor=1.0,
    scale=(0.02, 0.33),
    fill_value=None,
    value_range=(0, 255),
    seed=None,
    data_format=None,
    **kwargs
)

Random Erasing data augmentation technique.

Random Erasing is a data augmentation method where random patches of an image are erased (replaced by a constant value or noise) during training to improve generalization.

Note: This layer is safe to use inside a tf.data or grain pipeline (independently of which backend you're using).

References

Arguments

  • factor: A single float or a tuple of two floats. factor controls the probability of applying the transformation.
    • factor=0.0 ensures no erasing is applied.
    • factor=1.0 means erasing is always applied.
    • If a tuple (min, max) is provided, a probability value is sampled between min and max for each image.
    • If a single float is provided, a probability is sampled between 0.0 and the given float. Default is 1.0.
  • scale: A tuple of two floats representing the aspect ratio range of the erased patch. This defines the width-to-height ratio of the patch to be erased. It can help control the rw shape of the erased region. Default is (0.02, 0.33).
  • fill_value: A value to fill the erased region with. This can be set to a constant value or None to sample a random value from a normal distribution. Default is None.
  • 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.
  • seed: Integer. Used to create a random seed.
RandomErasing layer
RandomErasing class