Keras
Keras 3 API documentation
KerasCV
Layers
Augmentation layers
RandomCutout layer

RandomCutout layer

source

RandomCutout class 

keras_cv.layers.RandomCutout(
    height_factor, width_factor, fill_mode="constant", fill_value=0.0, seed=None, **kwargs
)

Randomly cut out rectangles from images and fill them.

Arguments

  • height_factor: A tuple of two floats, a single float or a keras_cv.FactorSampler. height_factor controls the size of the cutouts. height_factor=0.0 means the rectangle will be of size 0% of the image height, height_factor=0.1 means the rectangle will have a size of 10% of the image height, and so forth. Values should be between 0.0 and 1.0. If a tuple is used, a height_factor is sampled between the two values for every image augmented. If a single float is used, a value between 0.0 and the passed float is sampled. In order to ensure the value is always the same, please pass a tuple with two identical floats: (0.5, 0.5).
  • width_factor: A tuple of two floats, a single float or a keras_cv.FactorSampler. width_factor controls the size of the cutouts. width_factor=0.0 means the rectangle will be of size 0% of the image height, width_factor=0.1 means the rectangle will have a size of 10% of the image width, and so forth. Values should be between 0.0 and 1.0. If a tuple is used, a width_factor is sampled between the two values for every image augmented. If a single float is used, a value between 0.0 and the passed float is sampled. In order to ensure the value is always the same, please pass a tuple with two identical floats: (0.5, 0.5).
  • fill_mode: Pixels inside the patches are filled according to the given mode (one of {"constant", "gaussian_noise"}).
    • constant: Pixels are filled with the same constant value.
    • gaussian_noise: Pixels are filled with random gaussian noise.
  • fill_value: a float represents the value to be filled inside the patches when fill_mode="constant".
  • seed: Integer. Used to create a random seed.

Example

(images, labels), _ = keras.datasets.cifar10.load_data()
random_cutout = keras_cv.layers.preprocessing.RandomCutout(0.5, 0.5)
augmented_images = random_cutout(images)