Cutout / Random Erasing

This is a Cutout [1] / Random Erasing [2] implementation. In particular, it is easily used with ImageDataGenerator in Keras. Please check random_eraser.py for implementation details.

About Cutout / Random Erasing

Cutout or Random Erasing is a kind of image augmentation methods for convolutional neural networks (CNN). They are very similar methods and were proposed almost at the same time.

They try to regularize models using training images that are randomly masked with random values.

Usage

With ImageDataGenerator in Keras

It is very easy to use if you are using ImageDataGenerator in Keras; get eraser function by get_random_eraser(), and then pass it to ImageDataGenerator as preprocessing_function. By doing so, all images are randomly erased before standard augmentation done by ImageDataGenerator.

Please check cifar10_resnet.py, which is imported from official Keras examples.

What I did is adding only two lines:

...
from random_eraser import get_random_eraser  # added
...

    datagen = ImageDataGenerator(
    ...
        preprocessing_function=get_random_eraser(v_l=0, v_h=1))  # added

Erase a single image

Of cause, you can erase a single image using eraser function. Please note that eraser function works in inplace mode; the input image itself will be modified (therefore, img = eraser(img) can be replaced by eraser(img) in the following example).

from random_eraser import get_random_eraser
eraser = get_random_eraser()

# load image to img
img = eraser(img)

Pleae check example.ipynb for complete example.

Parameters

Parameters are fully configurable as:

get_random_eraser(p=0.5, s_l=0.02, s_h=0.4, r_1=0.3, r_2=1/0.3,
                  v_l=0, v_h=255, pixel_level=False)

• p : the probability that random erasing is performed
• s_l, s_h : minimum / maximum proportion of erased area against input image
• r_1, r_2 : minimum / maximum aspect ratio of erased area
• v_l, v_h : minimum / maximum value for erased area
• pixel_level : pixel-level randomization for erased area

Results

The original cifar10_resnet.py result (w/o cutout / random erasing):

Test loss: 0.539187009859
Test accuracy: 0.9077

With cutout / random erasing:

Test loss: 0.445597583055
Test accuracy: 0.9182

With cutout / random erasing (pixel-level):

Test loss: 0.446407950497
Test accuracy: 0.9213

References

[1] T. DeVries and G. W. Taylor, "Improved Regularization of Convolutional Neural Networks with Cutout," in arXiv:1708.04552, 2017.

[2] Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang, "Random Erasing Data Augmentation," in arXiv:1708.04896, 2017.