ARS-Aug
Augmented Random Search for Data Augmentation
Policy Found on CIFAR-10 and CIFAR-100:
[('Solarize', 0.66, 0.34), ('Equalize', 0.56, 0.21)],
[('Equalize', 0.43, 0.76), ('AutoContrast', 0.66, 0.98)],
[('Color', 0.72, 0.47), ('Contrast', 0.88, 0.86)],
[('Brightness', 0.84, 0.71), ('Color', 0.31, 0.74)],
[('Rotate', 0.68, 0.26), ('TranlateX', 0.38, 0.88)]]
[('TranslateY', 0.88, 0.96), ('TranslateY', 0.53, 0.79)],
[('AutoContrast', 0.44, 0.76), ('Solarize', 0.22, 0.48)],
[('AutoContrast', 0.93, 0.62), ('Solarize', 0.85, 0.26)],
[('Solarize', 0.55, 0.38), ('Equalize', 0.43, 0.68)],
[('TranslateY', 0.72, 0.93), ('AutoContrast', 0.83, 0.95)]]
[('Solarize', 0.43, 0.58), ('AutoContrast', 0.82, 0.26)],
[('TranslateY', 0.71, 0.79), ('AutoContrast', 0.81, 0.94)],
[('AutoContrast', 0.92, 0.18), ('TranslateY', 0.77, 0.85)],
[('Equalize', 0.71, 0.69), ('Color', 0.23, 0.33)],
[('Sharpness', 0.36, 0.98), ('Brightness', 0.72, 0.78)]]
[('Equalize', 0.74, 0.49), ('TranslateY', 0.86, 0.91)],
[('TranslateY', 0.82, 0.91), ('TranslateY', 0.96, 0.79)],
[('AutoContrast', 0.53, 0.37), ('Solarize', 0.39, 0.47)],
[('TranslateY', 0.22, 0.78), ('Color', 0.91, 0.65)],
[('Brightness', 0.82, 0.46), ('Color', 0.23, 0.91)]]
[('Cutout', 0.27, 0.45), ('Equalize', 0.37, 0.21)],
[('Color', 0.43, 0.23), ('Brightness', 0.65, 0.71)],
[('ShearX', 0.49, 0.31), ('AutoContrast', 0.92, 0.28)],
[('Equalize', 0.62, 0.59), ('Equalize', 0.38, 0.91)],
[('Solarize', 0.57, 0.31), ('Equalize', 0.61, 0.51)]]
Require:
Tensorflow, Python3
How to run: (Follow Autoaugment: Learning Augmentation Policies from Data https://arxiv.org/abs/1805.09501)
curl -o cifar-10-python.tar.gz https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
curl -o cifar-100-python.tar.gz https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
python train_cifar.py --model_name=wrn \
--checkpoint_dir=/tmp/training \
--data_path=/tmp/data \
--dataset='cifar10' \
--use_cpu=0