Unofficial implementation of the ImageNet, CIFAR 10 and SVHN Augmentation Policies learned by AutoAugment using pillow.
Unofficial implementation of the ImageNet, CIFAR10 and SVHN Augmentation Policies learned by AutoAugment, described in this Google AI Blogpost.
Update 18.06.2018: Changed order and functionality of many magnitudes. Higher magnitude now always apply the operation with higher intensity and the sign is randomly sampled (e.g. rotating for 20 degrees to the left or right). This seems to be more in line with how it was done in the paper (judging from the figures). Have asked the authors for more details and will update as soon as I know more.
Tested with Python 3.6. Needs pillow>=5.0.0
from autoaugment import ImageNetPolicy image = PIL.Image.open(path) policy = ImageNetPolicy() transformed = policy(image)
To see examples of all operations and magnitudes applied to images, take a look at AutoAugment_Exploration.ipynb.
Example as a PyTorch Transform - ImageNet
from autoaugment import ImageNetPolicy data = ImageFolder(rootdir, transform=transforms.Compose( [transforms.Resize(256), transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), ImageNetPolicy(), transforms.ToTensor(), transforms.Normalize(...)])) loader = DataLoader(data, ...)
From the paper it is not clear in what exact order to apply the preprocessing for ImageNet:
For baseline augmentation, we use the standard Inception-style pre-processing which involves scaling pixel values to [-1,1],
horizontal flips with 50% probability, and random distortions of colors. For models trained with AutoAugment, we use the baseline pre-processing
and the policy learned on ImageNet. We find that removing the random distortions of color does not change the results for AutoAugment.
Example as a PyTorch Transform - CIFAR10
from autoaugment import CIFAR10Policy data = ImageFolder(rootdir, transform=transforms.Compose( [transforms.RandomCrop(32, padding=4, fill=128), # gray fill value is important bc of the color operations transforms.RandomHorizontalFlip(), CIFAR10Policy(), transforms.ToTensor(), Cutout(n_holes=1, length=16), # (https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py) transforms.Normalize(...)])) loader = DataLoader(data, ...)
Example as a PyTorch Transform - SVHN
from autoaugment import SVHNPolicy data = ImageFolder(rootdir, transform=transforms.Compose( [SVHNPolicy(), transforms.ToTensor(), Cutout(n_holes=1, length=20), # (https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py) transforms.Normalize(...)])) loader = DataLoader(data, ...)
Results with AutoAugment
Generalizable Data Augmentations
Finally, we show that policies found on one task can generalize well across different models and datasets.
For example, the policy found on ImageNet leads to significant improvements on a variety of FGVC datasets. Even on datasets for
which fine-tuning weights pre-trained on ImageNet does not help significantly , e.g. Stanford
Cars  and FGVC Aircraft , training with the ImageNet policy reduces test set error by 1.16%
and 1.76%, respectively. This result suggests that transferring data augmentation policies offers an
alternative method for transfer learning.
Fine Grained Visual Classification Datasets
- How can you set the corner pixel values after rotating to gray instead of black?