AutoAugment - Learning Augmentation Policies from Data
Unofficial implementation of the ImageNet, CIFAR10 and SVHN Augmentation Policies learned by AutoAugment, described in this Google AI Blogpost.
Update July 13th, 2018: Wrote a Blogpost about AutoAugment and Double Transfer Learning.
Tested with Python 3.6. Needs pillow>=5.0.0
Example
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.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(), ImageNetPolicy(),
transforms.ToTensor(), transforms.Normalize(...)]))
loader = DataLoader(data, ...)
Example as a PyTorch Transform - CIFAR10
from autoaugment import CIFAR10Policy
data = ImageFolder(rootdir, transform=transforms.Compose(
[transforms.RandomCrop(32, padding=4, fill=128), # fill parameter needs torchvision installed from source
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 [26], e.g. Stanford Cars [27] and FGVC Aircraft [28], 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.
CIFAR 10
CIFAR 100
ImageNet
SVHN
Fine Grained Visual Classification Datasets
6 Sep 28, 2022
3 Nov 13, 2022
16 Nov 26, 2022
4 Jul 27, 2022
37 Mar 01, 2022
5 Dec 08, 2022
0 Dec 13, 2021
153 Dec 02, 2022
11 Dec 13, 2022
124 Dec 27, 2022
1 Jan 04, 2022
2.4k Jan 04, 2023
14 Apr 26, 2022
11 Dec 15, 2022
242 Dec 20, 2022
1 Dec 17, 2021
134 Dec 19, 2022
1 Jan 14, 2022
13 Nov 21, 2022
422 Jan 08, 2023