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https://github.com/DeepVoltaire/AutoAugment
Unofficial implementation of the ImageNet, CIFAR 10 and SVHN Augmentation Policies learned by AutoAugment using pillow
https://github.com/DeepVoltaire/AutoAugment
Last synced: 9 days ago
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Unofficial implementation of the ImageNet, CIFAR 10 and SVHN Augmentation Policies learned by AutoAugment using pillow
- Host: GitHub
- URL: https://github.com/DeepVoltaire/AutoAugment
- Owner: DeepVoltaire
- License: mit
- Created: 2018-06-05T18:36:14.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-05-16T16:40:38.000Z (over 1 year ago)
- Last Synced: 2024-11-05T03:42:31.480Z (9 days ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 29.4 MB
- Stars: 1,463
- Watchers: 22
- Forks: 199
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AutoAugment - Learning Augmentation Policies from Data
Unofficial implementation of the ImageNet, CIFAR10 and SVHN Augmentation Policies learned by [AutoAugment](https://arxiv.org/abs/1805.09501v1), described in this [Google AI Blogpost](https://ai.googleblog.com/2018/06/improving-deep-learning-performance.html).
__Update July 13th, 2018:__ Wrote a [Blogpost](https://towardsdatascience.com/how-to-improve-your-image-classifier-with-googles-autoaugment-77643f0be0c9) about AutoAugment and Double Transfer Learning.
##### Tested with Python 3.6. Needs pillow>=5.0.0
------------------
## Example
```python
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](AutoAugment_Exploration.ipynb).
## Example as a PyTorch Transform - ImageNet
```python
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
```python
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
```python
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
