ildoonet / Pytorch Randaugment
Licence: mit
Unofficial PyTorch Reimplementation of RandAugment.
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pytorch-randaugment
Unofficial PyTorch Reimplementation of RandAugment. Most of codes are from Fast AutoAugment.
Introduction
Models can be trained with RandAugment for the dataset of interest with no need for a separate proxy task. By only tuning two hyperparameters(N, M), you can achieve competitive performances as AutoAugments.
Install
$ pip install git+https://github.com/ildoonet/pytorch-randaugment
Usage
from torchvision.transforms import transforms
from RandAugment import RandAugment
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(_CIFAR_MEAN, _CIFAR_STD),
])
# Add RandAugment with N, M(hyperparameter)
transform_train.transforms.insert(0, RandAugment(N, M))
Experiment
We use same hyperparameters as the paper mentioned. We observed similar results as reported.
You can run an experiment with,
$ python RandAugment/train.py -c confs/wresnet28x10_cifar10_b256.yaml --save cifar10_wres28x10.pth
CIFAR-10 Classification
| Model | Paper's Result | Ours |
|---|---|---|
| Wide-ResNet 28x10 | 97.3 | 97.4 |
| Shake26 2x96d | 98.0 | 98.1 |
| Pyramid272 | 98.5 |
CIFAR-100 Classification
| Model | Paper's Result | Ours |
|---|---|---|
| Wide-ResNet 28x10 | 83.3 | 83.3 |
SVHN Classification
| Model | Paper's Result | Ours |
|---|---|---|
| Wide-ResNet 28x10 | 98.9 | 98.8 |
ImageNet Classification
I have experienced some difficulties while reproducing paper's result.
Issue : https://github.com/ildoonet/pytorch-randaugment/issues/9
| Model | Paper's Result | Ours |
|---|---|---|
| ResNet-50 | 77.6 / 92.8 | TODO |
| EfficientNet-B5 | 83.2 / 96.7 | TODO |
| EfficientNet-B7 | 84.4 / 97.1 | TODO |
References
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