Train Wide-ResNet, Shake-Shake and ShakeDrop models on CIFAR-10
and CIFAR-100 dataset with AutoAugment.
The CIFAR-10/CIFAR-100 data can be downloaded from:
https://www.cs.toronto.edu/~kriz/cifar.html. Use the Python version instead of the binary version.
The code replicates the results from Tables 1 and 2 on CIFAR-10/100 with the
following models: Wide-ResNet-28-10, Shake-Shake (26 2x32d), Shake-Shake (26
2x96d) and PyramidNet+ShakeDrop.
Related papers:
AutoAugment: Learning Augmentation Policies from Data
https://arxiv.org/abs/1805.09501
Wide Residual Networks
https://arxiv.org/abs/1605.07146
Shake-Shake regularization
https://arxiv.org/abs/1705.07485
ShakeDrop regularization
https://arxiv.org/abs/1802.02375
Settings:
CIFAR-10 Model | Learning Rate | Weight Decay | Num. Epochs | Batch Size
---------------------- | ------------- | ------------ | ----------- | ----------
Wide-ResNet-28-10 | 0.1 | 5e-4 | 200 | 128
Shake-Shake (26 2x32d) | 0.01 | 1e-3 | 1800 | 128
Shake-Shake (26 2x96d) | 0.01 | 1e-3 | 1800 | 128
PyramidNet + ShakeDrop | 0.05 | 5e-5 | 1800 | 64
Prerequisite:
1. Install TensorFlow. Be sure to run the code using python2 and not python3.
2. Download CIFAR-10/CIFAR-100 dataset.
```shell
curl -o cifar-10-binary.tar.gz https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
curl -o cifar-100-binary.tar.gz https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
```
How to run:
```shell
# cd to the your workspace.
# Specify the directory where dataset is located using the data_path flag.
# Note: User can split samples from training set into the eval set by changing train_size and validation_size.
# For example, to train the Wide-ResNet-28-10 model on a GPU.
python train_cifar.py --model_name=wrn \
--checkpoint_dir=/tmp/training \
--data_path=/tmp/data \
--dataset='cifar10' \
--use_cpu=0
```
## Contact for Issues
* Barret Zoph, @barretzoph
* Ekin Dogus Cubuk,