Fast Autoaugment

A Pytorch Implementation of Fast AutoAugment and EfficientNet.

Prerequisite

• torch==1.1.0
• torchvision==0.2.2
• hyperopt==0.1.2
• future==0.17.1
• tb-nightly==1.15.0a20190622

Usage

Training

CIFAR10

# ResNet20 (w/o FastAutoAugment)
python train.py --seed=24 --scale=3 --optimizer=sgd --fast_auto_augment=False

# ResNet20 (w/ FastAutoAugment)
python train.py --seed=24 --scale=3 --optimizer=sgd --fast_auto_augment=True

# ResNet20 (w/ FastAutoAugment, Pre-found policy)
python train.py --seed=24 --scale=3 --optimizer=sgd --fast_auto_augment=True \
                --augment_path=runs/ResNet_Scale3_FastAutoAugment/augmentation.cp

# ResNet32 (w/o FastAutoAugment)
python train.py --seed=24 --scale=5 --optimizer=sgd --fast_auto_augment=False

# ResNet32 (w/ FastAutoAugment)
python train.py --seed=24 --scale=5 --optimizer=sgd --fast_auto_augment=True

# EfficientNet (w/ FastAutoAugment)
python train.py --seed=24 --pi=0 --optimizer=adam --fast_auto_augment=True \
                --network=efficientnet_cifar10 --activation=swish

ImageNet (You can use any backbone networks in torchvision.models)

# BaseNet (w/o FastAutoAugment)
python train.py --seed=24 --dataset=imagenet --optimizer=adam --network=resnet50

# EfficientNet (w/ FastAutoAugment) (UnderConstruction)
python train.py --seed=24 --dataset=imagenet --pi=0 --optimizer=adam --fast_auto_augment=True \
                --network=efficientnet --activation=swish

Eval

# Single Image testing
python eval.py --model_path=runs/ResNet_Scale3_Basline

# 5-crops testing
python eval.py --model_path=runs/ResNet_Scale3_Basline --five_crops=True

Experiments

Fast AutoAugment

ResNet20 (CIFAR10)

• Pre-trained model [Download]
• Validation Curve

• Evaluation (Acc @1)

	Valid	Test(Single)
ResNet20	90.70	91.45
ResNet20 + FAA	92.46	91.45

ResNet34 (CIFAR10)

• Validation Curve

• Evaluation (Acc @1)

	Valid	Test(Single)
ResNet34	91.54	91.47
ResNet34 + FAA	92.76	91.99

Found Policy [Download]

Augmented images