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
