A Pytorch Implementation of Fast AutoAugment and EfficientNet.
# 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
# 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
# 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
Pre-trained model [Download]
Validation Curve
Evaluation (Acc @1)
Valid | Test(Single) | |
---|---|---|
ResNet20 | 90.70 | 91.45 |
ResNet20 + FAA | 92.46 | 91.45 |
Validation Curve
Evaluation (Acc @1)
Valid | Test(Single) | |
---|---|---|
ResNet34 | 91.54 | 91.47 |
ResNet34 + FAA | 92.76 | 91.99 |