GaussianMix: Data Augmentation focusing the structure of the Convolution Network

Prerequisites

• Python 3.5
• PyTorch 1.0
• GPU (recommended)

Datasets

• CIFAR-10/100: automatically downloaded by PyTorch scripts to data folder
• ImageNet: manually downloaded from ImageNet (ILSVRC2012 version) and moved to train and val folders in your dataroot path (e.g., ./imagenet/)

How to Train

Our script occupies all available GPUs. Please set environment CUDA_VISIBLE_DEVICES.

CIFAR-10 and WideResNet28-10

with SICAP

python main.py --dataset cifar10 --model WideResNetDropout --depth 28 --params 10 --beta_of_sicap 1.0 --postfix sicap1.0

without SICAP

python main.py --dataset cifar10 --model WideResNetDropout --depth 28 --params 10

We trained these models on a single GPU (GeForce GTX 1080).

CIFAR-100 and WideResNet28-10

with SICAP

python main.py --dataset cifar100 --model WideResNetDropout --depth 28 --params 10 --beta_of_sicap 0.3 --postfix SICAP1.0

without SICAP

python main.py --dataset cifar100 --model WideResNetDropout --depth 28 --params 10

We trained these models on a single GPU (GeForce GTX 1080).

ImageNet and WideResNetBottleneck50-2 for 100 epochs

with SICAP

python main.py --dataset ImageNet --dataroot [your imagenet folder path(like ./imagenet)] --model WideResNetBottleneck --depth 50 --epoch 100 --adlr 30,60,90 --droplr 0.1 --wd 1e-4 --batch 256 --params 2 --beta_of_ricap 0.3 --postfix ricap0.3

without SICAP

python main.py --dataset ImageNet --dataroot [your imagenet folder path(like ./imagenet)] --model WideResNetBottleneck --depth 50 --epoch 100 --adlr 30,60,90 --droplr 0.1 --wd 1e-4 --batch 256 --params 2