The implementation of SODA on CIFAR-10-C, CIFAR-100-C and ImageNet-C.
- python == 3.10.8
- cudatoolkit == 11.7
- pytorch ==1.13.1
- torchvision == 0.14.1
- numpy, PIL, argparse, collections, math, random
Please download and organize CIFAR-10-C, CIFAR-100-C and ImageNet-C in this structure:
(ImageNet-C data can also be generated following instructions in this repository)
BETA
├── data
├──CIFAR-10
│ ├── CIFAR-10-C
│ │ ├── brightness.npy
│ │ ├── contrast.npy
│ │ ├── ...
│ │ ├── labels.npy
├──CIFAR-100
│ ├── CIFAR-100-C
│ │ ├── brightness.npy
│ │ ├── contrast.npy
│ │ ├── ...
│ │ ├── labels.npy
├──ImageNet
│ ├── ImageNet-C
│ │ ├── brightness.pth
│ │ ├── contrast.pth
│ │ ├── ...
│ │ ├── labels.pth
The checkpoints of pre-trained Resnet-50 can be downloaded (197MB) using the following command:
mkdir -p results/cifar10_joint_resnet50 && cd results/cifar10_joint_resnet50
gdown https://drive.google.com/uc?id=1MZN19o-5b2w-BI1ObIlnsJ8XBZvMuL77 && cd ../..
mkdir -p results/cifar100_joint_resnet50 && cd results/cifar100_joint_resnet50
gdown https://drive.google.com/uc?id=1C7knE2S9kKDYZrqd4Bo4S5lOgp7Le_DP && cd ../..
mkdir -p results/imagenet && cd results/imagenet
gdown https://drive.google.com/uc?id=1GSGzOv0MNBBMEYeRRQlp1WGD1USDl0iP && cd ../..
The CIFAR-10/100 pre-trained models are obtained by training on the clean CIFAR-10/100 images using semi-supervised SimCLR. The ImageNet pre-trained model is obtained from TorchVision
# offline SODA
bash scripts/run_offline_soda_10.sh
# offline SODA-R
bash scripts/run_offline_soda_r_10.sh
# offline MA-SO
bash scripts/run_offline_ma_10.sh
# online SODA-O
bash scripts/run_online_soda_10.sh
# offline SODA
bash scripts/run_offline_soda_100.sh
# offline SODA-R
bash scripts/run_offline_soda_r_100.sh
# offline MA-SO
bash scripts/run_offline_ma_100.sh
# online SODA-O
bash scripts/run_online_soda_100.sh
# offline SODA
bash scripts/run_offline_soda_imagenet.sh
# offline SODA-R
bash scripts/run_offline_soda_r_imagenet.sh
# offline MA-SO
bash scripts/run_offline_ma_imagenet.sh
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent