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PRIME: A Few Primitives Can Boost Robustness to Common Corruptions
This is the official repository of PRIME, the data agumentation method introduced in the ECCV 2022 paper "PRIME: A Few Primitives Can Boost Robustness to Common Corruptions". PRIME is a generic, plug-n-play data augmentation scheme that consists of simple families of max-entropy image transformations for conferring robustness against common corruptions. PRIME leads to significant improvements in corruption robustness on multiple benchmarks.
<p align="center"> <img src="misc/prime-augmentations.png"/> </p>Pre-trained models
We provide different models trained with PRIME on CIFAR-10/100 and ImageNet datasets. You can download them from here.
Setup
This code has been tested with Python 3.8.5 and PyTorch 1.9.1. To install required dependencies run:
$ pip install -r requirements.txt
For corruption robustness evaluation, download and extract the CIFAR-10-C, CIFAR-100-C and ImageNet-C datasets from here.
Usage
We provide a script train.py for PRIME training on CIFAR-10/100, ImageNet-100 and ImageNet. For example, to train a ResNet-50 network on ImageNet with PRIME, run:
$ python -u train.py --config=config/imagenet_cfg.py \
--config.save_dir=<save_dir> \
--config.data_dir=<data_dir> \
--config.cc_dir=<common_corr_dir> \
--config.use_prime=True
Detailed configuration options for all the datasets can be found in config.
Results
Results on ImageNet/ImageNet-100 with a ResNet-50/ResNet-18 (†: without JSD loss)
| Dataset | Method | Clean ↑ | CC Acc ↑ | mCE ↓ |
|---|---|---|---|---|
| ImageNet | Standard | 76.1 | 38.1 | 76.1 |
| ImageNet | AugMix | 77.5 | 48.3 | 65.3 |
| ImageNet | DeepAugment | 76.7 | 52.6 | 60.4 |
| ImageNet | PRIME† | 77.0 | 55.0 | 57.5 |
| ImageNet | PRIME | 75.3 | 56.4 | 55.5 |
| ImageNet-100 | Standard | 88.0 | 49.7 | 100.0 |
| ImageNet-100 | AugMix | 88.7 | 60.7 | 79.1 |
| ImageNet-100 | DeepAugment | 86.3 | 67.7 | 68.1 |
| ImageNet-100 | PRIME | 85.9 | 71.6 | 61.0 |
Results on CIFAR-10/100 with a ResNet-18
| Dataset | Method | Clean ↑ | CC Acc ↑ | mCE ↓ |
|---|---|---|---|---|
| CIFAR-10 | Standard | 95.0 | 74.0 | 24.0 |
| CIFAR-10 | AugMix | 95.2 | 88.6 | 11.4 |
| CIFAR-10 | PRIME | 93.1 | 89.0 | 11.0 |
| CIFAR-100 | Standard | 76.7 | 51.9 | 48.1 |
| CIFAR-100 | AugMix | 78.2 | 64.9 | 35.1 |
| CIFAR-100 | PRIME | 77.6 | 68.3 | 31.7 |
Citing this work
@inproceedings{PRIME2022,
title = {PRIME: A Few Primitives Can Boost Robustness to Common Corruptions},
author = {Apostolos Modas and Rahul Rade and Guillermo {Ortiz-Jim\'enez} and Seyed-Mohsen {Moosavi-Dezfooli} and Pascal Frossard},
year = {2022},
booktitle = {European Conference on Computer Vision (ECCV)}
}