AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Google Research

Last update: Dec 17, 2022

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Deep Learning augmix

Overview

AugMix

Introduction

We propose AugMix, a data processing technique that mixes augmented images and enforces consistent embeddings of the augmented images, which results in increased robustness and improved uncertainty calibration. AugMix does not require tuning to work correctly, as with random cropping or CutOut, and thus enables plug-and-play data augmentation. AugMix significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance by more than half in some cases. With AugMix, we obtain state-of-the-art on ImageNet-C, ImageNet-P and in uncertainty estimation when the train and test distribution do not match.

For more details please see our ICLR 2020 paper.

Pseudocode

This directory includes a reference implementation in NumPy of the augmentation method used in AugMix in augment_and_mix.py. The full AugMix method also adds a Jensen-Shanon Divergence consistency loss to enforce consistent predictions between two different augmentations of the input image and the clean image itself.

We also include PyTorch re-implementations of AugMix on both CIFAR-10/100 and ImageNet in cifar.py and imagenet.py respectively, which both support training and evaluation on CIFAR-10/100-C and ImageNet-C.

Requirements

numpy>=1.15.0
Pillow>=6.1.0
torch==1.2.0
torchvision==0.2.2

Setup

Install PyTorch and other required python libraries with:
```
pip install -r requirements.txt
```

Download CIFAR-10-C and CIFAR-100-C datasets with:

mkdir -p ./data/cifar
curl -O https://zenodo.org/record/2535967/files/CIFAR-10-C.tar
curl -O https://zenodo.org/record/3555552/files/CIFAR-100-C.tar
tar -xvf CIFAR-100-C.tar -C data/cifar/
tar -xvf CIFAR-10-C.tar -C data/cifar/

Download ImageNet-C with:

mkdir -p ./data/imagenet/imagenet-c
curl -O https://zenodo.org/record/2235448/files/blur.tar
curl -O https://zenodo.org/record/2235448/files/digital.tar
curl -O https://zenodo.org/record/2235448/files/noise.tar
curl -O https://zenodo.org/record/2235448/files/weather.tar
tar -xvf blur.tar -C data/imagenet/imagenet-c
tar -xvf digital.tar -C data/imagenet/imagenet-c
tar -xvf noise.tar -C data/imagenet/imagenet-c
tar -xvf weather.tar -C data/imagenet/imagenet-c

Usage

The Jensen-Shannon Divergence loss term may be disabled for faster training at the cost of slightly lower performance by adding the flag --no-jsd.

Training recipes used in our paper:

WRN: python cifar.py

AllConv: python cifar.py -m allconv

ResNeXt: python cifar.py -m resnext -e 200

DenseNet: python cifar.py -m densenet -e 200 -wd 0.0001

ResNet-50: python imagenet.py <path/to/imagenet> <path/to/imagenet-c>

Pretrained weights

Weights for a ResNet-50 ImageNet classifier trained with AugMix for 180 epochs are available here.

This model has a 65.3 mean Corruption Error (mCE) and a 77.53% top-1 accuracy on clean ImageNet data.

Citation

If you find this useful for your work, please consider citing

@article{hendrycks2020augmix,
  title={{AugMix}: A Simple Data Processing Method to Improve Robustness and Uncertainty},
  author={Hendrycks, Dan and Mu, Norman and Cubuk, Ekin D. and Zoph, Barret and Gilmer, Justin and Lakshminarayanan, Balaji},
  journal={Proceedings of the International Conference on Learning Representations (ICLR)},
  year={2020}
}

Comments

Testing with the best model

I found that cifar.py code does not test the performance with the best model. https://github.com/google-research/augmix/blob/7c84885fe064435b4b7a8b596b937f0a879e458c/cifar.py#L431

In addition, why the validation set of CIFAR is not used in the model selection ??

opened by LeeDoYup 4
augmentations used in augmix
Hi,

I have a couple of questions about the augmentations used in augmix -

The AugMix paper mentions that contrast augmentations were removed from augmix as that would overlap with one of the tested corruptions (Contrast) - but I see that AutoContrast is still used in the code: https://github.com/google-research/augmix/blob/master/augmentations.py#L141

I am curious how or why the augmentations in augmix impact performance on these corruptions as the connection between them is not immediately clear. Do you have a take on this, perhaps through an ablation study of the augmentations in augmix?

Thank you.
opened by kiranchari 3
Issue loading pretrained weights

When I download the checkpoint.pth.tar file for the pretrained ImageNet model I get the error tar: Error opening archive: Unrecognized archive format, would it be possible to release the weights in another format (like a zip perhaps?) Unfortunately I don't have access to the ImageNet dataset so I can't retrain it myself

opened by humzaiqbal 3
Can you share any acc information per epoch?

Hi Author I am a college student studying deep learning in Korea. I read your paper very interesting. But I am running AugMix on imagenet and I have to wait 6 days with my GPU machine. So I'm wondering if learning is going well. To find out the trend in the middle, can you share any acc information per epoch?

opened by seominseok0429 3
ImageNet hparams
I'm having trouble achieving decent ImageNet results with the mixing + JSD loss. Are the hparams in the imagenet.py script what was used for paper results, was the same code used for the paper?

Any details on hparams for the paper results for ImageNet would be appreciated. Are these correct?

Epochs = 90 or 180?

JSD loss lambda = 12

batch size =256, that represents an effective batch size of 256*3=768, which is large for ResNet50 at FP32 and suggests 4+ GPU?

LR = 0.1

AugMix severity = 1

AugMix prob coeff = 0.1

Thanks
opened by rwightman 3
Corruption acc. of a Resnet50 trained on cifar10 with augmix

Hi there, I trained a Resnet50 on CIFAR10 using the cifar.py script in this repository.

The clean acc. was about 95% but corruption accuracy was less than reported in the original paper. I have pasted below accuracies for CIFAR10-C at Severity 5. The mean corruption acc. across all corruptions and severity levels was 81%. I understand the architecture used in the original work was different, so is this an expected corruption acc. variation with the architecture?

Thank you.

Corruption severity: 5 gaussian_noise 0.6097 shot_noise 0.6647 impulse_noise 0.6747 speckle_noise 0.6906 defocus_blur 0.79 glass_blur 0.5383 motion_blur 0.7412 zoom_blur 0.7565 gaussian_blur 0.738 snow 0.7785 frost 0.7448 fog 0.6972 brightness 0.8831 spatter 0.8641 contrast 0.454 elastic_transform 0.6711 pixelate 0.5521 jpeg_compression 0.753 saturate 0.8935

opened by kiranchari 2
Jensen–Shannon divergence

Hello,

I'm trying to understand Jensen–Shannon divergence, I still don't understand the math behind it, but someone asked me to investigate about it and Augmix because of this paragraph:

Alternatively, we can view each set as an empirical distribution and measure the distance between them using Kullback-Leibler (KL) or Jensen-Shannon (JS) divergence. The challenge for learning with KL or JS divergence is that no useful gradient is provided when the two empirical distributions have disjoint supports or have a non-empty intersection contained in a set of measure zero.

from here: https://arxiv.org/pdf/1907.10764.pdf

Is this problem presented in Augmix?

opened by LamyaMohaned 2
mean and std for every channel is 0.5 instead of (0.5071, 0.4867. 0.4408) as mean and (0.2657, 0.2565, 0.2761) as std

Hi, For normalizing cifar100 dataset, you used (0.5, 0.,5 0.,5) for mean and std instead of (0.5071, 0.4867. 0.4408) as mean and (0.2657, 0.2565, 0.2761) as std which is more commonly used. Is this by design? and if so, could you please explain in brief, as to why?

opened by shashankskagnihotri 2
There is a big gap between the results of the code and the results in the paper on CIFAR10-C
I run this script several times to test the performance of proposed method on CIFAR10-C and CIFAR100-C: python cifar.py -m resnext -e 200

10.9% error rate of CIFAR10-C is shown in the paper, however I got about 29% error rate. Then, I tried different models, but there is still a big gap between the results of this code and the results in the paper on CIFAR10-C.

But the results on the CIFAR100-C are close to the paper. BTW, I did not modify the code.

I would like to know:

Is the hyperparameter setting of cifar10 incorrectly given?

Are there more implementation details？

or could you please give some explanation?

THANKS A LOT!
opened by LinusWu 2
`depth` is constant each `width`
Hello, thank you for releasing a implementation.

In my understanding, we change number of op as depth each width , however this code should be same depth each width here. https://github.com/google-research/augmix/blob/7c84885fe064435b4b7a8b596b937f0a879e458c/augment_and_mix.py#L61

So, we change code like below, right?

depth = np.random.randint(1, depth + 1 if depth > 0 else 4)

Best, regard.
opened by yumion 2
CIFAR-10/ImageNet-P code

Hi, thank you for releasing the official implementation.

Do you plan to make public the codebase of CIFAR-10-P and ImageNet-P? Or are there official implementation for that elsewhere?

Thank you in advance.

opened by moskomule 2
Use combined with Object Detection

Is it possible a ease-to-use/integration with object detection model like DETR ? I mean, if AugMix works only over the image so the bounding boxes wont be touched and no extra manipulations gonna be required for bounding boxes. Could you give me any hint, please.

opened by ver0z 0
command for imagenet evaluation

Hi, do you have a command for imagenet evaluation on clean imagenet dataset and corrupted dataset? I have downloaded the pretrained weight but found it difficult to reproduce the result.

Thank you!

opened by Sarimuko 0

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Related tags

Overview

AugMix

Introduction

Pseudocode

Contents

Requirements

Setup

Usage

Pretrained weights

Citation

Comments

Owner

Google Research

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