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https://github.com/zurutech/anomaly-toolbox

Anomaly detection using GANs.
https://github.com/zurutech/anomaly-toolbox

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Anomaly detection using GANs.

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README 

        
# Anomaly Toolbox



## Description



_Anomaly Toolbox Powered by GANs._ 



This is the accompanying toolbox for the paper "**A 

Survey on GANs for Anomaly Detection**" (https://arxiv.org/pdf/1906.11632.pdf).



The toolbox is meant to be used by the user to explore the performance of different GAN based 

architectures (in our work aka "**experiments**"). It also already provides some datasets to 

perform experiments on: 

* _MNIST_, 

* _Corrupted MNIST_, 

* _Surface Cracks_ (https://www.kaggle.com/arunrk7/surface-crack-detection),

* _MVTec AD_ (https://www.mvtec.com/fileadmin/Redaktion/mvtec.

  com/company/research/datasets/mvtec_ad.pdf).



We provided the _MNIST_ dataset because the original works extensively use it. On the other hand, 

we have also added the previously listed datasets both because used by a particular 

architecture and because they contribute a good benchmark for the models we have implemented.



All the architectures were tested on commonly used datasets such as _MNIST_, _FashionMNIST_, 

_CIFAR-10_, and _KDD99_. Some of them were even tested on more specific datasets, such as an 

X-Ray dataset that, however, we could not provide because of the impossibility of getting the 

data (privacy reasons). 



The user can create their own dataset and use it to test the models.



## Quick Start



* First thing first, install the toolbox



```bash 

pip install anomaly-toolbox

```



Then you can choose what experiment to run. For example:



* Run the GANomaly experiment (i.e., the GANomaly architecture) with hyperparameters tuning 

  enabled, the pre-defined hyperparameters file _hparams.json_ and the _MNIST_ dataset:



```bash

anomaly-box.py --experiment GANomalyExperiment --hps-path path/to/config/hparams.json --dataset 

MNIST 

```

* Otherwise, you can run all the experiments using the pre-defined hyperparameters file _hparams.

  json_ and the _MNIST_ dataset:



```bash

anomaly-box.py --run-all --hps-path path/to/config/hparams.json --dataset MNIST 

```



For any other information, feel free to check the help:



```bash 

anomaly-box.py --help

```



## Contribution



This work is completely open source, and **we would appreciate any contribution to the code**. 

Any merge request to enhance, correct or expand the work is welcome.



## Notes



The structures of the models inside the toolbox come from their respective papers. We have tried to 

respect them as much as possible. However, sometimes, due to implementation issues, we had to make 

some minor-ish changes. For this reason, you could find out that, in some cases, some features 

such as the number of layers, the size of kernels, or other such things may differ from the 

originals. 



However, you don't have to worry. The heart and purpose of the architectures have remained intact.



## Installation



```console

pip install anomaly-toolbox

```



## Usage



```

Options:

  --experiment [AnoGANExperiment|DeScarGANExperiment|EGBADExperiment|GANomalyExperiment]

                                  Experiment to run.

  --hps-path PATH                 When running an experiment, the path of the

                                  JSON file where all the hyperparameters are

                                  located.  [required]

  --tuning BOOLEAN                If you want to use hyperparameters tuning,

                                  use 'True' here. Default is False.

  --dataset TEXT                  The dataset to use. Can be a ready to use

                                  dataset, or a .py file that implements the

                                  AnomalyDetectionDataset interface

                                  [required]

  --run-all BOOLEAN               Run all the available experiments

  --help                          Show this message and exit.

```



## Datasets and Custom Datasets



The provided datasets are:



* MNIST 

* Corrupted Mnist

* Surface Crack (https://www.kaggle.com/arunrk7/surface-crack-detection)

*  MVTec AD (https://www.mvtec.com/fileadmin/Redaktion/mvtec.com/company/research/datasets/mvtec_ad.pdf)



and are automatically downloaded when the user makes a specific choice: ["MNIST", 

"CorruptedMNIST", "SurfaceCracks","MVTecAD"].



The user can also add its own specific dataset. To do this, the new dataset should inherit from 

the `AnomalyDetectionDataset` abstract class implementing its own `configure` method. For a more 

detailed guide, the user can refer to the `README.md` file inside the 

`src/anomaly_toolbox/datasets` folder. Moreover, in the `examples` folder, the user can find a 

`dummy.py` module with the basic skeleton code to implement a dataset.



## References



- **GANomaly**:

    - Paper: https://arxiv.org/abs/1805.06725

    - Code: https://github.com/samet-akcay/ganomaly

- **EGBAD (BiGAN)**:

    - Paper: https://arxiv.org/abs/1802.06222

    - Code: https://github.com/houssamzenati/Efficient-GAN-Anomaly-Detection

- **AnoGAN**:

    - Paper: https://arxiv.org/abs/1703.05921

    - Code (not official): https://github.com/LeeDoYup/AnoGAN

    - Code (not official): https://github.com/tkwoo/anogan-keras

- **DeScarGAN**:

    - Paper: https://arxiv.org/abs/2007.14118

    - Code: https://github.com/JuliaWolleb/DeScarGAN