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:
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.
pip install anomaly-toolboxThen you can choose what experiment to run. For example:
anomaly-box.py --experiment GANomalyExperiment --hps-path path/to/config/hparams.json --dataset
MNIST anomaly-box.py --run-all --hps-path path/to/config/hparams.json --dataset MNIST For any other information, feel free to check the help:
anomaly-box.py --helpThis 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.
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.
pip install anomaly-toolboxOptions:
--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.The provided datasets are:
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.