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UPDATE:

This repo is no longer maintained. GANomaly implementation has been added to anomalib, the largest public collection of ready-to-use deep learning anomaly detection algorithms and benchmark datasets.

GANomaly

This repository contains PyTorch implementation of the following paper: GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training [1]

1. Table of Contents

GANomaly

2. Installation

First clone the repository

git clone https://github.com/samet-akcay/ganomaly.git

Create the virtual environment via conda
```
conda create -n ganomaly python=3.7
```
Activate the virtual environment.
```
conda activate ganomaly
```

Install the dependencies.

conda install -c intel mkl_fft
pip install --user --requirement requirements.txt

3. Experiment

To replicate the results in the paper for MNIST and CIFAR10 datasets, run the following commands:

# MNIST
sh experiments/run_mnist.sh

# CIFAR
sh experiments/run_cifar.sh # CIFAR10

4. Training

To list the arguments, run the following command:

python train.py -h

4.1. Training on MNIST

To train the model on MNIST dataset for a given anomaly class, run the following:

python train.py \
    --dataset mnist                         \
    --niter <number-of-epochs>              \
    --abnormal_class <0,1,2,3,4,5,6,7,8,9>  \
    --display                               # optional if you want to visualize

4.2. Training on CIFAR10

To train the model on CIFAR10 dataset for a given anomaly class, run the following:

python train.py \
    --dataset cifar10                                                   \
    --niter <number-of-epochs>                                          \
    --abnormal_class                                                    \
        <plane, car, bird, cat, deer, dog, frog, horse, ship, truck>    \
    --display                       # optional if you want to visualize

4.3. Train on Custom Dataset

To train the model on a custom dataset, the dataset should be copied into ./data directory, and should have the following directory & file structure:

Custom Dataset
├── test
│   ├── 0.normal
│   │   └── normal_tst_img_0.png
│   │   └── normal_tst_img_1.png
│   │   ...
│   │   └── normal_tst_img_n.png
│   ├── 1.abnormal
│   │   └── abnormal_tst_img_0.png
│   │   └── abnormal_tst_img_1.png
│   │   ...
│   │   └── abnormal_tst_img_m.png
├── train
│   ├── 0.normal
│   │   └── normal_tst_img_0.png
│   │   └── normal_tst_img_1.png
│   │   ...
│   │   └── normal_tst_img_t.png

Then model training is the same as training MNIST or CIFAR10 datasets explained above.

python train.py                     \
    --dataset <name-of-the-data>    \
    --isize <image-size>            \
    --niter <number-of-epochs>      \
    --display                       # optional if you want to visualize

For more training options, run python train.py -h.

5. Citing GANomaly

If you use this repository or would like to refer the paper, please use the following BibTeX entry

@inproceedings{akcay2018ganomaly,
  title={Ganomaly: Semi-supervised anomaly detection via adversarial training},
  author={Akcay, Samet and Atapour-Abarghouei, Amir and Breckon, Toby P},
  booktitle={Asian Conference on Computer Vision},
  pages={622--637},
  year={2018},
  organization={Springer}
}

6. Reference

[1] Akcay S., Atapour-Abarghouei A., Breckon T.P. (2019) GANomaly: Semi-supervised Anomaly Detection via Adversarial Training. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11363. Springer, Cham