# UPDATE: 
This repo is no longer maintained. [GANomaly](https://github.com/openvinotoolkit/anomalib/tree/development/anomalib/models/ganomaly) implementation has been added to [anomalib](https://github.com/openvinotoolkit/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]](#reference)

##  1. Table of Contents
- [GANomaly](#ganomaly)
    - [Table of Contents](#table-of-contents)
    - [Installation](#installation)
    - [Experiment](#experiment)
    - [Training](#training)
        - [Training on MNIST](#training-on-mnist)
        - [Training on CIFAR10](#training-on-cifar10)
        - [Train on Custom Dataset](#train-on-custom-dataset)
    - [Citing GANomaly](#citing-ganomaly)
    - [Reference](#reference)
    

## 2. Installation
1. First clone the repository
   ```
   git clone https://github.com/samet-akcay/ganomaly.git
   ```
2. Create the virtual environment via conda
    ```
    conda create -n ganomaly python=3.7
    ```
3. Activate the virtual environment.
    ```
    conda activate ganomaly
    ```
3. 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:

``` shell
# 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