This repository contains PyTorch implementation of the following paper: GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training [1]
- Linux or MacOS
- Python 3
- CPU or GPU + CUDA & CUDNN
- First clone the repository
git clone https://github.com:samet-akcay/ganomaly.git
- Install PyTorch and torchvision from https://pytorch.org
- Install the dependencies.
pip install -r requirements.txt
UPDATE: This repository now supports PyTorch v0.4. If you still would like to work with v0.3, you could use the branch named PyTorch.v0.3, which contains the previous version of the repo.
To replicate the results in the paper, run the following commands:
For MNIST experiments:
sh experiments/run_mnist.sh
For CIFAR experiments:
sh experiments/run_cifar.sh
To list the arguments, run the following command:
python train.py -h
To train the model on MNIST dataset for a given anomaly class, run the following:
python train.py \
--dataset mnist \
--niter <number-of-epochs> \
--anomaly_class <0,1,2,3,4,5,6,7,8,9>
To train the model on CIFAR10 dataset for a given anomaly class, run the following:
python train.py \
--dataset cifar10 \
--niter <number-of-epochs> \
--anomaly_class \
<plane, car, bird, cat, deer, dog, frog, horse, ship, truck>
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>
For more training options, run python train.py -h
as shown below:
usage: train.py [-h] [--dataset DATASET] [--dataroot DATAROOT]
[--batchsize BATCHSIZE] [--workers WORKERS] [--droplast]
[--isize ISIZE] [--nc NC] [--nz NZ] [--ngf NGF] [--ndf NDF]
[--extralayers EXTRALAYERS] [--gpu_ids GPU_IDS] [--ngpu NGPU]
[--name NAME] [--model MODEL]
[--display_server DISPLAY_SERVER]
[--display_port DISPLAY_PORT] [--display_id DISPLAY_ID]
[--display] [--outf OUTF] [--manualseed MANUALSEED]
[--anomaly_class ANOMALY_CLASS] [--print_freq PRINT_FREQ]
[--save_image_freq SAVE_IMAGE_FREQ] [--save_test_images]
[--load_weights] [--resume RESUME] [--phase PHASE]
[--iter ITER] [--niter NITER] [--beta1 BETA1] [--lr LR]
[--alpha ALPHA]
optional arguments:
-h, --help show this help message and exit
--dataset folder | cifar10 | mnist (default: cifar10)
--dataroot path to dataset (default: '')
--batchsize input batch size (default: 64)
--workers number of data loading workers (default: 8)
--droplast Drop last batch size. (default: True)
--isize input image size. (default: 32)
--nc input image channels (default: 3)
--nz size of the latent z vector (default: 100)
--ngf Number of features of the generator network
--ndf Number of features of the discriminator network.
--extralayers Number of extra layers on gen and disc (default: 0)
--gpu_ids gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU (default: 0)
--ngpu number of GPUs to use (default: 1)
--name name of the experiment (default: experiment_name)
--model chooses which model to use. (default:ganomaly)
--display_server visdom server of the web display (default: http://localhost)
--display_port visdom port of the web display (default: 8097)
--display_id window id of the web display (default: 0)
--display Use visdom. (default: False)
--outf folder to output images and model checkpoints (default: ./output)
--manualseed manual seed (default: None)
--anomaly_class Anomaly class idx for mnist and cifar datasets (default: car)
--print_freq frequency of showing training results on console (default: 100)
--save_image_freq frequency of saving real and fake images (default:100)
--save_test_images Save test images for demo. (default: False)
--load_weights Load the pretrained weights (default: False)
--resume path to checkpoints (to continue training) (default: '')
--phase train, val, test, etc (default: train)
--iter Start from iteration i (default: 0)
--niter number of epochs to train for (default: 15)
--beta1 momentum term of adam (default: 0.5)
--lr initial learning rate for adam (default: 0.0002)
--alpha alpha to weight l1 loss. default=500 (default: 50)
If you use this repository or would like to refer the paper, please use the following BibTeX entry
@article{Akcay2018,
author = {Akcay, S. and Atapour-Abarghouei, A. and Breckon, T.~P.},
title = "{GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprint = {1805.06725},
primaryClass = "cs.CV",
keywords = {Computer Science - Computer Vision and Pattern Recognition},
year = 2018,
month = may,
}