FastFlow

An unofficial PyTorch implementation of FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows (Jiawei Yu et al.).

We modified some of FrEIA module to output Jacobian determinant which has same shape of the input data, here.

Installation

1. Clone this repository.
2. Download MVTecAD dataset from https://www.mvtec.com/company/research/datasets/mvtec-ad and place it in the directory of your choice.
3. Install python packages on your system with pip install -r requirements.txt.

Versions of our system is listed below.

OS      : Ubuntu 18.04.5
CUDA    : 11.3
cudnn   : 8.2.0.53-1
python  : 3.7.11
FrEIA   : 0.2

Training models

1. Replace paths (and other configs if needed) in config.py to fit your environment.

mvtec_path = "/path/to/MVtecAD" ## path you placed the dataset.
weight_path = "./weights" ## directory to save fastflow model weights.
result_path = "./results" ## directory to save logs.

2. Run python main.py.

Evaluation on test dataset runs every validate_per_epoch(in config.py) epochs.

Metrics

Image level AUROC

category	bottle	cable	capsule	carpet	grid	hazelnut	leather	metul_nut	pill	screw	tile	toothbrush	transistor	wood	zipper
impl	1.000	0.919	0.977	1.000	0.998	1.000	1.000	0.998	0.992	0.846	0.999	0.872	0.965	0.987	0.942
paper	1.000	1.000	1.000	1.000	0.997	1.000	1.000	1.000	0.994	0.978	1.000	0.944	0.998	1.000	0.995
diff	0.000	-0.081	-0.023	0.000	0.001	0.000	0.000	-0.002	-0.002	-0.132	-0.001	-0.072	-0.033	-0.013	-0.053

Pixel Level AUROC

category	bottle	cable	capsule	carpet	grid	hazelnut	leather	metul_nut	pill	screw	tile	toothbrush	transistor	wood	zipper
impl	0.983	0.977	0.991	0.995	0.978	0.991	0.995	0.980	0.989	0.992	0.966	0.987	0.944	0.959	0.978
paper	0.977	0.984	0.991	0.994	0.983	0.991	0.995	0.985	0.992	0.994	0.963	0.989	0.973	0.970	0.987
diff	0.006	-0.007	0.000	0.001	-0.005	0.000	0.000	-0.005	-0.003	-0.002	0.003	-0.002	-0.029	-0.011	-0.009