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