gathierry / FastFlow

Apache License 2.0
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FastFlow

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

As the paper doesn't give all implementation details, it's kinda difficult to reproduce its result. A very close AUROC is achieved in this repo. But there are still some confusions and a lot of guesses:

Really appreciate the inspiring discussion with the community. Feel free to comment, raise new issues or PRs.

Installation

pip install -r requirements.txt

Data

We use MVTec-AD to verify the performance.

The dataset is organized in the following structure:

mvtec-ad
|- bottle
|  |- train
|  |- test
|  |- ground_truth
|- cable
|  |- train
|  |- test
|  |- ground_truth
...

Train and eval

Take ResNet18 as example

# train
python main.py -cfg configs/resnet18.yaml --data path/to/mvtec-ad -cat [category]
# a folder named _fastflow_experiment_checkpoints will be created automatically to save checkpoints

# eval
python main.py -cfg configs/resnet18.yaml --data path/to/mvtec-ad -cat [category] --eval -ckpt _fastflow_experiment_checkpoints/exp[index]/[epoch#].pt

Performance

As the training process is not stable, I paste both the performance of the last (500th) epoch and the best epoch.

AUROC (last/best) wide-resnet-50 resnet18 DeiT CaiT
bottle 0.987/0.989 0.975/0.979 0.931/0.959 0.926/0.976
cable 0.950/0.978 0.942/0.962 0.976/0.979 0.975/0.981
capsule 0.987/0.989 0.979/0.985 0.982/0.988 0.987/0.990
carpet 0.988/0.989 0.986/0.986 0.991/0.994 0.981/0.993
grid 0.991/0.993 0.973/0.985 0.965/0.980 0.968/0.970
hazel nut 0.957/0.984 0.922/0.963 0.982/0.990 0.981/0.992
leather 0.995/0.996 0.991/0.996 0.991/0.994 0.994/0.996
metal nut 0.968/0.986 0.950/0.966 0.980/0.988 0.977/0.984
pill 0.968/0.977 0.955/0.968 0.977/0.989 0.984/0.990
screw 0.969/0.987 0.952/0.957 0.990/0.990 0.991/0.993
tile 0.955/0.971 0.916/0.951 0.966/0.966 0.946/0.972
toothbrush 0.985/0.986 0.967/0.978 0.983/0.988 0.989/0.992
transistor 0.956/0.975 0.970/0.975 0.959/0.970 0.967/0.969
wood 0.948/0.964 0.894/0.954 0.960/0.963 0.950/0.959
zipper 0.980/0.987 0.969/0.979 0.966/0.974 0.972/0.984
MEAN 0.972/0.983 0.956/0.972 0.973/0.981 0.973/0.983
Paper 0.981 0.972 0.981 0.985