PyTorch implementation for ECCV2024 paper, Hierarchical Gaussian Mixture Normalizing Flows Modeling for Unified Anomaly Detection.
Install all packages with this command:
$ python3 -m pip install -U -r requirements.txt
Please download MVTecAD dataset from MVTecAD dataset, BTAD dataset from BTAD dataset, MVTecAD-3D dataset from MVTecAD-3D dataset, and VisA dataset VisA dataset.
- Run code for training MVTecAD
python main.py --dataset mvtec --seed 0 --gpu 0
Normally, you can obtain the following results:
| Category | Image/Pixel AUC | Category | Image/Pixel AUC | Category | Image/Pixel AUC |
|---|---|---|---|---|---|
| Carpet | 1.000/0.994 | Bottle | 1.000/0.986 | Pill | 0.966/0.988 |
| Grid | 0.997/0.991 | Cable | 0.970/0.959 | Screw | 0.961/0.993 |
| Leather | 1.000/0.996 | Capsule | 0.988/0.992 | Toothbrush | 0.911/0.990 |
| Tile | 1.000/0.961 | Hazelnut | 0.998/0.988 | Transistor | 0.977/0.913 |
| Wood | 0.996/0.959 | Metal nut | 1.000/0.981 | Zipper | 0.999/0.990 |
| Mean | 0.984/0.979 |
- Run code for training BTAD
python main.py --dataset btad --seed 0 --gpu 0
Normally, you can obtain the following results:
| Category | Image/Pixel AUC | Category | Image/Pixel AUC | Category | Image/Pixel AUC |
|---|---|---|---|---|---|
| 01 | 1.000/0.976 | 02 | 0.859/0.973 | 03 | 0.987/0.990 |
| Mean | 0.949/0.980 |
- Run code for training MVTecAD-3D
python main.py --dataset mvtec3d --seed 0 --gpu 0
Normally, you can obtain the following results:
| Category | Image/Pixel AUC | Category | Image/Pixel AUC | Category | Image/Pixel AUC |
|---|---|---|---|---|---|
| Bagel | 0.977/0.988 | Cable gland | 0.963/0.995 | Carrot | 0.889/0.988 |
| Cookie | 0.734/0.966 | Dowel | 0.960/0.992 | Foam | 0.811/0.917 |
| Peach | 0.829/0.994 | Potato | 0.690/0.950 | Rope | 0.976/0.992 |
| Tire | 0.876/0.986 | Mean | 0.871/0.977 |
- Run code for training VisA
python main.py --dataset visa --seed 0 --gpu 0
Normally, you can obtain the following results:
| Category | Image/Pixel AUC | Category | Image/Pixel AUC | Category | Image/Pixel AUC |
|---|---|---|---|---|---|
| Candle | 0.988/0.995 | Capsules | 0.956/0.990 | Cashew | 0.910/0.991 |
| Chewinggum | 0.999/0.996 | Fryum | 0.984/0.949 | Macaroni1 | 0.991/0.998 |
| Macaroni2 | 0.926/0.997 | Pcb1 | 0.976/0.995 | Pcb2 | 0.956/0.983 |
| Pcb3 | 0.986/0.994 | Pcb4 | 0.979/0.987 | Pipe fyrum | 0.996/0.993 |
| Mean | 0.971/0.989 |
- Run code for training Union dataset (combined by MVTecAD, BTAD, MVTecAD-3D, and VisA)
python main.py --dataset union --seed 0 --gpu 0
We also report the detailed results on the Union dataset as follows:
| Category | Image/Pixel AUC | Category | Image/Pixel AUC | Category | Image/Pixel AUC |
|---|---|---|---|---|---|
| Bottle | 1.000/0.982 | Cable | 0.951/0.860 | Capsule | 0.934/0.990 |
| Carpet | 1.000/0.993 | Grid | 0.986/0.983 | Hazelnut | 1.000/0.985 |
| Leather | 1.000/0.995 | Metal nut | 0.997/0.981 | Pill | 0.969/0.984 |
| Screw | 0.812/0.986 | Tile | 0.999/0.936 | Toothbrush | 0.961/0.992 |
| Transistor | 0.996/0.901 | Wood | 0.994/0.957 | Zipper | 0.999/0.992 |
| 01 | 0.997/0.974 | 02 | 0.838/0.969 | 03 | 0.995/0.997 |
| Bagel | 0.983/0.991 | Cable gland | 0.886/0.990 | Carrot | 0.815/0.990 |
| Cookie | 0.792/0.972 | Dowel | 0.896/0.978 | Foam | 0.798/0.913 |
| Peach | 0.856/0.993 | Potato | 0.625/0.958 | Rope | 0.929/0.994 |
| Tire | 0.835/0.965 | ||||
| Candle | 0.989/0.996 | Capsules | 0.939/0.975 | Cashew | 0.928/0.987 |
| Chewinggum | 0.996/0.996 | Fryum | 0.976/0.938 | Macaroni1 | 0.975/0.997 |
| Macaroni2 | 0.903/0.995 | Pcb1 | 0.964/0.992 | Pcb2 | 0.966/0.972 |
| Pcb3 | 0.964/0.990 | Pcb4 | 0.981/0.981 | Pipe fyrum | 0.991/0.992 |
| Mean | 0.935/0.975 |
Note: You need to set the root directory of your dataset in the main.py by setting args.data_path. For Union dataset, the dataset path can be set in the datasets/union.py script.
If you find this repository useful, please consider citing our work:
@article{HGAD,
title={Hierarchical Gaussian Mixture Normalizing Flows Modeling for Unified Anomaly Detection},
author={Xincheng Yao and Ruoqi Li and Zefeng Qian and Lu Wang and Chongyang Zhang},
year={2024},
booktitle={European Conference on Computer Vision 2024},
url={https://arxiv.org/abs/2403.13349},
primaryClass={cs.CV}
}
If you are interested in our work, you can also follow our other works: BGAD (CVPR2023), PMAD (AAAI2023), FOD (ICCV2023), ResAD (NeurIPS2024). Or, you can follow our github page xcyao00.