MLF-SC: Incorporating Multi-Layer Features to Sparse Coding for Unsupervised Anomaly Detection

MLF-SC (Multi-Layer Feature Sparse Coding) is an anomaly detection method that incorporates multi-scale features to sparse coding. This is a PyTorch implementation for MVTec datasets (Carpet, Grid, Leather, Tile, Wood, ...).

Output visualization of MVTec dataset (Grid, Hazelnut, and Bottle)

Quick Start

git clone [email protected]:LeapMind/MLF-SC.git
pip3 install -r requirements.txt
python3 main.py train cfg/sample_config.yml
python3 main.py test cfg/sample_config.yml

Download Dataset

wget ftp://guest:[email protected]/mvtec_anomaly_detection/mvtec_anomaly_detection.tar.xz
tar -xvf mvtec_anomaly_detection.tar.xz 

Usage

You can train and test for each texture dataset.

Set dataset path to config.yml

Set root in config.yml to texture dataset path (like path/to/mvtec_anomaly_detection/carpet/).

Train

$ python3 main.py train cfg/config.yml

Test and Visualize Output

$ python3 main.py test cfg/config.yml

Contribution

Anomaly detection performance for the texture categories of the MVTec AD dataset. For each cell in the R1 / R2 columns, the ratio of correctly classified samples of normal R1 and that of anomalous images R2 are shown with R1 / R2 notation. The maximum averages (R1 + R2) / 2 are marked with boldface. The performance for the non-sparse-coding-based methods are cited from Table 2 of (Bergmann et al., 2019). The AUROC columns show only sparse coding and MLF-SC.

	R1 / R2							AUROC
Category	AE (SSIM)	AE (L2)	AnoGAN	CNN Feature Dictionary	Texture Inspection	Sparse Coding	MLF-SC (Proposed)	Sparse Coding	MLF-SC (Proposed)
Carpet	0.43 / 0.90	0.57 / 0.42	0.82 / 0.16	0.89 / 0.36	0.57 / 0.61	0.43 / 0.79	1.00 / 0.98	0.58	0.99
Grid	0.38 / 1.00	0.57 / 0.98	0.90 / 0.12	0.57 / 0.33	1.00 / 0.05	0.76 / 0.72	1.00 / 0.88	0.89	0.97
Leather	0.00 / 0.92	0.06 / 0.82	0.91 / 0.12	0.63 / 0.71	0.00 / 0.99	0.84 / 0.96	0.97 / 0.97	0.95	0.99
Tile	1.00 / 0.04	1.00 / 0.54	0.97 / 0.05	0.97 / 0.44	1.00 / 0.43	0.94 / 0.60	0.94 / 0.76	0.86	0.92
Wood	0.84 / 0.82	1.00 / 0.47	0.89 / 0.47	0.79 / 0.88	0.42 / 1.00	0.84 / 0.60	0.95 / 0.98	0.97	0.99
Average	0.53 / 0.74	0.64 / 0.65	0.90 / 0.18	0.77 / 0.54	0.60 / 0.62	0.７６ / 0.81	0.97 / 0.91	0.85	0.97

License

Non-commercial, research purposes only

License of dependent libraries

See LICENSE directory.