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# Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection
This is the implementation of the [STFPM](https://arxiv.org/pdf/2103.04257.pdf) paper.
Model Type: Segmentation
## Description
STFPM algorithm which consists of a pre-trained teacher network and a student network with identical architecture. The student network learns the distribution of anomaly-free images by matching the features with the counterpart features in the teacher network. Multi-scale feature matching is used to enhance robustness. This hierarchical feature matching enables the student network to receive a mixture of multi-level knowledge from the feature pyramid thus allowing for anomaly detection of various sizes.
During inference, the feature pyramids of teacher and student networks are compared. Larger difference indicates a higher probability of anomaly occurrence.
## Architecture
## Usage
`python tools/train.py --model stfpm`
## Benchmark
All results gathered with seed `42`.
## [MVTec AD Dataset](https://www.mvtec.com/company/research/datasets/mvtec-ad)
### Image-Level AUC
| | Avg | Carpet | Grid | Leather | Tile | Wood | Bottle | Cable | Capsule | Hazelnut | Metal Nut | Pill | Screw | Toothbrush | Transistor | Zipper |
| -------------- | :---: | :----: | :---: | :-----: | :---: | :---: | :----: | :---: | :-----: | :------: | :-------: | :---: | :---: | :--------: | :--------: | :----: |
| ResNet-18 | 0.893 | 0.954 | 0.982 | 0.989 | 0.949 | 0.961 | 0.979 | 0.838 | 0.759 | 0.999 | 0.956 | 0.705 | 0.835 | 0.997 | 0.853 | 0.645 |
| Wide ResNet-50 | 0.876 | 0.957 | 0.977 | 0.981 | 0.976 | 0.939 | 0.987 | 0.878 | 0.732 | 0.995 | 0.973 | 0.652 | 0.825 | 0.5 | 0.875 | 0.899 |
### Pixel-Level AUC
| | Avg | Carpet | Grid | Leather | Tile | Wood | Bottle | Cable | Capsule | Hazelnut | Metal Nut | Pill | Screw | Toothbrush | Transistor | Zipper |
| -------------- | :---: | :----: | :---: | :-----: | :---: | :---: | :----: | :---: | :-----: | :------: | :-------: | :---: | :---: | :--------: | :--------: | :----: |
| ResNet-18 | 0.951 | 0.986 | 0.988 | 0.991 | 0.946 | 0.949 | 0.971 | 0.898 | 0.962 | 0.981 | 0.942 | 0.878 | 0.983 | 0.983 | 0.838 | 0.972 |
| Wide ResNet-50 | 0.903 | 0.987 | 0.989 | 0.980 | 0.966 | 0.956 | 0.966 | 0.913 | 0.956 | 0.974 | 0.961 | 0.946 | 0.988 | 0.178 | 0.807 | 0.980 |
### Image F1 Score
| | Avg | Carpet | Grid | Leather | Tile | Wood | Bottle | Cable | Capsule | Hazelnut | Metal Nut | Pill | Screw | Toothbrush | Transistor | Zipper |
| -------------- | :---: | :----: | :---: | :-----: | :---: | :---: | :----: | :---: | :-----: | :------: | :-------: | :---: | :---: | :--------: | :--------: | :----: |
| ResNet-18 | 0.932 | 0.961 | 0.982 | 0.989 | 0.930 | 0.951 | 0.984 | 0.819 | 0.918 | 0.993 | 0.973 | 0.918 | 0.887 | 0.984 | 0.790 | 0.908 |
| Wide ResNet-50 | 0.926 | 0.973 | 0.973 | 0.974 | 0.965 | 0.929 | 0.976 | 0.853 | 0.920 | 0.972 | 0.974 | 0.922 | 0.884 | 0.833 | 0.815 | 0.931 |
### Sample Results
|