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[ECCV2022 Oral] Registration based Few-Shot Anomaly Detection

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Registration based Few-Shot Anomaly Detection

This is an official implementation of “Registration based Few-Shot Anomaly Detection” (RegAD) with PyTorch, accepted by ECCV 2022 (Oral).

Paper Link

@inproceedings{huang2022regad,
  title={Registration based Few-Shot Anomaly Detection}
  author={Huang, Chaoqin and Guan, Haoyan and Jiang, Aofan and Zhang, Ya and Spratlin, Michael and Wang, Yanfeng},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2022}
}

Abstract: This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied setting for anomaly detection (AD), where only a limited number of normal images are provided for each category at training. So far, existing FSAD studies follow the one-model-per-category learning paradigm used for standard AD, and the inter-category commonality has not been explored. Inspired by how humans detect anomalies, i.e., comparing an image in question to normal images, we here leverage registration, an image alignment task that is inherently generalizable across categories, as the proxy task, to train a category-agnostic anomaly detection model. During testing, the anomalies are identified by comparing the registered features of the test image and its corresponding support (normal) images. As far as we know, this is the first FSAD method that trains a single generalizable model and requires no re-training or parameter fine-tuning for new categories.

Keywords: Anomaly Detection, Few-Shot Learning, Registration

Get Started

Environment

  • python >= 3.7.11
  • pytorch >= 1.11.0
  • torchvision >= 0.12.0
  • numpy >= 1.19.5
  • scipy >= 1.7.3
  • skimage >= 0.19.2
  • matplotlib >= 3.5.2
  • kornia >= 0.6.5
  • tqdm

Files Preparation

  1. Download the MVTec dataset here.
  2. Download the support dataset for few-shot anomaly detection on Google Drive or Baidu Disk (i9rx) and unzip the dataset. For those who have problem downloading the support set, please optional download categories of capsule and grid on Baidu Disk (pll9) and Baidu Disk (ns0n).
    tar -xvf support_set.tar
    
    We hope the followers could use these support datasets to make a fair comparison between different methods.
  3. Download the pre-train models on Google Drive or Baidu Disk (4qyo) and unzip the checkpoint files.
    tar -xvf save_checkpoints.tar
    

After the preparation work, the whole project should have the following structure:

./RegAD
├── README.md
├── train.py                                  # training code
├── test.py                                   # testing code
├── MVTec                                     # MVTec dataset files
│   ├── bottle
│   ├── cable
│   ├── ...                  
│   └── zippper
├── support_set                               # MVTec support dataset files
│   ├── 2
│   ├── 4                 
│   └── 8
├── models                                    # models and backbones
│   ├── stn.py  
│   └── siamese.py
├── losses                                    # losses
│   └── norm_loss.py  
├── datasets                                  # dataset                      
│   └── mvtec.py
├── save_checkpoints                          # model checkpoint files                  
└── utils                                     # utils
    ├── utils.py
    └── funcs.py

Quick Start

python test.py --obj $target-object --shot $few-shot-number --stn_mode rotation_scale

For example, if run on the category bottle with k=2:

python test.py --obj bottle --shot 2 --stn_mode rotation_scale

Training

python train.py --obj $target-object --shot $few-shot-number --data_type mvtec --data_path ./MVTec/ --epochs 50 --batch_size 32 --lr 0.0001 --momentum 0.9 --inferences 10 --stn_mode rotation_scale 

For example, to train a RegAD model on the MVTec dataset on bottle with k=2, simply run:

python train.py --obj bottle --shot 2 --data_type mvtec --data_path ./MVTec/ --epochs 50 --batch_size 32 --lr 0.0001 --momentum 0.9 --inferences 10 --stn_mode rotation_scale 

Then you can run the evaluation using:

python test.py --obj bottle --shot 2 --stn_mode rotation_scale

Results

Results of few-shot anomaly detection and localization with k=2:

AUC (%) Detection Localization
K=2 RegAD Inplementation RegAD Inplementation
bottle 99.4 99.7 98.0 98.6
cable 65.1 69.8 91.7 94.2
capsule 67.5 68.6 97.3 97.6
carpet 96.5 96.7 98.9 98.9
grid 84.0 79.1 77.4 77.5
hazelnut 96.0 96.3 98.1 98.2
leather 99.4 100 98.0 99.2
metal_nut 91.4 94.2 96.9 98.0
pill 81.3 66.1 93.6 97.0
screw 52.5 53.9 94.4 94.1
tile 94.3 98.9 94.3 95.1
toothbrush 86.6 86.8 98.2 98.2
transistor 86.0 82.2 93.4 93.3
wood 99.2 99.8 93.5 96.5
zipper 86.3 90.9 95.1 98.3
average 85.7 85.5 94.6 95.6

Results of few-shot anomaly detection and localization with k=4:

AUC (%) Detection Localization
K=4 RegAD Inplementation RegAD Inplementation
bottle 99.4 99.3 98.4 98.5
cable 76.1 82.9 92.7 95.5
capsule 72.4 77.3 97.6 98.3
carpet 97.9 97.9 98.9 98.9
grid 91.2 87 85.7 85.7
hazelnut 95.8 95.9 98.0 98.4
leather 100 99.9 99.1 99
metal_nut 94.6 94.3 97.8 96.5
pill 80.8 74.0 97.4 97.4
screw 56.6 59.3 95.0 96.0
tile 95.5 98.2 94.9 92.6
toothbrush 90.9 91.1 98.5 98.5
transistor 85.2 85.5 93.8 93.5
wood 98.6 98.9 94.7 96.3
zipper 88.5 95.8 94.0 98.6
average 88.2 89.2 95.8 96.2

Results of few-shot anomaly detection and localization with k=8:

AUC (%) Detection Localization
K=8 RegAD Inplementation RegAD Inplementation
bottle 99.8 99.8 97.5 98.5
cable 80.6 81.5 94.9 95.8
capsule 76.3 78.4 98.2 98.4
carpet 98.5 98.6 98.9 98.9
grid 91.5 91.5 88.7 88.7
hazelnut 96.5 97.3 98.5 98.5
leather 100 100 98.9 99.3
metal_nut 98.3 98.6 96.9 98.3
pill 80.6 77.8 97.8 97.7
screw 63.4 65.8 97.1 97.3
tile 97.4 99.6 95.2 96.1
toothbrush 98.5 96.6 98.7 99.0
transistor 93.4 90.3 96.8 95.9
wood 99.4 99.5 94.6 96.5
zipper 94.0 93.4 97.4 97.4
average 91.2 91.2 96.8 97.1

Visualization

Acknowledgement

We borrow some codes from SimSiam, STN and PaDiM

Contact

If you have any problem with this code, please feel free to contact [email protected].

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