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Industrial KNN-based Anomaly Detection

⭐Now has streamlit support!⭐ Run $ streamlit run streamlit_app.py

This repo aims to reproduce the results of the following KNN-based anomaly detection methods:

  1. SPADE (Cohen et al. 2021) - knn in z-space and distance to feature maps spade schematic
  2. PaDiM* (Defard et al. 2020) - distance to multivariate Gaussian of feature maps padim schematic
  3. PatchCore (Roth et al. 2021) - knn distance to avgpooled feature maps patchcore schematic

* actually does not have any knn mechanism, but shares many things implementation-wise.

Install

$ pipenv install -r requirements.txt

Usage

CLI:

$ python indad/run.py METHOD [--dataset DATASET]

Results can be found under ./results/.

Code example:

from indad.model import SPADE

model = SPADE(k=5, backbone_name="resnet18")

# feed healthy dataset
model.fit(...)

# get predictions
img_lvl_anom_score, pxl_lvl_anom_score = model.predict(...)

Custom datasets

👁️

Check out one of the downloaded MVTec datasets. Naming of images should correspond among folders. Right now there is no support for no ground truth pixel masks.

📂datasets
 ┗ 📂your_custom_dataset
  ┣ 📂 ground_truth/defective
  ┃ ┣ 📂 defect_type_1
  ┃ ┗ 📂 defect_type_2
  ┣ 📂 test
  ┃ ┣ 📂 defect_type_1
  ┃ ┣ 📂 defect_type_2
  ┃ ┗ 📂 good
  ┗ 📂 train/good

$ python indad/run.py METHOD --dataset your_custom_dataset

Results

📝 = paper, 👇 = this repo

Image-level

class SPADE 📝 SPADE 👇 PaDiM 📝 PaDiM 👇 PatchCore 📝 PatchCore 👇
bottle - 98.8 98.3 99.8 ■100.0■ ■100.0■
cable - 76.5 96.7 93.3 ■99.5■ 96.2
capsule - 84.6 ■98.5■ 88.3 98.1 95.3
carpet - 84.3 99.1 ■99.4 98.7 98.7
grid - 37.1 97.3 98.2 ■98.2■ 93.0
hazelnut - 88.7 98.2 83.7 ■100.0■ 100.0
leather - 97.1 99.2 99.9 ■100.0■ 100.0
metal_nut - 74.6 97.2 99.4 ■100.0■ 98.3
pill - 72.6 95.7 89.0 ■96.6■ 92.8
screw - 53.1 ■98.5■ 83.0 98.1 96.7
tile - 97.8 94.1 98.6 98.7 ■99.0■
toothbrush - 89.4 98.8 97.2 ■100.0■ 98.1
transistor - 89.2 97.5 96.8 ■100.0■ 99.7
wood - 98.3 94.7 98.9 ■99.2■ 98.8
zipper - 96.7 98.5 89.5 ■99.4■ 98.4
averages 85.5 82.6 97.5 94.3 ■99.1■ 97.7

Pixel-level

class SPADE 📝 SPADE 👇 PaDiM 📝 PaDiM 👇 PatchCore 📝 PatchCore 👇
bottle 97.5 97.7 94.8 97.8 ■98.6■ 97.8
cable 93.7 94.3 88.8 96.1 ■98.5■ 97.4
capsule 97.6 98.6 93.5 98.3 ■98.9■ 98.3
carpet 87.4 99.0 96.2 98.6 ■99.1■ 98.3
grid 88.5 96.1 94.6 97.2 ■98.7■ 96.7
hazelnut 98.4 98.1 92.6 97.5 ■98.7■ 98.1
leather 97.2 99.2 97.8 98.7 ■99.3■ 98.4
metal_nut ■99.0■ 96.1 85.6 96.5 98.4 96.2
pill ■99.1■ 93.5 92.7 93.2 97.6 98.7
screw 98.1 98.9 94.4 97.8 ■99.4■ 98.4
tile ■96.5■ 93.3 86.0 94.8 95.9 94.0
toothbrush ■98.9■ ■98.9■ 93.1 98.3 98.7 98.1
transistor ■97.9■ 96.3 84.5 97.2 96.4 97.5
wood 94.1 94.4 91.1 93.6 ■95.1■ 91.9
zipper 96.5 98.2 95.9 97.4 ■98.9■ 97.6
averages 96.9 96.8 92.1 96.9 ■98.1■ 97.2

PatchCore-10 was used.

Hyperparams

The following parameters were used to calculate the results. They more or less correspond to the parameters used in the papers.

spade:
  backbone: wide_resnet50_2
  k: 50
padim:
  backbone: wide_resnet50_2
  d_reduced: 250
  epsilon: 0.04
patchcore:
  backbone: wide_resnet50_2
  f_coreset: 0.1
  n_reweight: 3

Progress

  • Datasets
  • Code skeleton
  • Config files
  • CLI
  • Logging
  • SPADE
  • PADIM
  • PatchCore
  • Add custom dataset option
  • Add dataset progress bar
  • Add schematics
  • Unit tests

Design considerations

  • Data is processed in single images to avoid batch statistics interference.
  • I decided to implement greedy kcenter from scratch and there is room for improvement.
  • torch.nn.AdaptiveAvgPool2d for feature map resizing, torch.nn.functional.interpolate for score map resizing.
  • GPU is used for backbones and coreset selection. GPU coreset selection currently runs at:
    • 400-500 it/s @ float32 (RTX3080)
    • 1000+ it/s @ float16 (RTX3080)

Acknowledgements

  • hcw-00 for tipping sklearn.random_projection.SparseRandomProjection.
  • h1day for adding a custom range to the streamlit app.

References

SPADE:

@misc{cohen2021subimage,
      title={Sub-Image Anomaly Detection with Deep Pyramid Correspondences}, 
      author={Niv Cohen and Yedid Hoshen},
      year={2021},
      eprint={2005.02357},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

PaDiM:

@misc{defard2020padim,
      title={PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization}, 
      author={Thomas Defard and Aleksandr Setkov and Angelique Loesch and Romaric Audigier},
      year={2020},
      eprint={2011.08785},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

PatchCore:

@misc{roth2021total,
      title={Towards Total Recall in Industrial Anomaly Detection}, 
      author={Karsten Roth and Latha Pemula and Joaquin Zepeda and Bernhard Schölkopf and Thomas Brox and Peter Gehler},
      year={2021},
      eprint={2106.08265},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}