Unofficial implementation of PatchCore anomaly detection

Last update: Dec 22, 2022

Overview

PatchCore anomaly detection

Unofficial implementation of PatchCore(new SOTA) anomaly detection model

Original Paper : Towards Total Recall in Industrial Anomaly Detection (Jun 2021)
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, Peter Gehler

https://arxiv.org/abs/2106.08265
https://paperswithcode.com/sota/anomaly-detection-on-mvtec-ad

notice(21/06/18) :
This code is not yet verified. Any feedback is appreciated.
updates(21/06/21) :

I used sklearn's SparseRandomProjection(ep=0.9) for random projection. I'm not confident with this.
I think exact value of "b nearest patch-features" is not presented in the paper. I just set 9. (args.n_neighbors)
In terms of NN search, author used "faiss". but not implemented in this code yet.
sample embeddings/carpet/embedding.pickle => coreset_sampling_ratio=0.001

updates(21/06/26) :

A critical issue related to "locally aware patch" raised and fixed. Score table is updated.

Usage

# install python 3.6, torch==1.8.1, torchvision==0.9.1
pip install -r requirements.txt

python train.py --phase train or test --dataset_path .../mvtec_anomaly_detection --category carpet --project_root_path path/to/save/results --coreset_sampling_ratio 0.01 --n_neighbors 9'

# for fast try just specify your dataset_path and run
python train.py --phase test --dataset_path .../mvtec_anomaly_detection --project_root_path ./

MVTecAD AUROC score (PatchCore-1%, mean of n trials)

Category	Paper (image-level)	This code (image-level)	Paper (pixel-level)	This code (pixel-level)
carpet	0.980	0.991(1)	0.989	0.989(1)
grid	0.986	0.975(1)	0.986	0.975(1)
leather	1.000	1.000(1)	0.993	0.991(1)
tile	0.994	0.994(1)	0.961	0.949(1)
wood	0.992	0.989(1)	0.951	0.936(1)
bottle	1.000	1.000(1)	0.985	0.981(1)
cable	0.993	0.995(1)	0.982	0.983(1)
capsule	0.980	0.976(1)	0.988	0.989(1)
hazelnut	1.000	1.000(1)	0.986	0.985(1)
metal nut	0.997	0.999(1)	0.984	0.984(1)
pill	0.970	0.959(1)	0.971	0.977(1)
screw	0.964	0.949(1)	0.992	0.977(1)
toothbrush	1.000	1.000(1)	0.985	0.986(1)
transistor	0.999	1.000(1)	0.949	0.972(1)
zipper	0.992	0.995(1)	0.988	0.984(1)
mean	0.990	0.988	0.980	0.977

Code Reference

kcenter algorithm :
https://github.com/google/active-learning
embedding concat function :
https://github.com/xiahaifeng1995/PaDiM-Anomaly-Detection-Localization-master

Unofficial implementation of PatchCore anomaly detection

Related tags

Overview

PatchCore anomaly detection

Usage

MVTecAD AUROC score (PatchCore-1%, mean of n trials)

Code Reference

Owner

Changwoo Ha

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Unofficial implementation of PatchCore anomaly detection

Related tags

Overview

PatchCore anomaly detection

Usage

MVTecAD AUROC score (PatchCore-1%, mean of n trials)

Code Reference

Owner

Changwoo Ha

This is the paddle code for SeBoW(Self-Born wiring for neural trees), a kind of neural tree born form a large search space

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This was initially the repo for the project of [email protected] of Asaf Mazar, Millad Kassaie and Georgios Chochlakis named "Powered by the Will? Exploring Lay Theories of Behavior Change through Social Media"

Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.

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