62 lines
4.1 KiB
Markdown
62 lines
4.1 KiB
Markdown
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## CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows
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WACV 2022 preprint:[https://arxiv.org/abs/????.?????](https://arxiv.org/abs/????.?????)
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## Abstract
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Unsupervised anomaly detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly examples are completely missing in the train data. While recently proposed models for such data setup achieve high accuracy metrics, their complexity is a limiting factor for real-time processing. In this paper, we propose a real-time model and analytically derive its relationship to prior methods. Our CFLOW-AD model is based on a conditional normalizing flow framework adopted for anomaly detection with localization. In particular, CFLOW-AD consists of a discriminatively pretrained encoder followed by a multi-scale generative decoders where the latter explicitly estimate likelihood of the encoded features. Our approach results in a computationally and memory-efficient model: CFLOW-AD is faster and smaller by a factor of 10x than prior state-of-the-art with the same input setting. Our experiments on the MVTec dataset show that CFLOW-AD outperforms previous methods by 0.36% AUROC in detection task, by 1.12% AUROC and 2.5% AUPRO in localization task, respectively. We open-source our code with fully reproducible experiments.
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## BibTex Citation
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If you like our [paper](https://arxiv.org/abs/????.?????) or code, please cite its WACV 2022 preprint using the following BibTex:
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```
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@article{cflow_ad,
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title={CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows},
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author={Gudovskiy, Denis and Ishizaka, Shun and Kozuka, Kazuki and Tsukizawa, Sotaro},
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journal={arXiv:????.?????},
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year={2021}
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}
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```
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## Installation
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- Clone this repository: tested on Python 3.8
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- Install [PyTorch](http://pytorch.org/): tested on v1.8
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- Install [FrEIA Flows](https://github.com/VLL-HD/FrEIA): tested on [the recent branch](https://github.com/VLL-HD/FrEIA/tree/4e0c6ab42b26ec6e41b1ee2abb1a8b6562752b00)
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- Other dependencies in requirements.txt
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Install all packages with this command:
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```
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$ python3 -m pip install -U -r requirements.txt
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```
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## Datasets
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We support [MVTec AD dataset](https://www.mvtec.com/de/unternehmen/forschung/datasets/mvtec-ad/) for anomaly localization in factory setting and [Shanghai Tech Campus (STC)](https://svip-lab.github.io/dataset/campus_dataset.html) dataset with surveillance camera videos. Please, download dataset from URLs and extract to 'data' folder.
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## Code Organization
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- ./custom_datasets - contains dataloaders for MVTec and STC
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- ./custom_models - contains pretrained feature extractors
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## Running Experiments
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- Run code by selecting class name, feature extractor, input size, flow model etc.
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- The commands below should reproduce our reference MVTec results:
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```
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python3 main.py --gpu 0 --pro -inp 512 --dataset mvtec --class-name bottle
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python3 main.py --gpu 0 --pro -inp 256 --dataset mvtec --class-name cable
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python3 main.py --gpu 0 --pro -inp 256 --dataset mvtec --class-name capsule
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python3 main.py --gpu 0 --pro -inp 512 --dataset mvtec --class-name carpet
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python3 main.py --gpu 0 --pro -inp 512 --dataset mvtec --class-name grid
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python3 main.py --gpu 0 --pro -inp 256 --dataset mvtec --class-name hazelnut
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python3 main.py --gpu 0 --pro -inp 512 --dataset mvtec --class-name leather
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python3 main.py --gpu 0 --pro -inp 256 --dataset mvtec --class-name metal_nut
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python3 main.py --gpu 0 --pro -inp 256 --dataset mvtec --class-name pill
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python3 main.py --gpu 0 --pro -inp 512 --dataset mvtec --class-name screw
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python3 main.py --gpu 0 --pro -inp 512 --dataset mvtec --class-name tile
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python3 main.py --gpu 0 --pro -inp 512 --dataset mvtec --class-name toothbrush
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python3 main.py --gpu 0 --pro -inp 128 --dataset mvtec --class-name transistor
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python3 main.py --gpu 0 --pro -inp 512 --dataset mvtec --class-name wood
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python3 main.py --gpu 0 --pro -inp 512 --dataset mvtec --class-name zipper
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```
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## CFLOW-AD Architecture
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## Reference CFLOW-AD Results for MVTec
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