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EfficientAD

Unofficial implementation of paper https://arxiv.org/abs/2303.14535

PWC

Setup

Packages

install Python==3.10

conda create -n qcr3.10 python=3.10 conda activate qcr3.10 conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/

install pytorch==1.13.1 torchvision==0.14.1

conda install torch==1.13.1 torchvision==0.14.1 cpuonly -c pytorch conda install torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 cpuonly -c pytorch

CUDA 11.6

conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia

CUDA 11.7

conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia

CPU Only

conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 cpuonly -c pytorch

conda install requests pip install requests

pytorch==1.13.1
torchvision==0.14.1
tifffile==2021.7.30
tqdm==4.56.0
scikit-learn==1.2.2

Mvtec AD Dataset

For Mvtec evaluation code install:

numpy==1.18.5
Pillow==7.0.0
scipy==1.7.1
tabulate==0.8.7
tifffile==2021.7.30
tqdm==4.56.0

Download dataset (if you already have downloaded then set path to dataset (--mvtec_ad_path) when calling efficientad.py).

mkdir mvtec_anomaly_detection
cd mvtec_anomaly_detection
wget https://www.mydrive.ch/shares/38536/3830184030e49fe74747669442f0f282/download/420938113-1629952094/mvtec_anomaly_detection.tar.xz
tar -xvf mvtec_anomaly_detection.tar.xz
cd ..

Download evaluation code:

wget https://www.mydrive.ch/shares/60736/698155e0e6d0467c4ff6203b16a31dc9/download/439517473-1665667812/mvtec_ad_evaluation.tar.xz
tar -xvf mvtec_ad_evaluation.tar.xz
rm mvtec_ad_evaluation.tar.xz

efficientad.py

Training and inference:

python efficientad.py --dataset mvtec_ad --subdataset bottle

Evaluation with Mvtec evaluation code:

python mvtec_ad_evaluation/evaluate_experiment.py --dataset_base_dir './mvtec_anomaly_detection/' --anomaly_maps_dir './output/1/anomaly_maps/mvtec_ad/' --output_dir './output/1/metrics/mvtec_ad/' --evaluated_objects bottle

Reproduce paper results

Reproducing results from paper requires ImageNet stored somewhere. Download ImageNet training images from https://www.kaggle.com/competitions/imagenet-object-localization-challenge/data or set --imagenet_train_path of efficientad.py to other folder with general images in children folders for example downloaded https://drive.google.com/uc?id=1n6RF08sp7RDxzKYuUoMox4RM13hqB1Jo

Calls:

python efficientad.py --dataset mvtec_ad --subdataset bottle --model_size medium --weights models/teacher_medium.pth --imagenet_train_path ./ILSVRC/Data/CLS-LOC/train
python efficientad.py --dataset mvtec_ad --subdataset cable --model_size medium --weights models/teacher_medium.pth --imagenet_train_path ./ILSVRC/Data/CLS-LOC/train
python efficientad.py --dataset mvtec_ad --subdataset capsule --model_size medium --weights models/teacher_medium.pth --imagenet_train_path ./ILSVRC/Data/CLS-LOC/train
...

python efficientad.py --dataset mvtec_loco --subdataset breakfast_box --model_size medium --weights models/teacher_medium.pth --imagenet_train_path ./ILSVRC/Data/CLS-LOC/train
python efficientad.py --dataset mvtec_loco --subdataset juice_bottle --model_size medium --weights models/teacher_medium.pth --imagenet_train_path ./ILSVRC/Data/CLS-LOC/train
...

This produced the Mvtec AD results in results/mvtec_ad_medium.json.

Mvtec LOCO Dataset

Download dataset:

mkdir mvtec_loco_anomaly_detection
cd mvtec_loco_anomaly_detection
wget https://www.mydrive.ch/shares/48237/1b9106ccdfbb09a0c414bd49fe44a14a/download/430647091-1646842701/mvtec_loco_anomaly_detection.tar.xz
tar -xf mvtec_loco_anomaly_detection.tar.xz
cd ..

Download evaluation code:

wget https://www.mydrive.ch/shares/48245/a4e9922c5efa93f57b6a0ff9f5c6b969/download/430648014-1646847095/mvtec_loco_ad_evaluation.tar.xz
tar -xvf mvtec_loco_ad_evaluation.tar.xz
rm mvtec_loco_ad_evaluation.tar.xz

Install same packages as for Mvtec AD evaluation code, see above.

Training and inference for LOCO sub-dataset:

python efficientad.py --dataset mvtec_loco --subdataset breakfast_box

Evaluation with LOCO evaluation code:

python mvtec_loco_ad_evaluation/evaluate_experiment.py --dataset_base_dir './mvtec_loco_anomaly_detection/' --anomaly_maps_dir './output/1/anomaly_maps/mvtec_loco/' --output_dir './output/1/metrics/mvtec_loco/' --object_name breakfast_box