This code is adapted from the unofficial implementation of the paper https://arxiv.org/abs/2303.14535
Python==3.10
torch==1.13.0
torchvision==0.14.0
tifffile==2021.7.30
tqdm==4.56.0
scikit-learn==1.2.2
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
Training with augmented data, inference, and evalution. Augmented images are saved in mvtec_ad_path. Inference results such as anomaly maps are saved in output_dir. Evaluation results including F1 scores are printed out. Image-wise predictions are saved in evaluation_results.csv. MLflow trackings are saved in mlruns/.
python efficientad.py --dataset mvtec_ad --subdataset bottle --img_aug
Training without augmented data, inference, and evalution.
python efficientad.py --dataset mvtec_ad --subdataset bottle
If trained models are available, only conduct inference and evaluation:
python efficientad.py --dataset mvtec_ad --subdataset bottle --stage_inference
When evaluation results on individual classes are available, summarize and compute global F1 over all classes:
python results_summarization.py