Unofficial implementation of paper https://arxiv.org/abs/2303.14535
| Model | Dataset | Official Paper | efficientad.py |
|---|---|---|---|
| EfficientAD-M | Mvtec AD | 99.1 | 99.1 |
| EfficientAD-M | VisA | 98.1 | 98.2 |
| EfficientAD-M | Mvtec LOCO | 90.7 | 90.1 |
| EfficientAD-S | Mvtec AD | 98.8 | 99.0 |
| EfficientAD-S | VisA | 97.5 | 97.6 |
| EfficientAD-S | Mvtec LOCO | 90.0 | 89.5 |
| Model | GPU | Official Paper | benchmark.py |
|---|---|---|---|
| EfficientAD-M | A6000 | 4.5 ms | 4.4 ms |
| EfficientAD-M | A100 | - | 4.6 ms |
| EfficientAD-M | A5000 | 5.3 ms | 5.3 ms |
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 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
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.
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