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Using SPADE for anomaly detection on MVTEC LOCO AD dataset

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  1. Download the MVTEC-LOCO-AD dataset from the MVTEC website. Then organize the MVTEC-LOCO-AD dataset into MVTEC-AD format. There may be multiple GTs for one image in the MVTEC-LOCO-AD dataset, we only need to take any one of them.
  2. Change self.mvtec_folder_path in src/datasets/mvtec.py to your path of MVTEC LOCO AD dataset (In MVTEC AD format).
  3. Change the --dateset_base_dir in src/O_evaluation/evaluate_experiment.py -> def parse_arguments() to your own MVTEC LOCO AD dataset path (the original LOCO dataset downloaded, not in MVTEC AD format).
    Change --anomaly_maps_dir to your own path (log_metris folder is already included in this program!) .
  4. Run main.py. (The evaluation code was taken from this website.)
  • There is generally no randomness in SPADE.
  • The results of this project have a small difference in ROCAUC at the image-level from the original paper, but a large difference at the pixel-level.
  • I don't know where is wrong, if you have any idea please leave a comment to discuss.
Pixel-SPRO-AUC (Paper) Pixel-SPRO-AUC (This Code) Image-ROC-AUC (Paper) Image-ROC-AUC (This Code)
Breakfast Box 0.372 0.143 - 0.768
Screw Bag 0.331 0.421 - 0.532
Pushpins 0.234 0.251 - 0.569
Splicing Connectors 0.516 0.598 - 0.778
Juice Bottle 0.804 0.587 - 0.88
Mean 0.451 0.4 0.689 0.7054

Reference:https://github.com/byungjae89/SPADE-pytorch

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Using SPADE for anomaly detection on MVTEC LOCO AD dataset

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