MvtecAD unsupervised Anomaly Detection

Last update: Feb 25, 2022

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Overview

MvtecAD unsupervised Anomaly Detection

This respository is the unofficial implementations of DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

Result of 500 epochs trained model

Selects latent sizes of Autoencoder by PCA

Classes	latent size	Segmentation AUC	Detection AUC
bottle	116	97.2771%	99.8413%
cable	557	95.5101%	84.8951%
capsule	162	98.8928%	97.3275%
carpet	245	97.9116%	90.5297%
grid	145	97.2484	79.5322%
hazelnut	459	98.5848%	100%
leather	325	98.8649%	95.4484%
metal_nut	380	96.127%	97.263%
pill	292	98.0543%	94.108%
screw	283	99.3001%	92.0066%
tile	557	89.4887%	91.7388%
toothbrush	243	98.6729%	91.3889%
transistor	333	83.9157%	89.0833%
wood	364	91.7027%	98.9474%
zipper	115	95.6663%	83.2983%
mean		95.8141%	92.3606

How to run

requirements

pytorch scikit-learn matplotlib numpy pandas PIL wget

Train

python main.py --mode          train      
               --data_dir_head [Datapath] 
               --BATCH_SIZE    [BATCH_SIZE] 
               --LR            [Learning Rate] 
               --EPOCH         [Epochs] 
               --backbone      [Feature map of Conv in VGG19]
               --latent_dim    [Latent size of CAE] 
               --classes       [Default is all]

Download 500 Epochs Finetuned Models

Here provide the model of each classes in Drophox

python main.py --mode download

Evaluate the ROC-AUC of Test Set

python main.py --mode        evaluation    
               --classes     [Default is all]

Inference the model

python main.py --mode           inference    
               --heatmap_path   [Input path] 
               --heatmap_item   [Class of input] 
               --heatmap_gt     [GT path Default is None]
               --device         [cpu or cuda]
               --device         [Output path ]

Example run in main.py

if __name__ == "__main__":  
    cfg = config()
    cfg.mode = "inference"
    cfg.heatmap_path = 'mvtecad_unsupervise/bottle/test/broken_small/001.png'
    cfg.heatmap_item = 'bottle'
    cfg.heatmap_gt = 'mvtecad_unsupervise/bottle/ground_truth/broken_small/001_mask.png'
    cfg.device = 'cpu'
    cfg.heatmap_export = 'validate/Inferece.png'

validate/Inferece.png is

Code Reference

https://github.com/YoungGod/DFR

https://www.kaggle.com/danieldelro/unsupervised-anomaly-segmentation-of-screw-images

MvtecAD unsupervised Anomaly Detection

Related tags

Overview

MvtecAD unsupervised Anomaly Detection

Result of 500 epochs trained model

Selects latent sizes of Autoencoder by PCA

How to run

requirements

Train

Download 500 Epochs Finetuned Models

Evaluate the ROC-AUC of Test Set

Inference the model

Code Reference

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