AutoEncoder with SSIM loss

This is a third party implementation of the paper Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders.

Requirement

tensorflow==2.2.0
skimage

Datasets

MVTec AD datasets https://www.mvtec.com/company/research/datasets/mvtec-ad/

Code examples

Step 1. Set the DATASET_PATH variable.

Set the DATASET_PATH to the root path of the downloaded MVTec AD dataset.

Step 2. Train SSIM-AE and Test.

• bottle object

python train.py --name bottle --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --do_aug --p_rotate 0.
python test.py --name bottle --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --bg_mask W

• cable object

python train.py --name cable --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --do_aug --p_rotate 0. --p_horizonal_flip 0. --p_vertical_flip 0.
python test.py --name cable --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500

• capsule object

python train.py --name capsule --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --do_aug --p_rotate 0. --p_horizonal_flip 0. --p_vertical_flip 0.
python test.py --name capsule --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --bg_mask W

• carpet texture

python train.py --name carpet --loss ssim_loss --im_resize 512 --patch_size 128 --z_dim 100 --do_aug --rotate_angle_vari 10
python test.py --name carpet --loss ssim_loss --im_resize 512 --patch_size 128 --z_dim 100

• grid texture

python train.py --name grid --loss ssim_loss --im_resize 256 --patch_size 128 --z_dim 100 --grayscale --do_aug 
python test.py --name grid --loss ssim_loss --im_resize 256 --patch_size 128 --z_dim 100 --grayscale

• hazelnut object

python train.py --name hazelnut --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --do_aug --p_rotate_crop 0.
python test.py --name hazelnut --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --bg_mask B 

• leather texture

python train.py --name leather --loss ssim_loss --im_resize 256 --patch_size 128 --z_dim 100 --do_aug
python test.py --name leather --loss ssim_loss --im_resize 256 --patch_size 128 --z_dim 100

• metal_nut object

python train.py --name metal_nut --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --do_aug --p_rotate_crop 0. --p_horizonal_flip 0. --p_vertical_flip 0.
python test.py --name metal_nut --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --bg_mask B 

• pill object

python train.py --name pill --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --do_aug --p_rotate 0. --p_horizonal_flip 0. --p_vertical_flip 0.
python test.py --name pill --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --bg_mask B

• screw object

python train.py --name screw --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --grayscale --do_aug --p_rotate 0.
python test.py --name screw --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --grayscale --bg_mask W

• tile texture

python train.py --name tile --loss ssim_loss --im_resize 256 --patch_size 128 --z_dim 100 --do_aug
python test.py --name tile --loss ssim_loss --im_resize 256 --patch_size 128 --z_dim 100

• toothbrush object

python train.py --name toothbrush --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --do_aug --p_rotate 0. --p_vertical_flip 0.
python test.py --name toothbrush --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500

• transistor object

python train.py --name transistor --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --do_aug --p_rotate 0. --p_vertical_flip 0.
python test.py --name transistor --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 

• wood texture

python train.py --name wood --loss ssim_loss --im_resize 256 --patch_size 128 --z_dim 100 --do_aug --rotate_angle_vari 15
python test.py --name wood --loss ssim_loss --im_resize 256 --patch_size 128 --z_dim 100 

• zipper object

python train.py --name zipper --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --grayscale --do_aug --p_rotate 0.
python test.py --name zipper --loss ssim_loss --im_resize 266 --patch_size 256 --z_dim 500 --grayscale 

Overview of Results

Classification
During test, I simply classify a test image as defect if there is any anomalous response on the residual map. It is strict for anomaly-free images, resulting in relatively lower accuracy in the ok column shown as below.
Please note that the threshold makes a big difference to the outcome, which should be carefully selected.

Discussion

• SSIM + L1 metrics
  Since SSIM is a measure of similarity only between grayscale images, it cannot handle color defect in some cases. So here I use SSIM + L1 distance for anomaly segmentation.
• VAE
  I have tried VAE, observing no performances improvements.
• InstanceNorm
  I have also tried adding the IN layer for accelerating convergence, but the droplet artifact appears in some cases. It is also mentioned and discussed in StyleGAN-2 paper.

Supplementary materials

My notes https://www.yuque.com/books/share/8c7613f7-7571-4bfa-865a-689de3763c59?# password ixgg

References

@inproceedings{inproceedings, author = {Bergmann, Paul and Löwe, Sindy and Fauser, Michael and Sattlegger, David and Steger, Carsten}, year = {2019}, month = {01}, pages = {372-380}, title = {Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders}, doi = {10.5220/0007364503720380} }

Paul Bergmann, Michael Fauser, David Sattlegger, Carsten Steger. MVTec AD - A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection; in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019