arimousa / DDAD

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Anomaly Detection with Conditioned Denoising Diffusion Models.

Official implementation of DDAD

PWC PWC

Framework

Requirements

This repository is implemented and tested on Python 3.8 and PyTorch 2.1. To install requirements:

pip install -r requirements.txt

Train and Evaluation of the Model

You can download the model checkpoints directly from Checkpoints

To train the denoising UNet, run:

python main.py --train True

Modify the settings in the config.yaml file to train the model on different categories.

For fine-tuning the feature extractor, use the following command:

python main.py --domain_adaptation True

To evaluate and test the model, run:

python main.py --detection True

Dataset

You can download MVTec AD: MVTec Software and VisA Benchmarks. For preprocessing of VisA dataset check out the Data preparation section of this repository.

The dataset should be placed in the 'datasets' folder. The training dataset should only contain one subcategory consisting of nominal samples, which should be named 'good'. The test dataset should include one category named 'good' for nominal samples, and any other subcategories of anomalous samples. It should be made as follows:

Name_of_Dataset
|-- Category
|-----|----- ground_truth
|-----|----- test
|-----|--------|------ good
|-----|--------|------ ...
|-----|--------|------ ...
|-----|----- train
|-----|--------|------ good

Results

Running the code as explained in this file should achieve the following results for MVTec AD:

Anomaly Detection (Image AUROC) and Anomaly Localization (Pixel AUROC, PRO)

Expected results for MVTec AD:

Category Carpet Grid Leather Tile Wood Bottle Cable Capsule Hazel nut Metalnut Pill Screw Toothbrush Transistor Zipper Average
Detection 99.3% 100% 100% 100% 100% 100% 99.4% 99.4% 100% 100% 100% 99.0% 100% 100% 100% 99.8%
Localization (98.7%,93.9%) (99.4%,97.3%) (99.4%,97.7%) (98.2%,93.1%) (95.0%,82.9%) (98.7%,91.8%) (98.1%,88.9%) (95.7%,93.4%) (98.4%,86.7%) (99.0%,91.1%) (99.1%,95.5%) (99.3%,96.3%) (98.7%,92.6%) (95.3%,90.1%) (98.2%,93.2%) (98,1%,92.3%)

The settings used for these results are detailed in the table.

Categories Carpet Grid Leather Tile Wood Bottle Cable Capsule Hazelnut Metal nut Pill Screw Toothbrush Transistor Zipper
(w) 0 4 11 4 11 3 3 8 5 7 9 2 0 0 10
Training epochs 2500 2000 2000 1000 2000 1000 3000 1500 2000 3000 1000 2000 2000 2000 1000
FE epochs 0 6 8 0 16 5 0 8 3 1 4 4 2 0 6

Following is the expected results on VisA Dataset.

Category Candle Capsules Cashew Chewing gum Fryum Macaroni1 Macaroni2 PCB1 PCB2 PCB3 PCB4 Pipe fryum Average
Detection 99.9% 100% 94.5% 98.1% 99.0% 99.2% 99.2% 100% 99.7% 97.2% 100% 100% 98.9%
Localization (98.7%,96.6%) (99.5%,95.0%) (97.4%,80.3%) (96.5%,85.2%) (96.9%,94.2%) (98.7%,98.5%) (98.2%,99.3%) (93.4%,93.3%) (97.4%,93.3%) (96.3%,86.6%) (98.5%,95.5%) (99.5%,94.7%) (97.6%,92.7%)

The settings used for these results are detailed in the table.

Categories Candle Capsules Cashew Chewing gum Fryum Macaroni1 Macaroni2 PCB1 PCB2 PCB3 PCB4 Pipe fryum
(w) 6 5 0 6 4 5 2 9 5 6 6 8
Training epochs 1000 1000 1750 1250 1000 500 500 500 500 500 500 500
FE epochs 1 3 0 0 3 7 11 8 5 1 1 6

Framework

Citation

@article{mousakhan2023anomaly,
  title={Anomaly Detection with Conditioned Denoising Diffusion Models},
  author={Mousakhan, Arian and Brox, Thomas and Tayyub, Jawad},
  journal={arXiv preprint arXiv:2305.15956},
  year={2023}
}

Feedback

For any feedback or inquiries, please contact arian.mousakhan@gmail.com

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License:MIT License


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