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https://github.com/marco-rudolph/differnet

This is the official repository to the WACV 2021 paper "Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows" by Marco Rudolph, Bastian Wandt and Bodo Rosenhahn.
https://github.com/marco-rudolph/differnet

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This is the official repository to the WACV 2021 paper "Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows" by Marco Rudolph, Bastian Wandt and Bodo Rosenhahn.

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# DifferNet



This is the official repository to the WACV 2021 paper "[Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows](

https://arxiv.org/abs/2008.12577)" by Marco Rudolph, Bastian Wandt and Bodo Rosenhahn.



If the only reason you ended up here is because you made a typo on 'different' - what was our intention - here is a shortened summary: We introduce a method that is able to find anomalies like defects on image data without having some of them in the training set. Click [here](https://www.youtube.com/watch?v=lFxDtC34tk0) to watch our short presentation from WACV.



## Getting Started



You will need [Python 3.6](https://www.python.org/downloads) and the packages specified in _requirements.txt_.

We recommend setting up a [virtual environment with pip](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)

and installing the packages there.



Install packages with:



```

$ pip install -r requirements.txt

```



## Configure and Run



All configurations concerning data, model, training, visualization etc. can be made in _config.py_. The default configuration will run a training with paper-given parameters on the provided dummy dataset. This dataset contains images of 4 squares as normal examples and 4 circles as anomaly.



To start the training, just run _main.py_! If training on the dummy data does not lead to an AUROC of 1.0, something seems to be wrong. Don't be worried if the loss is negative. The loss reflects the negative log likelihood which may be negative.

Please report us if you have issues when using the code.



## Data



The given dummy dataset shows how the implementation expects the construction of a dataset. Coincidentally, the [MVTec AD dataset](https://www.mvtec.com/company/research/datasets/mvtec-ad) is constructed in this way.



Set the variables _dataset_path_ and _class_name_ in _config.py_ to run experiments on a dataset of your choice. The expected structure of the data is as follows:



``` 

train data:



        dataset_path/class_name/train/good/any_filename.png

        dataset_path/class_name/train/good/another_filename.tif

        dataset_path/class_name/train/good/xyz.png

        [...]



test data:



    'normal data' = non-anomalies



        dataset_path/class_name/test/good/name_the_file_as_you_like_as_long_as_there_is_an_image_extension.webp

        dataset_path/class_name/test/good/did_you_know_the_image_extension_webp?.png

        dataset_path/class_name/test/good/did_you_know_that_filenames_may_contain_question_marks????.png

        dataset_path/class_name/test/good/dont_know_how_it_is_with_windows.png

        dataset_path/class_name/test/good/just_dont_use_windows_for_this.png

        [...]



    anomalies - assume there are anomaly classes 'crack' and 'curved'



        dataset_path/class_name/test/crack/dat_crack_damn.png

        dataset_path/class_name/test/crack/let_it_crack.png

        dataset_path/class_name/test/crack/writing_docs_is_fun.png

        [...]



        dataset_path/class_name/test/curved/wont_make_a_difference_if_you_put_all_anomalies_in_one_class.png

        dataset_path/class_name/test/curved/but_this_code_is_practicable_for_the_mvtec_dataset.png

        [...]

``` 



## Credits



Some code of the [FrEIA framework](https://github.com/VLL-HD/FrEIA) was used for the implementation of Normalizing Flows. Follow [their tutorial](https://github.com/VLL-HD/FrEIA) if you need more documentation about it.



## Citation

Please cite our paper in your publications if it helps your research. Even if it does not, you are welcome to cite us.



    @inproceedings { RudWan2021,

    author = {Marco Rudolph and Bastian Wandt and Bodo Rosenhahn},

    title = {Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows},

    booktitle = {Winter Conference on Applications of Computer Vision (WACV)},

    year = {2021},

    month = jan

    }

    

Another paper link because you missed the first one:



* [Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows](

https://arxiv.org/abs/2008.12577)



## License



This project is licensed under the MIT License.