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        "author": "Evgenia Rusak, Benjamin Mitzkus",
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        "description": "# imagecorruptions\nThis package provides a set of corruptions that can be applied to images in order to benchmark the robustness of neural networks. These corruptions are not meant to be used as training data augmentation but rather to test the networks against unseen perturbations. For more information have a look at the paper on the original corruption package by Hendrycks and Dietterich: [Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations](https://arxiv.org/abs/1807.01697).\n\n![image corruptions](https://raw.githubusercontent.com/bethgelab/imagecorruptions/master/assets/corruptions_sev_3.png?token=ACY4L7YQWNOLTMRRO53U6FS5G3UF6)\n\n## Installation and Usage\nThis package is pip installable via `pip3 install imagecorruptions`. An example of how to use the corruption function is given below:\n```python\nfrom imagecorruptions import corrupt\n...\ncorrupted_image = corrupt(image, corruption_name='gaussian_blur', severity=1)\n...\n```\nLooping over all available corruptions can be done either by name or by index:\n```python\n# via name\nfrom imagecorruptions import get_corruption_names\nfor corruption in get_corruption_names():\n    for severity in range(5):\n        corrupted = corrupt(image, corruption_name=corruption, severity=severity+1)\n        ...\n\n# via number:\nfor i in range(15):\n    for severity in range(5):\n        corrupted = corrupt(image, corruption_number=i, severity=severity+1)\n        ...\n```\n\nNote that the first 15 image corruptions are the common corruptions (the ones you get via `get_corruption_names()`). If you really wish to use these as data augmentation, there exist four additional validation corruptions which can be accessed via `get_corruption_names('validation')` which should then be used to test the corruption robustness of the trained model.\n\n## Citation\n\nIf you use our code or the imagecorruptions package, please consider citing:\n```\n@article{michaelis2019dragon,\n  title={Benchmarking Robustness in Object Detection: \n    Autonomous Driving when Winter is Coming},\n  author={Michaelis, Claudio and Mitzkus, Benjamin and \n    Geirhos, Robert and Rusak, Evgenia and \n    Bringmann, Oliver and Ecker, Alexander S. and \n    Bethge, Matthias and Brendel, Wieland},\n  journal={arXiv preprint arXiv:1907.07484},\n  year={2019}\n}\n```\n\n## Credit and Changelog\nThis package is an extension of the image corruption functions provided by Dan Hendrycks in the repository  [corruptions](https://github.com/hendrycks/robustness). The image corruptions implemented by Hendrycks are generalized to work on images with arbitrary image dimensions and aspect ratios aswell as on grayscale images. We furthermore removed the dependency to `libmagickwand` and the python api `Wand` and reimplemented the `motion_blur` in python.\n\n\n",
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