├── .images-readme └── image-inference.png ├── LICENSE ├── README.md └── unsupervised-anomaly-detection.ipynb /.images-readme/image-inference.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dennishnf/unsupervised-anomaly-detection/0e46c74f1037881cb95e5938654db7a7515f3607/.images-readme/image-inference.png -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Dennis Núñez-Fernández 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.png)](https://www.python.org/) 2 | [![MIT Licence](https://img.shields.io/badge/License-MIT-blue.png)](https://opensource.org/licenses/mit-license.php) 3 | [![Ask Me Anything !](https://img.shields.io/badge/Ask%20me-anything-1abc9c.png)](https://github.com/dennishnf/unsupervised-anomaly-detection/issues) 4 | [![Open Source Love](https://img.shields.io/badge/Open%20Source-%E2%9D%A4-1abc9c.png)](https://github.com/dennishnf/unsupervised-anomaly-detection/) 5 | [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=Download%20and%20use%20the%20Project:%20Unsupervised%20anomaly%20detection&url=https://github.com/dennishnf/unsupervised-anomaly-detection&hashtags=anomaly,images,anomalib,unsupervised) 6 | 7 | Unsupervised anomaly detection using Anomalib 8 | ============================================= 9 | 10 | ## Description 11 | 12 | This repository describes the implementation of an unsupervised anomaly detector on metallic nuts using the Anomalib library. Thereby we evaluate several state-of-the-art deep learning models such as PaDiM, PatchCore, STFPM, FastFlow and Reverse Distillation. 13 | 14 | The data used was The MVTEC Anomaly Detection Dataset ([MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad)), but only the metal nut dataset was used. The training was performed locally on a laptop with an NVIDIA GeForce GTX 1050 Ti GPU and Ubuntu 20.04 LTS operating system. 15 | 16 | It is recommended to download the dataset from this [link](https://www.mvtec.com/company/research/datasets/mvtec-ad), and organize the dataset in the format shown in the main notebook. 17 | 18 | The implementation is fully described in the main notebook: **unsupervised-anomaly-detection.ipynb**. 19 | 20 |

21 | figure 22 |

23 | 24 | ## Author 25 | 26 | Dennis Hernando NÚÑEZ FERNÁNDEZ 27 | [https://dennishnf.com](https://dennishnf.com) 28 | 29 | 30 | ## References 31 | 32 | - Akcay, S., Ameln, D., Vaidya, A., Lakshmanan, B., Ahuja, N., & Genc, U. (2022). Anomalib: A Deep Learning Library for Anomaly Detection. doi:10.48550/ARXIV.2202.08341 33 | - https://blog.ml6.eu/a-practical-guide-to-anomaly-detection-using-anomalib-b2af78147934 34 | - https://openvinotoolkit.github.io/anomalib/ 35 | - https://pypi.org/project/anomalib/ 36 | - https://www.kaggle.com/code/ipythonx/mvtec-ad-anomaly-detection-with-anomalib-library/notebook 37 | - https://www.mvtec.com/company/research/datasets/mvtec-ad 38 | 39 | 40 | --------------------------------------------------------------------------------