https://github.com/dennishnf/unsupervised-anomaly-detection

This repository describes the implementation of an unsupervised anomaly detector using the Anomalib library.
https://github.com/dennishnf/unsupervised-anomaly-detection

anomaly-detection artificial-intelligence computer-vision deep-learning machine-learning neural-networks object-detection python torch

Last synced: 12 months ago
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This repository describes the implementation of an unsupervised anomaly detector using the Anomalib library.

• Host: GitHub
• URL: https://github.com/dennishnf/unsupervised-anomaly-detection
• Owner: dennishnf
• License: mit
• Created: 2022-10-06T00:33:31.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-10-06T00:50:35.000Z (over 3 years ago)
• Last Synced: 2023-04-05T08:59:50.774Z (about 3 years ago)
• Topics: anomaly-detection, artificial-intelligence, computer-vision, deep-learning, machine-learning, neural-networks, object-detection, python, torch
• Language: Jupyter Notebook
• Homepage:
• Size: 4.19 MB
• Stars: 21
• Watchers: 1
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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Unsupervised anomaly detection using Anomalib

=============================================

## Description

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. 

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.

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.

The implementation is fully described in the main notebook: **unsupervised-anomaly-detection.ipynb**.







## Author

Dennis Hernando NÚÑEZ FERNÁNDEZ    

[https://dennishnf.com](https://dennishnf.com)

## References

- 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    

- https://blog.ml6.eu/a-practical-guide-to-anomaly-detection-using-anomalib-b2af78147934    

- https://openvinotoolkit.github.io/anomalib/    

- https://pypi.org/project/anomalib/    

- https://www.kaggle.com/code/ipythonx/mvtec-ad-anomaly-detection-with-anomalib-library/notebook    

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