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), 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, 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.
Dennis Hernando NÚÑEZ FERNÁNDEZ
https://dennishnf.com
- 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