{"id":21441930,"url":"https://github.com/dennishnf/unsupervised-anomaly-detection","last_synced_at":"2025-07-14T17:32:31.010Z","repository":{"id":60877139,"uuid":"546355597","full_name":"dennishnf/unsupervised-anomaly-detection","owner":"dennishnf","description":"This repository describes the implementation of an unsupervised anomaly detector using the Anomalib library.","archived":false,"fork":false,"pushed_at":"2022-10-06T00:50:35.000Z","size":4392,"stargazers_count":21,"open_issues_count":0,"forks_count":6,"subscribers_count":1,"default_branch":"main","last_synced_at":"2023-04-05T08:59:50.774Z","etag":null,"topics":["anomaly-detection","artificial-intelligence","computer-vision","deep-learning","machine-learning","neural-networks","object-detection","python","torch"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dennishnf.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2022-10-06T00:33:31.000Z","updated_at":"2023-03-24T09:30:48.000Z","dependencies_parsed_at":"2022-10-06T05:34:17.872Z","dependency_job_id":null,"html_url":"https://github.com/dennishnf/unsupervised-anomaly-detection","commit_stats":null,"previous_names":[],"tags_count":null,"template":null,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dennishnf%2Funsupervised-anomaly-detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dennishnf%2Funsupervised-anomaly-detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dennishnf%2Funsupervised-anomaly-detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dennishnf%2Funsupervised-anomaly-detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dennishnf","download_url":"https://codeload.github.com/dennishnf/unsupervised-anomaly-detection/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":225990489,"owners_count":17556153,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["anomaly-detection","artificial-intelligence","computer-vision","deep-learning","machine-learning","neural-networks","object-detection","python","torch"],"created_at":"2024-11-23T01:45:12.763Z","updated_at":"2024-11-23T01:45:13.215Z","avatar_url":"https://github.com/dennishnf.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![made-with-python](https://img.shields.io/badge/Made%20with-Python-1f425f.png)](https://www.python.org/)\n[![MIT Licence](https://img.shields.io/badge/License-MIT-blue.png)](https://opensource.org/licenses/mit-license.php)\n[![Ask Me Anything !](https://img.shields.io/badge/Ask%20me-anything-1abc9c.png)](https://github.com/dennishnf/unsupervised-anomaly-detection/issues)\n[![Open Source Love](https://img.shields.io/badge/Open%20Source-%E2%9D%A4-1abc9c.png)](https://github.com/dennishnf/unsupervised-anomaly-detection/)\n[![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\u0026url=https://github.com/dennishnf/unsupervised-anomaly-detection\u0026hashtags=anomaly,images,anomalib,unsupervised)    \n\nUnsupervised anomaly detection using Anomalib\n=============================================\n\n## Description\n\nThis 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. \n\nThe 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.\n\nIt 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.\n\nThe implementation is fully described in the main notebook: **unsupervised-anomaly-detection.ipynb**.\n\n\u003cp align=\"center\"\u003e\n\u003cimg src=\".images-readme/image-inference.png\" alt=\"figure\" width=\"760\"/\u003e\n\u003c/p\u003e\n\n## Author\n\nDennis Hernando NÚÑEZ FERNÁNDEZ    \n[https://dennishnf.com](https://dennishnf.com)\n\n\n## References\n\n- Akcay, S., Ameln, D., Vaidya, A., Lakshmanan, B., Ahuja, N., \u0026 Genc, U. (2022). Anomalib: A Deep Learning Library for Anomaly Detection. doi:10.48550/ARXIV.2202.08341    \n- https://blog.ml6.eu/a-practical-guide-to-anomaly-detection-using-anomalib-b2af78147934    \n- https://openvinotoolkit.github.io/anomalib/    \n- https://pypi.org/project/anomalib/    \n- https://www.kaggle.com/code/ipythonx/mvtec-ad-anomaly-detection-with-anomalib-library/notebook    \n- https://www.mvtec.com/company/research/datasets/mvtec-ad    \n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdennishnf%2Funsupervised-anomaly-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdennishnf%2Funsupervised-anomaly-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdennishnf%2Funsupervised-anomaly-detection/lists"}