{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/visionad-a-software-package-of-performant","title":"VisionAD, a software package of performant anomaly detection algorithms, and Proportion Localised, an interpretable metric","arxiv_id":null,"date":"2024-06-07","proceeding":"Transactions of Machine Learning Research 2024 6","authors":["Alexander D. J. Taylor","Phillip Tregidgo","Jonathan James Morrison","Neill D. F. Campbell"],"abstract":"We release VisionAD, an anomaly detection library in the domain of images. The library forms the largest and most performant collection of such algorithms to date. Each algorithm is written through a standardised API, for ease of use. The library has a focus on fair benchmarking intended to mitigate the issue of cherry-picked results. It enables rapid experimentation and straightforward integration of new algorithms. In addition, we propose a new metric, Proportion Localised (PL). This reports the proportion of anomalies that are sufficiently localised via classifying each discrete anomaly as localised or not. The metric is far more intuitive as it has a real physical relation, meaning it is attractive to industry-based professionals. We also release the VisionADIndustrial (VADI) benchmark, a thorough benchmarking of the top anomaly detection algorithms. This benchmark calculates the mean across the pooled classes of the MVTec and VisA datasets. We are committed to hosting an updated version of this leaderboard online, and encourage researchers to add, tweak and improve algorithms to climb this leaderboard. VisionAD code is found at https://github.com/alext1995/VisionAD, and Proportion Localised code is found at https://github.com/alext1995/proportion_localised.","url_abs":"https://openreview.net/forum?id=o5kYH7bNe3","url_pdf":"https://openreview.net/pdf?id=o5kYH7bNe3","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"visionad-a-software-package-of-performant","repo_url":"https://github.com/alext1995/proportion_localised","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"visionad-a-software-package-of-performant","repo_url":"https://github.com/alext1995/VisionAD","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"benchmarking","task_name":"Benchmarking"}],"methods":[{"method_slug":"focus","method_name":"Focus"},{"method_slug":null,"method_name":"Library"}],"datasets_introduced":[{"slug":"visionadindustrial","name":"VisionADIndustrial","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}