{"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/dlib-ml-a-machine-learning-toolkit","title":"Dlib-ml: A Machine Learning Toolkit","arxiv_id":null,"date":"2009-01-01","proceeding":null,"authors":["Davis E. King"],"abstract":"There are many excellent toolkits which provide support for developing machine learning software in Python, R, Matlab, and similar environments. Dlib-ml is an open source library, targeted at both engineers and research scientists, which aims to provide a similarly rich environment for developing machine learning software in the C++ language. Towards this end, dlib-ml contains an extensible linear algebra toolkit with built in BLAS support. It also houses implementations of algorithms for performing inference in Bayesian networks and kernel-based methods for classification, regression, clustering, anomaly detection, and feature ranking. To enable easy use of these\r\ntools, the entire library has been developed with contract programming, which provides complete and precise documentation as well as powerful debugging tools","url_abs":"https://www.jmlr.org/papers/volume10/king09a/king09a.pdf","url_pdf":"https://www.jmlr.org/papers/volume10/king09a/king09a.pdf","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":"dlib-ml-a-machine-learning-toolkit","repo_url":"https://github.com/davisking/dlib","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"BSL-1.0"}},{"paper_slug":"dlib-ml-a-machine-learning-toolkit","repo_url":"https://github.com/serengil/deepface","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"face-verification","task_name":"Face Verification"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/face-verification-on-labeled-faces-in-the","task":"Face Verification","dataset":"Labeled Faces in the Wild","model":"Dlib","rank_in_archive_order":3,"of":7,"metrics":{"Accuracy":"99.38%"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}