{"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/jdll-a-library-to-run-deep-learning-models-on","title":"JDLL: A library to run Deep Learning models on Java bioimage informatics platforms","arxiv_id":"2306.04796","date":"2023-06-07","proceeding":null,"authors":["Carlos Garcia Lopez de Haro","Stephane Dallongeville","Thomas Musset","Estibaliz Gomez de Mariscal","Daniel Sage","Wei Ouyang","Arrate Munoz-Barrutia","Jean-Yves Tinevez","Jean-Christophe Olivo-Marin"],"abstract":"We present JDLL, an agile Java library that offers a comprehensive toolset/API to unify the development of high-end applications of DL for bioimage analysis and to streamline their installation and maintenance. JDLL provides all the functions required to consume DL models seamlessly, without being burdened by the configuration of the Python-based DL frameworks, within Java bioimage informatics platforms. Moreover, it allows the deployment of pre-trained models in the Bioimage Model Zoo (BMZ) by shipping the logic to connect to the BMZ website, download and run a selected model inference.","url_abs":"https://arxiv.org/abs/2306.04796v2","url_pdf":"https://arxiv.org/pdf/2306.04796v2.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":"jdll-a-library-to-run-deep-learning-models-on","repo_url":"https://github.com/bioimage-io/jdll","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[{"method_slug":null,"method_name":"Library"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}