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.
Cite
@article{arxiv.2306.04796,
title = {JDLL: A library to run Deep Learning models on Java bioimage informatics platforms},
author = {Carlos Garcia Lopez de Haro and Stephane Dallongeville and Thomas Musset and Estibaliz Gomez de Mariscal and Daniel Sage and Wei Ouyang and Arrate Munoz-Barrutia and Jean-Yves Tinevez and Jean-Christophe Olivo-Marin},
journal= {arXiv preprint arXiv:2306.04796},
year = {2023}
}