From Annotation Tools to Real-World AI: My Open-Source Journey with Labelme, Medical Imaging, and Industrial Vision

In practical artificial intelligence development, while model design is important, project success often depends more on data annotation workflows, toolchain integration, and engineering deployment. This talk draws on my real-world contributions to the open-source annotation tool Labelme, showing how practical user needs led to multilingual localization and feature improvements, as well as the technical trade-offs involved in collaborating with upstream maintainers and the community. Building on this experience, the session explores how open-source tools help bridge AI research and real-world applications, highlighting challenges such as data quality and privacy in medical image segmentation and data distribution shifts and real-time requirements in multi-site industrial vision deployment. Through cross-domain case studies, it emphasizes the critical role of open-source tooling in modern AI workflows and demonstrates how addressing real problems through community collaboration can effectively support engineering practice in medical and industrial AI systems.