Training Data for Your CNN: What You Need and How to Get it
Training Data for Your CNN: What You Need and How to Get it


A fundamental building block for AI development is the development of a proper training set to allows effective training of Neural Nets. Developing such a training set constitute a major challenge, requiring multi-disciplinary knowledge spacing data science, computer vision, machine learning and project management. This talk will provide an outline of common workflows in developing a training set for AI applications, touching base on how to start with the first steps, how to leverage existing tools and labeling companies and how to assess if the developed database is sufficiently comprehensive and capable of effectively sustaining AI algorithms development for computer vision applications.


Carlo Dal Mutto is a computer vision and machine learning engineer interested in the application of deep learning techniques to 3D data. He has received a Ph.D (Dottorato di ricerca) in Information Engineering from University of Padova, Italy in 2012. Currently he is CTO at Aquifi, focused on delivering 3D-AI solutions for logistics and manufacturing. He is inventor of several patents, he has been invited speaker at major technical conferences, and he has co-authored research papers, two book chapters and two books on 3D data acquisition and processing. He has served as a reviewer and TPC member for CVPR, ECCV, ICCV, 3DPVT, 3DIMPVT, ICME, IJCV, IEEE SPL, and Springer MVAP.

Open Data Science




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