This is a post co-written with Bernard Paques, CTO of Storm Reply, and Karl Herkt, Senior Strategist at Dassault Systèmes 3DExcite.
While computer vision can be crucial to industrial maintenance, manufacturing, logistics, and consumer applications, its adoption is limited by the manual creation of training datasets. The creation of labeled pictures in an industrial context is mainly done manually, which creates limited recognition capabilities, doesn’t scale, and results in labor costs and delays on business value realization. This goes against the business agility provided by rapid iterations in product design, product engineering, and product configuration. This process doesn’t scale for complex products such as cars, airplanes, or modern buildings, because in those scenarios every labeling project is unique (related to unique products). As result, computer vision technology can’t be easily applied to large-scale unique projects without a big effort in data preparation, sometimes limiting use case delivery.
In this post,

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