OpenVDNA Project Will Accelerate AI Adoption for Inspection and More
Source: Inside High Performance Computing
Fans of open source Machine Learning got a boost today with the launch of an Exascale Visual AI initiative to sequence Visual DNA from images using Volume Learning to enable new applications. The OpenVDNA project provides public resources for visual AI solutions, including a visual DNA catalog, visual AI search engine, and a visual AI toolkit. Visual DNA are taken from thousands of small pieces of images composing the fabric of the image.

Each visual DNA puzzle piece is analyzed and compared using over 16,000 feature metrics in four primary bases: Color, Shape, Texture and Glyphs. Visual DNA are learned and stored in the OpenVDNA catalog using Learning Agents to create structures representing higher-level objects, and then accessed via the OpenVDNA search engine and the OpenVDNA software toolkit.

The mission of the Visual Genomes Foundation is "OpenVDNA for everybody". "Visual DNA are part of the future of AI, and a standard part of the infrastructure of modern life", says Scott Krig, founder of the VGF. "A global backbone of supercomputers will learn, record and serve visual DNA to innumerable connected applications, providing basic visual AI services for industrial inspection, inventory, commerce and daily life. Volume learning and visual DNA will become ubiquitous, popular, simple and free."

Read the full article at Inside HPC.