Xnor.ai’s AI2GO debuts as a self-serve platform that enables developers and device creators to build smart, edge-based solutions without training or background in artificial intelligence (AI). Available now, the platform contains more than a hundred fully-trained models optimized to run on resource-constrained devices such as mobile devices, wearables, smart cameras, remote sensors and more.
AI2GO promises to change the scale and speed at which AI solutions can be built. As the first platform to offer hundreds of fully-trained edge AI models with state-of-the-art accuracy, developers can download the complete solution and are ready to go. In the coming months, the AI2GO platform will provide enterprise users access to fully optimized models along with additional custom features including automated training and re-training, and performance optimization for large-scale development teams.
Using AI2GO, users first select their preferred hardware (Raspberry Pi, Linux, Ambarella, etc.), then chooses an AI use case, for example a “pet classifier for a home security camera,” a “person detector for a dash cam,” or a “person segmenter for video conferencing applications.” Because AI2GO models are designed to run in resource-constrained environments, Xnor provides the user with the novel opportunity to tune their model for latency (milliseconds) and memory footprint (megabytes) to fit within the user’s set of constraints. Once the user has specified their constraints the available models are listed, ranked by accuracy. The user can then download an Xnor Bundle (XB), a module containing a deep learning model, an inference engine.
Xnor also provides an accompanying SDK that includes access to code samples, demo applications, benchmarking tools and technical documentation that makes it simple for everyone to start building a smart application. For more details, checkout AI2GO now.