This week Edge Impulse has announced its move into embedded Linux rolling out official support for the Raspberry Pi 4 mini PC. Now, users of Edge Impulse can select the right processor class for their embedded machine learning applications. Leverage our existing best-in-class support for low-power MCUs or venture into processor classes that run embedded Linux if highest performance is the objective, explains Edge Impulse. SDKs for Python, Node.js, Go, and C++ are provided so you can easily build your own custom apps for inferencing.
“We’ve brought the same great user experience our developers are already familiar with into the Linux domain (using full hardware acceleration on the Pi 4), with a refreshed set of tools and capabilities that makes deploying embedded machine learning models on Linux as easy as… Pi.”
“In addition, we are also thrilled to launch support for true object detection as part of our computer vision ML pipeline! Use a Raspberry Pi camera or plug in a standard USB web camera into one of the available USB slots on the Pi, and harness the raw power of higher performance compute and more sophisticated frameworks and libraries to facilitate computer vision applications. “
“In this tutorial we show you how to build a custom object detection system that can recognize things in your house. You learn how to sample data from real devices, how to use transfer learning to train a neural network, and how to deploy the model back to an embedded device. “
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