At alwaysAI we have the singular mission of making the process of building and deploying computer vision apps to edge devices as easy as possible. That includes training your model, building your app, and deploying your app to edge devices such as the Raspberry Pi, Jetson Nano, and many others. alwaysAI apps are built in Python and can run natively on Mac and Windows, and in our containerized edge runtime environment optimized...
Many models, including those for pose estimation, may have much better performance when run on a GPU rather than a CPU. In this tutorial, we’ll cover how to run pose estimation on the Jetson Nano B01 and cover some nuances of running starter apps on this edge device.
Building and running your app on alwaysAI can be done a few different ways, depending on the platform you want to develop on and the device you want to deploy on. We’ve concentrated these options in one place for your convenience and we’ll update this document as the platform evolves!
The Jetson Nano is a powerful compactly-packaged AI accelerator that allows you to run intensive models (such as the ones typically used for semantic segmentation and pose estimation) with shorter inference time, while meeting key performance requirements. The Jetson Nano also allows you to speed up lighter models, like those used for object detection, to the tune of 10-25 fps.
alwaysAI provides a platform to deploy computer-vision applications onto edge devices.Learn More