Jetson hardware, and the ‘jetson-inference’ package

I have been involved in several projects very recently (and two ongoing) where we have used NVIDIA ‘Jetson’ hardware (Nano, Xavier / NX, and ConnectTech Rudi NX).  These machines are roughly ‘credit-card sized’ (apart from the Rudi, which has a larger but very ‘rugged’ case) and are ideal for ‘edge’ or embedded systems.

The Jetson hardware is basically a small but powerful GPU, but also including a CPU and small ‘motherboard’ providing the usual USB ports, etc.  They run a modified version of Ubuntu Linux.

In some cases I developed software in-house using OpenCV (C++ and Python).  However, I am also making more and more use of the excellent ‘jetson-inference’ library of deep-learning tools, and have now built up quite a bit of experience in using this library and developing applications and solutions based on it.

In short, it is very good for developing solutions that need:

  • Image classification (e.g. cat vs dog, or labrador vs poodle, or beach vs park)
  • Object detection (i.e. accurate location, and classification of objects – can be trained to recognise new objects, including very small/distant)
  • Pose estimation (e.g. standing, sitting, walking, pointing, waving)

I have now developed a number of solutions that have ‘gone live’ using this hardware and toolkit.  I am also experienced in the ‘back end’ tasks of training new ‘models’ to recognise new, specfic classes of objects, and porting those models to the Jetson hardware.

Please contact me to discuss whether I can help you with your Jetson-based project.  tom [at]