If you haven’t already started thinking about building computer vision into your products, now is the time.

The Jetson TX2 is part of NVIDIA’s line of embedded AI modules enabling super fast computation on the edge. The TX2 is a leg up compared to the Nano and will give you faster inferencing times in your AI applications. In fact, the Jetson TX2 is the fastest, most power-efficient embedded AI computing device. This 7.5 watt supercomputer on a module brings true AI computing at the edge. 

Please note: This setup guide can only be followed if you have a Linux computer. VM support is un-verified.

The Raspberry Pi 3B+ and the Raspberry Pi 4 are ubiquitous among the hobbyist community of developers. They are reliable, easy to use single-board computers (SBCs) that are very affordable, making it easy to get your edge computer vision project up and running!

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.

The developer platform at alwaysAI fast-tracks the creation and deployment of computer vision applications on edge devices, making it easy for you to get started building applications in the field of computer vision.

At the AWS re:Invent conference, we deepened our collaboration with Qualcomm® Technologies by demonstrating real-time object detection and pose estimation on the Qualcomm® Robotics RB3 platform. Based on the Qualcomm® SDA845 system-on-a-chip (SoC), the RB3 platform enables the creation of high-performing computer vision applications on robots and other IoT devices. We built an application on a demo robot that showcased the powerful combination of the Qualcomm® Robotics RB3 platform, our deep...

In this tutorial, we will show you the steps needed to boost the performance of your edge device. You will need a hardware accelerator that is supported by alwaysAI – such as Intel’s Neural Compute Stick 2. We have more information on hardware accelerators in our documentation, which is available now. 

Recent advances in technology have greatly broadened the scope of object detection and related computer vision (CV) services. Hardware with advanced features paired with smarter neural networks has attracted developers and data scientists from numerous industries to start leveraging computer vision to solve complex business challenges. Combined with the rising popularity of embedded devices capturing data on the edge, computer vision on a grand scale has been exploding with seemingly endless...

Note: The method described in this article is not your only option for installing the base operating system on your Raspberry Pi. You can also use NOOBS, an operating system installation manager to install the base Raspbian operating system. For information on NOOBS go to this website https://www.raspberrypi.org/documentation/installation/noobs.md . If you do use NOOBS to do the initial operating system installation once finished go to Setting Up A Docker Container section of this document...

Something big is happening. The real world is being radically transformed by tech that has never had as much intelligence and capability as it does today. Sea changes are on the horizon and forward-thinking businesses are shifting their development focus to arguably the most significant change since the invention of the printed circuit board.