Nvidia launched the Jetson Xavier NX embedded System-on-Module (SoM) at the end of last year. It is pin-compatible with the Jetson Nano SoM and includes a CPU, a GPU, PMICs, DRAM, and flash storage. However, it was missing an important accessory, its own development kit. Since a SoM is an embedded board with just a row of connector pins, it is hard to use out-of-the-box. A development board connects all the pins on the module to ports like HDMI, Ethernet, and USB.
In this tutorial you will learn how to use my pre-configured NVIDIA Jetson Nano .img If you've ever configured an NVIDIA product such as the TX1, TX2, and even the Nano, you know that working with NVIDIA's Jetpack and installing libraries is far from straightforward. It is developed and supported by my team here at PyImageSearch to save you time and bring you up to speed quickly for developing your own embedded CV/DL projects and for following along with my new book Raspberry Pi for Computer Vision. If you purchase a copy of the Complete Bundle of Raspberry Pi for Computer Vision, you'll gain access to this accompanying .img. All you have to do is (1) download the .img
Have you thought about adding artificial intelligence to your project? It's easier than you think, and NVIDIA is making AI fully accessible to makers, self-taught developers, and embedded technology enthusiasts by providing the tools and inspiration to get up and running fast. In this contest, you'll tap into the power of the new NVIDIA Jetson Nano - a powerful, easy-to-use, mini AI computer that lets you run multiple neural networks in parallel. It's perfect for any project using image classification, object detection, segmentation, speech processing, and more. NVIDIA is giving away thousands in prizes including a paid trip to NVIDIA GTC 2020, a brand new Titan RTX, a Jetson AGX Xavier Developer Kit, and Public Cloud Compute credits that you can use for your next project.
We are using NVIDIA Jetson Nano Developer kit as our accelerator system.Which will contain Docker Container which will contain the dataset and trained model SSD (Single Shot Detector) MobileNetV2 which we will be used to for facial detection. Video would be recorded through the Camera attached to the accelerator system. Code of the SSD (Single Shot Detector) MobileNetV2 is written in Python Programming Language and Deep learning framework which has been used is PyTorch.To optimized the neural network layers.NVIDIA TensorRT is used for faster Inference during the run time.
AI has become the key driver for the adoption of edge computing. Originally, the edge computing layer was meant to deliver local compute, storage, and processing capabilities to IoT deployments. Sensitive data that cannot be sent to the cloud for processing and analysis is handled by the edge. It also reduces the latency involved in the roundtrip to the cloud. Most of the business logic that runs in the cloud is moving to the edge to provide low-latency, faster response time.