One and two are pre-trained deep learning models, meaning that they are provided to you as-is by OpenCV. The Movidius NCS will perform inference using each of these models. The third recognizer model is not a form of deep learning. Rather, it is our SVM machine learning face recognition model. The RPi CPU will have to handle making face recognition predictions using it. We also load our label encoder which holds the names of the people our model can recognize (Line 42). Let's initialize our video stream: Line 47 initializes and starts our VideoStream object. We wait for the camera sensor to warm up on Line 48. Line 51 initializes our FPS counter for benchmarking purposes.

Using Intel's Loihi neuromorphic research chip and ABR's Nengo Deep Learning toolkit, we analyze the inference speed, dynamic power consumption, and energy cost per inference of a two-layer neural network keyword spotter trained to recognize a single phrase. We perform comparative analyses of this keyword spotter running on more conventional hardware devices including a CPU, a GPU, Nvidia's Jetson TX1, and the Movidius Neural Compute Stick. Our results indicate that for this inference application, Loihi outperforms all of these alternatives on an energy cost per inference basis while maintaining near-equivalent inference accuracy. Furthermore, an analysis of tradeoffs between network size, inference speed, and energy cost indicates that Loihi's comparative advantage over other low-power computing devices improves for larger networks.

Today's post is inspired by Danielle, a PyImageSearch reader who emailed me last week and asked: I'm enjoying your blog and I especially liked last week's post about image classification with the Intel Movidius NCS. My project involves object detection with the Raspberry Pi where I'm using my own custom Caffe model. The benchmark scripts you supplied for applying object detection on the Pi's CPU were too slow and I need faster speeds. Would the NCS be a good choice for my project and help me achieve a higher FPS? The short answer is yes, you can use the Movidius NCS for object detection with your own custom Caffe model. You'll even achieve high frame rates if you're processing live or recorded video. I told Danielle that she'll need the full-blown Movidius SDK installed on her (Ubuntu 16.04) machine. I also mentioned that generating graph files from Caffe models isn't always straightforward. Inside today's post you will learn how to: After going through the post you'll have a good understanding of the Movidius NCS and whether it's appropriate for your Raspberry Pi object detection project. To get started with real-time object detection on the Raspberry Pi, just keep reading. Today's blog post is broken into five parts.