Accelerate Deep Learning on Raspberry Pi [Video]

More Information
  • Learn how to get Started with Raspberry Pi from Scratch
  • Discover various Object Detection, models
  • Introduction to Deep Learning and Tensorflow lite
  • Implement Object Detection using Movidius NC SDK

Learn how we implemented Deep Learning Object Detection Models on Raspberry Pi and accelerated them with Intel Movidius Neural Compute Stick. When we first got started in Deep Learning particularly in Computer Vision, we were really excited at the possibilities of this technology to help people. The only problem is, that image classification and object detection run just fine on our expensive, power consuming and bulky Deep Learning machines. However, not everyone can afford or implement AI for their practical applications. This is when we went searching for an affordable, compact, less power hungry alternative. Generally, if we'd want to shrink our IoT and automation projects, we'd often look to the Raspberry Pi which is a versatile computing solution for numerous problems. This made us ponder about how we can port out deep learning models to this compact computing unit. Not only that but how could we run it at close to real-time? Amongst the possible solutions, we arrived at using the raspberry pi in conjunction with an AI Accelerator USB stick that was made by Intel to boost our object detection frame-rate. However, it was not so simple to get it up and running. Implementing the documentation, we landed up with a series of bugs after bugs, which became a bit tedious. After endless posts on forums, tutorials and blogs, we have documented a seamless guide in the form of this course; which will show you, step-by-step, on how to implement your own Deep Learning Object Detection models on video and webcam without all the wasteful debugging. So essentially, we've structured this training to reduce debugging, speed up your time to market and get you results sooner. Let me help you get fast results. Enrol now, by clicking the button and let us show you how to develop Accelerated AI on Raspberry Pi.

All the code files are placed at

Style and Approach

A complete course packed with step-by-step instructions, working examples, and helpful advice. This course is clearly divided into small parts that will help you understand each part individually and help you learn at your own pace.

  • Getting Started with Raspberry Pi even if you are a beginner, Deep Learning Basics, Object Detection Models - Pros and Cons of each CNN,
  • Setup and Install Movidius Neural Compute Stick (NCS) SDK,
  • CURRENTLY, the NCS2 (the newest version of the Movidius) is not supported by the Raspberry Pi, if there will be some useful information about that, then we will make an announcement (or lecture) as soon as possible.
  • Run Yolo and Mobilenet SSD object detection models in the recorded or live video
Course Length 1 hour 17 minutes
ISBN 9781838640453
Date Of Publication 26 Feb 2019


Laszlo Benke

Augmented Startups have over 8 years experience in printed circuit board (PCB) design as well in image processing and embedded control. Author Ritesh Kanjee has completed his Master's Degree in Electronic Engineering and published two papers on the IEEE Database with one called "Vision-based adaptive Cruise Control using Pattern Matching" and the other called "A Three-Step Vehicle Detection Framework for Range Estimation Using a Single Camera" (on Google Scholar). His work was implemented in LabVIEW. He works as an embedded electronic engineer in defense research and has experience in FPGA design with programming in both VHDL and Verilog. Ritesh also has expertise in augmented reality and machine learning in which he shall be introducing new technologies through the video platform.

Laszlo Benke - Raspberry Pi expert, Python and AI engineer

He is an electrical engineer, he works as a Python software engineer freelancer. He uses Raspberry Pi and Computer vision technologies (AI, Object detection CNN) in my projects. You can find him on Upwork (freelancer projects) and Codementor (live teaching) also, for further information. Experienced Project Specialist with a demonstrated history of working in the industrial automation industry. Skilled in Embedded Software/Internet of Things, Python, C#, Home Automation especially in fast prototyping with Raspberry PI (MVP), Data Science and Machine learning (Tensorflow, Computer Vision, Object detection). Strong program and project management professional with an electrical engineer M.Sc. focused on machine learning (AI) and embedded systems from BME VIK (AUT).