Accelerate Deep Learning on Raspberry Pi

Learn how we implemented Deep Learning Object Detection Models on Raspberry Pi and accelerated them with Intel Movidius Neural Compute Stick. Click the link below to pre-register for FREE. First 10 Students get FREE Coupon to the Course

Click Here

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.

Accelerate Deep Learning on Raspberry Pi

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. Therefore, 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.

In this course, here are some of the things that you will learn:

  • 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,
  • Run Yolo and Mobilenet SSD object detection models in a recorded or live video

Click the link below to pre-register for FREE. First 10 Students get FREE Coupon to the Course

Click Here

Ritesh
administrator
Ritesh Kanjee has over 7 years in Printed Circuit Board (PCB) design as well in image processing and embedded control. He completed his Masters Degree in Electronic engineering and published a paper for IEEE called Vision-based adaptive Cruise control using Pattern matching (on Google Scholar). His work was implemented in LabVIEW. He works as an Embedded Electronic Engineer in defence research. He has experience in FPGA design with programming in both VHDL and Verilog.