Summary
This course provides a complete introduction on how to use the Intel® Neural Compute Stick 2 (Intel® NCS2) for low-power deep learning inference on edge devices. Topics covered include:
- How to install the Intel® Distribution of OpenVINO™ toolkit and configure the Intel NCS2
- The basics of deep learning vision applications and model topologies
- How to create computer vision applications in Python* using Intel NCS2 devices
By the end of this course, students will have practical knowledge of how to use the Intel NCS2 to:
- Analyze model performance with the included performance tools
- Deploy pretrained networks and custom networks on the Intel NCS2
- Deploy an object detection model on a Raspberry Pi* board
The course is structured around seven weeks of lectures and exercises. Each week requires up to three hours to complete. The code examples are implemented in Python*, so familiarity with the language is encouraged (you can learn along the way).
Prerequisites
Python* programming
Calculus
Linear algebra
Hardware Required:
Intel NCS2
Raspberry Pi 3 Model B board or newer
Week 1
Get an introduction to the Intel NCS2. Topics include:
- A comparison of the differences between traditional computer vision and deep learning
- A review of the Intel® AI Portfolio including hardware and tools
- An overview of edge inference with Intel® Movidius™ technology
- An introduction to the Intel Distribution of OpenVINO toolkit
Week 3
Learn how to deploy an image classifier model on the Intel NCS2. Topics include:
- Define an image classification model and explore a few popular image classification topologies
- A deeper look into the Intel Distribution of OpenVINO toolkit and learn to create and deploy your first image classifier
Week 5
See how to profile deep learning models using the Deep Learning Workbench. Topics include:
- Understand the capabilities of the Deep Learning Workbench
- Learn to install the Deep Learning Workbench directly on your system or using Docker* software
- Profile your first deep learning model using the Deep Learning Workbench
Week 6
Learn how to deploy custom models on the Intel NCS2 using the Intel Distribution of OpenVINO toolkit. Topics include:
- Understand what a custom model is and when to use one
- Go through the end-to-end training and inference workflow for a custom model on the Intel NCS2
- Implement your first custom layer using the toolkit
Week 7
Review how to deploy an object detection model on a Raspberry Pi board. Topics include:
- Reasons to use a low-powered embedded board
- Compare development and deployment modes of the Intel Distribution of OpenVINO toolkit
- Install the toolkit on a Raspberry Pi board and run an object detection model