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  • Hi.

  • I'm Priyanka from Intel.

  • In this video, we give you a summary

  • of all the videos in this series.

  • We also introduce some next steps, more advanced examples,

  • and dev kits to easily get started with your computer

  • visions solutions.


  • In the first video, we introduced you

  • to this computer vision series with Intel video solutions

  • for computer vision and deep learning applications.

  • In the second video, we introduced the OpenVINO toolkit

  • to accelerate computer vision application development

  • across Intel platforms.

  • We also talk about different components of the OpenVINO

  • toolkit to help optimize your deep learning inference.

  • Then we look at the model optimizer,

  • an important component of the deep learning deployment

  • toolkit, to do model conversion.

  • We discuss the model conversion techniques

  • and presented an example to convert a pre-trained model

  • to an intermediate format using model optimizer.

  • After that, in the fourth video, we deep

  • dive into the inference engine to run optimized inference

  • on different Intel platforms using a unified API.

  • We also look at a simple example to demonstrate inference engine

  • API usage and run the application on the CPU

  • and the integrated GPU.

  • Then, in the fifth video, we talk about the hetero plugin

  • from the inference engine to support hardware heterogeneity.

  • We also demonstrate running the computer vision application

  • on the Intel Movidius Compute Stick.

  • In the sixth video, we discuss optimization techniques

  • using OpenVINO toolkit to get better performance

  • for your computer vision application.

  • Finally, we discuss advanced video analytics

  • using OpenVINO toolkit and pre-trained models

  • included in the release package to expedite computer vision

  • application development.

  • To wrap up the series, I will walk you

  • through additional resources to use and help ramp

  • up on OpenVINO toolkit.

  • Along with core samples in the release package,

  • we also have included tutorials for building end

  • to end IoT reference solutions, addressing specific business

  • use cases.

  • For example, the store traffic monitor

  • reference implementation uses multiple video streams

  • that count people inside and outside of a facility,

  • and also counts product inventory.

  • You can check other reference implementations,

  • such as face access control, intruder detector, and people

  • counter from the links provided.

  • IEI and Aaeon introduced two developer kits

  • to help developers get started quickly

  • with their vision application development using Intel tools.

  • These kits ship with Intel System Studio and OpenVINO

  • pre-installed.

  • This makes them vision ready out of the box,

  • giving you the chance to try some of the code samples

  • and demos with just a few clicks.

  • To learn about tools, core samples, reference

  • implementations, and developer kits,

  • you can follow the links provided.

  • Thanks for watching the computer vision with Intel smart

  • video tools series.



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B1 openvino toolkit computer vision openvino toolkit vision inference

Series Summary | Intel Distribution of OpenVINO Toolkit | eWorkshop | Intel Software

  • 11 0
    alex posted on 2019/03/24
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