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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.
[MUSIC PLAYING]
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.
[MUSIC PLAYING]
提示:點選文章或是影片下面的字幕單字,可以直接快速翻譯喔!

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系列摘要| 英特爾®分佈式OpenVINO™工具包 (Series Summary | Intel Distribution of OpenVINO Toolkit | eWorkshop | Intel Software)

94 分類 收藏
alex 發佈於 2019 年 3 月 24 日
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