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

51 Folder Collection
alex published on March 24, 2019

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