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  • Hi, I'm Priyanka from Intel.

  • In this video, we walk you through various components

  • of the OpenVino toolkit.

  • Deep learning inference occurs at the edge.

  • which is optimized for power and performance.

  • Developers can learn how to write optimized code

  • for different hardware accelerators.

  • However, achieving proficiency will take a long time.

  • To accelerate deep learning application development,

  • Intel has introduced the OpenVino Toolkit,

  • or the Open Virtual Inference and Neural Network Optimization

  • Toolkit.

  • This convolutional neural network based toolkit

  • is designed to increase performance, reduce power,

  • maximize hardware utilization, and reduce time to market

  • for computer vision solutions.

  • Now, I will dive into different components of the OpenVino

  • toolkit.

  • It includes tools for deep learning,

  • as well as for traditional computer vision.

  • Let's start with the deep learning deployment toolkit.

  • It is a cross-platform tool to accelerate deep

  • learning inference performance.

  • It includes model optimizer and inference engine.

  • The model optimizer takes pre-trained models

  • from various frameworks, such as Caffe, Tensorflow, MXnet

  • and converts them into an intermediate representation

  • that is IR files.

  • The inference engine takes these IR files as input,

  • and then using the unified API, it deploys the computer vision

  • application on different platforms,

  • like CPU, integrated GPU, Movidius compute

  • stick, or FPGs without making any code changes.

  • The OpenVino Toolkit also includes OpenCV and OpenVX

  • for traditional computer vision.

  • It includes optimized OpenCV libraries

  • for Intel architecture.

  • Additionally, a new Intel photography vision library

  • with face detection, recognition, blink detection,

  • and smile detection is also added.

  • The OpenVino Toolkit also includes

  • the media SDK to support hardware optimized

  • encode, decode, and pre-processing of the input

  • image or video.

  • It supports OpenCL to run the application

  • on various platforms and add custom layers.

  • In addition to all these components,

  • this package also gives you the Intel FPGA Deep Learning

  • Acceleration suite, which includes

  • precompiled bitstreams and the Intel FPGA SDK for OpenCL.

  • Let's now look at how you can utilize OpenVino Toolkit

  • components to develop an optimized computer vision

  • application.

  • First, the OpenVino Toolkit takes three trained models

  • from frameworks such as Caffe, Tensorflow, and MXnet.

  • Model optimizer imports these models

  • and converts them into intermediate formats

  • to be processed by the inference engine.

  • Then, based on your choice, the inference engine API

  • runs the application on various hardware types,

  • such as CPU, integrated GPU, Movidius Neural Compute Stick,

  • or FPGA.

  • OpenVino also supports the heterogeneous architecture

  • with fallback to a different device for custom

  • or unsupported layers.

  • It also features an asynchronous execution,

  • which allows you to perform other tasks while the hardware

  • accelerator is crunching the current frame.

  • The OpenVino Toolkit comes with included samples that

  • showcase basic functionality.

  • These samples use Intel pre-trained detection

  • and recognition models for various tasks,

  • from age and gender recognition to multiple object detection,

  • and even headpose estimates.

  • We also offer a model downloader to access

  • several public pre-trained inference models, such as SSD,

  • VGG, DenseNet, and SqueezeNet.

  • To learn how to install the OpenVino Toolkit,

  • follow the link provided to the installation page.

  • The product website has a growing set of resources

  • to help you ramp up faster, including documentation,

  • training, and a support forum.

  • Thanks for watching.

  • In the next video, we cover the model optimizer in detail.

Hi, I'm Priyanka from Intel.

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B2 US toolkit openvino openvino toolkit inference deep learning computer vision

Toolkit Introduction | Intel Distribution of OpenVINO Toolkit | eWorkshop | Intel Software

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    alex posted on 2019/03/24
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