Develop Multiplatform Computer Vision Solutions


Explore the OpenVINO™ toolkit (formerly the Intel® Computer Vision SDK)

Make your vision a reality on Intel® platforms—from smart cameras and video surveillance to robotics, transportation, and more.

Your Computer Vision Apps...Now Faster

Develop applications and solutions that emulate human vision with the Open Visual Inference & Neural Network Optimization (OpenVINO™) toolkit. Based on convolutional neural networks (CNN), the toolkit extends workloads across Intel® hardware and maximizes performance.

  • Enables CNN-based deep learning inference on the edge
  • Supports heterogeneous execution across computer vision accelerators—CPU, GPU, Intel® Movidius™ Neural Compute Stick, and FPGA—using a common API
  • Speeds time to market via a library of functions and preoptimized kernels
  • Includes optimized calls for OpenCV and OpenVX*

Get Started

Discover the Capabilities

Deep Learning for Computer Vision

Accelerate and deploy CNNs on Intel® platforms with the Intel® Deep Learning Deployment Toolkit that's available in the OpenVINO toolkit and as a stand-alone download.

Hardware Acceleration

Harness the performance of Intel®-based accelerators: CPUs, GPUs, FPGAs, VPUs, and IPUs.

Who Needs This Product

Software developers and data scientists who:

  • Work on computer vision, neural network inference, and deep learning deployment capabilities
  • Want to accelerate their solutions across multiple platforms, including CPU, GPU, VPU, and FPGA

What's New?

Deep Learning Enhancements

  • Open Neural Network Exchange (ONNX) support covers all public models on the ONNX Model Zoo.
  • The Computer Vision Algorithms (CVA) component includes three prebuilt algorithms: emotion recognition, person re-identification, and crossroad object detection.
  • Four pretrained models target smart classroom and re-identification use cases.
  • The Intel® Deep Learning Deployment Toolkit supports all object detection models on TensorFlow Model Zoo*. To achieve increased performance, deploy the models using the OpenVINO toolkit.
  • Model optimizer support is extended to include features of graph freeze and graph summary. It introduces general support for dynamic input freezing (via the command line), which helps to deploy models like FaceNet.
  • The inference engine now includes a preview of the image preprocessing capability (resize and crop) and static shape inferencing.

Traditional Computer Vision Updates

  • OpenCV 3.4.3 provides improved performance. This library features an initial Intel® Advanced Vector Extensions 2 support via universal intrinsics. It also supports GStreamer as a multimedia backend on Linux*.

Hardware Support

  • Multicard support for Intel® FPGAs. Boost deep learning performance even further by adding multiple FPGAs to your solution.

Recent Updates

Release Notes

Product Brief

Key Specifications

Not all OpenVINO toolkit features run on all combinations of processor systems. For more information, see System Requirements.

Development Platform

Processors:

6th to 8th generation Intel® Core™ and Intel® Xeon® processors

Operating systems:

  • Ubuntu* 16.04.3 LTS (64 bit)
  • CentOS* 7.4 (64 bit)
  • Windows® 10 (64 bit)

Target System Platforms

CPU:

  • 6th to 8th generation Intel Core and Intel Xeon processors
  • Intel® Pentium® processor N4200/5, N3350/5, N3450/5 with Intel® HD Graphics

Graphics:

  • 6th to 8th generation Intel Core processor with Iris® Pro graphics and Intel HD Graphics
  • 6th to 8th generation Intel Xeon processor with Iris Pro graphics and Intel HD Graphics (excluding the e5 product family, which does not have graphics)

FPGA & VPU:

  • Intel® Arria® 10 FPGA GX development kit
  • Intel Movidius Neural Compute Stick

IPU:

  • Intel® Atom™ processor E3900

Operating systems:

  • Yocto Project* version Poky Jethro v2.0.3 (64 bit)
  • Ubuntu* 16.04.3 LTS (64 bit)
  • CentOS* 7.4 (64 bit)
  • Windows® 10 (64 bit)