Develop Multiplatform Computer Vision Solutions


Explore the Intel® Distribution of OpenVINO™ toolkit

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 Intel® Distribution of OpenVINO™ toolkit. Based on convolutional neural networks (CNN), the toolkit extends workloads across Intel® hardware (including accelerators) and maximizes performance.

  • Enables deep learning inference at the edge
  • Supports heterogeneous execution across computer vision accelerators—CPU, GPU, Intel® Movidius™ Neural Compute Stick, and FPGA—using a common API
  • Speeds up 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 neural network models on Intel® platforms with the Deep Learning Deployment Toolkit (DLDT) that's available in the Intel Distribution of OpenVINO toolkit.

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

 

Medical Imaging Powered by AI

Intel teamed up with Philips to deliver high performance, efficient deep-learning inference on X-rays and computed tomography (CT) scans without the need for accelerators. The solution runs on servers powered by Intel® Xeon® Scalable processors and was optimized by Intel® Distribution of OpenVINO™ toolkit.

What's New in the 2019 R2 Release

  • Adds a new Deep Learning Workbench profiler as a preview feature. It provides visualization of key performance metrics (latency, throughput, and performance counters) for neural network topologies and their layers. This tool includes simple configuration for inference experiments including int8 calibration, accuracy check, and automatic detection of optimal performance settings.
    For more information, see Workbench Overview.
  • Supports multidevice inference with automatic load balancing across available devices to achieve higher throughput when using multiple platforms simultaneously.
  • Provides new inference-engine-core APIs that automate direct mapping to available devices and saves time by eliminating manual loading of individual plug-ins. Includes a query API to get device configuration and metrics to help determine the best platforms for deploying deep learning applications.
  • Supports a serialized FP16 intermediate representation (IR) to work uniformly across supported platforms. This helps reduce model size by two times when compared to FP32 and improves the use of available device memory and model portability. (For CPUs, the inference remains at FP32.)
  • Enables new use cases for machine translation, natural language processing, and speech processing and recognition with support for popular nonvision topologies that include:
    • GNMT
    • BERT
    • TDNN (NNet3)
    • ESPNet
  • Provides new binary distribution methods that enable quick installation with minimal to no overhead in setting up the development environment:
    • Binary files at package managers like YUM* and APT
    • Docker* images on Docker Hub*
    • .Zip and .tgz files through GitHub*"

Release Notes

Product Brief

System Requirements

What You Can Do

Interactive Face Recognition

This example demonstrates face detection capabilities, featuring the ability to determine age, gender, and the position of a head.

 

Awarded by the
Embedded Vision Alliance*

What Customers Are Saying

“Intel® Xeon® Scalable processors and OpenVINO toolkit appears to be the right solution for medical imaging AI workloads. Our customers can use their existing hardware to its maximum potential, without having to complicate their infrastructure, while still aiming to achieve quality output resolution at exceptional speeds."

— Vijayananda J., chief architect and fellow, Data Science and AI, Philips HealthSuite Insights, India

White Paper

What Customers Are Saying

"Our strategic focus is bringing artificial intelligence on the edge. The challenge has been in implementing efficient algorithms to balance the workload between different hardware components. The OpenVINO toolkit bridges this gap, giving us a unique tool that speeds up the path to production, both for us and our partners."

— Fabrizio Del Maffeo, vice president and managing director, AAEON Technology, Europe

What Customers Are Saying

"Powered by the new OpenVINO toolkit, which delivers a comprehensive toolkit for developing and deploying [a] vision-oriented solution, and is capable of delivering higher computing and graphics performance, [several solution-ready packages from Advantech are launching] for vertical markets, including people counting, heat map analysis, facial recognition, and loss prevention. With the OpenVINO toolkit, we can easily process real-time video recording and transcoding, human detection and counting, as well as signage management."

— MC Chiang, associate vice president of Service-IoT, Advantech

What Customers Are Saying

"AI presents tremendous opportunity for industrial automation. With machine vision extensively used for guidance, identification, gauging, and inspection in manufacturing processes, the OpenVINO toolkit integrates deep learning tools, computer vision acceleration, and support for heterogeneous execution environments with high inference speed and accuracy for machine vision.”

— Edgar Chen, general manager of the embedded platforms and modules business unit, ADLINK*

What Customers Are Saying

"With the OpenVINO toolkit, we are now able to optimize inferencing across silicon from Intel, exceeding our throughput goals by almost six times. We want to not only keep deployment costs down for our customers, but also offer a flexible, high-performance solution for a new era of smarter medical imaging. Our partnership with Intel allows us to bring the power of AI to clinical diagnostic scanning and other healthcare workflows in a cost-effective manner.”

— David Chevalier, principal engineer, General Electric (GE) Healthcare*

What Customers Are Saying

"Dell EMC* surveillance labs validation program—and joint collaboration with Intel and its software tools—allows us to deliver proven solutions with the top surveillance camera and video software companies."

— Ken Mills, general manager, surveillance and security solutions for Dell EMC

Case Studies

Deep Learning for Healthcare Imaging

Intel and GE* bring the power of AI to clinical diagnostic scanning and other healthcare workflows.

Performance Boost for a Deep Learning Algorithm

GeoVision sped up its facial recognition solution using Intel® System Studio and the Intel Distribution of OpenVINO toolkit.

Digital Security & Surveillance Solutions

This toolkit is the centerpiece of Agent Vi*, which provides next-generation vision technology.

Robotics with AI for Industry 4.0

NexCOBOT offers a flexible, modular robotics solution that integrates AI with machine vision using tools from Intel.

Open Source Software

The OpenVINO™ toolkit is an open-source product. It contains the Deep Learning Deployment Toolkit (DLDT) for Intel® processors (for CPUs), Intel® Processor Graphics (for GPUs), and heterogeneous support. It includes an open model zoo with pretrained models, samples, and demos.

OpenVINO™ Toolkit

GitHub* for DLDT

GitHub for Open Model Zoo