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 CNN-based 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 CNNs 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 R1 Release

  • Supports second generation Intel® Xeon® processors and provides performance acceleration for inference with Intel® AVX512-Deep Learning Boost.
  • Extends support to macOS* on the CPU for key toolkit components (Model Optimizer, inference engine, OpenCV, and more).
  • Switches parallelism schemes from OpenMP* to Threading Building Blocks (TBB) to provide increased performance in a multinetwork scenario. The most common deployment pipelines run multiple network combinations, and TBB delivers optimal performance for these use cases. For more details, see Intel® Threading Building Blocks as a Default Parallelization.
  • Adds support for many new operations in ONNX*, TensorFlow*, and Apache MXNet* frameworks. Provides optimized inference on topologies like Tiny YOLO* version 3, the full DeepLabs* version 3, and bidirectional long short-term memory (LSTM) using the Deep Learning Deployment Toolkit.
  • Includes eight pretrained models for new use cases: gaze estimation, action recognition encoder, action recognition decoder, text recognition, and instance segmentation networks.
  • Introduces support for binary weights to further boost performance and adds four binary models: ResNet 50, and face, person, and vehicle detection.
  • Updates the FPGA plugin to the Deep Learning Accelerator 2019 R1 with new bitstreams for the Intel® Vision Accelerator Design, Intel® Arria® 10 FPGA developer kit, and Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA. Supports automatic scheduling between multiple FPGA devices.

Release Notes

Product Brief

System Requirements

Case Studies

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

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

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

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