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*
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.
Traditional Computer Vision
Develop and optimize classic computer vision applications built with the OpenCV library or OpenVX API.
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 2018 R5 Release
- Extends neural network support to include long short-term memory (LSTM) from ONNX*, TensorFlow*, MxNet frameworks, and 3D convolutional-based networks in a preview mode (CPU only) to support additional, new use cases beyond computer vision.
- Introduces the Neural Network Builder API in a preview mode, which provides the flexibility to create a graph from simple API calls. Directly deploy it using the Inference Engine without loading intermediate representation (IR) files.
- Delivers a significant boost in CPU performance, especially on multicore systems, through new parallelization techniques.
- Provides INT8-based primitives for Intel® Advanced Vector Extensions-512, Intel® Advanced Vector Extensions 2, and Single Instruction Multiple Data (SIMD) extensions (SSE4.2) platforms that deliver optimized performance on Intel® Xeon®, Intel® Core™, and Intel® Atom processors.
- Supports Raspberry Pi* hardware as a host for the Intel Movidius Neural Compute Stick 2 (preview). Offload your deep learning workloads seamlessly to this low-cost, low-power USB stick that's based on the Intel® Movidius™ Myriad™ X technology. It also supports the previous generation.
- Adds three optimized pretrained models (a total of 30 in the toolkit):
- Text detection of indoor and outdoor scenes
- Two single-image, super-resolution networks to enhance the resolution of an input image by a factor of three or four
Support
The developer community and our tech experts provide the help you need in this public forum.
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.
For more complete information about compiler optimizations, see our Optimization Notice.