Introduction to the OpenVINO™ Toolkit
The OpenVINO™ toolkit is a comprehensive toolkit that you can use to develop and deploy vision-oriented solutions on Intel® platforms. Vision-oriented means the solutions use images or videos to perform specific tasks. A few of the solutions use cases include autonomous navigation, digital surveillance cameras, robotics, and mixed-reality headsets.
The OpenVINO™ toolkit:
- Enables CNN-based deep learning inference on the edge
- Supports heterogeneous execution across an Intel® CPU, Intel® Integrated Graphics, Intel® Movidius™ Neural Compute Stick and Intel® Neural Compute Stick 2
- Speeds time-to-market via an easy-to-use library of computer vision functions and pre-optimized kernels
- Includes optimized calls for computer vision standards including OpenCV*, OpenCL™, and OpenVX*
The OpenVINO™ toolkit includes the following components:
- Intel® Deep Learning Deployment Toolkit (Intel® DLDT)
- Deep Learning Model Optimizer — A cross-platform command-line tool for importing models and preparing them for optimal execution with the Deep Learning Inference Engine. The Model Optimizer supports converting Caffe*, TensorFlow*, MXNet*, Kaldi*, ONNX* models.
- Deep Learning Inference Engine — A unified API to allow high performance inference on many hardware types including Intel® CPU, Intel® Processor Graphics, Intel® FPGA, Intel® Movidius™ Neural Compute Stick, and Intel® Neural Compute Stick 2.
- OpenCV — OpenCV* community version compiled for Intel® hardware. Includes PVL libraries for computer vision.
- Drivers and runtimes for OpenCL™ version 2.1
- Intel® Media SDK
- OpenVX* — Intel's implementation of OpenVX* optimized for running on Intel® hardware (CPU, GPU, IPU).
- Demos and samples.
This Guide provides overview of the Inference Engine describing the typical workflow for performing inference of a pre-trained and optimized deep learning model and a set of sample applications.
NOTE: Before you perform inference with the Inference Engine, your models should be converted to the Inference Engine format using the Model Optimizer. To learn about how to use Model Optimizer, refer to the Model Optimizer Developer Guide. To learn about the pre-trained and optimized models delivered with the OpenVINO™ toolkit, refer to Pre-Trained Models.
Table of Contents
Typical Next Step: Introduction to Intel® Deep Learning Deployment Toolkit