Introduction to OpenVINO™ Toolkit
OpenVINO™ toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNNs), the toolkit extends computer vision (CV) workloads across Intel® hardware, maximizing performance. The OpenVINO™ toolkit includes the Deep Learning Deployment Toolkit (DLDT).
OpenVINO™ toolkit:
- Enables CNN-based deep learning inference on the edge
- Supports heterogeneous execution across an Intel® CPU, Intel® Integrated Graphics, Intel® FPGA, Intel® Neural Compute Stick 2 and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
- 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* and OpenCL™
Toolkit Components
OpenVINO™ toolkit includes the following components:
- Deep Learning Deployment Toolkit (DLDT)
- Deep Learning Model Optimizer - A cross-platform command-line tool for importing models and preparing them for optimal execution with the Inference Engine. The Model Optimizer imports, converts, and optimizes models, which were trained in popular frameworks, such as Caffe*, TensorFlow*, MXNet*, Kaldi*, and ONNX*.
- Deep Learning Inference Engine - A unified API to allow high performance inference on many hardware types including the following:
- Intel® CPU
- Intel® Integrated Graphics
- Intel® Neural Compute Stick 2
- Intel® Vision Accelerator Design with Intel® Movidius™ vision processing unit (VPU)
- Inference Engine Code Samples - A set of simple console applications demonstrating how to use the Inference Engine in your applications
- Tools - A set of tools to work with your models
- Open Model Zoo
- Post-Training Optimization tool - A tool to calibrate a model and then execute it in the INT8 precision
- Deep Learning Workbench - A web-based graphical environment that allows you to easily use various sophisticated OpenVINO™ toolkit components
- Deep Learning Streamer (DL Streamer) – Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. DL Streamer can be installed by the Intel® Distribution of OpenVINO™ toolkit installer. Its open source version is available on GitHub. For the DL Streamer documentation, see:
- OpenCV - OpenCV* community version compiled for Intel® hardware
- Drivers and runtimes for OpenCL™ version 2.1
- Intel® Media SDK
Documentation Set Contents
OpenVINO™ toolkit documentation set includes the following documents:
Typical Next Step: Introduction to Deep Learning Deployment Toolkit