This section provides reference documents that guide you through the OpenVINO toolkit workflow, from obtaining models, optimizing them, to deploying them in your own deep learning applications.
Converting and Preparing Models¶
With Model Downloader and Model Optimizer guides, you will learn to download pre-trained models and convert them for use with OpenVINO™. You can use your own models or choose some from a broad selection provided in the Open Model Zoo.
Optimization and Performance¶
In this section you will find resources on how to test inference performance and how to increase it. It can be achieved by optimizing the model or optimizing inference at runtime.
This section explains the process of creating your own inference application using OpenVINO™ Runtime and documents the OpenVINO Runtime API for both Python and C++. It also provides a guide on deploying applications with OpenVINO and directs you to other sources on this topic.
Apart from the core components, OpenVINO offers tools, plugins, and expansions revolving around it, even if not constituting necessary parts of its workflow. This section will give you an overview of what makes up OpenVINO Toolkit.
Media Processing and Computer Vision Libraries¶
The OpenVINO™ toolkit also works with the following media processing frameworks and libraries:
Intel® Deep Learning Streamer (Intel® DL Streamer) — A streaming media analytics framework based on GStreamer, for creating complex media analytics pipelines optimized for Intel hardware platforms. Go to the Intel® DL Streamer documentation website to learn more.
Intel® oneAPI Video Processing Library (oneVPL) — A programming interface for video decoding, encoding, and processing to build portable media pipelines on CPUs, GPUs, and other accelerators.
You can also add computer vision capabilities to your application using optimized versions of OpenCV.