openvino/docs/documentation.md

Documentation

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.. toctree:: :maxdepth: 1 :hidden:

openvino_2_0_transition_guide API Reference <api/api_reference> Model Preparation <openvino_docs_model_processing_introduction> Model Optimization and Compression <openvino_docs_model_optimization_guide> Run Inference <openvino_docs_OV_UG_OV_Runtime_User_Guide> Deploy Locally <openvino_deployment_guide> Tool Ecosystem <openvino_ecosystem> OpenVINO Extensibility <openvino_docs_Extensibility_UG_Intro> Media Processing and CV Libraries <media_processing_cv_libraries> OpenVINO™ Security <openvino_docs_security_guide_introduction>

.. toctree:: :maxdepth: 1 :caption: Running Inference :hidden:

openvino_inference_engine_tools_compile_tool_README

.. toctree:: :maxdepth: 1 :caption: Optimization and Performance :hidden:

openvino_docs_optimization_guide_dldt_optimization_guide openvino_docs_MO_DG_Getting_Performance_Numbers openvino_docs_deployment_optimization_guide_dldt_optimization_guide openvino_docs_tuning_utilities openvino_docs_performance_benchmarks

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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](@ref omz_tools_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.

Deploying Inference

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.

OpenVINO Ecosystem

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)](@ref openvino_docs_dlstreamer) — 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.