openvino/docs/documentation.md

Documentation

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.. toctree:: :maxdepth: 1 :caption: API 2.0 :hidden:

openvino_2_0_transition_guide

.. toctree:: :maxdepth: 1 :caption: Model preparation :hidden:

openvino_docs_model_processing_introduction Supported_Model_Formats openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide omz_tools_downloader

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

openvino_docs_OV_UG_OV_Runtime_User_Guide 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_model_optimization_guide openvino_docs_deployment_optimization_guide_dldt_optimization_guide openvino_docs_tuning_utilities openvino_docs_performance_benchmarks

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

openvino_docs_deployment_guide_introduction openvino_deployment_guide

.. toctree:: :maxdepth: 1 :hidden: :caption: THE Ecosystem

openvino_ecosystem ovms_what_is_openvino_model_server ovsa_get_started ovtf_integration ote_documentation workbench_docs_Workbench_DG_Introduction

.. toctree:: :maxdepth: 1 :caption: OpenVINO Extensibility :hidden:

openvino_docs_Extensibility_UG_Intro openvino_docs_transformations OpenVINO Plugin Developer Guide <openvino_docs_ie_plugin_dg_overview>

.. toctree:: :maxdepth: 1 :hidden: :caption: Use OpenVINO™ Toolkit Securely

openvino_docs_security_guide_introduction openvino_docs_security_guide_workbench openvino_docs_OV_UG_protecting_model_guide ovsa_get_started

.. toctree:: :maxdepth: 1 :hidden: :caption: Media Processing and Computer Vision Libraries

Intel® Deep Learning Streamer <openvino_docs_dlstreamer> openvino_docs_gapi_gapi_intro OpenCV* Developer Guide https://docs.opencv.org/master/ OpenCL™ Developer Guide https://software.intel.com/en-us/openclsdk-devguide
OneVPL Developer Guide https://www.intel.com/content/www/us/en/developer/articles/release-notes/oneapi-video-processing-library-release-notes.html

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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.