openvino/docs/documentation.md

Documentation

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.. toctree:: :maxdepth: 1 :caption: Converting and Preparing Models :hidden:

openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide openvino_docs_HOWTO_Custom_Layers_Guide omz_tools_downloader

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

openvino_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide openvino_docs_install_guides_deployment_manager_tool openvino_inference_engine_tools_compile_tool_README

.. toctree:: :maxdepth: 1 :caption: Tuning for Performance :hidden:

openvino_docs_performance_benchmarks openvino_docs_optimization_guide_dldt_optimization_guide openvino_docs_MO_DG_Getting_Performance_Numbers pot_README openvino_docs_tuning_utilities

.. toctree:: :maxdepth: 1 :caption: Graphical Web Interface for OpenVINO™ toolkit
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workbench_docs_Workbench_DG_Introduction workbench_docs_Workbench_DG_Install workbench_docs_Workbench_DG_Work_with_Models_and_Sample_Datasets workbench_docs_Workbench_DG_User_Guide workbench_docs_security_Workbench workbench_docs_Workbench_DG_Troubleshooting

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

Intel® Deep Learning Streamer <openvino_docs_dlstreamer> openvino_docs_gapi_gapi_intro OpenVX Developer Guide https://software.intel.com/en-us/openvino-ovx-guide OpenVX API Reference https://khronos.org/openvx OpenCV* Developer Guide https://docs.opencv.org/master/ OpenCL™ Developer Guide https://software.intel.com/en-us/openclsdk-devguide

.. toctree:: :maxdepth: 1 :caption: Add-Ons :hidden:

openvino_docs_ovms ovsa_get_started

.. toctree:: :maxdepth: 1 :caption: Developing Inference Engine Plugins :hidden:

Inference Engine Plugin Developer Guide <openvino_docs_ie_plugin_dg_overview> groupie_dev_api Plugin Transformation Pipeline <openvino_docs_IE_DG_plugin_transformation_pipeline>

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

openvino_docs_security_guide_introduction openvino_docs_security_guide_workbench openvino_docs_IE_DG_protecting_model_guide ovsa_get_started

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This section provides reference documents that guide you through developing your own deep learning applications with the OpenVINO™ toolkit. These documents will most helpful if you have first gone through the Get Started guide.

Converting and Preparing Models

With the [Model Downloader](@ref omz_tools_downloader) and Model Optimizer guides, you will learn to download pre-trained models and convert them for use with the OpenVINO™ toolkit. You can provide your own model or choose a public or Intel model from a broad selection provided in the Open Model Zoo.

Deploying Inference

The OpenVINO™ Runtime User Guide explains the process of creating your own application that runs inference with the OpenVINO™ toolkit. The API Reference defines the Inference Engine API for Python, C++, and C and the nGraph API for Python and C++. The Inference Engine API is what you'll use to create an OpenVINO™ application, while the nGraph API is available for using enhanced operations sets and other features. After writing your application, you can use the Deployment Manager for deploying to target devices.

Tuning for Performance

The toolkit provides a Performance Optimization Guide and utilities for squeezing the best performance out of your application, including [Accuracy Checker](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool](@ref pot_README), and other tools for measuring accuracy, benchmarking performance, and tuning your application.

Graphical Web Interface for OpenVINO™ Toolkit

You can choose to use the [OpenVINO™ Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction), a web-based tool that guides you through the process of converting, measuring, optimizing, and deploying models. This tool also serves as a low-effort introduction to the toolkit and provides a variety of useful interactive charts for understanding performance.

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 and OpenVX.