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[DOCS] menu restructuring 2025 pass 4 port 25.0 (#28741)
port: #28738 Co-authored-by: sgolebiewski-intel <sebastianx.golebiewski@intel.com>
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docs/articles_en/about-openvino.rst

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Along with the primary components of model optimization and runtime, the toolkit also includes:
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* `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf>`__ - a tool for enhanced OpenVINO™ inference to get performance boost with minimal accuracy drop.
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* :doc:`Openvino Notebooks <learn-openvino/interactive-tutorials-python>`- Jupyter Python notebook, which demonstrate key features of the toolkit.
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* :doc:`Openvino Notebooks <get-started/learn-openvino/interactive-tutorials-python>`- Jupyter Python notebook, which demonstrate key features of the toolkit.
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* `OpenVINO Model Server <https://github.com/openvinotoolkit/model_server>`__ - a server that enables scalability via a serving microservice.
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* :doc:`OpenVINO Training Extensions <documentation/openvino-ecosystem/openvino-training-extensions>` – a convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference.
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* :doc:`Dataset Management Framework (Datumaro) <documentation/openvino-ecosystem/datumaro>` - a tool to build, transform, and analyze datasets.

docs/articles_en/about-openvino/performance-benchmarks/getting-performance-numbers.rst

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When comparing OpenVINO Runtime performance with the framework or reference code,
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make sure that both versions are as similar as possible:
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* Wrap the exact inference execution (for examples, see :doc:`Benchmark app <../../learn-openvino/openvino-samples/benchmark-tool>`).
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* Wrap the exact inference execution (for examples, see :doc:`Benchmark app <../../get-started/learn-openvino/openvino-samples/benchmark-tool>`).
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* Do not include model loading time.
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* Ensure that the inputs are identical for OpenVINO Runtime and the framework. For example, watch out for random values that can be used to populate the inputs.
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* In situations when any user-side pre-processing should be tracked separately, consider :doc:`image pre-processing and conversion <../../openvino-workflow/running-inference/optimize-inference/optimize-preprocessing>`.
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+++++++++++++++++++++++++++++++
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The default way of measuring OpenVINO performance is running a piece of code, referred to as
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:doc:`the benchmark tool <../../learn-openvino/openvino-samples/benchmark-tool>`.
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:doc:`the benchmark tool <../../get-started/learn-openvino/openvino-samples/benchmark-tool>`.
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For Python, it is part of the OpenVINO Runtime installation, while for C++, it is available as
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a code sample.
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More detailed insights into inference performance breakdown can be achieved with device-specific
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performance counters and/or execution graphs.
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Both :doc:`C++ and Python <../../learn-openvino/openvino-samples/benchmark-tool>`
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Both :doc:`C++ and Python <../../get-started/learn-openvino/openvino-samples/benchmark-tool>`
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versions of the benchmark_app support a ``-pc`` command-line parameter that outputs an internal
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execution breakdown.
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docs/articles_en/about-openvino/performance-benchmarks/performance-benchmarks-faq.rst

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All of the performance benchmarks on traditional network models are generated using the
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open-source tool within the Intel® Distribution of OpenVINO™ toolkit
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called :doc:`benchmark_app <../../learn-openvino/openvino-samples/benchmark-tool>`.
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called :doc:`benchmark_app <../../get-started/learn-openvino/openvino-samples/benchmark-tool>`.
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For diffusers (Stable-Diffusion) and foundational models (aka LLMs) please use the OpenVINO GenAI
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opensource repo `OpenVINO GenAI tools/llm_bench <https://github.com/openvinotoolkit/openvino.genai/tree/master/tools/llm_bench>`__

docs/articles_en/documentation/openvino-extensibility.rst

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########
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* :doc:`OpenVINO Transformations <openvino-extensibility/transformation-api>`
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* :doc:`Using OpenVINO Runtime Samples <../learn-openvino/openvino-samples>`
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* :doc:`Hello Shape Infer SSD sample <../learn-openvino/openvino-samples/hello-reshape-ssd>`
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* :doc:`Using OpenVINO Runtime Samples <../get-started/learn-openvino/openvino-samples>`
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* :doc:`Hello Shape Infer SSD sample <../get-started/learn-openvino/openvino-samples/hello-reshape-ssd>`
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docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/advanced-guides/low-precision-transformations.rst

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Inference
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The simplest way to infer the model and collect performance counters is :doc:`Benchmark Application <../../../../learn-openvino/openvino-samples/benchmark-tool>`.
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The simplest way to infer the model and collect performance counters is :doc:`Benchmark Application <../../../../get-started/learn-openvino/openvino-samples/benchmark-tool>`.
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.. code-block:: sh
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docs/articles_en/documentation/openvino-security.rst

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- Intel® Distribution of OpenVINO™ toolkit `home page <https://software.intel.com/en-us/openvino-toolkit>`__.
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- :doc:`Convert a Model <../openvino-workflow/model-preparation/convert-model-to-ir>`.
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- :doc:`OpenVINO™ Runtime User Guide <../openvino-workflow/running-inference>`.
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- For more information on Sample Applications, see the :doc:`OpenVINO Samples Overview <../learn-openvino/openvino-samples>`
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- For more information on Sample Applications, see the :doc:`OpenVINO Samples Overview <../get-started/learn-openvino/openvino-samples>`

docs/articles_en/get-started.rst

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:hidden:
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Install OpenVINO <get-started/install-openvino>
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Learn OpenVINO <get-started/learn-openvino>
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System Requirements <./about-openvino/release-notes-openvino/system-requirements>
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Interactive Tutorials - Jupyter Notebooks
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-----------------------------------------
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Start with :doc:`interactive Python <learn-openvino/interactive-tutorials-python>` that show the basics of model inference, the OpenVINO API, how to convert models to OpenVINO format, and more.
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Start with :doc:`interactive Python <get-started/learn-openvino/interactive-tutorials-python>` that show the basics of model inference, the OpenVINO API, how to convert models to OpenVINO format, and more.
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* `Hello Image Classification <notebooks/hello-world-with-output.html>`__ - Load an image classification model in OpenVINO and use it to apply a label to an image
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* `OpenVINO Runtime API Tutorial <notebooks/openvino-api-with-output.html>`__ - Learn the basic Python API for working with models in OpenVINO
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OpenVINO Code Samples
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---------------------
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View :doc:`sample code <learn-openvino/openvino-samples>` for various C++ and Python applications that can be used as a starting point for your own application. For C++ developers, step through the :doc:`Get Started with C++ Samples <learn-openvino/openvino-samples/get-started-demos>` to learn how to build and run an image classification program that uses OpenVINO’s C++ API.
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View :doc:`sample code <get-started/learn-openvino/openvino-samples>` for various C++ and Python applications that can be used as a starting point for your own application. For C++ developers, step through the :doc:`Get Started with C++ Samples <get-started/learn-openvino/openvino-samples/get-started-demos>` to learn how to build and run an image classification program that uses OpenVINO’s C++ API.
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.. _integrate-openvino:
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* :doc:`Automatic Batching <openvino-workflow/running-inference/inference-devices-and-modes/automatic-batching>` performs on-the-fly grouping of inference requests to maximize utilization of the target hardware’s memory and processing cores.
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* :doc:`Performance Hints <openvino-workflow/running-inference/optimize-inference/high-level-performance-hints>` automatically adjust runtime parameters to prioritize for low latency or high throughput
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* :doc:`Dynamic Shapes <openvino-workflow/running-inference/dynamic-shapes>` reshapes models to accept arbitrarily-sized inputs, increasing flexibility for applications that encounter different data shapes
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* :doc:`Benchmark Tool <learn-openvino/openvino-samples/benchmark-tool>` characterizes model performance in various hardware and pipeline configurations
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* :doc:`Benchmark Tool <get-started/learn-openvino/openvino-samples/benchmark-tool>` characterizes model performance in various hardware and pipeline configurations
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.. _additional-about-openvino/additional-resources:
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docs/articles_en/get-started/install-openvino/configurations.rst

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For GPU <configurations/configurations-intel-gpu>
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For NPU <configurations/configurations-intel-npu>
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GenAI Dependencies <configurations/genai-dependencies>
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Troubleshooting Guide for OpenVINO™ Installation & Configuration <troubleshooting-install-config.html>
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For certain use cases, you may need to install additional software, to benefit from the full
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potential of OpenVINO™. Check the following list for components used in your workflow:

docs/articles_en/get-started/install-openvino/install-openvino-apt.rst

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With the APT distribution, you can build OpenVINO sample files, as explained in the
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:doc:`guide for OpenVINO sample applications <../../../learn-openvino/openvino-samples>`.
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:doc:`guide for OpenVINO sample applications <../../../get-started/learn-openvino/openvino-samples>`.
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* Try the :doc:`C++ Quick Start Example <../../../learn-openvino/openvino-samples/get-started-demos>` for step-by-step
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* Try the :doc:`C++ Quick Start Example <../../../get-started/learn-openvino/openvino-samples/get-started-demos>` for step-by-step
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instructions on building and running a basic image classification C++ application.
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.. image:: https://user-images.githubusercontent.com/36741649/127170593-86976dc3-e5e4-40be-b0a6-206379cd7df5.jpg
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* Visit the :ref:`Samples <code samples>` page for other C++ example applications to get you started with OpenVINO, such as:
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* :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../../learn-openvino/openvino-samples/hello-reshape-ssd>`
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* :doc:`Object classification sample <../../../learn-openvino/openvino-samples/hello-classification>`
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* :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../../get-started/learn-openvino/openvino-samples/hello-reshape-ssd>`
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* :doc:`Object classification sample <../../../get-started/learn-openvino/openvino-samples/hello-classification>`
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* Learn more about :doc:`OpenVINO Workflow <../../../openvino-workflow>`.
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* To prepare your models for working with OpenVINO, see :doc:`Model Preparation <../../../openvino-workflow/model-preparation>`.
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* See pre-trained deep learning models on `Hugging Face <https://huggingface.co/OpenVINO>`__
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* Learn more about :doc:`Inference with OpenVINO Runtime <../../../openvino-workflow/running-inference>`.
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* See sample applications in :doc:`OpenVINO toolkit Samples Overview <../../../learn-openvino/openvino-samples>`.
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* See sample applications in :doc:`OpenVINO toolkit Samples Overview <../../../get-started/learn-openvino/openvino-samples>`.
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* Take a glance at the OpenVINO `product home page <https://software.intel.com/en-us/openvino-toolkit>`__ .
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docs/articles_en/get-started/install-openvino/install-openvino-archive-linux.rst

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.. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
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* `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__
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* `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__
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* :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../../learn-openvino/openvino-samples/hello-reshape-ssd>`
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* :doc:`Object classification sample <../../../learn-openvino/openvino-samples/hello-classification>`
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* :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../../get-started/learn-openvino/openvino-samples/hello-reshape-ssd>`
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* :doc:`Object classification sample <../../../get-started/learn-openvino/openvino-samples/hello-classification>`
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* :doc:`Troubleshooting Guide for OpenVINO Installation & Configuration <./configurations/troubleshooting-install-config>`
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* Converting models for use with OpenVINO™: :doc:`Convert a Model <../../../openvino-workflow/model-preparation>`
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* Writing your own OpenVINO™ applications: :doc:`OpenVINO™ Runtime User Guide <../../../openvino-workflow/running-inference>`
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* Sample applications: :doc:`OpenVINO™ Toolkit Samples Overview <../../../learn-openvino/openvino-samples>`
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* Sample applications: :doc:`OpenVINO™ Toolkit Samples Overview <../../../get-started/learn-openvino/openvino-samples>`
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* Pre-trained deep learning models on `Hugging Face <https://huggingface.co/OpenVINO>`__.

docs/articles_en/get-started/install-openvino/install-openvino-archive-macos.rst

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* `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__
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* `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__
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* :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../../learn-openvino/openvino-samples/hello-reshape-ssd>`
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* :doc:`Object classification sample <../../../learn-openvino/openvino-samples/hello-classification>`
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* :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../../get-started/learn-openvino/openvino-samples/hello-reshape-ssd>`
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* :doc:`Object classification sample <../../../get-started/learn-openvino/openvino-samples/hello-classification>`
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#####################################################
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* :doc:`Write your own OpenVINO™ applications <../../../openvino-workflow/running-inference/integrate-openvino-with-your-application>`
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* Sample applications: :doc:`OpenVINO™ Toolkit Samples Overview <../../../learn-openvino/openvino-samples>`
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* Sample applications: :doc:`OpenVINO™ Toolkit Samples Overview <../../../get-started/learn-openvino/openvino-samples>`
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* Pre-trained deep learning models on `Hugging Face <https://huggingface.co/OpenVINO>`__

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