92 lines
3.6 KiB
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92 lines
3.6 KiB
ReStructuredText
.. {#openvino_docs_OV_UG_Samples_Overview}
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OpenVINO™ Samples
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===================
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.. _code samples:
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.. meta::
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:description: OpenVINO™ samples include a collection of simple console applications
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that explain how to implement the capabilities and features of
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OpenVINO API into an application.
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.. toctree::
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:maxdepth: 1
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:hidden:
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Get Started with C++ Samples <openvino-samples/get-started-demos>
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openvino-samples/hello-classification
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openvino-samples/hello-nv12-input-classification
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openvino-samples/hello-query-device
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openvino-samples/hello-reshape-ssd
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openvino-samples/image-classification-async
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openvino-samples/model-creation
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openvino-samples/sync-benchmark
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openvino-samples/throughput-benchmark
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openvino-samples/bert-benchmark
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openvino-samples/benchmark-tool
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The OpenVINO™ samples are simple console applications that show how to utilize
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specific OpenVINO API capabilities within an application. They can assist you in
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executing specific tasks such as loading a model, running inference, querying
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specific device capabilities, etc.
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The applications include:
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.. important::
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All C++ samples support input paths containing only ASCII characters, except
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for the Hello Classification Sample, which supports Unicode.
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- :doc:`Hello Classification Sample <openvino-samples/hello-classification>` -
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Inference of image classification networks like AlexNet and GoogLeNet using
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Synchronous Inference Request API. Input of any size and layout can be set to
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an infer request which will be pre-processed automatically during inference.
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The sample supports only images as input and supports input paths containing
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only Unicode characters.
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- :doc:`Hello NV12 Input Classification Sample <openvino-samples/hello-nv12-input-classification>` -
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Input of any size and layout can be provided to an infer request. The sample
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transforms the input to the NV12 color format and pre-process it automatically
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during inference. The sample supports only images as input.
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- :doc:`Hello Query Device Sample <openvino-samples/hello-query-device>` -
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Query of available OpenVINO devices and their metrics, configuration values.
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- :doc:`Hello Reshape SSD Sample <openvino-samples/hello-reshape-ssd>` -
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Inference of SSD networks resized by ShapeInfer API according to an input size.
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- :doc:`Image Classification Async Sample <openvino-samples/image-classification-async>` -
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Inference of image classification networks like AlexNet and GoogLeNet using
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Asynchronous Inference Request API. The sample supports only images as inputs.
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- :doc:`OpenVINO Model Creation Sample <openvino-samples/model-creation>` -
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Construction of the LeNet model using the OpenVINO model creation sample.
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- **Benchmark Samples** - Simple estimation of a model inference performance
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- :doc:`Sync Samples <openvino-samples/sync-benchmark>`
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- :doc:`Throughput Samples <openvino-samples/throughput-benchmark>`
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- :doc:`Bert Python Sample <openvino-samples/bert-benchmark>`
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- :doc:`Benchmark Application <openvino-samples/benchmark-tool>` - Estimates deep
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learning inference performance on supported devices for synchronous and
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asynchronous modes.
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Python version of the benchmark tool is a core component of the OpenVINO
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installation package and may be executed with the following command:
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.. code-block:: console
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benchmark_app -m <model> -i <input> -d <device>
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Additional Resources
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####################
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* :doc:`Get Started with Samples <openvino-samples/get-started-demos>`
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* :doc:`OpenVINO Runtime User Guide <../openvino-workflow/running-inference>`
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