Feature/merge 2021 3 to master (#5307)
* Feature/azaytsev/cldnn doc fixes (#4600) * Legal fixes, removed the Generating docs section * Removed info regarding generating docs Co-authored-by: Trawinski, Dariusz <dariusz.trawinski@intel.com> * Feature/azaytsev/gna model link fixes (#4599) * Added info on DockerHub CI Framework * Feature/azaytsev/change layout (#3295) * Changes according to feedback comments * Replaced @ref's with html links * Fixed links, added a title page for installing from repos and images, fixed formatting issues * Added links * minor fix * Added DL Streamer to the list of components installed by default * Link fixes * Link fixes * ovms doc fix (#2988) * added OpenVINO Model Server * ovms doc fixes Co-authored-by: Trawinski, Dariusz <dariusz.trawinski@intel.com> * Updated openvino_docs.xml * Link Fixes Co-authored-by: Trawinski, Dariusz <dariusz.trawinski@intel.com> * Fix for broken CC in CPU plugin (#4595) * Azure CI: Add "ref: releases/2021/3" * Fixed clone rt info (#4597) * [.ci/azure] Enable CC build (#4619) * Formula fix (#4624) * Fixed transformation to pull constants into Loop body (cherry-pick of PR 4591) (#4607) * Cherry-pick of PR 4591 * Fixed typo * Moved a check into the parameter_unchanged_after_iteration function * Fixed KW hits (#4638) * [CPU] Supported ANY layout for inputs in inferRequest (#4621) * [.ci/azure] Add windows_conditional_compilation.yml (#4648) (#4655) * Fix for MKLDNN constant layers execution (#4642) * Fix for MKLDNN constant layers execution * Single mkldnn::engine for all MKLDNN graphs * Add workaround for control edges to support TF 2.4 RNN (#4634) Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Corrected PyYAML dependency (#4598) (#4620) 5.4.2 is absent on PyPI * [CPU] Statically analyzed issues. (#4637) * Docs api (#4657) * Updated API changes document * Comment for CVS-49440 * Add documentation on how to convert QuartzNet model (#4664) * Add documentation on how to convert QuartzNet model (#4422) * Add documentation on how to convert QuartzNet model * Apply review feedback * Small fix * Apply review feedback * Apply suggestions from code review Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Add reference to file Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Fixed bug in assign elimination transformation. (#4644) * [doc] Updated PyPI support OSes (#4643) (#4662) * [doc] Updated PyPI support OSes (#4643) * Updated PyPI support OSes * Added python versions for win and mac * Update pypi-openvino-dev.md * Update pypi-openvino-dev.md * Update pypi-openvino-rt.md * Update pypi-openvino-dev.md Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com> * [IE][VPU]: Fix empty output of CTCGreedyDecoderSeqLen (#4653) * Allow the second output of CTCGreedyDecoderSeqLen to be nullptr in cases when it is not used but calculated in the Myriad plugin. In this case, parse the second output as FakeData * It is a cherry-pick of #4652 * Update the firmware to release version * [VPU] WA for Segmentation fault on dlclose() issue (#4645) * Document TensorFlow 2* Update: Layers Support and Remove Beta Status (#4474) (#4711) * Document TensorFlow 2* Update: Layers Support and Remove Beta Status Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Update documentation based on latest test results and feedback Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Remove ConvLSTM2D from supported layers list Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Document Dot layer without limitation Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Address feedback upon DenseFeatures and RNN operations Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Do a grammar correction Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Do a grammar correction based on feedback Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Updated nGraph custom op documentation (#4604) * Updated nGraph custom op documentation * Fixed comments * [IE CLDNN] Fix missing variable initializations and types (#4669) * Fix NormalizeL2 creation in QueryNetwork (cherry pick from master PR 4310) (#4651) * Updated documentation about the supported YOLOv3 model from ONNX (#4722) (#4726) * Restored folded Operations for QueryNetwork (#4685) * Restored folded Operations for QueryNetwork * Fixed comment * Add unfolded constant operations to supported layers map * Add STN to list of supported models (#4728) * Fix python API for Loop/TensorIterator/Assign/ReadValue operations * Catch std::except in fuzz tests (#4695) Fuzz tests must catch all expected exceptions from IE. IE is using C++ std library which may raise standard exceptions which IE pass through. * Docs update (#4626) * Updated latency case desc to cover multi-socket machines * updated opt guide a bit * avoiding '#' which is interpreted as ref * Update CPU.md * Update docs/optimization_guide/dldt_optimization_guide.md Co-authored-by: Alina Alborova <alina.alborova@intel.com> * Update docs/optimization_guide/dldt_optimization_guide.md Co-authored-by: Alina Alborova <alina.alborova@intel.com> * Update docs/optimization_guide/dldt_optimization_guide.md Co-authored-by: Alina Alborova <alina.alborova@intel.com> * Update docs/optimization_guide/dldt_optimization_guide.md Co-authored-by: Alina Alborova <alina.alborova@intel.com> * Update docs/optimization_guide/dldt_optimization_guide.md Co-authored-by: Alina Alborova <alina.alborova@intel.com> Co-authored-by: Alina Alborova <alina.alborova@intel.com> * Blocked dims hwc 2021/3 (#4729) * Fix for BlockedDims * Added test for HWC layout * [GNA] Update documentation regarding splits and concatenations support (#4740) * Added mo.py to wheel packages (#4731) * Inserted a disclaimer (#4760) * Fixed some klockwork issues in C API samples (#4767) * Feature/vpu doc fixes 2021 3 (#4635) * Documentation fixes and updates for VPU * minor correction * minor correction * Fixed links * updated supported layers list for vpu * [DOCS] added iname/oname (#4735) * [VPU] Limit dlclose() WA to be used for Ubuntu only (#4806) * Fixed wrong link (#4817) * MKLDNN weights cache key calculation algorithm changed (#4790) * Updated PIP install instructions (#4821) * Document YOLACT support (#4749) * Document YOLACT support * Add preprocessing section * Apply suggestions from code review Co-authored-by: Tatiana Savina <tatiana.savina@intel.com> Co-authored-by: Tatiana Savina <tatiana.savina@intel.com> * Add documentation on how to convert F3Net model (#4863) * Add instruction for F3Net model pytorch->onnx conversion * Fix style * Fixed dead lock in telemetry (#4873) * Fixed dead lock in telemetry * Refactored TelemetrySender.send function * Refactored send function implementation to avoid deadlocks * Unit tests for telemetry sender function * Added legal header * avladimi/cvs-31369: Documented packages content to YUM/APT IGs (#4839) * Documented runtime/dev packages content * Minor formatting fixes * Implemented review comments * Update installing-openvino-apt.md Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com> * [DOC] Low-Precision 8-bit Integer Inference (#4834) * [DOC] Low-Precision 8-bit Integer Inference * [DOC] Low-Precision 8-bit Integer Inference: comment fixes * Update docs/IE_DG/Int8Inference.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update docs/IE_DG/Int8Inference.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update docs/IE_DG/Int8Inference.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update docs/IE_DG/Int8Inference.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update docs/IE_DG/Int8Inference.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update docs/IE_DG/Int8Inference.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update docs/IE_DG/Int8Inference.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update docs/IE_DG/Int8Inference.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update docs/IE_DG/Int8Inference.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * Update docs/IE_DG/Int8Inference.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * [DOC] LPT comments fix * [DOC] LPT comments fix: absolute links are updated to relative * Update Int8Inference.md * Update Int8Inference.md Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com> * Avladimi/cherry pick from master (#4892) * Fixed CVS-48061 * Reviewed and edited the Customization instructions * Fixed broken links in the TOC * Fixed links * Fixed formatting in the IG for Raspberry * Feature/benchmarks 2021 3 (#4910) * added new topics, changed the intro text * updated * Updates * Updates * Updates * Updates * Updates * Added yolo-v4-tf and unet-camvid-onnx graphs * Date for pricing is updated to March 15th * Feature/omz link changes (#4911) * Changed labels for demos and model downloader * Changed links to models and tools * Changed links to models and tools * Changed links to demos * [cherry-pick] Extensibility docs review (#4915) * Feature/ovsa docs 2021 3 (#4914) * Updated to 2021-3, fixed formatting issues * Fixed formatting issues * Fixed formatting issues * Fixed formatting issues * Update ovsa_get_started.md * Clarification of Low Latency Transformation and State API documentation (#4877) * Assign/ReadValue, LowLatency and StateAPI clarifications * Apply suggestions from code review: spelling mistakes Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> * fixed wording * cherry-pick missing commit to release branch: low latency documentation * Resolve review remarks Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> Co-authored-by: Svetlana Dolinina <svetlana.a.dolinina@intel.com> * DevCloud call outs (#4904) * [README.md] change latest release to 2021.3 * [49342] Update recommended CMake version on install guide in documentation (#4763) * Inserted a disclaimer * Another disclaimer * Update installing-openvino-windows.md * Update installing-openvino-windows.md * Update installing-openvino-windows.md * Feature/doc fixes 2021 3 (#4971) * Made changes for CVS-50424 * Changes for CVS-49349 * Minor change for CVS-49349 * Changes for CVS-49343 * Cherry-pick #PR4254 * Replaced /opt/intel/openvino/ with /opt/intel/openvino_2021/ as the default target directory * (CVS-50786) Added a new section Reference IMplementations to keep Speech Library and Speech Recognition Demos * Doc fixes * Replaced links to inference_engine_intro.md with Deep_Learning_Inference_Engine_DevGuide.md, fixed links * Fixed link * Fixes * Fixes * Reemoved Intel® Xeon® processor E family * fixes for graphs (#5057) * compression.configs.hardware config to package_data (#5066) * update OpenCV version to 4.5.2 (#5069) * update OpenCV version to 4.5.2 * Enable mo.front.common.extractors module (#5038) * Enable mo.front.common.extractors module (#5018) * Enable mo.front.common.extractors module * Update package_BOM.txt * Test MO wheel content * fix doc iframe issue - 2021.3 (#5090) * wrap with htmlonly * wrap with htmlonly * Add specification for ExperimentalDetectron* oprations (#5128) * Feature/benchmarks 2021 3 ehl (#5191) * Added EHL config * Updated graphs * improve table formatting * Wrap <iframe> tag with \htmlonly \endhtmlonly to avoid build errors * Updated graphs * Fixed links to TDP and Price for 8380 * Add PyTorch section to the documentation (#4972) * Add PyTorch section to the documentation * Apply review feedback * Remove section about loop * Apply review feedback * Apply review feedback * Apply review feedback * doc: add Red Hat docker registry (#5184) (#5253) * Incorporate changes in master Co-authored-by: Trawinski, Dariusz <dariusz.trawinski@intel.com> Co-authored-by: Vladislav Volkov <vladislav.volkov@intel.com> Co-authored-by: azhogov <alexander.zhogov@intel.com> Co-authored-by: Ilya Churaev <ilya.churaev@intel.com> Co-authored-by: Alina Kladieva <alina.kladieva@intel.com> Co-authored-by: Evgeny Lazarev <evgeny.lazarev@intel.com> Co-authored-by: Gorokhov Dmitriy <dmitry.gorokhov@intel.com> Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com> Co-authored-by: Mikhail Ryzhov <mikhail.ryzhov@intel.com> Co-authored-by: Nikolay Shchegolev <nikolay.shchegolev@intel.com> Co-authored-by: Ilya Lavrenov <ilya.lavrenov@intel.com> Co-authored-by: Maxim Vafin <maxim.vafin@intel.com> Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com> Co-authored-by: Anastasia Popova <anastasia.popova@intel.com> Co-authored-by: Maksim Doronin <maksim.doronin@intel.com> Co-authored-by: Andrew Bakalin <andrew.bakalin@intel.com> Co-authored-by: Mikhail Letavin <mikhail.letavin@intel.com> Co-authored-by: Anton Chetverikov <Anton.Chetverikov@intel.com> Co-authored-by: Ivan Tikhonov <ivan.tikhonov@intel.com> Co-authored-by: Andrey Somsikov <andrey.somsikov@intel.com> Co-authored-by: Maxim Shevtsov <maxim.y.shevtsov@intel.com> Co-authored-by: Alina Alborova <alina.alborova@intel.com> Co-authored-by: Elizaveta Lobanova <elizaveta.lobanova@intel.com> Co-authored-by: Andrey Dmitriev <andrey.dmitriev@intel.com> Co-authored-by: Helena Kloosterman <helena.kloosterman@intel.com> Co-authored-by: Tatiana Savina <tatiana.savina@intel.com> Co-authored-by: Edward Shogulin <edward.shogulin@intel.com> Co-authored-by: Svetlana Dolinina <svetlana.a.dolinina@intel.com> Co-authored-by: Alexey Suhov <alexey.suhov@intel.com> Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com> Co-authored-by: Dmitry Kurtaev <dmitry.kurtaev+github@gmail.com> Co-authored-by: Nikolay Tyukaev <nikolay.tyukaev@intel.com> Co-authored-by: Kate Generalova <kate.generalova@intel.com>
This commit is contained in:
Andrey Zaytsev
GitHub
@@ -9,13 +9,13 @@ In this guide, you will:
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[DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is a web-based graphical environment that enables you to easily use various sophisticated
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OpenVINO™ toolkit components:
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* [Model Downloader](@ref omz_tools_downloader_README) to download models from the [Intel® Open Model Zoo](@ref omz_models_intel_index)
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* [Model Downloader](@ref omz_tools_downloader) to download models from the [Intel® Open Model Zoo](@ref omz_models_group_intel)
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with pretrained models for a range of different tasks
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* [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) to transform models into
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the Intermediate Representation (IR) format
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* [Post-Training Optimization toolkit](@ref pot_README) to calibrate a model and then execute it in the
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INT8 precision
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* [Accuracy Checker](@ref omz_tools_accuracy_checker_README) to determine the accuracy of a model
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* [Accuracy Checker](@ref omz_tools_accuracy_checker) to determine the accuracy of a model
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* [Benchmark Tool](@ref openvino_inference_engine_samples_benchmark_app_README) to estimate inference performance on supported devices
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@@ -70,10 +70,10 @@ The simplified OpenVINO™ DL Workbench workflow is:
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## Run Baseline Inference
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This section illustrates a sample use case of how to infer a pretrained model from the [Intel® Open Model Zoo](@ref omz_models_intel_index) with an autogenerated noise dataset on a CPU device.
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This section illustrates a sample use case of how to infer a pretrained model from the [Intel® Open Model Zoo](@ref omz_models_group_intel) with an autogenerated noise dataset on a CPU device.
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\htmlonly
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<iframe width="560" height="315" src="https://www.youtube.com/embed/9TRJwEmY0K4" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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\endhtmlonly
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Once you log in to the DL Workbench, create a project, which is a combination of a model, a dataset, and a target device. Follow the steps below:
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@@ -18,7 +18,7 @@ In addition, demo scripts, code samples and demo applications are provided to he
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* **[Code Samples](../IE_DG/Samples_Overview.md)** - Small console applications that show you how to:
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* Utilize specific OpenVINO capabilities in an application
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* Perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more.
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* **[Demo Applications](@ref omz_demos_README)** - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
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* **[Demo Applications](@ref omz_demos)** - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
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## <a name="openvino-installation"></a>Intel® Distribution of OpenVINO™ toolkit Installation and Deployment Tools Directory Structure
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This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit installation and configuration steps. If you have not yet installed and configured the toolkit, see [Install Intel® Distribution of OpenVINO™ toolkit for Linux*](../install_guides/installing-openvino-linux.md).
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@@ -46,9 +46,9 @@ The primary tools for deploying your models and applications are installed to th
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| `samples/` | Inference Engine samples. Contains source code for C++ and Python* samples and build scripts. See the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md). |
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| `src/` | Source files for CPU extensions.|
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| `model_optimizer/` | Model Optimizer directory. Contains configuration scripts, scripts to run the Model Optimizer and other files. See the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
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| `open_model_zoo/` | Open Model Zoo directory. Includes the Model Downloader tool to download [pre-trained OpenVINO](@ref omz_models_intel_index) and public models, OpenVINO models documentation, demo applications and the Accuracy Checker tool to evaluate model accuracy.|
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| `open_model_zoo/` | Open Model Zoo directory. Includes the Model Downloader tool to download [pre-trained OpenVINO](@ref omz_models_group_intel) and public models, OpenVINO models documentation, demo applications and the Accuracy Checker tool to evaluate model accuracy.|
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| `demos/` | Demo applications for inference scenarios. Also includes documentation and build scripts.|
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| `intel_models/` | Pre-trained OpenVINO models and associated documentation. See the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index).|
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| `intel_models/` | Pre-trained OpenVINO models and associated documentation. See the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel).|
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| `tools/` | Model Downloader and Accuracy Checker tools. |
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| `tools/` | Contains a symbolic link to the Model Downloader folder and auxiliary tools to work with your models: Calibration tool, Benchmark and Collect Statistics tools.|
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@@ -197,7 +197,7 @@ Each demo and code sample is a separate application, but they use the same behav
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* [Code Samples](../IE_DG/Samples_Overview.md) - Small console applications that show how to utilize specific OpenVINO capabilities within an application and execute specific tasks such as loading a model, running inference, querying specific device capabilities, and more.
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* [Demo Applications](@ref omz_demos_README) - Console applications that provide robust application templates to support developers in implementing specific deep learning scenarios. They may also involve more complex processing pipelines that gather analysis from several models that run inference simultaneously. For example concurrently detecting a person in a video stream and detecting attributes such as age, gender and/or emotions.
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* [Demo Applications](@ref omz_demos) - Console applications that provide robust application templates to support developers in implementing specific deep learning scenarios. They may also involve more complex processing pipelines that gather analysis from several models that run inference simultaneously. For example concurrently detecting a person in a video stream and detecting attributes such as age, gender and/or emotions.
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Inputs you'll need to specify:
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- **A compiled OpenVINO™ code sample or demo application** that runs inferencing against a model that has been run through the Model Optimizer, resulting in an IR, using the other inputs you provide.
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@@ -209,7 +209,7 @@ Inputs you'll need to specify:
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To perform sample inference, run the Image Classification code sample and Security Barrier Camera demo application that were automatically compiled when you ran the Image Classification and Inference Pipeline demo scripts. The binary files are in the `~/inference_engine_cpp_samples_build/intel64/Release` and `~/inference_engine_demos_build/intel64/Release` directories, respectively.
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To run other sample code or demo applications, build them from the source files delivered as part of the OpenVINO toolkit. To learn how to build these, see the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and [Demo Applications Overview](@ref omz_demos_README) sections.
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To run other sample code or demo applications, build them from the source files delivered as part of the OpenVINO toolkit. To learn how to build these, see the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and [Demo Applications Overview](@ref omz_demos) sections.
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### <a name="download-models"></a> Step 1: Download the Models
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@@ -219,7 +219,7 @@ You must have a model that is specific for you inference task. Example model typ
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- Custom (Often based on SSD)
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Options to find a model suitable for the OpenVINO™ toolkit are:
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- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader_README).
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- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader).
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- Download from GitHub*, Caffe* Zoo, TensorFlow* Zoo, etc.
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- Train your own model.
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@@ -449,7 +449,7 @@ Throughput: 375.3339402 FPS
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### <a name="run-security-barrier"></a>Step 5: Run the Security Barrier Camera Demo Application
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> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you ran the Inference Pipeline demo scripts. If you want to build it manually, see the [Demo Applications Overview](@ref omz_demos_README) section.
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> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you ran the Inference Pipeline demo scripts. If you want to build it manually, see the [Demo Applications Overview](@ref omz_demos) section.
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To run the **Security Barrier Camera Demo Application** using an input image on the prepared IRs:
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@@ -18,7 +18,7 @@ In addition, demo scripts, code samples and demo applications are provided to he
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* **[Code Samples](../IE_DG/Samples_Overview.md)** - Small console applications that show you how to:
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* Utilize specific OpenVINO capabilities in an application.
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* Perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more.
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* **[Demo Applications](@ref omz_demos_README)** - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
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* **[Demo Applications](@ref omz_demos)** - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
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## <a name="openvino-installation"></a>Intel® Distribution of OpenVINO™ toolkit Installation and Deployment Tools Directory Structure
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This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit installation and configuration steps. If you have not yet installed and configured the toolkit, see [Install Intel® Distribution of OpenVINO™ toolkit for macOS*](../install_guides/installing-openvino-macos.md).
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@@ -48,9 +48,9 @@ The primary tools for deploying your models and applications are installed to th
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| `~intel_models/` | Symbolic link to the `intel_models` subfolder of the `open_model_zoo` folder.|
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| `model_optimizer/` | Model Optimizer directory. Contains configuration scripts, scripts to run the Model Optimizer and other files. See the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).|
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| `ngraph/` | nGraph directory. Includes the nGraph header and library files. |
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| `open_model_zoo/` | Open Model Zoo directory. Includes the Model Downloader tool to download [pre-trained OpenVINO](@ref omz_models_intel_index) and public models, OpenVINO models documentation, demo applications and the Accuracy Checker tool to evaluate model accuracy.|
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| `open_model_zoo/` | Open Model Zoo directory. Includes the Model Downloader tool to download [pre-trained OpenVINO](@ref omz_models_group_intel) and public models, OpenVINO models documentation, demo applications and the Accuracy Checker tool to evaluate model accuracy.|
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| `demos/` | Demo applications for inference scenarios. Also includes documentation and build scripts.|
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| `intel_models/` | Pre-trained OpenVINO models and associated documentation. See the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index).|
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| `intel_models/` | Pre-trained OpenVINO models and associated documentation. See the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel).|
|
||||
| `models` | Intel's trained and public models that can be obtained with Model Downloader.|
|
||||
| `tools/` | Model Downloader and Accuracy Checker tools. |
|
||||
| `tools/` | Contains a symbolic link to the Model Downloader folder and auxiliary tools to work with your models: Calibration tool, Benchmark and Collect Statistics tools.|
|
||||
@@ -200,7 +200,7 @@ Inputs you need to specify when using a code sample or demo application:
|
||||
|
||||
To perform sample inference, run the Image Classification code sample and Security Barrier Camera demo application that are automatically compiled when you run the Image Classification and Inference Pipeline demo scripts. The binary files are in the `~/inference_engine_samples_build/intel64/Release` and `~/inference_engine_demos_build/intel64/Release` directories, respectively.
|
||||
|
||||
You can also build all available sample code and demo applications from the source files delivered with the OpenVINO toolkit. To learn how to do this, see the instructions in the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and [Demo Applications Overview](@ref omz_demos_README) sections.
|
||||
You can also build all available sample code and demo applications from the source files delivered with the OpenVINO toolkit. To learn how to do this, see the instructions in the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and [Demo Applications Overview](@ref omz_demos) sections.
|
||||
|
||||
### <a name="download-models"></a> Step 1: Download the Models
|
||||
|
||||
@@ -210,7 +210,7 @@ You must have a model that is specific for you inference task. Example model typ
|
||||
- Custom (Often based on SSD)
|
||||
|
||||
Options to find a model suitable for the OpenVINO™ toolkit are:
|
||||
- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader_README).
|
||||
- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader).
|
||||
- Download from GitHub*, Caffe* Zoo, TensorFlow* Zoo, and other resources.
|
||||
- Train your own model.
|
||||
|
||||
@@ -422,7 +422,7 @@ classid probability label
|
||||
|
||||
### <a name="run-security-barrier"></a>Step 5: Run the Security Barrier Camera Demo Application
|
||||
|
||||
> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you run the Inference Pipeline demo scripts. If you want to build it manually, see the instructions in the [Demo Applications Overview](@ref omz_demos_README) section.
|
||||
> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you run the Inference Pipeline demo scripts. If you want to build it manually, see the instructions in the [Demo Applications Overview](@ref omz_demos) section.
|
||||
|
||||
To run the **Security Barrier Camera Demo Application** using an input image on the prepared IRs:
|
||||
|
||||
|
||||
@@ -43,8 +43,8 @@ The primary tools for deploying your models and applications are installed to th
|
||||
The OpenVINO™ workflow on Raspbian* OS is as follows:
|
||||
1. **Get a pre-trained model** for your inference task. If you want to use your model for inference, the model must be converted to the `.bin` and `.xml` Intermediate Representation (IR) files, which are used as input by Inference Engine. On Raspberry PI, OpenVINO™ toolkit includes only the Inference Engine module. The Model Optimizer is not supported on this platform. To get the optimized models you can use one of the following options:
|
||||
|
||||
* Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader_README).
|
||||
<br> For more information on pre-trained models, see [Pre-Trained Models Documentation](@ref omz_models_intel_index)
|
||||
* Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader).
|
||||
<br> For more information on pre-trained models, see [Pre-Trained Models Documentation](@ref omz_models_group_intel)
|
||||
|
||||
* Convert a model using the Model Optimizer from a full installation of Intel® Distribution of OpenVINO™ toolkit on one of the supported platforms. Installation instructions are available:
|
||||
* [Installation Guide for macOS*](../install_guides/installing-openvino-macos.md)
|
||||
@@ -62,10 +62,10 @@ Follow the steps below to run pre-trained Face Detection network using Inference
|
||||
```
|
||||
2. Build the Object Detection Sample with the following command:
|
||||
```sh
|
||||
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-march=armv7-a" /opt/intel/openvino/deployment_tools/inference_engine/samples/cpp
|
||||
cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-march=armv7-a" /opt/intel/openvino_2021/deployment_tools/inference_engine/samples/cpp
|
||||
make -j2 object_detection_sample_ssd
|
||||
```
|
||||
3. Download the pre-trained Face Detection model with the [Model Downloader tool](@ref omz_tools_downloader_README):
|
||||
3. Download the pre-trained Face Detection model with the [Model Downloader tool](@ref omz_tools_downloader):
|
||||
```sh
|
||||
git clone --depth 1 https://github.com/openvinotoolkit/open_model_zoo
|
||||
cd open_model_zoo/tools/downloader
|
||||
|
||||
@@ -19,7 +19,7 @@ In addition, demo scripts, code samples and demo applications are provided to he
|
||||
* **[Code Samples](../IE_DG/Samples_Overview.md)** - Small console applications that show you how to:
|
||||
* Utilize specific OpenVINO capabilities in an application.
|
||||
* Perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more.
|
||||
* **[Demo Applications](@ref omz_demos_README)** - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
|
||||
* **[Demo Applications](@ref omz_demos)** - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
|
||||
|
||||
## <a name="openvino-installation"></a>Intel® Distribution of OpenVINO™ toolkit Installation and Deployment Tools Directory Structure
|
||||
This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit installation and configuration steps. If you have not yet installed and configured the toolkit, see [Install Intel® Distribution of OpenVINO™ toolkit for Windows*](../install_guides/installing-openvino-windows.md).
|
||||
@@ -45,9 +45,9 @@ The primary tools for deploying your models and applications are installed to th
|
||||
| `~intel_models\` | Symbolic link to the `intel_models` subfolder of the `open_model_zoo` folder. |
|
||||
| `model_optimizer\` | Model Optimizer directory. Contains configuration scripts, scripts to run the Model Optimizer and other files. See the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). |
|
||||
| `ngraph\` | nGraph directory. Includes the nGraph header and library files. |
|
||||
| `open_model_zoo\` | Open Model Zoo directory. Includes the Model Downloader tool to download [pre-trained OpenVINO](@ref omz_models_intel_index) and public models, OpenVINO models documentation, demo applications and the Accuracy Checker tool to evaluate model accuracy.|
|
||||
| `open_model_zoo\` | Open Model Zoo directory. Includes the Model Downloader tool to download [pre-trained OpenVINO](@ref omz_models_group_intel) and public models, OpenVINO models documentation, demo applications and the Accuracy Checker tool to evaluate model accuracy.|
|
||||
| `demos\` | Demo applications for inference scenarios. Also includes documentation and build scripts.|
|
||||
| `intel_models\` | Pre-trained OpenVINO models and associated documentation. See the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index).|
|
||||
| `intel_models\` | Pre-trained OpenVINO models and associated documentation. See the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel).|
|
||||
| `models` | Intel's trained and public models that can be obtained with Model Downloader.|
|
||||
| `tools\` | Model Downloader and Accuracy Checker tools. |
|
||||
| `tools\` | Contains a symbolic link to the Model Downloader folder and auxiliary tools to work with your models: Calibration tool, Benchmark and Collect Statistics tools.|
|
||||
@@ -199,7 +199,7 @@ Inputs you need to specify when using a code sample or demo application:
|
||||
|
||||
To perform sample inference, run the Image Classification code sample and Security Barrier Camera demo application that are automatically compiled when you run the Image Classification and Inference Pipeline demo scripts. The binary files are in the `C:\Users\<USER_ID>\Intel\OpenVINO\inference_engine_cpp_samples_build\intel64\Release` and `C:\Users\<USER_ID>\Intel\OpenVINO\inference_engine_demos_build\intel64\Release` directories, respectively.
|
||||
|
||||
You can also build all available sample code and demo applications from the source files delivered with the OpenVINO™ toolkit. To learn how to do this, see the instruction in the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and [Demo Applications Overview](@ref omz_demos_README) sections.
|
||||
You can also build all available sample code and demo applications from the source files delivered with the OpenVINO™ toolkit. To learn how to do this, see the instruction in the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and [Demo Applications Overview](@ref omz_demos) sections.
|
||||
|
||||
### <a name="download-models"></a> Step 1: Download the Models
|
||||
|
||||
@@ -209,7 +209,7 @@ You must have a model that is specific for you inference task. Example model typ
|
||||
- Custom (Often based on SSD)
|
||||
|
||||
Options to find a model suitable for the OpenVINO™ toolkit are:
|
||||
- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using the [Model Downloader tool](@ref omz_tools_downloader_README).
|
||||
- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using the [Model Downloader tool](@ref omz_tools_downloader).
|
||||
- Download from GitHub*, Caffe* Zoo, TensorFlow* Zoo, and other resources.
|
||||
- Train your own model.
|
||||
|
||||
@@ -425,7 +425,7 @@ classid probability label
|
||||
|
||||
### <a name="run-security-barrier"></a>Step 5: Run the Security Barrier Camera Demo Application
|
||||
|
||||
> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you run the Inference Pipeline demo scripts. If you want to build it manually, see the instructions in the [Demo Applications Overview](@ref omz_demos_README) section.
|
||||
> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you run the Inference Pipeline demo scripts. If you want to build it manually, see the instructions in the [Demo Applications Overview](@ref omz_demos) section.
|
||||
|
||||
To run the **Security Barrier Camera Demo Application** using an input image on the prepared IRs:
|
||||
|
||||
|
||||
Reference in New Issue
Block a user