Files
openvino/docs/model_zoo.md
Sergey Lyalin adf7a24ec0 [DOCS] OVC/convert_model Documentation (#19555)
* Added OVC and ov.convert_model() description.

* Minor corrections.

* Small correction.

* Include page to toctree.

* WIP: Model Preparation

* Forked OVC/ov.convert_model documentation sub-directory; reworked model_introduction.md

* Reverted ovc-related changes in old MO_DG documentation

* State explicitly that MO is considered legacy API

* Reduced ovc description in model preparation part; added TF Hub exampe (via file)

* Grammar check; removed obsolexte parts not relevant to ovc; better wording

* Removed a duplicate of mo-to-ovc transition

* Fixed links and some other errors found in documentation build

* Resolved XYZ placeholder to the transition guide

* Fixed technical issues with links

* Up-to-date link to PTQ chapter (instead of obsolete POT)

* Fixed strong text ending

* Update docs/OV_Converter_UG/prepare_model/convert_model/MO_OVC_transition.md

Co-authored-by: Anastasiia Pnevskaia <anastasiia.pnevskaia@intel.com>

* Update docs/OV_Converter_UG/prepare_model/convert_model/MO_OVC_transition.md

Co-authored-by: Anastasiia Pnevskaia <anastasiia.pnevskaia@intel.com>

* Update docs/OV_Converter_UG/prepare_model/convert_model/MO_OVC_transition.md

Co-authored-by: Anastasiia Pnevskaia <anastasiia.pnevskaia@intel.com>

* Renamed Legacy conversion guides

* Fixed links and styles for inlined code

* Fixed style for code references

* Fixing technical syntax errors in docs

* Another attempt to fix docs

* Removed all unreferenced images

* Better content for Additional Resources in model preporation introduction

* MO to OVC transition guide. (#127)

* Examples code correction.

* Change format of example.

* Conflict fix.

* Remove wrong change.

* Added input_shapes example.

* batch example.

* Examples format changed.

* List item removed.

* Remove list for all examples.

* Corrected batch example.

* Transform example.

* Text corrections.

* Text correction.

* Example correction.

* Small correction.

* Small correction.

* Small correction.

* Small correction.

* Text corrections.

* Links corrected.

* Text corrections (#128)

* Text corrections.

* Example corrected.

* Update docs/install_guides/pypi-openvino-dev.md

Co-authored-by: Sergey Lyalin <sergey.lyalin@intel.com>

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Co-authored-by: Sergey Lyalin <sergey.lyalin@intel.com>

* Many technical fixes, description of recursive flattening of list and tuples

* Reorganized structure of Model Conversion toc tree. Removed fp16 dedicated page, merged to Conversion Parameters.

* Update docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md

Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>

* Update docs/Documentation/model_introduction.md

Co-authored-by: Maciej Smyk <maciejx.smyk@intel.com>

* Fixed example from tf hub. Removed input_shape references

* Update docs/Documentation/model_introduction.md

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Update docs/Documentation/model_introduction.md

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Update docs/Documentation/model_introduction.md

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Removed

* Update docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_ONNX.md

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Update docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_ONNX.md

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Update docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_ONNX.md

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Update docs/OV_Converter_UG/prepare_model/convert_model/Convert_Model_From_ONNX.md

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Fixed links

* Removed TODO for model flow

* Apply suggestions from code review

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Restored lost code-blocks that leaded to wrong rendering of the code snippets in some places

* Apply suggestions from code review

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

* Update docs/Documentation/model_introduction.md

* Fixed links to notebooks

* Apply suggestions from code review

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>

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Co-authored-by: Anastasiia Pnevskaia <anastasiia.pnevskaia@intel.com>
Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com>
Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>
Co-authored-by: Maciej Smyk <maciejx.smyk@intel.com>
Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>
2023-09-12 14:31:54 +04:00

1.6 KiB

Model Zoo

@sphinxdirective

.. _model zoo:

.. toctree:: :maxdepth: 1 :hidden:

omz_models_group_intel omz_models_group_public

.. toctree:: :maxdepth: 1 :hidden:

omz_tools_downloader omz_tools_accuracy_checker omz_data_datasets omz_demos

.. toctree:: :maxdepth: 1 :hidden:

omz_model_api_ovms_adapter

Open Model Zoo for OpenVINO™ toolkit delivers a wide variety of free, pre-trained deep learning models and demo applications that provide full application templates to help you implement deep learning in Python, C++, or OpenCV Graph API (G-API). Models and demos are available in the Open Model Zoo GitHub repo <https://github.com/openvinotoolkit/open_model_zoo>__ and licensed under Apache License Version 2.0.

Browse through over 200 neural network models, both :doc:public <omz_models_group_public> and from :doc:Intel <omz_models_group_intel>, and pick the right one for your solution. Types include object detection, classification, image segmentation, handwriting recognition, text to speech, pose estimation, and others. The Intel models have already been converted to work with OpenVINO™ toolkit, while public models can easily be converted using the :doc:OpenVINO Model Conversion API <openvino_docs_model_processing_introduction> utility.

Get started with simple :doc:step-by-step procedures <openvino_docs_get_started_get_started_demos> to learn how to build and run demo applications or discover the :doc:full set of demos <omz_demos> and adapt them for implementing specific deep learning scenarios in your applications.

@endsphinxdirective