Svetlana Dolinina 0ed3da0e86 add warning about order if both mean and scale set (#9284)
* add warning about order if both mean and scale set

* Update tools/mo/openvino/tools/mo/main.py

Co-authored-by: Anastasia Popova <anastasia.popova@intel.com>

* removed warning, added phrase in documentation

* fixed merge

* added phrase about order of ,mean and scale in MO help

* duplicate MO help phrase in doc

* Update docs/MO_DG/prepare_model/convert_model/Converting_Model.md

Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>

* Update docs/MO_DG/prepare_model/convert_model/Converting_Model.md

Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>

* Update docs/MO_DG/prepare_model/convert_model/Converting_Model.md

Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>

* Update tools/mo/openvino/tools/mo/utils/cli_parser.py

Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>

* Update tools/mo/openvino/tools/mo/utils/cli_parser.py

Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>

* remove tabs

* fix in order of reverse, mean, scale

Co-authored-by: Anastasia Popova <anastasia.popova@intel.com>
Co-authored-by: Anastasiya Ageeva <anastasiya.ageeva@intel.com>
2022-01-17 12:12:06 +03:00
2021-12-13 00:04:56 +03:00
2021-05-31 15:24:56 +03:00
2018-10-16 13:45:03 +03:00
2020-11-17 16:44:44 +03:00

OpenVINO™ Toolkit

Stable release Apache License Version 2.0 GitHub branch checks state Azure DevOps builds (branch) PyPI Downloads

This toolkit allows developers to deploy pre-trained deep learning models through a high-level C++ Inference Engine API integrated with application logic.

This open source version includes several components: namely Model Optimizer, nGraph and Inference Engine, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as Caffe*, TensorFlow*, MXNet* and ONNX*.

Repository components:

License

Deep Learning Deployment Toolkit is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.

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* Other names and brands may be claimed as the property of others.

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C++ 80.5%
Python 15.5%
C 2.8%
CMake 0.9%
Cython 0.1%