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TensorFlow Lite FrontEnd: documentation changes (#17187)

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* First glance doc changes

* Apply suggestions from code review

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

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

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

---------

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>
This commit is contained in:
Evgenya Stepyreva
2023-04-25 16:18:24 +04:00
committed by GitHub
parent 27210b6505
commit ee4ccec190
21 changed files with 165 additions and 21 deletions
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docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md
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@@ -19,7 +19,7 @@
Model Optimizer is a cross-platform command-line tool that facilitates the transition between training and deployment environments, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices.
To use it, you need a pre-trained deep learning model in one of the supported formats: TensorFlow, PyTorch, PaddlePaddle, MXNet, Caffe, Kaldi, or ONNX. Model Optimizer converts the model to the OpenVINO Intermediate Representation format (IR), which you can infer later with :doc:`OpenVINO™ Runtime <openvino_docs_OV_UG_OV_Runtime_User_Guide>`.
To use it, you need a pre-trained deep learning model in one of the supported formats: TensorFlow, PyTorch, PaddlePaddle, TensorFlow Lite, MXNet, Caffe, Kaldi, or ONNX. Model Optimizer converts the model to the OpenVINO Intermediate Representation format (IR), which you can infer later with :doc:`OpenVINO™ Runtime <openvino_docs_OV_UG_OV_Runtime_User_Guide>`.
Note that Model Optimizer does not infer models.