[DOCS] Port notes to 23.2 (#21471)

* change wording (#21428)

* add legacy note to mo section

* port note

* change header

* add note to pot

* port deprecation note

* resolve conflicts

* fix directive

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api.md

@@ -6,13 +6,13 @@
    :maxdepth: 1
    :hidden:
 
-   openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model
-   openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model
-   openvino_docs_MO_DG_Additional_Optimization_Use_Cases
-   openvino_docs_MO_DG_FP16_Compression
-   openvino_docs_MO_DG_Python_API
-   openvino_docs_MO_DG_prepare_model_Model_Optimizer_FAQ
-   Supported_Model_Formats_MO_DG
+   Setting Input Shapes <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>
+   Cutting Off Parts of a Model <openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model>
+   Embedding Preprocessing Computation <openvino_docs_MO_DG_Additional_Optimization_Use_Cases>
+   Compressing a Model to FP16 <openvino_docs_MO_DG_FP16_Compression>
+   Convert Models Represented as Python Objects <openvino_docs_MO_DG_Python_API>
+   Model Optimizer Frequently Asked Questions <openvino_docs_MO_DG_prepare_model_Model_Optimizer_FAQ>
+   Supported Model Formats <Supported_Model_Formats_MO_DG>
 
 .. meta::
    :description: Model conversion (MO) furthers the transition between training and

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/convert_python_model_objects.md

@@ -1,7 +1,13 @@
-# Convert Models Represented as Python Objects {#openvino_docs_MO_DG_Python_API}
+# [LEGACY] Convert Models Represented as Python Objects {#openvino_docs_MO_DG_Python_API}
 
 @sphinxdirective
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Model Preparation <openvino_docs_model_processing_introduction>` article.
+   
 Model conversion API is represented by ``convert_model()`` method in openvino.tools.mo namespace. ``convert_model()`` is compatible with types from openvino.runtime, like PartialShape, Layout, Type, etc.
 
 ``convert_model()`` has the ability available from the command-line tool, plus the ability to pass Python model objects, such as a PyTorch model or TensorFlow Keras model directly, without saving them into files and without leaving the training environment (Jupyter Notebook or training scripts). In addition to input models consumed directly from Python, ``convert_model`` can take OpenVINO extension objects constructed directly in Python for easier conversion of operations that are not supported in OpenVINO.

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/cutting_model.md

@@ -1,7 +1,11 @@
-# Cutting Off Parts of a Model  {#openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model}
+# [LEGACY] Cutting Off Parts of a Model  {#openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model}
 
 @sphinxdirective
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+   
 Sometimes, it is necessary to remove parts of a model when converting it to OpenVINO IR. This chapter describes how to do it, using model conversion API parameters. Model cutting applies mostly to TensorFlow models, which is why TensorFlow will be used in this chapter's examples, but it may be also useful for other frameworks.
 
 Purpose of Model Cutting

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/embedding_preprocessing_computation.md

@@ -1,7 +1,13 @@
-# Embedding Preprocessing Computation {#openvino_docs_MO_DG_Additional_Optimization_Use_Cases}
+# [LEGACY] Embedding Preprocessing Computation {#openvino_docs_MO_DG_Additional_Optimization_Use_Cases}
 
 @sphinxdirective
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Conversion Parameters <openvino_docs_OV_Converter_UG_Conversion_Options>` article. 
+   
 Input data for inference can be different from the training dataset and requires
 additional preprocessing before inference. To accelerate the whole pipeline including
 preprocessing and inference, model conversion API provides special parameters such as ``mean_values``,

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/fp16_compression.md

@@ -1,7 +1,13 @@
-# Compressing a Model to FP16 {#openvino_docs_MO_DG_FP16_Compression}
+# [LEGACY] Compressing a Model to FP16 {#openvino_docs_MO_DG_FP16_Compression}
 
 @sphinxdirective
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Conversion Parameters <openvino_docs_OV_Converter_UG_Conversion_Options>` article. 
+   
 By default, when IR is saved all relevant floating-point weights are compressed to ``FP16`` data type during model conversion.
 It results in creating a "compressed ``FP16`` model", which occupies about half of
 the original space in the file system. The compression may introduce a minor drop in accuracy,

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/model_optimizer_faq.md

@@ -1,4 +1,4 @@
-# Model Optimizer Frequently Asked Questions  {#openvino_docs_MO_DG_prepare_model_Model_Optimizer_FAQ}
+# [LEGACY] Model Optimizer Frequently Asked Questions  {#openvino_docs_MO_DG_prepare_model_Model_Optimizer_FAQ}
 
 @sphinxdirective
 

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/setting_input_shapes.md

@@ -1,8 +1,15 @@
-# Setting Input Shapes {#openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model}
+# [LEGACY] Setting Input Shapes {#openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model}
+
+@sphinxdirective
+
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the  :doc:`Setting Input Shapes <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Converting_Model>` article. 
 
 With model conversion API you can increase your model's efficiency by providing an additional shape definition, with these two parameters: `input_shape` and `static_shape`.
 
-@sphinxdirective
 
 .. meta:: 
    :description: Learn how to increase the efficiency of a model with MO by providing an additional shape definition with the input_shape and static_shape parameters.

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats.md

@@ -1,4 +1,4 @@
-# Supported Model Formats {#Supported_Model_Formats_MO_DG}
+# [LEGACY] Supported Model Formats {#Supported_Model_Formats_MO_DG}
 
 @sphinxdirective
 
@@ -6,17 +6,22 @@
    :maxdepth: 1
    :hidden:
 
-   openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow
-   openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX
-   openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch
-   openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite
-   openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle
-   openvino_docs_MO_DG_prepare_model_convert_model_tutorials
+   Converting a TensorFlow Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow>
+   Converting an ONNX Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX>
+   Converting a PyTorch Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch>
+   Converting a TensorFlow Lite Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite>
+   Converting a PaddlePaddle Model <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle>
+   Model Conversion Tutorials <openvino_docs_MO_DG_prepare_model_convert_model_tutorials>
 
 .. meta::
    :description: Learn about supported model formats and the methods used to convert, read, and compile them in OpenVINO™.
 
+.. danger::
 
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Supported Model Formats <Supported_Model_Formats>` article. 
+   
 **OpenVINO IR (Intermediate Representation)** - the proprietary and default format of OpenVINO, benefiting from the full extent of its features. All other supported model formats, as listed below, are converted to :doc:`OpenVINO IR <openvino_ir>` to enable inference. Consider storing your model in this format to minimize first-inference latency, perform model optimization, and, in some cases, save space on your drive.
 
 **PyTorch, TensorFlow, ONNX, and PaddlePaddle** - can be used with OpenVINO Runtime API directly, 

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/Convert_Model_From_ONNX.md

@@ -1,4 +1,4 @@
-# Converting an ONNX Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX}
+# [LEGACY] Converting an ONNX Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX}
 
 @sphinxdirective
 
@@ -6,6 +6,14 @@
    :description: Learn how to convert a model from the 
                  ONNX format to the OpenVINO Intermediate Representation.
 
+
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Converting an ONNX Model <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_ONNX>` article. 
+
+
 .. note:: ONNX models are supported via FrontEnd API. You may skip conversion to IR and read models directly by OpenVINO runtime API. Refer to the :doc:`inference example <openvino_docs_OV_UG_Integrate_OV_with_your_application>` for more details. Using ``convert_model`` is still necessary in more complex cases, such as new custom inputs/outputs in model pruning, adding pre-processing, or using Python conversion extensions.
 
 Converting an ONNX Model

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/Convert_Model_From_Paddle.md

@@ -1,4 +1,4 @@
-# Converting a PaddlePaddle Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle}
+# [LEGACY] Converting a PaddlePaddle Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle}
 
 @sphinxdirective
 
@@ -7,6 +7,13 @@
                  PaddlePaddle format to the OpenVINO Intermediate Representation. 
 
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Converting a PaddlePaddle Model <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_Paddle>` article.
+
+
 This page provides general instructions on how to convert a model from a PaddlePaddle format to the OpenVINO IR format using Model Optimizer. The instructions are different depending on PaddlePaddle model format.
 
 .. note:: PaddlePaddle models are supported via FrontEnd API. You may skip conversion to IR and read models directly by OpenVINO runtime API. Refer to the :doc:`inference example <openvino_docs_OV_UG_Integrate_OV_with_your_application>` for more details. Using ``convert_model`` is still necessary in more complex cases, such as new custom inputs/outputs in model pruning, adding pre-processing, or using Python conversion extensions.

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/Convert_Model_From_PyTorch.md

@@ -1,4 +1,4 @@
-# Converting a PyTorch Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch}
+# [LEGACY] Converting a PyTorch Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch}
 
 @sphinxdirective
 
@@ -7,6 +7,12 @@
                  PyTorch format to the OpenVINO Intermediate Representation. 
 
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Converting a PyTorch Model <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_PyTorch>` article. 
+
 This page provides instructions on how to convert a model from the PyTorch format to the OpenVINO IR format.
 
 The conversion is a required step to run inference using OpenVINO API.

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/Convert_Model_From_TensorFlow.md

@@ -1,4 +1,4 @@
-# Converting a TensorFlow Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow}
+# [LEGACY] Converting a TensorFlow Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow}
 
 @sphinxdirective
 
@@ -6,6 +6,12 @@
    :description: Learn how to convert a model from a 
                  TensorFlow format to the OpenVINO Intermediate Representation.
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Converting a TensorFlow Model <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_TensorFlow>` article. 
+
 
 .. note:: TensorFlow models are supported via :doc:`FrontEnd API <openvino_docs_MO_DG_TensorFlow_Frontend>`. You may skip conversion to IR and read models directly by OpenVINO runtime API. Refer to the :doc:`inference example <openvino_docs_OV_UG_Integrate_OV_with_your_application>` for more details. Using ``convert_model`` is still necessary in more complex cases, such as new custom inputs/outputs in model pruning, adding pre-processing, or using Python conversion extensions.
 

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/Convert_Model_From_TensorFlow_Lite.md

@@ -1,4 +1,4 @@
-# Converting a TensorFlow Lite Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite}
+# [LEGACY] Converting a TensorFlow Lite Model {#openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite}
 
 @sphinxdirective
 
@@ -6,6 +6,11 @@
    :description: Learn how to convert a model from a 
                  TensorFlow Lite format to the OpenVINO Intermediate Representation.
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Converting a TensorFlow Lite Model <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite>` article.
 
 To convert a TensorFlow Lite model, use the ``mo`` script and specify the path to the input ``.tflite`` model file:
 

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials.md

@@ -1,4 +1,4 @@
-# Model Conversion Tutorials {#openvino_docs_MO_DG_prepare_model_convert_model_tutorials}
+# [LEGACY] Model Conversion Tutorials {#openvino_docs_MO_DG_prepare_model_convert_model_tutorials}
 
 @sphinxdirective
 
@@ -37,6 +37,12 @@
    :description: Get to know conversion methods for specific TensorFlow, ONNX, PyTorch, MXNet, and Kaldi models.
 
 
+.. danger::
+
+   The code described in the tutorials has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+
 This section provides a set of tutorials that demonstrate conversion methods for specific 
 TensorFlow, ONNX, and PyTorch models. Note that these instructions do not cover all use 
 cases and may not reflect your particular needs.

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_AttentionOCR_From_Tensorflow.md

@@ -8,6 +8,12 @@
                  OpenVINO Intermediate Representation.
 
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+
 This tutorial explains how to convert the Attention OCR (AOCR) model from the `TensorFlow Attention OCR repository <https://github.com/emedvedev/attention-ocr>`__ to the Intermediate Representation (IR).
 
 Extracting a Model from ``aocr`` Library

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_BERT_From_Tensorflow.md

@@ -7,6 +7,12 @@
                  from TensorFlow to the OpenVINO Intermediate Representation.
 
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 Pretrained models for BERT (Bidirectional Encoder Representations from Transformers) are
 `publicly available <https://github.com/google-research/bert>`__.
 

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_Bert_ner.md

@@ -6,6 +6,11 @@
    :description: Learn how to convert a BERT-NER model
                  from PyTorch to the OpenVINO Intermediate Representation.
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
 
 The goal of this article is to present a step-by-step guide on how to convert PyTorch BERT-NER model to OpenVINO IR. First, you need to download the model and convert it to ONNX.
 

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_CRNN_From_Tensorflow.md

@@ -7,6 +7,12 @@
                  from TensorFlow to the OpenVINO Intermediate Representation.
 
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 This tutorial explains how to convert a CRNN model to OpenVINO™ Intermediate Representation (IR).
 
 There are several public versions of TensorFlow CRNN model implementation available on GitHub. This tutorial explains how to convert the model from

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_Cascade_RCNN_res101.md

@@ -7,6 +7,12 @@
                  model from PyTorch to the OpenVINO Intermediate Representation.
 
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 The goal of this article is to present a step-by-step guide on how to convert a PyTorch Cascade RCNN R-101 model to OpenVINO IR. First, you need to download the model and convert it to ONNX.
 
 Downloading and Converting Model to ONNX

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_DeepSpeech_From_Tensorflow.md

@@ -6,7 +6,12 @@
    :description: Learn how to convert a DeepSpeech model 
                  from TensorFlow to the OpenVINO Intermediate Representation.
 
+.. danger::
 
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 `DeepSpeech project <https://github.com/mozilla/DeepSpeech>`__ provides an engine to train speech-to-text models.
 
 Downloading the Pretrained DeepSpeech Model

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_EfficientDet_Models.md

@@ -7,6 +7,12 @@
                  from TensorFlow to the OpenVINO Intermediate Representation.
 
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 This tutorial explains how to convert EfficientDet public object detection models to the Intermediate Representation (IR).
 
 .. _efficientdet-to-ir:

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_F3Net.md

@@ -6,7 +6,12 @@
    :description: Learn how to convert a F3Net model
                  from PyTorch to the OpenVINO Intermediate Representation.
 
+.. danger::
 
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 `F3Net <https://github.com/weijun88/F3Net>`__ : Fusion, Feedback and Focus for Salient Object Detection
 
 Cloning the F3Net Repository

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_FaceNet_From_Tensorflow.md

@@ -6,7 +6,12 @@
    :description: Learn how to convert a FaceNet model 
                  from TensorFlow to the OpenVINO Intermediate Representation.
 
+.. danger::
 
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Supported Model Formats <Supported_Model_Formats>` article. 
+   
 `Public pre-trained FaceNet models <https://github.com/davidsandberg/facenet#pre-trained-models>`__ contain both training
 and inference part of graph. Switch between this two states is manageable with placeholder value.
 Intermediate Representation (IR) models are intended for inference, which means that train part is redundant.

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_Faster_RCNN.md

@@ -6,7 +6,12 @@
    :description: Learn how to convert a Faster R-CNN model 
                  from ONNX to the OpenVINO Intermediate Representation.
 
+.. danger::
 
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 The instructions below are applicable **only** to the Faster R-CNN model converted to the ONNX file format from the `maskrcnn-benchmark model <https://github.com/facebookresearch/maskrcnn-benchmark>`__:
 
 1. Download the pretrained model file from `onnx/models <https://github.com/onnx/models/tree/master/vision/object_detection_segmentation/faster-rcnn>`__ (commit-SHA: 8883e49e68de7b43e263d56b9ed156dfa1e03117).

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_GNMT_From_Tensorflow.md

@@ -6,7 +6,12 @@
    :description: Learn how to convert a GNMT model 
                  from TensorFlow to the OpenVINO Intermediate Representation.
 
+.. danger::
 
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 This tutorial explains how to convert Google Neural Machine Translation (GNMT) model to the Intermediate Representation (IR).
 
 There are several public versions of TensorFlow GNMT model implementation available on GitHub. This tutorial explains how to convert the GNMT model from the `TensorFlow Neural Machine Translation (NMT) repository <https://github.com/tensorflow/nmt>`__ to the IR.

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_GPT2.md

@@ -6,6 +6,11 @@
    :description: Learn how to convert a pre-trained GPT-2 
                  model from ONNX to the OpenVINO Intermediate Representation.
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
 
 `Public pre-trained GPT-2 model <https://github.com/onnx/models/tree/master/text/machine_comprehension/gpt-2>`__ is a large
 transformer-based language model with a simple objective: predict the next word, given all of the previous words within some text.

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_Mask_RCNN.md

@@ -6,6 +6,11 @@
    :description: Learn how to convert a pre-trained Mask 
                  R-CNN model from ONNX to the OpenVINO Intermediate Representation.
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
 
 The instructions below are applicable **only** to the Mask R-CNN model converted to the ONNX file format from the `maskrcnn-benchmark model <https://github.com/facebookresearch/maskrcnn-benchmark>`__.
 

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_NCF_From_Tensorflow.md

@@ -7,6 +7,11 @@
                  Filtering Model from TensorFlow to the OpenVINO Intermediate 
                  Representation.
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
 
 This tutorial explains how to convert Neural Collaborative Filtering (NCF) model to the OpenVINO Intermediate Representation.
 

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_Object_Detection_API_Models.md

@@ -8,6 +8,12 @@
                  Representation.
 
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 * Starting with the 2022.1 release, model conversion API can convert the TensorFlow Object Detection API Faster and Mask RCNNs topologies differently. By default, model conversion adds operation "Proposal" to the generated IR. This operation needs an additional input to the model with name "image_info" which should be fed with several values describing the preprocessing applied to the input image (refer to the :doc:`Proposal <openvino_docs_ops_detection_Proposal_4>` operation specification for more information). However, this input is redundant for the models trained and inferred with equal size images. Model conversion API can generate IR for such models and insert operation :doc:`DetectionOutput <openvino_docs_ops_detection_DetectionOutput_1>` instead of ``Proposal``. The `DetectionOutput` operation does not require additional model input "image_info". Moreover, for some models the produced inference results are closer to the original TensorFlow model. In order to trigger new behavior, the attribute "operation_to_add" in the corresponding JSON transformation configuration file should be set to value "DetectionOutput" instead of default one "Proposal".
 * Starting with the 2021.1 release, model conversion API converts the TensorFlow Object Detection API SSDs, Faster and Mask RCNNs topologies keeping shape-calculating sub-graphs by default, so topologies can be re-shaped in the OpenVINO Runtime using dedicated reshape API. Refer to the :doc:`Using Shape Inference <openvino_docs_OV_UG_ShapeInference>` guide for more information on how to use this feature. It is possible to change the both spatial dimensions of the input image and batch size.
 * To generate IRs for TF 1 SSD topologies, model conversion API creates a number of ``PriorBoxClustered`` operations instead of a constant node with prior boxes calculated for the particular input image size. This change allows you to reshape the topology in the OpenVINO Runtime using dedicated API. The reshaping is supported for all SSD topologies except FPNs, which contain hardcoded shapes for some operations preventing from changing topology input shape.

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_QuartzNet.md

@@ -6,7 +6,12 @@
    :description: Learn how to convert a QuartzNet model
                  from PyTorch to the OpenVINO Intermediate Representation.
 
+.. danger::
 
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 `NeMo project <https://github.com/NVIDIA/NeMo>`__ provides the QuartzNet model.
 
 Downloading the Pre-trained QuartzNet Model

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_RCAN.md

@@ -6,7 +6,12 @@
    :description: Learn how to convert a RCAN model
                  from PyTorch to the OpenVINO Intermediate Representation.
 
+.. danger::
 
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 `RCAN <https://github.com/yulunzhang/RCAN>`__ : Image Super-Resolution Using Very Deep Residual Channel Attention Networks
 
 Downloading and Converting the Model to ONNX

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_RNNT.md

@@ -6,7 +6,12 @@
    :description: Learn how to convert a RNN-T model
                  from PyTorch to the OpenVINO Intermediate Representation.
 
+.. danger::
 
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 This guide covers conversion of RNN-T model from `MLCommons <https://github.com/mlcommons>`__ repository. Follow
 the instructions below to export a PyTorch model into ONNX, before converting it to IR:
 

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_RetinaNet_From_Tensorflow.md

@@ -7,6 +7,12 @@
                  from TensorFlow to the OpenVINO Intermediate Representation.
 
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 This tutorial explains how to convert a RetinaNet model to the Intermediate Representation (IR).
 
 `Public RetinaNet model <https://github.com/fizyr/keras-retinanet>`__ does not contain pretrained TensorFlow weights.

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_Slim_Library_Models.md

@@ -7,6 +7,11 @@
                  Classification model from TensorFlow to the OpenVINO 
                  Intermediate Representation.
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
 
 `TensorFlow-Slim Image Classification Model Library <https://github.com/tensorflow/models/tree/master/research/slim/README.md>`__ is a library to define, train and evaluate classification models in TensorFlow. The library contains Python scripts defining the classification topologies together with checkpoint files for several pre-trained classification topologies. To convert a TensorFlow-Slim library model, complete the following steps:
 

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_WideAndDeep_Family_Models.md

@@ -7,6 +7,12 @@
                  models from TensorFlow to the OpenVINO Intermediate Representation.
 
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 The Wide and Deep models is a combination of wide and deep parts for memorization and generalization of object features respectively.
 These models can contain different types of object features such as numerical, categorical, sparse and sequential features. These feature types are specified
 through Tensorflow tf.feature_column API. Table below presents what feature types are supported by the OpenVINO toolkit.

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_XLNet_From_Tensorflow.md

@@ -6,7 +6,12 @@
    :description: Learn how to convert an XLNet model from 
                  TensorFlow to the OpenVINO Intermediate Representation.
 
+.. danger::
 
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 Pretrained models for XLNet (Bidirectional Encoder Representations from Transformers) are
 `publicly available <https://github.com/zihangdai/xlnet>`__.
 

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_YOLACT.md

@@ -7,6 +7,12 @@
                  from PyTorch to the OpenVINO Intermediate Representation.
 
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 You Only Look At CoefficienTs (YOLACT) is a simple, fully convolutional model for real-time instance segmentation.
 The PyTorch implementation is publicly available in `this GitHub repository <https://github.com/dbolya/yolact>`__.
 The YOLACT++ model is not supported, because it uses deformable convolutional layers that cannot be represented in ONNX format.

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_YOLO_From_Tensorflow.md

@@ -6,6 +6,11 @@
    :description: Learn how to convert YOLO models from 
                  TensorFlow to the OpenVINO Intermediate Representation.
 
+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
 
 This document explains how to convert real-time object detection YOLOv1, YOLOv2, YOLOv3 and YOLOv4 public models to the Intermediate Representation (IR). All YOLO models are originally implemented in the DarkNet framework and consist of two files:
 

docs/articles_en/documentation/openvino_legacy_features/mo_ovc_transition/legacy_conversion_api/supported_model_formats/convert_model_tutorials/Convert_lm_1b_From_Tensorflow.md

@@ -7,7 +7,12 @@
                  Model on One Billion Word Benchmark to the OpenVINO Intermediate 
                  Representation.
 
+.. danger::
 
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
+   This guide describes a deprecated conversion method. The guide on the new and recommended method can be found in the :doc:`Python tutorials <tutorials>`.
+   
 Downloading a Pre-trained Language Model on One Billion Word Benchmark
 ######################################################################
 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool.md

@@ -1,4 +1,4 @@
-# (Deprecated) Post-training Quantization with POT {#pot_introduction}
+# [Deprecated]  Post-training Quantization with POT {#pot_introduction}
 
 @sphinxdirective
 
@@ -12,15 +12,15 @@
    API Reference <pot_compression_api_README>
    Command-line Interface <pot_compression_cli_README>
    Examples <pot_examples_description>
-   pot_docs_FrequentlyAskedQuestions
+   Post-training Optimization Tool FAQ <pot_docs_FrequentlyAskedQuestions>
    (Experimental) Protecting Model <pot_ranger_README>
 
 
 
 
-.. note:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
-For the needs of post-training optimization, OpenVINO&trade; provides a **Post-training Optimization Tool (POT)** 
+For the needs of post-training optimization, OpenVINO provides a **Post-training Optimization Tool (POT)** 
 which supports the **uniform integer quantization** method. This method allows moving from floating-point precision 
 to integer precision (for example, 8-bit) for weights and activations during inference time. It helps to reduce 
 the model size, memory footprint and latency, as well as improve the computational efficiency, using integer arithmetic. 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_api_reference.md

@@ -1,7 +1,9 @@
-# API Reference  {#pot_compression_api_README}
+# [Deprecated] API Reference  {#pot_compression_api_README}
 
 @sphinxdirective
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 Post-training Optimization Tool API provides a full set of interfaces and helpers that allow users to implement a custom optimization pipeline for various types of DL models including cascaded or compound models. Below is a full specification of this API:
 
 DataLoader

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_cli.md

@@ -1,4 +1,4 @@
-# Use Post-Training Optimization Tool Command-Line Interface (Model Zoo flow){#pot_compression_cli_README}
+# [Deprecated] Use Post-Training Optimization Tool Command-Line Interface (Model Zoo flow){#pot_compression_cli_README}
 
 @sphinxdirective
 
@@ -7,9 +7,10 @@
    :hidden:
 
    Simplified Mode <pot_docs_simplified_mode>
-   pot_configs_README
+   Configuration File Description  <pot_configs_README>
 
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 Introduction
 ####################

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_cli/configuration_file_description.md

@@ -1,7 +1,9 @@
-# Configuration File Description {#pot_configs_README}
+# [Deprecated] Configuration File Description {#pot_configs_README}
 
 @sphinxdirective
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 The tool is designed to work with the configuration file where all the parameters required for the optimization are specified. These parameters are organized as a dictionary and stored in
 a JSON file. JSON file allows using comments that are supported by the ``jstyleson`` Python package.
 Logically all parameters are divided into three groups:

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_cli/simplified_mode.md

@@ -1,7 +1,9 @@
-# Optimization with Simplified Mode {#pot_docs_simplified_mode}
+# [Deprecated] Optimization with Simplified Mode {#pot_docs_simplified_mode}
 
 @sphinxdirective
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 Introduction
 ####################
 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples.md

@@ -1,4 +1,4 @@
-# Examples {#pot_examples_description}
+# [Deprecated] Examples {#pot_examples_description}
 
 @sphinxdirective
 
@@ -9,6 +9,7 @@
    API Examples <pot_example_README>
    Command-line Example <pot_configs_examples_README>
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 This section provides a set of examples that demonstrate how to apply the post-training optimization methods to optimize various models from different domains. It contains optimization recipes for concrete models, that unnecessarily cover your case, but which should be sufficient to reuse these recipes to optimize custom models:
 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_api_examples.md

@@ -1,4 +1,4 @@
-# Post-training Optimization Tool API Examples {#pot_example_README}
+# [Deprecated] Post-training Optimization Tool API Examples {#pot_example_README}
 
 @sphinxdirective
 
@@ -13,6 +13,8 @@
    Quantizing 3D Segmentation Model <pot_example_3d_segmentation_README>
    Quantizing for GNA Device <pot_example_speech_README>
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 
 The Post-training Optimization Tool contains multiple examples that demonstrate how to use its :doc:`API <pot_compression_api_README>` 
 to optimize DL models. All available examples can be found on `GitHub <https://github.com/openvinotoolkit/openvino/tree/master/tools/pot/openvino/tools/pot/api/samples>`__.

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_api_examples/pot_example_3d_segmentation.md

@@ -1,7 +1,9 @@
-# Quantizing 3D Segmentation Model {#pot_example_3d_segmentation_README}
+# [Deprecated] Quantizing 3D Segmentation Model {#pot_example_3d_segmentation_README}
 
 @sphinxdirective
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 This example demonstrates the use of the :doc:`Post-training Optimization Tool API <pot_compression_api_README>` for the task of quantizing a 3D segmentation model.
 The `Brain Tumor Segmentation <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/brain-tumor-segmentation-0002>`__ model from PyTorch is used for this purpose. A custom ``DataLoader`` is created to load images in NIfTI format from the `Medical Segmentation Decathlon BRATS 2017 <http://medicaldecathlon.com/>`__ dataset for 3D semantic segmentation task and the implementation of the Dice Index metric is used for the model evaluation. In addition, this example demonstrates how one can use image metadata obtained during image reading and preprocessing to post-process the model raw output. The code of the example is available on `GitHub <https://github.com/openvinotoolkit/openvino/tree/master/tools/pot/openvino/tools/pot/api/samples/3d_segmentation>`__.
 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_api_examples/pot_example_classification.md

@@ -1,7 +1,9 @@
-# Quantizing Image Classification Model {#pot_example_classification_README}
+# [Deprecated] Quantizing Image Classification Model {#pot_example_classification_README}
 
 @sphinxdirective
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 This example demonstrates the use of the :doc:`Post-training Optimization Tool API <pot_compression_api_README>` for the task of quantizing a classification model.
 The `MobilenetV2 <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mobilenet-v2-1.0-224>`__ model from TensorFlow is used for this purpose.
 A custom ``DataLoader`` is created to load the `ImageNet <http://www.image-net.org/>`__ classification dataset and the implementation of Accuracy at top-1 metric is used for the model evaluation. The code of the example is available on `GitHub <https://github.com/openvinotoolkit/openvino/tree/master/tools/pot/openvino/tools/pot/api/samples/classification>`__.

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_api_examples/pot_example_face_detection.md

@@ -1,7 +1,9 @@
-# Quantizing Cascaded Face detection Model {#pot_example_face_detection_README}
+# [Deprecated] Quantizing Cascaded Face detection Model {#pot_example_face_detection_README}
 
 @sphinxdirective
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 This example demonstrates the use of the :doc:`Post-training Optimization Tool API <pot_compression_api_README>` for the task of quantizing a face detection model.
 The `MTCNN <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/mtcnn>`__ model from Caffe is used for this purpose.
 A custom ``DataLoader`` is created to load the `WIDER FACE <http://shuoyang1213.me/WIDERFACE/>`__ dataset for a face detection task 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_api_examples/pot_example_object_detection.md

@@ -1,7 +1,9 @@
-# Quantizing Object Detection Model with Accuracy Control {#pot_example_object_detection_README}
+# [Deprecated] Quantizing Object Detection Model with Accuracy Control {#pot_example_object_detection_README}
 
 @sphinxdirective
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 This example demonstrates the use of the :doc:`Post-training Optimization Toolkit API <pot_compression_api_README>` to quantize an object detection model in the :doc:`accuracy-aware mode <accuracy_aware_README>`. The `MobileNetV1 FPN <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssd_mobilenet_v1_fpn_coco>`__ model from TensorFlow for object detection task is used for this purpose. A custom ``DataLoader`` is created to load the `COCO <https://cocodataset.org/>`__ dataset for object detection task and the implementation of mAP COCO is used for the model evaluation. The code of the example is available on `GitHub <https://github.com/openvinotoolkit/openvino/tree/master/tools/pot/openvino/tools/pot/api/samples/object_detection>`__.
 
 How to prepare the data

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_api_examples/pot_example_segmentation.md

@@ -1,7 +1,9 @@
-# Quantizing Semantic Segmentation Model {#pot_example_segmentation_README}
+# [Deprecated] Quantizing Semantic Segmentation Model {#pot_example_segmentation_README}
 
 @sphinxdirective
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 This example demonstrates the use of the :doc:`Post-training Optimization Tool API <pot_compression_api_README>` for the task of quantizing a segmentation model.
 The `DeepLabV3 <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/deeplabv3>` model from TensorFlow is used for this purpose.
 A custom `DataLoader` is created to load the `Pascal VOC 2012 <http://host.robots.ox.ac.uk/pascal/VOC/voc2012/>`__ dataset for semantic segmentation task 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_api_examples/pot_example_speech.md

@@ -1,7 +1,9 @@
-# Quantizing for GNA Device {#pot_example_speech_README}
+# [Deprecated] Quantizing for GNA Device {#pot_example_speech_README}
 
 @sphinxdirective
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 This example demonstrates the use of the :doc:`Post-training Optimization Tool API <pot_compression_api_README>` for the task of quantizing a speech model for :doc:`GNA <openvino_docs_OV_UG_supported_plugins_GNA>` device. Quantization for GNA is different from CPU quantization due to device specifics: GNA supports quantized inputs in INT16 and INT32 (for activations) precision and quantized weights in INT8 and INT16 precision.
 
 This example contains pre-selected quantization options based on the DefaultQuantization algorithm and created for models from `Kaldi <http://kaldi-asr.org/doc/>`__ framework, and its data format.

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_cli_example.md

@@ -1,7 +1,9 @@
-# End-to-end Command-line Interface Example {#pot_configs_examples_README}
+# [Deprecated] End-to-end Command-line Interface Example {#pot_configs_examples_README}
 
 @sphinxdirective
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 This tutorial describes an example of running post-training quantization for the **MobileNet v2 model from PyTorch** framework, 
 particularly by the DefaultQuantization algorithm.
 The example covers the following steps:

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_faq.md

@@ -1,8 +1,8 @@
-# Post-training Optimization Tool FAQ {#pot_docs_FrequentlyAskedQuestions}
+# [Deprecated]  Post-training Optimization Tool FAQ {#pot_docs_FrequentlyAskedQuestions}
 
 @sphinxdirective
 
-.. note:: 
+.. danger:: 
 
    Post-training Optimization Tool has been deprecated since OpenVINO 2023.0. 
    :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for post-training quantization instead.

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/protecting_model.md

@@ -1,7 +1,12 @@
-# Experimental: Protecting Deep Learning Model through Range Supervision ("RangeSupervision") {#pot_ranger_README}
+# [Deprecated] Experimental: Protecting Deep Learning Model through Range Supervision ("RangeSupervision") {#pot_ranger_README}
 
 @sphinxdirective
 
+.. danger:: 
+
+   Post-training Optimization Tool has been deprecated since OpenVINO 2023.0. 
+   :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for post-training quantization instead.
+   
 Introduction
 ####################
 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/quantization_best_practices.md

@@ -1,4 +1,4 @@
-#  Post-Training Quantization Best Practices {#pot_docs_BestPractices}
+#  [Deprecated] Post-Training Quantization Best Practices {#pot_docs_BestPractices}
 
 @sphinxdirective
 
@@ -8,6 +8,7 @@
 
    Saturation Issue <pot_saturation_issue>
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 The :doc:`Default Quantization <pot_default_quantization_usage>` of the Post-training Optimization Tool (POT) is
 the fastest and easiest way to get a quantized model. It requires only some unannotated representative dataset to be provided in most cases. Therefore, it is recommended to use it as a starting point when it comes to model optimization. However, it can lead to significant accuracy deviation in some cases. The purpose of this article is to provide tips to address this issue.

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/quantization_best_practices/saturation_issue.md

@@ -1,7 +1,9 @@
-# Saturation (overflow) Issue Workaround {#pot_saturation_issue}
+# [Deprecated] Saturation (overflow) Issue Workaround {#pot_saturation_issue}
 
 @sphinxdirective
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 Introduction
 ####################
 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/quantizing_models.md

@@ -1,4 +1,4 @@
-# Quantizing Models {#pot_default_quantization_usage}
+# [Deprecated] Quantizing Models {#pot_default_quantization_usage}
 
 
 @sphinxdirective
@@ -10,6 +10,8 @@
    DefaultQuantization Method <pot_compression_algorithms_quantization_default_README>
 
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 This guide describes how to apply model quantization with the Default Quantization method without accuracy control, using an unannotated dataset. 
 To use this method, create a Python script using an API of Post-Training Optimization Tool (POT) and implement data preparation logic and quantization pipeline. 
 If you are not familiar with Python, try :doc:`command-line interface <pot_compression_cli_README>` of POT which is designed to quantize models from 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/quantizing_models/default_quantization_algorithm.md

@@ -1,7 +1,9 @@
-# DefaultQuantization Parameters {#pot_compression_algorithms_quantization_default_README}
+# [Deprecated] DefaultQuantization Parameters {#pot_compression_algorithms_quantization_default_README}
 
 @sphinxdirective
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 The DefaultQuantization Algorithm is designed to perform fast and accurate quantization. It does not offer direct control over the accuracy metric itself but provides many options that can be used to improve it.
 
 Parameters

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/quantizing_models_with_accuracy.md

@@ -1,4 +1,4 @@
-# Quantizing Models with Accuracy Control {#pot_accuracyaware_usage}
+# [Deprecated] Quantizing Models with Accuracy Control {#pot_accuracyaware_usage}
 
 @sphinxdirective
 
@@ -8,6 +8,7 @@
 
    AccuracyAwareQuantization Method <accuracy_aware_README>
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 The Accuracy-aware Quantization algorithm allows performing quantization while maintaining accuracy within a pre-defined range. Note that it should be used only if the :doc:`Default Quantization <pot_default_quantization_usage>` introduces a significant accuracy degradation. The reason for it not being the primary choice is its potential for performance degradation, due to some layers getting reverted to the original precision.
 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/quantizing_models_with_accuracy/accuracy_aware_algorithm.md

@@ -1,7 +1,9 @@
-# AccuracyAwareQuantization Parameters {#accuracy_aware_README}
+# [Deprecated] AccuracyAwareQuantization Parameters {#accuracy_aware_README}
 
 @sphinxdirective
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 Introduction
 ####################
 

docs/articles_en/openvino_workflow/model_optimization_guide.md

@@ -11,7 +11,7 @@
    weight_compression
 
 
-Model optimization is an optional offline step of improving the final model performance and reducing the model size by applying special optimization methods, such as 8-bit quantization, pruning, etc. OpenVINO offers two optimization paths implemented in `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf>`__:
+Model optimization is an optional offline step of improving the final model performance and reducing the model size by applying special optimization methods, such as 8-bit quantization, pruning, etc. OpenVINO offers three optimization paths implemented in `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf>`__:
 
 - :doc:`Post-training Quantization <ptq_introduction>` is designed to optimize the inference of deep learning models by applying the post-training 8-bit integer quantization that does not require model retraining or fine-tuning.