Merge branch 'releases/2023/2' into releases/2023/2

.ci/azure/linux_coverity.yml

@@ -4,7 +4,7 @@ resources:
     type: github
     endpoint: openvinotoolkit
     name: openvinotoolkit/openvino_contrib
-    ref: master
+    ref: releases/2023/2
 
 variables:
   - group: github

.ci/azure/windows.yml

@@ -32,7 +32,7 @@ resources:
     type: github
     endpoint: openvinotoolkit
     name: openvinotoolkit/openvino_contrib
-    ref: master
+    ref: releases/2023/2
 
 jobs:
 - job: Win

.ci/azure/windows_conditional_compilation.yml

@@ -35,7 +35,7 @@ resources:
     type: github
     endpoint: openvinotoolkit
     name: openvinotoolkit/testdata
-    ref: master
+    ref: releases/2023/2
 
 variables:
   - group: github

.github/workflows/linux.yml

@@ -79,7 +79,7 @@ jobs:
           repository: 'openvinotoolkit/openvino_contrib'
           path: ${{ env.OPENVINO_CONTRIB_REPO }}
           submodules: 'true'
-          ref: 'master'
+          ref: 'releases/2023/2'
 
       #
       # Print system info
@@ -540,7 +540,7 @@ jobs:
             install_build_dependencies.sh
           sparse-checkout-cone-mode: false
           path: ${{ env.OPENVINO_REPO }}
-          ref: 'master'
+          ref: 'releases/2023/2'
 
       - name: Install git
         run: |
@@ -1443,7 +1443,7 @@ jobs:
         with:
           repository: 'openvinotoolkit/openvino_contrib'
           path: ${{ env.OPENVINO_CONTRIB_REPO }}
-          ref: 'master'
+          ref: 'releases/2023/2'
 
       #
       # Dependencies

.github/workflows/linux_conditional_compilation.yml

@@ -73,7 +73,7 @@ jobs:
           repository: 'openvinotoolkit/testdata'
           path: ${{ env.MODELS_PATH }}
           lfs: 'true'
-          ref: 'master'
+          ref: 'releases/2023/2'
 
       #
       # Print system info
@@ -243,7 +243,7 @@ jobs:
           repository: 'openvinotoolkit/testdata'
           path: ${{ env.MODELS_PATH }}
           lfs: 'true'
-          ref: 'master'
+          ref: 'releases/2023/2'
 
       - name: Download selective build statistics package
         uses: actions/download-artifact@v3

.github/workflows/windows.yml

@@ -60,7 +60,7 @@ jobs:
         with:
           repository: 'openvinotoolkit/openvino_contrib'
           path: 'openvino_contrib'
-          ref: 'master'
+          ref: 'releases/2023/2'
 
       #
       # Print system info

.github/workflows/windows_conditional_compilation.yml

@@ -62,7 +62,7 @@ jobs:
           repository: 'openvinotoolkit/testdata'
           path: 'testdata'
           lfs: 'true'
-          ref: 'master'
+          ref: 'releases/2023/2'
 
       #
       # Print system info
@@ -221,7 +221,7 @@ jobs:
           repository: 'openvinotoolkit/testdata'
           path: 'testdata'
           lfs: 'true'
-          ref: 'master'
+          ref: 'releases/2023/2'
 
       - name: Download selective build statistics package
         uses: actions/download-artifact@v3

docs/articles_en/documentation/openvino_ir/operation_sets/operations_specifications/sort/NMSRotated_13.md

@@ -27,13 +27,13 @@ The general algorithm is described below:
 
 Here ``func(rotated_iou(b_i, b)) = 1 if rotated_iou(b_i, b) <= iou_threshold else 0``.
 
-Having two bouding boxes ``B1`` and ``B2`` the following steps are performed to calculate ``rotated_iou(B1, B2)``:
+Having two bounding boxes ``B1`` and ``B2`` the following steps are performed to calculate ``rotated_iou(B1, B2)``:
 
 1. Calculate rotated vertices, (x, y) coordinates of the 4 corners of each box transformed by the corresponding angle in radians according to the direction specified by the *clockwise* attribute.
 2. Find all intersection points between edges of ``B1`` and ``B2``. Add them to the ``intersection_points``.
 3. Find all corners of ``B1`` within area of ``B2``, and all corners of ``B2`` within area of ``B1``. Add them to the ``intersection_points``.
 4. Calculate ``intersection_area`` of the polygon described by ``intersection_points`` (see Sholeace formula).
-5. Calculate ``union_area`` (the common area of ``B1`` and ``B2``), `union_area = (B1_area + B2_area) - intersection_area`.
+5. Calculate ``union_area`` (the common area of ``B1`` and ``B2``), `union_area = B1_area + B2_area`.
 6. Return intersection over union ``rotated_iou = intersection_area / (union_area - intersection_area)``.
 
 

docs/articles_en/get started/installing-openvino-overview/installing-openvino-shared/installing-openvino-vcpkg.md

@@ -65,37 +65,6 @@ Installing OpenVINO Runtime
 Note that the vcpkg installation means building all packages and dependencies from source, 
 which means the compiler stage will require additional time to complete the process. 
 
-.. important::
-
-   If you are building OpenVINO as dynamic libraries and you want to use either Paddle, TensorFlow or ONNX frontends, you need to create `custom vcpkg <https://learn.microsoft.com/en-us/vcpkg/users/triplets#per-port-customization>`__ triplet file, like ``<VCPKG_ROOT>/triplets/community/x64-linux-release-dynamic.cmake``, which builds ``protobuf`` dependency statically:
-
-   .. code-block:: sh
-
-      # typical values of vcpkg toolchain
-      set(VCPKG_TARGET_ARCHITECTURE x64)
-      set(VCPKG_CRT_LINKAGE dynamic)
-      # by default, all libraries are built dynamically
-      set(VCPKG_LIBRARY_LINKAGE dynamic)
-
-      set(VCPKG_CMAKE_SYSTEM_NAME Linux)
-      set(VCPKG_BUILD_TYPE release)
-
-      set(VCPKG_FIXUP_ELF_RPATH ON)
-
-      # OpenVINO specific additions: build statically the following internal dependencies
-      # IMPORTANT: you need to build at least protobuf statically, others can be dynamic
-      if(PORT MATCHES "^(ade|hwloc|onnx|protobuf|pugixml|snappy)$")
-          set(VCPKG_LIBRARY_LINKAGE static)
-      endif()
-
-
-   Then, you can use such a triplet file with the following command:
-
-   .. code-block:: sh
-
-      vcpkg install 'openvino:x64-linux-release-dynamic'
-
-
 After installation, you can use OpenVINO in your product's cmake scripts:
 
 .. code-block:: sh

docs/install_guides/pypi-openvino-dev.md

@@ -1,9 +1,6 @@
 # OpenVINO™ Development Tools
 
-<!--- The note below is intended for master branch only for pre-release purpose. Remove it for official releases. --->
-> **NOTE**: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.
-
-> **NOTE**: OpenVINO™ Development Tools package has been deprecated and will be discontinued with 2024.0 release. To learn more, refer to the [OpenVINO Legacy Features and Components page](https://docs.openvino.ai/2023.1/openvino_legacy_features.html).
+> **NOTE**: OpenVINO™ Development Tools package has been deprecated and will be discontinued with 2024.0 release. To learn more, refer to the [OpenVINO Legacy Features and Components page](https://docs.openvino.ai/2023.2/openvino_legacy_features.html).
 
 Intel® Distribution of OpenVINO™ toolkit is an open-source toolkit for optimizing and deploying AI inference. It can be used to develop applications and solutions based on deep learning tasks, such as: emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, etc. It provides high-performance and rich deployment options, from edge to cloud.
 
@@ -121,14 +118,14 @@ For example, to install and configure the components for working with TensorFlow
 
 | Component        | Console Script                                                                   | Description                                                                                                                                                                                                                                                                                                   |
 |------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [Legacy Model conversion API](https://docs.openvino.ai/nightly/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) | `mo` |**Model conversion API** imports, converts, and optimizes models that were trained in popular frameworks to a format usable by OpenVINO components. <br>Supported frameworks include Caffe\*, TensorFlow\*, MXNet\*, PaddlePaddle\*, and ONNX\*.                                               |                                         |
-| [Accuracy Checker](https://docs.openvino.ai/nightly/omz_tools_accuracy_checker.html) and <br> [Annotation Converter](https://docs.openvino.ai/nightly/omz_tools_accuracy_checker_annotation_converters.html) | `accuracy_check` <br> `convert_annotation` |**Accuracy Checker**  is a deep learning accuracy validation tool that allows you to collect accuracy metrics against popular datasets. The main advantages of the tool are the flexibility of configuration and a set of supported datasets, preprocessing, postprocessing, and metrics. <br> **Annotation Converter** is a utility that prepares datasets for evaluation with Accuracy Checker.                                             |
-| [Post-Training Optimization Tool](https://docs.openvino.ai/nightly/pot_introduction.html)| `pot` |**Post-Training Optimization Tool** allows you to optimize trained models with advanced capabilities, such as quantization and low-precision optimizations, without the need to retrain or fine-tune models.                                            |
-| [Model Downloader and other Open Model Zoo tools](https://docs.openvino.ai/nightly/omz_tools_downloader.html)| `omz_downloader` <br> `omz_converter` <br> `omz_quantizer` <br> `omz_info_dumper`| **Model Downloader** is a tool for getting access to the collection of high-quality and extremely fast pre-trained deep learning [public](@ref omz_models_group_public) and [Intel](@ref omz_models_group_intel)-trained models. These free pre-trained models can be used to speed up the development and production deployment process without training your own models. The tool downloads model files from online sources and, if necessary, patches them to make them more usable with model conversion API. A number of additional tools are also provided to automate the process of working with downloaded models:<br> **Model Converter** is a tool for converting Open Model Zoo models that are stored in an original deep learning framework format into the OpenVINO Intermediate Representation (IR) using model conversion API. <br> **Model Quantizer** is a tool for automatic quantization of full-precision models in the IR format into low-precision versions using the Post-Training Optimization Tool. <br> **Model Information Dumper** is a helper utility for dumping information about the models to a stable, machine-readable format.                                          |
+| [Legacy Model conversion API](https://docs.openvino.ai/2023.2/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) | `mo` |**Model conversion API** imports, converts, and optimizes models that were trained in popular frameworks to a format usable by OpenVINO components. <br>Supported frameworks include Caffe\*, TensorFlow\*, MXNet\*, PaddlePaddle\*, and ONNX\*.                                               |                                         |
+| [Accuracy Checker](https://docs.openvino.ai/2023.2/omz_tools_accuracy_checker.html) and <br> [Annotation Converter](https://docs.openvino.ai/2023.2/omz_tools_accuracy_checker_annotation_converters.html) | `accuracy_check` <br> `convert_annotation` |**Accuracy Checker**  is a deep learning accuracy validation tool that allows you to collect accuracy metrics against popular datasets. The main advantages of the tool are the flexibility of configuration and a set of supported datasets, preprocessing, postprocessing, and metrics. <br> **Annotation Converter** is a utility that prepares datasets for evaluation with Accuracy Checker.                                             |
+| [Post-Training Optimization Tool](https://docs.openvino.ai/2023.2/pot_introduction.html)| `pot` |**Post-Training Optimization Tool** allows you to optimize trained models with advanced capabilities, such as quantization and low-precision optimizations, without the need to retrain or fine-tune models.                                            |
+| [Model Downloader and other Open Model Zoo tools](https://docs.openvino.ai/2023.2/omz_tools_downloader.html)| `omz_downloader` <br> `omz_converter` <br> `omz_quantizer` <br> `omz_info_dumper`| **Model Downloader** is a tool for getting access to the collection of high-quality and extremely fast pre-trained deep learning [public](@ref omz_models_group_public) and [Intel](@ref omz_models_group_intel)-trained models. These free pre-trained models can be used to speed up the development and production deployment process without training your own models. The tool downloads model files from online sources and, if necessary, patches them to make them more usable with model conversion API. A number of additional tools are also provided to automate the process of working with downloaded models:<br> **Model Converter** is a tool for converting Open Model Zoo models that are stored in an original deep learning framework format into the OpenVINO Intermediate Representation (IR) using model conversion API. <br> **Model Quantizer** is a tool for automatic quantization of full-precision models in the IR format into low-precision versions using the Post-Training Optimization Tool. <br> **Model Information Dumper** is a helper utility for dumping information about the models to a stable, machine-readable format.                                          |
 
 ## Troubleshooting
 
-For general troubleshooting steps and issues, see [Troubleshooting Guide for OpenVINO Installation](https://docs.openvino.ai/2023.1/openvino_docs_get_started_guide_troubleshooting.html). The following sections also provide explanations to several error messages.
+For general troubleshooting steps and issues, see [Troubleshooting Guide for OpenVINO Installation](https://docs.openvino.ai/2023.2/openvino_docs_get_started_guide_troubleshooting.html). The following sections also provide explanations to several error messages.
 
 ### Errors with Installing via PIP for Users in China