Feature/ntyukaev/separate layout

docs/HOWTO/Custom_Layers_Guide.md

@@ -195,7 +195,7 @@ For a step-by-step walk-through creating and executing a custom layer, see [Cust
 - Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
 - OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org)
 - [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
-- [Kernel Extensivility in the Inference Engine Developer Guide](../IE_DG/Integrate_your_kernels_into_IE.md)
+- [Inference Engine Extensibility Mechanism](../IE_DG/Extensibility_DG/Intro.md)
 - [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
 - [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
 - [Inference Engine Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic)

docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md

@@ -42,8 +42,6 @@ inference of a pre-trained and optimized deep learning model and a set of sample
 
 ## Table of Contents
 
-* [Introduction to Intel® Deep Learning Deployment Toolkit](Introduction.md)
-
 * [Inference Engine API Changes History](API_Changes.md)
 
 * [Introduction to Inference Engine](inference_engine_intro.md)
@@ -87,4 +85,4 @@ inference of a pre-trained and optimized deep learning model and a set of sample
 
 * [Known Issues](Known_Issues_Limitations.md)
 
-**Typical Next Step:** [Introduction to Intel® Deep Learning Deployment Toolkit](Introduction.md)
+**Typical Next Step:** [Introduction to Inference Engine](inference_engine_intro.md)

docs/IE_DG/PythonPackage_Overview.md

@@ -12,4 +12,4 @@ The OpenVINO™ Python\* package includes the following sub-packages:
  - `openvino.tools.benchmark` - Measure latency and throughput.
 
 ## See Also
-* [Introduction to Intel's Deep Learning Inference Engine](Introduction.md)
+* [Introduction to Inference Engine](inference_engine_intro.md)

docs/IE_DG/Samples_Overview.md

@@ -183,4 +183,4 @@ sample, read the sample documentation by clicking the sample name in the samples
 list above.
 
 ## See Also
-* [Introduction to Intel's Deep Learning Inference Engine](Introduction.md)
+* [Introduction to Inference Engine](inference_engine_intro.md)

docs/IE_DG/Tools_Overview.md

@@ -14,4 +14,4 @@ The OpenVINO™ toolkit installation includes the following tools:
 
 
 ## See Also
-* [Introduction to Deep Learning Inference Engine](Introduction.md)
+* [Introduction to Inference Engine](inference_engine_intro.md)

docs/IE_DG/supported_plugins/MYRIAD.md

@@ -6,11 +6,12 @@ The Inference Engine MYRIAD plugin is developed for inference of neural networks
 
 ## Installation on Linux* OS
 
-For installation instructions, refer to the [Installation Guide for Linux*](../../../inference-engine/samples/benchmark_app/README.md).
+For installation instructions, refer to the [Installation Guide for Linux*](../../install_guides/installing-openvino-linux.md).
+
 
 ## Installation on Windows* OS
 
-For installation instructions, refer to the [Installation Guide for Windows*](../../../inference-engine/samples/benchmark_app/README.md).
+For installation instructions, refer to the [Installation Guide for Windows*](../../install_guides/installing-openvino-windows.md).
 
 ## Supported networks
 

docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md

@@ -63,8 +63,6 @@ Model Optimizer produces an Intermediate Representation (IR) of the network, whi
 
 ## Table of Content
 
-* [Introduction to OpenVINO™ Deep Learning Deployment Toolkit](../IE_DG/Introduction.md)
-
 * [Preparing and Optimizing your Trained Model with Model Optimizer](prepare_model/Prepare_Trained_Model.md)
     * [Configuring Model Optimizer](prepare_model/Config_Model_Optimizer.md)
     * [Converting a Model to Intermediate Representation (IR)](prepare_model/convert_model/Converting_Model.md)
@@ -107,4 +105,4 @@ Model Optimizer produces an Intermediate Representation (IR) of the network, whi
 
 * [Known Issues](Known_Issues_Limitations.md)
 
-**Typical Next Step:** [Introduction to Intel® Deep Learning Deployment Toolkit](../IE_DG/Introduction.md)
+**Typical Next Step:** [Preparing and Optimizing your Trained Model with Model Optimizer](prepare_model/Prepare_Trained_Model.md)

docs/MO_DG/prepare_model/Model_Optimizer_FAQ.md

@@ -615,3 +615,16 @@ You need to specify values for each input of the model. For more information, re
 #### 102. What does the message "Operation _contrib_box_nms is not supported ..." mean? <a name="question-102"></a>
 
 It means that you trying to convert the topology which contains '_contrib_box_nms' operation which is not supported directly. However the sub-graph of operations including the '_contrib_box_nms' could be replaced with DetectionOutput layer if your topology is one of the gluoncv topologies. Specify '--enable_ssd_gluoncv' command line parameter for the Model Optimizer to enable this transformation.
+
+\htmlonly
+
+<script>
+  window.addEventListener('load', function(){
+    var questionID = getURLParameter('question'); /* this function is defined in openvino-layout.js */
+    if (questionID) {
+      window.location = window.location.pathname + '#' + encodeURI(questionID);
+    }
+  });
+</script>
+
+\endhtmlonly

docs/benchmarks/performance_benchmarks.md

@@ -129,7 +129,7 @@ Testing by Intel done on: see test date for each HW platform below.
 
 |                      | Intel® Core™ i5-8500               | Intel® Core™ i7-8700T               | Intel® Core™ i9-10920X               | 11th Gen Intel® Core™ i5-1145G7E  |
 | -------------------- | ---------------------------------- | ----------------------------------- |--------------------------------------|-----------------------------------|
-| Motherboard          | ASUS* PRIME Z370-A                 | GIGABYTE* Z370M DS3H-CF             | ASUS* PRIME X299-A II                | Intel Corporation /<br>TigerLake U DDR4 SODIMM RVP |
+| Motherboard          | ASUS* PRIME Z370-A                 | GIGABYTE* Z370M DS3H-CF             | ASUS* PRIME X299-A II                | Intel Corporation<br>internal/Reference Validation Platform |
 | CPU                  | Intel® Core™ i5-8500 CPU @ 3.00GHz | Intel® Core™ i7-8700T CPU @ 2.40GHz | Intel® Core™ i9-10920X CPU @ 3.50GHz | 11th Gen Intel® Core™ i5-1145G7E @ 2.60GHz   |
 | Hyper Threading      | OFF                                | ON                                  | ON                                   | ON                                           |
 | Turbo Setting        | ON                                 | ON                                  | ON                                   | ON                                           |

docs/benchmarks/performance_benchmarks_faq.md

@@ -51,19 +51,16 @@ We published a set of guidelines and recommendations to optimize your models ava
 #### 9. Why are INT8 optimized models used for benchmarking on CPUs with no VNNI support?
 The benefit of low-precision optimization using the OpenVINO™ toolkit model optimizer extends beyond processors supporting VNNI through Intel® DL Boost. The reduced bit width of INT8 compared to FP32 allows Intel® CPU to process the data faster and thus offers better throughput on any converted model agnostic of the intrinsically supported low-precision optimizations within Intel® hardware. Please refer to [INT8 vs. FP32 Comparison on Select Networks and Platforms](./performance_int8_vs_fp32.html) for comparison on boost factors for different network models and a selection of Intel® CPU architectures, including AVX-2 with Intel® Core™ i7-8700T, and AVX-512 (VNNI) with Intel® Xeon® 5218T and Intel® Xeon® 8270.
 
-#### 10. Previous releases included benchmarks on googlenet-v1. Why is there no longer benchmarks on this neural network model?
-We replaced googlenet-v1 to [resnet-18-pytorch](https://github.com/opencv/open_model_zoo/blob/master/models/public/resnet-18-pytorch/resnet-18-pytorch.md) due to changes in developer usage. The public model resnet-18 is used by many developers as an Image Classification model. This pre-optimized model was also trained on the ImageNet database, similar to googlenet-v1. Both googlenet-v1 and resnet-18 will remain part of the Open Model Zoo. Developers are encouraged to utilize resnet-18-pytorch for Image Classification use cases.
-
-#### 11. Previous releases included benchmarks on googlenet-v1-CF (Caffe). Why is there no longer benchmarks on this neural network model?
+#### 10. Previous releases included benchmarks on googlenet-v1-CF (Caffe). Why is there no longer benchmarks on this neural network model?
 We replaced googlenet-v1-CF to resnet-18-pytorch due to changes in developer usage. The public model resnet-18 is used by many developers as an Image Classification model. This pre-optimized model was also trained on the ImageNet database, similar to googlenet-v1-CF. Both googlenet-v1-CF and resnet-18 will remain part of the Open Model Zoo. Developers are encouraged to utilize resnet-18-pytorch for Image Classification use cases.
 
-#### 12. Why have resnet-50-CF, mobilenet-v1-1.0-224-CF, mobilenet-v2-CF and resnet-101-CF been removed?
+#### 11. Why have resnet-50-CF, mobilenet-v1-1.0-224-CF, mobilenet-v2-CF and resnet-101-CF been removed?
 The CAFFE version of resnet-50, mobilenet-v1-1.0-224 and mobilenet-v2 have been replaced with their TensorFlow and PyTorch counterparts. Resnet-50-CF is replaced by resnet-50-TF, mobilenet-v1-1.0-224-CF is replaced by mobilenet-v1-1.0-224-TF and mobilenet-v2-CF is replaced by mobilenetv2-PyTorch. Resnet-50-CF an resnet-101-CF are no longer maintained at their public source repos.
 
-#### 13. Where can I search for OpenVINO™ performance results based on HW-platforms?
+#### 12. Where can I search for OpenVINO™ performance results based on HW-platforms?
 The web site format has changed in order to support the more common search approach of looking for the performance of a given neural network model on different HW-platforms. As opposed to review a given HW-platform's performance on different neural network models.
 
-#### 14. How is Latency measured?
+#### 13. How is Latency measured?
 Latency is measured by running the OpenVINO™ inference engine in synchronous mode. In synchronous mode each frame or image is processed through the entire set of stages (pre-processing, inference, post-processing) before the next frame or image is processed. This KPI is relevant for applications where the inference on a single image is required, for example the analysis of an ultra sound image in a medical application or the analysis of a seismic image in the oil & gas industry. Other use cases include real-time or near real-time applications like an industrial robot's response to changes in its environment and obstacle avoidance for autonomous vehicles where a quick response to the result of the inference is required.
 
 \htmlonly

docs/benchmarks/performance_int8_vs_fp32.md

@@ -10,7 +10,7 @@ The table below illustrates the speed-up factor for the performance gain by swit
     <th>Intel® Xeon® <br>Gold <br>5218T</th>
     <th>Intel® Xeon® <br>Platinum <br>8270</th>
     <th>Intel® Core™ <br>i7-1065G7</th>
-    <th>Intel® Core™ <br>i7-1145G7E</th>
+    <th>Intel® Core™ <br>i5-1145G7E</th>
   </tr>
   <tr align="left">
     <th>OpenVINO <br>benchmark <br>model name</th>

docs/doxygen/doxy_md_filter.py

@@ -72,9 +72,9 @@ def process(docs_folder):
         md_folder = os.path.dirname(md_file)
         with open(md_file, 'r', encoding='utf-8') as f:
             content = f.read()
-        inline_links = set(re.findall(r'!?\[.*?\]\(([\w\/\-\.]+\.(md|png|jpg|gif))\)', content, flags=re.IGNORECASE))
+        inline_links = set(re.findall(r'!?\[.*?\]\(([\w\/\-\.]+\.(md|png|jpg|gif|svg))\)', content, flags=re.IGNORECASE))
         github_md_links = set(re.findall(r'(\[(.+?)\]\((https:[\w\.\/-]+?\.md)\))', content, flags=re.IGNORECASE))
-        reference_links = set(re.findall(r'\[.+\]\:\s*?([\w\/\-\.]+\.(md|png|jpg|gif))', content, flags=re.IGNORECASE))
+        reference_links = set(re.findall(r'\[.+\]\:\s*?([\w\/\-\.]+\.(md|png|jpg|gif|svg))', content, flags=re.IGNORECASE))
         content = replace_links(content, inline_links, md_folder, labels, docs_folder)
         content = replace_links(content, reference_links, md_folder, labels, docs_folder)
         content = process_github_md_links(content, github_md_links)

docs/doxygen/ie_c_api.xml

@@ -3,12 +3,10 @@
   <!-- Navigation index tabs for HTML output -->
   <navindex>
     <tab type="mainpage" title="OpenVINO Home" url="../index.html"/>
-    <tab type="user" title="GETTING STARTED" url="../index.html"/>
-    <tab type="user" title="HOW TOs" url="../openvino_docs_how_tos_how_to_links.html"/>
-    <tab type="user" title="GUIDES" url="../openvino_docs_IE_DG_Introduction.html"/>
-    <tab type="user" title="RESOURCES" url="../openvino_docs_resources_introduction.html"/>
-    <tab type="user" title="PERFORMANCE BENCHMARKS" url="../openvino_docs_performance_benchmarks.html"/>
-    <tab type="usergroup" title="API REFERENCES" url="../usergroup14.html">
+    <tab type="user" title="Get Started" url="../index.html"/>
+    <tab type="user" title="Documentation" url="../documentation.html"/>
+    <tab type="user" title="Examples" url="../examples.html"/>
+    <tab type="usergroup" title="API REFERENCES" url="../api_references.html">
       <!-- OpenVX -->
       <tab type="user" title="OpenVX Developer Guide" url="https://khronos.org/openvx"/>
       <!-- OpenCV -->
@@ -31,6 +29,10 @@
       <tab type="user" title="Inference Engine Python API Reference" url="../ie_python_api/annotated.html"/>
       <!-- DL Streamer -->
       <tab type="user" title="DL Streamer API Reference" url="https://openvinotoolkit.github.io/dlstreamer_gst/"/>
+      <!-- nGraph C++ API -->
+      <tab type="user" title="nGraph C++ API Reference" url="../ngraph_cpp_api/annotated.html"/>
+      <!-- nGraph Python API -->
+      <tab type="user" title="nGraph Python API Reference" url="../ngraph_python_api/files.html"/>
     </tab>
     <!-- Chinese docs -->
     <tab type="user" title="中文文件" url="https://docs.openvinotoolkit.org/cn/index.html"/>