Feature/omz link changes (#4911)

* Changed labels for demos and model downloader

* Changed links to models and tools

* Changed links to models and tools

* Changed links to demos

docs/HOWTO/Custom_Layers_Guide.md

@@ -368,7 +368,7 @@ python3 mri_reconstruction_demo.py \
 - [Model Optimizer Extensibility](../MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.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)
+- [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel)
 - For IoT Libraries and Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).
 
 ## Converting Models:

docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md

@@ -36,7 +36,7 @@ This Guide provides overview of the Inference Engine describing the typical work
 inference of a pre-trained and optimized deep learning model and a set of sample applications.
 
 > **NOTES:**
-> - Before you perform inference with the Inference Engine, your models should be converted to the Inference Engine format using the Model Optimizer or built directly in run-time using nGraph API. To learn about how to use Model Optimizer, refer to the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). To learn about the pre-trained and optimized models delivered with the OpenVINO™ toolkit, refer to [Pre-Trained Models](@ref omz_models_intel_index).
+> - Before you perform inference with the Inference Engine, your models should be converted to the Inference Engine format using the Model Optimizer or built directly in run-time using nGraph API. To learn about how to use Model Optimizer, refer to the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). To learn about the pre-trained and optimized models delivered with the OpenVINO™ toolkit, refer to [Pre-Trained Models](@ref omz_models_group_intel).
 > - [Intel® System Studio](https://software.intel.com/en-us/system-studio) is an all-in-one, cross-platform tool suite, purpose-built to simplify system bring-up and improve system and IoT device application performance on Intel® platforms. If you are using the Intel® Distribution of OpenVINO™ with Intel® System Studio, go to [Get Started with Intel® System Studio](https://software.intel.com/en-us/articles/get-started-with-openvino-and-intel-system-studio-2019).
 
 
@@ -81,7 +81,7 @@ inference of a pre-trained and optimized deep learning model and a set of sample
     * [GNA](supported_plugins/GNA.md)
     * [MULTI](supported_plugins/MULTI.md)
 
-* [Pre-Trained Models](@ref omz_models_intel_index)
+* [Pre-Trained Models](@ref omz_models_group_intel)
 
 * [Known Issues](Known_Issues_Limitations.md)
 

docs/IE_DG/Int8Inference.md

@@ -23,11 +23,11 @@ A lot of investigation was made in the field of deep learning with the idea of u
 8-bit computations (referred to as `int8`) offer better performance compared to the results of inference in higher precision (for example, `fp32`), because they allow loading more data into a single processor instruction. Usually the cost for significant boost is a reduced accuracy. However, it is proved that an accuracy drop can be negligible and depends on task requirements, so that the application engineer can set up the maximum accuracy drop that is acceptable.
 
 
-Let's explore quantized [TensorFlow* implementation of ResNet-50](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-50-tf) model. Use [Model Downloader](@ref omz_tools_downloader_README) tool to download the `fp16` model from [OpenVINO™ Toolkit - Open Model Zoo repository](https://github.com/openvinotoolkit/open_model_zoo):
+Let's explore quantized [TensorFlow* implementation of ResNet-50](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/resnet-50-tf) model. Use [Model Downloader](@ref omz_tools_downloader) tool to download the `fp16` model from [OpenVINO™ Toolkit - Open Model Zoo repository](https://github.com/openvinotoolkit/open_model_zoo):
 ```sh
 ./downloader.py --name resnet-50-tf --precisions FP16-INT8
 ```
-After that you should quantize model by [Model Quantizer](@ref omz_tools_downloader_README) tool.
+After that you should quantize model by [Model Quantizer](@ref omz_tools_downloader) tool.
 ```sh
 ./quantizer.py --model_dir public/resnet-50-tf --dataset_dir <DATASET_DIR> --precisions=FP16-INT8
 ```

docs/IE_DG/Intro_to_Performance.md

@@ -29,7 +29,7 @@ Refer to the [Benchmark App](../../inference-engine/samples/benchmark_app/README
 ## Using Async API
 To gain better performance on accelerators, such as VPU, the Inference Engine uses the asynchronous approach (see
 [Integrating Inference Engine in Your Application (current API)](Integrate_with_customer_application_new_API.md)).
-The point is amortizing the costs of data transfers, by pipe-lining, see [Async API explained](@ref omz_demos_object_detection_demo_ssd_async_README).
+The point is amortizing the costs of data transfers, by pipe-lining, see [Async API explained](@ref omz_demos_object_detection_demo_cpp).
 Since the pipe-lining relies on the availability of the parallel slack, running multiple inference requests in parallel is essential.
 Refer to the [Benchmark App](../../inference-engine/samples/benchmark_app/README.md) sample, which enables running a number of inference requests in parallel. Specifying different number of request produces different throughput measurements.
 

docs/IE_DG/Samples_Overview.md

@@ -21,7 +21,7 @@ Inference Engine sample applications include the following:
 - **Hello Query Device Sample** – Query of available Inference Engine devices and their metrics, configuration values.
    - [Hello Query Device C++ Sample](../../inference-engine/samples/hello_query_device/README.md)
    - [Hello Query Device Python* Sample](../../inference-engine/ie_bridges/python/sample/hello_query_device/README.md)
-- **[Hello Reshape SSD C++ Sample**](../../inference-engine/samples/hello_reshape_ssd/README.md)** – Inference of SSD networks resized by ShapeInfer API according to an input size.
+- **[Hello Reshape SSD C++ Sample](../../inference-engine/samples/hello_reshape_ssd/README.md)** – Inference of SSD networks resized by ShapeInfer API according to an input size.
 - **Image Classification Sample Async** – Inference of image classification networks like AlexNet and GoogLeNet using Asynchronous Inference Request API (the sample supports only images as inputs). 
    - [Image Classification C++ Sample Async](../../inference-engine/samples/classification_sample_async/README.md)
    - [Image Classification Python* Sample Async](../../inference-engine/ie_bridges/python/sample/classification_sample_async/README.md)
@@ -43,7 +43,7 @@ To run the sample applications, you can use images and videos from the media fil
 
 ## Samples that Support Pre-Trained Models
 
-To run the sample, you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
+To run the sample, you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
 
 ## Build the Sample Applications
 

docs/IE_DG/ShapeInference.md

@@ -100,7 +100,7 @@ Here is a code example:
 
 @snippet snippets/ShapeInference.cpp part0
 
-Shape Inference feature is used in [Smart classroom sample](@ref omz_demos_smart_classroom_demo_README).
+Shape Inference feature is used in [Smart Classroom Demo](@ref omz_demos_smart_classroom_demo_cpp).
 
 ## Extensibility
 

docs/IE_DG/Tools_Overview.md

@@ -6,9 +6,9 @@ The OpenVINO™ toolkit installation includes the following tools:
 
 |Tool                                                                         | Location in the Installation Directory|
 |-----------------------------------------------------------------------------|---------------------------------------|
-|[Accuracy Checker Tool](@ref omz_tools_accuracy_checker_README)              | `<INSTALL_DIR>/deployment_tools/tools/open_model_zoo/tools/accuracy_checker`|
+|[Accuracy Checker Tool](@ref omz_tools_accuracy_checker)              | `<INSTALL_DIR>/deployment_tools/tools/open_model_zoo/tools/accuracy_checker`|
 |[Post-Training Optimization Tool](@ref pot_README)                           | `<INSTALL_DIR>/deployment_tools/tools/post_training_optimization_toolkit`|
-|[Model Downloader](@ref omz_tools_downloader_README)                         | `<INSTALL_DIR>/deployment_tools/tools/model_downloader`| 
+|[Model Downloader](@ref omz_tools_downloader)                         | `<INSTALL_DIR>/deployment_tools/tools/model_downloader`| 
 |[Cross Check Tool](../../inference-engine/tools/cross_check_tool/README.md)  | `<INSTALL_DIR>/deployment_tools/tools/cross_check_tool`|
 |[Compile Tool](../../inference-engine/tools/compile_tool/README.md)          | `<INSTALL_DIR>/deployment_tools/inference_engine/lib/intel64/`|
 

docs/IE_DG/protecting_model_guide.md

@@ -58,5 +58,5 @@ should be called with `weights` passed as an empty `Blob`.
 - Model Optimizer Developer Guide: [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
 - Inference Engine Developer Guide: [Inference Engine Developer Guide](Deep_Learning_Inference_Engine_DevGuide.md)
 - For more information on Sample Applications, see the [Inference Engine Samples Overview](Samples_Overview.md)
-- For information on a set of pre-trained models, see the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
+- For information on a set of pre-trained models, see the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel)
 - For IoT Libraries and Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).

docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_Object_Detection_API_Models.md

@@ -103,7 +103,7 @@ Models with `keep_aspect_ratio_resizer` were trained to recognize object in real
 Inference Engine comes with a number of samples that use Object Detection API models including:
 
 * [Object Detection for SSD Sample](../../../../../inference-engine/samples/object_detection_sample_ssd/README.md) --- for RFCN, SSD and Faster R-CNNs
-* [Mask R-CNN Sample for TensorFlow* Object Detection API Models](@ref omz_demos_mask_rcnn_demo_README) --- for Mask R-CNNs
+* [Mask R-CNN Sample for TensorFlow* Object Detection API Models](@ref omz_demos_mask_rcnn_demo_cpp) --- for Mask R-CNNs
 
 There are a number of important notes about feeding input images to the samples:
 
@@ -1044,4 +1044,4 @@ The Mask R-CNN models are cut at the end with the sub-graph replacer `ObjectDete
 
 ```SecondStageBoxPredictor_1/Conv_3/BiasAdd|SecondStageBoxPredictor_1/Conv_1/BiasAdd```
 
-One of these two nodes produces output mask tensors. The child nodes of these nodes are related to post-processing which is implemented in the [Mask R-CNN demo](@ref omz_demos_mask_rcnn_demo_README) and should be cut off.
+One of these two nodes produces output mask tensors. The child nodes of these nodes are related to post-processing which is implemented in the [Mask R-CNN demo](@ref omz_demos_mask_rcnn_demo_cpp) and should be cut off.

docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_YOLO_From_Tensorflow.md

@@ -110,7 +110,7 @@ where:
 
 > **NOTE:** The color channel order (RGB or BGR) of an input data should match the channel order of the model training dataset. If they are different, perform the `RGB<->BGR` conversion specifying the command-line parameter: `--reverse_input_channels`. Otherwise, inference results may be incorrect. For more information about the parameter, refer to **When to Reverse Input Channels** section of [Converting a Model Using General Conversion Parameters](../Converting_Model_General.md).
 
-OpenVINO&trade; toolkit provides a demo that uses YOLOv3 model. For more information, refer to [Object Detection C++ Demo](@ref omz_demos_object_detection_demo_ssd_async_README).
+OpenVINO&trade; toolkit provides a demo that uses YOLOv3 model. For more information, refer to [Object Detection C++ Demo](@ref omz_demos_object_detection_demo_cpp).
 
 ## Convert YOLOv1 and YOLOv2 Models to the IR
 

docs/gapi/face_beautification.md

@@ -12,11 +12,11 @@ This sample requires:
 
 * PC with GNU/Linux* or Microsoft Windows* (Apple macOS* is supported but was not tested)
 * OpenCV 4.2 or higher built with [Intel® Distribution of OpenVINO™ Toolkit](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html) (building with [Intel® TBB](https://www.threadingbuildingblocks.org/intel-tbb-tutorial) is a plus)
-* The following pre-trained models from the [Open Model Zoo](@ref omz_models_intel_index)
-      * [face-detection-adas-0001](@ref omz_models_intel_face_detection_adas_0001_description_face_detection_adas_0001)
-      * [facial-landmarks-35-adas-0002](@ref omz_models_intel_facial_landmarks_35_adas_0002_description_facial_landmarks_35_adas_0002)
+* The following pre-trained models from the [Open Model Zoo](@ref omz_models_group_intel)
+      * [face-detection-adas-0001](@ref omz_models_model_face_detection_adas_0001)
+      * [facial-landmarks-35-adas-0002](@ref omz_models_model_facial_landmarks_35_adas_0002)
 
-To download the models from the Open Model Zoo, use the [Model Downloader](@ref omz_tools_downloader_README) tool.
+To download the models from the Open Model Zoo, use the [Model Downloader](@ref omz_tools_downloader) tool.
 
 ## Face Beautification Algorithm
 We will implement a simple face beautification algorithm using a combination of modern Deep Learning techniques and traditional Computer Vision. The general idea behind the algorithm is to make face skin smoother while preserving face features like eyes or a mouth contrast. The algorithm identifies parts of the face using a DNN inference, applies different filters to the parts found, and then combines it into the final result using basic image arithmetics:

docs/gapi/gapi_face_analytics_pipeline.md

@@ -11,12 +11,12 @@ This sample requires:
 
 * PC with GNU/Linux* or Microsoft Windows* (Apple macOS* is supported but was not tested)
 * OpenCV 4.2 or higher built with [Intel® Distribution of OpenVINO™ Toolkit](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html) (building with [Intel® TBB](https://www.threadingbuildingblocks.org/intel-tbb-tutorial)
-* The following pre-trained models from the [Open Model Zoo](@ref omz_models_intel_index):
-    * [face-detection-adas-0001](@ref omz_models_intel_face_detection_adas_0001_description_face_detection_adas_0001)
-    * [age-gender-recognition-retail-0013](@ref omz_models_intel_age_gender_recognition_retail_0013_description_age_gender_recognition_retail_0013)
-    * [emotions-recognition-retail-0003](@ref omz_models_intel_emotions_recognition_retail_0003_description_emotions_recognition_retail_0003)
+* The following pre-trained models from the [Open Model Zoo](@ref omz_models_group_intel):
+    * [face-detection-adas-0001](@ref omz_models_model_face_detection_adas_0001)
+    * [age-gender-recognition-retail-0013](@ref omz_models_model_age_gender_recognition_retail_0013)
+    * [emotions-recognition-retail-0003](@ref omz_models_model_emotions_recognition_retail_0003)
 
-To download the models from the Open Model Zoo, use the [Model Downloader](@ref omz_tools_downloader_README) tool.
+To download the models from the Open Model Zoo, use the [Model Downloader](@ref omz_tools_downloader) tool.
 
 ## Introduction: Why G-API
 Many computer vision algorithms run on a video stream rather than on individual images. Stream processing usually consists of multiple steps – like decode, preprocessing, detection, tracking, classification (on detected objects), and visualization – forming a *video processing pipeline*. Moreover, many these steps of such pipeline can run in parallel – modern platforms have different hardware blocks on the same chip like decoders and GPUs, and extra accelerators can be plugged in as extensions, like Intel® Movidius™ Neural Compute Stick for deep learning offload.
@@ -26,7 +26,7 @@ Given all this manifold of options and a variety in video analytics algorithms,
 Starting with version 4.2, OpenCV offers a solution to this problem. OpenCV G-API now can manage Deep Learning inference (a cornerstone of any modern analytics pipeline) with a traditional Computer Vision as well as video capturing/decoding, all in a single pipeline. G-API takes care of pipelining itself – so if the algorithm or platform changes, the execution model adapts to it automatically.
 
 ## Pipeline Overview
-Our sample application is based on [Interactive Face Detection](omz_demos_interactive_face_detection_demo_README) demo from Open Model Zoo. A simplified pipeline consists of the following steps:
+Our sample application is based on [Interactive Face Detection](@ref omz_demos_interactive_face_detection_demo_cpp) demo from Open Model Zoo. A simplified pipeline consists of the following steps:
 
 1. Image acquisition and decode
 2. Detection with preprocessing

docs/get_started/get_started_dl_workbench.md

@@ -9,13 +9,13 @@ In this guide, you will:
 
 [DL Workbench](@ref workbench_docs_Workbench_DG_Introduction) is a web-based graphical environment that enables you to easily use various sophisticated
 OpenVINO™ toolkit components:
-* [Model Downloader](@ref omz_tools_downloader_README) to download models from the [Intel® Open Model Zoo](@ref omz_models_intel_index) 
+* [Model Downloader](@ref omz_tools_downloader) to download models from the [Intel® Open Model Zoo](@ref omz_models_group_intel) 
 with pretrained models for a range of different tasks
 * [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) to transform models into
 the Intermediate Representation (IR) format
 * [Post-Training Optimization toolkit](@ref pot_README) to calibrate a model and then execute it in the
  INT8 precision
-* [Accuracy Checker](@ref omz_tools_accuracy_checker_README) to determine the accuracy of a model
+* [Accuracy Checker](@ref omz_tools_accuracy_checker) to determine the accuracy of a model
 * [Benchmark Tool](@ref openvino_inference_engine_samples_benchmark_app_README) to estimate inference performance on supported devices
 
 ![](./dl_workbench_img/DL_Workbench.jpg)
@@ -70,7 +70,7 @@ The simplified OpenVINO™ DL Workbench workflow is:
 
 ## Run Baseline Inference
 
-This section illustrates a sample use case of how to infer a pretrained model from the [Intel® Open Model Zoo](@ref omz_models_intel_index) with an autogenerated noise dataset on a CPU device.
+This section illustrates a sample use case of how to infer a pretrained model from the [Intel® Open Model Zoo](@ref omz_models_group_intel) with an autogenerated noise dataset on a CPU device.
 
 <iframe width="560" height="315" src="https://www.youtube.com/embed/9TRJwEmY0K4" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
 

docs/get_started/get_started_linux.md

@@ -18,7 +18,7 @@ In addition, demo scripts, code samples and demo applications are provided to he
 * **[Code Samples](../IE_DG/Samples_Overview.md)** - Small console applications that show you how to: 
     * Utilize specific OpenVINO capabilities in an application
     * Perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more.
-* **[Demo Applications](@ref omz_demos_README)** - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
+* **[Demo Applications](@ref omz_demos)** - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
 
 ## <a name="openvino-installation"></a>Intel® Distribution of OpenVINO™ toolkit Installation and Deployment Tools Directory Structure
 This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit installation and configuration steps. If you have not yet installed and configured the toolkit, see [Install Intel® Distribution of OpenVINO™ toolkit for Linux*](../install_guides/installing-openvino-linux.md).
@@ -46,9 +46,9 @@ The primary tools for deploying your models and applications are installed to th
 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`samples/`      | Inference Engine samples. Contains source code for C++ and Python* samples and build scripts. See the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md). |
 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`src/`          | Source files for CPU extensions.|
 | `model_optimizer/`                      | Model Optimizer directory. Contains configuration scripts, scripts to run the Model Optimizer and other files. See the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
-| `open_model_zoo/`                       | Open Model Zoo directory. Includes the Model Downloader tool to download [pre-trained OpenVINO](@ref omz_models_intel_index) and public models, OpenVINO models documentation, demo applications and the Accuracy Checker tool to evaluate model accuracy.|
+| `open_model_zoo/`                       | Open Model Zoo directory. Includes the Model Downloader tool to download [pre-trained OpenVINO](@ref omz_models_group_intel) and public models, OpenVINO models documentation, demo applications and the Accuracy Checker tool to evaluate model accuracy.|
 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`demos/`        | Demo applications for inference scenarios. Also includes documentation and build scripts.| 
-| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`intel_models/` | Pre-trained OpenVINO models and associated documentation. See the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index).|
+| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`intel_models/` | Pre-trained OpenVINO models and associated documentation. See the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel).|
 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`tools/`        | Model Downloader and Accuracy Checker tools. |
 | `tools/`                                | Contains a symbolic link to the Model Downloader folder and auxiliary tools to work with your models: Calibration tool, Benchmark and Collect Statistics tools.|
 
@@ -197,7 +197,7 @@ Each demo and code sample is a separate application, but they use the same behav
 
 * [Code Samples](../IE_DG/Samples_Overview.md) - Small console applications that show how to utilize specific OpenVINO capabilities within an application and execute specific tasks such as loading a model, running inference, querying specific device capabilities, and more.
 
-* [Demo Applications](@ref omz_demos_README) - Console applications that provide robust application templates to support developers in implementing specific deep learning scenarios. They may also involve more complex processing pipelines that gather analysis from several models that run inference simultaneously. For example concurrently detecting a person in a video stream and detecting attributes such as age, gender and/or emotions.
+* [Demo Applications](@ref omz_demos) - Console applications that provide robust application templates to support developers in implementing specific deep learning scenarios. They may also involve more complex processing pipelines that gather analysis from several models that run inference simultaneously. For example concurrently detecting a person in a video stream and detecting attributes such as age, gender and/or emotions.
  
 Inputs you'll need to specify:
 - **A compiled OpenVINO™ code sample or demo application** that runs inferencing against a model that has been run through the Model Optimizer, resulting in an IR, using the other inputs you provide.
@@ -209,7 +209,7 @@ Inputs you'll need to specify:
 
 To perform sample inference, run the Image Classification code sample and Security Barrier Camera demo application that were automatically compiled when you ran the Image Classification and Inference Pipeline demo scripts. The binary files are in the `~/inference_engine_cpp_samples_build/intel64/Release` and `~/inference_engine_demos_build/intel64/Release` directories, respectively.
 
-To run other sample code or demo applications, build them from the source files delivered as part of the OpenVINO toolkit. To learn how to build these, see the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and [Demo Applications Overview](@ref omz_demos_README) sections.
+To run other sample code or demo applications, build them from the source files delivered as part of the OpenVINO toolkit. To learn how to build these, see the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and [Demo Applications Overview](@ref omz_demos) sections.
 
 ### <a name="download-models"></a> Step 1: Download the Models
 
@@ -219,7 +219,7 @@ You must have a model that is specific for you inference task. Example model typ
 - Custom (Often based on SSD)
 
 Options to find a model suitable for the OpenVINO™ toolkit are:
-- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader_README).
+- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader).
 - Download from GitHub*, Caffe* Zoo, TensorFlow* Zoo, etc.
 - Train your own model.
         
@@ -449,7 +449,7 @@ Throughput: 375.3339402 FPS
 
 ### <a name="run-security-barrier"></a>Step 5: Run the Security Barrier Camera Demo Application
 
-> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you ran the Inference Pipeline demo scripts. If you want to build it manually, see the [Demo Applications Overview](@ref omz_demos_README) section.
+> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you ran the Inference Pipeline demo scripts. If you want to build it manually, see the [Demo Applications Overview](@ref omz_demos) section.
 
 To run the **Security Barrier Camera Demo Application** using an input image on the prepared IRs:
 

docs/get_started/get_started_macos.md

@@ -18,7 +18,7 @@ In addition, demo scripts, code samples and demo applications are provided to he
 * **[Code Samples](../IE_DG/Samples_Overview.md)** - Small console applications that show you how to:
     * Utilize specific OpenVINO capabilities in an application.
     * Perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more.
-* **[Demo Applications](@ref omz_demos_README)** - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
+* **[Demo Applications](@ref omz_demos)** - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
 
 ## <a name="openvino-installation"></a>Intel® Distribution of OpenVINO™ toolkit Installation and Deployment Tools Directory Structure
 This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit installation and configuration steps. If you have not yet installed and configured the toolkit, see [Install Intel® Distribution of OpenVINO™ toolkit for macOS*](../install_guides/installing-openvino-macos.md).
@@ -48,9 +48,9 @@ The primary tools for deploying your models and applications are installed to th
 | `~intel_models/` | Symbolic link to the `intel_models` subfolder of the `open_model_zoo` folder.|
 | `model_optimizer/`                      | Model Optimizer directory. Contains configuration scripts, scripts to run the Model Optimizer and other files. See the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).|
 | `ngraph/`                               | nGraph directory. Includes the nGraph header and library files. |
-| `open_model_zoo/`                       | Open Model Zoo directory. Includes the Model Downloader tool to download [pre-trained OpenVINO](@ref omz_models_intel_index) and public models, OpenVINO models documentation, demo applications and the Accuracy Checker tool to evaluate model accuracy.|
+| `open_model_zoo/`                       | Open Model Zoo directory. Includes the Model Downloader tool to download [pre-trained OpenVINO](@ref omz_models_group_intel) and public models, OpenVINO models documentation, demo applications and the Accuracy Checker tool to evaluate model accuracy.|
 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`demos/`        | Demo applications for inference scenarios. Also includes documentation and build scripts.| 
-| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`intel_models/` | Pre-trained OpenVINO models and associated documentation. See the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index).|
+| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`intel_models/` | Pre-trained OpenVINO models and associated documentation. See the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel).|
 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`models`        | Intel's trained and public models that can be obtained with Model Downloader.|
 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`tools/`        | Model Downloader and Accuracy Checker tools. |
 | `tools/`                                | Contains a symbolic link to the Model Downloader folder and auxiliary tools to work with your models: Calibration tool, Benchmark and Collect Statistics tools.|
@@ -200,7 +200,7 @@ Inputs you need to specify when using a code sample or demo application:
 
 To perform sample inference, run the Image Classification code sample and Security Barrier Camera demo application that are automatically compiled when you run the Image Classification and Inference Pipeline demo scripts. The binary files are in the `~/inference_engine_samples_build/intel64/Release` and `~/inference_engine_demos_build/intel64/Release` directories, respectively.
 
-You can also build all available sample code and demo applications from the source files delivered with the OpenVINO toolkit. To learn how to do this, see the instructions in the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and [Demo Applications Overview](@ref omz_demos_README) sections.
+You can also build all available sample code and demo applications from the source files delivered with the OpenVINO toolkit. To learn how to do this, see the instructions in the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and [Demo Applications Overview](@ref omz_demos) sections.
 
 ### <a name="download-models"></a> Step 1: Download the Models
 
@@ -210,7 +210,7 @@ You must have a model that is specific for you inference task. Example model typ
 - Custom (Often based on SSD)
 
 Options to find a model suitable for the OpenVINO™ toolkit are:
-- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader_README). 
+- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader). 
 - Download from GitHub*, Caffe* Zoo, TensorFlow* Zoo, and other resources.
 - Train your own model.
         
@@ -422,7 +422,7 @@ classid probability label
 
 ### <a name="run-security-barrier"></a>Step 5: Run the Security Barrier Camera Demo Application
 
-> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you run the Inference Pipeline demo scripts. If you want to build it manually, see the instructions in the [Demo Applications Overview](@ref omz_demos_README) section.
+> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you run the Inference Pipeline demo scripts. If you want to build it manually, see the instructions in the [Demo Applications Overview](@ref omz_demos) section.
 
 To run the **Security Barrier Camera Demo Application** using an input image on the prepared IRs:
 

docs/get_started/get_started_raspbian.md

@@ -43,8 +43,8 @@ The primary tools for deploying your models and applications are installed to th
 The OpenVINO™ workflow on Raspbian* OS is as follows:
 1. **Get a pre-trained model** for your inference task. If you want to use your model for inference, the model must be converted to the `.bin` and `.xml` Intermediate Representation (IR) files, which are used as input by Inference Engine. On Raspberry PI, OpenVINO™ toolkit includes only the Inference Engine module. The Model Optimizer is not supported on this platform. To get the optimized models you can use one of the following options:
    
-   * Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader_README).
-    <br> For more information on pre-trained models, see [Pre-Trained Models Documentation](@ref omz_models_intel_index)
+   * Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader).
+    <br> For more information on pre-trained models, see [Pre-Trained Models Documentation](@ref omz_models_group_intel)
    
    * Convert a model using the Model Optimizer from a full installation of Intel® Distribution of OpenVINO™ toolkit on one of the supported platforms. Installation instructions are available:
      * [Installation Guide for macOS*](../install_guides/installing-openvino-macos.md)
@@ -65,7 +65,7 @@ Follow the steps below to run pre-trained Face Detection network using Inference
    cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="-march=armv7-a" /opt/intel/openvino/deployment_tools/inference_engine/samples/cpp
    make -j2 object_detection_sample_ssd
    ```
-3. Download the pre-trained Face Detection model with the [Model Downloader tool](@ref omz_tools_downloader_README):
+3. Download the pre-trained Face Detection model with the [Model Downloader tool](@ref omz_tools_downloader):
 ```sh
 git clone --depth 1 https://github.com/openvinotoolkit/open_model_zoo
 cd open_model_zoo/tools/downloader

docs/get_started/get_started_windows.md

@@ -19,7 +19,7 @@ In addition, demo scripts, code samples and demo applications are provided to he
 * **[Code Samples](../IE_DG/Samples_Overview.md)** - Small console applications that show you how to:
     * Utilize specific OpenVINO capabilities in an application.
     * Perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more.
-* **[Demo Applications](@ref omz_demos_README)** - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
+* **[Demo Applications](@ref omz_demos)** - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
 
 ## <a name="openvino-installation"></a>Intel® Distribution of OpenVINO™ toolkit Installation and Deployment Tools Directory Structure
 This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit installation and configuration steps. If you have not yet installed and configured the toolkit, see [Install Intel® Distribution of OpenVINO™ toolkit for Windows*](../install_guides/installing-openvino-windows.md).
@@ -45,9 +45,9 @@ The primary tools for deploying your models and applications are installed to th
 | `~intel_models\` | Symbolic link to the `intel_models` subfolder of the `open_model_zoo` folder. |
 | `model_optimizer\`                      | Model Optimizer directory. Contains configuration scripts, scripts to run the Model Optimizer and other files. See the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). |
 | `ngraph\`                               | nGraph directory. Includes the nGraph header and library files. |   
-| `open_model_zoo\`                       | Open Model Zoo directory. Includes the Model Downloader tool to download [pre-trained OpenVINO](@ref omz_models_intel_index) and public models, OpenVINO models documentation, demo applications and the Accuracy Checker tool to evaluate model accuracy.|
+| `open_model_zoo\`                       | Open Model Zoo directory. Includes the Model Downloader tool to download [pre-trained OpenVINO](@ref omz_models_group_intel) and public models, OpenVINO models documentation, demo applications and the Accuracy Checker tool to evaluate model accuracy.|
 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`demos\`        | Demo applications for inference scenarios. Also includes documentation and build scripts.| 
-| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`intel_models\` | Pre-trained OpenVINO models and associated documentation. See the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index).|
+| &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`intel_models\` | Pre-trained OpenVINO models and associated documentation. See the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel).|
 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`models`        | Intel's trained and public models that can be obtained with Model Downloader.|
 | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`tools\`        | Model Downloader and Accuracy Checker tools. |
 | `tools\`                                | Contains a symbolic link to the Model Downloader folder and auxiliary tools to work with your models: Calibration tool, Benchmark and Collect Statistics tools.|
@@ -199,7 +199,7 @@ Inputs you need to specify when using a code sample or demo application:
 
 To perform sample inference, run the Image Classification code sample and Security Barrier Camera demo application that are automatically compiled when you run the Image Classification and Inference Pipeline demo scripts. The binary files are in the `C:\Users\<USER_ID>\Intel\OpenVINO\inference_engine_cpp_samples_build\intel64\Release` and `C:\Users\<USER_ID>\Intel\OpenVINO\inference_engine_demos_build\intel64\Release` directories, respectively.
 
-You can also build all available sample code and demo applications from the source files delivered with the OpenVINO™ toolkit. To learn how to do this, see the instruction in the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and [Demo Applications Overview](@ref omz_demos_README) sections.
+You can also build all available sample code and demo applications from the source files delivered with the OpenVINO™ toolkit. To learn how to do this, see the instruction in the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and [Demo Applications Overview](@ref omz_demos) sections.
 
 ### <a name="download-models"></a> Step 1: Download the Models
 
@@ -209,7 +209,7 @@ You must have a model that is specific for you inference task. Example model typ
 - Custom (Often based on SSD)
 
 Options to find a model suitable for the OpenVINO™ toolkit are:
-- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using the [Model Downloader tool](@ref omz_tools_downloader_README).
+- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using the [Model Downloader tool](@ref omz_tools_downloader).
 - Download from GitHub*, Caffe* Zoo, TensorFlow* Zoo, and other resources.
 - Train your own model.
         
@@ -425,7 +425,7 @@ classid probability label
 
 ### <a name="run-security-barrier"></a>Step 5: Run the Security Barrier Camera Demo Application
 
-> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you run the Inference Pipeline demo scripts. If you want to build it manually, see the instructions in the [Demo Applications Overview](@ref omz_demos_README) section.
+> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you run the Inference Pipeline demo scripts. If you want to build it manually, see the instructions in the [Demo Applications Overview](@ref omz_demos) section.
 
 To run the **Security Barrier Camera Demo Application** using an input image on the prepared IRs:
 

docs/index.md

@@ -19,7 +19,7 @@ The following diagram illustrates the typical OpenVINO™ workflow (click to see
 ### Model Preparation, Conversion and Optimization
 
 You can use your framework of choice to prepare and train a Deep Learning model or just download a pretrained model from the Open Model Zoo. The Open Model Zoo includes Deep Learning solutions to a variety of vision problems, including object recognition, face recognition, pose estimation, text detection, and action recognition, at a range of measured complexities.
-Several of these pretrained models are used also in the [code samples](IE_DG/Samples_Overview.md) and [application demos](@ref omz_demos_README). To download models from the Open Model Zoo, the [Model Downloader](@ref omz_tools_downloader_README) tool is used.
+Several of these pretrained models are used also in the [code samples](IE_DG/Samples_Overview.md) and [application demos](@ref omz_demos). To download models from the Open Model Zoo, the [Model Downloader](@ref omz_tools_downloader) tool is used.
 
 One of the core component of the OpenVINO™ toolkit is the [Model Optimizer](MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) a cross-platform command-line
 tool that converts a trained neural network from its source framework to an open-source, nGraph-compatible [Intermediate Representation (IR)](MO_DG/IR_and_opsets.md) for use in inference operations. The Model Optimizer imports models trained in popular frameworks such as Caffe*, TensorFlow*, MXNet*, Kaldi*, and ONNX* and performs a few optimizations to remove excess layers and group operations when possible into simpler, faster graphs.
@@ -27,16 +27,17 @@ tool that converts a trained neural network from its source framework to an open
 
 If your neural network model contains layers that are not in the list of known layers for supported frameworks, you can adjust the conversion and optimization process through use of  [Custom Layers](HOWTO/Custom_Layers_Guide.md).
 
-Run the [Accuracy Checker utility](@ref omz_tools_accuracy_checker_README) either against source topologies or against the output representation to evaluate the accuracy of inference. The Accuracy Checker is also part of the [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction), an integrated web-based performance analysis studio.
+Run the [Accuracy Checker utility](@ref omz_tools_accuracy_checker) either against source topologies or against the output representation to evaluate the accuracy of inference. The Accuracy Checker is also part of the [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction), an integrated web-based performance analysis studio.
 
 Useful documents for model optimization:
 * [Model Optimizer Developer Guide](MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
 * [Intermediate Representation and Opsets](MO_DG/IR_and_opsets.md)
 * [Custom Layers Guide](HOWTO/Custom_Layers_Guide.md)
-* [Accuracy Checker utility](@ref omz_tools_accuracy_checker_README)
+* [Accuracy Checker utility](@ref omz_tools_accuracy_checker)
 * [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction)
-* [Model Downloader](@ref omz_tools_downloader_README) utility
-* [Pretrained Models (Open Model Zoo)](@ref omz_models_public_index)
+* [Model Downloader](@ref omz_tools_downloader) utility
+* [Intel's Pretrained Models (Open Model Zoo)](@ref omz_models_group_intel)
+* [Public Pretrained Models (Open Model Zoo)](@ref omz_models_group_public)
 
 ### Running and Tuning Inference
 The other core component of OpenVINO™ is the [Inference Engine](IE_DG/Deep_Learning_Inference_Engine_DevGuide.md), which manages the loading and compiling of the optimized neural network model, runs inference operations on input data, and outputs the results. Inference Engine can execute synchronously or asynchronously, and its plugin architecture manages the appropriate compilations for execution on multiple Intel® devices, including both workhorse CPUs and specialized graphics and video processing platforms (see below, Packaging and Deployment).
@@ -46,7 +47,7 @@ You can use OpenVINO™ Tuning Utilities with the Inference Engine to trial and
 For a full browser-based studio integrating these other key tuning utilities, try the [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction).
 ![](img/OV-diagram-step3.png)
 
-OpenVINO™ toolkit includes a set of [inference code samples](IE_DG/Samples_Overview.md) and [application demos](@ref omz_demos_README) showing how inference is run and output processed for use in retail environments, classrooms, smart camera applications, and other solutions.
+OpenVINO™ toolkit includes a set of [inference code samples](IE_DG/Samples_Overview.md) and [application demos](@ref omz_demos) showing how inference is run and output processed for use in retail environments, classrooms, smart camera applications, and other solutions.
 
 OpenVINO also makes use of open-Source and Intel™ tools for traditional graphics processing and performance management. Intel® Media SDK supports accelerated rich-media processing, including transcoding. OpenVINO™ optimizes calls to the rich OpenCV and OpenVX libraries for processing computer vision workloads. And the new DL Streamer integration further accelerates video pipelining and performance.
 
@@ -54,7 +55,7 @@ Useful documents for inference tuning:
 * [Inference Engine Developer Guide](IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
 * [Inference Engine API References](./api_references.html)
 * [Inference Code Samples](IE_DG/Samples_Overview.md)
-* [Application Demos](@ref omz_demos_README)
+* [Application Demos](@ref omz_demos)
 * [Post-Training Optimization Tool Guide](@ref pot_README)
 * [Deep Learning Workbench Guide](@ref workbench_docs_Workbench_DG_Introduction)
 * [Intel Media SDK](https://github.com/Intel-Media-SDK/MediaSDK)
@@ -87,10 +88,10 @@ Intel® Distribution of OpenVINO™ toolkit includes the following components:
 - [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) -  A web-based graphical environment that allows you to easily use various sophisticated OpenVINO™ toolkit components.
 - [Post-Training Optimization tool](@ref pot_README) - A tool to calibrate a model and then execute it in the INT8 precision.
 - Additional Tools - A set of tools to work with your models including [Benchmark App](../inference-engine/tools/benchmark_tool/README.md), [Cross Check Tool](../inference-engine/tools/cross_check_tool/README.md), [Compile tool](../inference-engine/tools/compile_tool/README.md).
-- [Open Model Zoo](@ref omz_models_intel_index)     
-    - [Demos](@ref omz_demos_README) - Console applications that provide robust application templates to help you implement specific deep learning scenarios.
-    - Additional Tools - A set of tools to work with your models including [Accuracy Checker Utility](@ref omz_tools_accuracy_checker_README) and [Model Downloader](@ref omz_tools_downloader_README).
-    - [Documentation for Pretrained Models](@ref omz_models_intel_index) - Documentation for pretrained models that are available in the [Open Model Zoo repository](https://github.com/opencv/open_model_zoo).
+- [Open Model Zoo](@ref omz_models_group_intel)     
+    - [Demos](@ref omz_demos) - Console applications that provide robust application templates to help you implement specific deep learning scenarios.
+    - Additional Tools - A set of tools to work with your models including [Accuracy Checker Utility](@ref omz_tools_accuracy_checker) and [Model Downloader](@ref omz_tools_downloader).
+    - [Documentation for Pretrained Models](@ref omz_models_group_intel) - Documentation for pretrained models that are available in the [Open Model Zoo repository](https://github.com/opencv/open_model_zoo).
 - Deep Learning Streamer (DL Streamer) – Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. DL Streamer can be installed by the Intel® Distribution of OpenVINO™ toolkit installer. Its open source version is available on [GitHub](https://github.com/opencv/gst-video-analytics). For the DL Streamer documentation, see:
     - [DL Streamer Samples](@ref gst_samples_README)
     - [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/)

docs/install_guides/installing-openvino-apt.md

@@ -26,9 +26,9 @@ The following components are installed with the OpenVINO developer package:
 | [Inference Engine](../IE_DG/inference_engine_intro.md) | The engine that runs a deep learning model. It includes a set of libraries for an easy inference integration into your applications.|
 | [OpenCV*](https://docs.opencv.org/master/) | OpenCV\* community version compiled for Intel® hardware |
 | [Sample Applications](../IE_DG/Samples_Overview.md)           | A set of simple console applications demonstrating how to use the Inference Engine in your applications. |
-| [Demo Applications](@ref omz_demos_README) | A set of console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use cases. |
-| Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker_README), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader_README) and other  |
-| [Documentation for Pre-Trained Models ](@ref omz_models_intel_index)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/opencv/open_model_zoo).  |
+| [Demo Applications](@ref omz_demos) | A set of console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use cases. |
+| Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and other  |
+| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/opencv/open_model_zoo).  |
 | Deep Learning Streamer (DL Streamer)   | Streaming analytics framework, based on GStreamer\*, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/opencv/gst-video-analytics/wiki/Elements), [Tutorial](https://github.com/opencv/gst-video-analytics/wiki/DL%20Streamer%20Tutorial). |
 
 ## Set up the Repository

docs/install_guides/installing-openvino-linux.md

@@ -26,20 +26,20 @@ The Intel® Distribution of OpenVINO™ toolkit for Linux\*:
 | Intel® Media SDK                                                                                    | Offers access to hardware accelerated video codecs and frame processing                                                                                                                                                                                                                                       |
 | [OpenCV](https://docs.opencv.org/master/)                                                           | OpenCV\* community version compiled for Intel® hardware                                                                                                                                                                                                                                                       |
 | [Inference Engine Code Samples](../IE_DG/Samples_Overview.md)           | A set of simple console applications demonstrating how to utilize specific OpenVINO capabilities in an application and how to perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more. |
-| [Demo Applications](@ref omz_demos_README)           | A set of simple console applications that provide robust application templates to help you implement specific deep learning scenarios. |
-| Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker_README), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader_README) and other  |
-| [Documentation for Pre-Trained Models ](@ref omz_models_intel_index)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/opencv/open_model_zoo).  |
+| [Demo Applications](@ref omz_demos)           | A set of simple console applications that provide robust application templates to help you implement specific deep learning scenarios. |
+| Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and other  |
+| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/opencv/open_model_zoo).  |
 | Deep Learning Streamer (DL Streamer)   | Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/opencv/gst-video-analytics/wiki/Elements), [Tutorial](https://github.com/opencv/gst-video-analytics/wiki/DL%20Streamer%20Tutorial). |
 
 **Could Be Optionally Installed**
 
 [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench) is a platform built upon OpenVINO™ and provides a web-based graphical environment that enables you to optimize, fine-tune, analyze, visualize, and compare performance of deep learning models on various Intel® architecture
 configurations. In the DL Workbench, you can use most of OpenVINO™ toolkit components:
-* [Model Downloader](@ref omz_tools_downloader_README)
-* [Intel® Open Model Zoo](@ref omz_models_intel_index)
+* [Model Downloader](@ref omz_tools_downloader)
+* [Intel® Open Model Zoo](@ref omz_models_group_intel)
 * [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
 * [Post-training Optimization Tool](@ref pot_README)
-* [Accuracy Checker](@ref omz_tools_accuracy_checker_README)
+* [Accuracy Checker](@ref omz_tools_accuracy_checker)
 * [Benchmark Tool](../../inference-engine/samples/benchmark_app/README.md)
 
 Proceed to an [easy installation from Docker](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub) to get started.
@@ -415,7 +415,7 @@ trusted-host = mirrors.aliyun.com
 - [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 - [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md).
 - For more information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
-- For information on a set of pre-trained models, see the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
+- For information on a set of pre-trained models, see the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel)
 - For IoT Libraries and Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).
 
 To learn more about converting models, go to:

docs/install_guides/installing-openvino-macos.md

@@ -27,19 +27,19 @@ The following components are installed by default:
 | [Inference Engine](../IE_DG/inference_engine_intro.md)               | This is the engine that runs a deep learning model. It includes a set of libraries for an easy inference integration into your applications.                                                                                                               |
 | [OpenCV\*](https://docs.opencv.org/master/)                                                         | OpenCV\* community version compiled for Intel® hardware                                                                                                                                                                                                      |
 | [Sample Applications](../IE_DG/Samples_Overview.md)                                                                                | A set of simple console applications demonstrating how to use the Inference Engine in your applications. |
-| [Demos](@ref omz_demos_README)                                   | A set of console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use-cases  |
-| Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker_README), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader_README) and other  |
-| [Documentation for Pre-Trained Models ](@ref omz_models_intel_index)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/opencv/open_model_zoo)  |
+| [Demos](@ref omz_demos)                                   | A set of console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use-cases  |
+| Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and other  |
+| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/opencv/open_model_zoo)  |
 
 **Could Be Optionally Installed**
 
 [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench) is a platform built upon OpenVINO™ and provides a web-based graphical environment that enables you to optimize, fine-tune, analyze, visualize, and compare performance of deep learning models on various Intel® architecture
 configurations. In the DL Workbench, you can use most of OpenVINO™ toolkit components:
-* [Model Downloader](@ref omz_tools_downloader_README)
-* [Intel® Open Model Zoo](@ref omz_models_intel_index)
+* [Model Downloader](@ref omz_tools_downloader)
+* [Intel® Open Model Zoo](@ref omz_models_group_intel)
 * [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
 * [Post-training Optimization Tool](@ref pot_README)
-* [Accuracy Checker](@ref omz_tools_accuracy_checker_README)
+* [Accuracy Checker](@ref omz_tools_accuracy_checker)
 * [Benchmark Tool](../../inference-engine/samples/benchmark_app/README.md)
 
 Proceed to an [easy installation from Docker](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub) to get started.
@@ -280,7 +280,7 @@ Follow the steps below to uninstall the Intel® Distribution of OpenVINO™ Tool
 
 - To learn more about the verification applications, see `README.txt` in `/opt/intel/openvino_2021/deployment_tools/demo/`.
 
-- For detailed description of the pre-trained models, go to the [Overview of OpenVINO toolkit Pre-Trained Models](@ref omz_models_intel_index) page.
+- For detailed description of the pre-trained models, go to the [Overview of OpenVINO toolkit Pre-Trained Models](@ref omz_models_group_intel) page.
 
 - More information on [sample applications](../IE_DG/Samples_Overview.md).
 

docs/install_guides/installing-openvino-raspbian.md

@@ -164,9 +164,9 @@ Read the next topic if you want to learn more about OpenVINO workflow for Raspbe
 
 If you want to use your model for inference, the model must be converted to the .bin and .xml Intermediate Representation (IR) files that are used as input by Inference Engine. OpenVINO™ toolkit support on Raspberry Pi only includes the Inference Engine module of the Intel® Distribution of OpenVINO™ toolkit. The Model Optimizer is not supported on this platform. To get the optimized models you can use one of the following options:
 
-* Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader_README).
+* Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader).
 
-   For more information on pre-trained models, see [Pre-Trained Models Documentation](@ref omz_models_intel_index)
+   For more information on pre-trained models, see [Pre-Trained Models Documentation](@ref omz_models_group_intel)
 
 * Convert the model using the Model Optimizer from a full installation of Intel® Distribution of OpenVINO™ toolkit on one of the supported platforms. Installation instructions are available:
 

docs/install_guides/installing-openvino-windows.md

@@ -62,19 +62,19 @@ The following components are installed by default:
 |[Inference Engine](../IE_DG/inference_engine_intro.md)               |This is the engine that runs the deep learning model. It includes a set of libraries for an easy inference integration into your applications.                                                                                                 |
 |[OpenCV\*](https://docs.opencv.org/master/)                                                         |OpenCV* community version compiled for Intel® hardware                                                                                                                                                                                         |
 |[Inference Engine Samples](../IE_DG/Samples_Overview.md)                             |A set of simple console applications demonstrating how to use Intel's Deep Learning Inference Engine in your applications.  |
-| [Demos](@ref omz_demos_README)                                   | A set of console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use-cases  |
-| Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker_README), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader_README) and other  |
-| [Documentation for Pre-Trained Models ](@ref omz_models_intel_index)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/opencv/open_model_zoo)  |
+| [Demos](@ref omz_demos)                                   | A set of console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use-cases  |
+| Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and other  |
+| [Documentation for Pre-Trained Models ](@ref omz_models_group_intel)                                   | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/opencv/open_model_zoo)  |
 
 **Could Be Optionally Installed**
 
 [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) (DL Workbench) is a platform built upon OpenVINO™ and provides a web-based graphical environment that enables you to optimize, fine-tune, analyze, visualize, and compare performance of deep learning models on various Intel® architecture
 configurations. In the DL Workbench, you can use most of OpenVINO™ toolkit components:
-* [Model Downloader](@ref omz_tools_downloader_README)
-* [Intel® Open Model Zoo](@ref omz_models_intel_index)
+* [Model Downloader](@ref omz_tools_downloader)
+* [Intel® Open Model Zoo](@ref omz_models_group_intel)
 * [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
 * [Post-training Optimization Tool](@ref pot_README)
-* [Accuracy Checker](@ref omz_tools_accuracy_checker_README)
+* [Accuracy Checker](@ref omz_tools_accuracy_checker)
 * [Benchmark Tool](../../inference-engine/samples/benchmark_app/README.md)
 
 Proceed to an [easy installation from Docker](@ref workbench_docs_Workbench_DG_Install_from_Docker_Hub) to get started.
@@ -355,7 +355,7 @@ To learn more about converting deep learning models, go to:
 - [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
 - [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
 - [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
-- [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
+- [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel)
 - [Intel® Neural Compute Stick 2 Get Started](https://software.intel.com/en-us/neural-compute-stick/get-started)
 
 

docs/install_guides/pypi-openvino-dev.md

@@ -13,7 +13,7 @@ OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applicatio
 | Component                                                                                           | Description                                                                                                                                                                                                                                                                                                   |  
 |-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
 | [Model Optimizer](https://docs.openvinotoolkit.org/latest/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) | This tool imports, converts, and optimizes models that were trained in popular frameworks to a format usable by Intel tools, especially the Inference Engine. <br>Popular frameworks include Caffe\*, TensorFlow\*, MXNet\*, and ONNX\*.                                                                              |
-| Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](https://docs.openvinotoolkit.org/latest/omz_tools_accuracy_checker_README.html), [Post-Training Optimization Tool](https://docs.openvinotoolkit.org/latest/pot_README.html)  |
+| Additional Tools                                   | A set of tools to work with your models including [Accuracy Checker utility](https://docs.openvinotoolkit.org/latest/omz_tools_accuracy_checker.html), [Post-Training Optimization Tool](https://docs.openvinotoolkit.org/latest/pot_README.html)  |
 
 **The Runtime Package Includes the Following Components Installed by Dependency:**
 

docs/optimization_guide/dldt_optimization_guide.md

@@ -445,7 +445,7 @@ There are important performance caveats though: for example, the tasks that run
 
 Also, if the inference is performed on the graphics processing unit (GPU), it can take little gain to do the encoding, for instance, of the resulting video, on the same GPU in parallel, because the device is already busy.
 
-Refer to the [Object Detection SSD Demo](@ref omz_demos_object_detection_demo_ssd_async_README) (latency-oriented Async API showcase) and [Benchmark App Sample](../../inference-engine/samples/benchmark_app/README.md) (which has both latency and throughput-oriented modes) for complete examples of the Async API in action.
+Refer to the [Object Detection SSD Demo](@ref omz_demos_object_detection_demo_cpp) (latency-oriented Async API showcase) and [Benchmark App Sample](../../inference-engine/samples/benchmark_app/README.md) (which has both latency and throughput-oriented modes) for complete examples of the Async API in action.
 
 ## Using Tools <a name="using-tools"></a>
 

docs/ovsa/ovsa_get_started.md

@@ -559,7 +559,7 @@ The Model Hosting components install the OpenVINO™ Security Add-on Runtime Doc
 
 This section requires interactions between the Model Developer/Independent Software vendor and the User. All roles must complete all applicable <a href="#setup-host">set up steps</a> and <a href="#ovsa-install">installation steps</a> before beginning this section.
 
-This document uses the [face-detection-retail-0004](@ref omz_models_intel_face_detection_retail_0004_description_face_detection_retail_0004) model as an example. 
+This document uses the [face-detection-retail-0004](@ref omz_models_model_face_detection_retail_0004) model as an example. 
 
 The following figure describes the interactions between the Model Developer, Independent Software Vendor, and User.
 

docs/resources/introduction.md

@@ -8,14 +8,14 @@
 
 ## Demos
 
-- [Demos](@ref omz_demos_README)
+- [Demos](@ref omz_demos)
 
 
 ## Additional Tools
 
-- A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker_README), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader_README) and other
+- A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader) and other
 
 ## Pre-Trained Models
 
-- [Intel's Pre-trained Models from Open Model Zoo](@ref omz_models_intel_index)
-- [Public Pre-trained Models Available with OpenVINO™ from Open Model Zoo](@ref omz_models_public_index)
+- [Intel's Pre-trained Models from Open Model Zoo](@ref omz_models_group_intel)
+- [Public Pre-trained Models Available with OpenVINO™ from Open Model Zoo](@ref omz_models_group_public)

inference-engine/ie_bridges/c/samples/hello_classification/README.md

@@ -14,7 +14,7 @@ To properly demonstrate this API, it is required to run several networks in pipe
 
 ## Running
 
-To run the sample, you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
+To run the sample, you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
 
 > **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 >

inference-engine/ie_bridges/c/samples/hello_nv12_input_classification/README.md

@@ -34,7 +34,7 @@ ffmpeg -i cat.jpg -pix_fmt nv12 cat.yuv
 
 ## Running
 
-To run the sample, you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
+To run the sample, you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
 
 > **NOTE**: Before running the sample with a trained model, make sure the model is converted to the
 > Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).

inference-engine/ie_bridges/c/samples/object_detection_sample_ssd/README.md

@@ -39,7 +39,7 @@ Options:
 
 Running the application with the empty list of options yields the usage message given above and an error message.
 
-To run the sample, you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
+To run the sample, you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
 
 > **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 >
@@ -71,4 +71,4 @@ classes of the detected objects along with the respective confidence values and
 
 ## See Also
 * [Model Optimizer](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
-* [Model Downloader](@ref omz_tools_downloader_README)
+* [Model Downloader](@ref omz_tools_downloader)

inference-engine/ie_bridges/python/sample/classification_sample_async/README.md

@@ -59,7 +59,7 @@ Options:
 
 Running the application with the empty list of options yields the usage message given above and an error message.
 
-To run the sample, you can use AlexNet and GoogLeNet or other image classification models. You can download [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models using the [Model Downloader](@ref omz_tools_downloader_README).
+To run the sample, you can use AlexNet and GoogLeNet or other image classification models. You can download [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models using the [Model Downloader](@ref omz_tools_downloader).
 
 > **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 > 

inference-engine/ie_bridges/python/sample/hello_classification/README.md

@@ -46,7 +46,7 @@ Options:
 
 Running the application with the empty list of options yields the usage message given above.
 
-To run the sample, you can use AlexNet and GoogLeNet or other image classification models. You can download [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models using the [Model Downloader](@ref omz_tools_downloader_README).
+To run the sample, you can use AlexNet and GoogLeNet or other image classification models. You can download [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models using the [Model Downloader](@ref omz_tools_downloader).
 
 > **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 > 
@@ -70,4 +70,4 @@ For example, to get the top-5 results on GPU, run the following command:
 ## See Also
 * [Using Inference Engine Samples](../../../../../docs/IE_DG/Samples_Overview.md)
 * [Model Optimizer tool](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
-* [Model Downloader](@ref omz_tools_downloader_README)
+* [Model Downloader](@ref omz_tools_downloader)

inference-engine/ie_bridges/python/sample/hello_reshape_ssd/README.md

@@ -7,7 +7,7 @@ networks like SSD-VGG. The sample shows how to use [Shape Inference feature](../
 
 ## Running
 
-To run the sample, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO [Model Downloader](@ref omz_tools_downloader_README).
+To run the sample, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO [Model Downloader](@ref omz_tools_downloader).
 
 > **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 >
@@ -26,5 +26,5 @@ rectangles to the standard output stream.
 
 ## See Also
 * [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
-* [Model Downloader](@ref omz_tools_downloader_README)
+* [Model Downloader](@ref omz_tools_downloader)
 * [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

inference-engine/ie_bridges/python/sample/object_detection_sample_ssd/README.md

@@ -55,13 +55,13 @@ Options:
 
 Running the application with the empty list of options yields the usage message given above and an error message.
 
-To run the sample, you can use RMNet_SSD or other object-detection models. You can download [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models using the [Model Downloader](@ref omz_tools_downloader_README).
+To run the sample, you can use RMNet_SSD or other object-detection models. You can download [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models using the [Model Downloader](@ref omz_tools_downloader).
 
 > **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 > 
 > The sample accepts models in ONNX format (.onnx) that do not require preprocessing.
 
-You can do inference of an image using the [person detection SSD model](@ref omz_models_intel_person_detection_retail_0013_description_person_detection_retail_0013) from the Open Model Zoo on CPU using the following command:
+You can do inference of an image using the [person detection SSD model](@ref omz_models_model_person_detection_retail_0013) from the Open Model Zoo on CPU using the following command:
 ```
     python3 object_detection_sample_ssd.py -i <path_to_image>/cat.bmp -m <path_to_model>/person-detection-retail-0013.xml -d CPU
 ```

inference-engine/samples/benchmark_app/README.md

@@ -123,7 +123,7 @@ If a model has only image input(s), please provide a folder with images or a pat
 If a model has some specific input(s) (not images), please prepare a binary file(s) that is filled with data of appropriate precision and provide a path to them as input.
 If a model has mixed input types, input folder should contain all required files. Image inputs are filled with image files one by one. Binary inputs are filled with binary inputs one by one.
 
-To run the tool, you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
+To run the tool, you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
 
 > **NOTE**: Before running the tool with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 >
@@ -195,4 +195,4 @@ Below are fragments of sample output for CPU and FPGA devices:
 ## See Also
 * [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
 * [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
-* [Model Downloader](@ref omz_tools_downloader_README)
+* [Model Downloader](@ref omz_tools_downloader)

inference-engine/samples/classification_sample_async/README.md

@@ -49,7 +49,7 @@ Options:
 
 Running the application with the empty list of options yields the usage message given above and an error message.
 
-To run the sample, use AlexNet and GoogLeNet or other public or pre-trained image classification models. You can download [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models using the [Model Downloader](@ref omz_tools_downloader_README).
+To run the sample, use AlexNet and GoogLeNet or other public or pre-trained image classification models. You can download [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models using the [Model Downloader](@ref omz_tools_downloader).
 
 > **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 > 
@@ -66,5 +66,5 @@ By default the application outputs top-10 inference results for each infer reque
 
 ## See Also
 * [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
-* [Model Downloader](@ref omz_tools_downloader_README)
+* [Model Downloader](@ref omz_tools_downloader)
 * [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

inference-engine/samples/hello_classification/README.md

@@ -10,8 +10,8 @@ It demonstrates how to use the following Inference Engine API in applications:
 
 There is also an API introduced to crop a ROI object and set it as input without additional memory re-allocation.
 To properly demonstrate this API, it is required to run several networks in pipeline which is out of scope of this sample.
-Please refer to [Security Barrier Camera Demo](@ref omz_demos_security_barrier_camera_demo_README), or
-[Crossroad Camera Demo](@ref omz_demos_crossroad_camera_demo_README) with an example of using of new crop ROI API.
+Please refer to [Security Barrier Camera Demo](@ref omz_demos_security_barrier_camera_demo_cpp), or
+[Crossroad Camera Demo](@ref omz_demos_crossroad_camera_demo_cpp) with an example of using of new crop ROI API.
 
 Refer to [Integrate the Inference Engine New Request API with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) for details.
 
@@ -19,7 +19,7 @@ Refer to [Integrate the Inference Engine New Request API with Your Application](
 
 ## Running
 
-To run the sample, you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
+To run the sample, you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
 
 > **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 >

inference-engine/samples/hello_nv12_input_classification/README.md

@@ -35,7 +35,7 @@ ffmpeg -i cat.jpg -pix_fmt nv12 cat.yuv
 
 ## Running
 
-To run the sample, you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
+To run the sample, you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
 
 > **NOTE**: Before running the sample with a trained model, make sure the model is converted to the
 > Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).

inference-engine/samples/hello_query_device/README.md

@@ -52,5 +52,5 @@ Available devices:
 
 ## See Also
 * [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
-* [Model Downloader](@ref omz_tools_downloader_README)
+* [Model Downloader](@ref omz_tools_downloader)
 * [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

inference-engine/samples/hello_reshape_ssd/README.md

@@ -7,7 +7,7 @@ networks like SSD-VGG. The sample shows how to use [Shape Inference feature](../
 
 ## Running
 
-To run the sample, you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
+To run the sample, you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
 
 > **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 >
@@ -26,5 +26,5 @@ rectangles to the standard output stream.
 
 ## See Also
 * [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
-* [Model Downloader](@ref omz_tools_downloader_README)
+* [Model Downloader](@ref omz_tools_downloader)
 * [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

inference-engine/samples/object_detection_sample_ssd/README.md

@@ -36,7 +36,7 @@ Options:
 
 Running the application with the empty list of options yields the usage message given above and an error message.
 
-To run the sample, you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
+To run the sample, you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
 
 > **NOTE**: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 >
@@ -62,4 +62,4 @@ rectangles to the standard output stream.
 ## See Also
 * [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
 * [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
-* [Model Downloader](@ref omz_tools_downloader_README)
+* [Model Downloader](@ref omz_tools_downloader)

inference-engine/samples/speech_sample/README.md

@@ -140,7 +140,7 @@ The following pre-trained models are available:
 * rm\_lstm4f
 * rm\_cnn4a\_smbr
 
-All of them can be downloaded from [https://storage.openvinotoolkit.org/models_contrib/speech/2021.2/](https://storage.openvinotoolkit.org/models_contrib/speech/2021.2/) or using the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) .
+All of them can be downloaded from [https://storage.openvinotoolkit.org/models_contrib/speech/2021.2/](https://storage.openvinotoolkit.org/models_contrib/speech/2021.2/) or using the OpenVINO [Model Downloader](@ref omz_tools_downloader) .
 
 
 ### Speech Inference
@@ -210,4 +210,4 @@ cat out.txt | utils/int2sym.pl -f 2- words.txt | sed s:\<UNK\>::g | compute-wer
 ## See Also
 * [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
 * [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
-* [Model Downloader](@ref omz_tools_downloader_README)
+* [Model Downloader](@ref omz_tools_downloader)

inference-engine/samples/style_transfer_sample/README.md

@@ -45,4 +45,4 @@ The application outputs an image (`out1.bmp`) or a sequence of images (`out1.bmp
 ## See Also
 * [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
 * [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
-* [Model Downloader](@ref omz_tools_downloader_README)
+* [Model Downloader](@ref omz_tools_downloader)

inference-engine/tools/benchmark_tool/README.md

@@ -143,7 +143,7 @@ If a model has only image input(s), please a provide folder with images or a pat
 If a model has some specific input(s) (not images), please prepare a binary file(s), which is filled with data of appropriate precision and provide a path to them as input.
 If a model has mixed input types, input folder should contain all required files. Image inputs are filled with image files one by one. Binary inputs are filled with binary inputs one by one.
 
-To run the tool, you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
+To run the tool, you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
 
 > **NOTE**: Before running the tool with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 
@@ -211,4 +211,4 @@ Below are fragments of sample output for CPU and FPGA devices:
 ## See Also
 * [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
 * [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
-* [Model Downloader](@ref omz_tools_downloader_README)
+* [Model Downloader](@ref omz_tools_downloader)

tools/benchmark/README.md

@@ -150,7 +150,7 @@ If a model has only image input(s), please a provide folder with images or a pat
 If a model has some specific input(s) (not images), please prepare a binary file(s), which is filled with data of appropriate precision and provide a path to them as input.
 If a model has mixed input types, input folder should contain all required files. Image inputs are filled with image files one by one. Binary inputs are filled with binary inputs one by one.
 
-To run the tool, you can use [public](@ref omz_models_public_index) or [Intel's](@ref omz_models_intel_index) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader_README).
+To run the tool, you can use [public](@ref omz_models_group_public) or [Intel's](@ref omz_models_group_intel) pre-trained models from the Open Model Zoo. The models can be downloaded using the [Model Downloader](@ref omz_tools_downloader).
 
 > **NOTE**: Before running the demo with a trained model, make sure the model is converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](./docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).