Merge pull request #3012 from Wovchena/openvinotoolkit.org-openvino.ai

openvinotoolkit.org->openvino.ai

Author: Wovchena

CONTRIBUTING.md

@@ -177,15 +177,15 @@ license: https://raw.githubusercontent.com/pudae/tensorflow-densenet/master/LICE
 
 ## Model Conversion
 
-Deep Learning Inference Engine (IE) supports models in the Intermediate Representation (IR) format. A model from any supported framework can be converted to IR using the Model Optimizer tool included in the OpenVINO™ toolkit. Find more information about conversion in the [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html). After a successful conversion you get a model in the IR format, with the `*.xml` file representing the net graph and the `*.bin` file containing the net parameters.
+Deep Learning Inference Engine (IE) supports models in the Intermediate Representation (IR) format. A model from any supported framework can be converted to IR using the Model Optimizer tool included in the OpenVINO™ toolkit. Find more information about conversion in the [Model Optimizer Developer Guide](https://docs.openvino.ai/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html). After a successful conversion you get a model in the IR format, with the `*.xml` file representing the net graph and the `*.bin` file containing the net parameters.
 
 > **NOTE 1**: Image preprocessing parameters (mean and scale) must be built into a converted model to simplify model usage.
 
 > **NOTE 2**: If a model input is a color image, color channel order should be `BGR`.
 
 ## Demo
 
-A demo shows the main idea of how to infer a model using IE. If your model solves one of the tasks supported by the Open Model Zoo, try to find an appropriate option from [demos](demos/README.md) or [samples](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html). Otherwise, you must provide your own demo (C++ or Python).
+A demo shows the main idea of how to infer a model using IE. If your model solves one of the tasks supported by the Open Model Zoo, try to find an appropriate option from [demos](demos/README.md) or [samples](https://docs.openvino.ai/latest/_docs_IE_DG_Samples_Overview.html). Otherwise, you must provide your own demo (C++ or Python).
 
 The demo's name should end with `_demo` suffix to follow the convention of the project.
 

README.md

@@ -1,4 +1,4 @@
-# [OpenVINO™ Toolkit](https://01.org/openvinotoolkit) - Open Model Zoo repository
+# [OpenVINO™ Toolkit](https://docs.openvino.ai/latest/index.html) - Open Model Zoo repository
 [![Stable release](https://img.shields.io/badge/version-2021.4-green.svg)](https://github.com/openvinotoolkit/open_model_zoo/releases/tag/2022.1)
 [![Gitter chat](https://badges.gitter.im/gitterHQ/gitter.png)](https://gitter.im/open_model_zoo/community)
 [![Apache License Version 2.0](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE)
@@ -19,12 +19,12 @@ Open Model Zoo is licensed under [Apache License Version 2.0](LICENSE).
 
 ## Online Documentation
 * [OpenVINO™ Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
-* [Pre-Trained Models](https://docs.openvinotoolkit.org/latest/trained_models.html)
-* [Demos and Samples](https://docs.openvinotoolkit.org/latest/omz_demos.html)
+* [Pre-Trained Models](https://docs.openvino.ai/latest/model_zoo.html)
+* [Demos and Samples](https://docs.openvino.ai/latest/omz_demos.html)
 
 ## Other Usage Examples
-* [Open Visual Cloud](https://01.org/openvisualcloud)
-  * [Tutorial: Build and Run the AD Insertion Sample on public cloud or local machine](https://01.org/openvisualcloud/documents/tutorial-build-and-run-ad-insertion-sample-public-cloud-or-local-machine)
+* [Open Visual Cloud](https://www.intel.com/content/www/us/en/developer/articles/technical/open-visual-cloud.html)
+  * [Tutorial: Running AD Insertion on Public Cloud](https://github.com/OpenVisualCloud/Ad-Insertion-Sample/wiki/Tutorial:-Running-AD-Insertion-on-Public-Cloud)
   * [GitHub Repo for Ad Insertion Sample](https://github.com/OpenVisualCloud/Ad-Insertion-Sample)
 * [OpenVINO for Smart City](https://github.com/incluit/OpenVino-For-SmartCity)
 * [OpenVINO Driver Behavior](https://github.com/incluit/OpenVino-Driver-Behaviour)

demos/3d_segmentation_demo/python/README.md

@@ -123,5 +123,5 @@ The demo reports
 ## See Also
 
 * [Open Model Zoo Demos](../../README.md)
-* [Model Optimizer](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
+* [Model Optimizer](https://docs.openvino.ai/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
 * [Model Downloader](../../../tools/model_tools/README.md)

demos/README.md

@@ -351,7 +351,7 @@ list above.
 
 ## See Also
 
-* [Intel OpenVINO Documentation](https://docs.openvinotoolkit.org/latest/documentation.html)
+* [Intel OpenVINO Documentation](https://docs.openvino.ai/latest/documentation.html)
 * [Overview of OpenVINO™ Toolkit Intel's Pre-Trained Models](../models/intel/index.md)
 * [Overview of OpenVINO™ Toolkit Public Pre-Trained Models](../models/public/index.md)
 

demos/action_recognition_demo/python/README.md

@@ -150,5 +150,5 @@ The application uses OpenCV to display the real-time action recognition results
 ## See Also
 
 * [Open Model Zoo Demos](../../README.md)
-* [Model Optimizer](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
+* [Model Optimizer](https://docs.openvino.ai/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
 * [Model Downloader](../../../tools/model_tools/README.md)

demos/bert_named_entity_recognition_demo/python/README.md

@@ -102,6 +102,6 @@ Notice that when the original "context" (text from the url) do not fit the model
 ## See Also
 
 * [Open Model Zoo Demos](../../README.md)
-* [Model Optimizer](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
+* [Model Optimizer](https://docs.openvino.ai/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
 * [Model Downloader](../../../tools/model_tools/README.md)
-* [Benchmark C++ Sample](https://docs.openvinotoolkit.org/latest/_inference_engine_samples_benchmark_app_README.html)
+* [Benchmark C++ Sample](https://docs.openvino.ai/latest/_inference_engine_samples_benchmark_app_README.html)

demos/bert_question_answering_demo/python/README.md

@@ -129,7 +129,7 @@ length of the context plus length of the question (both in tokens), if the resul
 sequence length that the network expects. This is performance (speed) and memory footprint saving option.
 Since some networks are not-reshapable (due to limitations of the internal layers) the reshaping might fail,
 so you will need to run the demo without it.
-Please see general [reshape intro and limitations](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_ShapeInference.html)
+Please see general [reshape intro and limitations](https://docs.openvino.ai/latest/_docs_IE_DG_ShapeInference.html)
 
 ## Demo Outputs
 
@@ -147,6 +147,6 @@ Thus, for the long texts, the network is called multiple times. The results are
 ## See Also
 
 * [Open Model Zoo Demos](../../README.md)
-* [Model Optimizer](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
+* [Model Optimizer](https://docs.openvino.ai/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
 * [Model Downloader](../../../tools/model_tools/README.md)
-* [Benchmark C++ Sample](https://docs.openvinotoolkit.org/latest/_inference_engine_samples_benchmark_app_README.html)
+* [Benchmark C++ Sample](https://docs.openvino.ai/latest/_inference_engine_samples_benchmark_app_README.html)

demos/bert_question_answering_embedding_demo/python/README.md

@@ -139,7 +139,7 @@ Notice that since order of inputs for the model does matter, the demo script che
 from the command line match the actual network inputs.
 The embedding model is reshaped by the demo to infer embedding vectors for long contexts and short question.
 Make sure that the original model converted by Model Optimizer with reshape option.
-Please see general [reshape intro and limitations](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_ShapeInference.html)
+Please see general [reshape intro and limitations](https://docs.openvino.ai/latest/_docs_IE_DG_ShapeInference.html)
 
 ## Demo Outputs
 
@@ -158,6 +158,6 @@ Thus, for the long paragraph texts, the network is called multiple times as for
 ## See Also
 
 * [Open Model Zoo Demos](../../README.md)
-* [Model Optimizer](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
+* [Model Optimizer](https://docs.openvino.ai/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
 * [Model Downloader](../../../tools/model_tools/README.md)
-* [Benchmark C++ Sample](https://docs.openvinotoolkit.org/latest/_inference_engine_samples_benchmark_app_README.html)
+* [Benchmark C++ Sample](https://docs.openvino.ai/latest/_inference_engine_samples_benchmark_app_README.html)

demos/classification_benchmark_demo/cpp/README.md

@@ -206,5 +206,5 @@ You can use these metrics to measure application-level performance.
 ## See Also
 
 * [Open Model Zoo Demos](../../README.md)
-* [Model Optimizer](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
+* [Model Optimizer](https://docs.openvino.ai/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
 * [Model Downloader](../../../tools/model_tools/README.md)

demos/classification_demo/python/README.md

@@ -242,5 +242,5 @@ You can use these metrics to measure application-level performance.
 ## See Also
 
 * [Open Model Zoo Demos](../../README.md)
-* [Model Optimizer](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
+* [Model Optimizer](https://docs.openvino.ai/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
 * [Model Downloader](../../../tools/model_tools/README.md)