Updating references to OMZ in docs

openvinotoolkit · Sep 10, 2024 · b4323dd · b4323dd
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diff --git a/docs/articles_en/about-openvino/release-notes-openvino.rst b/docs/articles_en/about-openvino/release-notes-openvino.rst
@@ -1028,7 +1028,7 @@ Discontinued in 2024
   * Deployment Manager. See :doc:`installation <../get-started/install-openvino>` and
     :doc:`deployment <../get-started/install-openvino>` guides for current distribution
     options.
-  * `Accuracy Checker <https://docs.openvino.ai/2023.3/omz_tools_accuracy_checker.html>`__.
+  * `Accuracy Checker <https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/README.md>`__.
   * `Post-Training Optimization Tool <https://docs.openvino.ai/2023.3/pot_introduction.html>`__
     (POT). Neural Network Compression Framework (NNCF) should be used instead.
   * A `Git patch <https://github.com/openvinotoolkit/nncf/tree/develop/third_party_integration/huggingface_transformers>`__
@@ -1065,15 +1065,15 @@ Deprecated and to be removed in the future
 
        * See alternative: `Optical Character Recognition (OCR) with OpenVINO™ <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/optical-character-recognition>`__,
        * See alternative: `PaddleOCR with OpenVINO™ <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/paddle-ocr-webcam>`__,
-       * See alternative: `Handwritten Text Recognition Demo <https://docs.openvino.ai/2024/omz_demos_handwritten_text_recognition_demo_python.html>`__
+       * See alternative: `Handwritten Text Recognition Demo <https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/handwritten_text_recognition_demo/python/README.md>`__
 
      * `Image In-painting with OpenVINO™ <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/image-inpainting>`__
 
-       * See alternative: `Image Inpainting Python Demo <https://docs.openvino.ai/2024/omz_demos_image_inpainting_demo_python.html>`__
+       * See alternative: `Image Inpainting Python Demo <https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/image_inpainting_demo/python/README.md>`__
 
      * `Interactive Machine Translation with OpenVINO <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/machine-translation>`__
 
-       * See alternative: `Machine Translation Python* Demo <https://docs.openvino.ai/2024/omz_demos_machine_translation_demo_python.html>`__
+       * See alternative: `Machine Translation Python* Demo <https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/machine_translation_demo/python/README.md>`__
 
      * `Open Model Zoo Tools Tutorial <https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/model-tools>`__
 

diff --git a/docs/articles_en/documentation/legacy-features/model-zoo.rst b/docs/articles_en/documentation/legacy-features/model-zoo.rst
@@ -1,32 +1,8 @@
 Model Zoo
 =========
 
-
 .. _model zoo:
 
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-   ../../omz_models_group_intel
-   ../../omz_models_group_public
-
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-   ../../omz_tools_downloader
-   ../../omz_tools_accuracy_checker
-   ../../omz_data_datasets
-   ../../omz_demos
-
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-   ../../omz_model_api_ovms_adapter
-
-
 .. note::
 
    Since the deprecation of Open Model Zoo, OpenVINO has significantly extended its presence on the
@@ -40,16 +16,14 @@ learning in Python, C++, or OpenCV Graph API (G-API). Models and demos are avail
 and licensed under Apache License Version 2.0.
 
 Browse through over 200 neural network models, both
-:doc:`public <../../omz_models_group_public>` and from
-:doc:`Intel <../../omz_models_group_intel>`, and pick the right one for your solution.
+`public <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/index.md>`__ and from
+`Intel <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/index.md>`__, and pick the right one for your solution.
 Types include object detection, classification, image segmentation, handwriting recognition,
 text to speech, pose estimation, and others. The Intel models have already been converted
 to work with OpenVINO™ toolkit, while public models can easily be converted using the
 :doc:`OpenVINO Model Conversion API <../../openvino-workflow/model-preparation>` utility.
 
-Get started with simple
-:doc:`step-by-step procedures <../../learn-openvino/openvino-samples/get-started-demos>`
-to learn how to build and run demo applications or discover the
-:doc:`full set of demos <../../omz_demos>` and adapt them for implementing specific deep
+Open Model Zoo offers a
+`comprehensive set of demos <https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/README.md>`__ that you can adapt for implementing specific deep
 learning scenarios in your applications.
 
diff --git a/...odel-formats/[legacy]-conversion-tutorials/convert-tensorflow-efficient-det.rst b/...odel-formats/[legacy]-conversion-tutorials/convert-tensorflow-efficient-det.rst
@@ -54,7 +54,7 @@ The attribute ``image_size`` specifies the shape to be defined for the model con
     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 the **When to Reverse Input Channels** section of the :doc:`Converting a Model to Intermediate Representation (IR) <../../[legacy]-setting-input-shapes>` guide.
 
 OpenVINO toolkit provides samples that can be used to infer EfficientDet model.
-For more information, refer to the :doc:`Open Model Zoo Demos <../../../../../../omz_demos>`.
+For more information, refer to the `Open Model Zoo Demos <https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/README.md>`__.
 
 .. important::
 

diff --git a/...d-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-retina-net.rst b/...d-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-retina-net.rst
@@ -16,7 +16,7 @@ Converting a TensorFlow RetinaNet Model
 This tutorial explains how to convert a RetinaNet model to the Intermediate Representation (IR).
 
 `Public RetinaNet model <https://github.com/fizyr/keras-retinanet>`__ does not contain pretrained TensorFlow weights.
-To convert this model to the TensorFlow format, follow the `Reproduce Keras to TensorFlow Conversion tutorial <https://docs.openvino.ai/2024/omz_models_model_retinanet_tf.html>`__.
+To convert this model to the TensorFlow format, follow the `Reproduce Keras to TensorFlow Conversion tutorial <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/retinanet-tf/README.md>`__.
 
 After converting the model to TensorFlow format, run the following command:
 

diff --git a/...pported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-yolo.rst b/...pported-model-formats/[legacy]-conversion-tutorials/convert-tensorflow-yolo.rst
@@ -216,7 +216,7 @@ where:
    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 the **When to Reverse Input Channels** section of the :doc:`Converting a Model to Intermediate Representation (IR) <../../[legacy]-setting-input-shapes>` guide.
 
 
-OpenVINO toolkit provides a demo that uses YOLOv3 model. Refer to the :doc:`Object Detection C++ Demo <../../../../../../omz_demos_object_detection_demo_cpp>` for more information.
+OpenVINO toolkit provides a demo that uses YOLOv3 model. Refer to the `Object Detection C++ Demo <https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/object_detection_demo/cpp/README.md>`__ for more information.
 
 Converting YOLOv1 and YOLOv2 Models to the IR
 #############################################

diff --git a/docs/articles_en/documentation/openvino-ecosystem/openvino-security-add-on.rst b/docs/articles_en/documentation/openvino-ecosystem/openvino-security-add-on.rst
@@ -735,7 +735,7 @@ How to Use the OpenVINO™ Security Add-on
 
 This section requires interactions between the Model Developer/Independent Software vendor and the User. All roles must complete all applicable :ref:`set up steps <setup_host>` and :ref:`installation steps <install_ovsa>` before beginning this section.
 
-This document uses the :doc:`face-detection-retail-0004 <../../omz_models_model_face_detection_retail_0004>` model as an example.
+This document uses the `face-detection-retail-0004 <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/face-detection-retail-0004/README.md>`__ model as an example.
 
 The following figure describes the interactions between the Model Developer, Independent Software Vendor, and User.
 

diff --git a/...ility/openvino-plugin-library/advanced-guides/low-precision-transformations.rst b/...ility/openvino-plugin-library/advanced-guides/low-precision-transformations.rst
@@ -312,13 +312,13 @@ This step is optional. It modifies the transformation function to a device-speci
 Result model overview
 #####################
 
-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 :doc:`Model Downloader <../../../../omz_tools_downloader>` 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 <https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/model_tools/README.md>`__ tool to download the ``fp16`` model from `OpenVINO™ Toolkit - Open Model Zoo repository <https://github.com/openvinotoolkit/open_model_zoo>`__:
 
 .. code-block:: sh
 
    omz_downloader --name resnet-50-tf --precisions FP16-INT8
 
-After that you should quantize model by the :doc:`Model Quantizer <../../../../omz_tools_downloader>` tool.
+After that you should quantize model by the `Model Quantizer <https://github.com/openvinotoolkit/open_model_zoo/blob/releases/2023/3/tools/model_tools/README.md>`__ tool.
 
 .. code-block:: sh
 

diff --git a/docs/articles_en/documentation/openvino-security.rst b/docs/articles_en/documentation/openvino-security.rst
@@ -72,5 +72,4 @@ Additional Resources
 - :doc:`Convert a Model <legacy-features/transition-legacy-conversion-api/legacy-conversion-api>`.
 - :doc:`OpenVINO™ Runtime User Guide <../openvino-workflow/running-inference>`.
 - For more information on Sample Applications, see the :doc:`OpenVINO Samples Overview <../learn-openvino/openvino-samples>`
-- For information on a set of pre-trained models, see the :doc:`Overview of OpenVINO™ Toolkit Pre-Trained Models <../omz_models_group_intel>`.
 - For IoT Libraries and Code Samples, see the `Intel® IoT Developer Kit <https://github.com/intel-iot-devkit>`__.
diff --git a/docs/articles_en/learn-openvino/openvino-samples/bert-benchmark.rst b/docs/articles_en/learn-openvino/openvino-samples/bert-benchmark.rst
@@ -7,7 +7,8 @@ Bert Benchmark Python Sample
 
 
 This sample demonstrates how to estimate performance of a Bert model using Asynchronous
-Inference Request API. Unlike `demos <https://docs.openvino.ai/2024/omz_demos.html>`__ this sample does not have
+Inference Request API. Unlike `demos <https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/README.md>`__
+this sample does not have
 configurable command line arguments. Feel free to modify sample's source code to
 try out different options.
 

diff --git a/docs/articles_en/learn-openvino/openvino-samples/hello-reshape-ssd.rst b/docs/articles_en/learn-openvino/openvino-samples/hello-reshape-ssd.rst
@@ -14,7 +14,7 @@ using the sample, refer to the following requirements:
 
 - Models with only one input and output are supported.
 - The sample accepts any file format supported by ``core.read_model``.
-- The sample has been validated with: `person-detection-retail-0013 <https://docs.openvino.ai/2024/omz_models_model_person_detection_retail_0013.html>`__
+- The sample has been validated with: `person-detection-retail-0013 <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/person-detection-retail-0013/README.md>`__
   models and the NCHW layout format.
 - To build the sample, use instructions available at :ref:`Build the Sample Applications <build-samples>`
   section in "Get Started with Samples" guide.

diff --git a/docs/articles_en/learn-openvino/openvino-samples/sync-benchmark.rst b/docs/articles_en/learn-openvino/openvino-samples/sync-benchmark.rst
@@ -9,13 +9,14 @@ Sync Benchmark Sample
 This sample demonstrates how to estimate performance of a model using Synchronous
 Inference Request API. It makes sense to use synchronous inference only in latency
 oriented scenarios. Models with static input shapes are supported. Unlike
-`demos <https://docs.openvino.ai/2024/omz_demos.html>`__ this sample does not have other configurable command-line
+`demos <https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/README.md>`__
+this sample does not have other configurable command-line
 arguments. Feel free to modify sample's source code to try out different options.
 Before using the sample, refer to the following requirements:
 
 - The sample accepts any file format supported by ``core.read_model``.
-- The sample has been validated with: `yolo-v3-tf <https://docs.openvino.ai/2024/omz_models_model_yolo_v3_tf.html>`__,
-  `face-detection-0200 <https://docs.openvino.ai/2024/omz_models_model_face_detection_0200.html>`__ models.
+- The sample has been validated with: `yolo-v3-tf <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/yolo-v3-tf/README.md>`__,
+  `face-detection-0200 <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/face-detection-0200/README.md>`__ models.
 - To build the sample, use instructions available at :ref:`Build the Sample Applications <build-samples>`
   section in "Get Started with Samples" guide.
 

diff --git a/docs/articles_en/learn-openvino/openvino-samples/throughput-benchmark.rst b/docs/articles_en/learn-openvino/openvino-samples/throughput-benchmark.rst
@@ -7,7 +7,7 @@ Throughput Benchmark Sample
 
 
 This sample demonstrates how to estimate performance of a model using Asynchronous
-Inference Request API in throughput mode. Unlike `demos <https://docs.openvino.ai/2024/omz_demos.html>`__ this sample
+Inference Request API in throughput mode. Unlike `demos <https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/README.md>`__ this sample
 does not have other configurable command-line arguments. Feel free to modify sample's
 source code to try out different options.
 
@@ -18,8 +18,8 @@ sets ``uint8``, while the sample uses default model precision which is usually `
 Before using the sample, refer to the following requirements:
 
 - The sample accepts any file format supported by ``core.read_model``.
-- The sample has been validated with: `yolo-v3-tf <https://docs.openvino.ai/2024/omz_models_model_yolo_v3_tf.html>`__,
-  `face-detection-0200 <https://docs.openvino.ai/2024/omz_models_model_face_detection_0200.html>`__ models.
+- The sample has been validated with: `yolo-v3-tf <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/yolo-v3-tf/README.md>`__,
+  `face-detection-0200 <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/face-detection-0200/README.md>`__ models.
 - To build the sample, use instructions available at :ref:`Build the Sample Applications <build-samples>`
   section in "Get Started with Samples" guide.
 

diff --git a/...penvino-workflow/running-inference/optimize-inference/general-optimizations.rst b/...penvino-workflow/running-inference/optimize-inference/general-optimizations.rst
@@ -60,7 +60,7 @@ Below are example-codes for the regular and async-based approaches to compare:
 
 
 The technique can be generalized to any available parallel slack. For example, you can do inference and simultaneously encode the resulting or previous frames or run further inference, like emotion detection on top of the face detection results.
-Refer to the `Object Detection C++ Demo <https://docs.openvino.ai/2024/omz_demos_object_detection_demo_cpp.html>`__ , `Object Detection Python Demo <https://docs.openvino.ai/2024/omz_demos_object_detection_demo_python.html>`__ (latency-oriented Async API showcase) and :doc:`Benchmark App Sample <../../../learn-openvino/openvino-samples/benchmark-tool>` for complete examples of the Async API in action.
+Refer to the `Object Detection C++ Demo <https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/object_detection_demo/cpp/README.md>`__ , `Object Detection Python Demo <https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/object_detection_demo/python/README.md>`__ (latency-oriented Async API showcase) and :doc:`Benchmark App Sample <../../../learn-openvino/openvino-samples/benchmark-tool>` for complete examples of the Async API in action.
 
 .. note::
 

diff --git a/docs/notebooks/explainable-ai-1-basic-with-output.rst b/docs/notebooks/explainable-ai-1-basic-with-output.rst
@@ -37,7 +37,7 @@ notebook:
 .. image:: https://github.com/openvinotoolkit/openvino_xai/assets/17028475/ccb67c0b-c58e-4beb-889f-af0aff21cb66
 
 A pre-trained `MobileNetV3
-model <https://docs.openvino.ai/2024/omz_models_model_mobilenet_v3_small_1_0_224_tf.html>`__
+model <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/mobilenet-v3-small-1.0-224-tf/README.md>`__
 from `Open Model
 Zoo <https://github.com/openvinotoolkit/open_model_zoo/>`__ is used in
 this tutorial.