[DOCS] Updating reference to OMZ (#2381)

Updating reference to Open Model Zoo repository. According to changes in
docs: openvinotoolkit/openvino#26523

notebooks/action-recognition-webcam/README.md

@@ -10,7 +10,7 @@ Human action recognition finds actions over time in a video. The list of actions
 
 ## Notebook Contents
 
-This notebook demonstrates live human action recognition with OpenVINO, using the [Action Recognition Models](https://docs.openvino.ai/2024/omz_models_group_intel.html#action-recognition-models) from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo), specifically the Encoder and Decoder from [action-recognition-0001](https://docs.openvino.ai/2024/omz_models_model_action_recognition_0001.html). Both models create a sequence to sequence (`"seq2seq"`)<sup id="a1">[1](#f1)</sup> system to identify the  human activities for [Kinetics-400 dataset](https://arxiv.org/pdf/1705.06950.pdf). The models use the Video Transformer approach with ResNet34 encoder<sup id="a2">[2](#f2)</sup>. The notebook shows how to create the following pipeline:
+This notebook demonstrates live human action recognition with OpenVINO, using the [Action Recognition Models](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/index.md#action-recognition-models) from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo), specifically the Encoder and Decoder from [action-recognition-0001](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/action-recognition-0001/README.md). Both models create a sequence to sequence (`"seq2seq"`)<sup id="a1">[1](#f1)</sup> system to identify the  human activities for [Kinetics-400 dataset](https://arxiv.org/pdf/1705.06950.pdf). The models use the Video Transformer approach with ResNet34 encoder<sup id="a2">[2](#f2)</sup>. The notebook shows how to create the following pipeline:
 
 <p align="center" width="100%">
     <img width="80%" src="https://user-images.githubusercontent.com/10940214/148401661-477aebcd-f2d0-4771-b107-4b37f94d0b1e.jpeg">
@@ -36,6 +36,6 @@ For details, please refer to [Installation Guide](../../README.md).
 
 * [OpenVINO notebooks](https://github.com/openvinotoolkit/openvino_notebooks)
 * [Model Conversion API](https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html)
-* [Action Recognition Demo (OpenVINO - No notebooks)](https://docs.openvino.ai/2024/omz_demos_action_recognition_demo_python.html)
+* [Action Recognition Demo (OpenVINO - No notebooks)](https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/action_recognition_demo/python/README.md)
 
 <img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=5b5a4db0-7875-4bfb-bdbd-01698b5b1a77&file=notebooks/action-recognition-webcam/README.md" />

notebooks/explainable-ai-1-basic/explainable-ai-1-basic.ipynb

@@ -24,7 +24,7 @@
     "\n",
     "![](https://github.com/openvinotoolkit/openvino_xai/assets/17028475/ccb67c0b-c58e-4beb-889f-af0aff21cb66)\n",
     "\n",
-    "A pre-trained [MobileNetV3 model](https://docs.openvino.ai/2024/omz_models_model_mobilenet_v3_small_1_0_224_tf.html) from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used in this tutorial.\n",
+    "A pre-trained [MobileNetV3 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.\n",
     "\n",
     "\n",
     "#### Table of contents:\n",

notebooks/handwritten-ocr/README.md

@@ -11,7 +11,7 @@ This tutorial demonstrates optical character recognition for handwritten Chinese
 
 ## Notebook Contents
 
-This notebook provides a tutorial on how to use OCR for handwritten Japanese and simplified Chinese. Models used for this notebook are [`handwritten-japanese-recognition-0001`](https://docs.openvino.ai/2024/omz_models_model_handwritten_japanese_recognition_0001.html) and [`handwritten-simplified-chinese-0001`](https://docs.openvino.ai/2024/omz_models_model_handwritten_simplified_chinese_recognition_0001.html). To decode model output to readable text [`kondate_nakayosi`](https://github.com/openvinotoolkit/open_model_zoo/blob/master/data/dataset_classes/kondate_nakayosi.txt) and [`scut_ept`](https://github.com/openvinotoolkit/open_model_zoo/blob/master/data/dataset_classes/scut_ept.txt) charlists are used. Both models are available from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/).
+This notebook provides a tutorial on how to use OCR for handwritten Japanese and simplified Chinese. Models used for this notebook are [`handwritten-japanese-recognition-0001`](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/handwritten-japanese-recognition-0001/README.md) and [`handwritten-simplified-chinese-0001`](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/handwritten-simplified-chinese-recognition-0001/README.md). To decode model output to readable text [`kondate_nakayosi`](https://github.com/openvinotoolkit/open_model_zoo/blob/master/data/dataset_classes/kondate_nakayosi.txt) and [`scut_ept`](https://github.com/openvinotoolkit/open_model_zoo/blob/master/data/dataset_classes/scut_ept.txt) charlists are used. Both models are available from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/).
 
 ## Installation Instructions
 

notebooks/handwritten-ocr/handwritten-ocr.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "In this tutorial, we perform optical character recognition (OCR) for handwritten Chinese (simplified) and Japanese. An OCR tutorial using the Latin alphabet is available in [notebook 208](../optical-character-recognition/optical-character-recognition.ipynb). This model is capable of processing only one line of symbols at a time.\n",
     "\n",
-    "The models used in this notebook are [`handwritten-japanese-recognition-0001`](https://docs.openvino.ai/2024/omz_models_model_handwritten_japanese_recognition_0001.html) and [`handwritten-simplified-chinese-0001`](https://docs.openvino.ai/2024/omz_models_model_handwritten_simplified_chinese_recognition_0001.html). To decode model outputs as readable text [`kondate_nakayosi`](https://github.com/openvinotoolkit/open_model_zoo/blob/master/data/dataset_classes/kondate_nakayosi.txt) and [`scut_ept`](https://github.com/openvinotoolkit/open_model_zoo/blob/master/data/dataset_classes/scut_ept.txt) charlists are used. Both models are available on [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/).\n",
+    "The models used in this notebook are [`handwritten-japanese-recognition-0001`](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/handwritten-japanese-recognition-0001/README.md) and [`handwritten-simplified-chinese-0001`](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/handwritten-simplified-chinese-recognition-0001/README.md). To decode model outputs as readable text [`kondate_nakayosi`](https://github.com/openvinotoolkit/open_model_zoo/blob/master/data/dataset_classes/kondate_nakayosi.txt) and [`scut_ept`](https://github.com/openvinotoolkit/open_model_zoo/blob/master/data/dataset_classes/scut_ept.txt) charlists are used. Both models are available on [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/).\n",
     "\n",
     "\n",
     "#### Table of contents:\n",

notebooks/hello-detection/README.md

@@ -12,7 +12,7 @@ This notebook demonstrates how to do inference with detection model.
 
 ## Notebook Contents
 
-In this basic introduction to detection with OpenVINO, the [horizontal-text-detection-0001](https://docs.openvino.ai/2024/omz_models_model_horizontal_text_detection_0001.html) model from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used. It detects text in images and returns blob of data in shape of `[100, 5]`. For each detection, a description is in the `[x_min, y_min, x_max, y_max, conf]` format.
+In this basic introduction to detection with OpenVINO, the [horizontal-text-detection-0001](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/horizontal-text-detection-0001/README.md) model from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used. It detects text in images and returns blob of data in shape of `[100, 5]`. For each detection, a description is in the `[x_min, y_min, x_max, y_max, conf]` format.
 
 ## Installation Instructions
 

notebooks/hello-detection/hello-detection.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "A very basic introduction to using object detection models with OpenVINO™.\n",
     "\n",
-    "The [horizontal-text-detection-0001](https://docs.openvino.ai/2024/omz_models_model_horizontal_text_detection_0001.html) model from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used. It detects horizontal text in images and returns a blob of data in the shape of `[100, 5]`. Each detected text box is stored in the `[x_min, y_min, x_max, y_max, conf]` format, where the\n",
+    "The [horizontal-text-detection-0001](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/horizontal-text-detection-0001/README.md) model from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used. It detects horizontal text in images and returns a blob of data in the shape of `[100, 5]`. Each detected text box is stored in the `[x_min, y_min, x_max, y_max, conf]` format, where the\n",
     "`(x_min, y_min)` are the coordinates of the top left bounding box corner, `(x_max, y_max)` are the coordinates of the bottom right bounding box corner and `conf` is the confidence for the predicted class.\n",
     "\n",
     "\n",

notebooks/hello-segmentation/README.md

@@ -11,7 +11,7 @@ This notebook demonstrates how to do inference with segmentation model.
 
 ## Notebook Contents
 
-A very basic introduction to segmentation with OpenVINO. This notebook uses the [`road-segmentation-adas-0001`](https://docs.openvino.ai/2024/omz_models_model_road_segmentation_adas_0001.html) model from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) and an input image downloaded from [Mapillary Vistas](https://www.mapillary.com/dataset/vistas). ADAS stands for Advanced Driver Assistance Services. The model recognizes four classes: background, road, curb and mark.
+A very basic introduction to segmentation with OpenVINO. This notebook uses the [`road-segmentation-adas-0001`](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/road-segmentation-adas-0001/README.md) model from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) and an input image downloaded from [Mapillary Vistas](https://www.mapillary.com/dataset/vistas). ADAS stands for Advanced Driver Assistance Services. The model recognizes four classes: background, road, curb and mark.
 
 ## Installation Instructions
 

notebooks/hello-segmentation/hello-segmentation.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "A very basic introduction to using segmentation models with OpenVINO™.\n",
     "\n",
-    "In this tutorial, a pre-trained [road-segmentation-adas-0001](https://docs.openvino.ai/2024/omz_models_model_road_segmentation_adas_0001.html) model from the [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used. ADAS stands for Advanced Driver Assistance Services. The model recognizes four classes: background, road, curb and mark.\n",
+    "In this tutorial, a pre-trained [road-segmentation-adas-0001](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/road-segmentation-adas-0001/README.md) model from the [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used. ADAS stands for Advanced Driver Assistance Services. The model recognizes four classes: background, road, curb and mark.\n",
     "\n",
     "\n",
     "#### Table of contents:\n",

notebooks/hello-world/hello-world.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "This basic introduction to OpenVINO™ shows how to do inference with an image classification model.\n",
     "\n",
-    "A pre-trained [MobileNetV3 model](https://docs.openvino.ai/2024/omz_models_model_mobilenet_v3_small_1_0_224_tf.html) from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/) is used in this tutorial. For more information about how OpenVINO IR models are created, refer to the [TensorFlow to OpenVINO](../tensorflow-classification-to-openvino/tensorflow-classification-to-openvino.ipynb) tutorial.\n",
+    "A pre-trained [MobileNetV3 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. For more information about how OpenVINO IR models are created, refer to the [TensorFlow to OpenVINO](../tensorflow-classification-to-openvino/tensorflow-classification-to-openvino.ipynb) tutorial.\n",
     "\n",
     "\n",
     "#### Table of contents:\n",

notebooks/optical-character-recognition/README.md

@@ -10,7 +10,7 @@ In this tutorial optical character recognition is presented. This notebook is a
 
 ## Notebook Contents
 
-In addition to previously used [horizontal-text-detection-0001](https://docs.openvino.ai/2024/omz_models_model_horizontal_text_detection_0001.html) model, a[text-recognition-resnet](https://docs.openvino.ai/2024/omz_models_model_text_recognition_resnet_fc.html) model is used. This model reads tight aligned crop with detected text converted to a grayscale image and returns tensor that is easily decoded to predicted text. Both models are from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/).
+In addition to previously used [horizontal-text-detection-0001](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/horizontal-text-detection-0001/README.md) model, a[text-recognition-resnet](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/text-recognition-resnet-fc/README.md) model is used. This model reads tight aligned crop with detected text converted to a grayscale image and returns tensor that is easily decoded to predicted text. Both models are from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo/).
 
 ## Installation Instructions
 

notebooks/optical-character-recognition/optical-character-recognition.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "This tutorial demonstrates how to perform optical character recognition (OCR) with OpenVINO models. It is a continuation of the [hello-detection](../hello-detection/hello-detection.ipynb) tutorial, which shows only text detection.\n",
     "\n",
-    "The [horizontal-text-detection-0001](https://docs.openvino.ai/2024/omz_models_model_horizontal_text_detection_0001.html) and [text-recognition-resnet](https://docs.openvino.ai/2024/omz_models_model_text_recognition_resnet_fc.html) models are used together for text detection and then text recognition.\n",
+    "The [horizontal-text-detection-0001](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/horizontal-text-detection-0001/README.md) and [text-recognition-resnet](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/text-recognition-resnet-fc/README.md) models are used together for text detection and then text recognition.\n",
     "\n",
     "In this tutorial, Open Model Zoo tools including Model Downloader, Model Converter and Info Dumper are used to download and convert the models from [Open Model Zoo](https://github.com/openvinotoolkit/open_model_zoo). For more information, refer to the [model-tools](../model-tools/model-tools.ipynb) tutorial.\n",
     "\n",

notebooks/person-tracking-webcam/README.md

@@ -14,7 +14,7 @@ This notebook shows a person tracking scenario: it reads frames from an input vi
 ## Notebook Contents
 
 This tutorial uses the [Deep SORT](https://arxiv.org/abs/1703.07402) algorithm to perform object tracking.
-[person detection model]( https://docs.openvino.ai/2024/omz_models_model_person_detection_0202.html) is deployed to detect the person in each frame of the video, and [reidentification model]( https://docs.openvino.ai/2024/omz_models_model_person_reidentification_retail_0287.html) is used to output embedding vector to match a pair of images of a person by the cosine distance.
+[person detection model]( https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/person-detection-0202/README.md) is deployed to detect the person in each frame of the video, and [reidentification model]( https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/person-reidentification-retail-0287/README.md) is used to output embedding vector to match a pair of images of a person by the cosine distance.
 
 ## Installation Instructions
 
@@ -26,6 +26,6 @@ For details, please refer to [Installation Guide](../../README.md).
 
 * [OpenVINO notebooks](https://github.com/openvinotoolkit/openvino_notebooks)
 * [Model Conversion API](https://docs.openvino.ai/2024/openvino-workflow/model-preparation/convert-model-to-ir.html)
-* [Pedestrian Tracker C++ Demo](https://docs.openvino.ai/2024/omz_demos_pedestrian_tracker_demo_cpp.html)
+* [Pedestrian Tracker C++ Demo](https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/pedestrian_tracker_demo/cpp/README.md)
 
 <img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=5b5a4db0-7875-4bfb-bdbd-01698b5b1a77&file=notebooks/person-tracking-webcam/README.md" />

notebooks/person-tracking-webcam/person-tracking.ipynb

@@ -164,10 +164,10 @@
     "\n",
     "> **NOTE**: Using a model outside the list can require different pre- and post-processing.\n",
     "\n",
-    "In this case, [person detection model]( https://docs.openvino.ai/2024/omz_models_model_person_detection_0202.html) is deployed to detect the person in each frame of the video, and [reidentification model]( https://docs.openvino.ai/2024/omz_models_model_person_reidentification_retail_0287.html) is used to output embedding vector to match a pair of images of a person by the cosine distance.\n",
+    "In this case, [person detection model]( https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/person-detection-0202/README.md) is deployed to detect the person in each frame of the video, and [reidentification model]( https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/person-reidentification-retail-0287/README.md) is used to output embedding vector to match a pair of images of a person by the cosine distance.\n",
     "\n",
     "\n",
-    "If you want to download another model (`person-detection-xxx` from [Object Detection Models list](https://docs.openvino.ai/2024/omz_models_group_intel.html#object-detection-models), `person-reidentification-retail-xxx` from [Reidentification Models list](https://docs.openvino.ai/2024/omz_models_group_intel.html#reidentification-models)), replace the name of the model in the code below."
+    "If you want to download another model (`person-detection-xxx` from [Object Detection Models list](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/index.md#object-detection-models), `person-reidentification-retail-xxx` from [Reidentification Models list](https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/index.md#reidentification-models)), replace the name of the model in the code below."
    ]
   },
   {

notebooks/style-transfer-webcam/README.md

@@ -22,5 +22,5 @@ For details, please refer to [Installation Guide](../../README.md).
 
 * [OpenVINO notebooks](https://github.com/openvinotoolkit/openvino_notebooks)
 * [Model Conversion API](https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html)
-* [Image Processing Demo](https://docs.openvino.ai/2024/omz_demos_image_processing_demo_cpp.html)
+* [Image Processing Demo](https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/image_processing_demo/cpp/README.md)
 <img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=5b5a4db0-7875-4bfb-bdbd-01698b5b1a77&file=notebooks/style-transfer-webcam/README.md" />