Replaced https://download.01.org/ links with https://storage.openvino…

…toolkit.org/

docs/IE_DG/Samples_Overview.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
 
-You can download the [pre-trained models](@ref omz_models_intel_index) using the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or from [https://download.01.org/opencv/](https://download.01.org/opencv/).
+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).
 
 ## Build the Sample Applications
 

docs/install_guides/installing-openvino-raspbian.md

@@ -60,7 +60,7 @@ This guide provides step-by-step instructions on how to install the OpenVINO™
 
 ## <a name="install-package"></a>Install the OpenVINO™ Toolkit for Raspbian* OS Package
 
-The guide assumes you downloaded the OpenVINO toolkit for Raspbian* OS. If you do not have a copy of the toolkit package file `l_openvino_toolkit_runtime_raspbian_p_<version>.tgz`, download the latest version from the [Intel® Open Source Technology Center](https://download.01.org/opencv/2021/openvinotoolkit/) and then return to this guide to proceed with the installation.
+The guide assumes you downloaded the OpenVINO toolkit for Raspbian* OS. If you do not have a copy of the toolkit package file `l_openvino_toolkit_runtime_raspbian_p_<version>.tgz`, download the latest version from the [OpenVINO™ Toolkit packages storage](https://storage.openvinotoolkit.org/repositories/openvino/packages/) and then return to this guide to proceed with the installation.
 
 > **NOTE**: The OpenVINO toolkit for Raspbian OS is distributed without installer, so you need to perform extra steps comparing to the [Intel® Distribution of OpenVINO™ toolkit for Linux* OS](installing-openvino-linux.md).
 

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 or pre-trained models. To download the pre-trained models, use the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or go to [https://download.01.org/opencv/](https://download.01.org/opencv/).
+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).
 
 > **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

@@ -34,9 +34,7 @@ ffmpeg -i cat.jpg -pix_fmt nv12 cat.yuv
 
 ## Running
 
-To run the sample, you can use public or pre-trained models. To download pre-trained models, use
-the OpenVINO™ [Model Downloader](@ref omz_tools_downloader_README)
-or go to [https://download.01.org/opencv/](https://download.01.org/opencv/).
+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).
 
 > **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

@@ -37,7 +37,7 @@ 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 or pre-trained models. To download the pre-trained models, use the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or go to [https://download.01.org/opencv/](https://download.01.org/opencv/).
+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).
 
 > **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/java/samples/README.md

@@ -57,9 +57,14 @@ Throughput: 148.29 FPS
 Upon the start-up the sample application reads command line parameters and loads a network and an image to the Inference
 Engine device. When inference is done, the application creates an output image/video.
 
-To download model ( .bin and .xml files must be downloaded) use:
-https://download.01.org/opencv/2019/open_model_zoo/R1/models_bin/face-detection-adas-0001/FP32/
+To download the model to your `my/download/directory`, use the [Model Downloader](@ref omz_tools_downloader_README):
 
+```sh
+cd <OPENVINO_DIR>/deployment_tools/tools/model_downloader/
+```
+```sh
+./downloader.py --name face-detection-adas-0001 --precisions FP32 -o my/download/directory
+```
 ## Build and run
 
 Build and run steps are similar to ```benchmark_app```, but you need to add an environment variable with OpenCV installation or build path before building:

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 the pre-trained models with the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or from [https://download.01.org/opencv/](https://download.01.org/opencv/).
+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).
 
 > **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 the pre-trained models with the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or from [https://download.01.org/opencv/](https://download.01.org/opencv/).
+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).
 
 > **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/object_detection_sample_ssd/README.md

@@ -55,7 +55,7 @@ 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 RMNet_SSD or other object-detection models. You can download the pre-trained models with the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or from [https://download.01.org/opencv/](https://download.01.org/opencv/).
+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).
 
 > **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/benchmark_app/README.md

@@ -120,7 +120,7 @@ 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 or Intel's pre-trained models. To download the models, use the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or go to [https://download.01.org/opencv/](https://download.01.org/opencv/).
+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).
 
 > **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).
 > 

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. To download the pre-trained models, use the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or go to [https://download.01.org/opencv/](https://download.01.org/opencv/).
+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).
 
 > **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_classification/README.md

@@ -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 or pre-trained models. To download the pre-trained models, use the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or go to [https://download.01.org/opencv/](https://download.01.org/opencv/).
+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).
 
 > **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

@@ -35,9 +35,7 @@ ffmpeg -i cat.jpg -pix_fmt nv12 cat.yuv
 
 ## Running
 
-To run the sample, you can use public or pre-trained models. To download pre-trained models, use
-the OpenVINO™ [Model Downloader](@ref omz_tools_downloader_README)
-or go to [https://download.01.org/opencv/](https://download.01.org/opencv/).
+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).
 
 > **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_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) or go to [https://download.01.org/opencv/](https://download.01.org/opencv/).
+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).
 
 > **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/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 or pre-trained models. To download the pre-trained models, use the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or go to [https://download.01.org/opencv/](https://download.01.org/opencv/).
+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).
 
 > **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/tools/benchmark_tool/README.md

@@ -136,7 +136,7 @@ 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 or Intel's pre-trained models. To download the models, use the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or go to [https://download.01.org/opencv/](https://download.01.org/opencv/).
+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).
 
 > **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).
 

tools/benchmark/README.md

@@ -144,7 +144,7 @@ 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 demo, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO [Model Downloader](https://github.com/opencv/open_model_zoo/tree/2018/model_downloader) or go to [https://download.01.org/opencv/](https://download.01.org/opencv/).
+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).
 
 > **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).