change repo name to openvino in readme files

README.md

@@ -1,5 +1,5 @@
 # [OpenVINO™ Toolkit](https://01.org/openvinotoolkit) - Deep Learning Deployment Toolkit repository
-[![Stable release](https://img.shields.io/badge/version-2020.1-green.svg)](https://github.com/opencv/dldt/releases/tag/2020.1)
+[![Stable release](https://img.shields.io/badge/version-2020.3-green.svg)](https://github.com/openvinotoolkit/openvino/releases/tag/2020.3.0)
 [![Apache License Version 2.0](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE)
 
 This toolkit allows developers to deploy pre-trained deep learning models 
@@ -36,7 +36,7 @@ with us doing the following steps:
 
 * Make sure you can build the product and run all tests and samples with your patch
 * In case of a larger feature, provide relevant unit tests and one or more sample
-* Submit a pull request at https://github.com/opencv/dldt/pulls
+* Submit a pull request at https://github.com/openvinotoolkit/openvino/pulls
 
 We will review your contribution and, if any additional fixes or modifications 
 are necessary, may give some feedback to guide you. Your pull request will be 
@@ -46,7 +46,7 @@ merged into GitHub* repositories if accepted.
 Please report questions, issues and suggestions using:
 
 * The `openvino` [tag on StackOverflow]\*
-* [GitHub* Issues](https://github.com/opencv/dldt/issues) 
+* [GitHub* Issues](https://github.com/openvinotoolkit/openvino/issues) 
 * [Forum](https://software.intel.com/en-us/forums/computer-vision)
 
 ---

build-instruction.md

@@ -28,7 +28,6 @@
 - [Add Inference Engine to Your Project](#add-inference-engine-to-your-project)
 - [(Optional) Additional Installation Steps for the Intel® Movidius™ Neural Compute Stick and Neural Compute Stick 2](#optional-additional-installation-steps-for-the-intel-movidius-neural-compute-stick-and-neural-compute-stick-2)
   - [For Linux, Raspbian Stretch* OS](#for-linux-raspbian-stretch-os)
-  - [For Windows](#for-windows-1)
 - [Next Steps](#next-steps)
 - [Additional Resources](#additional-resources)
 
@@ -60,12 +59,12 @@ The software was validated on:
 - [CMake]\* 3.11 or higher
 - GCC\* 4.8 or higher to build the Inference Engine
 - Python 2.7 or higher for Inference Engine Python API wrapper
-- (Optional) [Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 19.41.14441].
+- (Optional) [Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 20.13.16352].
 
 ### Build Steps
 1. Clone submodules:
     ```sh
-    cd dldt
+    cd openvino
     git submodule update --init --recursive
     ```
 2. Install build dependencies using the `install_dependencies.sh` script in the
@@ -78,7 +77,7 @@ The software was validated on:
    ```
 3. By default, the build enables the Inference Engine GPU plugin to infer models
    on your Intel® Processor Graphics. This requires you to
-   [Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 19.41.14441]
+   [Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 20.13.16352]
    before running the build. If you don't want to use the GPU plugin, use the
    `-DENABLE_CLDNN=OFF` CMake build option and skip the installation of the
    Intel® Graphics Compute Runtime for OpenCL™ Driver.
@@ -172,10 +171,10 @@ Native compilation of the Inference Engine is the most straightforward solution.
   sudo apt-get install -y git cmake libusb-1.0-0-dev
   ```
 
-2. Go to the cloned `dldt` repository:
+2. Go to the cloned `openvino` repository:
 
   ```bash
-  cd dldt
+  cd openvino
   ```
 
 3. Initialize submodules:
@@ -262,15 +261,15 @@ with the following content:
 5. Run Docker\* container with mounted source code folder from host:
 
   ```bash
-  docker run -it -v /absolute/path/to/dldt:/dldt ie_cross_armhf /bin/bash
+  docker run -it -v /absolute/path/to/openvino:/openvino ie_cross_armhf /bin/bash
   ```
 
 6. While in the container:
 
-    1. Go to the cloned `dldt` repository:
+    1. Go to the cloned `openvino` repository:
 
       ```bash
-      cd dldt
+      cd openvino
       ```
 
     2. Create a build folder:
@@ -291,8 +290,8 @@ with the following content:
       ```
 
 7. Press **Ctrl+D** to exit from Docker. You can find the resulting binaries
-   in the `dldt/bin/armv7l/` directory and the OpenCV*
-   installation in the `dldt/inference-engine/temp`.
+   in the `openvino/bin/armv7l/` directory and the OpenCV*
+   installation in the `openvino/inference-engine/temp`.
 
 >**NOTE**: Native applications that link to cross-compiled Inference Engine
 library require an extra compilation flag `-march=armv7-a`.
@@ -381,8 +380,8 @@ cmake -G "Visual Studio 15 2017 Win64" -T "Intel C++ Compiler 18.0" ^
 
 6. Before running the samples, add paths to the TBB and OpenCV binaries used for
    the build to the `%PATH%` environment variable. By default, TBB binaries are
-   downloaded by the CMake-based script to the `<dldt_repo>/inference-engine/temp/tbb/bin`
-   folder, OpenCV binaries to the `<dldt_repo>/inference-engine/temp/opencv_4.3.0/opencv/bin`
+   downloaded by the CMake-based script to the `<openvino_repo>/inference-engine/temp/tbb/bin`
+   folder, OpenCV binaries to the `<openvino_repo>/inference-engine/temp/opencv_4.3.0/opencv/bin`
    folder.
 
 ### Additional Build Options
@@ -437,7 +436,7 @@ cmake -G "Visual Studio 15 2017 Win64" -T "Intel C++ Compiler 18.0" ^
 call "C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2018\windows\bin\ipsxe-comp-vars.bat" intel64 vs2017
 set CXX=icl
 set CC=icl
-:: clean TBBROOT value set by ipsxe-comp-vars.bat, required TBB package will be downloaded by dldt cmake script
+:: clean TBBROOT value set by ipsxe-comp-vars.bat, required TBB package will be downloaded by openvino cmake script
 set TBBROOT=
 cmake -G Ninja -Wno-dev -DCMAKE_BUILD_TYPE=Release ..
 cmake --build . --config Release
@@ -461,7 +460,7 @@ The software was validated on:
 
 1. Clone submodules:
     ```sh
-    cd dldt
+    cd openvino
     git submodule update --init --recursive
     ```
 2. Install build dependencies using the `install_dependencies.sh` script in the
@@ -545,7 +544,7 @@ This section describes how to build Inference Engine for Android x86 (64-bit) op
 
 2. Clone submodules
   ```sh
-  cd dldt
+  cd openvino
   git submodule update --init --recursive
   ```
 
@@ -610,7 +609,7 @@ before running the Inference Engine build:
 For CMake projects, set the `InferenceEngine_DIR` environment variable:
 
 ```sh
-export InferenceEngine_DIR=/path/to/dldt/build/
+export InferenceEngine_DIR=/path/to/openvino/build/
 ```
 
 Then you can find Inference Engine by `find_package`:
@@ -660,20 +659,6 @@ sudo ldconfig
 rm 97-myriad-usbboot.rules
 ```
 
-### For Windows
-
-For Intel® Movidius™ Neural Compute Stick and Intel® Neural Compute Stick 2,
-install the Movidius™ VSC driver:
-
-1. Go to the `<DLDT_ROOT_DIR>/inference-engine/thirdparty/movidius/MovidiusDriver`
-   directory, where the `DLDT_ROOT_DIR` is the directory to which the DLDT
-   repository was cloned.
-2. Right click on the `Movidius_VSC_Device.inf` file and choose **Install** from
-   the pop-up menu.
-
-You have installed the driver for your Intel® Movidius™ Neural Compute Stick
-or Intel® Neural Compute Stick 2.
-
 ## Next Steps
 
 Congratulations, you have built the Inference Engine. To get started with the
@@ -706,7 +691,7 @@ This target collects all dependencies, prepares the nGraph package and copies it
 
 [Intel® Distribution of OpenVINO™]:https://software.intel.com/en-us/openvino-toolkit
 [CMake]:https://cmake.org/download/
-[Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 19.41.14441]:https://github.com/intel/compute-runtime/releases/tag/19.41.14441
+[Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 20.13.16352]:https://github.com/intel/compute-runtime/releases/tag/20.13.16352
 [MKL-DNN repository]:https://github.com/intel/mkl-dnn/releases/download/v0.19/mklml_lnx_2019.0.5.20190502.tgz
 [MKL-DNN repository for Windows]:(https://github.com/intel/mkl-dnn/releases/download/v0.19/mklml_win_2019.0.5.20190502.zip)
 [OpenBLAS]:https://sourceforge.net/projects/openblas/files/v0.2.14/OpenBLAS-v0.2.14-Win64-int64.zip/download

get-started-linux.md

@@ -1,7 +1,7 @@
 # Get Started with OpenVINO™ Deep Learning Deployment Toolkit (DLDT) on Linux*
 
 This guide provides you with the information that will help you to start using 
-the DLDT on Linux\*. With this guide, you will learn how to:
+the OpenVINO on Linux\*. With this guide, you will learn how to:
 
 1. [Configure the Model Optimizer](#configure-the-model-optimizer)
 2. [Prepare a model for sample inference](#prepare-a-model-for-sample-inference)
@@ -10,13 +10,13 @@ the DLDT on Linux\*. With this guide, you will learn how to:
 3. [Run the Image Classification Sample Application with the model](#run-the-image-classification-sample-application)
 
 ## Prerequisites
-1. This guide assumes that you have already cloned the `dldt` repo and 
+1. This guide assumes that you have already cloned the `openvino` repo and 
    successfully built the Inference Engine and Samples using the 
    [build instructions](inference-engine/README.md). 
 2. The original structure of the repository directories remains unchanged.
 
-> **NOTE**: Below, the directory to which the `dldt` repository is cloned is 
-referred to as `<DLDT_DIR>`.  
+> **NOTE**: Below, the directory to which the `openvino` repository is cloned is 
+referred to as `<OPENVINO_DIR>`.  
 
 ## Configure the Model Optimizer
 
@@ -53,7 +53,7 @@ If you see error messages, check for any missing dependencies.
 
 1.  Go to the Model Optimizer prerequisites directory:
 ```sh
-cd <DLDT_DIR>/model_optimizer/install_prerequisites
+cd <OPENVINO_DIR>/model_optimizer/install_prerequisites
 ```
 2.  Run the script to configure the Model Optimizer for Caffe,
     TensorFlow, MXNet, Kaldi\*, and ONNX:
@@ -68,7 +68,7 @@ Configure individual frameworks separately **ONLY** if you did not select
 
 1.  Go to the Model Optimizer prerequisites directory:
 ```sh
-cd <DLDT_DIR>/model_optimizer/install_prerequisites
+cd <OPENVINO_DIR>/model_optimizer/install_prerequisites
 ```
 2.  Run the script for your model framework. You can run more than one script:
 
@@ -162,12 +162,12 @@ as `<models_dir>` below) with the Model Downloader:
 
    **For CPU (FP32):**
    ```sh  
-   python3 <DLDT_DIR>/model_optimizer/mo.py --input_model <models_dir>/classification/squeezenet/1.1/caffe/squeezenet1.1.caffemodel --data_type FP32 --output_dir <ir_dir>
+   python3 <OPENVINO_DIR>/model_optimizer/mo.py --input_model <models_dir>/classification/squeezenet/1.1/caffe/squeezenet1.1.caffemodel --data_type FP32 --output_dir <ir_dir>
    ```
 
    **For GPU and MYRIAD (FP16):**
    ```sh  
-   python3 <DLDT_DIR>/model_optimizer/mo.py --input_model <models_dir>/classification/squeezenet/1.1/caffe/squeezenet1.1.caffemodel --data_type FP16 --output_dir <ir_dir>
+   python3 <OPENVINO_DIR>/model_optimizer/mo.py --input_model <models_dir>/classification/squeezenet/1.1/caffe/squeezenet1.1.caffemodel --data_type FP16 --output_dir <ir_dir>
    ``` 
    After the Model Optimizer script is completed, the produced IR files (`squeezenet1.1.xml`, `squeezenet1.1.bin`) are in the specified `<ir_dir>` directory.
 
@@ -184,14 +184,14 @@ Now you are ready to run the Image Classification Sample Application.
 
 The Inference Engine sample applications are automatically compiled when you 
 built the Inference Engine using the [build instructions](inference-engine/README.md). 
-The binary files are located in the `<DLDT_DIR>/inference-engine/bin/intel64/Release` 
+The binary files are located in the `<OPENVINO_DIR>/inference-engine/bin/intel64/Release` 
 directory.
 
 To run the Image Classification sample application with an input image on the prepared IR: 
 
 1. Go to the samples build directory:
    ```sh
-   cd <DLDT_DIR>/inference-engine/bin/intel64/Release
+   cd <OPENVINO_DIR>/inference-engine/bin/intel64/Release
 
 2. Run the sample executable with specifying the `car.png` file from the 
    `<OPENVINO_DIR>/scripts/demo/` directory as an input