<!-- GET STARTED category -->
<tab type="usergroup" title="GET STARTED" url="index.html">
<!-- Install Directly -->
- <tab type="usergroup" title="Install Directly" url=""><!--automatically generated-->
+ <tab type="usergroup" title="Installation Guides" url=""><!--automatically generated-->
<tab type="usergroup" title="Linux" url="@ref openvino_docs_install_guides_installing_openvino_linux">
<tab type="user" title="Install Intel® Distribution of OpenVINO™ toolkit for Linux* OS" url="@ref openvino_docs_install_guides_installing_openvino_linux"/>
<tab type="user" title="[DEPRECATED] Install Intel® Distribution of OpenVINO™ toolkit for Linux with FPGA Support" url="@ref openvino_docs_install_guides_installing_openvino_linux_fpga"/>
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<tab type="user" title="Raspbian OS" url="@ref openvino_docs_install_guides_installing_openvino_raspbian"/>
+ <tab type="user" title="DL Workbench Installation Guide" url="./workbench_docs_Workbench_DG_Install_Workbench.html"/><!-- Link to the original Workbench topic -->
</tab>
<!-- Install From Images and Repositories -->
- <tab type="usergroup" title="Install From Images and Repositories" url=""><!--automatically generated-->
+ <tab type="usergroup" title="Install From Images and Repositories" url="@ref openvino_docs_install_guides_installing_openvino_images">
<tab type="usergroup" title="Docker" url="@ref openvino_docs_install_guides_installing_openvino_docker_linux">
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<tab type="user" title="Install Intel® Distribution of OpenVINO™ toolkit for Windows* from a Docker* Image" url="@ref openvino_docs_install_guides_installing_openvino_docker_windows"/>
</tab>
+ <tab type="user" title="Docker with DL Workbench" url="./workbench_docs_Workbench_DG_Install_from_Docker_Hub.html"/><!-- Link to the original Workbench topic -->
<tab type="user" title="APT" url="@ref openvino_docs_install_guides_installing_openvino_apt"/>
<tab type="user" title="YUM" url="@ref openvino_docs_install_guides_installing_openvino_yum"/>
<tab type="user" title="Anaconda Cloud" url="@ref openvino_docs_install_guides_installing_openvino_conda"/>
<tab type="user" title="Linux" url="@ref openvino_docs_get_started_get_started_linux"/>
<tab type="user" title="Windows" url="@ref openvino_docs_get_started_get_started_windows"/>
<tab type="user" title="macOS" url="@ref openvino_docs_get_started_get_started_macos"/>
+ <tab type="user" title="Get Started with OpenVINO via DL Workbench" url="@ref openvino_docs_get_started_get_started_dl_workbench"/>
<tab type="user" title="Legal Information" url="@ref openvino_docs_Legal_Information"/>
+ <tab type="user" title="Introduction to DL Workbench" url="./openvino_docs_get_started_get_started_dl_workbench.html"/><!-- Link to the original Workbench topic -->
</tab>
<!-- Configuration for Hardware -->
<tab type="usergroup" title="Configuration for Hardware" url=""><!--automatically generated-->
<tab type="user" title="Automatic Speech Recognition C++ Sample" url="@ref openvino_inference_engine_samples_speech_sample_README"/>
<tab type="user" title="Neural Style Transfer C++ Sample" url="@ref openvino_inference_engine_samples_style_transfer_sample_README"/>
<tab type="user" title="Neural Style Transfer Python* Sample" url="@ref openvino_inference_engine_ie_bridges_python_sample_style_transfer_sample_README"/>
- <tab type="user" title="Benchmark C++ App" url="@ref openvino_inference_engine_samples_benchmark_app_README"/>
- <tab type="user" title="Benchmark Python* App" url="@ref openvino_inference_engine_tools_benchmark_tool_README"/>
+ <tab type="user" title="Benchmark C++ Tool" url="@ref openvino_inference_engine_samples_benchmark_app_README"/>
+ <tab type="user" title="Benchmark Python* Tool" url="@ref openvino_inference_engine_tools_benchmark_tool_README"/>
</tab>
<!-- DL Streamer Examples -->
## Introduction
-The Intel® Distribution of OpenVINO™ toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNN), the toolkit extends computer vision (CV) workloads across Intel® hardware, maximizing performance. The Intel® Distribution of OpenVINO™ toolkit includes the Intel® Deep Learning Deployment Toolkit (Intel® DLDT).
+OpenVINO™ toolkit is a comprehensive toolkit for quickly developing applications and solutions that solve a variety of tasks including emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, and many others. Based on latest generations of artificial neural networks, including Convolutional Neural Networks (CNNs), recurrent and attention-based networks, the toolkit extends computer vision and non-vision workloads across Intel® hardware, maximizing performance. It accelerates applications with high-performance, AI and deep learning inference deployed from edge to cloud.
The Intel® Distribution of OpenVINO™ toolkit for Linux\*:
- Enables CNN-based deep learning inference on the edge
| [Inference Engine Code Samples](../IE_DG/Samples_Overview.md) | A set of simple console applications demonstrating how to utilize specific OpenVINO capabilities in an application and how to perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more. |
| [Demo Applications](@ref omz_demos_README) | A set of simple console applications that provide robust application templates to help you implement specific deep learning scenarios. |
| Additional Tools | A set of tools to work with your models including [Accuracy Checker utility](@ref omz_tools_accuracy_checker_README), [Post-Training Optimization Tool Guide](@ref pot_README), [Model Downloader](@ref omz_tools_downloader_README) and other |
-| [Documentation for Pre-Trained Models ](@ref omz_models_intel_index) | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/opencv/open_model_zoo) |
+| [Documentation for Pre-Trained Models ](@ref omz_models_intel_index) | Documentation for the pre-trained models available in the [Open Model Zoo repo](https://github.com/opencv/open_model_zoo). |
+| Deep Learning Streamer (DL Streamer) | Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. For the DL Streamer documentation, see [DL Streamer Samples](@ref gst_samples_README), [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/), [Elements](https://github.com/opencv/gst-video-analytics/wiki/Elements), [Tutorial](https://github.com/opencv/gst-video-analytics/wiki/DL%20Streamer%20Tutorial). |
## System Requirements
```sh
cd ~/Downloads/
```
-By default, the file is saved as `l_openvino_toolkit_p_<version>.tgz`.
-
+ By default, the file is saved as `l_openvino_toolkit_p_<version>.tgz`.
3. Unpack the .tgz file:
```sh
tar -xvzf l_openvino_toolkit_p_<version>.tgz
```
-The files are unpacked to the `l_openvino_toolkit_p_<version>` directory.
-
+ The files are unpacked to the `l_openvino_toolkit_p_<version>` directory.
4. Go to the `l_openvino_toolkit_p_<version>` directory:
```sh
cd l_openvino_toolkit_p_<version>
```
-If you have a previous version of the Intel Distribution of OpenVINO
+ If you have a previous version of the Intel Distribution of OpenVINO
toolkit installed, rename or delete these two directories:
- `~/inference_engine_samples_build`
- `~/openvino_models`
-**Installation Notes:**
-
-- Choose an installation option and run the related script as root.
-- You can use either a GUI installation wizard or command-line instructions (CLI).
-- Screenshots are provided for the GUI, but not for CLI. The following information also applies to CLI and will be helpful to your installation where you will be presented with the same choices and tasks.
+ **Installation Notes:**
+ - Choose an installation option and run the related script as root.
+ - You can use either a GUI installation wizard or command-line instructions (CLI).
+ - Screenshots are provided for the GUI, but not for CLI. The following information also applies to CLI and will be helpful to your installation where you will be presented with the same choices and tasks.
5. Choose your installation option:
- **Option 1:** GUI Installation Wizard:
```sh
sudo -E ./install_openvino_dependencies.sh
```
-The dependencies are installed. Continue to the next section to set your environment variables.
+ The dependencies are installed. Continue to the next section to set your environment variables.
## <a name="set-the-environment-variables"></a>Set the Environment Variables
```sh
./demo_squeezenet_download_convert_run.sh
```
-This verification script downloads a SqueezeNet model, uses the Model Optimizer to convert the model to the .bin and .xml Intermediate Representation (IR) files. The Inference Engine requires this model conversion so it can use the IR as input and achieve optimum performance on Intel hardware.<br>
-This verification script builds the [Image Classification Sample Async](../../inference-engine/samples/classification_sample_async/README.md) application and run it with the `car.png` image located in the demo directory. When the verification script completes, you will have the label and confidence for the top-10 categories:
-
+ This verification script downloads a SqueezeNet model, uses the Model Optimizer to convert the model to the .bin and .xml Intermediate Representation (IR) files. The Inference Engine requires this model conversion so it can use the IR as input and achieve optimum performance on Intel hardware.<br>
+ This verification script builds the [Image Classification Sample Async](../../inference-engine/samples/classification_sample_async/README.md) application and run it with the `car.png` image located in the demo directory. When the verification script completes, you will have the label and confidence for the top-10 categories:
+ 
3. Run the **Inference Pipeline verification script**:
```sh
./demo_security_barrier_camera.sh
```
-This script downloads three pre-trained model IRs, builds the [Security Barrier Camera Demo](@ref omz_demos_security_barrier_camera_demo_README) application, and runs it with the downloaded models and the `car_1.bmp` image from the `demo` directory to show an inference pipeline. The verification script uses vehicle recognition in which vehicle attributes build on each other to narrow in on a specific attribute.
-
- First, an object is identified as a vehicle. This identification is used as input to the next model, which identifies specific vehicle attributes, including the license plate. Finally, the attributes identified as the license plate are used as input to the third model, which recognizes specific characters in the license plate.
-
- When the verification script completes, you will see an image that displays the resulting frame with detections rendered as bounding boxes, and text:
- 
+ This script downloads three pre-trained model IRs, builds the [Security Barrier Camera Demo](@ref omz_demos_security_barrier_camera_demo_README) application, and runs it with the downloaded models and the `car_1.bmp` image from the `demo` directory to show an inference pipeline. The verification script uses vehicle recognition in which vehicle attributes build on each other to narrow in on a specific attribute.<br>
+ First, an object is identified as a vehicle. This identification is used as input to the next model, which identifies specific vehicle attributes, including the license plate. Finally, the attributes identified as the license plate are used as input to the third model, which recognizes specific characters in the license plate.<br>
+ When the verification script completes, you will see an image that displays the resulting frame with detections rendered as bounding boxes, and text:
+ 
4. Close the image viewer window to complete the verification script.
```sh
./install_NEO_OCL_driver.sh
```
-The drivers are not included in the package and the script downloads them. Make sure you have the
-internet connection for this step.
-
-The script compares the driver version on the system to the current version.
-If the driver version on the system is higher or equal to the current version, the script does
-not install a new driver.
-If the version of the driver is lower than the current version, the script uninstalls the lower
-and installs the current version with your permission:
-
-Higher hardware versions require a higher driver version, namely 20.35 instead of 19.41.
-If the script fails to uninstall the driver, uninstall it manually.
-During the script execution, you may see the following command line output:
- - Add OpenCL user to video group
-Ignore this suggestion and continue.
+ The drivers are not included in the package and the script downloads them. Make sure you have the internet connection for this step.<br>
+ The script compares the driver version on the system to the current version. If the driver version on the system is higher or equal to the current version, the script does
+not install a new driver. If the version of the driver is lower than the current version, the script uninstalls the lower and installs the current version with your permission:
+ 
+ Higher hardware versions require a higher driver version, namely 20.35 instead of 19.41. If the script fails to uninstall the driver, uninstall it manually. During the script execution, you may see the following command line output:
+```sh
+Add OpenCL user to video group
+```
+ Ignore this suggestion and continue.
4. **Optional** Install header files to allow compiling a new code. You can find the header files at [Khronos OpenCL™ API Headers](https://github.com/KhronosGroup/OpenCL-Headers.git).
## <a name="additional-NCS-steps"></a>Steps for Intel® Neural Compute Stick 2
```sh
sudo usermod -a -G users "$(whoami)"
```
-Log out and log in for it to take effect.
-
+ Log out and log in for it to take effect.
2. To perform inference on Intel® Neural Compute Stick 2, install the USB rules as follows:
```sh
sudo cp /opt/intel/openvino_2021/inference_engine/external/97-myriad-usbboot.rules /etc/udev/rules.d/
projects / platform / upstream / dldt.git / commitdiff