- These steps apply to 32-bit Raspbian* OS, which is an official OS for Raspberry Pi* boards.
- These steps have been validated with Raspberry Pi 3*.
- All steps in this guide are required unless otherwise stated.
- An internet connection is required to follow the steps in this guide. If you have access to the Internet through the proxy server only, please make sure that it is configured in your OS environment.
The 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 OpenVINO toolkit includes the Intel® Deep Learning Deployment Toolkit (Intel® DLDT).
The OpenVINO™ toolkit for Raspbian* OS includes the Inference Engine and the MYRIAD plugins. You can use it with the Intel® Neural Compute Stick 2 plugged in one of USB ports.
The OpenVINO toolkit for Raspbian OS is an archive with pre-installed header files and libraries. The following components are installed by default:
|Inference Engine||This is the engine that runs the deep learning model. It includes a set of libraries for an easy inference integration into your applications.|
|OpenCV*||OpenCV* community version compiled for Intel® hardware.|
|Sample Applications||A set of simple console applications demonstrating how to use Intel's Deep Learning Inference Engine in your applications.|
NOTE: With OpenVINO™ 2020.4 release, Intel® Movidius™ Neural Compute Stick is no longer supported.
This guide provides step-by-step instructions on how to install the OpenVINO™ toolkit for Raspbian* OS. Links are provided for each type of compatible hardware including downloads, initialization and configuration steps. The following steps will be covered:
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 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.
~/Downloadsdirectory. If not, replace
~/Downloadswith the directory where the file is located.
Now the OpenVINO toolkit components are installed. Additional configuration steps are still required. Continue to the next sections to install External Software Dependencies, configure the environment and set up USB rules.
CMake* version 3.7.2 or higher is required for building the Inference Engine sample application. To install, open a Terminal* window and run the following command:
CMake is installed. Continue to the next section to set the environment variables.
You must update several environment variables before you can compile and run OpenVINO toolkit applications. Run the following script to temporarily set the environment variables:
**(Optional)** The OpenVINO environment variables are removed when you close the shell. As an option, you can permanently set the environment variables as follows:
To test your change, open a new terminal. You will see the following:
Continue to the next section to add USB rules for Intel® Neural Compute Stick 2 devices.
.bashrcto permanently set the environment variables, run
setupvars.shagain after logging in:
You are ready to compile and run the Object Detection sample to verify the Inference Engine installation.
Follow the next steps to run pre-trained Face Detection network using Inference Engine samples from the OpenVINO toolkit.
Download the pre-trained Face Detection model with the Model Downloader or copy it from the host machine:
```sh git clone –depth 1 https://github.com/openvinotoolkit/open_model_zoo cd open_model_zoo/tools/downloader python3 -m pip install -r requirements.in python3 downloader.py –name face-detection-adas-0001
The application outputs an image (
out_0.bmp) with detected faced enclosed in rectangles.
Congratulations, you have finished the OpenVINO™ toolkit for Raspbian* OS installation. You have completed all required installation, configuration and build steps in this guide.
Read the next topic if you want to learn more about OpenVINO workflow for Raspberry Pi.
If you want to use your model for inference, the model must be converted to the .bin and .xml Intermediate Representation (IR) files that are used as input by Inference Engine. OpenVINO™ toolkit support on Raspberry Pi only includes the Inference Engine module of the Intel® Distribution of OpenVINO™ toolkit. The Model Optimizer is not supported on this platform. To get the optimized models you can use one of the following options:
For more information on pre-trained models, see Pre-Trained Models Documentation
Convert the model using the Model Optimizer from a full installation of Intel® Distribution of OpenVINO™ toolkit on one of the supported platforms. Installation instructions are available:
For more information about how to use the Model Optimizer, see the Model Optimizer Developer Guide.