The Open Model Zoo demo applications are console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use-cases.
The Open Model Zoo includes the following demos:
Detectron
or maskrcnn-benchmark
.To run the demo applications, you can use images and videos from the media files collection available at https://github.com/intel-iot-devkit/sample-videos.
NOTE: Inference Engine HDDL and FPGA plugins are available in proprietary distribution only.
You can download the pre-trained models using the OpenVINO Model Downloader or from https://download.01.org/opencv/. The table below shows the correlation between models, demos, and supported plugins. The plugins names are exactly as they are passed to the demos with -d
option. The correlation between the plugins and supported devices see in the Supported Devices section.
NOTE: MYRIAD below stands for Intel® Movidius™ Neural Compute Stick, Intel® Neural Compute Stick 2, and Intel® Vision Accelerator Design with Intel® Movidius™ Vision Processing Units.
Model | Demos supported on the model | CPU | GPU | MYRIAD/HDDL | HETERO:FPGA,CPU |
---|---|---|---|---|---|
human-pose-estimation-3d-0001 | 3D Human Pose Estimation Python\* Demo | Supported | Supported | ||
action-recognition-0001-decoder | Action Recognition Demo | Supported | Supported | ||
action-recognition-0001-encoder | Action Recognition Demo | Supported | Supported | ||
driver-action-recognition-adas-0002-decoder | Action Recognition Demo | Supported | Supported | ||
driver-action-recognition-adas-0002-encoder | Action Recognition Demo | Supported | Supported | Supported | |
person-attributes-recognition-crossroad-0230 | Crossroad Camera Demo | Supported | Supported | Supported | Supported |
person-attributes-recognition-crossroad-0234 | Crossroad Camera Demo | Supported | |||
person-attributes-recognition-crossroad-0238 | Crossroad Camera Demo | Supported | |||
person-reidentification-retail-0031 | Crossroad Camera Demo | Supported | Supported | Supported | Supported |
person-reidentification-retail-0076 | Crossroad Camera Demo Multi-Camera Multi-Target Tracking Demo | Supported | Supported | Supported | Supported |
person-reidentification-retail-0079 | Crossroad Camera Demo Multi-Camera Multi-Target Tracking Demo | Supported | Supported | Supported | Supported |
person-vehicle-bike-detection-crossroad-0078 | Crossroad Camera Demo | Supported | Supported | Supported | Supported |
person-vehicle-bike-detection-crossroad-1016 | Crossroad Camera Demo | Supported | |||
person-vehicle-bike-detection-crossroad-yolov3-1020 | Object Detection for YOLO V3 Python\* Demo | Supported | |||
human-pose-estimation-0001 | Human Pose Estimation Demo | Supported | Supported | Supported | Supported |
image-retrieval-0001 | Image Retrieval Python\* Demo | Supported | Supported | Supported | Supported |
semantic-segmentation-adas-0001 | Image Segmentation Demo | Supported | Supported | Supported | |
instance-segmentation-security-0010 | Instance Segmentation Demo | Supported | Supported | ||
instance-segmentation-security-0050 | Instance Segmentation Demo | Supported | Supported | ||
instance-segmentation-security-0083 | Instance Segmentation Demo | Supported | Supported | ||
instance-segmentation-security-1025 | Instance Segmentation Demo | Supported | Supported | ||
age-gender-recognition-retail-0013 | Interactive Face Detection Demo | Supported | Supported | Supported | Supported |
emotions-recognition-retail-0003 | Interactive Face Detection Demo | Supported | Supported | Supported | Supported |
face-detection-adas-0001 | Interactive Face Detection Demo | Supported | Supported | Supported | Supported |
face-detection-adas-binary-0001 | Interactive Face Detection Demo | Supported | Supported | ||
face-detection-retail-0004 | Interactive Face Detection Demo | Supported | Supported | Supported | Supported |
facial-landmarks-35-adas-0002 | Interactive Face Detection Demo | Supported | Supported | Supported | Supported |
head-pose-estimation-adas-0001 | Interactive Face Detection Demo | Supported | Supported | Supported | Supported |
license-plate-recognition-barrier-0001 | Security Barrier Camera Demo | Supported | Supported | Supported | Supported |
vehicle-attributes-recognition-barrier-0039 | Security Barrier Camera Demo | Supported | Supported | Supported | Supported |
vehicle-attributes-recognition-barrier-0042 | Security Barrier Camera Demo | Supported | |||
vehicle-license-plate-detection-barrier-0106 | Security Barrier Camera Demo | Supported | Supported | Supported | Supported |
vehicle-license-plate-detection-barrier-0123 | Security Barrier Camera Demo | Supported | Supported | Supported | Supported |
landmarks-regression-retail-0009 | Smart Classroom Demo | Supported | Supported | Supported | Supported |
person-detection-action-recognition-0005 | Smart Classroom Demo | Supported | Supported | Supported | Supported |
person-detection-action-recognition-teacher-0002 | Smart Classroom Demo | Supported | Supported | Supported | |
single-human-pose-estimation-0001 | Single Human Pose Estimation Python\* Demo | Supported | Supported | ||
single-image-super-resolution-1032 | Super Resolution Demo | Supported | Supported | Supported | |
single-image-super-resolution-1033 | Super Resolution Demo | Supported | Supported | Supported | |
text-detection-0003 | Text Detection Demo | Supported | Supported | Supported | |
text-detection-0004 | Text Detection Demo | Supported | Supported | Supported | |
text-recognition-0012 | Text Detection Demo | Supported | Supported | ||
handwritten-japanese-recognition-0001 | Handwritten Japanese Recognition Python\* Demo | Supported | Supported | Supported | |
gaze-estimation-adas-0002 | Gaze Estimation Demo | Supported | Supported | Supported | Supported |
head-pose-estimation-adas-0001 | Gaze Estimation Demo | Supported | Supported | Supported | Supported |
facial-landmarks-35-adas-0002 | Gaze Estimation Demo | Supported | Supported | Supported | Supported |
pedestrian-and-vehicle-detector-adas-0001 | any demo that supports SSD*-based models, above | Supported | Supported | Supported | Supported |
pedestrian-detection-adas-0002 | any demo that supports SSD*-based models, above | Supported | Supported | Supported | Supported |
pedestrian-detection-adas-binary-0001 | any demo that supports SSD*-based models, above | Supported | Supported | ||
person-detection-retail-0002 | any demo that supports SSD*-based models, above | Supported | Supported | Supported | Supported |
person-detection-retail-0013 | any demo that supports SSD*-based models, above | Supported | Supported | Supported | Supported |
road-segmentation-adas-0001 | any demo that supports SSD*-based models, above | Supported | Supported | Supported | Supported |
vehicle-detection-adas-binary-0001 | any demo that supports SSD*-based models, above | Supported | Supported | ||
vehicle-detection-adas-0002 | any demo that supports SSD*-based models, above | Supported | Supported | Supported | Supported |
yolo-v2-tiny-vehicle-detection-0001 | Object Detection for YOLO V3 Python\* Demo | Supported |
Notice that the FPGA support comes through a heterogeneous execution, for example, when the post-processing is happening on the CPU.
To be able to build demos you need to source InferenceEngine and OpenCV environment from a binary package which is available as proprietary distribution. Please run the following command before the demos build (assuming that the binary package was installed to <INSTALL_DIR>
):
You can also build demos manually using Inference Engine built from the openvino repo. In this case please set InferenceEngine_DIR
environment variable to a folder containing InferenceEngineConfig.cmake
and ngraph_DIR
to a folder containing ngraphConfig.cmake
in a build folder. Please also set the OpenCV_DIR
to point to the OpenCV package to use. The same OpenCV version should be used both for Inference Engine and demos build. Alternatively these values can be provided via command line while running cmake
. See CMake's search procedure. Please refer to the Inference Engine build instructions for details. Please also add path to built Inference Engine libraries to LD_LIBRARY_PATH
(Linux*) or PATH
(Windows*) variable before building the demos.
The officially supported Linux* build environment is the following:
To build the demo applications for Linux, go to the directory with the build_demos.sh
script and run it:
You can also build the demo applications manually:
build
: make
to build the demos: For the release configuration, the demo application binaries are in <path_to_build_directory>/intel64/Release/
; for the debug configuration — in <path_to_build_directory>/intel64/Debug/
.
The recommended Windows* build environment is the following:
NOTE: If you want to use Microsoft Visual Studio 2019, you are required to install CMake 3.14.
To build the demo applications for Windows, go to the directory with the build_demos_msvc.bat
batch file and run it:
By default, the script automatically detects the highest Microsoft Visual Studio version installed on the machine and uses it to create and build a solution for a demo code. Optionally, you can also specify the preffered Microsoft Visual Studio version to be used by the script. Supported versions are: VS2015
, VS2017
, VS2019
. For example, to build the demos using the Microsoft Visual Studio 2017, use the following command:
The demo applications binaries are in the C:\Users\<username>\Documents\Intel\OpenVINO\omz_demos_build_build\intel64\Release
directory.
You can also build a generated solution by yourself, for example, if you want to build binaries in Debug configuration. Run the appropriate version of the Microsoft Visual Studio and open the generated solution file from the C:\Users\<username>\Documents\Intel\OpenVINO\omz_demos_build\Demos.sln
directory.
Some of the Python demo applications require native Python extension modules to be built before they can be run. This requires you to have Python development files (headers and import libraries) installed. To build these modules, follow the instructions for building the demo applications above, but add -DENABLE_PYTHON=ON
to either the cmake
or the build_demos*
command, depending on which you use. For example:
Before running compiled binary files, make sure your application can find the Inference Engine and OpenCV libraries. If you use a proprietary distribution to build demos, run the setupvars
script to set all necessary environment variables:
If you use your own Inference Engine and OpenCV binaries to build the demos please make sure you have added them to the LD_LIBRARY_PATH
environment variable.
**(Optional)**: The OpenVINO environment variables are removed when you close the shell. As an option, you can permanently set the environment variables as follows:
.bashrc
file in <user_home_directory>
: :wq
and press the Enter key.[setupvars.sh] OpenVINO environment initialized
.To run Python demo applications that require native Python extension modules, you must additionally set up the PYTHONPATH
environment variable as follows, where <bin_dir>
is the directory with the built demo applications:
You are ready to run the demo applications. To learn about how to run a particular demo, read the demo documentation by clicking the demo name in the demo list above.
Before running compiled binary files, make sure your application can find the Inference Engine and OpenCV libraries. Optionally download OpenCV community FFmpeg plugin. There is a downloader script in the OpenVINO package: <INSTALL_DIR>\opencv\ffmpeg-download.ps1
. If you use a proprietary distribution to build demos, run the setupvars
script to set all necessary environment variables:
If you use your own Inference Engine and OpenCV binaries to build the demos please make sure you have added to the PATH
environment variable.
To run Python demo applications that require native Python extension modules, you must additionally set up the PYTHONPATH
environment variable as follows, where <bin_dir>
is the directory with the built demo applications:
To debug or run the demos on Windows in Microsoft Visual Studio, make sure you have properly configured Debugging environment settings for the Debug and Release configurations. Set correct paths to the OpenCV libraries, and debug and release versions of the Inference Engine libraries. For example, for the Debug configuration, go to the project's Configuration Properties to the Debugging category and set the PATH
variable in the Environment field to the following:
where <INSTALL_DIR>
is the directory in which the OpenVINO toolkit is installed.
You are ready to run the demo applications. To learn about how to run a particular demo, read the demo documentation by clicking the demo name in the demos list above.