The Inference Engine sample applications are simple console applications that demonstrate how you can use the Inference Engine in your applications.
The OpenVINO™ toolkit installation includes the following sample applications available in the
*Few samples referenced above have simplified implementation in Python (
To run the sample applications, you can use images and videos from the media files collection available at https://github.com/intel-iot-devkit/sample-videos.
OpenVINO toolkit includes several pre-trained models. The table below shows the correlation between models and samples/plugins (the plugins names are exactly as they are passed to the samples with
-d option). The correlation between the plugins and supported devices see in the Supported Devices section. The samples are available in
|Model||Samples supported on the model||CPU||GPU||HETERO:FPGA,CPU||MYRIAD|
|face-detection-adas-0001||Interactive Face Detection Demo||Supported||Supported||Supported||Supported|
|age-gender-recognition-retail-0013||Interactive Face Detection Demo||Supported||Supported||Supported||Supported|
|head-pose-estimation-adas-0001||Interactive Face Detection Demo||Supported||Supported||Supported||Supported|
|emotions-recognition-retail-0003||Interactive Face Detection Demo||Supported||Supported||Supported||Supported|
|facial-landmarks-35-adas-0001||Interactive Face Detection Demo||Supported||Supported||Supported|
|vehicle-license-plate-detection-barrier-0106||Security Barrier Camera Demo||Supported||Supported||Supported||Supported|
|vehicle-attributes-recognition-barrier-0039||Security Barrier Camera Demo||Supported||Supported||Supported||Supported|
|license-plate-recognition-barrier-0001||Security Barrier Camera Demo||Supported||Supported||Supported||Supported|
|person-vehicle-bike-detection-crossroad-0078||Crossroad Camera Demo||Supported||Supported||Supported||Supported|
|person-attributes-recognition-crossroad-0200||Crossroad Camera Demo||Supported||Supported|
Crossroad Camera Demo
Pedestrian Tracker Demo
|person-reidentification-retail-0076||Crossroad Camera Demo||Supported||Supported||Supported||Supported|
|person-reidentification-retail-0079||Crossroad Camera Demo||Supported||Supported||Supported||Supported|
|road-segmentation-adas-0001||Image Segmentation Demo||Supported||Supported|
|semantic-segmentation-adas-0001||Image Segmentation Demo||Supported||Supported|
|person-detection-retail-0013||any demo that supports SSD*-based models, above
Pedestrian Tracker Demo
|person-detection-retail-0002||any demo that supports SSD*-based models, above||Supported||Supported||Supported||Supported|
|face-detection-retail-0004||any demo that supports SSD*-based models, above||Supported||Supported||Supported||Supported|
|face-person-detection-retail-0002||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|
|vehicle-detection-adas-0002||any demo that supports SSD*-based models, above||Supported||Supported||Supported||Supported|
|pedestrian-and-vehicle-detector-adas-0001||any demo that supports SSD*-based models, above||Supported||Supported||Supported|
|person-detection-action-recognition-0003||Smart Classroom Demo||Supported||Supported||Supported|
|landmarks-regression-retail-0009||Smart Classroom Demo||Supported||Supported||Supported|
|face-reidentification-retail-0095||Smart Classroom Demo||Supported||Supported|
|human-pose-estimation-0001||Human Pose Estimation Demo||Supported||Supported||Supported|
|single-image-super-resolution-0063||Super Resolution Demo||Supported|
|single-image-super-resolution-1011||Super Resolution Demo||Supported|
|single-image-super-resolution-1021||Super Resolution Demo||Supported|
|text-detection-0001||Text Detection Demo||Supported||Supported|
*Few samples referenced above have simplified equivalents in Python (
Notice that the FPGA support comes through a heterogeneous execution, for example, when the post-processing is happening on the CPU.
The officially supported Linux* build environment is the following:
Use the following steps to build sample application on Linux:
NOTE: If you have installed the product as a root user, switch to root mode before you continue:
NOTE: If you ran the Image Classification demo, the samples build directory was already created in your home directory:
maketo build the samples:
For the release configuration, the sample application binaries are in
<path_to_build_directory>/intel64/Release/; for the debug configuration — in
The recommended Windows* build environment is the following:
Use the following steps to build sample application on Windows:
create_msvc2017_solution.batfor 2015 or 2017 versions of Microsoft Visual Studio respectively. For example, for Microsoft Visual Studio 2015:
NOTE: When building either release or debug configurations in Microsoft Visual Studio, make sure that the corresponding build configuration, Release or Debug, is selected in the configuration panel.
NOTE: To debug or run samples in Microsoft Visual Studio, make sure you have properly configured Debugging settings for the Debug and Release configurations. For more information, refer to the Get Ready for Running the Sample Applications section.
The sample applications binaries are in your
Before running compiled binary files, make sure your application can find the Inference Engine and OpenCV libraries. Use the
setupvars script, which sets all necessary 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:
[setupvars.sh] OpenVINO environment initialized.
To debug or run the samples 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:
<INSTALL_DIR> is the directory in which the OpenVINO toolkit is installed.
You are ready to run sample applications. To learn about how to run a particular sample, read the sample documentation by clicking the sample name in the samples list above.