The Inference Engine demo applications are simple console applications that demonstrate how you can use the Intel's Deep Learning Inference Engine in your applications.
The Deep Learning Inference Engine release package provides the following demo applications available in the demos directory in the Inference Engine installation directory:
- Crossroad Camera Demo - Person Detection followed by the Person Attributes Recognition and Person Reidentification Retail, supports images/video and camera inputs. NEW MODELS SHOWCASE, below.
- End to end video analytics end2end_video_analytics_ie Demo - End to end demo application for image classification with inference engine.
- End to end video analytics end2end_video_analytics_opencv Demo - End to end demo application for image classification with OpenCVDNN.
- Human Pose Estimation Demo - Human pose estimation demo. NEW MODELS SHOWCASE, below.
- Image Segmentation Demo - Inference of image segmentation networks like FCN8 (the demo supports only images as inputs).
- Interactive Face Detection Demo - Face Detection coupled with Age/Gender, Head-Pose, Emotion, and Facial Landmarks detectors. Supports video and camera inputs. NEW MODELS SHOWCASE, below.
- Mask R-CNN Demo for TensorFlow* Object Detection API models - Inference of semantic segmentation networks created with TensorFlow* Object Detection API.
- Multi-Channel Face Detection Demo - Simultaneous Multi Camera Face Detection demo.
- Object Detection Demo - Inference of object detection networks like Faster R-CNN (the demo supports only images as inputs).
- Object Detection for SSD Demo app - Demo application for SSD-based Object Detection networks, new Async API performance showcase, and simple OpenCV interoperability (supports video and camera inputs).
- Object Detection for YOLO V3 Demo app - Demo application for YOLOV3-based Object Detection networks, new Async API performance showcase, and simple OpenCV interoperability (supports video and camera inputs).
- Pedestrian Tracker Demo - Demo application for pedestrian tracking scenario.
- Security Barrier Camera Demo - Vehicle Detection followed by the Vehicle Attributes and License-Plate Recognition, supports images/video and camera inputs. NEW MODELS SHOWCASE, below.
- Smart Classroom Demo - Face recognition and action detection demo for classroom environment. NEW MODELS SHOWCASE, below.
- Super Resolution Demo - Super Resolution demo (the demo supports only images as inputs).
Few demos referenced above have simplified equivalents in Python (python_demos subfolder).
(!) Important Note: Inference Engine MYRIAD and FPGA plugins are available in proprietary distribution only.
The product includes several pre-trained [models] (../intel_models/index.html).
The table below shows the correlation between models and demos/plugins (the plugins names are exactly as they are passed to the demos with -d option).
| Model | Demoss 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-detection-retail-0001 | Object Detection Demo | Supported | Supported | Supported | |
| person-vehicle-bike-detection-crossroad-0078 | Crossroad Camera Demo | Supported | Supported | Supported | Supported |
| person-attributes-recognition-crossroad-0031 | Crossroad Camera Demo | Supported | Supported | Supported | Supported |
| person-reidentification-retail-0031 | Crossroad Camera Demo Pedestrian Tracker Demo |
Supported | Supported | Supported | Supported |
| 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 |
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-0071 | Smart Classroom Demo | Supported | Supported | Supported | Supported |
| human-pose-estimation-0001 | Human Pose Estimation Demo | Supported | Supported | Supported | |
| single-image-super-resolution-0034 | Super Resolution Demo | Supported |
Few demos referenced above have simplified equivalents in Python (python_demos subfolder).
Notice that the FPGA support comes through a [heterogeneous execution](@ref PluginHETERO), 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 (assuming that the binary package was installed to <INSTALL_DIR>):
source <INSTALL_DIR>/deployment_tools/bin/setupvars.shAlso, you can build IE binaries from the dldt repo. In this case please set InferenceEngine_DIR to a CMake folder you built the dldt binaries from.
Please also set the OpenCV_DIR variable pointing to the required OpenCV package.
The officially supported Linux build environment is the following:
- Ubuntu* 16.04 LTS 64-bit or CentOS* 7.4 64-bit
- GCC* 5.4.0 (for Ubuntu* 16.04) or GCC* 4.8.5 (for CentOS* 7.4)
- CMake* version 2.8 or higher.
- OpenCV 3.3 or later (required for some demos and demos)
You can build the demo applications using the CMake file in the demos directory.
Create a new directory and change your current directory to the new one:
mkdir build
cd buildRun CMake to generate Make files:
cmake -DCMAKE_BUILD_TYPE=Release <path_to_inference_engine_demos_directory>To build demos with debug information, use the following command:
cmake -DCMAKE_BUILD_TYPE=Debug <path_to_inference_engine_demos_directory>Run Make to build the application:
makeAfter that you can find binaries for all demos applications in the intel64/Release subfolder.
The recommended Windows build environment is the following:
- Microsoft Windows* 10
- Microsoft* Visual Studio* 2015 including Microsoft Visual Studio 2015 Community or Microsoft Visual Studio 2017
- CMake* version 2.8 or later
- OpenCV* 3.3 or later
Generate Microsoft Visual Studio solution file using create_msvc2015_solution.bat file or create_msvc2017_solution.bat file
and then build the resulting solution Demos.sln in the Microsoft Visual Studio 2015 or Microsoft Visual Studio 2015 accordingly.
Before running compiled binary files, make sure your application can find the Inference Engine libraries.
Use the setupvars.sh script (or setupvars.bat on Windows), which will set all necessary environment variables pointing to the binaries from the installed
binary package you installed to the <INSTALL_DIR>.
For that, run:
source <INSTALL_DIR>/deployment_tools/bin/setupvars.sh
on Linux or
source <INSTALL_DIR>/deployment_tools/bin/setupvars.bat
to source required environment on Windows.
If you are using Inference Engine binaries from the dldt repository then you need to configure LD_LIBRARY_PATH variable (or PATH on Windows) manually.
What is left is running the required demo with appropriate commands, providing IR information (typically with "-m" command-line option). Please note that Inference Engine assumes that weights are in the same folder as .xml file.
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