using Open VINO as an optimizer to deploy a neuronal network for inference on Movidius edge device (or laptop cpu)

1. Intel demo on Movidius:
deploying an optimized neural
network on edge

2. overview
• Object detection using a caffe implementation of MobileNet-SSD
detection network, with pretrained weights
• Required:
• A hypervisor on the PC, I use Oracle Virtualbox – just download, plug&play
• Sufficient memory on the host, supported OS (e.g. Windows 10), etc.
• USB 3 slot
• Intel MYRIAD Neural Compute Stick (MOVIDIUS)
• Advantages
• Run inferences at a fraction of the power consumed on cpus
• Optimize a NN for deployment on edge devices
• Consistent software environment regardless of end user device

3. Guest OS
• Guest OS is Ubuntu by default when installing the VM

4. Movidius device -hardware

5. Movidius device -software

6. Using Open VINO for network optimization

7. plugins

8. Jupyter notebook with following steps:
• Creating Intermediate Representation (IR) model
• Choosing device
• Load the network into the plugin
• Load the labels for the different classes
• Input pre-processing (e.g. reshaping)
• The Movidius device only supports FP16
• Run inference (single shot)
• Run inference (video)

9. Inference output

10. Inference output – more

11. Object detection in video (this takes a long time on my PC, but I can
show the recorded video)

12. More demos
• Classification
• Pedestrian tracker demo
• Build (just make)
• Download a „pedestrian“ mp4, e.g. Pedestrians - 10840.mp4
• To copy from windows host to ubuntu vm: devices -> shared clipboard ->
bidirectional
• Run: ./pedestrian_tracker_demo -i /home/intel-ubuntu/Desktop/Pedestrians -
10840.mp4 -m_det
/opt/intel/computer_vision_sdk/deployment_tools/intel_models/person-detection-
retail-0013/FP32/person-detection-retail-0013.xml -d_det CPU -m_reid
/opt/intel/computer_vision_sdk/deployment_tools/intel_models//person-
reidentification-retail-0031/FP32/person-reidentification-retail-0031.xml -d_reid CPU