I had more luck running the ssd_mobilenet_v2_coco model from the TensorFlow model detection zoo on the NCS 2 than I did with YOLOv3. To convert from the .pb file to the OpenVINO-friendly files I used:
python3 mo_tf.py --input_model ssdmv2.pb --tensorflow_use_custom_operations_config ./extensions/front/tf/ssd_v2_support.json --tensorflow_object_detection_api_pipeline_config ssdmv2_pipeline.config --data_type FP16
In this case, I had renamed the frozen_instance_graph.pb from the download as ssdmv2.pb and renamed the pipeline.config file from the download as ssdmv2_pipeline.config. The screen capture above shows the object_detection_demo_ssd_async demo app running with the NCS 2. I didn’t sort out the labels for this test which is why it is just displaying numbers for the detected objects.
I also tried this using the CPU (using –data_type FP32) with this result:
It is worth noting that the video was running at 1920 x 1080 which is a significant challenge for just about anything. The CPU (an i7 5820K) is obviously a fair bit faster than the NCS 2 but a real advantage is the small physical footprint, low price, low power and CPU offload that the Myriad X VPU in the NCS 2 offers.