Keras+ tensorflow in NCS

[idata]

Am gonna to do real time object recognition process in keras+ tensorflow (backend) using raspberry pi 3 with help of Movidius neural stick. So I would like to know that can I do real time object recognition in keras+tf in movidius stick and raspberry pi..

Thanks in advance

[idata]

@Hashir If you want to use TensorFlow and NCS on the RPi, the only solution I know of that works is using Tiny Yolo V2. Although you will have to generate the graph file on a host machine and then transfer it over to the Pi if you are using the NCAPPZOO sample code: https://github.com/movidius/ncappzoo/tree/ncsdk2/tensorflow/tiny_yolo_v2. Then do a python3 run.py.

The NCSDK doesn't have support for Keras at the moment.

[idata]

@Tome_at_intel, Thanks for your valuable help. I ran my own tiny yolo model with no of class = 1. But it seems to be very slow processing. So do you know how to speed up detections (actually object counting only no recognition) process. One method I found out that use c++ instead of python.

Thanks in advance

Hashir

[idata]

Hi @Hashir

It probably is a little slower because you are running on a Raspberry Pi and Tiny Yolo requires some post processing done on the CPU. But both Tiny Yolo V1 & V2 have an native input size that is large compared to other networks (like SSD MobileNet). Since these networks were designed with an overall higher resolution for better accuracy, it means reduced speeds overall.

Best Regards,

Sahira

[idata]

@Sahira_at_Intel , but many of the article says that by eliminating tweaking and unwanted layers , we can boost the process speed in tiny yolo. How can i achieve this ?

Thanks in advance