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Api corresponding to distributed processing to multiple sticks is necessary, not cyclic processing.
Support for Tensorflow mobilenet SSD. The blog posts at movidius.github.io are quite good - additional topics by the same authors would be helpful.
I hope to see support for Mask R-CNN very soon.
@GoldenWings Thanks for the input. Any other suggestions or features you guys would want to see?
Would like to see support for squeezenet and Tensorflow mobilenet SSD object detection.
I would in general like to see more support for different tensorflow ops. This would enable greater freedom in design of networks which would be really cool. Also more tutorials for maybe custom Networks.
Tutorials on getting new networks not already in the ncappzoo working on the sticks. Like DSOD - Densely Supervised Object Detectors. MobileNets-SSD is slow to train on Caffe. (and will remain until they start supporting an efficient Depthwise Separable Layer, in Caffe-SSD)
@trevor you should be able to use this link for training SSD with efficient DWS convolution with CUDNN9: https://github.com/listenlink/caffe/tree/ssd
Please support Keras. I have everything setup with Keras in Ubuntu. I do not see particular reason to add Caffe in my system other than Movidius stick. Untill then example graph files that can be deployed in RPi would be appreciated.
How to use retrain.py and your own classifications, mnvcompile and use it on the stick.
Release date for Movidius X ??? ( this year !? )
It would be great if the stick will support Pytorch in the future
1) NCS kit in general is messy and loaded with Python dependencies. Straight C/C++ solutions would be best and a little python maybe for conversion / support.
2) install.sh just fails in so many ways on a vanilla clean install Ubuntu 16.04 - like it was never really tested.
3) find a way to enable a broader support community by open sourcing more tools (e.g. myriad compilers) and selecting a few outside partners to support smaller hobbyist / maker types.
4) get a partner program going - important to all of the above items
It would also be nice for better Raspberry Pi / TinkerBoard support during installation and compilation.
Internal processing FP32 compatible