I followed this tutorial for real-time object detection using Movidius NCS. It worked perfectly.
However, I noticed that it used a Caffemodel in the tutorial. I have a trained TensorFlow model that I want to compile onto the Movidius. I would you be very grateful if you could point me to a tutorial on how I can achieve that.
Here is an example of model I want to compile.
You need to use the mvNCCompile command in from the SDK. Not all models will be supported due to the limited ops support on the Movidius Neural Compute Stick.
There is a guide in the official documentation https://movidius.github.io/ncsdk/tf_compile_guidance.html
I have also written a brief guide for a personal project that is specifically what you're asking for. https://github.com/andrewginns/MSc-Project/blob/master/Movidius_Instructions.md
Basically you need to
1) Convert the .ckpt files to a frozen GraphDef
2) Convert that GraphDef into an inference optimised model
3) Run the mvNCCompile command to convert it to a movidius graph