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How to use darkflow with Intel Movidius Neural Compute Stick

Published Mar 31, 2019

Note: The following tutorial explains how to use darkflow framework, which is a tensorflow implementation, and integrate it with Intel Movidius Neural Compute Stick. This tutorial has been tested on Ubuntu 16.04LTS, CUDA9.0 and CUDNN8.0, and assumes you already have them installed.


It has come to my attention that edge image classification is highly under-discussed. This could be due to the lack of large community at the moment, or maybe frameworks are not optimized for edge computing. In the official documentation of NCSDK API, the 2 most used frameworks are caffe and tensorflow, which inspired me to use my knowledge on tensorflow to check out darkflow. However, Intel Movidius Neural Compute Stick is rarely used together with the said framework. Hence I began on my journey scouring the web and all documentations I can find to get darkflow to work on the Intel Movidius Neural Compute Stick.

Although caffe is a very powerful framework, and tensorflow provides a lot of fine tuning and (almost) complete comtrol over models, I have had very great experience with darknet, a C/C++ implemented framework for yolov2 model, hence I was curious to see what darkflow, the tensorflow implementation of darknet, has in store. FUrthermore, I have found that caffe’s method of labeling and improving training speed to be confusing, darkflow however is faster to market as labeling is simpler, training is straightforward, and produces good results.

With that being said, finding proper source code for interpreting the graph file passed into movidius stick is difficult, hence here at, I would like to show you my research results and steps required for proper training.

Note this blogpost is not for beginners. Check out my blog for 7 steps to Image Classification with AI for the basic idea of the steps I follow.

The aim of this tutorial is just to setup darkflow, setup NCSDK2 and compile a graph file that is intepretable by the Intel Movidius Neural Compute Stick, and read the output, ending off with an exmaple.


Clone the darkflow repository:

git clone

Please see the official docs for training steps, but for TLDR:

  • get training data
  • use a labeling image tool to label your images in xml format, link here
  • generate your labels in .txt file
  • edit cfg file (eg, filter and classes in yolov2 model)
  • pass the above to darkflow with the following command
VALUE = 0.0 to 1.0 (amount of GPU used)
./flow --labels labels.txt --model model.cfg --dataset /path/to/dataset --annotations /path/to/xml/annotations \
 --train --trainer YOUR_PREFERENCE --batch size BATCH_SIZE --gpu VALUE --load YOUR_CHECKPOINT

After your training, you should see .meta files and .pb files in ckpt directory of darkflow. These are graph files compatible with Tensorflow, but not with NCSDK2.

Generate the frozen pb file with the following command:

./flow --model /path/to/cfg --load (your tensorflow weights) --savepb /path/to/output

Next, clone the following 2 repositories: yolo-darkflow-movidius & NCSDK2.

git clone
git clone t clone -b ncsdk2 && cd ncsdk && make install

use -b ncsdk2 to clone NCSDK2, you can clone NCSDK, but will need to make modifications according to this website.

if you have caffe installed, comment out the path to your caffe directory, as ncsdk examples depend on and comes with a caffe installation. ncsdk2 also uninstalls any installation of OpenCV. If you however wish to ignore their examples, feel free to use make api instead of make, as we will show below.

Editing the cfg file follows the same format for darknet, please read the documentation here.

Preparing for Intel Movidius Neural Compute Stick

The command to convert the graph file output to a graph file that complies to Intel Movidius Stick is the command below: mvNCCompile -s 12 /path/to/graph.pb please refer here for more arguments.

the -s argument indicates the amount of SHAVES used. SHAVES is analogous to core counts, max is 12 SHAVES. Read here for more details.

Next, we need to create a symbolic link in yolo-darkflow-movidius directory:

cd yolo-darkflow-movidius/
pip install --upgrade cython

ln -s ../darkflow/darkflow darkflow

At this point, preparations are all done!

Example on PC/Laptop

To demonstrate an example, run the following commands to prepare:

cd ../darkflow
wget -O tiny-yolo-voc.cfg
wget -O tiny-yolo-voc.weights

python3 ./flow --model tiny-yolo-voc.cfg --load tiny-yolo-voc.weights --savepb
cd ../yolo-darkflow-movidius
mv  darkflow/built_graph/
mvNCCompile built_graph/tiny-yolo-voc.pb -s 12 -in input -on output -o built_graph/tiny-yolo-voc.graph 

Now, run this to see the result, with your Intel Movidius Neural Compute Stick plugged in:

python3 -i video.avi

Congrats, you have succesfully run a TinyYOLO model on movidius stick! Expect similar results when exporting them to the edge!

Edge example

Grab a coffee and something to do because the procedures are largely the same, but taking a lot longer in an environment like raspberry pi or other device. Do note to use make api instead of make when building ncsdk2.

This time, you do not need to train a model, but just import a model like the example above.

Hope this blog post has been of help. Drop me an email if you have any questions. Thanks.

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