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This project shows how to run tiny yolo v2 with movidius stick.
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YOLOv2 for Intel/Movidius Neural Compute Stick (NCS)

This project shows how to run tiny yolov2 (20 classes) with movidius stick:

  • A python convertor from yolo to caffe
  • A c/c++ implementation and python wrapper for region layer of yolov2
  • A sample for running yolov2 with movidius stick in images or videos


  • Support NCSDK 2.0 (Thanks cpagravel!)
  • Release 1.0 for NCSDK v1.0
  • Refine output bboxes according to letterbox_image in YOLOV2, 01/03/2018, 01/12/2018 (Thanks nathiyaa!)
  • Support multiple sticks, 12/29/2017 (Thanks ichigoi7e!)
  • Process video in the sample, 12/15/2017 (Thanks ichigoi7e!)
  • Fix confident offset issues in nms, 12/12/2017

How To Use

The following experiments are done on an Intel NUC with ubuntu 16.04.


Please install NCSDK following

Step 1. Compile Python Wrapper


Step 2. Convert Caffe to NCS

mvNCCompile ./models/caffemodels/yoloV2Tiny20.prototxt -w ./models/caffemodels/yoloV2Tiny20.caffemodel -s 12

There will be a file graph generated as converted models for NCS.

Step 3. Run tests

python3 ./detectionExample/ --image ./data/dog.jpg

This loads graph by default and results will be like this:

Run Other YoloV2 models

Convert Yolo to Caffe

Install caffe and config the python environment path.
sh ./models/


Please ignore the error message similar as "Region layer is not supported".

The converted caffe models should end with "prototxt" and "caffemodel".

Update parameters

Please update parameters (biases, object names, etc) in ./src/CRegionLayer.cpp, and parameters (dim, blockwd, targetBlockwd, classe, etc) in ./detectionExample/

Please read ./src/CRegionLayer.cpp and ./detectionExample/ for details.




Research Only


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