YOLOv2 for PYNQ-Z1 and Intel/Movidius Neural Compute Stick (NCS)

This project shows how to run tiny yolov2 (20 classes) with PYNQ-Z1 and 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

Updates

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 a PYNQ-Z1. You'll also need a Linux PC to compile the graph file with NCSDK.

Step 1. Setup your PYNQ-Z1 to work with Movidius NCS

Follow this guide to setup your PYNQ-Z1 for the Movidius NCS.

For this project, you will also need to install Boost Python on the PYNQ-Z1:

apt-get install libboost-python-dev

Step 2. Clone this repo on PYNQ-Z1 and compile Python Wrapper

cd /home/xilinx/jupyter_notebooks
git clone https://github.com/fpgadeveloper/pynq-ncs-yolo-v2.git
make

Step 3. Clone this repo to a development Linux PC and Convert Caffe to NCS

To generate the graph file you will have to install NCSDK on a Linux PC, then clone this repository to the dev PC.

mkdir ~/workspace
cd ~/workspace
git clone https://github.com/fpgadeveloper/pynq-ncs-yolo-v2.git

Then generate the graph file with this command:

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

Finally you must copy this graph file to /home/xilinx/jupyter_notebooks/pynq-ncs-yolo-v2 on the PYNQ-Z1.

Step 4. Run tests

Use the Jupyter notebooks in the notebooks directory of this repo to test YOLOv2 with an image file, a webcam or a HDMI input. You can also run the detection example script by using this command in the terminal:

python3.6 ./detectionExample/Main.py --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/convertyo.sh

Tips:

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/ObjectWrapper.py.

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

References

License

Research Only

Author

duangenquan@gmail.com Mods for PYNQ-Z1 and Jupyter notebooks by Jeff Johnson

yluo39github/pynq-ncs-yolo-v2