MaixPy_YoloV2

This repository helps you to extend the models is to detect objects using YOLO-V2 on a MaixPY

DIY

Install Docker on your machine and create a notebooks folder inside e.g. Documents:

curl -sSL https://get.docker.com | sh
mkdir ~/Documents/notebooks/

Then, deploy the tensorflow/tensorflow:latest-py3-jupyter image using:

sudo docker run -d -p 8888:8888 -v ~/Documents/notebooks/:/tf/notebooks/ tensorflow/tensorflow:latest-py3-jupyter

I explained the -v flag here. But, it is a "Bind Mount". This means the ~/Documents/notebooks/ folder is connected to the /tf/notebooks/ folder inside the container. This makes the data inside the folder /tf/notebooks/ (container) persistent. Otherwise, if the container is stopped you lose the files.

Then, clone this repository inside ~/Documents/notebooks/

cd ~/Documents/notebooks/
git clone https://github.com/lemariva/MaixPy_YoloV2

Open the following URL in your host web browser: http://localhost:8888

You need a token to log in. The token is inside the container. List the container to get the ID with following command:

$ docker container ls
CONTAINER ID        IMAGE    [....]
5082a85283bb        tensorflow/  [....]

Then, read the logs typing:

$ docker logs 5082a85283bb

[...]
The Jupyter Notebook is running at:
[...] http://ac64d540a1cb:8888/?token=df050fa3b53de5f9203ca862e5f3656962b665dc224243a8
[...]

The hash after token= is the token to log in.

I added a training example with the brio 33594. You can train the model running the code inside training.ipynb.

Note: Some additional libraries are required inside the container and they are installed on the first cell block of the Notebook. You don't need to run the cell every time that you compile the model. However, if you start a new container (not restart the stopped one), you need to install them again. You can extend the container to include these libraries per default. You can find more info about that on this tutorial.

More Info

Visit the following tutorial for more information: MAixPy: Object detector - MobileNet and YOLOv2 on Sipeed MAix Dock

Acknowledgement

• Ported from penny4860/Yolo-digit-detector to Jupyter Notebooks and upgraded to Tensorflow 2.0.