train_scripts

You can also train on Maixhub.com, just upload your datasets and you will get the result(kmodel and usage code)

Train type

Object classification(Mobilenet V1): judge class of image
Object detection(YOLO v2): find a recognizable object in the picture

Usage

0. Prepare

only support Linux
Prepare environment, use CPU or GPU to train At your fist time train, CPU is recommended, just

pip3 install -r requirements.txt

or use aliyun's source if you are in China

pip3 install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/

Download nncase and unzip it to tools/ncc/ncc_v0.1, and the executable path is tools/ncc/ncc_v0.1/ncc
python3 train.py init
Edit instance/config.py according to your hardware
Prepare dataset, in the datasets directory has some example datasets, input size if 224x224 or you just fllow maixhub's conduct

1. Object classification (Mobilenet V1)

python3 train.py -t classifier -z datasets/test_classifier_datasets.zip train

or assign datasets directory

python3 train.py -t classifier -d datasets/test_classifier_datasets train

more command seepython3 train.py -h

and you will see output in the out directory, packed as a zip file

2. Object detection (YOLO V2)

python3 train.py -t detector -z datasets/test_detector_xml_format.zip train

more command seepython3 train.py -h

and you will see output in the out directory, packed as a zip file

Use GPU

Use docker or install tensorflow with GPU in your local environment

Tensorflow's version should >= 2.0, tested on 2.1

Use docker(recommend)

see tensorflow official website (或者可以参考这篇教程)

Install docker
Install nvidia-docker
Pull docker image

docker pull neucrack/tensorflow-gpu-py3-jupyterlab

docker pull daocloud.io/neucrack/tensorflow-gpu-py3-jupyterlab

Test environment

docker run --gpus all -it --rm neucrack/tensorflow-gpu-py3-jupyterlab python -c "import tensorflow as tf; print('-----version:{}, gpu:{}, 1+2={}'.format(tf.__version__, tf.test.is_gpu_available(), tf.add(1, 2).numpy()) );"

if output is-----version:2.1.0, gpu:True, 1+2=3, that's ok(maybe version can > 2.1.0)

Create docker container

docker run --gpus all --name jupyterlab-gpu -it -p 8889:8889 -e USER_NAME=$USER -e USER_ID=`id -u $USER` -e GROUP_NAME=`id -gn $USER` -e GROUP_ID=`id -g $USER` -v /home/${USER}:/tf neucrack/tensorflow-gpu-py3-jupyterlab

If used daocloud, image name should be change to daocloud.io/neucrack/tensorflow-gpu-py3-jupyterlab

This will mount your/home/$USER directory to /tf directory of container, the /tf is the root dir of jupyterlab

Stop by docker stop jupyterlab-gpu, start again by docker start jupyterlab-gpu To use sudo command, edit user password by

docker exec -it jupyterlab_gpu /bin/bash
passwd $USER
passwd root

use jupyterlab

Open http://127.0.0.1:8889/lab? in browser, input token(see docker start log) and set new password

Use docker stop jupyterlab-gpu to stop server Use docker start jupyterlab-gpu to start service again

Install on local environment

refer to tensorflow official website

License

Apache 2.0, see LICENSE

Geekiter/maix_train