You can also train on Maixhub.com, just upload your datasets and you will get the result(kmodel and usage code)
- Object classification(Mobilenet V1): judge class of image
- Object detection(YOLO v2): find a recognizable object in the picture
- 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 istools/ncc/ncc_v0.1/ncc python3 train.py init- Edit
instance/config.pyaccording to your hardware - Prepare dataset, in the
datasetsdirectory has some example datasets, input size if224x224or you just fllow maixhub's conduct
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
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 docker or install tensorflow with GPU in your local environment
Tensorflow's version should >= 2.0, tested on 2.1
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
or
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-jupyterlabIf 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
refer to tensorflow official website
Apache 2.0, see LICENSE
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