ease training

prepare a dataset and you are ready to train, zero coding | 只要准备好训练数据集，就可以开始训练了，无需编码

requirements | 需求

• linux
• docker
• docker-compose
• nvidia-docker

useage | 用法

clone this project and cd project folder | 克隆本项目并 cd 到项目目录

./prepare.sh
docker-compose up -d

then open http://localhost:3000 (or replace localhost with lan IP) | 打开 http://localhost:3000 或 localhost 更换为局域网 IP

home page	create dataset
dataset page	epoch mean AP chart

use docker only | 只使用 docker

prepare your custom dataset and map to /dataset.zip and map out where you generate classes.py and parameters: | 准备数据集映射到 /dataset.zip，并向外映射 classes.py 和训练结果

docker run -it --rm --gpus all --shm-size=32G -v $(pwd)/parameters:/parameters -v $(pwd)/dataset.zip:/dataset.zip -v $(pwd):/out-classes postor/ease-training train_yolo3.py --gpus=0 --save-prefix=/parameters/

# or cache models and train with more params | 缓存下载的模型以及更多的训练参数
docker run -it --rm --gpus all --shm-size=32G -v ~/.mxnet:/root/.mxnet -v $(pwd)/parameters:/parameters -v $(pwd)/dataset.zip:/dataset.zip -v $(pwd):/out-classes --shm-size 32G postor/ease-training train_yolo3.py --batch-size=2 --gpus=1,2 --lr=0.0001 --epochs=500 --network=darknet53 --save-prefix=/parameters/

replace train_yolo3.py --network=darknet53 --data-shape=416 with train_${detector}.py --network=${network} --data-shape=${dataShape} as needed, check supported network && data shape

params or logics refer https://gluon-cv.mxnet.io/build/examples_detection/index.html and training/predict.py | 参数及逻辑参考 https://gluon-cv.mxnet.io/build/examples_detection/index.html 和 training/predict.py

after training, parameters shall be in your $(pwd)/parameters folder | 训练之后，训练结果会产生在 $(pwd)/parameters 目录

to predict, you need some sample images, put them into a folder, like $(pwd)/test, run this to generate result to $(pwd)/result | 要进行预测，你需要准备些样例图片，放到一个文件夹里，比如 $(pwd)/test，运行以命令码将预测结果生成到 $(pwd)/result

docker run -it --rm --gpus all  -v $(pwd)/parameters:/training/parameters -v $(pwd)/test:/test -v $(pwd)/result:/result -v $(pwd)/classes.py:/training/classes.py postor/ease-training:predict --model=yolo3_darknet53 --data-shape=416 --input-folder=/test --output-folder=/result

replace --model=yolo3_darknet53 --data-shape=416 with --model=${detector}_${network} --data-shape=${dataShape} if needed

supported network && data shape | 支持的 network && data shape

• yolo3
  • darknet53
    • 320
    • 416
    • 608
  • mobilenet0.25
    • 320
    • 416
    • 608
  • mobilenet1.0
    • 320
    • 416
    • 608
• ssd
  • mobilenet0.25
    • 300
  • vgg16_atrous
    • 300
    • 512
  • mobilenet1.0
    • 512
  • resnet50_v1
    • 512