3 篇文章带有标签 “Darknet”

FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu18.04
LABEL maintainer="wang-junjian@qq.com"

#auto install tzdata(opencv depend)
ENV DEBIAN_FRONTEND=noninteractive

RUN apt-get update && apt-get install -y \
    git wget nano \
    libopencv-dev python3-opencv \
    && rm -rf /var/lib/apt/lists/*

#set your localtime
RUN ln -fs /usr/share/zoneinfo/Asia/Shanghai /etc/localtime

WORKDIR /
// ...

• 构建容器

docker build -t darknet:latest-gpu-yolov4 .

• 训练的样本：train.txt

images/IMG_9255.JPG
images/IMG_9266.JPG
images/IMG_9280.JPG

• 验证的样本：valid.txt

images/IMG_9263.JPG

• 标注类型：voc.names

close
open

• 举例：这里以platen-switch为例

  platen-switch/
  ├── cfg
  │   └── voc.names
  ├── images
  │   ├── IMG_9255.JPG
  │   ├── IMG_9263.JPG
  │   ├── IMG_9266.JPG
  │   └── IMG_9280.JPG
  ├── labels
  │   ├── IMG_9255.txt
  │   ├── IMG_9263.txt
  │   ├── IMG_9266.txt
  │   └── IMG_9280.txt
  └── test
  ├── IMG_9256.JPG
  └── IMG_9271.JPG
  

2. 运行darknet容器
   • 将工程目录作为挂载点绑定到容器

   # 使用您的工程绝对路径设置变量 project_dir
   $ project_dir='/home/wjunjian/github/gouchicao/darknet/model-zoo/platen-switch'
   $ sudo docker run --runtime=nvidia -it --name=darknet \
       --volume=$project_dir:/darknet/project \
       gouchicao/darknet:latest-gpu
   

创建工程，用于模型训练 创建工程，自动生成训练前需要的数据 $ python3 create_project

• 训练的样本：train.txt

yolos/IMG_9255.JPG
yolos/IMG_9266.JPG
yolos/IMG_9280.JPG

• 验证的样本：valid.txt

yolos/IMG_9263.JPG

• 标注类型：voc.names

close
open

• 配置文件：voc.data

classes= 2
train  = cfg/train.txt
valid  = cfg/valid.txt
names = cfg/voc.names
backup = backup

• 修改YOLO神经网络文件：yolov3.cfg

603行：filters=21    # (classes + 5)*3
610行：classes=2
689行：filters=21
696行：classes=2
776行：filters=21
783行：classes=2

• 使用LabelImg标注图像样本集

# python3 labelImg.py [图像目录] [标注名字文件] [标注目录]
python3 labelImg.py open-close/yolos/ open-close/cfg/yolo.names

• 下载基于imagenet的预训练模型 darknet53.conv.74

wget https://pjreddie.com/media/files/darknet53.conv.74