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README
Apache-2.0

ssd.pytorch

介绍

  1. 用pytorch复现ssd并在自己的数据集上进行行人检测

  2. docker镜像已经安装了pytorch以及各种依赖,免除安装环境的痛苦(强烈安利docker!!!)

    SSD的源码来源于https://github.com/amdegroot/ssd.pytorch.githttps://github.com/acm5656/ssd_pytorch.git

    自己根据需求进行了差异化修改,按照下边的使用说明可直接运行出结果

安装教程

  1. 服务器安装docker以及nvidia-docker

     安装docker参照官网 https://docs.docker.com/install/linux/docker-ce/ubuntu/#install-docker-ce-1
    
     安装nvidia-docker参照 https://github.com/NVIDIA/nvidia-docker
    
     操作docker参照 http://www.runoob.com/docker/docker-command-manual.html
  2. 下载docker镜像

     docker pull qinzhenyi1314/pytorch:1.1.0-cuda10.0-cudnn7.5-py3-vnc-jpd-ph
  3. 下载本项目代码

     git clone https://gitee.com/qinzhenyi1314/ssd.pytorch.git

使用说明

  1. 运行镜像

         docker run --runtime=nvidia  -it --rm --shm-size=3072m -w /data -p 6868:22 -p 5901:5901 -v  /home/js180/qzy/deeplearning/:/data qinzhenyi1314/pytorch:1.1.0-cuda10.0-cudnn7.5-py3-vnc-jpd-ph
    
         -v 是为了将服务器路径挂载到容器
    
         /home/test/qzy/deeplearning/是服务器代码存放路径
    
         /data是容器内的路径
  2. 下载数据集

     原始的voc2007以及voc2012都是20+1(背景)类,由于自己做的是行人检测 1+1(背景)类
    
     公司的数据集不能分享,暂提供一个很小的数据进行验证
    
     链接:https://pan.baidu.com/s/1-luJwOIhJhLWRHICoJItcw 提取码:42mh
    
     放入data/VOCdevkit下
    
     格式参照data/VOCdevkit/readme.txt
  3. 下载预训练模型

     链接:https://pan.baidu.com/s/1t4uG3YjCy2uIKFG3IZXQKA 提取码:5qhg
    
     主要使用
    
     1. vgg16_reducedfc.pth 用来训练用,是原始VGG16在ImageNet训练的模型
    
     2. ssd300_VOC_120000.pth 用来测试 自己模型在50000张训练集上训练120000次得到 map0.7936,还在继续优化!
  4. 打开docker内的VNC桌面并远程连接

     在镜像内输入 vncserver :1 注意有空格
    
     window上打开VNC客户端 在VNCServer处输入 xxx.xxx.xxx.xxx:5901  前边的xxx是你服务器Ip 比如 192.168.5.180
    
     密码输入 USRname 即可登录,现在就有桌面啦~~
    
     分享一个免安装的VNCServer
    
     链接:https://pan.baidu.com/s/1PLaUuy5mMY6npiWkbMk53g 提取码:9fyz
  5. 打开pycharm

     到 /home/qzy/pycharm-community-2019.1.1/bin/ 下运行
    
     bash ./pycharm.sh
    
     即可打开pycharm,然后选择加载我们的SSD项目
  6. 运行测试

     python test.py
    
     运行后将挑选3张图片将检测框画在图片上并保存在test文件夹下,可以根据自己需求更改
  7. 运行训练

     python train.py
    
     运行后会加载vgg16_reducedfc.pth预训练模型,并在weights生成相应的训练模型 xxx.pth
    
     一些超参数可自行设置查看效果
  8. 运行评价

     python eval.py
    
     运行后会在eval文件夹下生成eval.txt文件,保存了所有检测结果的置信度和框坐标
    
     到eval文件夹下
    
     运行eval_pro.py
    
     会在当前路径下生成map和pr结果并保存
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用pytorch复现的ssd进行物体检测 展开 收起
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