All Projects → liuguiyangnwpu → DL.EyeSight

liuguiyangnwpu / DL.EyeSight

Licence: GPL-3.0 license
Mainly use SSD, YOLO and other models to solve the target detection problem in image and video !

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python
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图像视频中的目标检测

本工程主要目的是集成深度学习中常用的目标检测模型,并利用目标检测模型进行图像和视频中的检测!

开发环境

OS: Ubuntu 16.04
Python: Python 3.6.0
Tensorflow: 1.4.1 version
Opencv: 3.2.0 version for python

实现模型介绍

  • SSD+VGG
  • SSD+Res
  • SSD+Inception
  • SSD+SqueezeNet
  • SSD+Deconvolution
  • YOLO

运行说明

  1. 先准备好数据集
cat /Volumes/projects/DataSets/VOC2007/voc_train.txt
image_path01 xmin ymin xmax ymax class_id xmin ymin xmax ymax class_id
image_path02 xmin ymin xmax ymax class_id xmin ymin xmax ymax class_id
image_path03 xmin ymin xmax ymax class_id xmin ymin xmax ymax class_id

PS:Class_id从0开始编号,顺序同cfg文件中的label顺序一致
  1. 修改配置文件 配置文件存放在根目录下:conf/ssd_train.cfg 其中还有若干配置项,进行修改

  2. 运行程序 进入到example/ssd目录中

python vgg_trainer.py -c ../../conf/ssd_train.cfg

TODOLISTS

  • 整理文件目录结构,按照设计模式进行
  • 增加数据预处理的PipeLine
    • 图像插值
    • 图像镜像操作(左右,上下)
    • 添加随机噪声(各种模糊操作)
    • 对比度拉伸
    • 饱和度变化
    • 图像锐化
  • 提高模型训练速度
    • RawData ---> TFRecords
    • Single Process ---> Multi Processes
  • 检测过程的可视化
  • 编写检测网络结构模型文件
  • 对数据集的处理结构的统一接口
  • 编写对模块的测试文件

实验结果

  • YOLOv1模型在Pascal VOC数据集上的表现

联系我

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