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deeplabv3-tensorflow's Introduction

基于deeplabv3对遥感图像的语义分割

数据集:

CCF卫星影像的AI分类与识别提供的数据集初赛复赛训练集,一共五张卫星遥感影像

dataset
├── origin //5张遥感图片,有标签,用来做训练
├── test   //3张遥感图片,无标签,用来做测试
└── train  //为空,通过`python preprocess.py`随机采样生成
    ├── images       
    └── labels

采用方法:

  • 使用模型为Deeplab_v3,使用预训练好的resnet_v2_50 fine-tuning
  • 将原始的遥感图像裁成大小为(256x256)的图片块,裁剪的方法为随机采样,并进行数据增强

依赖:

  • GPU Nvidia Tesla V100 (16G)
  • tensorflow
  • opencv-python
  • python3

单卡跑一天就可以收敛~

How To Train?

  1. 将百度云中的数据集文件夹dataset下载并存放到项目主目录下
  2. python proprecess.py 生成训练集 时间稍长,需要等待
  3. python main.py 开始训练

TODO:

Pull requests are welcome.

  • Resnet_v2_50 as Network Backbone using tensorflow API.
  • Ready for slim pretrained parameters.
  • Xception as Network Backbone
  • Multi-GPU support
  • TFRecords
  • 后处理优化,比如消除拼接痕迹,CRF后处理

测试图片:

训练曲线:

deeplabv3-tensorflow's People

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