/Detection-of-river-water-level-and-illegal-buildings-based-on-deep-learning

基于深度学习的河道水位和违章建筑检测

Primary LanguagePythonMIT LicenseMIT

SSD: Single Shot MultiBox Object Detector, in PyTorch

A PyTorch implementation of Single Shot MultiBox Detector from the 2016 paper by Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang, and Alexander C. Berg. The official and original Caffe code can be found here.

Table of Contents

       

Installation

  • Install PyTorch by selecting your environment on the website and running the appropriate command.
  • Clone this repository.
    • Note: We currently only support Python 3+.
  • Then download the dataset by following the instructions below.
  • We now support Visdom for real-time loss visualization during training!
    • To use Visdom in the browser:
    # First install Python server and client
pip install visdom
# Start the server (probably in a screen or tmux)
python -m visdom.server
    • Then (during training) navigate to http://localhost:8097/ (see the Train section below for training details).
  • Note: For training, we currently support VOC and COCO, and aim to add ImageNet support soon.

train

实验4.1

使用gauge.zip的数据训练一个水位尺检测模型

使用mark.zip的数据训练一个实际水位和警戒水位检测模型

实验4.2

使用buildingwater.zip的数据训练一个河道区域和建筑区域的检测模型

注意

修改data/custom.py中的CUSTOM_CLASSES以适应不同的数据集

修改data/config.py中的num_classes, lr_steps, max_iter以适应不同的数据集