ConvRnet

This is the implementation of the ConvRnet model proposed in the paper, Research on Underground 3-D Displacement Measurement Based on Convolutional Neural Networks and Dual Mutual Inductance Voltages, for predicting 3D underground displacements.

Getting Started 🚀

Prerequisites 🛠️

It is recommended that you have an Nvidia GPU with at least 8GB of memory, as this will significantly reduce the time required for training and validation.

Software Requirements 🖥️

numpy~=1.24.1  
torch~=2.2.1+cu118  
torchvision~=0.17.1+cu118  
scipy~=1.10.1  
matplotlib~=3.7.5  
pandas~=2.0.3

Installation 💻

1. Clone the repository

git clone https://github.com/ZHN202/ConvRnet.git

2. Install dependencies

pip install -r requirements.txt

Usage ℹ️

Training 🏋️

Models to choose from:
1 ---> Linear MLP  
2 ---> Conv1d  
3 ---> ConvRnet  
4 ---> ConvRnet_linear  
5 ---> ConvRnet_without_CBAM  
6 ---> ConvRnet_without_DM  
7 ---> ConvMLP  
8 ---> RBF  
9 ---> RBF_MLP  

python train_for_k_fold.py --ChooseModel=1

Validation ✔️

python val_to_file.py --dir_path=your/path/to/20-4-Fold-Dataset-1

Citations 📚

If you use this code in your research, please cite:

@article{jia2024research,
  title={Research on Underground 3-D Displacement Measurement Based on Convolutional Neural Networks and Dual Mutual Inductance Voltages},
  author={Jia, Shengyao and Zhou, Haonan and Shi, Ge and Chen, Haiwei and Han, Jianqiang and Li, Qing},
  journal={IEEE Sensors Journal},
  volume={24},
  number={1},
  pages={526--532},
  year={2024}
}