https://github.com/imperialcollegelondon/ggiecn

https://github.com/imperialcollegelondon/ggiecn

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 2 DOI reference(s) in README
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (4.2%) to scientific vocabulary
Last synced: 4 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: ImperialCollegeLondon
  • License: mit
  • Language: Python
  • Default Branch: main
  • Size: 17.6 KB
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  • Stars: 0
  • Watchers: 1
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  • Releases: 1
Created about 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

GGIECN

paper submitted

Prerequisites

conda install pytorch torchvision cudatoolkit=11.0 -c pytorch

  • opencv

pip install opencv-python

Usage

Train and predict

python GGIECN.py optional parameter: --data_path --backbone_type --crop_h --crop_w --batch_size --epochs --save_path --model_weight --predict_save_path

How to cite:

[1] Yang Gao, Sajjad Foroughi, Zhuangzhuang Ma, Sanyi Yuan, Lizhi Xiao, Branko Bijeljic, Martin J. Blunt. "Gradient Information Enhanced Image Segmentation and Automatic In Situ Contact Angle Measurement Applied to Images of Multiphase Flow in Porous Media." Water Resources Reseach (2024).

By citing our work, you contribute to the recognition of our research efforts and help others understand the foundation of this code.

Owner

  • Name: Imperial College London
  • Login: ImperialCollegeLondon
  • Kind: organization
  • Email: icgithub-support@imperial.ac.uk
  • Location: Imperial College London

Imperial College main code repository

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