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One Research Framework To Rule Them All

License: MIT License

Shell 0.38% C++ 0.30% Python 62.90% MATLAB 0.19% CSS 0.16% TeX 0.01% Cuda 0.73% Makefile 0.01% HTML 25.20% Batchfile 0.01% Jupyter Notebook 10.10% Dockerfile 0.02%

mon's Introduction

🐈 MON

Documentation

🐈 mon is an all-in-one research framework built using Python and PyTorch.
It covers a wide range of research topics in computer vision and machine learning.
The full documentation of the framework can be found here (still work-in-progress).

Installation

git clone https://[email protected]/phlong3105/mon
cd mon
chmod +x install.sh
bash -i install.sh

Directory Organization

Look at "private.docx" for more information.

code
 |_ mon
     |_ bin                  # Executable, main() files, CLI.
     |_ data                 # Default location to store working datasets.
     |_ docs                 # Documentation.
     |_ env                  # Environment variables.
     |_ src                  # Source code.
     |   |_ mon              # Python code.
     |       |_ config       # Configuration functionality.
     |       |_ core         # Base functionality for other packages.
     |       |_ data         # Data processing package.
     |       |_ nn           # Machine learning package.
     |       |_ vision       # Computer vision package.
     |_ zoo                  # Model zoo.
     |_ .gitignore           # 
     |_ install.sh           # Installation script.
     |_ LICENSE              #
     |_ mkdocs.yaml          # mkdocs setup.
     |_ pyproject.toml       # 
     |_ README.md            # Github Readme.

Cite

If you find our work useful, please cite the following:

@misc{Pham2022,  
    author       = {Long Hoang Pham, Duong Nguyen-Ngoc Tran, Quoc Pham-Nam Ho},  
    title        = {🐈 mon},  
    publisher    = {GitHub},
    journal      = {GitHub repository},
    howpublished = {https://github.com/phlong3105/mon},
    year         = {2022},
}