/Lunar-Landscape-Image-Segmentation

ML2 Final Project - Lunar Landscape Segmentation

Primary LanguageJupyter NotebookMIT LicenseMIT

Final-Project-Group5

George Washington University, Machine Learning II - DATS203_10, Fall 2022

Project

Lunar Landscape Imagery Segmentation

sample_diagram

Table of Contents

  1. Team Members
  2. How to Run
  3. Folder Structure
  4. Timeline
  5. Topic Proposal
  6. Datasets
  7. Presentation
  8. Report
  9. References
  10. Licensing

Team Members

How to Run

  1. Clone this repo

  2. Install python packages. After cloning the repo and download python packages.

    cd Final-Project-Group5/Code/
pip install -r requirements.txt

  3. Execute Main Script with options...

    Test (~10 minutes): This will skip any of the training and run the testing loops with the models downloaded from Google Drive

    python3 main.py --method 'test'

    Train (~10 hours): This will run the full training loops, overwriting the downloaded Models (if any) and then test the results.

    python3 main.py --method 'train'

    Debug (~10 hours): This will run the training loops along with any debugging code, this includes checks for the data loaders and plotting outputs between models in addition to at the end of the loops.

    python3 main.py --method 'debug'

    EDA (additional 10+ minutes): Running with EDA set to True will run the EDA python script before any modeling code, this will allow the EDA notebook to be executed without errors. If you don't want to execute the EDA notebook then this argument should be left out as the default is False.

    python3 main.py --method 'test' --EDA True

Folder Structure

.
├── Code                                # Final code for the project, navigate here to run.
│   ├── LunarModules                    # Modules to support codebase
│   ├── plots                           # Plots folder to save plots
├── Final-Group-Presentation            # Presentation Slides PDF
├── Final-Group-Presentation            # Final Report
├── Group-Proposal                      # Group Proposal Report
├── joshua-ting-individual-project      # Individual report - Josh
├── sahara-ensley-individual-project    # Individual report - Sahara
├── Results                             # This folder contains results from the models we tuned. The GUI pulls from this folder.
│ 
└── requirements.txt        # Python package requirements

Timeline

  • Proposal - 11/8/2022
  • Environment Setup - 11/8/2022
  • EDA - 11/11/2022
  • Start Model Training - 11/18/2022
  • Final Model and Results - 12/02/2022
  • Google Drive Models Download
  • Google Drive Data Download
  • Main Script with option to run saved model or train from scratch
  • Freeze requirements.txt
  • Finalize README
  • Test On Clean EC2
  • Final Report - 12/12/2022
  • Individual Reports/Code
  • Final Presentation - 12/12/2022

Topic Proposal

Datasets

Presentation

Report

References

Licensing