Kit-Kars

ML project on Car Segmentation with Deloitte, for DTU Deep Learning course.

TODO before running anything:

• run make requirements from dl_kitkars folder
• run wandb login, then go to the Wandb of our project, copy the key and get WANDB authorization
• download unet_carvana_scale0.5_epoch2.pth in models folder
• download clean_data files (3521 files) in data/raw/carseg_data/clean_data

Useful Tips

• To debug: import pdb; pdb.set_trace()
• Start a tmux session: tmux new -s <someName>
• Attach the tmux session later: tmux attach -t <someName>
• Kill a tmux session: tmux kill-session
• Remove a local commit without any changes: git reset HEAD~1 --soft
• HPC keywords: voltash, sxm2sh or a100sh

Useful Links

• About tmux: https://linuxize.com/post/getting-started-with-tmux/
• HPC GPU available: https://www.hpc.dtu.dk/?page_id=2129
• MLOps course website: https://skaftenicki.github.io/dtu_mlops/

Project Organization

---

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.*
|       └── carseg_data
|           └── clean_data
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

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Project based on the cookiecutter data science project template. #cookiecutterdatascience