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Project Organization

├── LICENSE
├── Makefile                <- Makefile with commands like `make train`.
├── README.md               <- The top-level README for developers using this project.
├── setup.py                <- makes project pip installable (pip install -e .) so src can be imported.
├── tox.ini
├── requirements.txt        <- The requirements file for reproducing the analysis environment.
├── requirements_tests.txt
├── conf                    <- The overall configurations for the project.
│   ├── __init__.py
│   ├── config.yaml
│   ├── data                <- Configurations for dataset.
│   │   └── imagenet-mini.yaml
│   ├── experiment          <- Configurations for training.
│   │   └── exp1.yaml
│   ├── predict.yaml
│   └── sweep.yaml
├── src                     <- Source code for use in this project.
│   ├── __init__.py
│   ├── data                <- Scripts to download or generate data.
│   │   ├── __init__.py
│   │   └── make_dataset.py
│   └── models              <- Scripts to train models and then use trained models to make predictions.
│       ├── __init__.py
│       ├── model.py
│       ├── predict_model.py
│       ├── script_model.py
│       └── train_model.py
├── tests                   <- Unit tests code for dataset and models.
│   ├── __init__.py
│   ├── test_data.py
│   └── test_model.py
├── app                     <- A self-contained fastapi to do inference.
│   ├── __init__.py
│   ├── cloud_deployment.py
│   ├── cloud_function.py
│   ├── index_to_name.json
│   ├── predict_image.py
│   └── requirements.txt
├── data
│   ├── processed           <- The final, canonical data sets for modeling.
│   │   ├── train_dataset.pt
│   │   └── val_dataset.pt
│   └── raw                 <- The original, immutable data dump.
│       └── imagenet-mini
├── data.dvc                <- dvc tracking for the data folder.
├── models                  <- Trained and serialized models.
│   ├── deployable_model.pt
│   └── exp4
│       └── epoch=03-val_accuracy=0.5455.ckpt
├── models.dvc              <- dvc tracking for the models folder.
├── trainer.dockerfile      <- dockerfile for the training process.
├── prediction.dockerfile   <- dockerfile for the prediction process.
├── entrypoint.sh
├── cloudbuild.yaml
├── cml.yaml
├── codecov.yml
├── docs                    <- sphinx documentation of the project's codebase.
├── profiles
│   └── exp4
└── wandb                   <- wandb log for each experiment(training) and hyperparameter sweeping.
    ├── run-20250425_003418-9mldez9v
    │   ├── files
    │   ├── logs
    │   ├── run-9mldez9v.wandb
    │   └── tmp
    └── sweep-rwh3k86x
        └── config-3f06mlo8.yaml