Barring #5 , project should work out of the box. However, this is hard for someone who is handed this directory, and in the interest of reproducibility and following best practices I want to do some restructuring and cleaning.
Some points to focus on:
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Images directory is a bit bloated since it contains a mixture of raw and augmented images, post training/results figures, and random datasets from SQL queries. These should be decoupled in some fashion.
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I want someone to be able to do the usual python -m venv my_env + pip install -r requirements.txt workflow to get up and running. Ideally a new user should only need to run one python script and maybe change some hyperparameters in a config file. Unfortunately I was a little reliant on testing in a bunch of different notebooks, and then nbconverting perseusResNet50.ipynb to a .py script to run on the cluster.
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The old way I handled datasets is a nightmare, intelligently using class methods like convention recommends makes this much cleaner.
Barring #5 , project should work out of the box. However, this is hard for someone who is handed this directory, and in the interest of reproducibility and following best practices I want to do some restructuring and cleaning.
Some points to focus on:
Images directory is a bit bloated since it contains a mixture of raw and augmented images, post training/results figures, and random datasets from SQL queries. These should be decoupled in some fashion.
I want someone to be able to do the usual
python -m venv my_env+pip install -r requirements.txtworkflow to get up and running. Ideally a new user should only need to run one python script and maybe change some hyperparameters in a config file. Unfortunately I was a little reliant on testing in a bunch of different notebooks, and then nbconvertingperseusResNet50.ipynbto a .py script to run on the cluster.The old way I handled datasets is a nightmare, intelligently using class methods like convention recommends makes this much cleaner.