Use this seed to start new deep learning / ML projects.

• Built in setup.py
• Built in requirements
• Examples with MNIST
• Badges
• Bibtex

The goal of this seed is to structure ML paper-code the same so that work can easily be extended and replicated.

What it does

First, install dependencies

# clone project   
git clone https://github.com/YourGithubName/deep-learning-project-template

# install project   
cd deep-learning-project-template 
pip install -e .   
pip install -r requirements.txt

Next, navigate to any file and run it.

# module folder
cd project

# run module (example: mnist as your main contribution)   
python lit_classifier_main.py    

This project is setup as a package which means you can now easily import any file into any other file like so:

from project.datasets.mnist import mnist
from project.lit_classifier_main import LitClassifier
from pytorch_lightning import Trainer

# model
model = LitClassifier()

# data
train, val, test = mnist()

# train
trainer = Trainer()
trainer.fit(model, train, val)

# test using the best model!
trainer.test(test_dataloaders=test)

@article{YourName,
  title={Your Title},
  author={Your team},
  journal={Location},
  year={Year}
}