With lighter, focus on your deep learning experiments and forget about boilerplate through:

1. Task-agnostic training logic already implemented for you (classification, segmentation, self-supervised, etc.)
2. Configuration-based approach that will ensure that you can always reproduce your experiments and know what hyperparameters you used.
3. Extremely simple integration of custom models, datasets, transforms, or any other components to your experiments.

 

lighter stands on the shoulder of these two giants:

• MONAI Bundle - Configuration system. Similar to Hydra, but with additional features.
• PyTorch Lightning - Our LighterSystem is based on the PyTorch Lightning LightningModule and implements all the necessary training logic for you. Couple it with the PyTorch Lightning Trainer and you're good to go.

Simply put, lighter = config(trainer + system) 😇

📖 Usage

• 📚 Documentation
• 🎥 YouTube Channel

🚀 Install

Current release:

pip install project-lighter

Pre-release (up-to-date with the main branch):

pip install project-lighter --pre

For development:

make setup
make install             # Install lighter via Poetry
make pre-commit-install  # Set up the pre-commit hook for code formatting
poetry shell             # Once installed, activate the poetry shell

💡 Projects

Projects that use lighter:

Project	Description
Foundation Models for Quantitative Imaging Biomarker Discovery in Cancer Imaging	A foundation model for lesions on CT scans that can be applied to down-stream tasks related to tumor radiomics, nodule classification, etc.

📄 Cite:

If you find lighter useful in your research or project, please consider citing it:

@software{lighter,
  author       = {Ibrahim Hadzic and
                  Suraj Pai and
                  Keno Bressem and
                  Hugo Aerts},
  title        = {Lighter},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.8007711},
  url          = {https://doi.org/10.5281/zenodo.8007711}
}

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