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With `lighter`, focus on your deep learning experiments and forget about boilerplate through:
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1.**Task-agnostic** training logic already implemented for you (classification, segmentation, self-supervised, etc.)
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2.**Configuration-based** approach that will ensure that you can always reproduce your experiments and know what hyperparameters you used.
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3. Extremely **simple integration of custom** models, datasets, transforms, or any other components to your experiments.
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Focus on your deep learning experiments and forget about (re)writing code. `lighter` is:
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1.**Task-agnostic**
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Whether you’re working on classification, segmentation, or self-supervised learning, `lighter` provides generalized training logic that you can use out-of-the-box.
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2.**Configuration-based**
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Easily define, track, and reproduce experiments with `lighter`’s configuration-driven approach, keeping all your hyperparameters organized.
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3.**Customizable**
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Seamlessly integrate your custom models, datasets, or transformations into `lighter`’s flexible framework.
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`lighter` stands on the shoulder of these two giants:
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-[MONAI Bundle](https://docs.monai.io/en/stable/bundle_intro.html) - Configuration system. Similar to [Hydra](https://github.com/facebookresearch/hydra), but with additional features.
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-[PyTorch Lightning](https://github.com/Lightning-AI/lightning) - Our [`LighterSystem`](https://project-lighter.github.io/lighter/reference/system/) is based on the PyTorch Lightning [`LightningModule`](https://lightning.ai/docs/pytorch/stable/common/lightning_module.html) and implements all the necessary training logic for you. Couple it with the PyTorch Lightning [Trainer](https://lightning.ai/docs/pytorch/stable/common/trainer.html) and you're good to go.
make pre-commit-install # Set up the pre-commit hook for code formatting
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poetry shell # Once installed, activate the poetry shell
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```
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poetry shell # Once installed, activate the poetry shell</code></pre>
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</details>
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<br/>
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## 💡 Projects
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Projects that use `lighter`:
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| --- | --- |
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|[Foundation Models for Quantitative Imaging Biomarker Discovery in Cancer Imaging](https://aim.hms.harvard.edu/foundation-cancer-image-biomarker)| A foundation model for lesions on CT scans that can be applied to down-stream tasks related to tumor radiomics, nodule classification, etc. |
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<br/>
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## 📄 Cite:
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If you find `lighter` useful in your research or project, please consider citing it:
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