Minerva ======= Minerva is a PyTorch-Lightning-based framework for training machine learning models, designed for researchers. It provides a robust and flexible framework for those working on machine learning projects. Minerva includes various utilities and modules for: - Data transformation - Model creation - Analysis metrics - Reproducibility Designed to be modular and extensible, Minerva allows researchers to easily add new features and functionalities. Features -------- Minerva offers a wide range of features to support your machine learning projects: - **Model Creation** Offers a variety of models and architectures to choose from. - **Training and Evaluation** Provides tools to train and evaluate models, including loss functions, optimizers, and evaluation metrics. - **Data Transformation** Includes tools for preprocessing and transforming data, such as data loaders, data augmentation, and normalization. - **Modular Design** Designed to be modular and extensible, allowing easy integration of new features and functionalities. - **Reproducibility** Ensures reproducibility with tools for versioning, configuration, and experiment logging. - **Self-Supervised Learning (SSL) Support** Supports Self-Supervised Learning (SSL) for training models with limited labeled data. - **Development Environment** Provides a pre-configured development environment with all dependencies installed. Minerva is a framework for training machine learning models for researchers. We provide a set models, datasets, and tools to help you get started with your research. Also, we provide a set of tools to help you configurate experiments, train models, and analyze results in a reproducible way, logging all the information you need to reproduce your experiments. .. toctree:: :maxdepth: 1 :caption: Contents installation getting_started design experiments contributing tutorials api Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search`