You got a research idea? It shouldn't take you more than 10 minutes to start from scratch and get it running with the ability to produce high quality figures/tables from the results: that's the goal of stable-SSL.
We achieve that by taking the best--and only the best--from the most eponymous AI libraries: PytorchLightning, VISSL, Wandb, Hydra, Submitit.
stable-SSL implements all the basic boilerplate code, including data loader, logging, checkpointing, optimization, etc. You only need to implement 3 methods to get started: your loss, your model, and your prediction (see example <#own_trainer>_ below). But if you want to customize more things, simply inherit the base BaseModel and override any method! This could include different metrics, different data samples, different training loops, etc.
.. .. image:: https://github.com/rbalestr-lab/stable-SSL/raw/main/docs/source/figures/logo.png .. :alt: ssl logo .. :width: 200px .. :align: right
.. .. contents:: Table of Contents .. :depth: 2
.. _why:
A quick search of AI libraries or Self Supervised Learning libraries will return hundreds of results. 99% will be independent project-centric libraries that can't be reused for general purpose AI research. The other 1% includes:
Hence our goal is to fill that void.
.. _installation:
The library is not yet available on PyPI. You can install it from the source code, as follows.
.. code-block:: bash
pip install -e .
Or you can also run:
.. code-block:: bash
pip install -U git+https://github.com/rbalestr-lab/stable-SSL