Barlow Twins with timm

This is an implementation of Barlow Twins: Self-Supervised Learning via Redundancy Reduction.

I borrow from the original repo and mostly refactor to make configurable (via hydra), loggable via wandb, and integrate the timm library for fast experimentation.

Usage

Requirements

Install dependencies into a virtualenv:

$ python -m venv env
$ source env/bin/activate
(env) $ pip install -r requirements.txt

Written with python version 3.8.11

Data and Configuration

Custom datasets can be placed in the data/ dir. Edits should be made to the conf/data/default.yaml file to reflect the correct properties of the data. All other configuration hyperparameters can be set in the hydra configs.

Train

Once properly configured, a model can be trained via python train.py.

Citations

@misc{zbontar2021barlow,
      title={Barlow Twins: Self-Supervised Learning via Redundancy Reduction},
      author={Jure Zbontar and Li Jing and Ishan Misra and Yann LeCun and Stéphane Deny},
      year={2021},
      eprint={2103.03230},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}