Awesome Open Source
Awesome Open Source

Powerful Benchmarker

PyPi version

Documentation

View the documentation here

Google Colab Examples

See the examples folder for notebooks that show a bit of this library's functionality.

A Metric Learning Reality Check

See supplementary material for the ECCV 2020 paper.

Benchmark results:

Benefits of this library

  1. Highly configurable:
  2. Customizable:
  3. Easy hyperparameter optimization:
  4. Extensive logging:
  5. Reproducible:
  6. Trackable changes:

Installation

pip install powerful-benchmarker

Citing the benchmark results or code

If you'd like to cite the benchmark results, please cite this paper:

@misc{musgrave2020metric,
    title={A Metric Learning Reality Check},
    author={Kevin Musgrave and Serge Belongie and Ser-Nam Lim},
    year={2020},
    eprint={2003.08505},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Acknowledgements

Thank you to Ser-Nam Lim at Facebook AI, and my research advisor, Professor Serge Belongie. This project began during my internship at Facebook AI where I received valuable feedback from Ser-Nam, and his team of computer vision and machine learning engineers and research scientists.


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