A highly-configurable tool that enables thorough evaluation of deep metric learning algorithms.
Project description
Powerful Benchmarker
Documentation
A Metric Learning Reality Check
This library was used for A Metric Learning Reality Check. See the documentation for supplementary material.
Benchmark results:
Benefits of this library
- Highly configurable
- Use the default configs files, merge in your own, or override options via the command line.
- Extensive logging
- View experiment data in tensorboard, csv, and sqlite format.
- Easy hyperparameter optimization
- Simply append ~BAYESIAN~ to the hyperparameters you want to optimize.
- Customizable
- Benchmark your own losses, miners, datasets etc. with a simple function call.
Installation
pip install powerful-benchmarker
Citing the benchmark results
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}
}
Citing the code
If you'd like to cite the powerful-benchmarker code, you can use this bibtex:
@misc{Musgrave2019,
author = {Musgrave, Kevin and Lim, Ser-Nam and Belongie, Serge},
title = {Powerful Benchmarker},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/KevinMusgrave/powerful-benchmarker}},
}
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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