powerful-benchmarker 0.9.33

Description:

powerfulbenchmarker 0.9.33

Powerful Benchmarker







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:

4-fold cross validation, test on 2nd-half of classes

Benefits of this library

Highly configurable:

Yaml files for organized configuration
A powerful command line syntax that allows you to merge, override, swap, apply, and delete config options.


Customizable:

Benchmark your own losses, miners, datasets etc. with a simple function call.


Easy hyperparameter optimization:

Append the ~BAYESIAN~ flag to the names of hyperparameters you want to optimize.


Extensive logging:

View experiment data in tensorboard, CSV and SQLite format.


Reproducible:

Config files are saved with each experiment and are easily reproduced.


Trackable changes:

Keep track of changes to an experiment's configuration.



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.