A PyTorch library for benchmarking deep metric learning. It's powerful.
Project description
Powerful Benchmarker
Documentation
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
- 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.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for powerful-benchmarker-0.9.33.tar.gz
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | de41a435a140d71cb938518958d906b68a450a60a36682f5e174048c08a9c080 |
|
| MD5 | b76c6c9eb9e14c0d2f039dbc963fa1ba |
|
| BLAKE2b-256 | 74cfe4e1a6d31367f71b91e642264c4b3c1609e01eb83461e7f70eb0d2082022 |
Hashes for powerful_benchmarker-0.9.33-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 8ec67e423a8c2c4cfcbc14e9ffd41b6cab4adfd892b85c4edc86e550a951e33c |
|
| MD5 | 030305a322acce541fb99a949992b71d |
|
| BLAKE2b-256 | bce64c395dddae84980855215a411838282a3fb28e4aeb465bb98a8dd10c1433 |
