Metadata-Version: 2.1
Name: powerful-benchmarker
Version: 0.9.29
Summary: A highly-configurable tool that enables thorough evaluation of deep metric learning algorithms. 
Home-page: https://github.com/KevinMusgrave/powerful-benchmarker
Author: Kevin Musgrave
Author-email: tkm45@cornell.edu
License: UNKNOWN
Description: <h1 align="center">
         Powerful Benchmarker
        </h2>
        <p align="center">
        	
        </h2>
        <p align="center">
         <a href="https://badge.fury.io/py/powerful-benchmarker">
             <img alt="PyPi version" src="https://badge.fury.io/py/powerful-benchmarker.svg">
         </a>
         
        
        ## Documentation
        [**View the documentation here**](https://kevinmusgrave.github.io/powerful-benchmarker/)
        
        ## A Metric Learning Reality Check
        This library was used for [A Metric Learning Reality Check](https://arxiv.org/abs/2003.08505). See [the documentation](https://kevinmusgrave.github.io/powerful-benchmarker/papers/mlrc) for supplementary material.
        
        ## Benchmark results: 
        - [4-fold cross validation, test on 2nd-half of classes](https://docs.google.com/spreadsheets/d/1brUBishNxmld-KLDAJewIc43A4EVZk3gY6yKe8OIKbY/edit?usp=sharing)
        
        ## Benefits of this library
        1. Highly configurable
            - Use the default configs files, merge in your own, or override options via the command line.
        2. Extensive logging
            - View experiment data in tensorboard, csv, and sqlite format.
        3. Easy hyperparameter optimization
            - Simply append \~BAYESIAN\~ to the hyperparameters you want to optimize.
        4. 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:
        ```latex
        @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:
        ```latex
        @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.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.0
Description-Content-Type: text/markdown
