Metadata-Version: 2.1
Name: onemetric
Version: 0.1.0
Summary: Metrics Library to Evaluate Machine Learning Algorithms in Python
Home-page: https://github.com/SkalskiP/onemetric
Author: Piotr Skalski
Author-email: piotr.skalski92@gmail.com
License: BSD
Description: [![PyPI version](https://badge.fury.io/py/onemetric.svg)](https://badge.fury.io/py/onemetric)
        ![PyPI - Downloads](https://img.shields.io/pypi/dm/onemetric)
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        [![codecov](https://codecov.io/gh/SkalskiP/onemetric/branch/master/graph/badge.svg?token=ZFSEYF9WN4)](https://codecov.io/gh/SkalskiP/onemetric)
        ![tests](https://github.com/SkalskiP/onemetric/actions/workflows/tests.yml/badge.svg)
        ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/onemetric)
        
        <h1 align="center">onemetric</h1>
        
        <p align="center"> 
            <img width="150" src="https://onemetric-images.s3.eu-central-1.amazonaws.com/favicon.png" alt="Logo">
        </p>
        
        ## Installation
        
        ```terminal
        pip install onemetric
        ```
        
        ## Documentation
        
        The official documentation is hosted on Github Pages: https://skalskip.github.io/onemetric
        
        ## Contribute
        
        Feel free to file [issues](https://github.com/SkalskiP/onemetric/issues) or [pull requests](https://github.com/SkalskiP/onemetric/pulls). Let us know what metrics should be part of onemetric!
        
        ## Citation
        
        Please cite onemetric in your publications if this is useful for your research. Here is an example BibTeX entry:
        
        ```BibTeX
        @MISC{onemetric,
           author = {Piotr Skalski},
           title = {{onemetric}},
           howpublished = "\url{https://github.com/SkalskiP/onemetric/}",
           year = {2021},
        }
        ```
        
        ## License
        
        This project is licensed under the BSD 3 - see the [LICENSE][1] file for details.
        
        ## Acknowledgements
        
        Building onemetric would have been much more difficult if not for the efforts and persistence of many open-source developers. Their ideas were the help and inspiration in creating this library. **Thank you!**
        
        1. Confusion Matrix for Object Detection [link](2) by [kaanakan](3).
        2. Mean Average Precision for Object Detection [link](4) by [bes-dev](5).
        3. YOLOv3 in PyTorch [link](6) by [ultralytics](7).
        
        [1]: https://github.com/SkalskiP/onemetric/blob/master/LICENSE
        [2]: https://github.com/kaanakan/object_detection_confusion_matrix
        [3]: https://github.com/kaanakan
        [4]: https://github.com/bes-dev/mean_average_precision
        [5]: https://github.com/bes-dev
        [6]: https://github.com/ultralytics/yolov3
        [7]: https://github.com/ultralytics
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Typing :: Typed
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: tests
