Metadata-Version: 2.1
Name: debobo
Version: 0.1.4
Summary: Package for evaluating object detection models
Home-page: https://github.com/dseuss/debobo.git
Author: Daniel Suess
Author-email: daniel@dsuess.me
License: UNKNOWN
Description: D.E. Bobo - Detection Evaluation for Bounding Boxes
        ===================================================
        
        <div style="text-align:center">
          <img src="https://raw.githubusercontent.com/dseuss/debobo/2c5e651ff89d1c189a2a33ef4857061bf9eb7e6a/assets/the_dj.jpg" width="360">
        </div>
        
        DEBobo is a library providing an easy-to-use evaluation code for object detection models. 
        It's main motivation was to replace the part of [pycocotools](https://github.com/cocodataset/cocoapi/tree/master/PythonAPI/pycocotools) responsible for evaluation as it doesn't work well with custom datasets.
        Additionally, I found the workflow of pycocotools doesn't work well with high-level training libraries such as [ignite](https://github.com/pytorch/ignite). 
        
        
        ## Installation
        
        Most end users should be able to get away with
        
        ```
        pip install debobo
        ```
        
        For development, install the library with symlinks and with the additional test requirements using
        
        ```
        pip install -e .[test]
        ```
        
        To run the tests, first download the test-data using `./fetch_testdata.sh`. 
        The test is run via 
        
        ```
        pytest tests/
        ```
        
        It compares the results obtained from debobo to the result obtained using pycocotools.
        
        
        ## Usage
        
        We provide ready-to-use metrics for ignite in `debobo.adapters.ignite`. 
        Feel free to request other adapters as an issue.
        Also, check the [tests](tests/test_detection.py) for how to use the low-level routines.
        
        
        ## Thanks
        
        Thanks to [@martiningram](https://github.com/martiningram/) for the header image.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
