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        "author": "Daniel Suess",
        "author_email": "daniel@dsuess.me",
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            "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)",
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        "description": "D.E. Bobo - Detection Evaluation for Bounding Boxes\n===================================================\n\n<div style=\"text-align:center\">\n  <img src=\"https://raw.githubusercontent.com/dseuss/debobo/2c5e651ff89d1c189a2a33ef4857061bf9eb7e6a/assets/the_dj.jpg\" width=\"360\">\n</div>\n\nDEBobo is a library providing an easy-to-use evaluation code for object detection models. \nIt'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.\nAdditionally, I found the workflow of pycocotools doesn't work well with high-level training libraries such as [ignite](https://github.com/pytorch/ignite). \n\n\n## Installation\n\nMost end users should be able to get away with\n\n```\npip install debobo\n```\n\nFor development, install the library with symlinks and with the additional test requirements using\n\n```\npip install -e .[test]\n```\n\nTo run the tests, first download the test-data using `./fetch_testdata.sh`. \nThe test is run via \n\n```\npytest tests/\n```\n\nIt compares the results obtained from debobo to the result obtained using pycocotools.\n\n\n## Usage\n\nWe provide ready-to-use metrics for ignite in `debobo.adapters.ignite`. \nFeel free to request other adapters as an issue.\nAlso, check the [tests](tests/test_detection.py) for how to use the low-level routines.\n\n\n## Thanks\n\nThanks to [@martiningram](https://github.com/martiningram/) for the header image.",
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