This file contains code for performing non-maximum suppression (NMS) on bounding boxes obtained from multiple object detection models. The code was originally taken from the following GitHub repository:
This implementation provides functions for standard NMS, linear soft-NMS, and gaussian soft-NMS. It also includes a method for preparing the boxes, scores, and labels before applying NMS.
Along with some motification to match the conventional OSML styles such as typings and docstrings.
Checklist
Before you submit a pull request, please make sure you have the following:
[x] Code changes are compact and well-structured to facilitate easy review
[x] Changes are documented in the README.md and other relevant documentation pages
[x] PR title and description accurately reflect the changes and are detailed enough for historical tracking
[x] PR contains tests that cover all new code and the code has been manual tested
[x] All new dependencies are declared (if any), and no unnecessary libraries are added
[x] Performance impacts (if any) of the changes are evaluated and documented
[x] Security implications of the changes (if any) are reviewed and addressed
Issue #, if available: n/a
Notes
This file contains code for performing non-maximum suppression (NMS) on bounding boxes obtained from multiple object detection models. The code was originally taken from the following GitHub repository:
https://github.com/ZFTurbo/Weighted-Boxes-Fusion/blob/master/ensemble_boxes/ensemble_boxes_nms.py
The original author is 'ZFTurbo' (https://kaggle.com/zfturbo).
This implementation provides functions for standard NMS, linear soft-NMS, and gaussian soft-NMS. It also includes a method for preparing the boxes, scores, and labels before applying NMS.
Along with some motification to match the conventional OSML styles such as typings and docstrings.
Checklist
Before you submit a pull request, please make sure you have the following:
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.