feat: Filter out low-confidence bounding box detections - #3154
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MMathisLab merged 10 commits intoJan 13, 2026
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fixes issue with len(keepers). Since keepers is a tuple with indices, it's len() is not indicative of the number of bboxes that should be kept. This is resolved by using Any()
… reproducibility. instead of a default filtering threshold of 0.25, the filtering is made optional and by default all bboxes are kept.
Low-confidence bounding boxes are removed when min_bbox_score parameter is provided. Default behavior remains unchanged.
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deruyter92
marked this pull request as ready for review
December 4, 2025 10:51
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Thank you for picking up my PR and running with it, your edits all look good to me! |
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Pull request overview
This PR adds functionality to filter out low-confidence bounding box detections during top-down pose estimation. The feature helps reduce false positives by removing detections below a configurable confidence threshold.
Key changes:
- Added a new RemoveLowConfidenceBoxes postprocessor class to filter bounding boxes based on score threshold
- Added min_bbox_score parameter to get_inference_runners, get_detector_inference_runner, and get_filtered_coco_detector_inference_runner functions
- Updated build_detector_postprocessor to optionally apply the low-confidence box filtering
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 7 comments.
| File | Description |
|---|---|
| deeplabcut/pose_estimation_pytorch/data/postprocessor.py | Added RemoveLowConfidenceBoxes class and updated build_detector_postprocessor to accept min_bbox_score parameter for filtering low-confidence detections |
| deeplabcut/pose_estimation_pytorch/apis/utils.py | Added min_bbox_score parameter to three inference runner functions (get_inference_runners, get_detector_inference_runner, get_filtered_coco_detector_inference_runner) with appropriate documentation |
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… quotes and logging instead of print.
(should be interpreted as the *minimum acceptible confidence)
MMathisLab
approved these changes
Jan 13, 2026
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Revised version of PR #3118 by @tlancaster6
Feature
Filter out low-confidence bounding box detections during top-down pose estimation to reduce false-positives.
Function get_inference_runners in deeplabcut.pose_estimation_pytorch now accepts an optional argument min_bbox_score. Low-confidence bounding boxes are removed when min_bbox_score parameter is provided. Default behavior remains unchanged (i.e. all bounding boxes are kept).