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Fix Faster R-CNN detector inference crashing on box_score_thresh: null by rysk-t · Pull Request #3470 · DeepLabCut/DeepLabCut · GitHub
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Fix Faster R-CNN detector inference crashing on box_score_thresh: null #3470

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Merged
deruyter92 merged 14 commits into from
Sep 3, 2026
Merged

Fix Faster R-CNN detector inference crashing on box_score_thresh: null #3470

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fcfb6eb
Treat a null box_score_thresh as the default detector threshold
rysk-t Aug 30, 2026
6db842d
Normalize detector box score threshold
C-Achard Aug 31, 2026
21a0eb9
Refactor detector threshold tests to config level
C-Achard Aug 31, 2026
c61ddcc
Fix incorrect import
C-Achard Aug 31, 2026
aac861c
Fix model config test defaults for TD
C-Achard Sep 1, 2026
85072d9
Use config cutoff for detector boxes
C-Achard Sep 1, 2026
7893a35
Fix detector box_score_thresh handling
C-Achard Sep 1, 2026
5e58815
Clarify detector threshold docs
C-Achard Sep 1, 2026
3e2611e
Fix `create_video_with_all_detections` -> read default `bbox_pcutoff`…
deruyter92 Sep 1, 2026
baff692
revert shared box score threshold default -> inline config default
deruyter92 Sep 1, 2026
0f5330a
update docstring for `DetectorModelConfig`
deruyter92 Sep 1, 2026
256dfb9
add test for building config with null threshold
deruyter92 Sep 1, 2026
2a00c40
Make `ProjectConfig.bboxes_pcutoff`` a property rather than a field
deruyter92 Sep 1, 2026
2689d30
add xfail for `ProjectConfig.bboxes_pcutoff`
deruyter92 Sep 1, 2026
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Make ProjectConfig.bboxes_pcutoff` a property rather than a field 
This avoids breaking compatibility, and keeps the config schema internally consistent for the current version
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@deruyter92
deruyter92 committed Sep 1, 2026
commit 2a00c40ae9260ecc00547e552c7341a947f33ed7
15 changes: 13 additions & 2 deletions deeplabcut/core/config/project_config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,7 +54,6 @@ class ProjectConfig(DLCVersionedConfig):
skeleton: Skeleton connectivity for plotting.
skeleton_color: Skeleton color for plotting.
pcutoff: Confidence cutoff for plotting.
bboxes_pcutoff: Confidence cutoff for plotting bounding boxes from detectors.
dotsize: Dot size for visualization.
alphavalue: Alpha value for visualization.
colormap: Colormap for visualization.
Expand Down Expand Up @@ -131,7 +130,6 @@ class ProjectConfig(DLCVersionedConfig):
)
skeleton_color: str = "black"
pcutoff: Fraction = 0.6
bboxes_pcutoff: Fraction = 0.6
dotsize: NonNegativeInt = 12
alphavalue: Fraction = 0.7
colormap: str = "rainbow"
Expand Down Expand Up @@ -191,6 +189,19 @@ class ProjectConfig(DLCVersionedConfig):
)
croppedtraining: bool | None = None

# TODO @deruyter92 2026-09-01: This should be included as field in the
# config. It requires a version bump of the schema version.
# see https://github.com/DeepLabCut/DeepLabCut/pull/3470
@property
def bboxes_pcutoff(self) -> Fraction:
"""Default confidence cutoff for plotting bounding boxes from detectors.

Note: This property is introduced in v3.1.0 as prospective config
field, but is intentionally not yet defined as such in the schema to
avoid breaking compatibility with older configs.
"""
return 0.6

@property
def bodyparts_list(self) -> list[str]:
# Animal-count agnostic; Always return a list (never "MULTI!", None, etc.)
Expand Down
10 changes: 7 additions & 3 deletions deeplabcut/pose_estimation_pytorch/apis/evaluation.py
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Expand Up @@ -462,6 +462,7 @@ def evaluate_snapshot(
per_keypoint_evaluation: bool = False,
detector_snapshot: Snapshot | None = None,
pcutoff: float | list[float] | dict[str, float] | None = None,
bboxes_pcutoff: float | None = None,
) -> pd.DataFrame:
"""Evaluates a snapshot. The evaluation results are stored in the .h5 and .csv file
under the subdirectory 'evaluation_results'.
Expand Down Expand Up @@ -490,7 +491,12 @@ def evaluate_snapshot(
(if there are any). If a dict is provided, the keys should be bodyparts
mapping to pcutoff values for each bodypart. Bodyparts that are not defined
in the dict will have pcutoff set to 0.6.
bboxes_pcutoff: The cutoff to use for plotting bounding boxes from detectors.
When `None`, the cutoff will be loaded from the project config.
"""
if bboxes_pcutoff is None:
bboxes_pcutoff = cfg.bboxes_pcutoff

head_type = loader.model_cfg["model"]["heads"]["bodypart"]["type"]
if head_type == "DLCRNetHead":
prune_paf_graph.benchmark_paf_graphs(
Expand Down Expand Up @@ -622,8 +628,6 @@ def evaluate_snapshot(

df_ground_truth = ensure_multianimal_df_format(loader.df)

bboxes_cutoff = cfg.bboxes_pcutoff

for mode in ["train", "test"]:
df_combined = predictions[mode].merge(df_ground_truth, left_index=True, right_index=True)
bboxes_split = bounding_boxes[mode]
Expand All @@ -642,7 +646,7 @@ def evaluate_snapshot(
alpha_value=cfg["alphavalue"],
p_cutoff=cfg["pcutoff"],
bounding_boxes=bboxes_split,
bboxes_cutoff=bboxes_cutoff,
bboxes_cutoff=bboxes_pcutoff,
)

return df_predictions
Expand Down