roboflow/supervision · error · ValueError

2D boolean mask column count {mask.shape[1]} does not match

Error message

2D boolean mask column count {mask.shape[1]} does not match keypoint count {self.xy.shape[1]}.

What it means

F1Score._make_empty_content() returns the shaped empty array for empty Detections per metric target and, like its siblings, ends with a defensive ValueError for any MetricTarget it does not know. Reachable only with version skew or injected enum values, and only when compute() processes empty Detections.

Source

Thrown at src/supervision/key_points/core.py:870

                stored in a uniform `(n, k, ...)` array.

        Returns:
            A new `KeyPoints` instance containing only the keypoints selected by
            the mask for each object.

        Raises:
            ValueError: If `mask.shape[0]` does not match the number of objects, if
                `mask.shape[1]` does not match the number of keypoints, or if
                different rows of the mask select different numbers of `True` values.
        """
        n = len(self.xy)
        if mask.shape[0] != n:
            raise ValueError(
                f"2D boolean mask row count {mask.shape[0]} does not match "
                f"object count {n}."
            )
        if mask.shape[1] != self.xy.shape[1]:
            raise ValueError(
                f"2D boolean mask column count {mask.shape[1]} does not match "
                f"keypoint count {self.xy.shape[1]}."
            )
        counts = np.sum(mask, axis=1)
        if n > 0 and not np.all(counts == counts[0]):
            raise ValueError(
                "Cannot filter keypoints with a 2D boolean mask where rows have "
                "different numbers of True values. "
                "All objects must select the same number of keypoints. "
                f"Got counts per object: {counts.tolist()}"
            )
        k = int(counts[0]) if n > 0 else 0
        xy_selected = np.zeros((n, k, self.xy.shape[2]), dtype=self.xy.dtype)
        keypoint_confidence_selected: npt.NDArray[np.float32] | None = None
        if self.keypoint_confidence is not None:
            keypoint_confidence_selected = cast(
                npt.NDArray[np.float32],
                np.zeros((n, k), dtype=self.keypoint_confidence.dtype),

View on GitHub (pinned to 7f254d9784)

Solutions

Align the supervision version (reinstall/upgrade)
Restrict metric_target to supported members
Remove enum patches

Defensive patterns

Strategy: validation

Validate before calling

SUPPORTED = {sv.MetricTarget.BOXES, sv.MetricTarget.MASKS, sv.MetricTarget.ORIENTED_BOUNDING_BOXES}
assert target in SUPPORTED

Type guard

def is_supported_f1_target(target: sv.MetricTarget) -> bool:
    return target in {sv.MetricTarget.BOXES, sv.MetricTarget.MASKS, sv.MetricTarget.ORIENTED_BOUNDING_BOXES}

Prevention

Validate the metric target once at startup
Run a minimal end-to-end metric call in CI to catch version-skew breakage early

When it happens

Trigger: An unsupported metric_target value combined with empty predictions/targets lists during F1Score.compute().

Common situations: Version-mismatched supervision installs or monkey-patched enums, surfaced when evaluating images with zero detections.

Related errors

AI-assisted analysis of roboflow/supervision@7f254d9784 (2026-08-15). Data as JSON: /api/errors/baf9bb7d4e639fa1. Report an issue: GitHub.