roboflow/supervision · error · ValueError

confidence must be 1d np.ndarray with (n, ) shape

Error message

confidence must be 1d np.ndarray with (n, ) shape

What it means

Raised by `sv.Classifications.__post_init__` when `confidence` is provided but is not a 1-D np.ndarray whose length equals `len(class_id)`. Confidence is optional (may be None), but when present it must align one-to-one with `class_id` so `get_top_k` and annotators can index both consistently.

Source

Thrown at src/supervision/classification/core.py:29

def _validate_class_ids(class_id: Any, n: int) -> None:
    """
    Ensure that class_id is a 1d np.ndarray with (n, ) shape.
    """
    is_valid = isinstance(class_id, np.ndarray) and class_id.shape == (n,)
    if not is_valid:
        raise ValueError("class_id must be 1d np.ndarray with (n, ) shape")


def _validate_confidence(confidence: Any, n: int) -> None:
    """
    Ensure that confidence is a 1d np.ndarray with (n, ) shape.
    """
    if confidence is not None:
        is_valid = isinstance(confidence, np.ndarray) and confidence.shape == (n,)
        if not is_valid:
            raise ValueError("confidence must be 1d np.ndarray with (n, ) shape")


@dataclass
class Classifications:
    class_id: npt.NDArray[np.int_]
    confidence: npt.NDArray[np.floating] | None = None

    def __post_init__(self) -> None:
        """
        Validate the classification inputs.
        """
        n = len(self.class_id)

        _validate_class_ids(self.class_id, n)
        _validate_confidence(self.confidence, n)

    def __eq__(self, other: object) -> bool:
        """

View on GitHub (pinned to 7f254d9784)

Solutions

1. Ensure `len(confidence) == len(class_id)` and convert with `np.asarray(confidence)`.
2. When filtering classifications, apply the same mask to both arrays.
3. For score matrices, pass a 1-D slice: `scores.max(axis=1)` or the selected class scores.

Example fix

# before
sv.Classifications(class_id=np.array([0, 1, 2]), confidence=np.array([0.9]))
# after
sv.Classifications(class_id=np.array([0, 1, 2]), confidence=np.array([0.9, 0.5, 0.1]))

class_id = np.asarray(class_id)
if confidence is not None:
    confidence = np.asarray(confidence)
    assert confidence.shape == class_id.shape, 'confidence must match class_id length'

def confidence_aligned(confidence: Any, n: int) -> bool:
    return confidence is None or (
        isinstance(confidence, np.ndarray) and confidence.shape == (n,)
    )

When it happens

Trigger: Passing `confidence=[0.9, 0.8]` (list, 2 items) with `class_id` of length 3; passing a confidence array from a previous inference run against a filtered class_id list; passing a 2-D scores array without selecting a column.

Common situations: Top-k filtering of class ids without filtering confidences; slicing one array and not the other after NMS; model score matrices where `scores[:, 0]` extraction was forgotten.

Related errors

• class_id must be 1d np.ndarray with (n, ) shape
• Shape of np.ndarray for key '{key}' must be ({n},)
• First dimension of np.ndarray for key '{key}' must have size
• top_k could not be calculated, confidence is None
• NumPy image must have at least 2 dimensions (H, W, ...). Rec

AI-assisted analysis of roboflow/supervision@7f254d9784 (2026-08-15). Data as JSON: /api/errors/b4c03bbeaf4abc45. Report an issue: GitHub.