roboflow/supervision · error · ValueError

Invalid metric type

Error message

Invalid metric type

What it means

Raised by get_size_category() in metrics/utils/object_size.py when the metric_target argument is not one of MetricTarget.BOXES, MASKS, or ORIENTED_BOUNDING_BOXES. The function dispatches on the enum to pick the right size-category computation (bbox area, mask pixel count, or OBB shoelace area). This is the final fall-through for an unknown enum value.

Source

Thrown at src/supervision/metrics/utils/object_size.py:87

        ...     [0, 0, 10, 10],    # 100 (Small)
        ...     [0, 0, 50, 50],    # 2500 (Medium)
        ...     [0, 0, 100, 100]   # 10000 (Large)
        ... ])
        >>> get_object_size_category(xyxy, MetricTarget.BOXES)
        array([1, 2, 3])

        ```
    """
    if metric_target == MetricTarget.BOXES:
        bbox_data = cast(npt.NDArray[np.number], data)
        return get_bbox_size_category(bbox_data)
    if metric_target == MetricTarget.MASKS:
        mask_data = cast(npt.NDArray[np.bool_], data)
        return get_mask_size_category(mask_data)
    if metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
        obb_data = cast(npt.NDArray[np.number], data)
        return get_obb_size_category(obb_data)
    raise ValueError("Invalid metric type")


def get_bbox_size_category(xyxy: npt.NDArray[np.number]) -> npt.NDArray[np.int_]:
    """
    Get the size category of a bounding boxes array.

    Args:
        xyxy: The bounding boxes array shaped (N, 4).

    Returns:
        The size category of each bounding box, matching
        the enum values of ObjectSizeCategory. Shaped (N,).

    Example:
        ```pycon
        >>> import numpy as np
        >>> from supervision.metrics.utils.object_size import get_bbox_size_category
        >>> xyxy = np.array([

View on GitHub (pinned to 7f254d9784)

Solutions

1. Convert config values to the enum: MetricTarget(config_value) wrapped in try/except, or map strings explicitly
2. Check the enum members supported in your supervision version before calling

Example fix

# before
cats = get_size_category(data, metric_target="masks")  # str not handled

# after
from supervision.metrics.metric_target import MetricTarget

cats = get_size_category(data, MetricTarget("masks"))

from supervision.metrics.metric_target import MetricTarget

if not isinstance(metric_target, MetricTarget):
    metric_target = MetricTarget(metric_target)  # raises cleanly on bad values

from supervision.metrics.metric_target import MetricTarget

def is_valid_metric_target(value: object) -> bool:
    """True when value is a MetricTarget enum member."""
    return isinstance(value, MetricTarget)

try:
    cats = get_size_category(data, metric_target)
except ValueError as e:
    if 'Invalid metric type' in str(e):
        cats = get_size_category(data, MetricTarget.BOXES)
    else:
        raise

When it happens

Trigger: Calling get_size_category(data, metric_target) with a value outside the handled MetricTarget members (e.g. a raw int/string, or a newly added enum member like class-agnostic variants).

Common situations: Passing metric_target loaded from a YAML/JSON config without converting to the enum; forward-compatibility mismatch when supervision adds a new MetricTarget that this helper does not yet support; passing None.

Related errors

• Invalid metric target: {self._metric_target}
• Invalid metric target: {self._metric_target}
• Detection area metadata must be shaped (N,) and aligned with
• Invalid vlm value: {vlm}. Must be one of {[e.value for e in
• Invalid value type: {type(value)}. Must be an instance of {c

AI-assisted analysis of roboflow/supervision@7f254d9784 (2026-08-15). Data as JSON: /api/errors/6c165a6d4c5c0e87. Report an issue: GitHub.