roboflow/supervision · error · ValueError

Conflicting metadata for key: '{key}'.

Error message

Conflicting metadata for key: '{key}'.

What it means

Raised by merge_metadata when the same metadata key holds different non-array (scalar/plain) values in two Detections being merged. After the ndarray special cases, plain inequality of the existing merged value and the new value is treated as a conflict, because merge_metadata has no policy for choosing between competing scalars.

Source

Thrown at src/supervision/detection/utils/internal.py:648

                merged_metadata[key] = value
                continue

            other_value = merged_metadata[key]
            if isinstance(value, np.ndarray) and isinstance(other_value, np.ndarray):
                if not np.array_equal(merged_metadata[key], value):
                    raise ValueError(
                        f"Conflicting metadata for key: '{key}': "
                        f"{type(value)}, {type(other_value)}."
                    )
            elif isinstance(value, np.ndarray) or isinstance(other_value, np.ndarray):
                # Since [] == np.array([]).
                raise ValueError(
                    f"Conflicting metadata for key: '{key}': "
                    f"{type(value)}, {type(other_value)}."
                )
            else:
                if merged_metadata[key] != value:
                    raise ValueError(f"Conflicting metadata for key: '{key}'.")

    return merged_metadata


def get_data_item(
    data: _DetectionDataType,
    index: int | slice | list[int] | npt.NDArray[np.integer | np.bool_],
) -> _DetectionDataType:
    """
    Retrieve a subset of the data dictionary based on the given index.

    Args:
        data: The data dictionary of the Detections object.
        index: The index or indices specifying the subset to retrieve.

    Returns:
        A subset of the data dictionary corresponding to the specified index.
    """

View on GitHub (pinned to 7f254d9784)

Solutions

1. Merge only Detections that share the metadata value (group inputs by metadata before merging), e.g. group by d.metadata['video_id'] and merge within each group
2. Overwrite the conflicting key with a single intended value on all inputs before merge
3. Drop the per-source key from metadata if it is not needed post-merge

Example fix

# before
merged = sv.Detections.merge([d_v1, d_v2])  # video_id 1 vs 2
# after
from itertools import groupby
groups = {}
for d in detections_list:
    groups.setdefault(d.metadata.get('video_id'), []).append(d)
merged = [sv.Detections.merge(g) for g in groups.values()]

def split_by_metadata(detections_list, key):
    groups = {}
    for d in detections_list:
        groups.setdefault(d.metadata.get(key), []).append(d)
    return list(groups.values())
# merge within each group only

When it happens

Trigger: sv.Detections.merge([d1, d2]) with d1.metadata={'video_id': 1} and d2.metadata={'video_id': 2}; also the internal pair merge in core.py:3462 and annotations merge (core.py:3366) when inputs come from different videos/cameras.

Common situations: Concatenating Detections accumulated from multiple videos or camera streams where each carries its own video_id/source_id; batching detections per frame with frame-index metadata and merging across frames; reusing a merge helper on heterogeneous batches.

Related errors

• All metadata dictionaries must have the same keys to merge.
• Conflicting metadata for key: '{key}': {type(value)}, {type(
• Both Detections should have exactly 1 detected object.
• Value must be a np.ndarray or a list
• Detections confidence must be given for NMM to be executed.

AI-assisted analysis of roboflow/supervision@7f254d9784 (2026-08-15). Data as JSON: /api/errors/c1e0f1651ce40e93. Report an issue: GitHub.