roboflow/supervision · error · ValueError

Precision metric requires `class_id` and `confidence` on pre

Error message

Precision metric requires `class_id` and `confidence` on predictions.

What it means

Error "Precision metric requires `class_id` and `confidence` on predictions." thrown in roboflow/supervision.

Source

Thrown at src/supervision/metrics/precision.py:188

            prediction_size_mask = np.ones(len(predictions), dtype=bool)
            target_size_mask = np.ones(len(targets), dtype=bool)
            if size_category != ObjectSizeCategory.ANY:
                if len(predictions) > 0:
                    prediction_size_mask = (
                        get_detection_size_category(predictions, self._metric_target)
                        == size_category.value
                    )
                if len(targets) > 0:
                    target_size_mask = (
                        get_detection_size_category(targets, self._metric_target)
                        == size_category.value
                    )

            if len(targets) == 0 and len(predictions) > 0:
                # Only predictions are present (e.g. a background image); every
                # prediction is a false positive.
                if predictions.class_id is None or predictions.confidence is None:
                    raise ValueError(
                        "Precision metric requires `class_id` and `confidence` "
                        "on predictions."
                    )
                prediction_class_ids = np.asarray(predictions.class_id, dtype=np.int32)[
                    prediction_size_mask
                ]
                prediction_confidence = np.asarray(
                    predictions.confidence, dtype=np.float32
                )[prediction_size_mask]
                if len(prediction_class_ids) == 0:
                    continue
                stats.append(
                    (
                        np.zeros(
                            (len(prediction_class_ids), iou_thresholds.size),
                            dtype=np.bool_,
                        ),
                        np.zeros(

View on GitHub (pinned to 7f254d9784)

Solutions

1. Ensure prediction Detections have both class_id and confidence set before computing Precision.
2. Populate class_id and confidence from your model outputs when constructing Detections.

When it happens

Trigger: Thrown at src/supervision/metrics/precision.py:188 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of roboflow/supervision@7f254d9784 (2026-08-15). Data as JSON: /api/errors/4bbc68f4084d62a9. Report an issue: GitHub.