Recall

Recall

Bases: Metric

Recall is a metric used to evaluate object detection models. It is the ratio of true positive detections to the total number of ground truth instances. We calculate it at different IoU thresholds.

In simple terms, Recall is a measure of a model's completeness, calculated as:

Recall = TP / (TP + FN)

Here, TP is the number of true positives (correct detections), and FN is the number of false negatives (missed detections).

Example

import supervision as sv
from supervision.metrics import Recall

predictions = sv.Detections(...)
targets = sv.Detections(...)

recall_metric = Recall()
recall_result = recall_metric.update(predictions, targets).compute()

print(recall_result)
print(recall_result.recall_at_50)
print(recall_result.small_objects.recall_at_50)

Source code in supervision/metrics/recall.py

class Recall(Metric):
    """
    Recall is a metric used to evaluate object detection models. It is the ratio of
    true positive detections to the total number of ground truth instances. We calculate
    it at different IoU thresholds.

    In simple terms, Recall is a measure of a model's completeness, calculated as:

    `Recall = TP / (TP + FN)`

    Here, `TP` is the number of true positives (correct detections), and `FN` is the
    number of false negatives (missed detections).

    Example:
        ```python
        import supervision as sv
        from supervision.metrics import Recall

        predictions = sv.Detections(...)
        targets = sv.Detections(...)

        recall_metric = Recall()
        recall_result = recall_metric.update(predictions, targets).compute()

        print(recall_result)
        print(recall_result.recall_at_50)
        print(recall_result.small_objects.recall_at_50)
        ```
    """

    def __init__(
        self,
        metric_target: MetricTarget = MetricTarget.BOXES,
        averaging_method: AveragingMethod = AveragingMethod.WEIGHTED,
    ):
        """
        Initialize the Recall metric.

        Args:
            metric_target (MetricTarget): The type of detection data to use.
            averaging_method (AveragingMethod): The averaging method used to compute the
                recall. Determines how the recall is aggregated across classes.
        """
        self._metric_target = metric_target
        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
            raise NotImplementedError(
                "Recall is not implemented for oriented bounding boxes."
            )

        self._metric_target = metric_target
        self.averaging_method = averaging_method
        self._predictions_list: List[Detections] = []
        self._targets_list: List[Detections] = []

    def reset(self) -> None:
        """
        Reset the metric to its initial state, clearing all stored data.
        """
        self._predictions_list = []
        self._targets_list = []

    def update(
        self,
        predictions: Union[Detections, List[Detections]],
        targets: Union[Detections, List[Detections]],
    ) -> Recall:
        """
        Add new predictions and targets to the metric, but do not compute the result.

        Args:
            predictions (Union[Detections, List[Detections]]): The predicted detections.
            targets (Union[Detections, List[Detections]]): The target detections.

        Returns:
            (Recall): The updated metric instance.
        """
        if not isinstance(predictions, list):
            predictions = [predictions]
        if not isinstance(targets, list):
            targets = [targets]

        if len(predictions) != len(targets):
            raise ValueError(
                f"The number of predictions ({len(predictions)}) and"
                f" targets ({len(targets)}) during the update must be the same."
            )

        self._predictions_list.extend(predictions)
        self._targets_list.extend(targets)

        return self

    def compute(self) -> RecallResult:
        """
        Calculate the precision metric based on the stored predictions and ground-truth
        data, at different IoU thresholds.

        Returns:
            (RecallResult): The precision metric result.
        """
        result = self._compute(self._predictions_list, self._targets_list)

        small_predictions, small_targets = self._filter_predictions_and_targets_by_size(
            self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL
        )
        result.small_objects = self._compute(small_predictions, small_targets)

        medium_predictions, medium_targets = (
            self._filter_predictions_and_targets_by_size(
                self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM
            )
        )
        result.medium_objects = self._compute(medium_predictions, medium_targets)

        large_predictions, large_targets = self._filter_predictions_and_targets_by_size(
            self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE
        )
        result.large_objects = self._compute(large_predictions, large_targets)

        return result

    def _compute(
        self, predictions_list: List[Detections], targets_list: List[Detections]
    ) -> RecallResult:
        iou_thresholds = np.linspace(0.5, 0.95, 10)
        stats = []

        for predictions, targets in zip(predictions_list, targets_list):
            prediction_contents = self._detections_content(predictions)
            target_contents = self._detections_content(targets)

            if len(targets) > 0:
                if len(predictions) == 0:
                    stats.append(
                        (
                            np.zeros((0, iou_thresholds.size), dtype=bool),
                            np.zeros((0,), dtype=np.float32),
                            np.zeros((0,), dtype=int),
                            targets.class_id,
                        )
                    )

                else:
                    if self._metric_target == MetricTarget.BOXES:
                        iou = box_iou_batch(target_contents, prediction_contents)
                    elif self._metric_target == MetricTarget.MASKS:
                        iou = mask_iou_batch(target_contents, prediction_contents)
                    else:
                        raise NotImplementedError(
                            "Unsupported metric target for IoU calculation"
                        )

                    matches = self._match_detection_batch(
                        predictions.class_id, targets.class_id, iou, iou_thresholds
                    )
                    stats.append(
                        (
                            matches,
                            predictions.confidence,
                            predictions.class_id,
                            targets.class_id,
                        )
                    )

        if not stats:
            return RecallResult(
                metric_target=self._metric_target,
                averaging_method=self.averaging_method,
                recall_scores=np.zeros(iou_thresholds.shape[0]),
                recall_per_class=np.zeros((0, iou_thresholds.shape[0])),
                iou_thresholds=iou_thresholds,
                matched_classes=np.array([], dtype=int),
                small_objects=None,
                medium_objects=None,
                large_objects=None,
            )

        concatenated_stats = [np.concatenate(items, 0) for items in zip(*stats)]
        recall_scores, recall_per_class, unique_classes = (
            self._compute_recall_for_classes(*concatenated_stats)
        )

        return RecallResult(
            metric_target=self._metric_target,
            averaging_method=self.averaging_method,
            recall_scores=recall_scores,
            recall_per_class=recall_per_class,
            iou_thresholds=iou_thresholds,
            matched_classes=unique_classes,
            small_objects=None,
            medium_objects=None,
            large_objects=None,
        )

    def _compute_recall_for_classes(
        self,
        matches: np.ndarray,
        prediction_confidence: np.ndarray,
        prediction_class_ids: np.ndarray,
        true_class_ids: np.ndarray,
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        sorted_indices = np.argsort(-prediction_confidence)
        matches = matches[sorted_indices]
        prediction_class_ids = prediction_class_ids[sorted_indices]
        unique_classes, class_counts = np.unique(true_class_ids, return_counts=True)

        # Shape: PxTh,P,C,C -> CxThx3
        confusion_matrix = self._compute_confusion_matrix(
            matches, prediction_class_ids, unique_classes, class_counts
        )

        # Shape: CxThx3 -> CxTh
        recall_per_class = self._compute_recall(confusion_matrix)

        # Shape: CxTh -> Th
        if self.averaging_method == AveragingMethod.MACRO:
            recall_scores = np.mean(recall_per_class, axis=0)
        elif self.averaging_method == AveragingMethod.MICRO:
            confusion_matrix_merged = confusion_matrix.sum(0)
            recall_scores = self._compute_recall(confusion_matrix_merged)
        elif self.averaging_method == AveragingMethod.WEIGHTED:
            class_counts = class_counts.astype(np.float32)
            recall_scores = np.average(recall_per_class, axis=0, weights=class_counts)

        return recall_scores, recall_per_class, unique_classes

    @staticmethod
    def _match_detection_batch(
        predictions_classes: np.ndarray,
        target_classes: np.ndarray,
        iou: np.ndarray,
        iou_thresholds: np.ndarray,
    ) -> np.ndarray:
        num_predictions, num_iou_levels = (
            predictions_classes.shape[0],
            iou_thresholds.shape[0],
        )
        correct = np.zeros((num_predictions, num_iou_levels), dtype=bool)
        correct_class = target_classes[:, None] == predictions_classes

        for i, iou_level in enumerate(iou_thresholds):
            matched_indices = np.where((iou >= iou_level) & correct_class)

            if matched_indices[0].shape[0]:
                combined_indices = np.stack(matched_indices, axis=1)
                iou_values = iou[matched_indices][:, None]
                matches = np.hstack([combined_indices, iou_values])

                if matched_indices[0].shape[0] > 1:
                    matches = matches[matches[:, 2].argsort()[::-1]]
                    matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
                    matches = matches[np.unique(matches[:, 0], return_index=True)[1]]

                correct[matches[:, 1].astype(int), i] = True

        return correct

    @staticmethod
    def _compute_confusion_matrix(
        sorted_matches: np.ndarray,
        sorted_prediction_class_ids: np.ndarray,
        unique_classes: np.ndarray,
        class_counts: np.ndarray,
    ) -> np.ndarray:
        """
        Compute the confusion matrix for each class and IoU threshold.

        Assumes the matches and prediction_class_ids are sorted by confidence
        in descending order.

        Arguments:
            sorted_matches: np.ndarray, bool, shape (P, Th), that is True
                if the prediction is a true positive at the given IoU threshold.
            sorted_prediction_class_ids: np.ndarray, int, shape (P,), containing
                the class id for each prediction.
            unique_classes: np.ndarray, int, shape (C,), containing the unique
                class ids.
            class_counts: np.ndarray, int, shape (C,), containing the number
                of true instances for each class.

        Returns:
            np.ndarray, shape (C, Th, 3), containing the true positives, false
                positives, and false negatives for each class and IoU threshold.
        """

        num_thresholds = sorted_matches.shape[1]
        num_classes = unique_classes.shape[0]

        confusion_matrix = np.zeros((num_classes, num_thresholds, 3))
        for class_idx, class_id in enumerate(unique_classes):
            is_class = sorted_prediction_class_ids == class_id
            num_true = class_counts[class_idx]
            num_predictions = is_class.sum()

            if num_predictions == 0:
                true_positives = np.zeros(num_thresholds)
                false_positives = np.zeros(num_thresholds)
                false_negatives = np.full(num_thresholds, num_true)
            elif num_true == 0:
                true_positives = np.zeros(num_thresholds)
                false_positives = np.full(num_thresholds, num_predictions)
                false_negatives = np.zeros(num_thresholds)
            else:
                true_positives = sorted_matches[is_class].sum(0)
                false_positives = (1 - sorted_matches[is_class]).sum(0)
                false_negatives = num_true - true_positives
            confusion_matrix[class_idx] = np.stack(
                [true_positives, false_positives, false_negatives], axis=1
            )

        return confusion_matrix

    @staticmethod
    def _compute_recall(confusion_matrix: np.ndarray) -> np.ndarray:
        """
        Broadcastable function, computing the recall from the confusion matrix.

        Arguments:
            confusion_matrix: np.ndarray, shape (N, ..., 3), where the last dimension
                contains the true positives, false positives, and false negatives.

        Returns:
            np.ndarray, shape (N, ...), containing the recall for each element.
        """
        if not confusion_matrix.shape[-1] == 3:
            raise ValueError(
                f"Confusion matrix must have shape (..., 3), got "
                f"{confusion_matrix.shape}"
            )
        true_positives = confusion_matrix[..., 0]
        false_negatives = confusion_matrix[..., 2]

        denominator = true_positives + false_negatives
        recall = np.where(denominator == 0, 0, true_positives / denominator)

        return recall

    def _detections_content(self, detections: Detections) -> np.ndarray:
        """Return boxes, masks or oriented bounding boxes from detections."""
        if self._metric_target == MetricTarget.BOXES:
            return detections.xyxy
        if self._metric_target == MetricTarget.MASKS:
            return (
                detections.mask
                if detections.mask is not None
                else np.empty((0, 0, 0), dtype=bool)
            )
        if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
            if obb := detections.data.get(ORIENTED_BOX_COORDINATES):
                return np.ndarray(obb, dtype=np.float32)
            return np.empty((0, 8), dtype=np.float32)
        raise ValueError(f"Invalid metric target: {self._metric_target}")

    def _filter_detections_by_size(
        self, detections: Detections, size_category: ObjectSizeCategory
    ) -> Detections:
        """Return a copy of detections with contents filtered by object size."""
        new_detections = deepcopy(detections)
        if detections.is_empty() or size_category == ObjectSizeCategory.ANY:
            return new_detections

        sizes = get_detection_size_category(new_detections, self._metric_target)
        size_mask = sizes == size_category.value

        new_detections.xyxy = new_detections.xyxy[size_mask]
        if new_detections.mask is not None:
            new_detections.mask = new_detections.mask[size_mask]
        if new_detections.class_id is not None:
            new_detections.class_id = new_detections.class_id[size_mask]
        if new_detections.confidence is not None:
            new_detections.confidence = new_detections.confidence[size_mask]
        if new_detections.tracker_id is not None:
            new_detections.tracker_id = new_detections.tracker_id[size_mask]
        if new_detections.data is not None:
            for key, value in new_detections.data.items():
                new_detections.data[key] = np.array(value)[size_mask]

        return new_detections

    def _filter_predictions_and_targets_by_size(
        self,
        predictions_list: List[Detections],
        targets_list: List[Detections],
        size_category: ObjectSizeCategory,
    ) -> Tuple[List[Detections], List[Detections]]:
        """
        Filter predictions and targets by object size category.
        """
        new_predictions_list = []
        new_targets_list = []
        for predictions, targets in zip(predictions_list, targets_list):
            new_predictions_list.append(
                self._filter_detections_by_size(predictions, size_category)
            )
            new_targets_list.append(
                self._filter_detections_by_size(targets, size_category)
            )
        return new_predictions_list, new_targets_list

Functions

__init__(metric_target=MetricTarget.BOXES, averaging_method=AveragingMethod.WEIGHTED)

Initialize the Recall metric.

Parameters:

Name	Type	Description	Default
metric_target	MetricTarget	The type of detection data to use.	BOXES
averaging_method	AveragingMethod	The averaging method used to compute the recall. Determines how the recall is aggregated across classes.	WEIGHTED

Source code in supervision/metrics/recall.py

def __init__(
    self,
    metric_target: MetricTarget = MetricTarget.BOXES,
    averaging_method: AveragingMethod = AveragingMethod.WEIGHTED,
):
    """
    Initialize the Recall metric.

    Args:
        metric_target (MetricTarget): The type of detection data to use.
        averaging_method (AveragingMethod): The averaging method used to compute the
            recall. Determines how the recall is aggregated across classes.
    """
    self._metric_target = metric_target
    if self._metric_target == MetricTarget.ORIENTED_BOUNDING_BOXES:
        raise NotImplementedError(
            "Recall is not implemented for oriented bounding boxes."
        )

    self._metric_target = metric_target
    self.averaging_method = averaging_method
    self._predictions_list: List[Detections] = []
    self._targets_list: List[Detections] = []

compute()

Calculate the precision metric based on the stored predictions and ground-truth data, at different IoU thresholds.

Returns:

Type	Description
RecallResult	The precision metric result.

Source code in supervision/metrics/recall.py

def compute(self) -> RecallResult:
    """
    Calculate the precision metric based on the stored predictions and ground-truth
    data, at different IoU thresholds.

    Returns:
        (RecallResult): The precision metric result.
    """
    result = self._compute(self._predictions_list, self._targets_list)

    small_predictions, small_targets = self._filter_predictions_and_targets_by_size(
        self._predictions_list, self._targets_list, ObjectSizeCategory.SMALL
    )
    result.small_objects = self._compute(small_predictions, small_targets)

    medium_predictions, medium_targets = (
        self._filter_predictions_and_targets_by_size(
            self._predictions_list, self._targets_list, ObjectSizeCategory.MEDIUM
        )
    )
    result.medium_objects = self._compute(medium_predictions, medium_targets)

    large_predictions, large_targets = self._filter_predictions_and_targets_by_size(
        self._predictions_list, self._targets_list, ObjectSizeCategory.LARGE
    )
    result.large_objects = self._compute(large_predictions, large_targets)

    return result

reset()

Reset the metric to its initial state, clearing all stored data.

Source code in supervision/metrics/recall.py

def reset(self) -> None:
    """
    Reset the metric to its initial state, clearing all stored data.
    """
    self._predictions_list = []
    self._targets_list = []

update(predictions, targets)

Add new predictions and targets to the metric, but do not compute the result.

Parameters:

Name	Type	Description	Default
predictions	Union[Detections, List[Detections]]	The predicted detections.	required
targets	Union[Detections, List[Detections]]	The target detections.	required

Returns:

Type	Description
Recall	The updated metric instance.

Source code in supervision/metrics/recall.py

def update(
    self,
    predictions: Union[Detections, List[Detections]],
    targets: Union[Detections, List[Detections]],
) -> Recall:
    """
    Add new predictions and targets to the metric, but do not compute the result.

    Args:
        predictions (Union[Detections, List[Detections]]): The predicted detections.
        targets (Union[Detections, List[Detections]]): The target detections.

    Returns:
        (Recall): The updated metric instance.
    """
    if not isinstance(predictions, list):
        predictions = [predictions]
    if not isinstance(targets, list):
        targets = [targets]

    if len(predictions) != len(targets):
        raise ValueError(
            f"The number of predictions ({len(predictions)}) and"
            f" targets ({len(targets)}) during the update must be the same."
        )

    self._predictions_list.extend(predictions)
    self._targets_list.extend(targets)

    return self

RecallResult

The results of the recall metric calculation.

Defaults to 0 if no detections or targets were provided.

Attributes:

Name	Type	Description
metric_target	MetricTarget	the type of data used for the metric - boxes, masks or oriented bounding boxes.
averaging_method	AveragingMethod	the averaging method used to compute the recall. Determines how the recall is aggregated across classes.
recall_at_50	float	the recall at IoU threshold of 0.5.
recall_at_75	float	the recall at IoU threshold of 0.75.
recall_scores	ndarray	the recall scores at each IoU threshold. Shape: (num_iou_thresholds,)
recall_per_class	ndarray	the recall scores per class and IoU threshold. Shape: (num_target_classes, num_iou_thresholds)
iou_thresholds	ndarray	the IoU thresholds used in the calculations.
matched_classes	ndarray	the class IDs of all matched classes. Corresponds to the rows of recall_per_class.
small_objects	Optional[RecallResult]	the Recall metric results for small objects.
medium_objects	Optional[RecallResult]	the Recall metric results for medium objects.
large_objects	Optional[RecallResult]	the Recall metric results for large objects.

Source code in supervision/metrics/recall.py

@dataclass
class RecallResult:
    """
    The results of the recall metric calculation.

    Defaults to `0` if no detections or targets were provided.

    Attributes:
        metric_target (MetricTarget): the type of data used for the metric -
            boxes, masks or oriented bounding boxes.
        averaging_method (AveragingMethod): the averaging method used to compute the
            recall. Determines how the recall is aggregated across classes.
        recall_at_50 (float): the recall at IoU threshold of `0.5`.
        recall_at_75 (float): the recall at IoU threshold of `0.75`.
        recall_scores (np.ndarray): the recall scores at each IoU threshold.
            Shape: `(num_iou_thresholds,)`
        recall_per_class (np.ndarray): the recall scores per class and IoU threshold.
            Shape: `(num_target_classes, num_iou_thresholds)`
        iou_thresholds (np.ndarray): the IoU thresholds used in the calculations.
        matched_classes (np.ndarray): the class IDs of all matched classes.
            Corresponds to the rows of `recall_per_class`.
        small_objects (Optional[RecallResult]): the Recall metric results
            for small objects.
        medium_objects (Optional[RecallResult]): the Recall metric results
            for medium objects.
        large_objects (Optional[RecallResult]): the Recall metric results
            for large objects.
    """

    metric_target: MetricTarget
    averaging_method: AveragingMethod

    @property
    def recall_at_50(self) -> float:
        return self.recall_scores[0]

    @property
    def recall_at_75(self) -> float:
        return self.recall_scores[5]

    recall_scores: np.ndarray
    recall_per_class: np.ndarray
    iou_thresholds: np.ndarray
    matched_classes: np.ndarray

    small_objects: Optional[RecallResult]
    medium_objects: Optional[RecallResult]
    large_objects: Optional[RecallResult]

    def __str__(self) -> str:
        """
        Format as a pretty string.

        Example:
            ```python
            print(recall_result)
            ```
        """
        out_str = (
            f"{self.__class__.__name__}:\n"
            f"Metric target:    {self.metric_target}\n"
            f"Averaging method: {self.averaging_method}\n"
            f"R @ 50:     {self.recall_at_50:.4f}\n"
            f"R @ 75:     {self.recall_at_75:.4f}\n"
            f"R @ thresh: {self.recall_scores}\n"
            f"IoU thresh: {self.iou_thresholds}\n"
            f"Recall per class:\n"
        )
        if self.recall_per_class.size == 0:
            out_str += "  No results\n"
        for class_id, recall_of_class in zip(
            self.matched_classes, self.recall_per_class
        ):
            out_str += f"  {class_id}: {recall_of_class}\n"

        indent = "  "
        if self.small_objects is not None:
            indented = indent + str(self.small_objects).replace("\n", f"\n{indent}")
            out_str += f"\nSmall objects:\n{indented}"
        if self.medium_objects is not None:
            indented = indent + str(self.medium_objects).replace("\n", f"\n{indent}")
            out_str += f"\nMedium objects:\n{indented}"
        if self.large_objects is not None:
            indented = indent + str(self.large_objects).replace("\n", f"\n{indent}")
            out_str += f"\nLarge objects:\n{indented}"

        return out_str

    def to_pandas(self) -> "pd.DataFrame":
        """
        Convert the result to a pandas DataFrame.

        Returns:
            (pd.DataFrame): The result as a DataFrame.
        """
        ensure_pandas_installed()
        import pandas as pd

        pandas_data = {
            "R@50": self.recall_at_50,
            "R@75": self.recall_at_75,
        }

        if self.small_objects is not None:
            small_objects_df = self.small_objects.to_pandas()
            for key, value in small_objects_df.items():
                pandas_data[f"small_objects_{key}"] = value
        if self.medium_objects is not None:
            medium_objects_df = self.medium_objects.to_pandas()
            for key, value in medium_objects_df.items():
                pandas_data[f"medium_objects_{key}"] = value
        if self.large_objects is not None:
            large_objects_df = self.large_objects.to_pandas()
            for key, value in large_objects_df.items():
                pandas_data[f"large_objects_{key}"] = value

        return pd.DataFrame(pandas_data, index=[0])

    def plot(self):
        """
        Plot the recall results.
        """

        labels = ["Recall@50", "Recall@75"]
        values = [self.recall_at_50, self.recall_at_75]
        colors = [LEGACY_COLOR_PALETTE[0]] * 2

        if self.small_objects is not None:
            small_objects = self.small_objects
            labels += ["Small: R@50", "Small: R@75"]
            values += [small_objects.recall_at_50, small_objects.recall_at_75]
            colors += [LEGACY_COLOR_PALETTE[3]] * 2

        if self.medium_objects is not None:
            medium_objects = self.medium_objects
            labels += ["Medium: R@50", "Medium: R@75"]
            values += [medium_objects.recall_at_50, medium_objects.recall_at_75]
            colors += [LEGACY_COLOR_PALETTE[2]] * 2

        if self.large_objects is not None:
            large_objects = self.large_objects
            labels += ["Large: R@50", "Large: R@75"]
            values += [large_objects.recall_at_50, large_objects.recall_at_75]
            colors += [LEGACY_COLOR_PALETTE[4]] * 2

        plt.rcParams["font.family"] = "monospace"

        _, ax = plt.subplots(figsize=(10, 6))
        ax.set_ylim(0, 1)
        ax.set_ylabel("Value", fontweight="bold")
        title = (
            f"Recall, by Object Size"
            f"\n(target: {self.metric_target.value},"
            f" averaging: {self.averaging_method.value})"
        )
        ax.set_title(title, fontweight="bold")

        x_positions = range(len(labels))
        bars = ax.bar(x_positions, values, color=colors, align="center")

        ax.set_xticks(x_positions)
        ax.set_xticklabels(labels, rotation=45, ha="right")

        for bar in bars:
            y_value = bar.get_height()
            ax.text(
                bar.get_x() + bar.get_width() / 2,
                y_value + 0.02,
                f"{y_value:.2f}",
                ha="center",
                va="bottom",
            )

        plt.rcParams["font.family"] = "sans-serif"

        plt.tight_layout()
        plt.show()

Functions

__str__()

Format as a pretty string.

Example

print(recall_result)

Source code in supervision/metrics/recall.py

def __str__(self) -> str:
    """
    Format as a pretty string.

    Example:
        ```python
        print(recall_result)
        ```
    """
    out_str = (
        f"{self.__class__.__name__}:\n"
        f"Metric target:    {self.metric_target}\n"
        f"Averaging method: {self.averaging_method}\n"
        f"R @ 50:     {self.recall_at_50:.4f}\n"
        f"R @ 75:     {self.recall_at_75:.4f}\n"
        f"R @ thresh: {self.recall_scores}\n"
        f"IoU thresh: {self.iou_thresholds}\n"
        f"Recall per class:\n"
    )
    if self.recall_per_class.size == 0:
        out_str += "  No results\n"
    for class_id, recall_of_class in zip(
        self.matched_classes, self.recall_per_class
    ):
        out_str += f"  {class_id}: {recall_of_class}\n"

    indent = "  "
    if self.small_objects is not None:
        indented = indent + str(self.small_objects).replace("\n", f"\n{indent}")
        out_str += f"\nSmall objects:\n{indented}"
    if self.medium_objects is not None:
        indented = indent + str(self.medium_objects).replace("\n", f"\n{indent}")
        out_str += f"\nMedium objects:\n{indented}"
    if self.large_objects is not None:
        indented = indent + str(self.large_objects).replace("\n", f"\n{indent}")
        out_str += f"\nLarge objects:\n{indented}"

    return out_str

plot()

Plot the recall results.

Source code in supervision/metrics/recall.py

def plot(self):
    """
    Plot the recall results.
    """

    labels = ["Recall@50", "Recall@75"]
    values = [self.recall_at_50, self.recall_at_75]
    colors = [LEGACY_COLOR_PALETTE[0]] * 2

    if self.small_objects is not None:
        small_objects = self.small_objects
        labels += ["Small: R@50", "Small: R@75"]
        values += [small_objects.recall_at_50, small_objects.recall_at_75]
        colors += [LEGACY_COLOR_PALETTE[3]] * 2

    if self.medium_objects is not None:
        medium_objects = self.medium_objects
        labels += ["Medium: R@50", "Medium: R@75"]
        values += [medium_objects.recall_at_50, medium_objects.recall_at_75]
        colors += [LEGACY_COLOR_PALETTE[2]] * 2

    if self.large_objects is not None:
        large_objects = self.large_objects
        labels += ["Large: R@50", "Large: R@75"]
        values += [large_objects.recall_at_50, large_objects.recall_at_75]
        colors += [LEGACY_COLOR_PALETTE[4]] * 2

    plt.rcParams["font.family"] = "monospace"

    _, ax = plt.subplots(figsize=(10, 6))
    ax.set_ylim(0, 1)
    ax.set_ylabel("Value", fontweight="bold")
    title = (
        f"Recall, by Object Size"
        f"\n(target: {self.metric_target.value},"
        f" averaging: {self.averaging_method.value})"
    )
    ax.set_title(title, fontweight="bold")

    x_positions = range(len(labels))
    bars = ax.bar(x_positions, values, color=colors, align="center")

    ax.set_xticks(x_positions)
    ax.set_xticklabels(labels, rotation=45, ha="right")

    for bar in bars:
        y_value = bar.get_height()
        ax.text(
            bar.get_x() + bar.get_width() / 2,
            y_value + 0.02,
            f"{y_value:.2f}",
            ha="center",
            va="bottom",
        )

    plt.rcParams["font.family"] = "sans-serif"

    plt.tight_layout()
    plt.show()

to_pandas()

Convert the result to a pandas DataFrame.

Returns:

Type	Description
DataFrame	The result as a DataFrame.

Source code in supervision/metrics/recall.py

def to_pandas(self) -> "pd.DataFrame":
    """
    Convert the result to a pandas DataFrame.

    Returns:
        (pd.DataFrame): The result as a DataFrame.
    """
    ensure_pandas_installed()
    import pandas as pd

    pandas_data = {
        "R@50": self.recall_at_50,
        "R@75": self.recall_at_75,
    }

    if self.small_objects is not None:
        small_objects_df = self.small_objects.to_pandas()
        for key, value in small_objects_df.items():
            pandas_data[f"small_objects_{key}"] = value
    if self.medium_objects is not None:
        medium_objects_df = self.medium_objects.to_pandas()
        for key, value in medium_objects_df.items():
            pandas_data[f"medium_objects_{key}"] = value
    if self.large_objects is not None:
        large_objects_df = self.large_objects.to_pandas()
        for key, value in large_objects_df.items():
            pandas_data[f"large_objects_{key}"] = value

    return pd.DataFrame(pandas_data, index=[0])

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