diff --git a/supervision/metrics/__init__.py b/supervision/metrics/__init__.py
index 17a6cd48..8ae33e63 100644
--- a/supervision/metrics/__init__.py
+++ b/supervision/metrics/__init__.py
@@ -1,5 +1,4 @@
 from supervision.metrics.core import (
-    CLASS_ID_NONE,
     AveragingMethod,
     Metric,
     MetricTarget,
diff --git a/supervision/metrics/core.py b/supervision/metrics/core.py
index 1440fd43..d1818441 100644
--- a/supervision/metrics/core.py
+++ b/supervision/metrics/core.py
@@ -4,9 +4,6 @@ from abc import ABC, abstractmethod
 from enum import Enum
 from typing import Any
 
-CLASS_ID_NONE = -1
-"""Used by metrics module as class ID, when none is present"""
-
 
 class Metric(ABC):
     """
diff --git a/supervision/metrics/mean_average_precision.py b/supervision/metrics/mean_average_precision.py
index cc15f565..dbd60b2e 100644
--- a/supervision/metrics/mean_average_precision.py
+++ b/supervision/metrics/mean_average_precision.py
@@ -76,6 +76,15 @@ class MeanAveragePrecision(Metric):
                 f" targets ({len(targets)}) during the update must be the same."
             )
 
+        if self._class_agnostic:
+            predictions = deepcopy(predictions)
+            targets = deepcopy(targets)
+
+            for prediction in predictions:
+                prediction.class_id[:] = -1
+            for target in targets:
+                target.class_id[:] = -1
+
         self._predictions_list.extend(predictions)
         self._targets_list.extend(targets)
 
@@ -180,6 +189,7 @@ class MeanAveragePrecision(Metric):
                     matches = self._match_detection_batch(
                         predictions.class_id, targets.class_id, iou, iou_thresholds
                     )
+
                     stats.append(
                         (
                             matches,
@@ -203,6 +213,7 @@ class MeanAveragePrecision(Metric):
 
         return MeanAveragePrecisionResult(
             metric_target=self._metric_target,
+            is_class_agnostic=self._class_agnostic,
             mAP_scores=mAP_scores,
             iou_thresholds=iou_thresholds,
             matched_classes=unique_classes,
@@ -245,6 +256,7 @@ class MeanAveragePrecision(Metric):
             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):
@@ -383,6 +395,8 @@ class MeanAveragePrecisionResult:
     Attributes:
         metric_target (MetricTarget): the type of data used for the metric -
             boxes, masks or oriented bounding boxes.
+        class_agnostic (bool): When computing class-agnostic results, class ID
+            is set to `-1`.
         mAP_map50_95 (float): the mAP score at IoU thresholds from `0.5` to `0.95`.
         mAP_map50 (float): the mAP score at IoU threshold of `0.5`.
         mAP_map75 (float): the mAP score at IoU threshold of `0.75`.
@@ -402,6 +416,7 @@ class MeanAveragePrecisionResult:
     """
 
     metric_target: MetricTarget
+    is_class_agnostic: bool
 
     @property
     def map50_95(self) -> float:
@@ -436,6 +451,7 @@ class MeanAveragePrecisionResult:
         out_str = (
             f"{self.__class__.__name__}:\n"
             f"Metric target: {self.metric_target}\n"
+            f"Class agnostic: {self.is_class_agnostic}\n"
             f"mAP @ 50:95: {self.map50_95:.4f}\n"
             f"mAP @ 50:    {self.map50:.4f}\n"
             f"mAP @ 75:    {self.map75:.4f}\n"