diff --git a/supervision/metrics/detection.py b/supervision/metrics/detection.py
index de1ffe47..39bead32 100644
--- a/supervision/metrics/detection.py
+++ b/supervision/metrics/detection.py
@@ -464,9 +464,6 @@ class ConfusionMatrix:
         return fig
 
 
-import torch
-
-
 @dataclass(frozen=True)
 class MeanAveragePrecision:
     map: float
@@ -572,7 +569,7 @@ class MeanAveragePrecision:
         stats = [np.concatenate(x, 0) for x in zip(*stats)]
 
         if len(stats) and stats[0].any():
-            tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, names=classes)
+            tp, fp, p, r, f1, ap, ap_class = MeanAveragePrecision.ap_per_class(*stats, names=classes)
             ap50, ap = ap[:, 0], ap.mean(1)  # AP@0.5, AP@0.5:0.95
             mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
 
@@ -633,82 +630,84 @@ class MeanAveragePrecision:
             matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
         return matches
 
+    @staticmethod
+    def compute_ap(recall, precision):
+        """Compute the average precision, given the recall and precision curves
+        # Arguments
+            recall:    The recall curve (list)
+            precision: The precision curve (list)
+        # Returns
+            Average precision, precision curve, recall curve
+        """
+        # Append sentinel values to beginning and end
+        mrec = np.concatenate(([0.0], recall, [1.0]))
+        mpre = np.concatenate(([1.0], precision, [0.0]))
 
-def ap_per_class(tp, conf, pred_cls, target_cls, eps=1e-16):
-    i = np.argsort(-conf)
-    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
+        # Compute the precision envelope
+        mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
 
-    unique_classes, nt = np.unique(target_cls, return_counts=True)
-    nc = unique_classes.shape[0]  # number of classes, number of detections
+        # Integrate area under curve
+        method = "interp"  # methods: 'continuous', 'interp'
+        if method == "interp":
+            x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
+            ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
+        else:  # 'continuous'
+            i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes
+            ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
 
-    px, py = np.linspace(0, 1, 1000), []  # for plotting
-    ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
-    for ci, c in enumerate(unique_classes):
-        i = pred_cls == c
-        n_l = nt[ci]  # number of labels
-        n_p = i.sum()  # number of predictions
-        if n_p == 0 or n_l == 0:
-            continue
+        return ap, mpre, mrec
 
-        # Accumulate FPs and TPs
-        fpc = (1 - tp[i]).cumsum(0)
-        tpc = tp[i].cumsum(0)
+    @staticmethod
+    def ap_per_class(tp, conf, pred_cls, target_cls, eps=1e-16):
+        i = np.argsort(-conf)
+        tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
 
-        # Recall
-        recall = tpc / (n_l + eps)  # recall curve
-        r[ci] = np.interp(
-            -px, -conf[i], recall[:, 0], left=0
-        )  # negative x, xp because xp decreases
+        unique_classes, nt = np.unique(target_cls, return_counts=True)
+        nc = unique_classes.shape[0]  # number of classes, number of detections
 
-        # Precision
-        precision = tpc / (tpc + fpc)  # precision curve
-        p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score
+        px, py = np.linspace(0, 1, 1000), []  # for plotting
+        ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
+        for ci, c in enumerate(unique_classes):
+            i = pred_cls == c
+            n_l = nt[ci]  # number of labels
+            n_p = i.sum()  # number of predictions
+            if n_p == 0 or n_l == 0:
+                continue
 
-        # AP from recall-precision curve
-        for j in range(tp.shape[1]):
-            ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
+            # Accumulate FPs and TPs
+            fpc = (1 - tp[i]).cumsum(0)
+            tpc = tp[i].cumsum(0)
 
-    # Compute F1 (harmonic mean of precision and recall)
-    f1 = 2 * p * r / (p + r + eps)
+            # Recall
+            recall = tpc / (n_l + eps)  # recall curve
+            r[ci] = np.interp(
+                -px, -conf[i], recall[:, 0], left=0
+            )  # negative x, xp because xp decreases
 
-    i = smooth(f1.mean(0), 0.1).argmax()  # max F1 index
-    p, r, f1 = p[:, i], r[:, i], f1[:, i]
-    tp = (r * nt).round()  # true positives
-    fp = (tp / (p + eps) - tp).round()  # false positives
-    return tp, fp, p, r, f1, ap, unique_classes.astype(int)
+            # Precision
+            precision = tpc / (tpc + fpc)  # precision curve
+            p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score
+
+            # AP from recall-precision curve
+            for j in range(tp.shape[1]):
+                ap[ci, j], mpre, mrec = MeanAveragePrecision.compute_ap(recall[:, j], precision[:, j])
+
+        # Compute F1 (harmonic mean of precision and recall)
+        f1 = 2 * p * r / (p + r + eps)
+
+        i = MeanAveragePrecision.smooth(f1.mean(0), 0.1).argmax()  # max F1 index
+        p, r, f1 = p[:, i], r[:, i], f1[:, i]
+        tp = (r * nt).round()  # true positives
+        fp = (tp / (p + eps) - tp).round()  # false positives
+        return tp, fp, p, r, f1, ap, unique_classes.astype(int)
+
+    @staticmethod
+    def smooth(y, f=0.05):
+        # Box filter of fraction f
+        nf = round(len(y) * f * 2) // 2 + 1  # number of filter elements (must be odd)
+        p = np.ones(nf // 2)  # ones padding
+        yp = np.concatenate((p * y[0], y, p * y[-1]), 0)  # y padded
+        return np.convolve(yp, np.ones(nf) / nf, mode="valid")  # y-smoothed
 
 
-def smooth(y, f=0.05):
-    # Box filter of fraction f
-    nf = round(len(y) * f * 2) // 2 + 1  # number of filter elements (must be odd)
-    p = np.ones(nf // 2)  # ones padding
-    yp = np.concatenate((p * y[0], y, p * y[-1]), 0)  # y padded
-    return np.convolve(yp, np.ones(nf) / nf, mode="valid")  # y-smoothed
 
-
-def compute_ap(recall, precision):
-    """Compute the average precision, given the recall and precision curves
-    # Arguments
-        recall:    The recall curve (list)
-        precision: The precision curve (list)
-    # Returns
-        Average precision, precision curve, recall curve
-    """
-
-    # Append sentinel values to beginning and end
-    mrec = np.concatenate(([0.0], recall, [1.0]))
-    mpre = np.concatenate(([1.0], precision, [0.0]))
-
-    # Compute the precision envelope
-    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
-
-    # Integrate area under curve
-    method = "interp"  # methods: 'continuous', 'interp'
-    if method == "interp":
-        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
-        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
-    else:  # 'continuous'
-        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes
-        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
-
-    return ap, mpre, mrec