diff --git a/unstructured/metrics/object_detection.py b/unstructured/metrics/object_detection.py
index 7c28721518..6eb8175b47 100644
--- a/unstructured/metrics/object_detection.py
+++ b/unstructured/metrics/object_detection.py
@@ -8,6 +8,7 @@
 
 import numpy as np
 import torch
+from numba import njit
 
 IOU_THRESHOLDS = torch.tensor(
     [0.5000, 0.5500, 0.6000, 0.6500, 0.7000, 0.7500, 0.8000, 0.8500, 0.9000, 0.9500]
@@ -303,8 +304,8 @@ def _change_bbox_bounds_for_image_size(
         Returns:
             clipped_boxes:  Clipped bboxes in XYXY format of [..., 4] shape
         """
-        boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(min=0, max=img_shape[1])
-        boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(min=0, max=img_shape[0])
+        # Use Numba-accelerated function for inplace fast clipping
+        _numba_clip_boxes(boxes, img_shape[0], img_shape[1])
         return boxes
 
     @staticmethod
@@ -697,6 +698,42 @@ def _compute_detection_metrics_per_cls(
         return ap, precision, recall
 
 
+@njit(cache=True, fastmath=True)
+def _numba_clip_boxes(boxes: np.ndarray, img_height: int, img_width: int) -> np.ndarray:
+    # This helper efficiently clips _inplace_ for (..., 4)-shaped XYXY boxes:
+    #   x1 = min(max(x1, 0), img_width)
+    #   y1 = min(max(y1, 0), img_height)
+    #   x2 = min(max(x2, 0), img_width)
+    #   y2 = min(max(y2, 0), img_height)
+    #
+    # Works with both 2D (N, 4) and higher dimensional (..., 4) arrays.
+    boxes.shape
+    boxes_reshaped = boxes.reshape(-1, 4)
+    for i in range(boxes_reshaped.shape[0]):
+        # x1
+        if boxes_reshaped[i, 0] < 0:
+            boxes_reshaped[i, 0] = 0
+        if boxes_reshaped[i, 0] > img_width:
+            boxes_reshaped[i, 0] = img_width
+        # y1
+        if boxes_reshaped[i, 1] < 0:
+            boxes_reshaped[i, 1] = 0
+        if boxes_reshaped[i, 1] > img_height:
+            boxes_reshaped[i, 1] = img_height
+        # x2
+        if boxes_reshaped[i, 2] < 0:
+            boxes_reshaped[i, 2] = 0
+        if boxes_reshaped[i, 2] > img_width:
+            boxes_reshaped[i, 2] = img_width
+        # y2
+        if boxes_reshaped[i, 3] < 0:
+            boxes_reshaped[i, 3] = 0
+        if boxes_reshaped[i, 3] > img_height:
+            boxes_reshaped[i, 3] = img_height
+    # No need to reshape as it is a view of original
+    return boxes
+
+
 if __name__ == "__main__":
     from dataclasses import asdict