diff --git a/mrcnn/utils.py b/mrcnn/utils.py
index 0133dbf914439276b6076e656c158c535846c853..85e51487894cd240bb1d261da0e96d0aff4e5086 100644
--- a/mrcnn/utils.py
+++ b/mrcnn/utils.py
@@ -753,6 +753,30 @@ def compute_ap(gt_boxes, gt_class_ids, gt_masks,
     return mAP, precisions, recalls, overlaps
 
 
+def compute_ap_range(gt_box, gt_class_id, gt_mask,
+                     pred_box, pred_class_id, pred_score, pred_mask,
+                     iou_thresholds=None, verbose=1):
+    """Compute AP over a range or IoU thresholds. Default range is 0.5-0.95."""
+    # Default is 0.5 to 0.95 with increments of 0.05
+    iou_thresholds = iou_thresholds or np.arange(0.5, 1.0, 0.05)
+    
+    # Compute AP over range of IoU thresholds
+    AP = []
+    for iou_threshold in iou_thresholds:
+        ap, precisions, recalls, overlaps =\
+            compute_ap(gt_box, gt_class_id, gt_mask,
+                        pred_box, pred_class_id, pred_score, pred_mask,
+                        iou_threshold=iou_threshold)
+        if verbose:
+            print("AP @{:.2f}:\t {:.3f}".format(iou_threshold, ap))
+        AP.append(ap)
+    AP = np.array(AP).mean()
+    if verbose:
+        print("AP @{:.2f}-{:.2f}:\t {:.3f}".format(
+            iou_thresholds[0], iou_thresholds[-1], AP))
+    return AP
+
+
 def compute_recall(pred_boxes, gt_boxes, iou):
     """Compute the recall at the given IoU threshold. It's an indication
     of how many GT boxes were found by the given prediction boxes.