diff --git a/docs/en/reference/utils/ops.md b/docs/en/reference/utils/ops.md
index 5f5443bfb..0d7028c3f 100644
--- a/docs/en/reference/utils/ops.md
+++ b/docs/en/reference/utils/ops.md
@@ -99,10 +99,6 @@ keywords: Ultralytics, utility operations, non-max suppression, bounding box tra
 
 <br><br>
 
-## ::: ultralytics.utils.ops.process_mask_upsample
-
-<br><br>
-
 ## ::: ultralytics.utils.ops.process_mask
 
 <br><br>
diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index 7a7595c52..a547711ed 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.59"
+__version__ = "8.2.60"
 
 import os
 
diff --git a/ultralytics/models/yolo/segment/val.py b/ultralytics/models/yolo/segment/val.py
index 301899938..edd1a1856 100644
--- a/ultralytics/models/yolo/segment/val.py
+++ b/ultralytics/models/yolo/segment/val.py
@@ -49,7 +49,7 @@ class SegmentationValidator(DetectionValidator):
         if self.args.save_json:
             check_requirements("pycocotools>=2.0.6")
         # more accurate vs faster
-        self.process = ops.process_mask_upsample if self.args.save_json or self.args.save_txt else ops.process_mask
+        self.process = ops.process_mask_native if self.args.save_json or self.args.save_txt else ops.process_mask
         self.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
 
     def get_desc(self):
diff --git a/ultralytics/utils/ops.py b/ultralytics/utils/ops.py
index 4c79feff1..2c15f107f 100644
--- a/ultralytics/utils/ops.py
+++ b/ultralytics/utils/ops.py
@@ -652,27 +652,6 @@ def crop_mask(masks, boxes):
     return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2))
 
 
-def process_mask_upsample(protos, masks_in, bboxes, shape):
-    """
-    Takes the output of the mask head, and applies the mask to the bounding boxes. This produces masks of higher quality
-    but is slower.
-
-    Args:
-        protos (torch.Tensor): [mask_dim, mask_h, mask_w]
-        masks_in (torch.Tensor): [n, mask_dim], n is number of masks after nms
-        bboxes (torch.Tensor): [n, 4], n is number of masks after nms
-        shape (tuple): the size of the input image (h,w)
-
-    Returns:
-        (torch.Tensor): The upsampled masks.
-    """
-    c, mh, mw = protos.shape  # CHW
-    masks = (masks_in @ protos.float().view(c, -1)).view(-1, mh, mw)
-    masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0]  # CHW
-    masks = crop_mask(masks, bboxes)  # CHW
-    return masks.gt_(0.0)
-
-
 def process_mask(protos, masks_in, bboxes, shape, upsample=False):
     """
     Apply masks to bounding boxes using the output of the mask head.