diff --git a/src/microwink/seg.py b/src/microwink/seg.py
index b7c30e5..fbb405b 100644
--- a/src/microwink/seg.py
+++ b/src/microwink/seg.py
@@ -194,28 +194,33 @@ def postprocess(
         L = len(x)
         assert x.shape == (L, C)
         x = np.c_[
-            x[..., :4],  # boxes
-            x[..., 4 : 4 + NUM_CLASSES].max(axis=1),  # scores
-            x[..., 4 : 4 + NUM_CLASSES].argmax(axis=1),  # class_ids
-            x[..., 4 + NUM_CLASSES :],  # masks
+            x[:, :4],  # boxes
+            x[:, 4 : 4 + NUM_CLASSES].max(axis=1),  # scores
+            x[:, 4 : 4 + NUM_CLASSES].argmax(axis=1),  # class_ids
+            x[:, 4 + NUM_CLASSES :],  # masks
         ]
         assert x.shape == (L, 1 + C)
-        keep = cv2.dnn.NMSBoxes(x[:, :4], x[:, 4], conf_threshold, iou_threshold)
+        keep = self.nms(
+            x[:, :4],
+            x[:, 4],
+            conf_threshold=conf_threshold,
+            iou_threshold=iou_threshold,
+        )
         N = len(keep)
         x = x[keep]
         assert x.shape == (N, 1 + C)
         if N == 0:
             return None
 
-        x[..., [0, 1]] -= x[..., [2, 3]] / 2
-        x[..., [2, 3]] += x[..., [0, 1]]
+        x[:, [0, 1]] -= x[:, [2, 3]] / 2
+        x[:, [2, 3]] += x[:, [0, 1]]
 
-        x[..., :4] -= [pad_w, pad_h, pad_w, pad_h]
-        x[..., :4] /= ratio
+        x[:, :4] -= [pad_w, pad_h, pad_w, pad_h]
+        x[:, :4] /= ratio
 
         ih, iw = img_size
-        x[..., [0, 2]] = x[:, [0, 2]].clip(0, iw)
-        x[..., [1, 3]] = x[:, [1, 3]].clip(0, ih)
+        x[:, [0, 2]] = x[:, [0, 2]].clip(0, iw)
+        x[:, [1, 3]] = x[:, [1, 3]].clip(0, ih)
 
         boxes = x[:, :4]
         masks = self.process_mask(protos, x[:, 6:], boxes, (ih, iw))
@@ -224,6 +229,25 @@ def postprocess(
         assert dets.shape == (N, 6)
         return dets, masks
 
+    @staticmethod
+    def nms(
+        boxes: np.ndarray,
+        scores: np.ndarray,
+        *,
+        conf_threshold: float,
+        iou_threshold: float,
+    ) -> list[int]:
+        N = len(boxes)
+        assert boxes.shape == (N, 4)
+        assert scores.shape == (N,)
+        keep = cv2.dnn.NMSBoxes(
+            boxes,  # type: ignore
+            scores,  # type: ignore
+            conf_threshold,
+            iou_threshold,
+        )
+        return list(keep)
+
     def process_mask(
         self,
         protos: np.ndarray,