diff --git a/micro_sam/_vendored.py b/micro_sam/_vendored.py
index 9cbe239d..f574012a 100644
--- a/micro_sam/_vendored.py
+++ b/micro_sam/_vendored.py
@@ -83,7 +83,10 @@ def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
     out = []
     for mask in tensor:
         diffs = mask[1:] != mask[:-1]  # pairwise unequal (string safe)
-        indices = np.append(np.where(diffs), n - 1)  # must include last element posi
-        counts = np.diff(np.append(-1, indices))  # run lengths
-        out.append({"size": [h, w], "counts": counts.tolist()})
+        indices = np.append(np.where(diffs), n - 1)  # must include last element position
+        # count needs to start with 0 if the mask begins with 1
+        counts = [] if mask[0] == 0 else [0]
+        # compute the actual RLE
+        counts += np.diff(np.append(-1, indices)).tolist()
+        out.append({"size": [h, w], "counts": counts})
     return out
diff --git a/test/test_vendored.py b/test/test_vendored.py
index 07429b4b..a0d7f703 100644
--- a/test/test_vendored.py
+++ b/test/test_vendored.py
@@ -52,14 +52,7 @@ def _get_mask_to_rle_pytorch_data(self):
             one_hot[i, masks == idx] = 1
         one_hot = torch.from_numpy(one_hot)
 
-        # make sure that all corner pixels are zero
-        one_hot[:, 0, 0] = 0
-        one_hot[:, -1, -1] = 0
-        one_hot[:, 0, -1] = 0
-        one_hot[:, -1, 0] = 0
-
         expected_result = mask_to_rle_pytorch_sam(one_hot)
-
         return one_hot, expected_result
 
     def test_mask_to_rle_pytorch(self):