diff --git a/test/test_datapoints.py b/test/test_datapoints.py
index 5b875a6ef20..39c05123333 100644
--- a/test/test_datapoints.py
+++ b/test/test_datapoints.py
@@ -28,5 +28,5 @@ def test_bbox_instance(data, format):
     assert isinstance(bboxes, torch.Tensor)
     assert bboxes.ndim == 2 and bboxes.shape[1] == 4
     if isinstance(format, str):
-        format = datapoints.BoundingBoxFormat.from_str(format.upper())
+        format = datapoints.BoundingBoxFormat[(format.upper())]
     assert bboxes.format == format
diff --git a/torchvision/datapoints/_bounding_box.py b/torchvision/datapoints/_bounding_box.py
index 1dc46f8f21a..75e779f0b21 100644
--- a/torchvision/datapoints/_bounding_box.py
+++ b/torchvision/datapoints/_bounding_box.py
@@ -1,18 +1,18 @@
 from __future__ import annotations
 
+from enum import Enum
 from typing import Any, List, Optional, Sequence, Tuple, Union
 
 import torch
-from torchvision._utils import StrEnum
 from torchvision.transforms import InterpolationMode  # TODO: this needs to be moved out of transforms
 
 from ._datapoint import _FillTypeJIT, Datapoint
 
 
-class BoundingBoxFormat(StrEnum):
-    XYXY = StrEnum.auto()
-    XYWH = StrEnum.auto()
-    CXCYWH = StrEnum.auto()
+class BoundingBoxFormat(Enum):
+    XYXY = "XYXY"
+    XYWH = "XYWH"
+    CXCYWH = "CXCYWH"
 
 
 class BoundingBox(Datapoint):
@@ -39,7 +39,7 @@ def __new__(
         tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
 
         if isinstance(format, str):
-            format = BoundingBoxFormat.from_str(format.upper())
+            format = BoundingBoxFormat[format.upper()]
 
         return cls._wrap(tensor, format=format, spatial_size=spatial_size)