diff --git a/test/prototype_transforms_kernel_infos.py b/test/prototype_transforms_kernel_infos.py
index e65fb2d6a6a..8852f9864c8 100644
--- a/test/prototype_transforms_kernel_infos.py
+++ b/test/prototype_transforms_kernel_infos.py
@@ -835,7 +835,7 @@ def sample_inputs_rotate_video():
             F.rotate_bounding_box,
             sample_inputs_fn=sample_inputs_rotate_bounding_box,
             closeness_kwargs={
-                **scripted_vs_eager_double_pixel_difference("cpu", atol=1e-6, rtol=1e-6),
+                **scripted_vs_eager_double_pixel_difference("cpu", atol=1e-5, rtol=1e-5),
                 **scripted_vs_eager_double_pixel_difference("cuda", atol=1e-5, rtol=1e-5),
             },
         ),
diff --git a/test/test_prototype_datapoints.py b/test/test_prototype_datapoints.py
index e6d2321fc80..4663cdac3da 100644
--- a/test/test_prototype_datapoints.py
+++ b/test/test_prototype_datapoints.py
@@ -1,5 +1,7 @@
 import pytest
 import torch
+
+from PIL import Image
 from torchvision.prototype import datapoints
 
 
@@ -130,3 +132,30 @@ def test_wrap_like():
     assert type(label_new) is datapoints.Label
     assert label_new.data_ptr() == output.data_ptr()
     assert label_new.categories is label.categories
+
+
+@pytest.mark.parametrize("data", [torch.rand(3, 32, 32), Image.new("RGB", (32, 32), color=123)])
+def test_image_instance(data):
+    image = datapoints.Image(data)
+    assert isinstance(image, torch.Tensor)
+    assert image.ndim == 3 and image.shape[0] == 3
+
+
+@pytest.mark.parametrize("data", [torch.randint(0, 10, size=(1, 32, 32)), Image.new("L", (32, 32), color=2)])
+def test_mask_instance(data):
+    mask = datapoints.Mask(data)
+    assert isinstance(mask, torch.Tensor)
+    assert mask.ndim == 3 and mask.shape[0] == 1
+
+
+@pytest.mark.parametrize("data", [torch.randint(0, 32, size=(5, 4)), [[0, 0, 5, 5], [2, 2, 7, 7]]])
+@pytest.mark.parametrize(
+    "format", ["XYXY", "CXCYWH", datapoints.BoundingBoxFormat.XYXY, datapoints.BoundingBoxFormat.XYWH]
+)
+def test_bbox_instance(data, format):
+    bboxes = datapoints.BoundingBox(data, format=format, spatial_size=(32, 32))
+    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())
+    assert bboxes.format == format
diff --git a/torchvision/prototype/datapoints/_dataset_wrapper.py b/torchvision/prototype/datapoints/_dataset_wrapper.py
index 910d2bb24ac..e60d61e5f90 100644
--- a/torchvision/prototype/datapoints/_dataset_wrapper.py
+++ b/torchvision/prototype/datapoints/_dataset_wrapper.py
@@ -99,7 +99,7 @@ def identity(item):
 
 
 def pil_image_to_mask(pil_image):
-    return datapoints.Mask(F.to_image_tensor(pil_image).squeeze(0))
+    return datapoints.Mask(pil_image)
 
 
 def list_of_dicts_to_dict_of_lists(list_of_dicts):
diff --git a/torchvision/prototype/datapoints/_image.py b/torchvision/prototype/datapoints/_image.py
index 4ffeb37d5eb..e999d8243e3 100644
--- a/torchvision/prototype/datapoints/_image.py
+++ b/torchvision/prototype/datapoints/_image.py
@@ -23,6 +23,11 @@ def __new__(
         device: Optional[Union[torch.device, str, int]] = None,
         requires_grad: Optional[bool] = None,
     ) -> Image:
+        if isinstance(data, PIL.Image.Image):
+            from torchvision.prototype.transforms import functional as F
+
+            data = F.pil_to_tensor(data)
+
         tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
         if tensor.ndim < 2:
             raise ValueError
diff --git a/torchvision/prototype/datapoints/_mask.py b/torchvision/prototype/datapoints/_mask.py
index 834f990512b..55476cd503d 100644
--- a/torchvision/prototype/datapoints/_mask.py
+++ b/torchvision/prototype/datapoints/_mask.py
@@ -2,6 +2,7 @@
 
 from typing import Any, List, Optional, Tuple, Union
 
+import PIL.Image
 import torch
 from torchvision.transforms import InterpolationMode
 
@@ -21,6 +22,11 @@ def __new__(
         device: Optional[Union[torch.device, str, int]] = None,
         requires_grad: Optional[bool] = None,
     ) -> Mask:
+        if isinstance(data, PIL.Image.Image):
+            from torchvision.prototype.transforms import functional as F
+
+            data = F.pil_to_tensor(data)
+
         tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
         return cls._wrap(tensor)