diff --git a/test/test_utils.py b/test/test_utils.py
index f2597162905..cb6aa7cf6d1 100644
--- a/test/test_utils.py
+++ b/test/test_utils.py
@@ -11,6 +11,7 @@
 import torchvision.utils as utils
 from common_utils import assert_equal, cpu_and_cuda
 from PIL import __version__ as PILLOW_VERSION, Image, ImageColor
+from torchvision.transforms.v2.functional import to_dtype
 
 
 PILLOW_VERSION = tuple(int(x) for x in PILLOW_VERSION.split("."))
@@ -246,6 +247,26 @@ def test_draw_segmentation_masks(colors, alpha, device):
         torch.testing.assert_close(out[:, mask], interpolated_color, rtol=0.0, atol=1.0)
 
 
+def test_draw_segmentation_masks_dtypes():
+    num_masks, h, w = 2, 100, 100
+
+    masks = torch.randint(0, 2, (num_masks, h, w), dtype=torch.bool)
+
+    img_uint8 = torch.randint(0, 256, size=(3, h, w), dtype=torch.uint8)
+    out_uint8 = utils.draw_segmentation_masks(img_uint8, masks)
+
+    assert img_uint8 is not out_uint8
+    assert out_uint8.dtype == torch.uint8
+
+    img_float = to_dtype(img_uint8, torch.float, scale=True)
+    out_float = utils.draw_segmentation_masks(img_float, masks)
+
+    assert img_float is not out_float
+    assert out_float.is_floating_point()
+
+    torch.testing.assert_close(out_uint8, to_dtype(out_float, torch.uint8, scale=True), rtol=0, atol=1)
+
+
 @pytest.mark.parametrize("device", cpu_and_cuda())
 def test_draw_segmentation_masks_errors(device):
     h, w = 10, 10
diff --git a/torchvision/utils.py b/torchvision/utils.py
index 6ec19a0e0a1..630eada5cbc 100644
--- a/torchvision/utils.py
+++ b/torchvision/utils.py
@@ -10,6 +10,7 @@
 import torch
 from PIL import Image, ImageColor, ImageDraw, ImageFont
 
+
 __all__ = [
     "make_grid",
     "save_image",
@@ -262,10 +263,10 @@ def draw_segmentation_masks(
 
     """
     Draws segmentation masks on given RGB image.
-    The values of the input image should be uint8 between 0 and 255.
+    The image values should be uint8 in [0, 255] or float in [0, 1].
 
     Args:
-        image (Tensor): Tensor of shape (3, H, W) and dtype uint8.
+        image (Tensor): Tensor of shape (3, H, W) and dtype uint8 or float.
         masks (Tensor): Tensor of shape (num_masks, H, W) or (H, W) and dtype bool.
         alpha (float): Float number between 0 and 1 denoting the transparency of the masks.
             0 means full transparency, 1 means no transparency.
@@ -282,8 +283,8 @@ def draw_segmentation_masks(
         _log_api_usage_once(draw_segmentation_masks)
     if not isinstance(image, torch.Tensor):
         raise TypeError(f"The image must be a tensor, got {type(image)}")
-    elif image.dtype != torch.uint8:
-        raise ValueError(f"The image dtype must be uint8, got {image.dtype}")
+    elif not (image.dtype == torch.uint8 or image.is_floating_point()):
+        raise ValueError(f"The image dtype must be uint8 or float, got {image.dtype}")
     elif image.dim() != 3:
         raise ValueError("Pass individual images, not batches")
     elif image.size()[0] != 3:
@@ -303,10 +304,10 @@ def draw_segmentation_masks(
         warnings.warn("masks doesn't contain any mask. No mask was drawn")
         return image
 
-    out_dtype = torch.uint8
+    original_dtype = image.dtype
     colors = [
-        torch.tensor(color, dtype=out_dtype, device=image.device)
-        for color in _parse_colors(colors, num_objects=num_masks)
+        torch.tensor(color, dtype=original_dtype, device=image.device)
+        for color in _parse_colors(colors, num_objects=num_masks, dtype=original_dtype)
     ]
 
     img_to_draw = image.detach().clone()
@@ -315,7 +316,8 @@ def draw_segmentation_masks(
         img_to_draw[:, mask] = color[:, None]
 
     out = image * (1 - alpha) + img_to_draw * alpha
-    return out.to(out_dtype)
+    # Note: at this point, out is a float tensor in [0, 1] or [0, 255] depending on original_dtype
+    return out.to(original_dtype)
 
 
 @torch.no_grad()
@@ -516,6 +518,7 @@ def _parse_colors(
     colors: Union[None, str, Tuple[int, int, int], List[Union[str, Tuple[int, int, int]]]],
     *,
     num_objects: int,
+    dtype: torch.dtype = torch.uint8,
 ) -> List[Tuple[int, int, int]]:
     """
     Parses a specification of colors for a set of objects.
@@ -552,7 +555,10 @@ def _parse_colors(
     else:  # colors specifies a single color for all objects
         colors = [colors] * num_objects
 
-    return [ImageColor.getrgb(color) if isinstance(color, str) else color for color in colors]
+    colors = [ImageColor.getrgb(color) if isinstance(color, str) else color for color in colors]
+    if dtype.is_floating_point:  # [0, 255] -> [0, 1]
+        colors = [tuple(v / 255 for v in color) for color in colors]
+    return colors
 
 
 def _log_api_usage_once(obj: Any) -> None: