diff --git a/torchvision/models/video/s3d.py b/torchvision/models/video/s3d.py
index f80d849683c..90861e57191 100644
--- a/torchvision/models/video/s3d.py
+++ b/torchvision/models/video/s3d.py
@@ -160,6 +160,7 @@ class S3D_Weights(WeightsEnum):
             resize_size=(256, 256),
             mean=(0.5, 0.5, 0.5),
             std=(0.5, 0.5, 0.5),
+            channel_order=(2, 1, 0),  # RGB to BGR
         ),
         meta={
             "min_size": (224, 224),
diff --git a/torchvision/transforms/_presets.py b/torchvision/transforms/_presets.py
index 33b94d01c9d..1b41f68d672 100644
--- a/torchvision/transforms/_presets.py
+++ b/torchvision/transforms/_presets.py
@@ -89,6 +89,7 @@ def __init__(
         mean: Tuple[float, ...] = (0.43216, 0.394666, 0.37645),
         std: Tuple[float, ...] = (0.22803, 0.22145, 0.216989),
         interpolation: InterpolationMode = InterpolationMode.BILINEAR,
+        channel_order: Optional[Tuple[int, int, int]] = None,
     ) -> None:
         super().__init__()
         self.crop_size = list(crop_size)
@@ -96,6 +97,7 @@ def __init__(
         self.mean = list(mean)
         self.std = list(std)
         self.interpolation = interpolation
+        self.channel_order = channel_order
 
     def forward(self, vid: Tensor) -> Tensor:
         need_squeeze = False
@@ -109,6 +111,8 @@ def forward(self, vid: Tensor) -> Tensor:
         vid = F.center_crop(vid, self.crop_size)
         vid = F.convert_image_dtype(vid, torch.float)
         vid = F.normalize(vid, mean=self.mean, std=self.std)
+        if self.channel_order is not None:
+            vid = vid[:, self.channel_order]
         H, W = self.crop_size
         vid = vid.view(N, T, C, H, W)
         vid = vid.permute(0, 2, 1, 3, 4)  # (N, T, C, H, W) => (N, C, T, H, W)
@@ -124,17 +128,21 @@ def __repr__(self) -> str:
         format_string += f"\n    mean={self.mean}"
         format_string += f"\n    std={self.std}"
         format_string += f"\n    interpolation={self.interpolation}"
+        format_string += f"\n    channel_order={self.channel_order}"
         format_string += "\n)"
         return format_string
 
     def describe(self) -> str:
-        return (
+        s = (
             "Accepts batched ``(B, T, C, H, W)`` and single ``(T, C, H, W)`` video frame ``torch.Tensor`` objects. "
             f"The frames are resized to ``resize_size={self.resize_size}`` using ``interpolation={self.interpolation}``, "
             f"followed by a central crop of ``crop_size={self.crop_size}``. Finally the values are first rescaled to "
-            f"``[0.0, 1.0]`` and then normalized using ``mean={self.mean}`` and ``std={self.std}``. Finally the output "
-            "dimensions are permuted to ``(..., C, T, H, W)`` tensors."
+            f"``[0.0, 1.0]`` and then normalized using ``mean={self.mean}`` and ``std={self.std}``. "
         )
+        if self.channel_order is not None:
+            s += f"Remaps the order within the channels dimension using ``channel_order={self.channel_order}``. "
+        s += "Finally the output dimensions are permuted to ``(..., C, T, H, W)`` tensors."
+        return s
 
 
 class SemanticSegmentation(nn.Module):