diff --git a/lerobot/common/datasets/factory.py b/lerobot/common/datasets/factory.py
index d9ce7a0f..370d1640 100644
--- a/lerobot/common/datasets/factory.py
+++ b/lerobot/common/datasets/factory.py
@@ -17,9 +17,9 @@ import logging
 
 import torch
 from omegaconf import ListConfig, OmegaConf
-from torchvision.transforms import v2
 
 from lerobot.common.datasets.lerobot_dataset import LeRobotDataset, MultiLeRobotDataset
+from lerobot.common.datasets.transforms import make_transforms
 
 
 def resolve_delta_timestamps(cfg):
@@ -72,22 +72,7 @@ def make_dataset(cfg, split: str = "train") -> LeRobotDataset | MultiLeRobotData
 
     resolve_delta_timestamps(cfg)
 
-    if cfg.image_transform.enable:
-        transform = v2.Compose(
-            [
-                v2.ColorJitter(
-                    brightness=cfg.image_transform.colorjitter_factor,
-                    contrast=cfg.image_transform.colorjitter_factor,
-                ),
-                v2.RandomAdjustSharpness(
-                    cfg.image_transform.sharpness_factor, p=cfg.image_transform.sharpness_p
-                ),
-                v2.RandomAdjustSharpness(cfg.image_transform.blur_factor, p=cfg.image_transform.blur_p),
-                v2.ToDtype(torch.float32, scale=True),
-            ]
-        )
-    else:
-        transform = None
+    transform = make_transforms(cfg.image_transform) if cfg.image_transform.enable else None
 
     if isinstance(cfg.dataset_repo_id, str):
         dataset = LeRobotDataset(
diff --git a/lerobot/common/datasets/transforms.py b/lerobot/common/datasets/transforms.py
new file mode 100644
index 00000000..e5bec5e6
--- /dev/null
+++ b/lerobot/common/datasets/transforms.py
@@ -0,0 +1,58 @@
+from typing import Any, Callable, Sequence
+
+import torch
+from torchvision.transforms import v2
+from torchvision.transforms.v2 import Transform
+
+
+class RandomSubsetApply(Transform):
+    """
+    Apply a random subset of N transformations from a list of transformations in a random order.
+
+    Args:
+        transforms (sequence or torch.nn.Module): list of transformations
+        N (int): number of transformations to apply
+    """
+
+    def __init__(self, transforms: Sequence[Callable], n_subset: int) -> None:
+        super().__init__()
+        if not isinstance(transforms, Sequence):
+            raise TypeError("Argument transforms should be a sequence of callables")
+        if not (0 <= n_subset <= len(transforms)):
+            raise ValueError(f"N should be in the interval [0, {len(transforms)}]")
+
+        self.transforms = transforms
+        self.N = n_subset
+
+    def forward(self, *inputs: Any) -> Any:
+        needs_unpacking = len(inputs) > 1
+
+        # Randomly pick N transforms
+        selected_transforms = torch.randperm(len(self.transforms))[: self.N]
+
+        # Apply selected transforms in random order
+        for idx in selected_transforms:
+            transform = self.transforms[idx]
+            outputs = transform(*inputs)
+            inputs = outputs if needs_unpacking else (outputs,)
+
+        return outputs
+
+    def extra_repr(self) -> str:
+        format_string = [f"N={self.N}"]
+        for t in self.transforms:
+            format_string.append(f"    {t}")
+        return "\n".join(format_string)
+
+
+def make_transforms(cfg):
+    image_transforms = [
+        v2.ColorJitter(
+            brightness=cfg.colorjitter_factor,
+            contrast=cfg.colorjitter_factor,
+        ),
+        v2.RandomAdjustSharpness(cfg.sharpness_factor, p=cfg.sharpness_p),
+        v2.RandomAdjustSharpness(cfg.blur_factor, p=cfg.blur_p),
+        v2.ToDtype(torch.float32, scale=True),
+    ]
+    return RandomSubsetApply(image_transforms, n_subset=cfg.n_subset)