diff --git a/aloscene/frame.py b/aloscene/frame.py
index 304fc6fd..ba71711a 100644
--- a/aloscene/frame.py
+++ b/aloscene/frame.py
@@ -465,6 +465,9 @@ def norm_minmax_sym(self):
             tensor = 2 * tensor - 1.0
         elif tensor.normalization == "255":
             tensor = 2 * (tensor / 255.0) - 1.0
+        elif tensor.mean_std is not None:
+            tensor = tensor.norm01()
+            tensor = 2 * tensor - 1.0
         else:
             raise Exception(f"Can't convert from {tensor.normalization} to norm255")
         tensor.mean_std = None
@@ -484,7 +487,7 @@ def mean_std_norm(self, mean, std, name) -> Frame:
         """
         tensor = self
         mean_tensor, std_tensor = self._get_mean_std_tensor(
-            tensor.shape, tensor.names, tensor._resnet_mean_std, device=tensor.device
+            tensor.shape, tensor.names, (mean, std), device=tensor.device
         )
         if tensor.normalization == "01":
             tensor = tensor - mean_tensor
@@ -507,10 +510,27 @@ def mean_std_norm(self, mean, std, name) -> Frame:
 
         return tensor
 
+    def norm_meanstd(self, mean_std=None, name=None) -> Frame:
+        """Returns z-norm of the current frame.
+        This method will simply call `frame.mean_std_norm()` with the mean/std property of the frame and the selected name.
+        Instead of a custom mean/std, you can use the resnet norm based on the normalized use of resnet on pytorch.
+        Examples
+        --------
+        >>> frame_resnet = frame.norm_resnet(name="resnet")
+        >>> frame_custom_norm = frame.norm_resnet(name="custom")
+        """
+        if name == "resnet":
+            return self.norm_resnet()
+        elif mean_std is not None and len(mean_std) == 2:
+            return self.mean_std_norm(mean=mean_std[0], std=mean_std[1], name=name)
+        elif self.mean_std is not None:
+            return self.mean_std_norm(mean=self.mean_std[0], std=self.mean_std[1], name=name)
+        else:
+            raise Exception("Please pass a mean_std tuple or use the resnet norm")
+
     def norm_resnet(self) -> Frame:
         """Normalized the current frame based on the normalized use on resnet on pytorch. This method will
         simply call `frame.mean_std_norm()` with the resnet mean/std and the name `resnet`.
-
         Examples
         --------
         >>> frame_resnet = frame.norm_resnet()