diff --git a/ultralytics/nn/tasks.py b/ultralytics/nn/tasks.py
index cfbc122..aaeef5b 100644
--- a/ultralytics/nn/tasks.py
+++ b/ultralytics/nn/tasks.py
@@ -54,6 +54,22 @@ class BaseModel(nn.Module):
             visualize (bool): Save the feature maps of the model if True, defaults to False.
             augment (bool): Augment image during prediction, defaults to False.
 
+        Returns:
+            (torch.Tensor): The last output of the model.
+        """
+        if augment:
+            return self._predict_augment(x)
+        return self._predict_once(x, profile, visualize)
+
+    def _predict_once(self, x, profile=False, visualize=False):
+        """
+        Perform a forward pass through the network.
+
+        Args:
+            x (torch.Tensor): The input tensor to the model.
+            profile (bool):  Print the computation time of each layer if True, defaults to False.
+            visualize (bool): Save the feature maps of the model if True, defaults to False.
+
         Returns:
             (torch.Tensor): The last output of the model.
         """
@@ -69,6 +85,13 @@ class BaseModel(nn.Module):
                 feature_visualization(x, m.type, m.i, save_dir=visualize)
         return x
 
+    def _predict_augment(self, x):
+        """Perform augmentations on input image x and return augmented inference."""
+        LOGGER.warning(
+            f'WARNING ⚠️ {self.__class__.__name__} has not supported augment inference yet! Now using single-scale inference instead.'
+        )
+        return self._predict_once(x)
+
     def _profile_one_layer(self, m, x, dt):
         """
         Profile the computation time and FLOPs of a single layer of the model on a given input.
@@ -225,13 +248,7 @@ class DetectionModel(BaseModel):
             self.info()
             LOGGER.info('')
 
-    def predict(self, x, augment=False, profile=False, visualize=False):
-        """Run forward pass on input image(s) with optional augmentation and profiling."""
-        if augment:
-            return self._forward_augment(x)  # augmented inference, None
-        return super().predict(x, profile=profile, visualize=visualize)  # single-scale inference, train
-
-    def _forward_augment(self, x):
+    def _predict_augment(self, x):
         """Perform augmentations on input image x and return augmented inference and train outputs."""
         img_size = x.shape[-2:]  # height, width
         s = [1, 0.83, 0.67]  # scales
@@ -279,13 +296,16 @@ class SegmentationModel(DetectionModel):
         """Initialize YOLOv8 segmentation model with given config and parameters."""
         super().__init__(cfg=cfg, ch=ch, nc=nc, verbose=verbose)
 
-    def _forward_augment(self, x):
-        """Undocumented function."""
-        raise NotImplementedError(emojis('WARNING ⚠️ SegmentationModel has not supported augment inference yet!'))
-
     def init_criterion(self):
         return v8SegmentationLoss(self)
 
+    def _predict_augment(self, x):
+        """Perform augmentations on input image x and return augmented inference."""
+        LOGGER.warning(
+            f'WARNING ⚠️ {self.__class__.__name__} has not supported augment inference yet! Now using single-scale inference instead.'
+        )
+        return self._predict_once(x)
+
 
 class PoseModel(DetectionModel):
     """YOLOv8 pose model."""
@@ -302,9 +322,12 @@ class PoseModel(DetectionModel):
     def init_criterion(self):
         return v8PoseLoss(self)
 
-    def _forward_augment(self, x):
-        """Undocumented function."""
-        raise NotImplementedError(emojis('WARNING ⚠️ PoseModel has not supported augment inference yet!'))
+    def _predict_augment(self, x):
+        """Perform augmentations on input image x and return augmented inference."""
+        LOGGER.warning(
+            f'WARNING ⚠️ {self.__class__.__name__} has not supported augment inference yet! Now using single-scale inference instead.'
+        )
+        return self._predict_once(x)
 
 
 class ClassificationModel(BaseModel):
@@ -448,10 +471,6 @@ class RTDETRDetectionModel(DetectionModel):
         x = head([y[j] for j in head.f], batch)  # head inference
         return x
 
-    def _forward_augment(self, x):
-        """Undocumented function."""
-        raise NotImplementedError(emojis('WARNING ⚠️ RTDETRModel has not supported augment inference yet!'))
-
 
 class Ensemble(nn.ModuleList):
     """Ensemble of models."""