diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index 9d19f6ab..bd608b1d 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.3.44"
+__version__ = "8.3.47"
 
 import os
 
diff --git a/ultralytics/engine/exporter.py b/ultralytics/engine/exporter.py
index c0e29e7e..ae84cab9 100644
--- a/ultralytics/engine/exporter.py
+++ b/ultralytics/engine/exporter.py
@@ -73,7 +73,7 @@ from ultralytics.data import build_dataloader
 from ultralytics.data.dataset import YOLODataset
 from ultralytics.data.utils import check_cls_dataset, check_det_dataset
 from ultralytics.nn.autobackend import check_class_names, default_class_names
-from ultralytics.nn.modules import C2f, Detect, RTDETRDecoder
+from ultralytics.nn.modules import C2f, Classify, Detect, RTDETRDecoder
 from ultralytics.nn.tasks import DetectionModel, SegmentationModel, WorldModel
 from ultralytics.utils import (
     ARM64,
@@ -287,6 +287,8 @@ class Exporter:
 
             model = FXModel(model)
         for m in model.modules():
+            if isinstance(m, Classify):
+                m.export = True
             if isinstance(m, (Detect, RTDETRDecoder)):  # includes all Detect subclasses like Segment, Pose, OBB
                 m.dynamic = self.args.dynamic
                 m.export = True
diff --git a/ultralytics/models/yolo/classify/predict.py b/ultralytics/models/yolo/classify/predict.py
index b75a1949..385f75bc 100644
--- a/ultralytics/models/yolo/classify/predict.py
+++ b/ultralytics/models/yolo/classify/predict.py
@@ -53,7 +53,8 @@ class ClassificationPredictor(BasePredictor):
         if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
             orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
 
+        preds = preds[0] if isinstance(preds, (list, tuple)) else preds
         return [
-            Results(orig_img, path=img_path, names=self.model.names, probs=pred.softmax(0))
+            Results(orig_img, path=img_path, names=self.model.names, probs=pred)
             for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0])
         ]
diff --git a/ultralytics/models/yolo/classify/val.py b/ultralytics/models/yolo/classify/val.py
index e54f0411..67333f26 100644
--- a/ultralytics/models/yolo/classify/val.py
+++ b/ultralytics/models/yolo/classify/val.py
@@ -71,6 +71,10 @@ class ClassificationValidator(BaseValidator):
         self.metrics.confusion_matrix = self.confusion_matrix
         self.metrics.save_dir = self.save_dir
 
+    def postprocess(self, preds):
+        """Preprocesses the classification predictions."""
+        return preds[0] if isinstance(preds, (list, tuple)) else preds
+
     def get_stats(self):
         """Returns a dictionary of metrics obtained by processing targets and predictions."""
         self.metrics.process(self.targets, self.pred)
diff --git a/ultralytics/nn/modules/head.py b/ultralytics/nn/modules/head.py
index 25964ac2..0afb5fd1 100644
--- a/ultralytics/nn/modules/head.py
+++ b/ultralytics/nn/modules/head.py
@@ -282,6 +282,8 @@ class Pose(Detect):
 class Classify(nn.Module):
     """YOLO classification head, i.e. x(b,c1,20,20) to x(b,c2)."""
 
+    export = False  # export mode
+
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1):
         """Initializes YOLO classification head to transform input tensor from (b,c1,20,20) to (b,c2) shape."""
         super().__init__()
@@ -296,7 +298,10 @@ class Classify(nn.Module):
         if isinstance(x, list):
             x = torch.cat(x, 1)
         x = self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
-        return x
+        if self.training:
+            return x
+        y = x.softmax(1)  # get final output
+        return y if self.export else (y, x)
 
 
 class WorldDetect(Detect):
diff --git a/ultralytics/utils/loss.py b/ultralytics/utils/loss.py
index 73952868..c6557df4 100644
--- a/ultralytics/utils/loss.py
+++ b/ultralytics/utils/loss.py
@@ -604,6 +604,7 @@ class v8ClassificationLoss:
 
     def __call__(self, preds, batch):
         """Compute the classification loss between predictions and true labels."""
+        preds = preds[1] if isinstance(preds, (list, tuple)) else preds
         loss = F.cross_entropy(preds, batch["cls"], reduction="mean")
         loss_items = loss.detach()
         return loss, loss_items