diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
index cf9cf9e2..7b5793a2 100644
--- a/ultralytics/__init__.py
+++ b/ultralytics/__init__.py
@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.42"
+__version__ = "8.2.43"
 
 import os
 
diff --git a/ultralytics/utils/ops.py b/ultralytics/utils/ops.py
index 0ec7e4a5..4c79feff 100644
--- a/ultralytics/utils/ops.py
+++ b/ultralytics/utils/ops.py
@@ -199,6 +199,7 @@ def non_max_suppression(
         max_nms (int): The maximum number of boxes into torchvision.ops.nms().
         max_wh (int): The maximum box width and height in pixels.
         in_place (bool): If True, the input prediction tensor will be modified in place.
+        rotated (bool): If Oriented Bounding Boxes (OBB) are being passed for NMS.
 
     Returns:
         (List[torch.Tensor]): A list of length batch_size, where each element is a tensor of
@@ -212,11 +213,16 @@ def non_max_suppression(
     assert 0 <= iou_thres <= 1, f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0"
     if isinstance(prediction, (list, tuple)):  # YOLOv8 model in validation model, output = (inference_out, loss_out)
         prediction = prediction[0]  # select only inference output
+    if classes is not None:
+        classes = torch.tensor(classes, device=prediction.device)
 
-    if prediction.shape[-1] == 6:  # end-to-end model
-        return [pred[pred[:, 4] > conf_thres] for pred in prediction]
+    if prediction.shape[-1] == 6:  # end-to-end model (BNC, i.e. 1,300,6)
+        output = [pred[pred[:, 4] > conf_thres] for pred in prediction]
+        if classes is not None:
+            output = [pred[(pred[:, 5:6] == classes).any(1)] for pred in output]
+        return output
 
-    bs = prediction.shape[0]  # batch size
+    bs = prediction.shape[0]  # batch size (BCN, i.e. 1,84,6300)
     nc = nc or (prediction.shape[1] - 4)  # number of classes
     nm = prediction.shape[1] - nc - 4  # number of masks
     mi = 4 + nc  # mask start index
@@ -265,7 +271,7 @@ def non_max_suppression(
 
         # Filter by class
         if classes is not None:
-            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
+            x = x[(x[:, 5:6] == classes).any(1)]
 
         # Check shape
         n = x.shape[0]  # number of boxes