diff --git a/ultralytics/models/yolov10/predict.py b/ultralytics/models/yolov10/predict.py
index 29f21a9c..e3670f20 100644
--- a/ultralytics/models/yolov10/predict.py
+++ b/ultralytics/models/yolov10/predict.py
@@ -12,32 +12,25 @@ class YOLOv10DetectionPredictor(DetectionPredictor):
         if isinstance(preds, (list, tuple)):
             preds = preds[0]
 
-        if preds.shape[-1] != 6:
+        if preds.shape[-1] == 6:
+            pass
+        else:
             preds = preds.transpose(-1, -2)
-            bboxes, scores, labels = ops.v10postprocess(
-                preds, self.args.max_det, preds.shape[-1] - 4
-            )
+            bboxes, scores, labels = ops.v10postprocess(preds, self.args.max_det, preds.shape[-1]-4)
             bboxes = ops.xywh2xyxy(bboxes)
-            preds = torch.cat(
-                [bboxes, scores.unsqueeze(-1), labels.unsqueeze(-1)], dim=-1
-            )
+            preds = torch.cat([bboxes, scores.unsqueeze(-1), labels.unsqueeze(-1)], dim=-1)
 
         mask = preds[..., 4] > self.args.conf
 
-        # Filter predictions using the mask and keep batch dimension
-        filtered_preds = [p[mask[idx]] for idx, p in enumerate(preds)]
+        preds = [p[mask[idx]] for idx, p in enumerate(preds)]
 
-        if not isinstance(
-            orig_imgs, list
-        ):  # input images are a torch.Tensor, not a list
+        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
             orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
 
         results = []
-        for i, pred in enumerate(filtered_preds):
+        for i, pred in enumerate(preds):
             orig_img = orig_imgs[i]
             pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
             img_path = self.batch[0][i]
-            results.append(
-                Results(orig_img, path=img_path, names=self.model.names, boxes=pred)
-            )
+            results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
         return results