diff --git a/mmdeploy/codebase/mmcls/deploy/classification.py b/mmdeploy/codebase/mmcls/deploy/classification.py
index 9d6cb4430..6e3ef7038 100644
--- a/mmdeploy/codebase/mmcls/deploy/classification.py
+++ b/mmdeploy/codebase/mmcls/deploy/classification.py
@@ -161,7 +161,6 @@ class Classification(BaseTask):
                 `np.ndarray`, `torch.Tensor`.
             input_shape (list[int]): A list of two integer in (width, height)
                 format specifying input shape. Default: None.
-
         Returns:
             tuple: (data, img), meta information for the input image and input.
         """
@@ -170,6 +169,11 @@ class Classification(BaseTask):
         model_cfg = process_model_config(self.model_cfg, imgs, input_shape)
         from mmengine.dataset import Compose
         pipeline = deepcopy(model_cfg.test_pipeline)
+        move_pipeline = []
+        while pipeline[-1]['type'] != 'PackClsInputs':
+            sub_pipeline = pipeline.pop(-1)
+            move_pipeline = [sub_pipeline] + move_pipeline
+        pipeline = pipeline[:-1] + move_pipeline + pipeline[-1:]
         pipeline = Compose(pipeline)
 
         if isinstance(imgs, str):
@@ -248,6 +252,13 @@ class Classification(BaseTask):
         transforms = [
             item for item in pipeline if 'Random' not in item['type']
         ]
+        move_pipeline = []
+        import re
+        while re.search('Pack[a-z | A-Z]+Inputs',
+                        transforms[-1]['type']) is None:
+            sub_pipeline = transforms.pop(-1)
+            move_pipeline = [sub_pipeline] + move_pipeline
+            transforms = transforms[:-1] + move_pipeline + transforms[-1:]
         for i, transform in enumerate(transforms):
             if transform['type'] == 'PackClsInputs':
                 meta_keys += transform[