diff --git a/ppdet/utils/checkpoint.py b/ppdet/utils/checkpoint.py
index ed0433764ba6a13ec62f29298af536ddac3a3b6e..f3dafd40f0f8596c5ad0ac2ad0e99600b0134aab 100644
--- a/ppdet/utils/checkpoint.py
+++ b/ppdet/utils/checkpoint.py
@@ -155,7 +155,7 @@ def match_state_dict(model_state_dict, weight_state_dict, mode='default'):
         return a == b or a.endswith("." + b) or b.endswith("." + a)
 
     def match(a, b):
-        if a.startswith('backbone.res5'):
+        if b.startswith('backbone.res5'):
             b = b[9:]
         return a == b or a.endswith("." + b)
 
@@ -174,15 +174,28 @@ def match_state_dict(model_state_dict, weight_state_dict, mode='default'):
     max_id = match_matrix.argmax(1)
     max_len = match_matrix.max(1)
     max_id[max_len == 0] = -1
+    load_id = set(max_id)
+    load_id.discard(-1)
     not_load_weight_name = []
+    if weight_keys[0].startswith('modelStudent') or weight_keys[0].startswith(
+            'modelTeacher'):
+        for match_idx in range(len(max_id)):
+            if max_id[match_idx] == -1:
+                not_load_weight_name.append(model_keys[match_idx])
+        if len(not_load_weight_name) > 0:
+            logger.info('{} in model is not matched with pretrained weights, '
+                        'and its will be trained from scratch'.format(
+                            not_load_weight_name))
 
-    for match_idx in range(len(max_id)):
-        if max_id[match_idx] == -1:
-            not_load_weight_name.append(model_keys[match_idx])
-    if len(not_load_weight_name) > 0:
-        logger.info('{} in model is not matched with pretrained weights, '
-                    'and its will be trained from scratch'.format(
-                        not_load_weight_name))
+    else:
+        for idx in range(len(weight_keys)):
+            if idx not in load_id:
+                not_load_weight_name.append(weight_keys[idx])
+
+        if len(not_load_weight_name) > 0:
+            logger.info('{} in pretrained weight is not used in the model, '
+                        'and its will not be loaded'.format(
+                            not_load_weight_name))
     matched_keys = {}
     result_state_dict = {}
     for model_id, weight_id in enumerate(max_id):