diff --git a/deploy/python/postprocess.py b/deploy/python/postprocess.py
index c66b1abeb..2e35e996b 100644
--- a/deploy/python/postprocess.py
+++ b/deploy/python/postprocess.py
@@ -374,10 +374,7 @@ class FaceAttribute(object):
         gender_list = [["Male", "男性"], ["Female", "女性"]]
         age_list = [["Young", "年轻人"], ["Old", "老年人"]]
         batch_res = []
-        if self.convert_cn:
-            index = 1
-        else:
-            index = 0
+        index = 1 if self.convert_cn else 0
         for idx, res in enumerate(x):
             res = res.tolist()
             label_res = []
diff --git a/ppcls/data/postprocess/attr_rec.py b/ppcls/data/postprocess/attr_rec.py
index db84769ba..ff6dcee16 100644
--- a/ppcls/data/postprocess/attr_rec.py
+++ b/ppcls/data/postprocess/attr_rec.py
@@ -84,7 +84,6 @@ class PersonAttribute(object):
         if isinstance(x, dict):
             x = x['logits']
         assert isinstance(x, paddle.Tensor)
-
         if file_names is not None:
             assert x.shape[0] == len(file_names)
         x = F.sigmoid(x).numpy()
@@ -99,7 +98,6 @@ class PersonAttribute(object):
             'Skirt&Dress'
         ]
         batch_res = []
-
         for idx, res in enumerate(x):
             res = res.tolist()
             label_res = []
@@ -209,10 +207,7 @@ class FaceAttribute(object):
         gender_list = [["Male", "男性"], ["Female", "女性"]]
         age_list = [["Young", "年轻人"], ["Old", "老年人"]]
         batch_res = []
-        if self.convert_cn:
-            index = 1
-        else:
-            index = 0
+        index = 1 if self.convert_cn else 0
         for idx, res in enumerate(x):
             res = res.tolist()
             label_res = []
diff --git a/ppcls/metric/metrics.py b/ppcls/metric/metrics.py
index f00ef0b58..b69bc65a3 100644
--- a/ppcls/metric/metrics.py
+++ b/ppcls/metric/metrics.py
@@ -219,12 +219,17 @@ class TprAtFpr(nn.Layer):
 
 
 class MultilabelMeanAccuracy(nn.Layer):
-    def __init__(self, class_num=40):
+    def __init__(self,
+                 start_threshold=0.4,
+                 num_iterations=10,
+                 end_threshold=0.9):
         super().__init__()
+        self.start_threshold = start_threshold
+        self.num_iterations = num_iterations
+        self.end_threshold = end_threshold
         self.gt_all_score_list = []
         self.gt_label_score_list = []
         self.max_acc = 0.
-        self.class_num = class_num
 
     def forward(self, x, label):
         if isinstance(x, dict):
@@ -251,8 +256,10 @@ class MultilabelMeanAccuracy(nn.Layer):
         result = ""
         gt_all_score_list = np.array(self.gt_all_score_list)
         gt_label_score_list = np.array(self.gt_label_score_list)
-        for i in range(10):
-            threshold = 0.4 + i * 0.05
+        for i in range(self.num_iterations):
+            threshold = self.start_threshold + i * (self.end_threshold -
+                                                    self.start_threshold
+                                                    ) / self.num_iterations
             pred_label = (gt_all_score_list > threshold).astype(int)
             TP = np.sum(
                 (gt_label_score_list == 1) * (pred_label == 1)).astype(float)
@@ -262,8 +269,8 @@ class MultilabelMeanAccuracy(nn.Layer):
             if max_acc <= acc:
                 max_acc = acc
                 result = "threshold: {}, mean_acc: {}".format(
-                    threshold, max_acc / self.class_num)
-        self.max_acc = max_acc / self.class_num
+                    threshold, max_acc / len(gt_label_score_list[0]))
+        self.max_acc = max_acc / len(gt_label_score_list[0])
         return result