diff --git a/ppcls/arch/gears/bnneck.py b/ppcls/arch/gears/bnneck.py
index 9cd051ab6292d34da7a2b2d3c3107cb514b980ec..2a63ebe6e5df38bba66bc49d5e6687ec94752d1a 100644
--- a/ppcls/arch/gears/bnneck.py
+++ b/ppcls/arch/gears/bnneck.py
@@ -8,8 +8,8 @@ class BNNeck(paddle.nn.Layer):
 
         self.bn = paddle.nn.BatchNorm1D(
             self.num_filters)
-        if not trainable:
-            self.bn.bias.trainable = False
+        # if not trainable:
+        #     self.bn.bias.trainable = False
 
     def forward(self, input, label=None):
         out = self.bn(input)
diff --git a/ppcls/arch/gears/fc.py b/ppcls/arch/gears/fc.py
index b32474195e1ada4cd0a17b493f68f65a242d82cd..9ad0ed8ef664774378a49f361a98d1ae73977e00 100644
--- a/ppcls/arch/gears/fc.py
+++ b/ppcls/arch/gears/fc.py
@@ -21,14 +21,14 @@ import paddle.nn as nn
 
 
 class FC(nn.Layer):
-    def __init__(self, embedding_size, class_num):
+    def __init__(self, embedding_size, class_num, bias_attr=None):
         super(FC, self).__init__()
         self.embedding_size = embedding_size
         self.class_num = class_num
         weight_attr = paddle.ParamAttr(
             initializer=paddle.nn.initializer.XavierNormal())
         self.fc = paddle.nn.Linear(
-            self.embedding_size, self.class_num, weight_attr=weight_attr)
+            self.embedding_size, self.class_num, weight_attr=weight_attr, bias_attr=bias_attr)
 
     def forward(self, input, label=None):
         out = self.fc(input)
diff --git a/ppcls/configs/Pedestrian/strong_baseline_m1.yaml b/ppcls/configs/Pedestrian/strong_baseline_m1.yaml
index 7edb9349b6760d67dece7446f46aa0aff9694526..6ec16b0ed0bbf2ea33560ad08d495f3ba0c5326e 100644
--- a/ppcls/configs/Pedestrian/strong_baseline_m1.yaml
+++ b/ppcls/configs/Pedestrian/strong_baseline_m1.yaml
@@ -26,14 +26,14 @@ Arch:
     stem_act: null
   BackboneStopLayer:
     name: "flatten"
-  #Neck:
-  #  name: BNNeck
-  #  num_filters: 2048
-  #  trainable: false
+  Neck:
+    name: BNNeck
+    num_filters: 2048
   Head:
     name: "FC"
     embedding_size: 2048
     class_num: 751
+    bias_attr: false
 
 # loss function config for traing/eval process
 Loss:
diff --git a/ppcls/engine/evaluation/retrieval.py b/ppcls/engine/evaluation/retrieval.py
index 8471a42c7421648b75a56e426bf3e1ab9c14a5fd..45b6c41eb7546a81c4c1449fae3dcecaf56b33af 100644
--- a/ppcls/engine/evaluation/retrieval.py
+++ b/ppcls/engine/evaluation/retrieval.py
@@ -125,7 +125,7 @@ def cal_feature(engine, name='gallery'):
         out = engine.model(batch[0], batch[1])
         if "Student" in out:
             out = out["Student"]
-        batch_feas = out["features"]
+        batch_feas = out["backbone"]
 
         # do norm
         if engine.config["Global"].get("feature_normalize", True):
diff --git a/ppcls/loss/triplet.py b/ppcls/loss/triplet.py
index d1c7eec9e6031aa7e51a1a3575094e7d1a4f90df..5df48b0d373e077cbb714a0d00a868e30b517fd7 100644
--- a/ppcls/loss/triplet.py
+++ b/ppcls/loss/triplet.py
@@ -24,7 +24,7 @@ class TripletLossV2(nn.Layer):
             inputs: feature matrix with shape (batch_size, feat_dim)
             target: ground truth labels with shape (num_classes)
         """
-        inputs = input["features"]
+        inputs = input["backbone"]
 
         if self.normalize_feature:
             inputs = 1. * inputs / (paddle.expand_as(