diff --git a/ppcls/configs/Pedestrian/strong_baseline_m1_centerloss.yaml b/ppcls/configs/Pedestrian/strong_baseline_m1_centerloss.yaml
index c4ea70b26672025f75aa4ce6dc1b6760e5fe5fef..042cdffdec49852200ecdfe4dd9d2e52434eb873 100644
--- a/ppcls/configs/Pedestrian/strong_baseline_m1_centerloss.yaml
+++ b/ppcls/configs/Pedestrian/strong_baseline_m1_centerloss.yaml
@@ -25,7 +25,7 @@ Arch:
   infer_add_softmax: False
   Backbone:
     name: "ResNet50_last_stage_stride1"
-    pretrained: "./dataset/resnet50-19c8e357_for_strong_baseline"
+    pretrained: True
     stem_act: null
   BackboneStopLayer:
     name: "flatten"
@@ -73,7 +73,7 @@ Loss:
 
 Optimizer:
   - Adam:
-      scope: model
+      scope: RecModel
       lr:
         name: Piecewise
         decay_epochs: [30, 60]
diff --git a/ppcls/optimizer/__init__.py b/ppcls/optimizer/__init__.py
index d27f1100eef871db48b8da9ab86eba6af8aecee8..c354f6cb9c7e38bca0fd5df6e0cf7846a2ef8794 100644
--- a/ppcls/optimizer/__init__.py
+++ b/ppcls/optimizer/__init__.py
@@ -71,7 +71,7 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None):
         optim_cfg = optim_item[optim_name]  # get optim_cfg
 
         lr = build_lr_scheduler(optim_cfg.pop('lr'), epochs, step_each_epoch)
-        logger.debug("build lr ({}) for scope ({}) success..".format(
+        logger.info("build lr ({}) for scope ({}) success..".format(
             lr, optim_scope))
         # step2 build regularization
         if 'regularizer' in optim_cfg and optim_cfg['regularizer'] is not None:
@@ -83,8 +83,8 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None):
             reg_name = reg_config.pop('name') + 'Decay'
             reg = getattr(paddle.regularizer, reg_name)(**reg_config)
             optim_cfg["weight_decay"] = reg
-            logger.debug("build regularizer ({}) for scope ({}) success..".
-                         format(reg, optim_scope))
+            logger.info("build regularizer ({}) for scope ({}) success..".
+                        format(reg, optim_scope))
         # step3 build optimizer
         if 'clip_norm' in optim_cfg:
             clip_norm = optim_cfg.pop('clip_norm')
@@ -115,7 +115,9 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None):
                             optim_model.append(m)
                 else:
                     # opmizer for module in model, such as backbone, neck, head...
-                    if hasattr(model_list[i], optim_scope):
+                    if optim_scope == model_list[i].__class__.__name__:
+                        optim_model.append(model_list[i])
+                    elif hasattr(model_list[i], optim_scope):
                         optim_model.append(getattr(model_list[i], optim_scope))
 
         assert len(optim_model) == 1, \
@@ -123,7 +125,7 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None):
         optim = getattr(optimizer, optim_name)(
             learning_rate=lr, grad_clip=grad_clip,
             **optim_cfg)(model_list=optim_model)
-        logger.debug("build optimizer ({}) for scope ({}) success..".format(
+        logger.info("build optimizer ({}) for scope ({}) success..".format(
             optim, optim_scope))
         optim_list.append(optim)
         lr_list.append(lr)