diff --git a/mindcv/optim/optim_factory.py b/mindcv/optim/optim_factory.py
index 9d5e54ccc..5d8c14504 100644
--- a/mindcv/optim/optim_factory.py
+++ b/mindcv/optim/optim_factory.py
@@ -15,7 +15,7 @@
 _logger = logging.getLogger(__name__)
 
 
-def init_group_params(params, weight_decay, weight_decay_filter):
+def init_group_params(params, weight_decay, weight_decay_filter, no_weight_decay):
     if weight_decay_filter == "disable":
         return [
             {"params": params, "weight_decay": weight_decay},
@@ -24,11 +24,12 @@ def init_group_params(params, weight_decay, weight_decay_filter):
 
     decay_params = []
     no_decay_params = []
+    no_weight_decay = set(no_weight_decay)
     for param in params:
-        if "beta" not in param.name and "gamma" not in param.name and "bias" not in param.name:
-            decay_params.append(param)
-        else:
+        if "beta" in param.name or "gamma" in param.name or "bias" in param.name or param.name in no_weight_decay:
             no_decay_params.append(param)
+        else:
+            decay_params.append(param)
     return [
         {"params": decay_params, "weight_decay": weight_decay},
         {"params": no_decay_params, "weight_decay": 0.0},
@@ -37,7 +38,7 @@ def init_group_params(params, weight_decay, weight_decay_filter):
 
 
 def create_optimizer(
-    params,
+    model_or_params,
     opt: str = "adam",
     lr: Optional[float] = 1e-3,
     weight_decay: float = 0,
@@ -78,7 +79,16 @@ def create_optimizer(
         Optimizer object
     """
 
-    opt = opt.lower()
+    no_weight_decay = {}
+    if isinstance(model_or_params, nn.Cell):
+        # a model was passed in, extract parameters and add weight decays to appropriate layers
+        if hasattr(model_or_params, "no_weight_decay"):
+            no_weight_decay = model_or_params.no_weight_decay()
+        params = model_or_params.trainable_params()
+
+    else:
+        params = model_or_params
+
     if weight_decay_filter == "auto":
         _logger.warning(
             "You are using AUTO weight decay filter, which means the weight decay filter isn't explicitly pass in "
@@ -86,13 +96,14 @@ def create_optimizer(
             "NOTE: mindspore.nn.Optimizer will filter Norm parmas from weight decay. "
         )
     elif weight_decay_filter == "disable" or "norm_and_bias":
-        params = init_group_params(params, weight_decay, weight_decay_filter)
+        params = init_group_params(params, weight_decay, weight_decay_filter, no_weight_decay)
         weight_decay = 0.0
     else:
         raise ValueError(
             f"weight decay filter only support ['disable', 'auto', 'norm_and_bias'], but got{weight_decay_filter}."
         )
 
+    opt = opt.lower()
     opt_args = dict(**kwargs)
     # if lr is not None:
     #    opt_args.setdefault('lr', lr)