diff --git a/detection/mmdet_custom/models/backbones/intern_image.py b/detection/mmdet_custom/models/backbones/intern_image.py
index dc87c8d9..a3737bcb 100644
--- a/detection/mmdet_custom/models/backbones/intern_image.py
+++ b/detection/mmdet_custom/models/backbones/intern_image.py
@@ -15,7 +15,7 @@
 from mmdet.models.builder import BACKBONES
 import torch.nn.functional as F
 
-from ops_dcnv3 import modules as dcnv3
+from ops_dcnv3 import modules as opsm
 
 
 class to_channels_first(nn.Module):
@@ -365,8 +365,7 @@ def __init__(self,
                  with_cp=False,
                  dw_kernel_size=None, # for InternImage-H/G
                  res_post_norm=False, # for InternImage-H/G
-                 center_feature_scale=False,
-                 use_dcn_v4_op=False): # for InternImage-H/G
+                 center_feature_scale=False): # for InternImage-H/G
         super().__init__()
         self.channels = channels
         self.groups = groups
@@ -386,8 +385,7 @@ def __init__(self,
             act_layer=act_layer,
             norm_layer=norm_layer,
             dw_kernel_size=dw_kernel_size, # for InternImage-H/G
-            center_feature_scale=center_feature_scale,
-            use_dcn_v4_op=use_dcn_v4_op) # for InternImage-H/G
+            center_feature_scale=center_feature_scale) # for InternImage-H/G
         self.drop_path = DropPath(drop_path) if drop_path > 0. \
             else nn.Identity()
         self.norm2 = build_norm_layer(channels, 'LN')
@@ -471,8 +469,7 @@ def __init__(self,
                  dw_kernel_size=None, # for InternImage-H/G
                  post_norm_block_ids=None, # for InternImage-H/G
                  res_post_norm=False, # for InternImage-H/G
-                 center_feature_scale=False, # for InternImage-H/G
-                 use_dcn_v4_op=False):
+                 center_feature_scale=False): # for InternImage-H/G
         super().__init__()
         self.channels = channels
         self.depth = depth
@@ -496,8 +493,7 @@ def __init__(self,
                 with_cp=with_cp,
                 dw_kernel_size=dw_kernel_size, # for InternImage-H/G
                 res_post_norm=res_post_norm, # for InternImage-H/G
-                center_feature_scale=center_feature_scale, # for InternImage-H/G
-                use_dcn_v4_op=use_dcn_v4_op
+                center_feature_scale=center_feature_scale # for InternImage-H/G
             ) for i in range(depth)
         ])
         if not self.post_norm or center_feature_scale:
@@ -573,11 +569,12 @@ def __init__(self,
                  level2_post_norm_block_ids=None,  # for InternImage-H/G
                  res_post_norm=False,  # for InternImage-H/G
                  center_feature_scale=False,  # for InternImage-H/G
-                 use_dcn_v4_op=False,
                  out_indices=(0, 1, 2, 3),
                  init_cfg=None,
+                 frozen_stages=-1, # you can freez level 1 -> num_levels(len(depths))
                  **kwargs):
         super().__init__()
+        self.frozen_stages = frozen_stages
         self.core_op = core_op
         self.num_levels = len(depths)
         self.depths = depths
@@ -596,7 +593,6 @@ def __init__(self,
         logger.info(f"level2_post_norm: {level2_post_norm}")
         logger.info(f"level2_post_norm_block_ids: {level2_post_norm_block_ids}")
         logger.info(f"res_post_norm: {res_post_norm}")
-        logger.info(f"use_dcn_v4_op: {use_dcn_v4_op}")
 
         in_chans = 3
         self.patch_embed = StemLayer(in_chans=in_chans,
@@ -617,7 +613,7 @@ def __init__(self,
             post_norm_block_ids = level2_post_norm_block_ids if level2_post_norm and (
                 i == 2) else None # for InternImage-H/G
             level = InternImageBlock(
-                core_op=getattr(dcnv3, core_op),
+                core_op=getattr(opsm, core_op),
                 channels=int(channels * 2**i),
                 depth=depths[i],
                 groups=groups[i],
@@ -634,14 +630,27 @@ def __init__(self,
                 dw_kernel_size=dw_kernel_size,  # for InternImage-H/G
                 post_norm_block_ids=post_norm_block_ids, # for InternImage-H/G
                 res_post_norm=res_post_norm, # for InternImage-H/G
-                center_feature_scale=center_feature_scale, # for InternImage-H/G
-                use_dcn_v4_op=use_dcn_v4_op,
+                center_feature_scale=center_feature_scale # for InternImage-H/G
             )
             self.levels.append(level)
 
         self.num_layers = len(depths)
         self.apply(self._init_weights)
         self.apply(self._init_deform_weights)
+        self._freeze_stages()
+
+    def train(self, mode=True):
+        """Convert the model into training mode while keep normalization layer
+        freezed."""
+        super(InternImage, self).train(mode)
+        self._freeze_stages()
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            for level in self.levels[:self.frozen_stages]:
+                level.eval()
+                for param in level.parameters():
+                    param.requires_grad = False
 
     def init_weights(self):
         logger = get_root_logger()
@@ -694,7 +703,7 @@ def _init_weights(self, m):
             nn.init.constant_(m.weight, 1.0)
 
     def _init_deform_weights(self, m):
-        if isinstance(m, getattr(dcnv3, self.core_op)):
+        if isinstance(m, getattr(opsm, self.core_op)):
             m._reset_parameters()
 
     def forward(self, x):