diff --git a/scripts/aux_swa.py b/scripts/make_swa_model.py
similarity index 50%
rename from scripts/aux_swa.py
rename to scripts/make_swa_model.py
index 6c10a164..d017b423 100644
--- a/scripts/aux_swa.py
+++ b/scripts/make_swa_model.py
@@ -1,5 +1,5 @@
 
-# Usage: python3 script/aux_swa.py [FINAL_EPOCH] [EPOCHS] [EPOCH_STEP]
+# Usage: python3 script/make_swa_model.py [FINAL_EPOCH] [EPOCHS] [EPOCH_STEP]
 
 import os
 import sys
@@ -44,6 +44,28 @@ def _avg_fn(averaged_model_parameter, model_parameter, num_averaged):
     model.load_state_dict(torch.load(os.path.join(model_dir, model_id)), strict=True)
     swa_model.update_parameters(model)
 
+# Update BN
+# get averaged running stats from SWA model
+averaged_running_stats = {}
+for model_id in model_ids:
+    model.load_state_dict(torch.load(os.path.join(model_dir, model_id)), strict=True)
+    for name, module in dict(model.named_modules()).items():
+        if not issubclass(module.__class__, torch.nn.modules.batchnorm._BatchNorm):
+            continue
+        if name not in averaged_running_stats:
+            averaged_running_stats[name] = {
+                'running_mean': torch.zeros_like(module.running_mean),
+                'running_var': torch.zeros_like(module.running_var),}
+        averaged_running_stats[name]['running_mean'] += module.running_mean / len(model_ids)
+        averaged_running_stats[name]['running_var'] += module.running_var / len(model_ids)
+
+# set averaged running stats into SWA model
+for name, module in dict(swa_model.module.named_modules()).items():
+    if issubclass(module.__class__, torch.nn.modules.batchnorm._BatchNorm):
+        module.running_mean.copy_(averaged_running_stats[name]['running_mean'])
+        module.running_var.copy_(averaged_running_stats[name]['running_var'])
+
+# Save SWA model
 torch.save(swa_model.module.state_dict(), saved_model_path)
 
 print('Saved %s' % saved_model_path)