diff --git a/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x0_5.yaml b/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x0_5.yaml
index b1a017e29cac7b54174d8bdce0f04de123eae5d2..32fb6b939839de845b58e442472bc8e62f6d053d 100644
--- a/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x0_5.yaml
+++ b/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x0_5.yaml
@@ -49,13 +49,11 @@ Optimizer:
   weight_decay: 0.004
   one_dim_param_no_weight_decay: True
   lr:
-    # for 8 cards
     name: Cosine
-    learning_rate: 0.009
+    learning_rate: 0.009  # for total batch size 1024
     eta_min: 0.0009
     warmup_epoch: 16  # 20000 iterations
     warmup_start_lr: 1e-6
-    # by_epoch: True
   clip_norm: 10
 
 # data loader for train and eval
diff --git a/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x1_0.yaml b/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x1_0.yaml
index 3dae2020508e8e1d385ba397f010dd0279168d8a..7e77a6399551c9edbc023fed7dbfc616a3db0aaf 100644
--- a/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x1_0.yaml
+++ b/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x1_0.yaml
@@ -49,13 +49,11 @@ Optimizer:
   weight_decay: 0.013
   one_dim_param_no_weight_decay: True
   lr:
-    # for 8 cards
     name: Cosine
-    learning_rate: 0.0075
+    learning_rate: 0.0075  # for total batch size 1024
     eta_min: 0.00075
     warmup_epoch: 16  # 20000 iterations
     warmup_start_lr: 1e-6
-    # by_epoch: True
   clip_norm: 10
 
 # data loader for train and eval
diff --git a/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x1_5.yaml b/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x1_5.yaml
index 072d1b184e916c8b3776dc5fee9069521ecb0cb9..9f1df77a7d97c400399cbcac7812b4ad69a34156 100644
--- a/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x1_5.yaml
+++ b/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x1_5.yaml
@@ -14,7 +14,6 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
   use_dali: False
-  update_freq: 2  # for 4 gpus
 
 # mixed precision training
 AMP:
@@ -50,13 +49,11 @@ Optimizer:
   weight_decay: 0.029
   one_dim_param_no_weight_decay: True
   lr:
-    # for 8 cards
     name: Cosine
     learning_rate: 0.0035  # for total batch size 1024
     eta_min: 0.00035
     warmup_epoch: 16  # 20000 iterations
     warmup_start_lr: 1e-6
-    # by_epoch: True
   clip_norm: 10
 
 # data loader for train and eval
diff --git a/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x2_0.yaml b/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x2_0.yaml
index 55c008450aa95e10579dc38513f76a3e4ed9862e..851cd64e77a4ca759265124296be762a210dd952 100644
--- a/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x2_0.yaml
+++ b/ppcls/configs/ImageNet/MobileViTV2/MobileViTV2_x2_0.yaml
@@ -49,13 +49,11 @@ Optimizer:
   weight_decay: 0.05
   one_dim_param_no_weight_decay: True
   lr:
-    # for 8 cards
     name: Cosine
-    learning_rate: 0.002
+    learning_rate: 0.002  # for total batch size 1024
     eta_min: 0.0002
     warmup_epoch: 16  # 20000 iterations
     warmup_start_lr: 1e-6
-    # by_epoch: True
   clip_norm: 10
 
 # data loader for train and eval