diff --git a/model_zoo/official/cv/ctpn/scripts/run_standalone_train_ascend.sh b/model_zoo/official/cv/ctpn/scripts/run_standalone_train_ascend.sh
index 1649590a04..394ea84531 100644
--- a/model_zoo/official/cv/ctpn/scripts/run_standalone_train_ascend.sh
+++ b/model_zoo/official/cv/ctpn/scripts/run_standalone_train_ascend.sh
@@ -13,9 +13,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-if [ $# -ne 2 ]
+echo "=============================================================================================================="
+echo "Please run the script as: "
+echo "sh run_standalone_train.sh [TASK_TYPE] [PRETRAINED_PATH] [DEVICE_ID]"
+echo "for example: sh run_standalone_train.sh Pretraining /path/vgg16_backbone.ckpt 0"
+echo "when device id is occupied, choose for another one"
+echo "It is better to use absolute path."
+echo "=============================================================================================================="
+if [ $# -ne 3 ]
 then 
-    echo "Usage: sh run_distribute_train_ascend.sh [TASK_TYPE] [PRETRAINED_PATH]"
+    echo "Usage: sh run_standalone_train_ascend.sh [TASK_TYPE] [PRETRAINED_PATH] [DEVICE_ID]"
 exit 1
 fi
 
@@ -38,7 +45,7 @@ fi
 
 ulimit -u unlimited
 export DEVICE_NUM=1
-export DEVICE_ID=0
+export DEVICE_ID=$3
 export RANK_ID=0
 export RANK_SIZE=1
 
diff --git a/model_zoo/official/cv/resnet/train.py b/model_zoo/official/cv/resnet/train.py
index f03a7e2a9b..4298331aaf 100755
--- a/model_zoo/official/cv/resnet/train.py
+++ b/model_zoo/official/cv/resnet/train.py
@@ -218,7 +218,7 @@ if __name__ == '__main__':
     metrics = {"acc"}
     if args_opt.run_distribute:
         metrics = {'acc': DistAccuracy(batch_size=config.batch_size, device_num=args_opt.device_num)}
-    if (args_opt.net not in ("resnet18", "resnet50", "resnet101")) or \
+    if (args_opt.net not in ("resnet18", "resnet50", "resnet101", "se-resnet50")) or \
         args_opt.parameter_server or target == "CPU":
         ## fp32 training
         model = Model(net, loss_fn=loss, optimizer=opt, metrics=metrics, eval_network=dist_eval_network)