diff --git a/model_zoo/official/cv/MCNN/README.md b/model_zoo/official/cv/MCNN/README.md
index 0d7514aa5d2..b738cd467e2 100644
--- a/model_zoo/official/cv/MCNN/README.md
+++ b/model_zoo/official/cv/MCNN/README.md
@@ -53,6 +53,8 @@ Dataset used: [ShanghaitechA](<https://www.dropbox.com/s/fipgjqxl7uj8hd5/Shangha
                     ├─ground_truth_csv
 ```
 
+- note: formatted_trainval dir is generated by file [create_training_set_shtech](https://github.com/svishwa/crowdcount-mcnn/blob/master/data_preparation/create_training_set_shtech.m)
+
 # [Environment Requirements](#contents)
 
 - Hardware (Ascend)
@@ -68,10 +70,10 @@ Dataset used: [ShanghaitechA](<https://www.dropbox.com/s/fipgjqxl7uj8hd5/Shangha
 After installing MindSpore via the official website, you can start training and evaluation as follows:
 
 ```bash
-# enter script dir, train AlexNet
-sh run_standalone_train_ascend.sh [DATA_PATH] [CKPT_SAVE_PATH]
-# enter script dir, evaluate AlexNet
-sh run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME]
+# enter script dir, train MCNN example
+sh run_standalone_train_ascend.sh 0 ./formatted_trainval/shanghaitech_part_A_patches_9/train ./formatted_trainval/shanghaitech_part_A_patches_9/train_den ./formatted_trainval/shanghaitech_part_A_patches_9/val ./formatted_trainval/shanghaitech_part_A_patches_9/val_den ./ckpt
+# enter script dir, evaluate MCNN example
+sh run_standalone_eval_ascend.sh 0 ./original/shanghaitech/part_A_final/test_data/images ./original/shanghaitech/part_A_final/test_data/ground_truth_csv ./train/ckpt/best.ckpt
 ```
 
 # [Script Description](#contents)
@@ -123,11 +125,10 @@ Major parameters in train.py and config.py as follows:
 - running on Ascend
 
   ```bash
-  # python train.py
-  # or enter script dir, and run the distribute script
-  sh run_distribute_train.sh
-  # or enter script dir, and run the standalone script
-  sh run_standalone_train.sh
+  # enter script dir, and run the distribute script
+  sh run_distribute_train.sh ./hccl_table.json ./formatted_trainval/shanghaitech_part_A_patches_9/train ./formatted_trainval/shanghaitech_part_A_patches_9/train_den ./formatted_trainval/shanghaitech_part_A_patches_9/val ./formatted_trainval/shanghaitech_part_A_patches_9/val_den ./ckpt
+  # enter script dir, and run the standalone script
+  sh run_standalone_train_ascend.sh 0 ./formatted_trainval/shanghaitech_part_A_patches_9/train ./formatted_trainval/shanghaitech_part_A_patches_9/train_den ./formatted_trainval/shanghaitech_part_A_patches_9/val ./formatted_trainval/shanghaitech_part_A_patches_9/val_den ./ckpt
   ```
 
   After training, the loss value will be achieved as follows:
@@ -154,9 +155,8 @@ Before running the command below, please check the checkpoint path used for eval
 - running on Ascend
 
   ```bash
-  # python eval.py
-  # or enter script dir, and run the script
-  sh run_eval.sh
+  # enter script dir, and run the script
+  sh run_standalone_eval_ascend.sh 0 ./original/shanghaitech/part_A_final/test_data/images ./original/shanghaitech/part_A_final/test_data/ground_truth_csv ./train/ckpt/best.ckpt
   ```
 
   You can view the results through the file "eval_log". The accuracy of the test dataset will be as follows:
diff --git a/model_zoo/official/cv/MCNN/eval.py b/model_zoo/official/cv/MCNN/eval.py
index f320df55bdd..476af8e9421 100644
--- a/model_zoo/official/cv/MCNN/eval.py
+++ b/model_zoo/official/cv/MCNN/eval.py
@@ -36,17 +36,18 @@ ckptpath = "obs://lhb1234/MCNN/ckpt"
 
 parser = argparse.ArgumentParser(description='MindSpore MCNN Example')
 parser.add_argument('--run_offline', type=ast.literal_eval,
-                    default=False, help='run in offline is False or True')
-parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
+                    default=True, help='run in offline is False or True')
+parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend'],
                     help='device where the code will be implemented (default: Ascend)')
+parser.add_argument('--device_id', type=int, default=0, help='device id of Ascend. (Default: 0)')
 parser.add_argument('--ckpt_path', type=str, default="/cache/train_output", help='Location of ckpt.')
 parser.add_argument('--data_url', default=None, help='Location of data.')
 parser.add_argument('--train_url', default=None, help='Location of training outputs.')
 parser.add_argument('--val_path', required=True,
-                    default='obs://lhb1234/mcnn-pure/data/formatted_trainval/shanghaitech_part_A_patches_9/val',
+                    default='/data/mcnn/original/shanghaitech/part_A_final/test_data/images',
                     help='Location of data.')
 parser.add_argument('--val_gt_path', required=True,
-                    default='obs://lhb1234/mcnn-pure/data/formatted_trainval/shanghaitech_part_A_patches_9/val_den',
+                    default='/data/mcnn/original/shanghaitech/part_A_final/test_data/ground_truth_csv',
                     help='Location of data.')
 args = parser.parse_args()
 set_seed(64678)
@@ -54,14 +55,13 @@ set_seed(64678)
 
 if __name__ == "__main__":
     device_num = int(os.getenv("RANK_SIZE"))
-    device_id = int(os.getenv("DEVICE_ID"))
 
     device_target = args.device_target
     context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
     context.set_context(save_graphs=False)
 
     if device_target == "Ascend":
-        context.set_context(device_id=device_id)
+        context.set_context(device_id=args.device_id)
     else:
         raise ValueError("Unsupported platform.")
 
@@ -81,7 +81,7 @@ if __name__ == "__main__":
     ds_val = ds_val.batch(1)
     network = MCNN()
 
-    model_name = os.path.join(local_ckpt_url, 'best.ckpt')
+    model_name = local_ckpt_url
     print(model_name)
     mae = 0.0
     mse = 0.0
diff --git a/model_zoo/official/cv/MCNN/scripts/run_distribute_train.sh b/model_zoo/official/cv/MCNN/scripts/run_distribute_train.sh
index e402cc20d25..42af2eb97f3 100644
--- a/model_zoo/official/cv/MCNN/scripts/run_distribute_train.sh
+++ b/model_zoo/official/cv/MCNN/scripts/run_distribute_train.sh
@@ -15,10 +15,9 @@
 # ============================================================================
 
 ulimit -u unlimited
-export DEVICE_NUM=8
 export RANK_SIZE=8
-export RANK_TABLE_FILE=/home/wks/hccl_8p_01234567_127.0.0.1.json
-export RUN_OFFLINE=$1
+export DEVICE_NUM=8
+export RANK_TABLE_FILE=$1
 export TRAIN_PATH=$2
 export TRAIN_GT_PATH=$3
 export VAL_PATH=$4
@@ -40,7 +39,7 @@ do
     cd ./train_parallel$i || exit
     echo "start training for rank $RANK_ID, device $DEVICE_ID"
     env > env.log
-    python train.py --run_offline=$RUN_OFFLINE --train_path=$TRAIN_PATH --train_gt_path=$TRAIN_GT_PATH \
-                    --val_path=$VAL_PATH --val_gt_path=$VAL_GT_PATH --ckpt_path=$CKPT_PATH &> log &
+    python -u train.py --device_id=$DEVICE_ID --train_path=$TRAIN_PATH --train_gt_path=$TRAIN_GT_PATH \
+                       --val_path=$VAL_PATH --val_gt_path=$VAL_GT_PATH --ckpt_path=$CKPT_PATH &> log &
     cd ..
 done
diff --git a/model_zoo/official/cv/MCNN/scripts/run_eval.sh b/model_zoo/official/cv/MCNN/scripts/run_eval.sh
index a7fec47c8b4..5a138dc548f 100644
--- a/model_zoo/official/cv/MCNN/scripts/run_eval.sh
+++ b/model_zoo/official/cv/MCNN/scripts/run_eval.sh
@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2021 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -16,14 +16,13 @@
 
 if [ $# != 4 ]
 then
-    echo "Usage: sh run_eval.sh [se-resnet50] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH] [0|1|2|3|4|5|6|7] "
+    echo "Usage: sh run_eval.sh [DEVICE_ID] [VAL_PATH] [VAL_GT_PATH] [CKPT_PATH] "
 exit 1
 fi
 
 ulimit -u unlimited
-export DEVICE_ID=0
 export RANK_SIZE=1
-export RUN_OFFLINE=$1
+export DEVICE_ID=$1
 export VAL_PATH=$2
 export VAL_GT_PATH=$3
 export CKPT_PATH=$4
@@ -40,6 +39,6 @@ cp -r ../src ./eval
 cd ./eval || exit
 env > env.log
 echo "start evaluation for device $DEVICE_ID"
-python eval.py --run_offline=$RUN_OFFLINE --val_path=$VAL_PATH \
-               --val_gt_path=$VAL_GT_PATH --ckpt_path=$CKPT_PATH &> log &
+python -u eval.py --device_id=$DEVICE_ID --val_path=$VAL_PATH \
+                  --val_gt_path=$VAL_GT_PATH --ckpt_path=$CKPT_PATH &> log &
 cd ..
diff --git a/model_zoo/official/cv/MCNN/scripts/run_standalone_train.sh b/model_zoo/official/cv/MCNN/scripts/run_standalone_train.sh
index f5fdcf08f1e..c0733eefa95 100644
--- a/model_zoo/official/cv/MCNN/scripts/run_standalone_train.sh
+++ b/model_zoo/official/cv/MCNN/scripts/run_standalone_train.sh
@@ -14,9 +14,8 @@
 # limitations under the License.
 # ============================================================================
 ulimit -u unlimited
-export DEVICE_ID=1
 export RANK_SIZE=1
-export RUN_OFFLINE=$1
+export DEVICE_ID=$1
 export TRAIN_PATH=$2
 export TRAIN_GT_PATH=$3
 export VAL_PATH=$4
@@ -37,8 +36,8 @@ env > env.
 
 if [ $# == 6 ]
 then
-    python train.py --run_offline=$RUN_OFFLINE --train_path=$TRAIN_PATH --train_gt_path=$TRAIN_GT_PATH \
-                    --val_path=$VAL_PATH --val_gt_path=$VAL_GT_PATH --ckpt_path=$CKPT_PATH &> log &
+    python -u train.py --device_id=$DEVICE_ID --train_path=$TRAIN_PATH --train_gt_path=$TRAIN_GT_PATH \
+                       --val_path=$VAL_PATH --val_gt_path=$VAL_GT_PATH --ckpt_path=$CKPT_PATH &> log &
 fi
 cd ..
 
diff --git a/model_zoo/official/cv/MCNN/train.py b/model_zoo/official/cv/MCNN/train.py
index d159df12021..f4dfd969826 100644
--- a/model_zoo/official/cv/MCNN/train.py
+++ b/model_zoo/official/cv/MCNN/train.py
@@ -36,9 +36,10 @@ from src.Mcnn_Callback import mcnn_callback
 
 parser = argparse.ArgumentParser(description='MindSpore MCNN Example')
 parser.add_argument('--run_offline', type=ast.literal_eval,
-                    default=False, help='run in offline is False or True')
-parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'],
+                    default=True, help='run in offline is False or True')
+parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend'],
                     help='device where the code will be implemented (default: Ascend)')
+parser.add_argument('--device_id', type=int, default=0, help='device id of Ascend. (Default: 0)')
 parser.add_argument('--ckpt_path', type=str, default="/cache/train_output", help='Location of ckpt.')
 
 parser.add_argument('--data_url', default=None, help='Location of data.')
@@ -47,10 +48,10 @@ parser.add_argument('--train_url', default=None, help='Location of training outp
 parser.add_argument('--train_path', required=True, default=None, help='Location of data.')
 parser.add_argument('--train_gt_path', required=True, default=None, help='Location of data.')
 parser.add_argument('--val_path', required=True,
-                    default='/lhb1234/mcnn/data/formatted_trainval/shanghaitech_part_A_patches_9/val',
+                    default='/data/formatted_trainval/shanghaitech_part_A_patches_9/val',
                     help='Location of data.')
 parser.add_argument('--val_gt_path', required=True,
-                    default='/lhb1234/mcnn/data/formatted_trainval/shanghaitech_part_A_patches_9/val_den',
+                    default='/data/formatted_trainval/shanghaitech_part_A_patches_9/val_den',
                     help='Location of data.')
 args = parser.parse_args()
 rand_seed = 64678
@@ -58,26 +59,20 @@ np.random.seed(rand_seed)
 
 if __name__ == "__main__":
     device_num = int(os.getenv("RANK_SIZE"))
-    device_id = int(os.getenv("DEVICE_ID"))
 
-    print("device_id:", device_id)
     print("device_num:", device_num)
     device_target = args.device_target
     context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
     context.set_context(save_graphs=False)
 
     if device_target == "Ascend":
-        context.set_context(device_id=device_id)
+        context.set_context(device_id=args.device_id)
 
         if device_num > 1:
             context.reset_auto_parallel_context()
             context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                               gradients_mean=True)
             init()
-            # local_data1_url=os.path.join(local_data1_url,str(device_id)) # 可以删除
-            # local_data2_url=os.path.join(local_data2_url,str(device_id))
-            # local_data3_url=os.path.join(local_data3_url,str(device_id))
-            # local_data4_url=os.path.join(local_data4_url,str(device_id))
     else:
         raise ValueError("Unsupported platform.")
     if args.run_offline: