diff --git a/model_zoo/official/cv/xception/README.md b/model_zoo/official/cv/xception/README.md
index 0ab75ae016..731d54fea0 100644
--- a/model_zoo/official/cv/xception/README.md
+++ b/model_zoo/official/cv/xception/README.md
@@ -129,7 +129,6 @@ sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
 ```
 
 > Notes: RANK_TABLE_FILE can refer to [Link](https://www.mindspore.cn/tutorial/training/en/master/advanced_use/distributed_training_ascend.html), and the device_ip can be got as [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
-> This is processor cores binding operation regarding the `device_num` and total processor numbers. If you are not expect to do it, remove the operations `taskset` in `scripts/run_distribute_train.sh`
 
 ### Launch
 
@@ -155,7 +154,7 @@ Training result will be stored in the example path. Checkpoints will be stored a
 epoch: 1 step: 1251, loss is 4.8427444
 epoch time: 701242.350 ms, per step time: 560.545 ms
 epoch: 2 step: 1251, loss is 4.0637593
-epoch time: 598591.422 ms, per step time: 478.490ms
+epoch time: 598591.422 ms, per step time: 478.490 ms
 ```
 
 ## [Eval process](#contents)
@@ -219,11 +218,11 @@ result: {'Loss': 1.7797744848789312, 'Top_1_Acc': 0.7985777243589743, 'Top_5_Acc
 | ------------------- | --------------------------- |
 | Model Version       | Xception                    |
 | Resource            | HUAWEI CLOUD Modelarts      |
-| Uploaded Date       | 11/15/2020                  |
-| MindSpore Version   | 1.0.0                       |
+| Uploaded Date       | 12/10/2020                  |
+| MindSpore Version   | 1.1.0                       |
 | Dataset             | 50k images                  |
 | Batch_size          | 128                         |
-| Accuracy            | Top1[79.9%] Top5[94.9%]     |
+| Accuracy            | Top1[79.8%] Top5[94.8%]     |
 | Total time          | 3mins                       |
 
 # [Description of Random Situation](#contents)