diff --git a/model_zoo/official/cv/ssd/README.md b/model_zoo/official/cv/ssd/README.md
index ded107e499..73ebc5f7f2 100644
--- a/model_zoo/official/cv/ssd/README.md
+++ b/model_zoo/official/cv/ssd/README.md
@@ -66,7 +66,7 @@ To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will ge
     sh run_distribute_train.sh 8 500 0.2 coco /data/hccl.json
     ```
 
-    The input parameters are device numbers, epoch size, learning rate, dataset mode and [hccl json configuration file](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html). **It is better to use absolute path.** 
+    The input parameters are device numbers, epoch size, learning rate, dataset mode and [hccl json configuration file](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). **It is better to use absolute path.** 
 
 You will get the loss value of each step as following:
 
diff --git a/model_zoo/official/cv/yolov3_darknet53_quant/README.md b/model_zoo/official/cv/yolov3_darknet53_quant/README.md
index 5cd863f876..8a2b93c081 100644
--- a/model_zoo/official/cv/yolov3_darknet53_quant/README.md
+++ b/model_zoo/official/cv/yolov3_darknet53_quant/README.md
@@ -71,7 +71,7 @@ sh run_distribute_train.sh dataset/coco2014 yolov3_darknet_noquant_ckpt/0-320_10
 sh run_standalone_train.sh dataset/coco2014 yolov3_darknet_noquant_ckpt/0-320_102400.ckpt
 ```
 
-> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html).
+> About rank_table.json, You can generate it by using the [hccl json configuration file](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
 
 #### Result
 
@@ -108,14 +108,14 @@ epoch[134], iter[86500], loss:34.303755, 145.18 imgs/sec, lr:1.6245529650404933e
 
 ```
 # infer
-sh run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH]
+sh run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH] [DEVICE_ID]
 ```
 
 #### Launch
 
 ```bash
 # infer with checkpoint
-sh run_eval.sh dataset/coco2014/ checkpoint/0-135.ckpt
+sh run_eval.sh dataset/coco2014/ checkpoint/0-131.ckpt 0
 
 ```
 
diff --git a/model_zoo/official/cv/yolov3_resnet18/README.md b/model_zoo/official/cv/yolov3_resnet18/README.md
index 5d5b36de08..17582c5960 100644
--- a/model_zoo/official/cv/yolov3_resnet18/README.md
+++ b/model_zoo/official/cv/yolov3_resnet18/README.md
@@ -51,7 +51,7 @@ To train the model, run `train.py` with the dataset `image_dir`, `anno_path` and
     sh run_distribute_train.sh 8 150 /data/Mindrecord_train /data /data/train.txt /data/hccl.json
     ```
 
-    The input variables are device numbers, epoch size, mindrecord directory path, dataset directory path, train TXT file path and [hccl json configuration file](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html). **It is better to use absolute path.**
+    The input variables are device numbers, epoch size, mindrecord directory path, dataset directory path, train TXT file path and [hccl json configuration file](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools). **It is better to use absolute path.**
 
 You will get the loss value and time of each step as following: