diff --git a/doc/doc_ch/detection.md b/doc/doc_ch/detection.md
index 8db64664f6ff560450a5ee99d708313c931989fc..e69f7355390f5c16cb1b498389a6552b5d40ca0a 100644
--- a/doc/doc_ch/detection.md
+++ b/doc/doc_ch/detection.md
@@ -96,7 +96,7 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml \
 # 单机多卡训练，通过 --gpus 参数设置使用的GPU ID
 python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \
      -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
- 
+
 # 多机多卡训练，通过 --ips 参数设置使用的机器IP地址，通过 --gpus 参数设置使用的GPU ID
 python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \
      -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
@@ -104,14 +104,14 @@ python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1
 
 上述指令中，通过-c 选择训练使用configs/det/det_db_mv3.yml配置文件。
 有关配置文件的详细解释，请参考[链接](./config.md)。
- 
+
 您也可以通过-o参数在不需要修改yml文件的情况下，改变训练的参数，比如，调整训练的学习率为0.0001
 ```shell
 python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
 ```
- 
-**注意:** 采用多机多卡训练时，需要替换上面命令中的ips值为您机器的地址，机器之间需要能够相互ping通。查看机器ip地址的命令为`ifconfig`。
- 
+
+**注意:** 采用多机多卡训练时，需要替换上面命令中的ips值为您机器的地址，机器之间需要能够相互ping通。另外，训练时需要在多个机器上分别启动命令。查看机器ip地址的命令为`ifconfig`。
+
 如果您想进一步加快训练速度，可以使用[自动混合精度训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_cn.html)， 以单机单卡为例，命令如下：
 ```shell
 python3 tools/train.py -c configs/det/det_mv3_db.yml \
diff --git a/doc/doc_en/detection_en.md b/doc/doc_en/detection_en.md
index 948733e16cebea2ce819367a863948434ece5ae5..586ed30bb841122717e66966337b5c99b9cf3397 100644
--- a/doc/doc_en/detection_en.md
+++ b/doc/doc_en/detection_en.md
@@ -98,14 +98,14 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o   \
 # multi-GPU training
 # Set the GPU ID used by the '--gpus' parameter.
 python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
- 
+
 # multi-Node, multi-GPU training
 # Set the IPs of your nodes used by the '--ips' parameter. Set the GPU ID used by the '--gpus' parameter.
 python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \
      -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
 ```
-**Note:** For multi-Node multi-GPU training, you need to replace the `ips` value in the preceding command with the address of your machine, and the machines must be able to ping each other. The command for viewing the IP address of the machine is `ifconfig`.
- 
+**Note:** For multi-Node multi-GPU training, you need to replace the `ips` value in the preceding command with the address of your machine, and the machines must be able to ping each other. In addition, it requires activating commands separately on multiple machines when we start the training. The command for viewing the IP address of the machine is `ifconfig`.
+
 If you want to further speed up the training, you can use [automatic mixed precision training](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_en.html). for single card training, the command is as follows:
 ```
 python3 tools/train.py -c configs/det/det_mv3_db.yml \