diff --git a/docs/en/application/mainbody_detection_en.md b/docs/en/application/mainbody_detection_en.md
index 46373c379894ce526585f335b2379e3cb7e262b2..c4fbed6db98451ca470bc30c36794b44331fb6eb 100644
--- a/docs/en/application/mainbody_detection_en.md
+++ b/docs/en/application/mainbody_detection_en.md
@@ -64,9 +64,7 @@ cd PaddleDetection
 pip install -r requirements.txt
 ```
 
-For more installation tutorials, please refer to [Installation tutorial]()
-
-更多安装教程，请参考: [安装文档](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/INSTALL.md)
+For more installation tutorials, please refer to [Installation tutorial](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/INSTALL.md)
 
 ### 3.2 Prepare for the dataset
 
@@ -126,6 +124,16 @@ python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppy
 
 --eval：eval during training
 
+* (**Recommend**) Model finetune
+If you want to finetune the model on your own dataset, you can run the following command to train the model.
+
+```bash
+export CUDA_VISIBLE_DEVICES=0
+# assign pretrain_weights, load the general mainbody-detection pretrained model
+python tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml -o pretrain_weights=https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/ppyolov2_r50vd_dcn_mainbody_v1.0_pretrained.pdparams
+```
+
+
 * Resume training: you can use `-r` to load checkpoints and resume training.
 
 ```bash
diff --git a/docs/zh_CN/application/mainbody_detection.md b/docs/zh_CN/application/mainbody_detection.md
index 897e48f815ca2d8cb9eb007019417a66fb33f98f..e3cba4b52cbba7e1ce352fc2a18f9fd8e38e8a86 100644
--- a/docs/zh_CN/application/mainbody_detection.md
+++ b/docs/zh_CN/application/mainbody_detection.md
@@ -117,7 +117,18 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
 python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval
 ```
 
---eval：表示边训练边验证
+--eval：表示边训练边验证。
+
+
+* (**推荐**)模型微调
+如果希望加载PaddleClas中已经训练好的主体检测模型，在自己的数据集上进行模型微调，可以使用下面的命令进行训练。
+
+```bash
+export CUDA_VISIBLE_DEVICES=0
+# 指定pretrain_weights参数，加载通用的主体检测预训练模型
+python tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml -o pretrain_weights=https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/ppyolov2_r50vd_dcn_mainbody_v1.0_pretrained.pdparams
+```
+
 
 * 模型恢复训练
 
diff --git a/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_384.yaml b/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_384.yaml
index 7e5c98ce27afeaea017c9ba316db44582e4a3bcd..0c76f694ab8122d506f6816875aee1cb02b142fa 100644
--- a/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_384.yaml
+++ b/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_384.yaml
@@ -16,7 +16,7 @@ Global:
 
 # model architecture
 Arch:
-  name: ViT_base_patch16_224
+  name: ViT_base_patch16_384
   class_num: 1000
  
 # loss function config for traing/eval process