diff --git a/configs/ppyolo/README.md b/configs/ppyolo/README.md
index b3589f1a9ccef4436b5f3d58e0ecddb62da0864a..749a91b98445d19cc7845b8ad2af4bd5451932af 100644
--- a/configs/ppyolo/README.md
+++ b/configs/ppyolo/README.md
@@ -42,14 +42,14 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
 | YOLOv4(AlexyAB)          |     -      |      -     | CSPDarknet |     512     |           -          |         43.0          |       83       |          138.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml)                   |
 | YOLOv4(AlexyAB)          |     -      |      -     | CSPDarknet |     416     |           -          |         41.2          |       96       |          164.0          | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml)                   |
 | YOLOv4(AlexyAB)          |     -      |      -     | CSPDarknet |     320     |           -          |         38.0          |      123       |          199.0          | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml)                   |
-| PP-YOLO                  |     8      |     24    | ResNet50vd  |     608     |         44.8         |         45.2          |      72.9      |          155.6          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
-| PP-YOLO                  |     8      |     24    | ResNet50vd  |     512     |         43.9         |         44.4          |      89.9      |          188.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
-| PP-YOLO                  |     8      |     24    | ResNet50vd  |     416     |         42.1         |         42.5          |     109.1      |          215.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
-| PP-YOLO                  |     8      |     24    | ResNet50vd  |     320     |         38.9         |         39.3          |     132.2      |          242.2          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
-| PP-YOLO_2x               |     8      |     24    | ResNet50vd  |     608     |         45.3         |         45.9          |      72.9      |          155.6          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_2x.yml)                   |
-| PP-YOLO_2x               |     8      |     24    | ResNet50vd  |     512     |         44.4         |         45.0          |      89.9      |          188.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_2x.yml)                   |
-| PP-YOLO_2x               |     8      |     24    | ResNet50vd  |     416     |         42.7         |         43.2          |     109.1      |          215.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_2x.yml)                   |
-| PP-YOLO_2x               |     8      |     24    | ResNet50vd  |     320     |         39.5         |         40.1          |     132.2      |          242.2          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_2x.yml)                   |
+| PP-YOLO                  |     8      |     24     | ResNet50vd |     608     |         44.8         |         45.2          |      72.9      |          155.6          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
+| PP-YOLO                  |     8      |     24     | ResNet50vd |     512     |         43.9         |         44.4          |      89.9      |          188.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
+| PP-YOLO                  |     8      |     24     | ResNet50vd |     416     |         42.1         |         42.5          |     109.1      |          215.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
+| PP-YOLO                  |     8      |     24     | ResNet50vd |     320     |         38.9         |         39.3          |     132.2      |          242.2          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml)                   |
+| PP-YOLO_2x               |     8      |     24     | ResNet50vd |     608     |         45.3         |         45.9          |      72.9      |          155.6          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_2x.yml)                   |
+| PP-YOLO_2x               |     8      |     24     | ResNet50vd |     512     |         44.4         |         45.0          |      89.9      |          188.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_2x.yml)                   |
+| PP-YOLO_2x               |     8      |     24     | ResNet50vd |     416     |         42.7         |         43.2          |     109.1      |          215.4          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_2x.yml)                   |
+| PP-YOLO_2x               |     8      |     24     | ResNet50vd |     320     |         39.5         |         40.1          |     132.2      |          242.2          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_2x.yml)                   |
 
 **Notes:**
 
@@ -61,16 +61,16 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
 - YOLOv4(AlexyAB) performance and inference speed is copy from single Tesla V100 testing results in [YOLOv4 github repo](https://github.com/AlexeyAB/darknet), Tesla V100 TensorRT FP16 inference speed is testing with tkDNN configuration and TensorRT 5.1.2.2 on single Tesla V100 based on [AlexyAB/darknet repo](https://github.com/AlexeyAB/darknet).
 - Download and configuration of YOLOv4(AlexyAB) is reproduced model of YOLOv4 in PaddleDetection, whose evaluation performance is same as YOLOv4(AlexyAB), and finetune training is supported in PaddleDetection currently, reproducing by training from backbone pretrain weights is on working, see [PaddleDetection YOLOv4](../yolov4/README.md) for details.
 
-### PP-YOLO tiny
+### PP-YOLO for mobile
 
 |          Model           | GPU number | images/GPU |  backbone  | input shape | Box AP50<sup>val</sup> | Box AP50<sup>test</sup> | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config  |
 |:------------------------:|:----------:|:----------:|:----------:| :----------:| :--------------------: | :---------------------: | :------------: | :---------------------: | :------: | :-----: |
-| PP-YOLO tiny             |     4      |      32    | ResNet18vd |     416     |          47.0          |          47.7           |     401.6      |          724.6          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_tiny.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_tiny.yml)                   |
-| PP-YOLO tiny             |     4      |      32    | ResNet18vd |     320     |          43.7          |          44.4           |     478.5      |          791.3          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_tiny.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_tiny.yml)                   |
+| PP-YOLO_r18vd            |     4      |      32    | ResNet18vd |     416     |          47.0          |          47.7           |     401.6      |          724.6          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml)                   |
+| PP-YOLO_r18vd            |     4      |      32    | ResNet18vd |     320     |          43.7          |          44.4           |     478.5      |          791.3          | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml)                   |
 
-- PP-YOLO tiny is trained on COCO train2017 datast and evaluated on val2017 & test-dev2017 dataset，Box AP50<sup>val</sup> is evaluation results of `mAP(IoU=0.5)`.
-- PP-YOLO tiny used 4 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](../../docs/FAQ.md).
-- PP-YOLO tiny inference speeding testing environment and configuration is same as PP-YOLO above.
+- PP-YOLO_r18vd is trained on COCO train2017 datast and evaluated on val2017 & test-dev2017 dataset，Box AP50<sup>val</sup> is evaluation results of `mAP(IoU=0.5)`.
+- PP-YOLO_r18vd used 4 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](../../docs/FAQ.md).
+- PP-YOLO_r18vd inference speeding testing environment and configuration is same as PP-YOLO above.
 
 ## Getting Start
 
diff --git a/configs/ppyolo/README_cn.md b/configs/ppyolo/README_cn.md
index 909f04a4b12f8fb00fb2f7fb50901f7327b2b419..1626c582b9ef9a3b580ca191c0386e43e7e29029 100644
--- a/configs/ppyolo/README_cn.md
+++ b/configs/ppyolo/README_cn.md
@@ -53,7 +53,7 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
 
 **注意:**
 
-- PP-YOLO模型使用COCO数据集中train2017作为训练集，使用test-dev2017作为测试集，Box AP<sup>test</sup>为`mAP(IoU=0.5:0.95)`评估结果。
+- PP-YOLO模型使用COCO数据集中train2017作为训练集，使用val2017和test-dev2017作为测试集，Box AP<sup>test</sup>为`mAP(IoU=0.5:0.95)`评估结果。
 - PP-YOLO模型训练过程中使用8 GPUs，每GPU batch size为24进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](../../docs/FAQ.md)调整学习率和迭代次数。
 - PP-YOLO模型推理速度测试采用单卡V100，batch size=1进行测试，使用CUDA 10.2, CUDNN 7.5.1，TensorRT推理速度测试使用TensorRT 5.1.2.2。
 - PP-YOLO模型FP32的推理速度测试数据为使用`tools/export_model.py`脚本导出模型后，使用`deploy/python/infer.py`脚本中的`--run_benchnark`参数使用Paddle预测库进行推理速度benchmark测试结果, 且测试的均为不包含数据预处理和模型输出后处理(NMS)的数据(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。
@@ -62,16 +62,16 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
 - PP-YOLO模型推理速度测试采用单卡V100，batch size=1进行测试，使用CUDA 10.2, CUDNN 7.5.1，TensorRT推理速度测试使用TensorRT 5.1.2.2。
 - YOLOv4(AlexyAB)行`模型下载`和`配置文件`为PaddleDetection复现的YOLOv4模型，目前评估精度已对齐，支持finetune，训练精度对齐中，可参见[PaddleDetection YOLOv4 模型](../yolov4/README.md)
 
-### PP-YOLO tiny模型
+### PP-YOLO 移动端模型
 
 |          模型            | GPU个数 | 每GPU图片个数 |  骨干网络  | 输入尺寸 | Box AP50<sup>val</sup> | Box AP50<sup>test</sup> | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件 |
 |:------------------------:|:-------:|:-------------:|:----------:| :-------:| :--------------------: | : :---------------------: |------------: | :---------------------: | :------: | :------: |
-| PP-YOLO tiny              |    4    |      32      | ResNet18vd |   416    |         47.0           |            47.7           |   401.6      |          724.6          | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_tiny.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_tiny.yml)                   |
-| PP-YOLO tiny              |    4    |      32      | ResNet18vd |   320    |         43.7           |            44.4           |   478.5      |          791.3          | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_tiny.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_tiny.yml)                   |
+| PP-YOLO_r18vd              |    4    |      32      | ResNet18vd |   416    |         47.0           |            47.7           |   401.6      |          724.6          | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml)                   |
+| PP-YOLO_r18vd              |    4    |      32      | ResNet18vd |   320    |         43.7           |            44.4           |   478.5      |          791.3          | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml)                   |
 
-- PP-YOLO tiny模型使用COCO数据集中train2017作为训练集，使用val2017作为测试集，Box AP50<sup>val</sup>为`mAP(IoU=0.5)`评估结果。
-- PP-YOLO tiny模型训练过程中使用4GPU，每GPU batch size为32进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](../../docs/FAQ.md)调整学习率和迭代次数。
-- PP-YOLO tiny模型推理速度测试环境配置和测试方法与PP-YOLO模型一致。
+- PP-YOLO_r18vd 模型使用COCO数据集中train2017作为训练集，使用val2017和test-dev2017作为测试集，Box AP50<sup>val</sup>为`mAP(IoU=0.5)`评估结果。
+- PP-YOLO_r18vd 模型训练过程中使用4GPU，每GPU batch size为32进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](../../docs/FAQ.md)调整学习率和迭代次数。
+- PP-YOLO_r18vd 模型推理速度测试环境配置和测试方法与PP-YOLO模型一致。
 
 ## 使用说明
 
@@ -149,7 +149,7 @@ CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output/ppyolo -
 ## 未来工作
 
 1. 发布PP-YOLO-tiny模型
-2. 发布更多骨干网络的PP-YOLO及PP-YOLO-tiny模型
+2. 发布更多骨干网络的PP-YOLO模型
 
 ## 附录
 
diff --git a/configs/ppyolo/ppyolo_tiny.yml b/configs/ppyolo/ppyolo_r18vd.yml
similarity index 100%
rename from configs/ppyolo/ppyolo_tiny.yml
rename to configs/ppyolo/ppyolo_r18vd.yml