diff --git a/configs/mobile/README.md b/configs/mobile/README.md
index ff967105bb696e96e574ae6b57a22288db226cf1..6cd8b8b65cd6572831f6e563088acdf206ecd862 100755
--- a/configs/mobile/README.md
+++ b/configs/mobile/README.md
@@ -1,39 +1,41 @@
-# Mobile Model Zoo
+[English](README_en.md) | 简体中文
+# 移动端模型库
-## Models
-This directory contains models optimized for mobile applications, at present the following models included:
+## 模型
-| Backbone | Architecture | Input | Image/gpu 1 | Lr schd | Box AP | Download 2 |
+PaddleDetection目前提供一系列针对移动应用进行优化的模型，主要支持以下结构:
+
+| 骨干网络 | 结构 | 输入大小 | 图片/gpu 1 | 学习率策略 | Box AP | 下载 2 |
|--------------------------|---------------------------|-------|------------------------|---------------|--------|-----------------------|
-| MobileNetV3 Small | SSDLite | 320 | 64 | 400K (cosine) | 16.6 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/ssdlite_mobilenet_v3_small.tar.gz) |
-| MobileNetV3 Large | SSDLite | 320 | 64 | 400K (cosine) | 22.8 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/ssdlite_mobilenet_v3_large.tar.gz) |
-| MobileNetV3 Large w/ FPN | Cascade RCNN | 320 | 2 | 500k (cosine) | 25.0 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_mobilenetv3_fpn_320.tar.gz) |
-| MobileNetV3 Large w/ FPN | Cascade RCNN | 640 | 2 | 500k (cosine) | 30.2 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_mobilenetv3_fpn_640.tar.gz) |
-| MobileNetV3 Large | YOLOv3 | 320 | 8 | 500K | 27.1 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3.tar.gz) |
-| MobileNetV3 Large | YOLOv3 Prune 3 | 320 | 8 | - | 24.6 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3_prune86_FPGM_320.tar.gz) |
+| MobileNetV3 Small | SSDLite | 320 | 64 | 400K (cosine) | 16.6 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/ssdlite_mobilenet_v3_small.tar.gz) |
+| MobileNetV3 Large | SSDLite | 320 | 64 | 400K (cosine) | 22.8 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/ssdlite_mobilenet_v3_large.tar.gz) |
+| MobileNetV3 Large w/ FPN | Cascade RCNN | 320 | 2 | 500k (cosine) | 25.0 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_mobilenetv3_fpn_320.tar.gz) |
+| MobileNetV3 Large w/ FPN | Cascade RCNN | 640 | 2 | 500k (cosine) | 30.2 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_mobilenetv3_fpn_640.tar.gz) |
+| MobileNetV3 Large | YOLOv3 | 320 | 8 | 500K | 27.1 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3.tar.gz) |
+| MobileNetV3 Large | YOLOv3 Prune 3 | 320 | 8 | - | 24.6 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3_prune86_FPGM_320.tar.gz) |
-**Notes**:
+**注意**:
-- [1] All models are trained on 8 GPUs.
-- [2] Each tarball contains the following files
- - model weight file (`.pdparams` or `.tar`)
- - inference model files (`__model__` and `__params__`)
- - Paddle-Lite model file (`.nb`)
-- [3] See the note section on how YOLO head is pruned
+- [1] 模型统一使用8卡训练.
+- [2] 压缩包包括下列文件
+ - 模型权重文件 (`.pdparams` or `.tar`)
+ - inference model 文件 (`__model__` and `__params__`)
+ - Paddle-Lite 模型文件 (`.nb`)
+- [3] 参考下面关于YOLO剪裁的说明
-## Benchmarks Results
+## 评测结果
-- Models are benched on following chipsets with [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) 2.6 (to be released)
+- 模型使用 [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) 2.6 (即将发布) 在下列平台上进行了测试
- Qualcomm Snapdragon 625
- Qualcomm Snapdragon 835
- Qualcomm Snapdragon 845
- Qualcomm Snapdragon 855
- HiSilicon Kirin 970
- HiSilicon Kirin 980
-- With 1 CPU thread (latency numbers are in ms)
+- 单CPU线程 (单位： ms)
| | SD625 | SD835 | SD845 | SD855 | Kirin 970 | Kirin 980 |
|------------------|---------|---------|---------|---------|-----------|-----------|
@@ -43,7 +45,7 @@ This directory contains models optimized for mobile applications, at present the
| YOLOv3 prune | 253.98 | 131.279 | 89.4124 | 48.2856 | 122.732 | 55.8626 |
| Cascade RCNN 320 | 286.526 | 125.635 | 87.404 | 46.184 | 149.179 | 52.9994 |
| Cascade RCNN 640 | 1115.66 | 495.926 | 351.361 | 189.722 | 573.558 | 207.917 |
-- With 4 CPU threads (latency numbers are in ms)
+- 4 CPU线程 (单位： ms)
| | SD625 | SD835 | SD845 | SD855 | Kirin 970 | Kirin 980 |
|------------------|---------|---------|---------|---------|-----------|-----------|
@@ -55,26 +57,26 @@ This directory contains models optimized for mobile applications, at present the
| Cascade RCNN 640 | 473.083 | 224.543 | 156.205 | 100.686 | 231.108 | 138.391 |
-## Notes on YOLOv3 pruning
+## YOLOv3剪裁说明
-We pruned the YOLO-head and distill the pruned model with YOLOv3-ResNet34 as the teacher, which has a higher mAP on COCO (31.4 with 320\*320 input).
+首先对YOLO检测头进行剪裁，然后再使用 YOLOv3-ResNet34 作为teacher网络对剪裁后的模型进行蒸馏, teacher网络在COCO的mAP为31.4 (输入大小320\*320).
-The following configurations can be used for pruning:
+可以使用如下两种方式进行剪裁:
-- Prune with fixed ratio, overall prune ratios is 86%
+- 固定比例剪裁, 整体剪裁率是86%
```shell
--pruned_params="yolo_block.0.0.0.conv.weights,yolo_block.0.0.1.conv.weights,yolo_block.0.1.0.conv.weights,yolo_block.0.1.1.conv.weights,yolo_block.0.2.conv.weights,yolo_block.0.tip.conv.weights,yolo_block.1.0.0.conv.weights,yolo_block.1.0.1.conv.weights,yolo_block.1.1.0.conv.weights,yolo_block.1.1.1.conv.weights,yolo_block.1.2.conv.weights,yolo_block.1.tip.conv.weights,yolo_block.2.0.0.conv.weights,yolo_block.2.0.1.conv.weights,yolo_block.2.1.0.conv.weights,yolo_block.2.1.1.conv.weights,yolo_block.2.2.conv.weights,yolo_block.2.tip.conv.weights" \
--pruned_ratios="0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.875,0.875,0.875,0.875,0.875,0.875"
```
-- Prune filters using [FPGM](https://arxiv.org/abs/1811.00250) algorithm:
+- 使用 [FPGM](https://arxiv.org/abs/1811.00250) 算法剪裁:
```shell
--prune_criterion=geometry_median
```
-## Upcoming
+## 敬请关注后续发布
-- [ ] More models configurations
-- [ ] Quantized models
+- [ ] 更多模型
+- [ ] 量化模型
diff --git a/configs/mobile/README_en.md b/configs/mobile/README_en.md
new file mode 100755
index 0000000000000000000000000000000000000000..0108e8c67c1a8f08958b8d52ffb4b145c1001798
--- /dev/null
+++ b/configs/mobile/README_en.md
@@ -0,0 +1,82 @@
+English | [简体中文](README.md)
+
+# Mobile Model Zoo
+
+
+## Models
+
+This directory contains models optimized for mobile applications, at present the following models included:
+
+| Backbone | Architecture | Input | Image/gpu 1 | Lr schd | Box AP | Download 2 |
+|--------------------------|---------------------------|-------|------------------------|---------------|--------|-----------------------|
+| MobileNetV3 Small | SSDLite | 320 | 64 | 400K (cosine) | 16.6 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/ssdlite_mobilenet_v3_small.tar.gz) |
+| MobileNetV3 Large | SSDLite | 320 | 64 | 400K (cosine) | 22.8 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/ssdlite_mobilenet_v3_large.tar.gz) |
+| MobileNetV3 Large w/ FPN | Cascade RCNN | 320 | 2 | 500k (cosine) | 25.0 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_mobilenetv3_fpn_320.tar.gz) |
+| MobileNetV3 Large w/ FPN | Cascade RCNN | 640 | 2 | 500k (cosine) | 30.2 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_mobilenetv3_fpn_640.tar.gz) |
+| MobileNetV3 Large | YOLOv3 | 320 | 8 | 500K | 27.1 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3.tar.gz) |
+| MobileNetV3 Large | YOLOv3 Prune 3 | 320 | 8 | - | 24.6 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3_prune86_FPGM_320.tar.gz) |
+
+**Notes**:
+
+- [1] All models are trained on 8 GPUs.
+- [2] Each tarball contains the following files
+ - model weight file (`.pdparams` or `.tar`)
+ - inference model files (`__model__` and `__params__`)
+ - Paddle-Lite model file (`.nb`)
+- [3] See the note section on how YOLO head is pruned
+
+
+## Benchmarks Results
+
+- Models are benched on following chipsets with [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) 2.6 (to be released)
+ - Qualcomm Snapdragon 625
+ - Qualcomm Snapdragon 835
+ - Qualcomm Snapdragon 845
+ - Qualcomm Snapdragon 855
+ - HiSilicon Kirin 970
+ - HiSilicon Kirin 980
+- With 1 CPU thread (latency numbers are in ms)
+
+ | | SD625 | SD835 | SD845 | SD855 | Kirin 970 | Kirin 980 |
+ |------------------|---------|---------|---------|---------|-----------|-----------|
+ | SSDLite Large | 289.071 | 134.408 | 91.933 | 48.2206 | 144.914 | 55.1186 |
+ | SSDLite Small | 122.932 | 57.1914 | 41.003 | 22.0694 | 61.5468 | 25.2106 |
+ | YOLOv3 baseline | 1082.5 | 435.77 | 317.189 | 155.948 | 536.987 | 178.999 |
+ | YOLOv3 prune | 253.98 | 131.279 | 89.4124 | 48.2856 | 122.732 | 55.8626 |
+ | Cascade RCNN 320 | 286.526 | 125.635 | 87.404 | 46.184 | 149.179 | 52.9994 |
+ | Cascade RCNN 640 | 1115.66 | 495.926 | 351.361 | 189.722 | 573.558 | 207.917 |
+- With 4 CPU threads (latency numbers are in ms)
+
+ | | SD625 | SD835 | SD845 | SD855 | Kirin 970 | Kirin 980 |
+ |------------------|---------|---------|---------|---------|-----------|-----------|
+ | SSDLite Large | 107.535 | 51.1382 | 34.6392 | 20.4978 | 50.5598 | 24.5318 |
+ | SSDLite Small | 51.5704 | 24.5156 | 18.5486 | 11.4218 | 24.9946 | 16.7158 |
+ | YOLOv3 baseline | 413.486 | 184.248 | 133.624 | 75.7354 | 202.263 | 126.435 |
+ | YOLOv3 prune | 98.5472 | 53.6228 | 34.4306 | 21.3112 | 44.0722 | 31.201 |
+ | Cascade RCNN 320 | 131.515 | 59.6026 | 39.4338 | 23.5802 | 58.5046 | 36.9486 |
+ | Cascade RCNN 640 | 473.083 | 224.543 | 156.205 | 100.686 | 231.108 | 138.391 |
+
+
+## Notes on YOLOv3 pruning
+
+We pruned the YOLO-head and distill the pruned model with YOLOv3-ResNet34 as the teacher, which has a higher mAP on COCO (31.4 with 320\*320 input).
+
+The following configurations can be used for pruning:
+
+- Prune with fixed ratio, overall prune ratios is 86%
+
+ ```shell
+ --pruned_params="yolo_block.0.0.0.conv.weights,yolo_block.0.0.1.conv.weights,yolo_block.0.1.0.conv.weights,yolo_block.0.1.1.conv.weights,yolo_block.0.2.conv.weights,yolo_block.0.tip.conv.weights,yolo_block.1.0.0.conv.weights,yolo_block.1.0.1.conv.weights,yolo_block.1.1.0.conv.weights,yolo_block.1.1.1.conv.weights,yolo_block.1.2.conv.weights,yolo_block.1.tip.conv.weights,yolo_block.2.0.0.conv.weights,yolo_block.2.0.1.conv.weights,yolo_block.2.1.0.conv.weights,yolo_block.2.1.1.conv.weights,yolo_block.2.2.conv.weights,yolo_block.2.tip.conv.weights" \
+ --pruned_ratios="0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.875,0.875,0.875,0.875,0.875,0.875"
+ ```
+- Prune filters using [FPGM](https://arxiv.org/abs/1811.00250) algorithm:
+
+ ```shell
+ --prune_criterion=geometry_median
+ ```
+
+
+## Upcoming
+
+- [ ] More models configurations
+- [ ] Quantized models