diff --git a/docs/en/model_zoo_en.md b/docs/en/model_zoo_en.md
index 47155978e39c995f5063afc199988d8722e565d1..6350be720814139c998af26c7173d616b9a8dbad 100644
--- a/docs/en/model_zoo_en.md
+++ b/docs/en/model_zoo_en.md
@@ -50,27 +50,28 @@ PaddleLite latency(ms)
| Kirin 970 | ResNet50 | quant_aware | 488.361 | 260.1697 | 142.416 | 479.5668 | 249.8485 | 138.1742 |
| Kirin 970 | ResNet50 | quant_post | 489.6188 | 258.3279 | 142.6063 | 480.0064 | 249.5339 | 138.5284 |
-
-
-
-
### 1.2 Pruning
+PaddleLite:
-| Model | Method | Top-1/Top-5 Acc | Model Size（MB） | GFLOPs | Download |
-|:--:|:---:|:--:|:--:|:--:|:--:|
-| MobileNetV1 | Baseline | 70.99%/89.68% | 17 | 1.11 | [model](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar) |
-| MobileNetV1 | uniform -50% | 69.4%/88.66% (-1.59%/-1.02%) | 9 | 0.56 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_uniform-50.tar) |
-| MobileNetV1 | sensitive -30% | 70.4%/89.3% (-0.59%/-0.38%) | 12 | 0.74 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_sensitive-30.tar) |
-| MobileNetV1 | sensitive -50% | 69.8% / 88.9% (-1.19%/-0.78%) | 9 | 0.56 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_sensitive-50.tar) |
-| MobileNetV2 | - | 72.15%/90.65% | 15 | 0.59 | [model](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) |
-| MobileNetV2 | uniform -50% | 65.79%/86.11% (-6.35%/-4.47%) | 11 | 0.296 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_uniform-50.tar) |
-| ResNet34 | - | 72.15%/90.65% | 84 | 7.36 | [model](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar) |
-| ResNet34 | uniform -50% | 70.99%/89.95% (-1.36%/-0.87%) | 41 | 3.67 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet34_uniform-50.tar) |
-| ResNet34 | auto -55.05% | 70.24%/89.63% (-2.04%/-1.06%) | 33 | 3.31 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet34_auto-55.tar) |
+env: Qualcomm SnapDragon 845 + armv8
+
+criterion: time cost in Thread1/Thread2/Thread4
+PaddleLite version: v2.3
+|Model | Method | Top-1/Top-5 Acc | ModelSize(MB) | GFLOPs |PaddleLite cost(ms)|TensorRT speed(FPS)| download |
+|:--:|:---:|:--:|:--:|:--:|:--:|:--:|:--:|
+| MobileNetV1 | Baseline | 70.99%/89.68% | 17 | 1.11 |66.052\35.8014\19.5762|-| [download](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar) |
+| MobileNetV1 | uniform -50% | 69.4%/88.66% (-1.59%/-1.02%) | 9 | 0.56 | 33.5636\18.6834\10.5076|-|[download](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_uniform-50.tar) |
+| MobileNetV1 | sensitive -30% | 70.4%/89.3% (-0.59%/-0.38%) | 12 | 0.74 | 46.5958\25.3098\13.6982|-|[download](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_sensitive-30.tar) |
+| MobileNetV1 | sensitive -50% | 69.8% / 88.9% (-1.19%/-0.78%) | 9 | 0.56 |37.9892\20.7882\11.3144|-| [download](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_sensitive-50.tar) |
+| MobileNetV2 | - | 72.15%/90.65% | 15 | 0.59 |41.7874\23.375\13.3998|-| [download](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) |
+| MobileNetV2 | uniform -50% | 65.79%/86.11% (-6.35%/-4.47%) | 11 | 0.296 |23.8842\13.8698\8.5572|-| [download](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_uniform-50.tar) |
+| ResNet34 | - | 72.15%/90.65% | 84 | 7.36 |217.808\139.943\96.7504|342.32| [download](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar) |
+| ResNet34 | uniform -50% | 70.99%/89.95% (-1.36%/-0.87%) | 41 | 3.67 |114.787\75.0332\51.8438|452.41| [download](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet34_uniform-50.tar) |
+| ResNet34 | auto -55.05% | 70.24%/89.63% (-2.04%/-1.06%) | 33 | 3.31 |105.924\69.3222\48.0246|457.25| [download](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet34_auto-55.tar) |
### 1.3 Distillation
@@ -85,9 +86,7 @@ PaddleLite latency(ms)
|ResNet101|teacher|77.56%/93.64%| 173 | [model](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar) |
| ResNet50 | ResNet101 distill | 77.29%/93.65% (+0.79%/+0.65%) | 99 | [model](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_distilled.tar) |
-!!! note "Note"
-
- [1]：The `_vd` suffix indicates that the pre-trained model uses Mixup. Please refer to the detailed introduction: [mixup: Beyond Empirical Risk Minimization](https://arxiv.org/abs/1710.09412)
+Note: The `_vd` suffix indicates that the pre-trained model uses Mixup. Please refer to the detailed introduction: [mixup: Beyond Empirical Risk Minimization](https://arxiv.org/abs/1710.09412)
### 1.4 NAS
@@ -174,9 +173,7 @@ Dataset: WIDER-FACE
| BlazeFace-NAS | - | 8 | 640 | 83.7/80.7/65.8 | 244 | 21.117 |[model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) |
| BlazeFace-NAS1 | SANAS | 8 | 640 | 87.0/83.7/68.5 | 389 | 22.558 | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas2.tar) |
-!!! note "Note"
-
- [1]: latency is based on latency_855.txt, the file is test on 855 by PaddleLite。
+Note: latency is based on latency_855.txt, the file is test on 855 by PaddleLite。
## 3. Image Segmentation
diff --git a/docs/zh_cn/model_zoo.md b/docs/zh_cn/model_zoo.md
index 7414ac26a799c0862744c1fba816ccffabf6fa4f..df462be50b94e61d530a9ef1f285bc01f2aac353 100644
--- a/docs/zh_cn/model_zoo.md
+++ b/docs/zh_cn/model_zoo.md
@@ -57,20 +57,27 @@
### 1.2 剪裁
-| 模型 | 压缩方法 | Top-1/Top-5 Acc | 模型体积（MB） | GFLOPs | 下载 |
-|:--:|:---:|:--:|:--:|:--:|:--:|
-| MobileNetV1 | Baseline | 70.99%/89.68% | 17 | 1.11 | [下载链接](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar) |
-| MobileNetV1 | uniform -50% | 69.4%/88.66% (-1.59%/-1.02%) | 9 | 0.56 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_uniform-50.tar) |
-| MobileNetV1 | sensitive -30% | 70.4%/89.3% (-0.59%/-0.38%) | 12 | 0.74 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_sensitive-30.tar) |
-| MobileNetV1 | sensitive -50% | 69.8% / 88.9% (-1.19%/-0.78%) | 9 | 0.56 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_sensitive-50.tar) |
-| MobileNetV2 | - | 72.15%/90.65% | 15 | 0.59 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) |
-| MobileNetV2 | uniform -50% | 65.79%/86.11% (-6.35%/-4.47%) | 11 | 0.296 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_uniform-50.tar) |
-| ResNet34 | - | 72.15%/90.65% | 84 | 7.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar) |
-| ResNet34 | uniform -50% | 70.99%/89.95% (-1.36%/-0.87%) | 41 | 3.67 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet34_uniform-50.tar) |
-| ResNet34 | auto -55.05% | 70.24%/89.63% (-2.04%/-1.06%) | 33 | 3.31 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet34_auto-55.tar) |
+PaddleLite推理耗时说明：
+
+环境：Qualcomm SnapDragon 845 + armv8
+
+速度指标：Thread1/Thread2/Thread4耗时
+PaddleLite版本： v2.3
+| 模型 | 压缩方法 | Top-1/Top-5 Acc | 模型体积（MB） | GFLOPs |PaddleLite推理耗时|TensorRT推理速度(FPS)| 下载 |
+|:--:|:---:|:--:|:--:|:--:|:--:|:--:|:--:|
+| MobileNetV1 | Baseline | 70.99%/89.68% | 17 | 1.11 |66.052\35.8014\19.5762|-| [下载链接](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar) |
+| MobileNetV1 | uniform -50% | 69.4%/88.66% (-1.59%/-1.02%) | 9 | 0.56 | 33.5636\18.6834\10.5076|-|[下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_uniform-50.tar) |
+| MobileNetV1 | sensitive -30% | 70.4%/89.3% (-0.59%/-0.38%) | 12 | 0.74 | 46.5958\25.3098\13.6982|-|[下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_sensitive-30.tar) |
+| MobileNetV1 | sensitive -50% | 69.8% / 88.9% (-1.19%/-0.78%) | 9 | 0.56 |37.9892\20.7882\11.3144|-| [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_sensitive-50.tar) |
+| MobileNetV2 | - | 72.15%/90.65% | 15 | 0.59 |41.7874\23.375\13.3998|-| [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) |
+| MobileNetV2 | uniform -50% | 65.79%/86.11% (-6.35%/-4.47%) | 11 | 0.296 |23.8842\13.8698\8.5572|-| [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_uniform-50.tar) |
+| ResNet34 | - | 72.15%/90.65% | 84 | 7.36 |217.808\139.943\96.7504|342.32| [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar) |
+| ResNet34 | uniform -50% | 70.99%/89.95% (-1.36%/-0.87%) | 41 | 3.67 |114.787\75.0332\51.8438|452.41| [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet34_uniform-50.tar) |
+| ResNet34 | auto -55.05% | 70.24%/89.63% (-2.04%/-1.06%) | 33 | 3.31 |105.924\69.3222\48.0246|457.25| [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet34_auto-55.tar) |
+
### 1.3 蒸馏
@@ -85,9 +92,7 @@
|ResNet101|teacher|77.56%/93.64%| 173 | [下载链接](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar) |
| ResNet50 | ResNet101 distill | 77.29%/93.65% (+0.79%/+0.65%) | 99 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_distilled.tar) |
-!!! note "Note"
-
- [1]：带_vd后缀代表该预训练模型使用了Mixup，Mixup相关介绍参考[mixup: Beyond Empirical Risk Minimization](https://arxiv.org/abs/1710.09412)
+注意：带"_vd"后缀代表该预训练模型使用了Mixup，Mixup相关介绍参考[mixup: Beyond Empirical Risk Minimization](https://arxiv.org/abs/1710.09412)
### 1.4 搜索
@@ -173,9 +178,7 @@
| BlazeFace-NAS | - | 8 | 640 | 83.7/80.7/65.8 | 244 | 21.117 |[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) |
| BlazeFace-NAS1 | SANAS | 8 | 640 | 87.0/83.7/68.5 | 389 | 22.558 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas2.tar) |
-!!! note "Note"
-
- [1]: 硬件延时时间是利用提供的硬件延时表得到的，硬件延时表是在855芯片上基于PaddleLite测试的结果。
+>Note: 硬件延时时间是利用提供的硬件延时表得到的，硬件延时表是在855芯片上基于PaddleLite测试的结果。
## 3. 图像分割