diff --git a/paddle/fluid/inference/tests/api/int8_mkldnn_quantization.md b/paddle/fluid/inference/tests/api/int8_mkldnn_quantization.md
index 6f0e0d14bdf8c4f27761b278fd9bc617cf1cd527..2d73c43da9218df6ee0758268c8c7f48d0c593f8 100644
--- a/paddle/fluid/inference/tests/api/int8_mkldnn_quantization.md
+++ b/paddle/fluid/inference/tests/api/int8_mkldnn_quantization.md
@@ -42,25 +42,25 @@ We provide the results of accuracy and performance measured on Intel(R) Xeon(R)
 
 | Model        | FP32 Accuracy   | INT8 Accuracy   | Accuracy Diff(FP32-INT8)   |
 | :----------: | :-------------: | :------------:  | :--------------:           |
-| GoogleNet    |  70.50%         |  69.81%         |   0.69%                    |
-| MobileNet-V1 |  70.78%         |  70.42%         |   0.36%                    |
-| MobileNet-V2 |  71.90%         |  71.35%         |   0.55%                    |
-| ResNet-101   |  77.50%         |  77.42%         |   0.08%                    |
-| ResNet-50    |  76.63%         |  76.52%         |   0.11%                    |
-| VGG16        |  72.08%         |  72.03%         |   0.05%                    |
-| VGG19        |  72.57%         |  72.55%         |   0.02%                    |
+| GoogleNet    |  70.50%         |  70.08%         |   0.42%                    |
+| MobileNet-V1 |  70.78%         |  70.41%         |   0.37%                    |
+| MobileNet-V2 |  71.90%         |  71.34%         |   0.56%                    |
+| ResNet-101   |  77.50%         |  77.43%         |   0.07%                    |
+| ResNet-50    |  76.63%         |  76.57%         |   0.06%                    |
+| VGG16        |  72.08%         |  72.05%         |   0.03%                    |
+| VGG19        |  72.57%         |  72.57%         |   0.00%                    |
 
 >**II. Throughput on Intel(R) Xeon(R) Gold 6271 (batch size 1 on single core)**
 
 | Model        | FP32 Throughput(images/s)  | INT8 Throughput(images/s) | Ratio(INT8/FP32)|
 | :-----------:| :------------:             | :------------:            | :------------:  |
-| GoogleNet    |    34.06                   |    72.79                  |   2.14          |
-| MobileNet-V1 |    80.02                   |   230.65                  |   2.88          |
-| MobileNet-V2 |    99.38                   |   206.92                  |   2.08          |
-| ResNet-101   |     7.38                   |    27.31                  |   3.70          |
-| ResNet-50    |    13.71                   |    50.55                  |   3.69          |
-| VGG16        |     3.64                   |    10.56                  |   2.90          |
-| VGG19        |     2.95                   |     9.02                  |   3.05          |
+| GoogleNet    |    32.76                   |    67.43                  |   2.06          |
+| MobileNet-V1 |    73.96                   |   218.82                  |   2.96          |
+| MobileNet-V2 |    87.94                   |   193.70                  |   2.20          |
+| ResNet-101   |     7.17                   |    26.37                  |   3.42          |
+| ResNet-50    |    13.26                   |    48.72                  |   3.67          |
+| VGG16        |     3.47                   |    10.10                  |   2.91          |
+| VGG19        |     2.82                   |     8.68                  |   3.07          |
 
 
 * ## Prepare dataset
diff --git a/python/paddle/fluid/contrib/slim/tests/QAT_mkldnn_int8_readme.md b/python/paddle/fluid/contrib/slim/tests/QAT_mkldnn_int8_readme.md
index 115ba489bff257581a052691cda4a31fd74648dc..dcf69d05e6a12f8229ffd1891c725441a24287dd 100644
--- a/python/paddle/fluid/contrib/slim/tests/QAT_mkldnn_int8_readme.md
+++ b/python/paddle/fluid/contrib/slim/tests/QAT_mkldnn_int8_readme.md
@@ -65,12 +65,12 @@ Notes:
 
 | Model        | Fake QAT Original Throughput(images/s) | INT8 Throughput(images/s) | Ratio(INT8/FP32)|
 | :-----------:| :-------------------------:            | :------------:            | :------------:  |
-| MobileNet-V1 |    13.66                               | 114.98                    |   8.42          |
-| MobileNet-V2 |    10.22                               |  79.78                    |   7.81          |
-| ResNet101    |    2.65                                |  18.97                    |   7.16          |
-| ResNet50     |    4.58                                |  35.09                    |   7.66          |
-| VGG16        |    2.38                                |  9.93                     |   4.17          |
-| VGG19        |    2.03                                |  8.53                     |   4.20          |
+| MobileNet-V1 |    12.86                               | 118.05                    |   9.18          |
+| MobileNet-V2 |    9.76                                |  85.89                    |   8.80          |
+| ResNet101    |    2.55                                |  19.40                    |   7.61          |
+| ResNet50     |    4.39                                |  35.78                    |   8.15          |
+| VGG16        |    2.26                                |  9.89                     |   4.38          |
+| VGG19        |    1.96                                |  8.41                     |   4.29          |
 
 ## 3. How to reproduce the results
 Three steps to reproduce the above-mentioned accuracy results, and we take ResNet50 benchmark as an example:
@@ -95,7 +95,7 @@ cd /PATH/TO/DOWNLOAD/MODEL/
 wget http://paddle-inference-dist.bj.bcebos.com/int8/${MODEL_FILE_NAME}
 ```
 
-To download and verify all the 7 models, you need to set `MODEL_NAME` to one of the following values in command line:
+Unzip the downloaded model to the folder.To verify all the 7 models, you need to set `MODEL_NAME` to one of the following values in command line:
 ```text
 QAT MKL-DNN 1.0
 MODEL_NAME=ResNet50, ResNet101, GoogleNet, MobileNetV1, MobileNetV2, VGG16, VGG19