diff --git a/docs/source_en/benchmark.md b/docs/source_en/benchmark.md
index fd02b194fba11050b0811d8e9a25156664275560..13a2238d6772fad14aa598ba6ffa75330a816575 100644
--- a/docs/source_en/benchmark.md
+++ b/docs/source_en/benchmark.md
@@ -9,11 +9,11 @@ For details about the MindSpore pre-trained model, see [Model Zoo](https://gitee
 
 ### ResNet
 
-| Network |     Network Type | Dataset | MindSpore Version | Resource                 | Precision | Batch Size | Throughput | Speedup |
+| Network | Network Type | Dataset | MindSpore Version | Resource                 | Precision | Batch Size | Throughput | Speedup |
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-| ResNet-50 v1.5 | CNN | ImageNet2012 | 0.2.0-alpha | Ascend: 1 * Ascend 910 </br> CPU：24 Cores | Mixed | 32 | 1787 images/sec | - |
-|  |  |  |  | Ascend: 8 * Ascend 910 </br> CPU：192 Cores | Mixed | 32 | 13689 images/sec | 0.95 |
-|  |  |  |  | Ascend: 16 * Ascend 910 </br> CPU：384 Cores | Mixed | 32 | 27090 images/sec | 0.94 |
+| ResNet-50 v1.5 | CNN | ImageNet2012 | 0.5.0-beta | Ascend: 1 * Ascend 910 </br> CPU：24 Cores | Mixed | 256 | 2115 images/sec | - |
+|  |  |  |  | Ascend: 8 * Ascend 910 </br> CPU：192 Cores | Mixed | 256 | 16600 images/sec | 0.98 |
+|  |  |  |  | Ascend: 16 * Ascend 910 </br> CPU：384 Cores | Mixed | 256 | 32768 images/sec | 0.96 |
 
 1. The preceding performance is obtained based on ModelArts, the HUAWEI CLOUD AI development platform. It is the average performance obtained by the Ascend 910 AI processor during the overall training process. 
 2. For details about other open source frameworks, see [ResNet-50 v1.5 for TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Classification/RN50v1.5#nvidia-dgx-2-16x-v100-32g).
diff --git a/docs/source_zh_cn/benchmark.md b/docs/source_zh_cn/benchmark.md
index 3329170adb5588364cae7ac6ad4144cfc1f9f3bc..e401cfde8770e8bf7c54880051fee77c8d1f70f8 100644
--- a/docs/source_zh_cn/benchmark.md
+++ b/docs/source_zh_cn/benchmark.md
@@ -8,11 +8,11 @@
 
 ### ResNet
 
-| Network |     Network Type | Dataset | MindSpore Version | Resource &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | Precision | Batch Size | Throughput | Speedup |
+| Network | Network Type | Dataset | MindSpore Version | Resource &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | Precision | Batch Size | Throughput | Speedup |
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |
-| ResNet-50 v1.5 | CNN | ImageNet2012 | 0.2.0-alpha | Ascend: 1 * Ascend 910 </br> CPU：24 Cores | Mixed | 32 | 1787 images/sec | - |
-|  |  |  |  | Ascend: 8 * Ascend 910 </br> CPU：192 Cores | Mixed | 32 | 13689 images/sec | 0.95 |
-|  |  |  |  | Ascend: 16 * Ascend 910 </br> CPU：384 Cores | Mixed | 32 | 27090 images/sec | 0.94 |
+| ResNet-50 v1.5 | CNN | ImageNet2012 | 0.5.0-beta | Ascend: 1 * Ascend 910 </br> CPU：24 Cores | Mixed | 256 | 2115 images/sec | - |
+|  |  |  |  | Ascend: 8 * Ascend 910 </br> CPU：192 Cores | Mixed | 256 | 16600 images/sec | 0.98 |
+|  |  |  |  | Ascend: 16 * Ascend 910 </br> CPU：384 Cores | Mixed | 256 | 32768 images/sec | 0.96 |
 
 1. 以上数据基于华为云AI开发平台ModelArts测试获得，是训练过程整体下沉至Ascend 910 AI处理器执行所得的平均性能。
 2. 业界其他开源框架数据可参考：[ResNet-50 v1.5 for TensorFlow](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/Classification/RN50v1.5#nvidia-dgx-2-16x-v100-32g)。