diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/README_ENG.md b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/README_ENG.md
new file mode 100644
index 0000000000000000000000000000000000000000..a97ce199b75a139165111fbb029182e52e098cae
--- /dev/null
+++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/README_ENG.md
@@ -0,0 +1,84 @@
+English|[中文](README.md)
+
+# Resnet50v1.5 Inference for Tensorflow 
+
+This repository provides a script and recipe to Inference of the Resnet50v1.5 model.
+
+## Notice
+**This sample only provides reference for you to learn the Ascend software stack and is not for commercial purposes.**
+
+Before starting, please pay attention to the following adaptation conditions. If they do not match, may leading in failure.
+
+| Conditions | Need |
+| --- | --- |
+| CANN Version | >=5.0.3 |
+| Chip Platform| Ascend310/Ascend310P3 |
+| 3rd Party Requirements| Please follow the 'requirements.txt' |
+
+## Quick Start Guide
+
+### 1. Clone the respository
+
+```shell
+git clone https://gitee.com/ascend/ModelZoo-TensorFlow.git
+cd Modelzoo-TensorFlow/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL
+```
+
+### 2. Download and preprocess the dataset
+
+1. Download the ImageNet2012 Validation dataset by yourself. You can get the validation pictures(50000 JPEGS and a ILSVRC2012val-label-index.txt)
+
+2. Put JPEGS to **'scripts/ILSVRC2012val'** and label text to **'scripts/'**
+
+3. Images Preprocess:
+```
+cd scripts
+mkdir input_bins
+python3 resnet50v15_preprocessing.py ./ILSVRC2012val/ ./input_bins/
+```
+The jpegs pictures will be preprocessed to bin fils.
+
+### 3. Offline Inference
+
+**Convert pb to om.**
+
+- configure the env
+
+  Please follow the [guide](https://gitee.com/ascend/ModelZoo-TensorFlow/wikis/02.%E7%A6%BB%E7%BA%BF%E6%8E%A8%E7%90%86%E6%A1%88%E4%BE%8B/Ascend%E5%B9%B3%E5%8F%B0%E6%8E%A8%E7%90%86%E7%8E%AF%E5%A2%83%E5%8F%98%E9%87%8F%E8%AE%BE%E7%BD%AE?sort_id=6458719) to set the envs 
+
+- convert pb to om
+
+  [pb download link](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/modelzoo/Official/cv/Resnet50v1.5_for_ACL/resnet50v15_tf.pb)
+
+  ```
+  atc --model=resnet50v15_tf.pb --framework=3 --output=resnet50v15_tf_1batch --output_type=FP32 --soc_version=Ascend310 --input_shape="input_tensor:1,224,224,3" --insert_op_conf=resnet50v15_aipp.cfg --enable_small_channel=1 --log=info
+  ```
+
+- Build the program
+
+  ```
+  bash build.sh
+  ```
+
+- Run the program:
+
+  ```
+  cd scripts
+  bash benchmark_tf.sh
+  ```
+
+## Performance
+
+### Result
+
+Our result was obtained by running the applicable inference script. To achieve the same results, follow the steps in the Quick Start Guide.
+
+#### Inference accuracy results
+
+|       model       | **data**  |    Top1/Top5    |
+| :---------------: | :-------: | :-------------: |
+| offline Inference | 50000 images | 76.5 %/ 93.1% |
+
+## Reference
+
+[1] https://github.com/IntelAI/models  
\ No newline at end of file
diff --git a/ACL_TensorFlow/built-in/cv/Vgg16_for_ACL/README.md b/ACL_TensorFlow/built-in/cv/Vgg16_for_ACL/README.md
index bdbe61a39bed7872b482a5638904200c807225ca..9152adbd3f70a710444ad3eb4e6f272c42d9da03 100644
--- a/ACL_TensorFlow/built-in/cv/Vgg16_for_ACL/README.md
+++ b/ACL_TensorFlow/built-in/cv/Vgg16_for_ACL/README.md
@@ -77,7 +77,7 @@ cd Modelzoo-TensorFlow/ACL_TensorFlow/built-in/cv/Vgg16_for_ACL
 
 ### 结果
 
-本结果是通过运行上面适配的推理脚本获得的。要获得相同的结果，请按照《快速入门指南》中的步骤操作。
+本结果是通过运行上面适配的推理脚本获得的。要获得相同的结果，请按照上述《快速指南》中的步骤操作。
 
 #### 推理精度结果