From 21187aa861cd9b688e164594e033fefb70249941 Mon Sep 17 00:00:00 2001
From: lrving <2415912278@qq.com>
Date: Wed, 19 Oct 2022 06:38:54 +0000
Subject: [PATCH 1/9] add
 ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL/README_EN.md.

Signed-off-by: lrving <2415912278@qq.com>
---
 .../built-in/cv/YOLOv3_for_ACL/README_EN.md   | 126 ++++++++++++++++++
 1 file changed, 126 insertions(+)
 create mode 100644 ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL/README_EN.md

diff --git a/ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL/README_EN.md b/ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL/README_EN.md
new file mode 100644
index 000000000..806f8b624
--- /dev/null
+++ b/ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL/README_EN.md
@@ -0,0 +1,126 @@
+
+
+# Yolov3 Inference for Tensorflow 
+
+This repository provides a script and recipe to Inference the Yolov3 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/YOLOv3_for_ACL
+```
+
+### 2. Requirements
+
+opencv-python==4.2.0.34
+
+
+### 3. Download and preprocess the dataset
+
+1. dataset
+  To compare with official implement, for example, we use [get_coco_dataset.sh](https://github.com/pjreddie/darknet/blob/master/scripts/get_coco_dataset.sh) to prepare our dataset.
+
+2. annotation file
+
+   cd scripts
+
+   Using script generate `coco2014_minival.txt` file. Modify the path in `coco_minival_anns.py` and `5k.txt`, then execute:
+
+   ```
+   python3 coco_minival_anns.py
+   ```
+
+   One line for one image, in the format like `image_index image_absolute_path img_width img_height box_1 box_2 ... box_n`.    
+   Box_x format: 
+
+   - `label_index x_min y_min x_max y_max`. (The origin of coordinates is at the left top corner, left top => (xmin, ymin), right bottom => (xmax, ymax).)    
+   - `image_index` is the line index which starts from zero. `label_index` is in range [0, class_num - 1].
+
+   For example:
+
+   ```
+   0 xxx/xxx/a.jpg 1920 1080 0 453 369 473 391 1 588 245 608 268
+   1 xxx/xxx/b.jpg 1920 1080 1 466 403 485 422 2 793 300 809 320
+   ...
+   ```
+
+
+### 3. Offline Inference
+
+**Convert pb to om.**
+
+- Configure the env according to your installation path 
+
+  ```
+  #Please modify the environment settings as needed
+  export install_path=/usr/local/Ascend
+  export PATH=/usr/local/python3.7.5/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH
+  export PYTHONPATH=${install_path}/atc/python/site-packages:${install_path}/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/atc/python/site-packages/schedule_search.egg:$PYTHONPATH
+  export LD_LIBRARY_PATH=${install_path}/atc/lib64:${install_path}/acllib/lib64:$LD_LIBRARY_PATH
+  export ASCEND_OPP_PATH=${install_path}/opp
+  ```
+
+- convert pb to om
+
+  [pb download link](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/modelzoo/yolov3_tf.pb)
+
+  For Ascend310:
+  ```
+  atc --model=yolov3_tf.pb --framework=3 --output=yolov3_tf_aipp --output_type=FP32 --soc_version=Ascend310 --input_shape="input:1,416,416,3" --log=info --insert_op_conf=yolov3_tf_aipp.cfg
+  ```
+  For Ascend310P3:
+  ```
+  atc --model=yolov3_tf.pb --framework=3 --output=yolov3_tf_aipp --output_type=FP32 --soc_version=Ascend310P3 --input_shape="input:1,416,416,3" --log=info --insert_op_conf=yolov3_tf_aipp.cfg
+  ```
+
+- Build the program
+
+  For Ascend310:
+  ```
+  unset ASCEND310P3_DVPP
+  bash build.sh
+  ```
+  For Ascend310P3:
+  ```
+  export ASCEND310P3_DVPP=1
+  bash build.sh
+  ```
+
+- Run the program:
+
+  ```
+  cd scripts
+  bash benchmark_tf.sh --batchSize=1 --modelType=yolov3 --imgType=raw --precision=fp16 --outputType=fp32 --useDvpp=1 --deviceId=0 --modelPath=yolov3_tf_aipp.om --trueValuePath=instance_val2014.json --imgInfoFile=coco2014_minival.txt --classNamePath=../../coco.names
+  ```
+
+
+
+## Performance
+
+### Result
+
+Our result were obtained by running the applicable inference script. To achieve the same results, follow the steps in the Quick Start Guide.
+
+#### Inference accuracy results
+
+IoU=0.5
+| model  | Npu_nums | **mAP** | 
+| :----: | :------: | :-----: | 
+| Yolov3 |    1     |  55.3%   | 
+
+## Reference
+[1] https://gitee.com/ascend/ModelZoo-TensorFlow/tree/master/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow
-- 
Gitee


From a9010cdceb331db5003343e2100ddfd6900aeefe Mon Sep 17 00:00:00 2001
From: lrving <2415912278@qq.com>
Date: Wed, 19 Oct 2022 06:41:59 +0000
Subject: [PATCH 2/9] update
 ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL/README_EN.md.

Signed-off-by: lrving <2415912278@qq.com>
---
 .../built-in/cv/YOLOv3_for_ACL/README_EN.md          | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL/README_EN.md b/ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL/README_EN.md
index 806f8b624..efdce8678 100644
--- a/ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL/README_EN.md
+++ b/ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL/README_EN.md
@@ -1,4 +1,4 @@
-
+English|[中文](README.md)
 
 # Yolov3 Inference for Tensorflow 
 
@@ -63,15 +63,9 @@ opencv-python==4.2.0.34
 
 **Convert pb to om.**
 
-- Configure the env according to your installation path 
+- configure the env
 
-  ```
-  #Please modify the environment settings as needed
-  export install_path=/usr/local/Ascend
-  export PATH=/usr/local/python3.7.5/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH
-  export PYTHONPATH=${install_path}/atc/python/site-packages:${install_path}/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/atc/python/site-packages/schedule_search.egg:$PYTHONPATH
-  export LD_LIBRARY_PATH=${install_path}/atc/lib64:${install_path}/acllib/lib64:$LD_LIBRARY_PATH
-  export ASCEND_OPP_PATH=${install_path}/opp
+  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
-- 
Gitee


From bc95afe6002a4ca32f6f70c14f08998d8269cc8c Mon Sep 17 00:00:00 2001
From: lrving <2415912278@qq.com>
Date: Wed, 19 Oct 2022 07:04:22 +0000
Subject: [PATCH 3/9] update
 ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL/README.md.

Signed-off-by: lrving <2415912278@qq.com>
---
 .../built-in/cv/YOLOv3_for_ACL/README.md      | 67 +++++++++----------
 1 file changed, 30 insertions(+), 37 deletions(-)

diff --git a/ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL/README.md b/ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL/README.md
index f7d191f51..78e184231 100644
--- a/ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL/README.md
+++ b/ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL/README.md
@@ -1,40 +1,40 @@
+中文|[English](README_EN.md)
 
+# Yolov3 TensorFlow离线推理 
 
-# Yolov3 Inference for Tensorflow 
+此链接提供Yolov3 TensorFlow模型在NPU上离线推理的脚本和方法
 
-This repository provides a script and recipe to Inference the Yolov3 model.
+## 注意
+**此案例仅为您学习Ascend软件栈提供参考，不用于商业目的。**
 
-## 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.
+在开始之前，请注意以下适配条件。如果不匹配，可能导致运行失败。
 
 | Conditions | Need |
 | --- | --- |
-| CANN Version | >=5.0.3 |
-| Chip Platform| Ascend310/Ascend310P3 |
-| 3rd Party Requirements| Please follow the 'requirements.txt' |
+| CANN版本 | >=5.0.3 |
+| 芯片平台| Ascend310/Ascend310P3 |
+| 第三方依赖| 请参考 'requirements.txt' |
 
-## Quick Start Guide
+## 快速指南
 
-### 1. Clone the respository
+### 1. 拷贝代码
 
 ```shell
 git clone https://gitee.com/ascend/ModelZoo-TensorFlow.git
 cd Modelzoo-TensorFlow/ACL_TensorFlow/built-in/cv/YOLOv3_for_ACL
 ```
 
-### 2. Requirements
+### 2. 必要条件
 
 opencv-python==4.2.0.34
 
 
-### 3. Download and preprocess the dataset
+### 3. 下载数据集和预处理
 
-1. dataset
-  To compare with official implement, for example, we use [get_coco_dataset.sh](https://github.com/pjreddie/darknet/blob/master/scripts/get_coco_dataset.sh) to prepare our dataset.
+1. 数据集
+  例如，与官方实施相比，我们使用 [get_coco_dataset.sh](https://github.com/pjreddie/darknet/blob/master/scripts/get_coco_dataset.sh) 准备我们的数据集。
 
-2. annotation file
+2. 注释文件
 
    cd scripts
 
@@ -50,7 +50,7 @@ opencv-python==4.2.0.34
    - `label_index x_min y_min x_max y_max`. (The origin of coordinates is at the left top corner, left top => (xmin, ymin), right bottom => (xmax, ymax).)    
    - `image_index` is the line index which starts from zero. `label_index` is in range [0, class_num - 1].
 
-   For example:
+   例如:
 
    ```
    0 xxx/xxx/a.jpg 1920 1080 0 453 369 473 391 1 588 245 608 268
@@ -59,24 +59,17 @@ opencv-python==4.2.0.34
    ```
 
 
-### 3. Offline Inference
+### 3. 离线推理
 
-**Convert pb to om.**
+**离线模型转换**
 
-- Configure the env according to your installation path 
+- 环境变量设置
 
-  ```
-  #Please modify the environment settings as needed
-  export install_path=/usr/local/Ascend
-  export PATH=/usr/local/python3.7.5/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH
-  export PYTHONPATH=${install_path}/atc/python/site-packages:${install_path}/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/atc/python/site-packages/schedule_search.egg:$PYTHONPATH
-  export LD_LIBRARY_PATH=${install_path}/atc/lib64:${install_path}/acllib/lib64:$LD_LIBRARY_PATH
-  export ASCEND_OPP_PATH=${install_path}/opp
-  ```
+  请参考[说明](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)，设置环境变量
 
-- convert pb to om
+- Pb模型转换为om模型
 
-  [pb download link](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/modelzoo/yolov3_tf.pb)
+  [pb模型下载链接](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/modelzoo/yolov3_tf.pb)
 
   For Ascend310:
   ```
@@ -87,7 +80,7 @@ opencv-python==4.2.0.34
   atc --model=yolov3_tf.pb --framework=3 --output=yolov3_tf_aipp --output_type=FP32 --soc_version=Ascend310P3 --input_shape="input:1,416,416,3" --log=info --insert_op_conf=yolov3_tf_aipp.cfg
   ```
 
-- Build the program
+- 编译程序
 
   For Ascend310:
   ```
@@ -100,7 +93,7 @@ opencv-python==4.2.0.34
   bash build.sh
   ```
 
-- Run the program:
+- 开始运行:
 
   ```
   cd scripts
@@ -109,18 +102,18 @@ opencv-python==4.2.0.34
 
 
 
-## Performance
+## 推理结果
 
-### Result
+### 结果
 
-Our result were obtained by running the applicable inference script. To achieve the same results, follow the steps in the Quick Start Guide.
+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《快速指南》中的步骤操作。
 
-#### Inference accuracy results
+#### 推理精度结果
 
 IoU=0.5
 | model  | Npu_nums | **mAP** | 
 | :----: | :------: | :-----: | 
 | Yolov3 |    1     |  55.3%   | 
 
-## Reference
+## 参考
 [1] https://gitee.com/ascend/ModelZoo-TensorFlow/tree/master/TensorFlow/built-in/cv/detection/YoloV3_ID0076_for_TensorFlow
-- 
Gitee


From 232135bcf288d0a9dd8bdff3d516f92b6cc28e51 Mon Sep 17 00:00:00 2001
From: lrving <2415912278@qq.com>
Date: Wed, 19 Oct 2022 07:14:32 +0000
Subject: [PATCH 4/9] add
 ACL_TensorFlow/built-in/cv/YOLOv4_for_ACL/README_EN.md.

Signed-off-by: lrving <2415912278@qq.com>
---
 .../built-in/cv/YOLOv4_for_ACL/README_EN.md   | 91 +++++++++++++++++++
 1 file changed, 91 insertions(+)
 create mode 100644 ACL_TensorFlow/built-in/cv/YOLOv4_for_ACL/README_EN.md

diff --git a/ACL_TensorFlow/built-in/cv/YOLOv4_for_ACL/README_EN.md b/ACL_TensorFlow/built-in/cv/YOLOv4_for_ACL/README_EN.md
new file mode 100644
index 000000000..8f8cc59ac
--- /dev/null
+++ b/ACL_TensorFlow/built-in/cv/YOLOv4_for_ACL/README_EN.md
@@ -0,0 +1,91 @@
+English|[中文](README.md)
+
+# YOLOv4 Inference for Tensorflow 
+
+This repository provides a script and recipe to Inference of the YOLOv4 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/YOLOv4_for_ACL
+```
+
+### 2. Download and preprocess the dataset
+
+1. Download the COCO-2017 validation dataset by yourself. 
+
+2. Put pictures to **'scripts/val2017'**
+
+3. Images Preprocess:
+```
+cd scripts
+mkdir input_bins
+python3 preprocess.py ./val2017/ ./input_bins/
+```
+   The pictures will be preprocessed to bin files.
+
+4. Split ground-truth labels from **instances_val2017.json**
+```
+python3 load_coco_json.py
+```
+
+### 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/yolov4_tf.pb)
+
+  ```
+  atc --model=yolov4_tf.pb --framework=3 --output=yolov4_tf_1batch --output_type=FP32 --soc_version=Ascend310 --input_shape="x:1,416,416,3" --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**  |    mAP    |
+| :---------------: | :-------: | :-------------: |
+| offline Inference | 5000 images | 60.7% |
+
+
+## Reference
+[1] https://github.com/hunglc007/tensorflow-yolov4-tflite
+
+[2] https://github.com/rafaelpadilla/Object-Detection-Metrics
-- 
Gitee


From 64ea11cc6689b2463ef60aa97e8d413905c17d48 Mon Sep 17 00:00:00 2001
From: lrving <2415912278@qq.com>
Date: Wed, 19 Oct 2022 07:21:23 +0000
Subject: [PATCH 5/9] update
 ACL_TensorFlow/built-in/cv/YOLOv4_for_ACL/README.md.

Signed-off-by: lrving <2415912278@qq.com>
---
 .../built-in/cv/YOLOv4_for_ACL/README.md      | 63 +++++++++----------
 1 file changed, 29 insertions(+), 34 deletions(-)

diff --git a/ACL_TensorFlow/built-in/cv/YOLOv4_for_ACL/README.md b/ACL_TensorFlow/built-in/cv/YOLOv4_for_ACL/README.md
index 70a81cab7..17bcc4b9d 100644
--- a/ACL_TensorFlow/built-in/cv/YOLOv4_for_ACL/README.md
+++ b/ACL_TensorFlow/built-in/cv/YOLOv4_for_ACL/README.md
@@ -1,36 +1,36 @@
+中文|[English](README_EN.md)
 
+# YOLOv4 TensorFlow离线推理 
 
-# YOLOv4 Inference for Tensorflow 
+此链接提供YOLOv4 TensorFlow模型在NPU上离线推理的脚本和方法
 
-This repository provides a script and recipe to Inference of the YOLOv4 model.
+## 注意
+**此案例仅为您学习Ascend软件栈提供参考，不用于商业目的。**
 
-## 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.
+在开始之前，请注意以下适配条件。如果不匹配，可能导致运行失败。
 
 | Conditions | Need |
 | --- | --- |
-| CANN Version | >=5.0.3 |
-| Chip Platform| Ascend310/Ascend310P3 |
-| 3rd Party Requirements| Please follow the 'requirements.txt' |
+| CANN版本 | >=5.0.3 |
+| 芯片平台| Ascend310/Ascend310P3 |
+| 第三方依赖| 请参考 'requirements.txt' |
 
-## Quick Start Guide
+## 快速指南
 
-### 1. Clone the respository
+### 1. 拷贝代码
 
 ```shell
 git clone https://gitee.com/ascend/ModelZoo-TensorFlow.git
 cd Modelzoo-TensorFlow/ACL_TensorFlow/built-in/cv/YOLOv4_for_ACL
 ```
 
-### 2. Download and preprocess the dataset
+### 2. 下载数据集和预处理
 
-1. Download the COCO-2017 validation dataset by yourself. 
+1. 自行下载COCO-2017验证数据集。
 
-2. Put pictures to **'scripts/val2017'**
+2. 将图片放到 **'scripts/val2017'**
 
-3. Images Preprocess:
+3. 图像预处理:
 ```
 cd scripts
 mkdir input_bins
@@ -43,55 +43,50 @@ python3 preprocess.py ./val2017/ ./input_bins/
 python3 load_coco_json.py
 ```
 
-### 3. Offline Inference
+### 3. 离线推理
 
-**Convert pb to om.**
+**离线模型转换**
 
-- configure the env
+- 环境变量设置
 
-  ```
-  export install_path=/usr/local/Ascend
-  export PATH=/usr/local/python3.7.5/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH
-  export PYTHONPATH=${install_path}/atc/python/site-packages:${install_path}/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/atc/python/site-packages/schedule_search.egg:$PYTHONPATH
-  export LD_LIBRARY_PATH=${install_path}/atc/lib64:${install_path}/acllib/lib64:$LD_LIBRARY_PATH
-  export ASCEND_OPP_PATH=${install_path}/opp
-  ```
+  请参考[说明](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)，设置环境变量
+
+- Pb模型转换为om模型
 
-- convert pb to om
 
-  [pb download link](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/modelzoo/yolov4_tf.pb)
+  [pb模型下载链接](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/modelzoo/yolov4_tf.pb)
 
   ```
   atc --model=yolov4_tf.pb --framework=3 --output=yolov4_tf_1batch --output_type=FP32 --soc_version=Ascend310 --input_shape="x:1,416,416,3" --log=info
   ```
 
-- Build the program
+- 编译程序
 
   ```
   bash build.sh
   ```
 
-- Run the program:
+- 开始运行:
 
   ```
   cd scripts
   bash benchmark_tf.sh
   ```
 
-## Performance
+## 推理结果
 
-### Result
+### 结果
 
-Our result was obtained by running the applicable inference script. To achieve the same results, follow the steps in the Quick Start Guide.
+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《快速指南》中的步骤操作。
 
-#### Inference accuracy results
+#### 推理精度结果
 
 |       model       | **data**  |    mAP    |
 | :---------------: | :-------: | :-------------: |
 | offline Inference | 5000 images | 60.7% |
 
 
-## Reference
+## 参考
 [1] https://github.com/hunglc007/tensorflow-yolov4-tflite
 
 [2] https://github.com/rafaelpadilla/Object-Detection-Metrics
-- 
Gitee


From 295bd38a4a2c697206c39cb7649efd73579b81ff Mon Sep 17 00:00:00 2001
From: lrving <2415912278@qq.com>
Date: Wed, 19 Oct 2022 07:34:45 +0000
Subject: [PATCH 6/9] add
 ACL_TensorFlow/contrib/cv/Yolov5_for_ACL/README_EN.md.

Signed-off-by: lrving <2415912278@qq.com>
---
 .../contrib/cv/Yolov5_for_ACL/README_EN.md    | 95 +++++++++++++++++++
 1 file changed, 95 insertions(+)
 create mode 100644 ACL_TensorFlow/contrib/cv/Yolov5_for_ACL/README_EN.md

diff --git a/ACL_TensorFlow/contrib/cv/Yolov5_for_ACL/README_EN.md b/ACL_TensorFlow/contrib/cv/Yolov5_for_ACL/README_EN.md
new file mode 100644
index 000000000..794499120
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/Yolov5_for_ACL/README_EN.md
@@ -0,0 +1,95 @@
+English|[中文](README.md)
+
+# Yolov5 Inference for Tensorflow 
+
+This repository provides a script and recipe to Inference of the Yolov5 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/contrib/cv/Yolov5_for_ACL
+```
+
+### 2. Download and preprocess the dataset
+
+1. Refer to this [url](https://github.com/hunglc007/tensorflow-yolov4-tflite/README.md) to download and preprocess the dataset
+The operation is as follows:
+```
+# run script in /script/get_coco_dataset_2017.sh to download COCO 2017 Dataset
+# preprocess coco dataset
+cd data
+mkdir dataset
+cd ..
+cd scripts
+python coco_convert.py --input ./coco/annotations/instances_val2017.json --output val2017.pkl
+python coco_annotation.py --coco_path ./coco 
+```
+There will generate coco2017 test data set under *data/dataset/*.
+
+### 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
+
+  ```
+  atc --model=yolov5_tf2_gpu.pb --framework=3 --output=yolov5_tf2_gpu --soc_version=Ascend310 --input_shape="Input:1,640,640,3" --out_nodes="Identity:0;Identity_1:0;Identity_2:0;Identity_3:0;Identity_4:0;Identity_5:0" --log=info
+  ```
+
+- Build the program
+
+  ```
+  bash build.sh
+  ```
+
+- Run the program:
+
+  ```
+  cd offline_inference
+  bash benchmark_tf.sh
+  ```
+  
+- Run the post process:
+
+  ```
+  cd ..
+  python3 offline_inference/postprocess.py
+  ```
+  
+## Performance
+
+### Result
+
+Our result were 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**  |   AP/AR   |
+| :---------------: | :-------: | :-----------: |
+| offline Inference | 4952 images | 0.221/0.214 |
+  
+
+## Reference
+[1] https://github.com/hunglc007/tensorflow-yolov4-tflite
+
+[2] https://github.com/ultralytics/yolov5
+
+[3]https://github.com/khoadinh44/YOLOv5_customized_data
-- 
Gitee


From e0eecdc2ebf498e7c3b78fbbc2ea15aee8d85cd3 Mon Sep 17 00:00:00 2001
From: lrving <2415912278@qq.com>
Date: Wed, 19 Oct 2022 07:40:35 +0000
Subject: [PATCH 7/9] update
 ACL_TensorFlow/contrib/cv/Yolov5_for_ACL/README.md.

Signed-off-by: lrving <2415912278@qq.com>
---
 .../contrib/cv/Yolov5_for_ACL/README.md       | 62 +++++++++----------
 1 file changed, 28 insertions(+), 34 deletions(-)

diff --git a/ACL_TensorFlow/contrib/cv/Yolov5_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/Yolov5_for_ACL/README.md
index d7d5bed61..bff0e02ee 100644
--- a/ACL_TensorFlow/contrib/cv/Yolov5_for_ACL/README.md
+++ b/ACL_TensorFlow/contrib/cv/Yolov5_for_ACL/README.md
@@ -1,33 +1,33 @@
+中文|[English](README_EN.md)
 
+# Yolov5 TensorFlow离线推理
 
-# Yolov5 Inference for Tensorflow 
+此链接提供Yolov5 TensorFlow模型在NPU上离线推理的脚本和方法
 
-This repository provides a script and recipe to Inference of the Yolov5 model.
+## 注意
+**此案例仅为您学习Ascend软件栈提供参考，不用于商业目的。**
 
-## 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.
+在开始之前，请注意以下适配条件。如果不匹配，可能导致运行失败。
 
 | Conditions | Need |
 | --- | --- |
-| CANN Version | >=5.0.3 |
-| Chip Platform| Ascend310/Ascend310P3 |
-| 3rd Party Requirements| Please follow the 'requirements.txt' |
+| CANN版本 | >=5.0.3 |
+| 芯片平台| Ascend310/Ascend310P3 |
+| 第三方依赖| 请参考 'requirements.txt' |
 
-## Quick Start Guide
+## 快速指南
 
-### 1. Clone the respository
+### 1. 拷贝代码
 
 ```shell
 git clone https://gitee.com/ascend/ModelZoo-TensorFlow.git
 cd Modelzoo-TensorFlow/ACL_TensorFlow/contrib/cv/Yolov5_for_ACL
 ```
 
-### 2. Download and preprocess the dataset
+### 2. 下载数据集和预处理
 
-1. Refer to this [url](https://github.com/hunglc007/tensorflow-yolov4-tflite/README.md) to download and preprocess the dataset
-The operation is as follows:
+1. 参考此URL[url](https://github.com/hunglc007/tensorflow-yolov4-tflite/README.md)下载并预处理数据集
+操作如下:
 ```
 # run script in /script/get_coco_dataset_2017.sh to download COCO 2017 Dataset
 # preprocess coco dataset
@@ -38,62 +38,56 @@ cd scripts
 python coco_convert.py --input ./coco/annotations/instances_val2017.json --output val2017.pkl
 python coco_annotation.py --coco_path ./coco 
 ```
-There will generate coco2017 test data set under *data/dataset/*.
+生成coco2017测试数据集目录 *data/dataset/*.
 
-### 3. Offline Inference
+### 3. 离线推理
 
-**Convert pb to om.**
+**离线模型转换**
 
-- configure the env
+- 环境变量设置
 
-  ```
-  export install_path=/usr/local/Ascend
-  export PATH=/usr/local/python3.7.5/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH
-  export PYTHONPATH=${install_path}/atc/python/site-packages:${install_path}/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/atc/python/site-packages/schedule_search.egg:$PYTHONPATH
-  export LD_LIBRARY_PATH=${install_path}/atc/lib64:${install_path}/acllib/lib64:$LD_LIBRARY_PATH
-  export ASCEND_OPP_PATH=${install_path}/opp
-  ```
+  请参考[说明](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)，设置环境变量
 
-- convert pb to om
+- Pb模型转换为om模型
 
   ```
   atc --model=yolov5_tf2_gpu.pb --framework=3 --output=yolov5_tf2_gpu --soc_version=Ascend310 --input_shape="Input:1,640,640,3" --out_nodes="Identity:0;Identity_1:0;Identity_2:0;Identity_3:0;Identity_4:0;Identity_5:0" --log=info
   ```
 
-- Build the program
+- 编译程序
 
   ```
   bash build.sh
   ```
 
-- Run the program:
+- 开始运行:
 
   ```
   cd offline_inference
   bash benchmark_tf.sh
   ```
   
-- Run the post process:
+- 运行后期处理:
 
   ```
   cd ..
   python3 offline_inference/postprocess.py
   ```
   
-## Performance
+## 推理结果
 
-### Result
+### 结果
 
-Our result were obtained by running the applicable inference script. To achieve the same results, follow the steps in the Quick Start Guide.
+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《快速指南》中的步骤操作。
 
-#### Inference accuracy results
+#### 推理精度结果
 
 |       model       | **data**  |   AP/AR   |
 | :---------------: | :-------: | :-----------: |
 | offline Inference | 4952 images | 0.221/0.214 |
   
 
-## Reference
+## 参考
 [1] https://github.com/hunglc007/tensorflow-yolov4-tflite
 
 [2] https://github.com/ultralytics/yolov5
-- 
Gitee


From 69efc944cb6faffbc5988aa8d735e8649bb3c6c3 Mon Sep 17 00:00:00 2001
From: lrving <2415912278@qq.com>
Date: Wed, 19 Oct 2022 07:53:57 +0000
Subject: [PATCH 8/9] add
 ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/README_EN.md.

Signed-off-by: lrving <2415912278@qq.com>
---
 .../cv/Resnet50v1.5_for_ACL/README_EN.md      | 84 +++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/README_EN.md

diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/README_EN.md b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/README_EN.md
new file mode 100644
index 000000000..ba5015856
--- /dev/null
+++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/README_EN.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
-- 
Gitee


From f0b35c2b631d3e619681bc37c702940f8ad3043a Mon Sep 17 00:00:00 2001
From: lrving <2415912278@qq.com>
Date: Wed, 19 Oct 2022 07:58:55 +0000
Subject: [PATCH 9/9] update
 ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/README.md.

Signed-off-by: lrving <2415912278@qq.com>
---
 .../cv/Resnet50v1.5_for_ACL/README.md         | 62 +++++++++----------
 1 file changed, 28 insertions(+), 34 deletions(-)

diff --git a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/README.md b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/README.md
index 694ccc3ad..e5cc170ec 100644
--- a/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/README.md
+++ b/ACL_TensorFlow/built-in/cv/Resnet50v1.5_for_ACL/README.md
@@ -1,90 +1,84 @@
+中文|[English](README_EN.md)
 
+# Resnet50v1.5 TensorFlow离线推理
 
-# Resnet50v1.5 Inference for Tensorflow 
+此链接提供Resnet50v1.5 TensorFlow模型在NPU上离线推理的脚本和方法
 
-This repository provides a script and recipe to Inference of the Resnet50v1.5 model.
+## 注意
+**此案例仅为您学习Ascend软件栈提供参考，不用于商业目的。**
 
-## 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.
+在开始之前，请注意以下适配条件。如果不匹配，可能导致运行失败。
 
 | Conditions | Need |
 | --- | --- |
-| CANN Version | >=5.0.3 |
-| Chip Platform| Ascend310/Ascend310P3 |
-| 3rd Party Requirements| Please follow the 'requirements.txt' |
+| CANN版本 | >=5.0.3 |
+| 芯片平台| Ascend310/Ascend310P3 |
+| 第三方依赖| 请参考 'requirements.txt' |
 
-## Quick Start Guide
+## 快速指南
 
-### 1. Clone the respository
+### 1. 拷贝代码
 
 ```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
+### 2. 下载数据集和预处理
 
-1. Download the ImageNet2012 Validation dataset by yourself. You can get the validation pictures(50000 JPEGS and a ILSVRC2012val-label-index.txt)
+1. 请自行下载ImageNet2012测试数据集。 你可以获得验证图片(50000 JPEGS and a ILSVRC2012val-label-index.txt)
 
 2. Put JPEGS to **'scripts/ILSVRC2012val'** and label text to **'scripts/'**
 
-3. Images Preprocess:
+3. 图像预处理:
 ```
 cd scripts
 mkdir input_bins
 python3 resnet50v15_preprocessing.py ./ILSVRC2012val/ ./input_bins/
 ```
-The jpegs pictures will be preprocessed to bin fils.
+jpegs图片将被预处理为bin-fils。
 
-### 3. Offline Inference
+### 3. 离线推理
 
-**Convert pb to om.**
+**离线模型转换**
 
-- configure the env
+- 环境变量设置
 
-  ```
-  export install_path=/usr/local/Ascend
-  export PATH=/usr/local/python3.7.5/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH
-  export PYTHONPATH=${install_path}/atc/python/site-packages:${install_path}/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/atc/python/site-packages/schedule_search.egg:$PYTHONPATH
-  export LD_LIBRARY_PATH=${install_path}/atc/lib64:${install_path}/acllib/lib64:$LD_LIBRARY_PATH
-  export ASCEND_OPP_PATH=${install_path}/opp
-  ```
+  请参考[说明](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)，设置环境变量
 
-- convert pb to om
+- Pb模型转换为om模型
 
-  [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)
+  [pb模型下载链接](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
+- 编译程序
 
   ```
   bash build.sh
   ```
 
-- Run the program:
+- 开始运行:
 
   ```
   cd scripts
   bash benchmark_tf.sh
   ```
 
-## Performance
+## 推理结果
 
-### Result
+### 结果
 
-Our result was obtained by running the applicable inference script. To achieve the same results, follow the steps in the Quick Start Guide.
+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《快速指南》中的步骤操作。
 
-#### Inference accuracy results
+#### 推理精度结果
 
 |       model       | **data**  |    Top1/Top5    |
 | :---------------: | :-------: | :-------------: |
 | offline Inference | 50000 images | 76.5 %/ 93.1% |
 
-## Reference
+## 参考
 
 [1] https://github.com/IntelAI/models
-- 
Gitee