@@ -36,7 +36,7 @@ In addition, you can use the preset model to perform migration learning to imple
## Converting a Model
After you retrain a model provided by MindSpore, export the model in the [.mindir format](https://www.mindspore.cn/tutorial/en/master/use/saving_and_loading_model_parameters.html#mindir). Use the MindSpore Lite [model conversion tool](https://gitee.com/mindspore/docs/blob/master/lite/tutorials/source_en/use/converter_tool.md) to convert the .mindir model to a .ms model.
After you retrain a model provided by MindSpore, export the model in the [.mindir format](https://www.mindspore.cn/tutorial/en/master/use/saving_and_loading_model_parameters.html#mindir). Use the MindSpore Lite [model conversion tool](https://www.mindspore.cn/lite/tutorial/zh-CN/master/use/converter_tool.html) to convert the .mindir model to a .ms model.
Take the MindSpore MobileNetV2 model as an example. Execute the following script to convert a model into a MindSpore Lite model for on-device inference.
```bash
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@@ -86,7 +86,7 @@ The following section describes how to build and execute an on-device image clas
## Detailed Description of the Sample Program
This image classification sample program on the Android device includes a Java layer and a JNI layer. At the Java layer, the Android Camera 2 API is used to enable a camera to obtain image frames and process images. At the JNI layer, the model inference process is completed in [Runtime](https://gitee.com/mindspore/docs/blob/master/lite/tutorials/source_en/use/runtime.md).
This image classification sample program on the Android device includes a Java layer and a JNI layer. At the Java layer, the Android Camera 2 API is used to enable a camera to obtain image frames and process images. At the JNI layer, the model inference process is completed in [Runtime](https://www.mindspore.cn/lite/tutorial/zh-CN/master/use/runtime.html).
> This following describes the JNI layer implementation of the sample program. At the Java layer, the Android Camera 2 API is used to enable a device camera and process image frames. Readers are expected to have the basic Android development knowledge.
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@@ -132,7 +132,7 @@ app
### Configuring MindSpore Lite Dependencies
When MindSpore C++ APIs are called at the Android JNI layer, related library files are required. You can use MindSpore Lite [source code compilation](https://gitee.com/mindspore/docs/blob/master/lite/tutorials/source_en/compile.md) to generate the `libmindspore-lite.so` library file.
When MindSpore C++ APIs are called at the Android JNI layer, related library files are required. You can use MindSpore Lite [source code compilation](https://www.mindspore.cn/lite/docs/zh-CN/master/compile.html) to generate the `libmindspore-lite.so` library file.
In Android Studio, place the compiled `libmindspore-lite.so` library file (which can contain multiple compatible architectures) in the `app/libs/ARM64-V8a` (Arm64) or `app/libs/armeabi-v7a` (Arm32) directory of the application project. In the `build.gradle` file of the application, configure the compilation support of CMake, `arm64-v8a`, and `armeabi-v7a`.
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@@ -178,7 +178,7 @@ target_link_libraries(
In this example, the download.gradle File configuration auto download ` libmindspot-lite.so `and OpenCV_ Java4.so library file, placed in the 'app / libs / arm64-v8a' directory.
In this example, the download.gradle File configuration auto download ` libmindspot-lite.so `and `libopencv_ Java4.so` library file, placed in the 'app / libs / arm64-v8a' directory.
Note: if the automatic download fails, please manually download the relevant library files and put them in the corresponding location.
@@ -90,7 +90,7 @@ MindSpore Model Zoo中图像分类模型可[在此下载]((https://download.mind
## 示例程序详细说明
本端侧图像分类Android示例程序分为JAVA层和JNI层，其中，JAVA层主要通过Android Camera 2 API实现摄像头获取图像帧，以及相应的图像处理等功能；JNI层在[Runtime](https://gitee.com/mindspore/docs/blob/master/lite/tutorials/source_zh_cn/use/runtime.md)中完成模型推理的过程。
本端侧图像分类Android示例程序分为JAVA层和JNI层，其中，JAVA层主要通过Android Camera 2 API实现摄像头获取图像帧，以及相应的图像处理等功能；JNI层在[Runtime](https://www.mindspore.cn/lite/tutorial/zh-CN/master/use/runtime.html)中完成模型推理的过程。
> 此处详细说明示例程序的JNI层实现，JAVA层运用Android Camera 2 API实现开启设备摄像头以及图像帧处理等功能，需读者具备一定的Android开发基础知识。
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@@ -134,19 +134,19 @@ app
### 配置MindSpore Lite依赖项
Android JNI层调用MindSpore C++ API时，需要相关库文件支持。可通过MindSpore Lite[源码编译]https://gitee.com/mindspore/docs/blob/master/lite/tutorials/source_zh_cn/compile.md)生成`libmindspore-lite.so`库文件。
Android JNI层调用MindSpore C++ API时，需要相关库文件支持。可通过MindSpore Lite[源码编译](https://www.mindspore.cn/lite/docs/zh-CN/master/compile.html)生成`libmindspore-lite.so`库文件。
