diff --git a/lite/tutorials/source_en/quick_start/quick_start.md b/lite/tutorials/source_en/quick_start/quick_start.md index 1155439d33ee347bfc65bcac4c17551aa3347947..349d7a4ddeae35ca90d1dc172503cd4e136693d9 100644 --- a/lite/tutorials/source_en/quick_start/quick_start.md +++ b/lite/tutorials/source_en/quick_start/quick_start.md @@ -41,9 +41,9 @@ In addition, you can use the preset model to perform migration learning to imple 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/en/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. +Take the mobilenetv2 model as an example. Execute the following script to convert a model into a MindSpore Lite model for on-device inference. ```bash -./converter_lite --fmk=MS --modelFile=mobilenet_v2.mindir --outputFile=mobilenet_v2.ms +./converter_lite --fmk=MS --modelFile=mobilenetv2.mindir --outputFile=mobilenetv2.ms ``` ## Deploying an Application @@ -54,7 +54,7 @@ The following section describes how to build and execute an on-device image clas - Android Studio 3.2 or later (Android 4.0 or later is recommended.) - Native development kit (NDK) 21.3 -- CMake 10.1 +- CMake 3.10.2 - Android software development kit (SDK) 26 or later - OpenCV 4.0.0 or later (included in the sample code) @@ -133,7 +133,7 @@ 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://www.mindspore.cn/lite/docs/en/master/compile.html) 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/tutorial/en/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`.　　 @@ -195,7 +195,7 @@ libopencv include [libopencv include]( https://download.mindspore.cn/model_zoo/o ### Downloading and Deploying a Model File -In this example, the download.gradle File configuration auto download `mobilenet_v2.ms `and placed in the 'app / libs / arm64-v8a' directory. +In this example, the download.gradle File configuration auto download `mobilenetv2.ms `and 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. diff --git a/lite/tutorials/source_zh_cn/quick_start/quick_start.md b/lite/tutorials/source_zh_cn/quick_start/quick_start.md index 8ba907d0bc959f9b2bd46d2e43c1bb066d3b1604..f7bb41de2df45a84d477ab2d61a8fe032d3a24ff 100644 --- a/lite/tutorials/source_zh_cn/quick_start/quick_start.md +++ b/lite/tutorials/source_zh_cn/quick_start/quick_start.md @@ -40,9 +40,9 @@ MindSpore Model Zoo中图像分类模型可[在此下载]((https://download.mind 如果预置模型已经满足你要求，请跳过本章节。 如果你需要对MindSpore提供的模型进行重训，重训完成后，需要将模型导出为[.mindir格式](https://www.mindspore.cn/tutorial/zh-CN/master/use/saving_and_loading_model_parameters.html#mindir)。然后使用MindSpore Lite[模型转换工具](https://www.mindspore.cn/lite/tutorial/zh-CN/master/use/converter_tool.html)将.mindir模型转换成.ms格式。 -以MindSpore MobilenetV2模型为例，如下脚本将其转换为MindSpore Lite模型用于端侧推理。 +以mobilenetv2模型为例，如下脚本将其转换为MindSpore Lite模型用于端侧推理。 ```bash -./converter_lite --fmk=MS --modelFile=mobilenet_v2.mindir --outputFile=mobilenet_v2.ms +./converter_lite --fmk=MS --modelFile=mobilenetv2.mindir --outputFile=mobilenetv2.ms ``` ## 部署应用 @@ -53,7 +53,7 @@ MindSpore Model Zoo中图像分类模型可[在此下载]((https://download.mind - Android Studio >= 3.2 (推荐4.0以上版本) - NDK 21.3 -- CMake 10.1 +- CMake 3.10.2 - Android SDK >= 26 - OpenCV >= 4.0.0 （本示例代码已包含） @@ -134,7 +134,7 @@ 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://www.mindspore.cn/lite/docs/zh-CN/master/compile.html)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/tutorial/zh-CN/master/compile.html)生成`libmindspore-lite.so`库文件。 本示例中，bulid过程由download.gradle文件配置自动下载`libmindspore-lite.so`以及OpenCV的`libopencv_java4.so`库文件，并放置在`app/libs/arm64-v8a`目录下。 @@ -192,7 +192,7 @@ target_link_libraries( ### 下载及部署模型文件 -从MindSpore Model Hub中下载模型文件，本示例程序中使用的终端图像分类模型文件为`mobilenet_v2.ms`，同样通过`download.gradle`脚本在APP构建时自动下载，并放置在`app/src/main/assets`工程目录下。 +从MindSpore Model Hub中下载模型文件，本示例程序中使用的终端图像分类模型文件为`mobilenetv2.ms`，同样通过`download.gradle`脚本在APP构建时自动下载，并放置在`app/src/main/assets`工程目录下。 注：若下载失败请手工下载模型文件，mobilenetv2.ms [下载链接](https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetv2.ms)