From 9fc531bc95c38dbe24937e63734c76024aa6e7ce Mon Sep 17 00:00:00 2001 From: Chery <376067609@qq.com> Date: Mon, 31 Aug 2020 14:44:52 +0800 Subject: [PATCH] update title. --- lite/tutorials/source_en/quick_start/quick_start.md | 6 +++--- lite/tutorials/source_zh_cn/quick_start/quick_start.md | 8 ++++---- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/lite/tutorials/source_en/quick_start/quick_start.md b/lite/tutorials/source_en/quick_start/quick_start.md index 349d7a4dde..5a7bfb88af 100644 --- a/lite/tutorials/source_en/quick_start/quick_start.md +++ b/lite/tutorials/source_en/quick_start/quick_start.md @@ -1,8 +1,8 @@ -# Quick Start +# Implementing an Image Classification Application -- [Quick Start ](#quick-start) +- [Implementing an Image Classification Application](#implementing-an-image-classification-application) - [Overview](#overview) - [Selecting a Model](#selecting-a-model) - [Converting a Model](#converting-a-model) @@ -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/tutorial/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/build.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`.　　 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 f7bb41de2d..8c264bd3dc 100644 --- a/lite/tutorials/source_zh_cn/quick_start/quick_start.md +++ b/lite/tutorials/source_zh_cn/quick_start/quick_start.md @@ -1,8 +1,8 @@ -# 快速入门 +# 实现一个图像分类应用 -- [快速入门](#快速入门) +- [实现一个图像分类应用](#实现一个图像分类应用) - [概述](#概述) - [选择模型](#选择模型) - [转换模型](#转换模型) @@ -33,7 +33,7 @@ ## 选择模型 MindSpore团队提供了一系列预置终端模型，你可以在应用程序中使用这些预置的终端模型。 -MindSpore Model Zoo中图像分类模型可[在此下载]((https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetv2.ms))。 +MindSpore Model Zoo中图像分类模型可[在此下载](https://download.mindspore.cn/model_zoo/official/lite/mobilenetv2_openimage_lite/mobilenetv2.ms)。 同时，你也可以使用预置模型做迁移学习，以实现自己的图像分类任务。 ## 转换模型 @@ -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/tutorial/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/build.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。 -- Gitee