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快速使用

创建一个ScyllaDB数据库容器, 点击start_database.bat启动数据库, 点击vegetable_recognition_backend.exe启动后端,确保model.safetensors在可执行文件同级目录下,

将app-debug.apk安装到Android设备上, 打开软件输入可访问的后端地址即可开始使用

源码说明

已去除训练数据集,如需训练可按照保留的验证集格式添加数据 rust后端调试需把model.safetensors放在CARGO_TARGET_DIR\debug\model.safetensors flutter前端只能在debug模式下运行

1 启动服务器 startup server

1.1 数据库 database

1.1.1 第一次启动 first time

docker pull scylladb/scylla
docker run --name scylla -p 9042:9042 -d scylladb/scylla

1.1.2 后续启动 subsequent time

docker start scylla

1.2 启动后端 startup backend

model.safetensors放在可执行文件vegetable_recognition_backend.exe同级目录下,然后运行vegetable_recognition_backend.exe

put model.safetensors in the same directory as the executable file vegetable_recognition_backend.exe, and then run vegetable_recognition_backend.exe

2 启动前端 startup frontend

2.1 编译Android应用 compile Android app

2.1.1 安装依赖 install dependencies

flutter pub get

2.1.2 编译 compile

flutter build apk

2.2 安装Android应用 install Android app

build/app/outputs/apk/release/app-debug.apkbuild/app/outputs/flutter-apk/app-debug.apk安装到Android设备上

put build/app/outputs/apk/release/app-release.apk or build/app/outputs/flutter-apk/app-debug.apk on Android device

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image classification with pytorch candle flutter

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