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cribbing-detection's Introduction

作弊检测

目录结构

├── README.md                
├── datasets    # 预处理好的数据集        
├── model       # 存放训练的模型         
├── model_web   # 存放训练的模型      
├── prepare     # 准备数据
├── training    # 训练模型的 Python 程序
└── webui       # 前端展示，加载训练好的模型

使用方法

准备数据

cd prepare
python build_imagenet_data.py

产生的数据生成在 datasets 下面

训练模型

cd training
python mobilenet_train.py

转换模型供 js 脚本使用

tensorflowjs_converter  --input_format tf_frozen_model \
                        --output_format tfjs_graph_model \
                        --output_node_names='MobilenetV1/Predictions/Reshape_1'  \
                        model/xxx.pb model_web

因训练设备性能有限，训练模型仅使用了 ImageNet 的样例数据，未使用所有数据。

因此 model_web 下最终加载的模型为预训练好的模型。

提供模型下载服务

cd model_web
env FLASK_APP=tfjs-serving.py flask run

端口 5000

运行前端

保证存在 npm 运行环境并安装了 yarn

安装相关依赖

cd webui
yarn install

dev 方式运行 node 项目

yarn watch

cribbing-detection's People

Contributors

templezhou avatar dependabot[bot] avatar

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