face-anti-spoofing:防欺骗,mob​​ilenetv2,焦点丢失

上传者: 42138703 | 上传时间: 2022-08-11 22:16:21 | 文件大小: 9.89MB | 文件类型: ZIP
使用MXNet进行FaceNet Spoofing的MobileNetV2的复制 我建议使用mxnet版本进行分类,该版本已在ImageNet上进行了预训练。 项目说明 创建于: lxy和shj 时间: 2018/12/10 15:09 项目面部反欺骗 公司: 版本: 0.1 工具: python 2.7 修改的: 描述:培训和测试代码 要求 张量流> = 1.5.0 python> = 2.7.15 opencv> = 3.4.0 咖啡 ga 训练数据 培训数据使用工具从Internet下载 我们创建了包括4类的数据集(手机:1电视:2 telectroller:3背景:0)。 运行培训和测试演示 配置参数位于Root / src / configs / config.py中 目录 数据用于存储训练和测试数据。 日志用于存储训练日志。 模型用于存储网络参数。 src用于存储培训和测

文件下载

资源详情

[{"title":"( 40 个子文件 9.89MB ) face-anti-spoofing:防欺骗,mob​​ilenetv2,焦点丢失","children":[{"title":"face-anti-spoofing-master","children":[{"title":"data","children":[{"title":"FaceAnti","children":[{"title":"property.txt <span style='color:#111;'> 6B </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"src","children":[{"title":"prepare_data","children":[{"title":"convert_data_to_tfrecord.py <span style='color:#111;'> 8.61KB </span>","children":null,"spread":false},{"title":"read_multi_tfrecord.py <span style='color:#111;'> 2.25KB </span>","children":null,"spread":false},{"title":"image_preprocess.py <span style='color:#111;'> 10.85KB </span>","children":null,"spread":false},{"title":"run.sh <span style='color:#111;'> 1.34KB </span>","children":null,"spread":false},{"title":"run_script.sh <span style='color:#111;'> 5.74KB </span>","children":null,"spread":false},{"title":"read_tfrecord.py <span style='color:#111;'> 5.96KB </span>","children":null,"spread":false},{"title":"aug_failed.txt <span style='color:#111;'> 0B </span>","children":null,"spread":false}],"spread":true},{"title":"tf2caffe.py <span style='color:#111;'> 29.79KB </span>","children":null,"spread":false},{"title":"face_test","children":[{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"tools_matrix.py <span style='color:#111;'> 9.38KB </span>","children":null,"spread":false},{"title":"align.py <span style='color:#111;'> 6.30KB </span>","children":null,"spread":false},{"title":"run.sh <span style='color:#111;'> 87B </span>","children":null,"spread":false},{"title":"test.py <span style='color:#111;'> 13.72KB </span>","children":null,"spread":false},{"title":"Detector.py <span style='color:#111;'> 8.96KB </span>","children":null,"spread":false}],"spread":true},{"title":"losses","children":[{"title":"loss.py <span style='color:#111;'> 5.62KB </span>","children":null,"spread":false}],"spread":true},{"title":"utils","children":[{"title":"get_property.py <span style='color:#111;'> 537B </span>","children":null,"spread":false},{"title":"transform.py <span style='color:#111;'> 17.95KB </span>","children":null,"spread":false},{"title":"imgpad.py <span style='color:#111;'> 3.89KB </span>","children":null,"spread":false}],"spread":true},{"title":"network","children":[{"title":"lenet5.py <span style='color:#111;'> 3.66KB </span>","children":null,"spread":false},{"title":"mobilenetV2.py <span style='color:#111;'> 7.31KB </span>","children":null,"spread":false},{"title":"resnet.py <span style='color:#111;'> 6.51KB </span>","children":null,"spread":false}],"spread":true},{"title":"configs","children":[{"title":"config.py <span style='color:#111;'> 2.92KB </span>","children":null,"spread":false}],"spread":true},{"title":"train","children":[{"title":"run.sh <span style='color:#111;'> 82B </span>","children":null,"spread":false},{"title":"train.py <span style='color:#111;'> 10.83KB </span>","children":null,"spread":false}],"spread":true},{"title":"test","children":[{"title":"get_model.py <span style='color:#111;'> 4.86KB </span>","children":null,"spread":false},{"title":"face_anti.py <span style='color:#111;'> 3.70KB </span>","children":null,"spread":false},{"title":"run.sh <span style='color:#111;'> 832B </span>","children":null,"spread":false},{"title":"demo.py <span style='color:#111;'> 7.64KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"models","children":[{"title":"FaceDetect","children":[{"title":"48net.caffemodel <span style='color:#111;'> 1.49MB </span>","children":null,"spread":false},{"title":"24net.prototxt <span style='color:#111;'> 2.90KB </span>","children":null,"spread":false},{"title":"48net.prototxt <span style='color:#111;'> 3.84KB </span>","children":null,"spread":false},{"title":"12net.caffemodel <span style='color:#111;'> 27.50KB </span>","children":null,"spread":false},{"title":"12net.prototxt <span style='color:#111;'> 2.30KB </span>","children":null,"spread":false},{"title":"24net.caffemodel <span style='color:#111;'> 398.35KB </span>","children":null,"spread":false}],"spread":true},{"title":"FaceAnti","children":[{"title":"mobilenetv2-1_1-symbol.json <span style='color:#111;'> 113.98KB </span>","children":null,"spread":false},{"title":"mobilenetv2-1_0-0000.params <span style='color:#111;'> 8.64MB </span>","children":null,"spread":false},{"title":"checkpoint <span style='color:#111;'> 120B </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"README.md <span style='color:#111;'> 2.22KB </span>","children":null,"spread":false},{"title":"logs","children":[{"title":"events.out.tfevents.1557998098.lxy-ThinkPad-E480 <span style='color:#111;'> 649.54KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明