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项目名称为:自建数据集人脸识别。该项目利用电脑自带摄像头或者已有照片进行人脸数据集建立,再进行人脸检测,人脸识别,人脸预测,包括数据采集、数据预处理、建模、模型训练、模型使用预测全过程。项目使用Openc3进行数据采集、数据预处理,Keras 进行建模,模型参考了VGG16网络,包含4个卷积层,5个LeRu层,2个池化层,3个Dropout层,2个全连接层,1个flatten层,1个分类层,共18层。
项目名称为:SSD Keras实现。SSD是一个基于回归的目标检测与识别网络,其原论文是用CAFFE实现的,本项目将其用Keras实现,并用中文做了注释。SSD网络集成了Fast RCNN的损失函数,Faster RCNN 的Anchor机制,VGG16分类等**,是一个经典网络,很值得学习研究。
本项目实现了ocr主流算法gru/lstm+ctc+cnn架构,进行不定长度验证码识别,达到不分割字符而识别验证码内容的效果。验证码内容包含了大小字母以及数字,并增加点、线、颜色、位置、字体等干扰项。本项目对gru +ctc+cnn、lstm+ctc+cnn、cnn三种架构进行了对比,实践说明同等训练下gru/lstm+ctc+cnn架构准确率和速度均明显优于cnn架构,gru +ctc+cnn优于lstm+ctc+cnn,在实验2500个样本数据200轮训练时,gru +ctc+cnn架构在500样本测试准确率达90.2%。本项目技术能够训练长序列的ocr识别,更换数据集和相关调整,即可用于比如身份证号码、车牌、手机号、邮编等识别任务,也可用于汉字识别。
Spring Boot
MTCNN提出了一种Multi-task的人脸检测框架,将人脸检测和人脸特征点检测同时进行。提出一个新的基于CNN的级联型框架,用于联和(joint)人脸检测和对齐;还设计轻量级的CNN架构使得速度上可以达到实时;提出一个有效的online hard sample mining方法来提高表现能力;在人脸检测和人脸对齐上提高了不少精度。论文原文采用caffe实现,本项目用keras/tensorflow+python实现。
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