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。
你好！运行gen_middle_wideface_data.py r_net之后neg：120个，part：37个，pos：9个，landmark文件夹生成了，但是不能训练r_net.h5文件
Hi,
Thank you for your work.
Do you have a better README.md about usage of this project?
About Data, Train, Test and Inference
Thank you.
您好，为什么生成O_Net训练数据的时候，有很多的neg和part数据，但是pos数据却为0呢？
你好哦！我已经把pos、neg、part、landmark四个数据集训练好了（win10+pycharm+anaconda），p_net 网络的.h5文件也训练好了（loss为0.0897，acc为0.1426），请问一下如何生成r_net、o_net网络中的.h5文件。您在read.pdf 中说r_net、o_net对的训练数据正样本、负样本、部分样本是使用前面已经训练好的网络进行预测的，我尝试用gen_training_h5_data.py进行r_net网络，产生了pos、neg、part三个数据集及对应的TXT文件和middle_wideface_data_det.pkl文件，您能帮我解答一下疑问吗？谢谢
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