Face Landmark Detection Model MTCNN Implemented with PaddlePaddle

The article introduces the process of using MTCNN (Multi-Task Convolutional Neural Network) for face detection, which includes three hierarchical networks: P-Net, R-Net, and O-Net. P-Net is used to generate candidate windows, R-Net performs precise selection and regresses bounding boxes and key points, while O-Net further refines the output to get the final bounding box and key point locations. The project source code is hosted on GitHub and implemented using PaddlePaddle 2.0.1. The model training consists of three steps: first, training the PNet to generate candidate windows; then, using PNet data to train the RNet for... (Note: The original Chinese text appears to be truncated at this point; the translation continues as per the provided content.)