我正在使用 helen 数据集为 194 个面部地标训练DLIB的shape_predictor ，该数据集用于通过face_landmark_detection_ex.cppdlib 库检测面部地标。
现在它给了我一个大约45 MBsp.dat的二进制文件，与68 个面部标志的给定文件（ http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2 ）相比要少。在培训中
当我使用经过训练的数据来获取人脸地标位置时，我得到了结果..
这与从 68 个地标得到的结果有很大的偏差
68个地标图片：
为什么？
好的，看起来您还没有阅读代码注释（？）：
shape_predictor_trainer trainer;
// This algorithm has a bunch of parameters you can mess with. The
// documentation for the shape_predictor_trainer explains all of them.
// You should also read Kazemi's paper which explains all the parameters
// in great detail. However, here I'm just setting three of them
// differently than their default values. I'm doing this because we
// have a very small dataset. In particular, setting the oversampling
// to a high amount (300) effectively boosts the training set size, so
// that helps this example.
trainer.set_oversampling_amount(300);
// I'm also reducing the capacity of the model by explicitly increasing
// the regularization (making nu smaller) and by using trees with
// smaller depths.
trainer.set_nu(0.05);
trainer.set_tree_depth(2);
看看Kazemi 论文，按 ctrl-f 字符串 'parameter' 并阅读...