DLIB：训练194个地标的Shape_predictor（海伦数据集）

Question

我正在使用 helen数据集训练 DLIB shape_predictor 194个面部地标，用于通过{检测面部地标dlib库的{1}}。

现在它给了我一个约{strong> 45 MB 的 int SaveChangesWithDelete(int userId) { using (var tx= Database.BeginTransaction()) //Begin a new transaction { var entitiyGroups = ChangeTracker.Entries() .Where(e => e.State == EntityState.Deleted && e.Entity is Auditable) .Select(e => e.Entity) .GroupBy(d => d.GetType(); foreach (var entityGroup in entitiyGroups) //Loop through deleted entities and run a manual UPDATE { string query = string.Format(@" UPDATE {0} SET UpdatedBy = {1} WHERE Id IN ({2}) ", entityGroup.Key.Name, userId, string.Join(",", entityGroup.Select(e => PrimaryKeyValue(e)))); Database.ExecuteSqlCommand(query); //Execute the query - this triggers the audit framework } int result = SaveChanges(); //save the context changes as normal tx.Commit(); //commit the transaction return result; //return the result } } 二进制文件，与68个面部地标的给定文件（http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2）相比较少。在培训中

• 平均训练错误：0.0203811
• 平均测试错误：0.0204511

当我使用经过训练的数据获得面部地标位置时，IN结果我得到了..

与68个地标的结果非常不同

68具有里程碑意义的形象：

为什么？

Answer 1

好的，好像你还没看过code评论（？）：

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);

查看Kazemi paper，ctrl-f字符串'参数'并阅读...