dlib C++ Library

Training deep learning models can be a pain. In particular, there is this perception that one of the reasons it's a pain is because you have to fiddle with learning rates. For example, arguably the most popular strategy for setting learning rates looks...

I get asked a lot of questions about dlib's landmarking tools. Some of the most common questions are about how to prepare a good training dataset. One of the most useful tricks for creating a dataset is to mirror the data, since this effectively doubles...

Here is a common problem: you have some machine learning algorithm you want to use but it has these damn hyperparameters. These are numbers like weight decay magnitude, Gaussian kernel width, and so forth. The algorithm doesn't set them, instead, it...

Dlib 19.8 is officially out. There are a lot of changes, but the two most interesting ones are probably the new global optimizer and semantic segmentation examples.  The global optimizer is definitely my favorite as it allows you to easily find the optimal...