HRNet is a state-of-the-art algorithm in the field of semantic segmentation, facial landmark detection, and human pose estimation. It has shown superior results in semantic segmentation on datasets like PASCAL Context, LIP, Cityscapes, AFLW, COFW, and 300W. But first, let's understand what the fields mean and what kind of algorithm hides behind HRNet. Semantic Segmentation is used to categorize structures of an image into certain classes. This is done by labeling each pixel with a certain class [3].

Face landmark detection is a computer vision task where we want to detect and track keypoints from a human face. This task applies to many problems. For example, we can use the keypoints for detecting a human's head pose position and rotation. With that, we can track whether a driver is paying attention or not. Also, we can use the keypoints for applying an augmented reality easier.

Dlib is a library for applying machine learning and computer vision solutions. This library is based on the C language, but we can use a language like Python for using the library. One of the solutions that we can apply by using this library is face landmark detection. Now let's get into the implementation. Installing a library can become a problem.

Face Detection Systems have great uses in today's world which demands security, accessibility or joy! Today, we will be building a model that can plot 15 key points on a face. Face Landmark Detection models form various features we see in social media apps. The face filters you find on Instagram are a common use case.