Developing Kubeflow Pipelines: Kaggle’s Facial Keypoints Detection Competition

Detect the location of keypoints on face images with machine learning.

Aug 24, 2022 06:00 AM Pacific Time (US and Canada)

Kubeflow familiarity
Beginner to Intermediate

About the Workshop
In this workshop we’ll show how to turn Kaggle’s Facial Keypoints Detection competition into a Kubeflow Pipeline using the KFP SDK and the Kale JupyterLab extension.

About the Kaggle Competition
The objective of the Facial Keypoints Detection Kaggle competition, as the site notes, is to predict keypoint positions on face images. This can be used as a building block in several applications, such as:

  • Tracking faces in images and video
  • Analyzing facial expressions
  • Detecting dysmorphic facial signs for medical diagnosis
  • Biometrics/face recognition
In the world of machine learning, it is well known that detecting facial keypoints is a very difficult problem to solve. This is because facial features vary significantly from person to person, and even for a specific individual, there is a large amount of variation in facial images due to changing conditions such as 3D pose, size, position, viewing angle, and lighting. Although computer vision research has come a long way in addressing these difficulties, there still remain many opportunities for improvement!

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