This hands-on workshop explores computer vision techniques for automotive damage detection using the
CarDD dataset - the largest public dataset specifically designed for vehicle damage analysis.
Participants will learn how to leverage
FiftyOne, a powerful computer vision experimentation platform, to explore, visualize, and build models for detecting six common types of vehicle damage: dents, scratches, cracks, glass shatter, tire flats, and broken lamps.
The workshop consists of five comprehensive modules that guide participants from initial setup to advanced model deployment:
This workshop is designed for computer vision practitioners, automotive industry professionals, and data scientists interested in applying modern deep learning techniques to vehicle damage detection. Participants will gain practical experience working with a production-grade dataset while learning best practices for model development, evaluation, and deployment.