Hands-on Data Centric Visual AI
Coursera
9 Courses
UC Davis is a top-tier public research university that offers graduate and undergraduate degrees in more than 100 fields of study. Its campus is situated near Sacramento in the Central Valley of California.
Overview
This comprehensive course is a hands-on guide to developing and maintaining high-quality datasets for visual AI applications. Learners will gain in-depth knowledge and practical skills in:
- Discovering and implementing various labeling approaches, from manual to fully automated methods
- Assessing and improving annotation quality for object detection tasks, including identifying and correcting common labeling issues
- Analyzing the impact of bounding box quality on model performance and developing strategies to enhance label consistency
- Using advanced tools like FiftyOne and CVAT for dataset exploration, error correction, and annotation refinement
- Addressing complex challenges in computer vision, such as overlapping detections, occlusions, and small object detection
- Implementing data augmentation techniques to improve model robustness and generalization
- Applying concepts like sample hardness and entropy in the context of model training and dataset curation
Through a combination of theoretical knowledge and hands-on exercises, students will learn to create, maintain, and optimize datasets that lead to more accurate and reliable visual AI models.
University: University of California, Davis
Provider: Coursera
Categories: Computer Vision Courses, Object Detection Courses, Data Augmentation Courses
