Hands-on Data Centric Visual AI

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

Subjects