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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; use advanced tools like FiftyOne and CVAT for dataset...
Syllabus
- Getting Started and the Data-Centric AI Paradigm
- Image Quality and Its Impact on Model Performance
- Label Quality and Its Impact on Model Performance
- Putting It All Together
University of California, Davis via Coursera
15 hours 51 minutes
Paid Certificate Available
English
On-Demand
Intermediate
Instructor
Hartpreet Sahota
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