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Inicio 4 June 2026 03:23
Fin 4 June 2026
Hands-on Data Centric Visual AI
University of California, Davis
15 Cursos
UC Davis es una universidad pública de investigación altamente clasificada que ofrece grados de posgrado y pregrado en más de 100 campos de estudio. Su campus está ubicado cerca de Sacramento en el Valle Central de California.
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Resumen
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
