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Débute 4 June 2026 04:45
Se termine 4 June 2026
Hands-on Data Centric Visual AI
University of California, Davis
15 Cours
UC Davis est une université de recherche publique très bien classée qui offre des diplômes de premier cycle et de deuxième cycle dans plus de 100 domaines d'études. Son campus est situé près de Sacramento dans la vallée centrale de Californie.
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Free
Amélioration optionnelle disponible
Aperçu
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
