University of California, Davis

Hands-on Data Centric Visual AI

University of California, Davis via Coursera

Coursera

8 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.

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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
  • 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

Syllabus


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