Hands-on Data Centric Visual AI

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 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; and 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.