Skills you'll gain

There are 4 modules in this course

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.

Image Quality and Its Impact on Model Performance

Label Quality and Its Impact on Model Performance

Putting It All Together

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