5 Ways to Improve The Quality of Labeled Data

Computer vision models are becoming increasingly sophisticated and accurate, but their effectiveness relies heavily on the quality of labeled datasets. Poorly labeled or inaccurate data can lead to significant problems for machine learning teams. Common errors include inaccurate labels, mislabeled images, missing labels, unbalanced data, and insufficient data to account for edge cases. To improve dataset quality, organizations should use complex ontological structures for their labels, AI-assisted labeling tools, identify badly labeled data, manage annotators effectively, and utilize platforms like Encord to enhance model development with data-driven insights.