Yadira Romancelly

• Annotated and validated large-scale image and video datasets for human pose estimation and object detection tasks.
• Applied structured labeling schemas for keypoint detection, object classification, and spatial tracking.
• Performed QA audits on annotated datasets, identifying inconsistencies and improving labeling accuracy.
• Built preprocessing pipelines including dataset cleaning, normalization, and augmentation to improve model training quality.
• Evaluated model outputs and flagged misclassifications, contributing to improved model precision and recall.
• Identified and documented edge cases to refine annotation guidelines and improve dataset robustness.
• Collaborated with cross-functional teams to ensure consistency between labeling standards and model requirements.

Key Results:

• Improved dataset quality and model accuracy (mAP) through iterative annotation refinement and QA processes.
• Reduced labeling inconsistencies by implementing validation workflows and automated checks.
• Scaled dataset preparation workflows to support high-volume training data pipelines.
• Automated portions of dataset labeling and validation, increasing throughput and efficiency.