I recently led a full cycle data labeling project for a computer vision model designed to detect product defects in manufacturing environments. My responsibilities included creating detailed annotation guidelines, performing high‑accuracy bounding‑box and polygon segmentation, and conducting multi‑stage quality reviews to ensure dataset consistency. I collaborated closely with the client’s ML team to refine edge‑case definitions, improve inter‑annotator agreement, and deliver a clean, production‑ready dataset of more than 12,000 images. The final model achieved a measurable improvement in precision and recall after integrating my annotated dataset. This project reflects my strengths in accuracy, domain understanding, and communicationm qualities I bring to every annotation task.
ImageClassification
This project involved preparing a high‑quality dataset for a computer‑vision model used in industrial defect detection. The scope included reviewing raw image data, defining annotation rules, performing detailed labeling, and conducting multi‑stage quality checks to ensure the dataset met the client’s model‑training requirements. I collaborated with the client to clarify edge cases, refine annotation guidelines, and maintain consistency across the entire dataset.
This project involved preparing a high‑quality dataset for a computer‑vision model used in industrial defect detection. The scope included reviewing raw image data, defining annotation rules, performing detailed labeling, and conducting multi‑stage quality checks to ensure the dataset met the client’s model‑training requirements. I collaborated with the client to clarify edge cases, refine annotation guidelines, and maintain consistency across the entire dataset.
2023 - 2026