High-Precision Computer Vision Labeling for Agriculture & Retail AI
CVATVideoBounding BoxPolygon
Led data annotation efforts on large-scale image and video datasets for AI model training in agriculture and retail sectors. Tasks included detailed object detection and classification of crops, products, packaging, pests, and shelf items using bounding boxes, segmentation, and polygon annotations. Worked closely with QA teams and ML engineers to ensure labeling consistency, accuracy, and alignment with project goals. Maintained a high annotation accuracy rate (>98%) across over 50,000 labeled images and video frames.
Ensured ethical data practices and scalability by developing clear labeling guidelines and providing feedback on edge cases to improve annotation quality and model performance.
Led data annotation efforts on large-scale image and video datasets for AI model training in agriculture and retail sectors. Tasks included detailed object detection and classification of crops, products, packaging, pests, and shelf items using bounding boxes, segmentation, and polygon annotations. Worked closely with QA teams and ML engineers to ensure labeling consistency, accuracy, and alignment with project goals. Maintained a high annotation accuracy rate (>98%) across over 50,000 labeled images and video frames. Ensured ethical data practices and scalability by developing clear labeling guidelines and providing feedback on edge cases to improve annotation quality and model performance.
2019 - 2024