Data Annontator
During my project with TELUS International, I contributed to image data annotation tasks aimed at improving computer vision models used for object recognition and scene understanding. The scope of the project involved reviewing and labeling large sets of images according to predefined guidelines to help train and evaluate AI systems. My tasks included identifying objects within images, applying appropriate labels or bounding boxes where required, categorizing visual elements, and validating previously labeled data to ensure consistency with annotation standards. The project involved working with thousands of image samples within structured annotation workflows. To maintain high dataset quality, strict quality assurance measures were followed, including adherence to detailed labeling guidelines, performing cross-checks, and maintaining accuracy thresholds defined by the project team. I consistently reviewed annotations for precision, corrected inconsistencies, and ensured that all labeled data met the required standards before submission. These measures helped ensure that the training datasets were reliable and suitable for improving the performance of computer vision models.