Muhammad Tariq

I am an experienced Data Annotation Specialist with over four years delivering precise, training-ready datasets for computer vision and NLP applications. I focus on accurate labeling, validation, and preparing large unstructured datasets to improve AI/ML model performance.

My background includes live and stale image annotation, bounding boxes, polygons, segmentation, and video object tracking using tools such as CVAT, LabelImg, LabelMe, VGG, Roboflow and MakeSense.ai. I have transcribed audio, performed text annotation, and produced structured outputs like CSVs for downstream systems.

I have executed projects that include traffic dataset annotation (20,000+ images), age-prediction validation (40,000 images), smart-cart live-session annotation, and digitizing restaurant menus for eCommerce platforms. I also perform QA workflows and manual functional testing to ensure end-to-end dataset quality.

I hold a BSc in Computer Sciences and prioritize consistency, attention to detail, and collaboration to meet annotation guidelines and project requirements. I thrive on improving model accuracy through rigorous validation and tailored annotation strategies.