Abdulbasit Isa

Key Skills

Software

iMerit

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Bounding Box

Polygon

Segmentation

Object Detection

Freelancer Overview

I have hands-on experience in data labeling and AI training data projects, where I’ve worked on annotating text, image, and audio datasets to improve model accuracy and performance. My work has involved text classification, sentiment analysis, data categorization, image bounding boxes, and audio transcription with quality control checks to ensure high annotation standards. I am comfortable working with structured guidelines, meeting strict accuracy thresholds, and maintaining consistency across large datasets. What sets me apart is my strong attention to detail, ability to quickly understand annotation schemas, and commitment to delivering high-quality outputs under tight deadlines. With a background in tech-focused roles and experience handling large volumes of data, I am efficient, reliable, and adaptable to new tools and labeling platforms. I also prioritize continuous learning to stay aligned with evolving AI training standards and best practices.

Labeling Experience

Image Annotation Project

ImeritImageBounding BoxPolygon

Worked on a large-scale computer vision dataset for autonomous driving systems, focusing on object detection and scene understanding. The project involved annotating over 50,000 street-level images captured from vehicle-mounted cameras. Key tasks included drawing precise bounding boxes around vehicles, pedestrians, cyclists, and traffic signs; polygon annotations for irregular objects; and semantic segmentation for road surfaces and lane markings. Maintained a minimum accuracy rate of 98% through strict adherence to annotation guidelines and multi-stage quality control processes, including peer review and auditor feedback cycles. Ensured consistency across complex urban and highway scenarios, including varying weather and lighting conditions. Delivered tasks within tight deadlines while meeting SLA requirements.

Worked on a large-scale computer vision dataset for autonomous driving systems, focusing on object detection and scene understanding. The project involved annotating over 50,000 street-level images captured from vehicle-mounted cameras. Key tasks included drawing precise bounding boxes around vehicles, pedestrians, cyclists, and traffic signs; polygon annotations for irregular objects; and semantic segmentation for road surfaces and lane markings. Maintained a minimum accuracy rate of 98% through strict adherence to annotation guidelines and multi-stage quality control processes, including peer review and auditor feedback cycles. Ensured consistency across complex urban and highway scenarios, including varying weather and lighting conditions. Delivered tasks within tight deadlines while meeting SLA requirements.

2025 - 2025

Education

Work History