Ekomobong J.

Key Skills

Software

Clickworker

Appen

Anno-Mage

Top Subject Matter

Autonomous Vehicles – Image annotation

Healthcare – Medical image labeling or clinical text annotation

E-commerce – Product categorization, review sentiment analysis

Top Data Types

Image

Video

Text

Top Task Types

Bounding Box

Polygon

Freelancer Overview

With over two years in data labeling and AI training data, I’ve worked extensively on computer vision (bounding boxes, segmentation) and NLP tasks (text categorization, entity recognition). I consistently maintain 98%+ inter-annotator agreement using tools like Labelbox and Supervisely, and I’m skilled at interpreting complex guidelines while flagging edge cases. What sets me apart is my focus on efficiency and quality: I once streamlined ambiguous-case decision trees, cutting review time by 20% without losing accuracy, and I’ve helped create annotation playbooks and provide peer feedback. My goal is always to deliver consistent, high‑quality training data that directly improves model performance.

Labeling Experience

Computer Vision Annotation for Autonomous Vehicle Perception

ImageBounding Box

Led image annotation for a high‑volume autonomous driving perception project (50,000+ labeled frames). Tasks included drawing tight bounding boxes for vehicles, pedestrians, cyclists, and traffic signs, plus polygon segmentation for irregular objects like debris and construction zones. Maintained 99% inter‑annotator agreement by rigorously following 50+ page guidelines and conducting weekly calibration sessions with a team of 10 labelers. Implemented a quality assurance workflow that reduced rework by 25% and delivered all batches on time for model training iterations

2022 - Present

Education

Work History