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Emmanuel T
@emmanuel_tayie1
Nigeria
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Sobre mí
Human enterprise-Grade Data Processing for AI and Automation
We deliver high-quality, time-sensitive image and video annotation services designed to accelerate your artificial intelligence initiatives. Leveraging a highly scalable, human-in-the-loop workforce, we efficiently process massive, enterprise-level datasets without compromising on accuracy.
Backed by four years of specialized industry experience—including successful collaborations with leading firms like AI Workforce—we provide expert data labeling tailored to cutting-edge sectors.
Habilidades
Emmanuel T
desconectado •
Tiempo medio de respuesta: 2 horas
Revisa mis servicios
Etiquetado y anotación de datos
I will do data annotation, image, video, map and label data polygon and object tracking
Etiquetado y anotación de datos
I will annotate by cvat, roboflow, labelbox, image annotation, polygon and bounding box
Porfolio
Experiencia laboral
video labeler
Atlas Group • Tiempo parcial
Jan 2026 - Present • 6 mos
High-Precision Video Annotation: Specialized in labeling complex video datasets by identifying and tagging specific human and object actions frame-by-frame. Strict Guideline Adherence: Consistently followed detailed client rules and edge-case workflows to ensure 100% data consistency and eliminate annotation bias. Quality-First Approach: Prioritized absolute correctness over speed, maintaining a near-zero error rate to deliver pristine datasets ready for machine learning and deep learning algorithms. Platform Expertise: Efficiently utilized data labeling platforms (such as Atlas Capture) to handle time-sensitive, large-scale enterprise video projects.
Data annotation
AI Medical Technology • Tiempo parcial
Apr 2024 - Sep 2025 • 1 yr 5 mos
High-Precision Medical Annotation: Expertly annotated complex medical datasets—including X-rays, CT scans, MRIs, and ultrasound images—to train advanced computer vision and deep learning models. Strict Guideline Adherence: Consistently followed rigorous clinical protocols, anatomical guidelines, and edge-case workflows to ensure 100% accuracy and eliminate annotation bias. Advanced Segmentation Techniques: Specialized in semantic and instance segmentation to precisely outline tumors, lesions, organs, and tissue boundaries for diagnostic automation. Quality-First Approach: Prioritized absolute correctness and patient data integrity over speed, maintaining a near-zero error rate to deliver pristine datasets ready for healthcare AI initiatives.
