Farouq Olalekan

Key Skills

Software

CrowdSource

Top Subject Matter

No subject matter listed

Top Data Types

Video

Top Label Types

Bounding Box

Polygon

Segmentation

Data Collection

Transcription

Freelancer Overview

I am a detail-oriented AI data trainer with four years of experience specializing in data labeling, annotation, and training datasets for multimodal AI systems. My background includes recording and evaluating high-quality English audio, conducting linguistic analysis, and performing precise annotation across text, audio, and other data types. I have contributed to projects that enhance AI understanding of speech patterns, tone, and natural language, working both independently and collaboratively to ensure dataset accuracy and quality. My strengths lie in my English language proficiency, keen attention to detail, and ability to deliver consistent, high-quality results for advanced AI research and development.

Labeling Experience

Data Labeller

CrowdsourceVideoBounding BoxPolygon

Worked on large-scale video data labeling projects focused on training and improving computer vision models. Responsibilities included annotating video frames using bounding boxes and polygons to identify and track objects across sequences, as well as performing semantic segmentation for precise object boundaries. Ensured high annotation accuracy by following strict labeling guidelines, consistency rules, and quality control standards. Regularly participated in review cycles, corrected flagged annotations, and met accuracy benchmarks required for production-ready AI datasets. The project involved labeling thousands of video frames used to train and validate machine learning models for object detection and motion tracking.

Worked on large-scale video data labeling projects focused on training and improving computer vision models. Responsibilities included annotating video frames using bounding boxes and polygons to identify and track objects across sequences, as well as performing semantic segmentation for precise object boundaries. Ensured high annotation accuracy by following strict labeling guidelines, consistency rules, and quality control standards. Regularly participated in review cycles, corrected flagged annotations, and met accuracy benchmarks required for production-ready AI datasets. The project involved labeling thousands of video frames used to train and validate machine learning models for object detection and motion tracking.

2024 - 2025

Education

Work History