Phielinah Kamau

Key Skills

Software

CVAT

Data Annotation Tech

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Bounding Box

Transcription

Freelancer Overview

I am a detail-oriented AI data labeler with hands-on experience in image annotation and classification for computer vision projects. I specialize in drawing accurate bounding boxes, especially around fast-moving objects such as volleyballs during sports rallies, and classifying actions like spike, serve, block, and receive. My expertise includes using CVAT and similar annotation tools, and I consistently deliver high-quality, accurate data under tight deadlines. I am skilled at following detailed annotation guidelines, performing quality control checks, and managing tasks efficiently with tools like Google Sheets and Excel. My commitment to accuracy and my ability to adapt quickly make me a valuable contributor to any AI training data project.

Labeling Experience

Data labelling

Data Annotation TechImageBounding BoxTranscription

Object Detection – Image Annotation Project Type: Computer Vision Dataset Size: 8,000+ images Duration: 4 weeks Tasks Performed: Drew accurate bounding boxes and polygons around objects (vehicles, pedestrians, road signs) Classified objects based on predefined categories Reviewed annotations to correct overlaps and mislabeling Followed project-specific annotation guidelines strictly Quality Measures Adhered To: Maintained 95%+ annotation accuracy during QA audits Ensured tight bounding boxes with minimal background noise Passed random quality checks and rework reviews Followed consistency rules across similar objects

Object Detection – Image Annotation Project Type: Computer Vision Dataset Size: 8,000+ images Duration: 4 weeks Tasks Performed: Drew accurate bounding boxes and polygons around objects (vehicles, pedestrians, road signs) Classified objects based on predefined categories Reviewed annotations to correct overlaps and mislabeling Followed project-specific annotation guidelines strictly Quality Measures Adhered To: Maintained 95%+ annotation accuracy during QA audits Ensured tight bounding boxes with minimal background noise Passed random quality checks and rework reviews Followed consistency rules across similar objects

2024 - 2024

Education

Work History