Bonface Kiprono

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Top Label Types

Bounding Box

Polygon

Polyline

Segmentation

Freelancer Overview

I am a detail-oriented data annotator with hands-on experience in labeling and structuring large volumes of image, text, and video data for AI and machine learning projects. My background in computer science and practical work with Python have enabled me to manage datasets, streamline annotation workflows, and apply quality assurance measures that ensure accuracy and consistency. I have contributed to AI experiments and anomaly detection tasks, working both independently and with startups to organize and curate data for model training. My strengths include identifying inconsistencies, maintaining high annotation standards, and adapting quickly to new tools and guidelines. I am fluent in English, French, and Swahili, which helps me work with diverse datasets and international teams.

Labeling Experience

Data annotator

Scale AI3D SensorBounding BoxPolygon

I worked on a structured AI image annotation project focused on preparing high-quality training data for an object detection model, where I labeled over 8,000 images using bounding boxes and classification tags for objects such as vehicles, pedestrians, and traffic signs. I followed detailed annotation guidelines to ensure consistency and precision, handled edge cases like overlapping or partially visible objects, and performed regular quality assurance checks to maintain over 95% accuracy. I also organized the labeled data into structured CSV and JSON formats, maintained clear naming conventions, and reviewed dataset consistency to ensure it was clean, scalable, and ready for machine learning training, strengthening my attention to detail and understanding of how accurate data labeling directly improves model performance.

I worked on a structured AI image annotation project focused on preparing high-quality training data for an object detection model, where I labeled over 8,000 images using bounding boxes and classification tags for objects such as vehicles, pedestrians, and traffic signs. I followed detailed annotation guidelines to ensure consistency and precision, handled edge cases like overlapping or partially visible objects, and performed regular quality assurance checks to maintain over 95% accuracy. I also organized the labeled data into structured CSV and JSON formats, maintained clear naming conventions, and reviewed dataset consistency to ensure it was clean, scalable, and ready for machine learning training, strengthening my attention to detail and understanding of how accurate data labeling directly improves model performance.

2020 - 2023

Education

Work History