Redempter Lupalasa

Key Skills

Software

Labelbox

LabelImg

Prodigy

CVAT

Top Subject Matter

Machine Learning

Legal Services & Contract Review

Regulatory Compliance & Risk Analysis

Top Data Types

Audio

Image

Text

Document

Top Task Types

Classification

Data Collection

Object Detection

Segmentation

Bounding Box

Polygon

Polyline

Cuboid

Transcription

Freelancer Overview

Data Labeling Specialist (General Experience). Brings 13+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include N and A. Education includes Bachelor of Commerce, Kenyatta University (2015) and Kenya Certificate of Secondary Education, St. Marys Girls Mumias (2009). AI-training focus includes data types such as N and A and labeling workflows including N and A.

Labeling Experience

Annotator

LabelboxTextSegmentationClassification

Project Title: Autonomous Vehicle Object Detection Dataset Scope: The project involved annotating a large dataset of street-level images captured from autonomous vehicles. The goal was to create a high-quality labeled dataset to train and validate object detection models, enhancing their ability to recognize and classify various road elements in real-time. Specific Data Tasks Performed 1. Object Detection: o Tasks: Annotated images with bounding boxes around various objects such as pedestrians, vehicles (cars, trucks, buses), traffic signs, and road markings. o Tools: Used LabelImg for bounding box annotations and Labelbox for managing annotations and quality control. 2. Categorization: o Tasks: Labeled each bounding box with a specific class, such as "pedestrian," "vehicle," "traffic sign," or "road marking," to ensure accurate classification. o Tools: Employed a predefined set of labels consistent with industry standards.

Project Title: Autonomous Vehicle Object Detection Dataset Scope: The project involved annotating a large dataset of street-level images captured from autonomous vehicles. The goal was to create a high-quality labeled dataset to train and validate object detection models, enhancing their ability to recognize and classify various road elements in real-time. Specific Data Tasks Performed 1. Object Detection: o Tasks: Annotated images with bounding boxes around various objects such as pedestrians, vehicles (cars, trucks, buses), traffic signs, and road markings. o Tools: Used LabelImg for bounding box annotations and Labelbox for managing annotations and quality control. 2. Categorization: o Tasks: Labeled each bounding box with a specific class, such as "pedestrian," "vehicle," "traffic sign," or "road marking," to ensure accurate classification. o Tools: Employed a predefined set of labels consistent with industry standards.

2021

Data Labeling Specialist (General Experience)

I excel in data labelling by implementing precise annotation strategies, ensuring high-quality labelled datasets for machine learning. My expertise covers diverse labelling types, including classification, object detection, and segmentation, using both manual and automated tools. I emphasize quality assurance through consistency checks, inter-annotator agreement, and detailed guidelines to maintain accuracy and efficiency. • Implemented high-quality annotation strategies for robust machine learning training data. • Applied classification, object detection, and segmentation labeling types. • Used both manual and automated annotation tools to maintain efficiency. • Prioritized quality assurance and consistency in all labeling projects.

I excel in data labelling by implementing precise annotation strategies, ensuring high-quality labelled datasets for machine learning. My expertise covers diverse labelling types, including classification, object detection, and segmentation, using both manual and automated tools. I emphasize quality assurance through consistency checks, inter-annotator agreement, and detailed guidelines to maintain accuracy and efficiency. • Implemented high-quality annotation strategies for robust machine learning training data. • Applied classification, object detection, and segmentation labeling types. • Used both manual and automated annotation tools to maintain efficiency. • Prioritized quality assurance and consistency in all labeling projects.

Not specified

Education

Work History