Darrell Howard Taylor

Key Skills

Software

CVAT

Data Annotation Tech

Labelbox

Prodigy

SuperAnnotate

Telus

Top Subject Matter

Healthcare: Experienced in medical data analysis and labeling, particularly in medical imaging and patient records.

E-commerce: Proficient in product categorization and classification for online retail platforms, helping improve search algorithms and user experience.

Education: Familiar with educational content development and assessment, including question answering and text summarization for learning materials.

Top Data Types

Audio

Image

Text

Top Task Types

Classification

Object Detection

Text Generation

Freelancer Overview

I have over three years of experience in data labeling and AI training, specializing in annotating diverse datasets for machine learning applications. My proficiency spans various data types, including text, images, and audio, with a strong focus on ensuring high-quality, consistent annotations that meet project requirements. I have successfully utilized industry-standard tools such as Labelbox, Amazon SageMaker Ground Truth, and CVAT, which have enhanced my ability to deliver accurate and reliable data for AI model training. My attention to detail and commitment to quality have enabled me to achieve a 98% accuracy rate in text annotations, significantly contributing to the development of natural language processing models. One of my key projects involved annotating over 10,000 images for an object detection model, where I implemented bounding boxes and labels to improve model performance. Additionally, I worked on a sentiment analysis project, where I labeled text data with sentiment tags, achieving a 95% agreement rate with expert annotations. My background in computer science, combined with certifications in data annotation and machine learning, equips me with a solid foundation in AI concepts and data preprocessing techniques. I am passionate about leveraging my skills to contribute to innovative AI solutions and enhance the quality of training data in various applications.

Labeling Experience

Object Detection Annotation for Autonomous Vehicles

LabelboxImageBounding BoxObject Detection

I worked on a project focused on annotating images for an autonomous vehicle's object detection system. The project involved labeling over 15,000 images captured from various driving scenarios, including urban, rural, and highway environments. My specific tasks included creating bounding boxes around key objects such as pedestrians, vehicles, traffic signs, and obstacles to train the AI model effectively. I adhered to strict quality measures, conducting regular quality checks to ensure that annotations met the required accuracy standards. The project utilized Labelbox for initial annotations and CVAT for quality assurance, resulting in a final accuracy rate of 97% in the labeled dataset.

I worked on a project focused on annotating images for an autonomous vehicle's object detection system. The project involved labeling over 15,000 images captured from various driving scenarios, including urban, rural, and highway environments. My specific tasks included creating bounding boxes around key objects such as pedestrians, vehicles, traffic signs, and obstacles to train the AI model effectively. I adhered to strict quality measures, conducting regular quality checks to ensure that annotations met the required accuracy standards. The project utilized Labelbox for initial annotations and CVAT for quality assurance, resulting in a final accuracy rate of 97% in the labeled dataset.

2022 - 2022

Education

Work History