Howard Adeniyi

Key Skills

Software

AWS SageMaker

Appen

CVAT

Data Annotation Tech

Humanatic

iMerit

Img Lab

Labelbox

LabelImg

OneForma

Remotasks

SuperAnnotate

Toloka

Telus

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Text

Top Task Types

Classification

Computer Programming Coding

Evaluation Rating

Relationship

Text Summarization

Freelancer Overview

I have extensive experience in data labeling and AI training data preparation, specializing in creating high-quality datasets for machine learning models in industries such as eCommerce and computer vision. My expertise includes annotating large-scale datasets using tools like Labelbox and AWS SageMaker Ground Truth, with a focus on accuracy and consistency. I have worked on projects such as retail product image annotation, where I labeled over 10,000 product images with bounding boxes, categorization tags, and attributes to enhance AI-powered recommendation systems. These efforts resulted in a 15% improvement in model performance and accelerated deployment timelines. What sets me apart is my attention to detail, adherence to strict annotation guidelines, and ability to collaborate effectively with data science teams to refine workflows and resolve edge cases. I am proficient in semantic segmentation, bounding box labeling, and quality assurance audits, ensuring datasets exceed client expectations. My contributions have consistently driven AI model accuracy, efficiency, and time-to-market, making me a reliable asset in the AI training data lifecycle.

Labeling Experience

Retail Product Image Annotation for eCommerce AI

LabelboxImageBounding BoxRelationship

Annotated 10,000+ retail product images with bounding boxes, categorization tags, and attributes (e.g., color, size, style). Used Labelbox and AWS SageMaker Ground Truth for data labeling, ensuring consistency across large datasets. Collaborated with the data science team to refine labeling instructions and resolve edge cases. Conducted quality assurance audits to achieve a dataset accuracy rate of 98.5%, surpassing client expectations.

Annotated 10,000+ retail product images with bounding boxes, categorization tags, and attributes (e.g., color, size, style). Used Labelbox and AWS SageMaker Ground Truth for data labeling, ensuring consistency across large datasets. Collaborated with the data science team to refine labeling instructions and resolve edge cases. Conducted quality assurance audits to achieve a dataset accuracy rate of 98.5%, surpassing client expectations.

2024 - 2024

Autonomous Vehicle Video Annotation

LabelboxTextPoint Key PointSegmentation

Annotated thousands of hours of video footage to train AI models for pedestrian and vehicle recognition in urban environments. Responsibilities: Used Labelbox to annotate bounding boxes for vehicles, pedestrians, traffic signs, and lane markers. Reviewed and audited dataset for accuracy and edge cases. Collaborated with data engineers to refine annotation guidelines.

Annotated thousands of hours of video footage to train AI models for pedestrian and vehicle recognition in urban environments. Responsibilities: Used Labelbox to annotate bounding boxes for vehicles, pedestrians, traffic signs, and lane markers. Reviewed and audited dataset for accuracy and edge cases. Collaborated with data engineers to refine annotation guidelines.

2024 - 2024

Education

Work History