Harrison Amolo

Key Skills

Software

CVAT

LabelImg

Labelbox

Top Subject Matter

Object detection in images for machine learning

Sentiment analysis and text classification for AI

Quality control and verification for AI training datasets

Top Data Types

Image

Text

Document

Top Task Types

Bounding Box

Classification

Freelancer Overview

Image Annotation Project. Core strengths include CVAT, LabelImg, and Labelbox. Education includes Bachelor of Education, Karatina University (2027). AI-training focus includes data types such as Image and Text and labeling workflows including Bounding Box, Classification, and Evaluation.

Labeling Experience

Data Quality Review Task

LabelboxImage

I performed quality review of labeled datasets by identifying and correcting annotation errors to ensure compliance with quality standards. This required careful attention to detail and systematic error detection processes across both image and text data. Reliability and consistency were critical to upholding the requirements of AI training datasets. • Audited previously labeled datasets for annotation errors and inconsistencies • Corrected mistakes to meet defined accuracy thresholds • Developed attention to detail skills for improved quality control • Conducted reviews using Labelbox and spreadsheet-based workflows

I performed quality review of labeled datasets by identifying and correcting annotation errors to ensure compliance with quality standards. This required careful attention to detail and systematic error detection processes across both image and text data. Reliability and consistency were critical to upholding the requirements of AI training datasets. • Audited previously labeled datasets for annotation errors and inconsistencies • Corrected mistakes to meet defined accuracy thresholds • Developed attention to detail skills for improved quality control • Conducted reviews using Labelbox and spreadsheet-based workflows

Not specified

Text Classification Project

LabelboxTextClassification

I labeled and categorized text samples into sentiment classes to help build sentiment analysis models for AI solutions. This required accurately interpreting text context and applying well-defined classification rules. Consistency and speed were maintained to ensure a high-quality labeled dataset. • Sorted text data into positive, negative, and neutral sentiment classes • Enhanced annotation pace while keeping accuracy high • Applied complex labeling guidelines to a diverse set of text samples • Used Labelbox and spreadsheet tools for annotation and review

I labeled and categorized text samples into sentiment classes to help build sentiment analysis models for AI solutions. This required accurately interpreting text context and applying well-defined classification rules. Consistency and speed were maintained to ensure a high-quality labeled dataset. • Sorted text data into positive, negative, and neutral sentiment classes • Enhanced annotation pace while keeping accuracy high • Applied complex labeling guidelines to a diverse set of text samples • Used Labelbox and spreadsheet tools for annotation and review

Not specified

Image Annotation Project

CVATImageBounding Box

I annotated a large volume of images by carefully drawing bounding boxes around specified objects to support object detection model training. I maintained high consistency and accuracy throughout the project while rigorously following provided annotation guidelines. The work involved identifying and labeling a variety of items often present in real-world scenarios. • Processed 1,500+ images with bounding boxes for machine learning applications • Labeled objects including vehicles, humans, and everyday items • Achieved over 95% consistency across the dataset • Utilized CVAT, LabelImg, and Labelbox for annotation

I annotated a large volume of images by carefully drawing bounding boxes around specified objects to support object detection model training. I maintained high consistency and accuracy throughout the project while rigorously following provided annotation guidelines. The work involved identifying and labeling a variety of items often present in real-world scenarios. • Processed 1,500+ images with bounding boxes for machine learning applications • Labeled objects including vehicles, humans, and everyday items • Achieved over 95% consistency across the dataset • Utilized CVAT, LabelImg, and Labelbox for annotation

Not specified

Education

Work History