Patrick Wakulwa

Key Skills

Software

Appen

Data Annotation Tech

OneForma

Other

CVAT

Prodigy

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Task Types

Data Collection

Text Summarization

Translation Localization

Freelancer Overview

I have extensive experience in data labeling and AI training data management, working across diverse domains including computer vision, natural language processing, and speech recognition. My expertise spans annotating large-scale datasets with high accuracy, designing labeling guidelines, and implementing quality control processes to ensure consistency and reliability. I am proficient with tools such as Labelbox, CVAT, and Amazon SageMaker Ground Truth, and skilled in managing both image, video, and text-based datasets for supervised learning tasks. I have contributed to multiple AI projects, including object detection and classification models, sentiment analysis datasets, and speech-to-text training corpora. My strengths lie in understanding complex project requirements, maintaining data integrity, and optimizing workflows for efficiency and scalability. With a keen eye for detail and strong analytical skills, I consistently deliver high-quality labeled datasets that enable AI models to achieve robust and accurate performance.Do you like this personality?

Labeling Experience

Text Annotation and Translation for Multilingual AI Models

ProdigyTextTranslation Localization

Worked on annotating and translating large-scale text datasets to support multilingual AI model development. Tasks included translating content between English and target languages, labeling entities, classifying sentiment, and preparing question-answer pairs. Ensured linguistic accuracy and cultural relevance by following strict quality control protocols, including cross-reviewing translations and maintaining consistency across datasets. The project helped improve AI performance in understanding and generating text across multiple languages.

Worked on annotating and translating large-scale text datasets to support multilingual AI model development. Tasks included translating content between English and target languages, labeling entities, classifying sentiment, and preparing question-answer pairs. Ensured linguistic accuracy and cultural relevance by following strict quality control protocols, including cross-reviewing translations and maintaining consistency across datasets. The project helped improve AI performance in understanding and generating text across multiple languages.

2025 - 2025

Image Annotation for Autonomous Vehicle Object Detection

CVATImageBounding BoxSegmentation

Worked on a large-scale dataset of road images to support the development of an autonomous vehicle object detection model. Tasks included annotating vehicles, pedestrians, traffic signs, and road obstacles using bounding boxes and segmentation masks. Ensured labeling consistency and high accuracy by following detailed annotation guidelines and performing regular quality checks. The project involved over 50,000 images, contributing directly to improving model precision and safety.

Worked on a large-scale dataset of road images to support the development of an autonomous vehicle object detection model. Tasks included annotating vehicles, pedestrians, traffic signs, and road obstacles using bounding boxes and segmentation masks. Ensured labeling consistency and high accuracy by following detailed annotation guidelines and performing regular quality checks. The project involved over 50,000 images, contributing directly to improving model precision and safety.

2024 - 2024

Image Annotation for Object Detection and Segmentation

Label StudioImageTracking

Annotated large-scale image datasets to train AI models for object detection, segmentation, and tracking. Tasks included labeling vehicles, pedestrians, products, and other objects using bounding boxes and polygons, ensuring pixel-level accuracy for segmentation tasks. Implemented strict quality control procedures, including inter-annotator agreement checks and regular guideline updates, to maintain high data reliability. The project involved over 50,000 images, contributing directly to improving AI model performance and accuracy.

Annotated large-scale image datasets to train AI models for object detection, segmentation, and tracking. Tasks included labeling vehicles, pedestrians, products, and other objects using bounding boxes and polygons, ensuring pixel-level accuracy for segmentation tasks. Implemented strict quality control procedures, including inter-annotator agreement checks and regular guideline updates, to maintain high data reliability. The project involved over 50,000 images, contributing directly to improving AI model performance and accuracy.

2023 - 2023

Text Annotation for Sentiment Analysis

AppenTextEntity Ner ClassificationClassification

Annotated thousands of customer reviews and feedback messages to train a sentiment analysis model. Tasks included labeling text as positive, negative, or neutral, identifying key entities (e.g., product names, features), and tagging emotions expressed in the text. Applied strict quality control measures, including double-review protocols and inter-annotator agreement checks, to ensure high dataset accuracy. This dataset improved the AI’s ability to automatically classify sentiment and extract actionable insights from customer feedback.

Annotated thousands of customer reviews and feedback messages to train a sentiment analysis model. Tasks included labeling text as positive, negative, or neutral, identifying key entities (e.g., product names, features), and tagging emotions expressed in the text. Applied strict quality control measures, including double-review protocols and inter-annotator agreement checks, to ensure high dataset accuracy. This dataset improved the AI’s ability to automatically classify sentiment and extract actionable insights from customer feedback.

2023 - 2023

Education

Work History