Peter Kakula

Key Skills

Software

Labelbox

CVAT

Label Studio

OpenCV AI Kit (OAK)

Remotasks

Scale AI

Toloka

Telus

Top Subject Matter

Computer Vision and Natural Language Processing

Top Data Types

Image

Audio

Text

Top Task Types

Bounding Box

Polygon

Classification

Segmentation

Point Key Point

Polyline

Cuboid

Object Detection

Text Generation

Question Answering

Text Summarization

RLHF

Fine Tuning

Data Collection

Evaluation Rating

Transcription

Freelancer Overview

Data Annotation Specialist at ISON Xperiences. Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox. Education includes Bachelor of Science, Jomo Kenyatta University of Agriculture and Technology. AI-training focus includes data types such as Image and labeling workflows including Bounding Box.

Labeling Experience

Data Annotation Specialist at ISON Xperiences

LabelboxImageBounding Box

Oversaw large-scale annotation projects involving image, video, and text data for machine learning purposes. Implemented rigorous quality validation, ensuring 100% data integrity for computer vision and NLP models. Executed object detection, temporal segment labeling, and sentiment analysis with strict adherence to labeling taxonomies. • Labeled images using bounding boxes, polygons, and key-point annotations for object localization and semantic segmentation. • Tagged and categorized text for sentiment analysis and entity recognition in NLP pipelines. • Annotated videos via object tracking and temporal segmentation for real-time AI model training. • Used Labelbox, CVAT, LabelImg, and VGG Image Annotator in conjunction with Python and Excel for labeling and validation.

Oversaw large-scale annotation projects involving image, video, and text data for machine learning purposes. Implemented rigorous quality validation, ensuring 100% data integrity for computer vision and NLP models. Executed object detection, temporal segment labeling, and sentiment analysis with strict adherence to labeling taxonomies. • Labeled images using bounding boxes, polygons, and key-point annotations for object localization and semantic segmentation. • Tagged and categorized text for sentiment analysis and entity recognition in NLP pipelines. • Annotated videos via object tracking and temporal segmentation for real-time AI model training. • Used Labelbox, CVAT, LabelImg, and VGG Image Annotator in conjunction with Python and Excel for labeling and validation.

2024 - 2026

Education

Work History