Daniel O.

Key Skills

Software

CVAT

Label Studio

Top Subject Matter

Simulated autonomous-vehicle object detection

Customer reviews and product description NLP labeling

Conversational AI audio transcription and diarization

Top Data Types

Image

Text

Audio

Top Task Types

Object Detection

Entity (NER) Classification

Transcription

Freelancer Overview

Image Annotation for Object Detection (Personal/Academic Project). Core strengths include CVAT, Label Studio, and N. Education includes Bachelor of Science, Kwara State University. AI-training focus includes data types such as Image, Text, and Audio and labeling workflows including Object Detection, Entity (NER) Classification, and Transcription.

Labeling Experience

NLP Text Classification & Sentiment Labeling (Personal Project)

Label StudioTextEntity (NER) Classification

Labeled 1,000+ customer review snippets with sentiment and intent categories for a text classification dataset using Label Studio. Performed NER tagging on product descriptions to identify brands, locations, and dates while documenting edge-case decisions. Maintained reproducibility by recording annotation guidelines and consistent labeling rules for ambiguous inputs. • Tooling: Label Studio • Tasks: sentiment, intent categories, NER tagging • Artifacts: documented guidelines and edge-case rulings • Dataset size: 1,000+ review snippets

2024

Image Annotation for Object Detection (Personal/Academic Project)

CVATImageObject Detection

Annotated 500+ images for a simulated autonomous-vehicle object detection dataset using bounding boxes and polygon segmentation. Followed detailed style guides to keep label quality consistent across multiple object categories including pedestrians, vehicles, and traffic signs. Achieved over 95% inter-annotator agreement by comparing outputs against provided reference annotations. • Tooling: CVAT • Tasks: bounding boxes, polygon segmentation • Domain: autonomous-vehicle imagery • Quality: >95% inter-annotator agreement vs reference

2024

Audio Transcription & Speaker Diarization (Volunteer/Self-Directed)

AudioTranscription

Transcribed 10+ hours of podcast and interview audio using strict verbatim and clean-read standards aligned to client guidelines. Labeled speaker turns and background audio events such as laughter, applause, and noise to support conversational AI training data. Maintained turnaround time under 3x real-time while preserving accuracy above 98%. • Tasks: transcription, speaker diarization, audio event tagging • Output labeling: speaker turns + background event labels • Standards: verbatim vs clean-read per guidelines • Performance: >98% accuracy, <3x real-time turnaround

2023

Education

Work History