Marklet
Ayodele David

Data Annotator & AI Specialist

Ayodele David

Preparing High-Quality Data for Intelligent AI Systems.

I am a trained Data Annotator with practical, course-based experience in image and video annotation and knowledge of multimodal AI/ML labelling workflows.

About Me

I completed the Data Annotation for AI/ML Bootcamp (Udemy, Georgi Smarts, 2026). I have practical training with CVAT, familiarity with Labelbox and experience with Microsoft Excel for text-data work. My knowledge covers image, video, text and audio annotation. I hold a B.Eng. in Civil Engineering (Second Class Honours, Upper Division, 2024). I also have civil engineering QA/QC experience at Julius Berger Nigeria, strengthening my attention to detail, technical documentation and quality-focused approach.

Technical Skills

  • Image: Bounding Boxes, Polygons, Polylines, Keypoints/Landmarks, Ellipses, 3D Cuboids, Pixel-Level Masks, Segmentation and Object Detection.
  • Video: Frame Annotation, Bounding Boxes, Object Tracking, Track Merge, Group, Split, Join and Slice.
  • Text: Text Classification, Named Entity Recognition (NER), Sequence Labelling, Relation Tags and LLM Data Annotation.
  • Audio: Verbatim/Clean Transcription, Speaker Diarization, Sound Event Tagging, Emotion Tagging, Transcription Notation and Transcript Repair.
  • Tools: CVAT, Labelbox and Microsoft Excel.

Image Annotation

PROJECT 01 — IMAGE ANNOTATION: ROAD SCENE LABELLING

Image annotation · Object detection · Bounding boxes · Polygons · Region delineation · Guideline-based labelling

Tool: CVAT. Objective: Annotate a road-scene image according to supplied guidelines.

Tool
CVAT

Challenge

Annotate a road-scene image according to supplied guidelines.

Stages

  1. Polygons
  2. Bounding Boxes
  3. Truck Labelling

Polygons

Used polygons to annotate the road and pedestrian crossing.

Bounding Boxes

Applied bounding boxes to identify individual pedestrians.

Truck Labelling

Identified and labelled a truck separately.

Solution

Executed precise image annotation following strict project guidelines.

Result

Accurately delivered delineated regions, object detection, and bounding boxes.

See image annotation

Video Annotation

PROJECT 02 — VIDEO ANNOTATION: VEHICLE TRACKING

Video annotation · Bounding-box placement · Object tracking · Temporal consistency

Tool: CVAT. Objective: Track vehicles moving through a video sequence according to supplied guidelines.

Tool
CVAT

Challenge

Track vehicles moving through a video sequence according to supplied guidelines.

Stages

  1. Tracking Tool
  2. Bounding Boxes
  3. Object Tracks

Tracking Tool

Used CVAT’s tracking tool across successive frames.

Bounding Boxes

Applied bounding boxes to locate vehicles.

Object Tracks

Maintained object tracks to follow vehicles as they moved.

Solution

Maintained temporal consistency and accurate object tracking across video frames.

Result

Successfully completed a six-second tracking demonstration.

WATCH VIDEO DEMONSTRATION

Quality & Working Approach

Highlight annotation guidelines, consistent labelling, accuracy, edge-case judgement, Inter-Annotator Agreement, gold-task evaluation, QA, dataset bias awareness, mitigation and ethical annotation. Connect these principles to my civil engineering QA/QC background.

Education & Experience

  • B.Eng. Civil Engineering

    Second Class Honours (Upper Division)

    2024

  • Civil Engineering Intern — QA/QC Support

    Julius Berger Nigeria

    2025 — 2026

  • Data Annotation for AI/ML Bootcamp

    Udemy

    2026
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