John Mak

Key Skills

Software

Labelbox

Scale AI

CVAT

Prodigy

Appen

Top Subject Matter

Computer Vision

AI Model Training

Medical Imaging

Top Data Types

Image

Video

Top Task Types

Segmentation

Bounding Box

Action Recognition

Freelancer Overview

Data Annotator – Hugo Tech. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox, Scale AI, and CVAT. Education includes Bachelor of Technology, Federal University of Technology, Akure (2018). AI-training focus includes data types such as Image, Video, and Medical and labeling workflows including Segmentation, Bounding Box, and Action Recognition.

Labeling Experience

Multi-class Image Segmentation — Medical Imaging – Hugo Tech

LabelboxSegmentation

At Hugo Tech, I worked on multi-class image segmentation tasks specifically in the medical imaging domain. My role involved labeling anatomical structures in medical scans for AI diagnostic models. I adhered to strict quality and safety standards for high-stakes use cases. • Performed detailed semantic and instance segmentation on medical DICOM images. • Maintained zero-tolerance for errors in critical anatomical regions. • Supported the creation of AI-powered diagnostic support tools. • Ensured annotation accuracy for regulatory and clinical requirements.

At Hugo Tech, I worked on multi-class image segmentation tasks specifically in the medical imaging domain. My role involved labeling anatomical structures in medical scans for AI diagnostic models. I adhered to strict quality and safety standards for high-stakes use cases. • Performed detailed semantic and instance segmentation on medical DICOM images. • Maintained zero-tolerance for errors in critical anatomical regions. • Supported the creation of AI-powered diagnostic support tools. • Ensured annotation accuracy for regulatory and clinical requirements.

2024 - Present

Data Annotator – Hugo Tech

LabelboxImageSegmentation

As a Data Annotator at Hugo Tech, I performed detailed image and video annotation for computer vision pipelines. This included executing bounding box, instance segmentation, and polygon labeling across diverse client projects. I ensured high accuracy through rigorous quality audits and rapid adaptation to new guidelines and platforms. • Conducted object detection and tracking on video datasets, maintaining temporal consistency. • Participated in calibration sessions and peer quality checks for team alignment. • Worked with varied annotation tools including Labelbox, Scale AI, and CVAT. • Maintained inter-annotator agreement scores exceeding project thresholds.

As a Data Annotator at Hugo Tech, I performed detailed image and video annotation for computer vision pipelines. This included executing bounding box, instance segmentation, and polygon labeling across diverse client projects. I ensured high accuracy through rigorous quality audits and rapid adaptation to new guidelines and platforms. • Conducted object detection and tracking on video datasets, maintaining temporal consistency. • Participated in calibration sessions and peer quality checks for team alignment. • Worked with varied annotation tools including Labelbox, Scale AI, and CVAT. • Maintained inter-annotator agreement scores exceeding project thresholds.

2024 - Present

Object Detection Dataset — Autonomous Vehicles – Micro One

LabelboxImageBounding Box

I contributed to an object detection dataset for autonomous vehicles at Micro One by annotating images with bounding boxes and polygon masks. The project required labeling across multiple object classes and maintaining high QA pass rates. My work was frequently prioritized for fast-track batches due to consistent quality. • Labeled over 15,000 images for vehicle, pedestrian, cyclist, and signage detection. • Achieved a 97% QA pass rate on all submitted batches. • Ensured precise annotation for safety-critical perception models. • Collaborated on annotation guidelines and project processes.

I contributed to an object detection dataset for autonomous vehicles at Micro One by annotating images with bounding boxes and polygon masks. The project required labeling across multiple object classes and maintaining high QA pass rates. My work was frequently prioritized for fast-track batches due to consistent quality. • Labeled over 15,000 images for vehicle, pedestrian, cyclist, and signage detection. • Achieved a 97% QA pass rate on all submitted batches. • Ensured precise annotation for safety-critical perception models. • Collaborated on annotation guidelines and project processes.

2023 - 2024

Image & Video Annotation Specialist – Micro One

LabelboxImageSegmentation

At Micro One, I specialized in large-scale image and video annotation for computer vision model training. My tasks included bounding boxes, polygon masks, semantic segmentation, keypoint labeling, and multi-object tracking. I maintained high annotation accuracy and contributed significantly to guideline development. • Annotated over 10,000 images and video frames for object detection projects. • Reviewed peer annotations and flagged complex edge cases. • Adapted to multiple annotation platforms swiftly as project needs changed. • Collaborated globally to deliver high-volume annotation batches on tight deadlines.

At Micro One, I specialized in large-scale image and video annotation for computer vision model training. My tasks included bounding boxes, polygon masks, semantic segmentation, keypoint labeling, and multi-object tracking. I maintained high annotation accuracy and contributed significantly to guideline development. • Annotated over 10,000 images and video frames for object detection projects. • Reviewed peer annotations and flagged complex edge cases. • Adapted to multiple annotation platforms swiftly as project needs changed. • Collaborated globally to deliver high-volume annotation batches on tight deadlines.

2023 - 2024

Video Annotation — Action Recognition – Micro One

LabelboxVideoAction Recognition

For the Action Recognition Video Annotation project at Micro One, I performed frame-by-frame keypoint labeling and object tracking on thousands of video clips. My annotations supported the development of human activity recognition models. I maintained temporal consistency and received QA commendations for reducing training noise. • Labeled over 5,000 video clips focused on human activity detection. • Performed detailed tracking to ensure clean temporal labeling across sequences. • Reduced model label noise through careful annotation review. • Achieved recognition from project QA leads for annotation quality.

For the Action Recognition Video Annotation project at Micro One, I performed frame-by-frame keypoint labeling and object tracking on thousands of video clips. My annotations supported the development of human activity recognition models. I maintained temporal consistency and received QA commendations for reducing training noise. • Labeled over 5,000 video clips focused on human activity detection. • Performed detailed tracking to ensure clean temporal labeling across sequences. • Reduced model label noise through careful annotation review. • Achieved recognition from project QA leads for annotation quality.

2023 - 2023

Education

Work History