Amado Lawson

Key Skills

Software

CVAT

Labelbox

Prodigy

Supervisely

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Audio

Top Task Types

Bounding Box

Point Key Point

Segmentation

Object Detection

Tracking

Action Recognition

Classification

Emotion Recognition

Transcription

Freelancer Overview

I am an AI Trainer and Computer Vision Specialist with over 3 years of experience in high-accuracy data annotation and dataset validation for machine learning systems. My expertise spans image, video, and audio labeling, with a strong focus on object detection (YOLO v5/v8), multi-object tracking, semantic segmentation, and keypoint annotation. I am skilled in using tools such as Labelbox, CVAT, Supervisely, and Prodigy, and I have a solid background in structuring datasets in JSON and YOLO formats, as well as ensuring annotation quality through rigorous QA procedures. I am passionate about delivering production-grade datasets that enhance model accuracy and reliability, and I have a proven track record of reducing labeling errors and improving dataset consistency through standardized guidelines and validation checks. My technical proficiency includes Python, OpenCV, TensorFlow, and PyTorch, and I am committed to supporting AI model training and performance improvement in computer vision and multimodal data domains.

Labeling Experience

Audio Annotation & Transcription for AI Training

LabelboxAudioClassificationEmotion Recognition

Annotated and transcribed over 8,000 audio samples including speaker segmentation, emotion tagging, and speech classification. Ensured timestamp alignment and clarity standards for training speech recognition models. Conducted quality validation through transcript accuracy checks and consistency calibration.

Annotated and transcribed over 8,000 audio samples including speaker segmentation, emotion tagging, and speech classification. Ensured timestamp alignment and clarity standards for training speech recognition models. Conducted quality validation through transcript accuracy checks and consistency calibration.

2023

Video Annotation & Multi-Object Tracking

CVATVideoBounding BoxAction Recognition

Performed frame-by-frame annotation and multi-object tracking across 15,000+ video sequences. Labeled moving objects, behavioral interactions, and motion patterns to support supervised learning models. Ensured temporal consistency of tracking IDs and reduced annotation drift. Applied QA protocols to validate frame continuity and object persistence across sequences.

Performed frame-by-frame annotation and multi-object tracking across 15,000+ video sequences. Labeled moving objects, behavioral interactions, and motion patterns to support supervised learning models. Ensured temporal consistency of tracking IDs and reduced annotation drift. Applied QA protocols to validate frame continuity and object persistence across sequences.

2022 - 2023

Computer Vision Data Annotation – Object Detection (YOLO)

LabelboxImageBounding BoxPoint Key Point

Annotated over 120,000 images for object detection models using YOLO (v5/v8) standards. Created precise bounding boxes and segmentation masks for vehicles, pedestrians, infrastructure elements, and environmental objects. Structured datasets in YOLO-compatible formats and validated coordinate accuracy. Conducted quality assurance audits and implemented inter-annotator agreement checks to maintain high labeling consistency and reduce classification errors.

Annotated over 120,000 images for object detection models using YOLO (v5/v8) standards. Created precise bounding boxes and segmentation masks for vehicles, pedestrians, infrastructure elements, and environmental objects. Structured datasets in YOLO-compatible formats and validated coordinate accuracy. Conducted quality assurance audits and implemented inter-annotator agreement checks to maintain high labeling consistency and reduce classification errors.

2021 - 2023

Education

Work History