Muhammad Usman Akram

Key Skills

Software

CVAT

Labelbox

SuperAnnotate

V7 Labs

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Video

Top Label Types

Action Recognition

Bounding Box

Classification

Data Collection

Emotion Recognition

Object Detection

Polygon

Segmentation

Text Summarization

Transcription

Translation Localization

Freelancer Overview

I am a detail-oriented data professional with a strong foundation in data analytics and a dedicated focus on data annotation for AI and machine learning projects. I have completed the full MxD Data Annotator certification series (Levels 100-300), equipping me with advanced skills in image, text, audio, and video annotation. My hands-on experience includes using CVAT to create high-quality training datasets through precise bounding boxes, polygons, and semantic segmentation for computer vision tasks such as object detection and classification. I am skilled in metadata tagging, quality assurance, and annotation workflows, ensuring that all labeled data meets strict quality standards to support robust AI/ML model development. My analytical mindset and commitment to accuracy make me a valuable contributor to any data labeling or annotation team.

Labeling Experience

Data Annotator (Practice Exercise)

CVATImageBounding Box

As a Data Annotator, I utilized CVAT to accurately label images using bounding boxes, polygons, and semantic segmentation. I generated high-quality training datasets for object detection and classification models in alignment with strict annotation protocols. My work emphasized the creation of precise and reliable datasets for AI and machine learning tasks. • Applied bounding boxes and polygons for detailed object annotations. • Performed semantic segmentation for pixel-level image analysis. • Ensured data quality through established validation workflows. • Supported model training in object detection and classification domains.

As a Data Annotator, I utilized CVAT to accurately label images using bounding boxes, polygons, and semantic segmentation. I generated high-quality training datasets for object detection and classification models in alignment with strict annotation protocols. My work emphasized the creation of precise and reliable datasets for AI and machine learning tasks. • Applied bounding boxes and polygons for detailed object annotations. • Performed semantic segmentation for pixel-level image analysis. • Ensured data quality through established validation workflows. • Supported model training in object detection and classification domains.

2026

Education

Work History