Mufidat Muhammed

Key Skills

Software

Labelbox

CVAT

Supervisely

Top Subject Matter

Crop Detection in Satellite Imagery

Chatbot Intent Recognition

Medical Image Diagnosis (Pneumonia Detection)

Top Data Types

Image

Text

Top Task Types

Polygon

Classification

Object Detection

Freelancer Overview

Junior Data Annotator – Satellite Image Labeling. Core strengths include Labelbox, CVAT, and Supervisely. Education includes Bachelor of Science, Ahmadu Bello University (2023). AI-training focus includes data types such as Image, Text, and Medical and labeling workflows including Polygon, Classification, and Object Detection.

Labeling Experience

Junior Data Annotator – Text Intent Classification

LabelboxTextClassification

I classified over 12,000 short text snippets into specified intent categories for chatbot training. This involved meticulous attention to labeling guidelines and maintaining consistent throughput and quality. My contributions supported machine learning training for conversational AI models. • Used Labelbox to categorize text according to predefined intent categories • Maintained a 95% accuracy rate across ongoing annotation batches • Delivered 120+ labels per hour consistently without drops in quality • Utilized Jira to monitor and resolve 200+ discrepancies swiftly

I classified over 12,000 short text snippets into specified intent categories for chatbot training. This involved meticulous attention to labeling guidelines and maintaining consistent throughput and quality. My contributions supported machine learning training for conversational AI models. • Used Labelbox to categorize text according to predefined intent categories • Maintained a 95% accuracy rate across ongoing annotation batches • Delivered 120+ labels per hour consistently without drops in quality • Utilized Jira to monitor and resolve 200+ discrepancies swiftly

2024 - 2025

Junior Data Annotator – Satellite Image Labeling

LabelboxImagePolygon

As a Junior Data Annotator at TanlentBridge Data Solutions, I labeled over 8,000 satellite images for crop detection tasks. The work required precision and adherence to gold-standard labels, ensuring above 97% accuracy through regular QA review. I was recognized for my annotation speed and consistency in quality across the team. • Used Labelbox and CVAT to annotate images with complex agricultural objects • Resolved annotation discrepancies through regular feedback from senior reviewers • Maintained weekly throughput targets while upholding 98% QA consistency • Contributed to guideline optimization for improved project onboarding

As a Junior Data Annotator at TanlentBridge Data Solutions, I labeled over 8,000 satellite images for crop detection tasks. The work required precision and adherence to gold-standard labels, ensuring above 97% accuracy through regular QA review. I was recognized for my annotation speed and consistency in quality across the team. • Used Labelbox and CVAT to annotate images with complex agricultural objects • Resolved annotation discrepancies through regular feedback from senior reviewers • Maintained weekly throughput targets while upholding 98% QA consistency • Contributed to guideline optimization for improved project onboarding

2024 - 2025

Medical Image Annotator – RSNA Pneumonia Detection Challenge

SuperviselyObject Detection

I participated in annotating 2,000 medical images for the RSNA Pneumonia Detection Challenge on Kaggle. The work focused on identifying and labeling regions of interest in chest X-rays for use in diagnosis model training. Careful adherence to medical annotation standards was critical throughout the project. • Used Supervisely and Labelbox for precise region annotation • Collaborated with other annotators to ensure consistency in labeling standards • Maintained attention to medical privacy and data handling protocols • Supported open-source machine learning efforts with clinically relevant data

I participated in annotating 2,000 medical images for the RSNA Pneumonia Detection Challenge on Kaggle. The work focused on identifying and labeling regions of interest in chest X-rays for use in diagnosis model training. Careful adherence to medical annotation standards was critical throughout the project. • Used Supervisely and Labelbox for precise region annotation • Collaborated with other annotators to ensure consistency in labeling standards • Maintained attention to medical privacy and data handling protocols • Supported open-source machine learning efforts with clinically relevant data

2024 - 2024

Education

Work History