John Mbao

Key Skills

Software

Roboflow

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Bounding Box

Freelancer Overview

I am a detail-oriented data annotation professional with hands-on experience labeling images, short texts, and audio samples using open-source tools. My skills include sentiment analysis tagging, text categorization, and entity marking, with a strong focus on accuracy and quality control. I am proficient in English, both written and spoken, and excel at following guidelines and meeting deadlines while working independently. My background in creative writing further enhances my ability to understand linguistic nuances, making me well-suited for AI training data tasks in domains such as natural language processing and computer vision. I am a fast learner, adaptable, and committed to delivering reliable, high-quality results in remote settings.

Labeling Experience

Cats and Dogs image annotation and preprocessing using Roboflow

RoboflowImageBounding Box

Annotated and labeled a Cats and Dogs image dataset from Kaggle using bounding boxes on Roboflow to create a high-quality training set for a computer vision model. The annotated data was used to train a model that independently identifies cats and dogs with ~90% accuracy. Skills Demonstrated: Image annotation • Data labeling • Dataset preprocessing • Model training • Computer vision

Annotated and labeled a Cats and Dogs image dataset from Kaggle using bounding boxes on Roboflow to create a high-quality training set for a computer vision model. The annotated data was used to train a model that independently identifies cats and dogs with ~90% accuracy. Skills Demonstrated: Image annotation • Data labeling • Dataset preprocessing • Model training • Computer vision

2025 - 2025

Apple Leaf Disease Annotation & Classification Model

RoboflowImageBounding Box

Annotated and labeled images of apple leaves to classify plant health status, including healthy leaves and leaves affected by scab, rust, or a combination of both diseases. The curated dataset was used to train a computer vision model capable of independently identifying diseased apple leaves with ~75% accuracy. Skills Demonstrated: Image annotation • Data labeling • Dataset preprocessing • Model training • Computer vision

Annotated and labeled images of apple leaves to classify plant health status, including healthy leaves and leaves affected by scab, rust, or a combination of both diseases. The curated dataset was used to train a computer vision model capable of independently identifying diseased apple leaves with ~75% accuracy. Skills Demonstrated: Image annotation • Data labeling • Dataset preprocessing • Model training • Computer vision

2025 - 2025

Education

Work History