Maria G.

Key Skills

Software

Roboflow

Appen

Clickworker

Labelbox

Top Subject Matter

Healthcare - Records

Automotive -Car Analysis

Agriculture- Plant Analysis

Top Data Types

Image

Text

Video

Top Task Types

Bounding Box

Segmentation

Classification

Polyline

Object Detection

Freelancer Overview

I have been working with data annotation and training data tasks since my college years, where I gained hands-on experience preparing, organizing, and labeling datasets for research and machine learning projects. Through these experiences, I developed strong attention to detail, accuracy, and consistency when handling large volumes of data. I understand the importance of high-quality annotations in improving AI model performance and ensuring reliable training outcomes. One of my strengths is my ability to work efficiently without compromising accuracy. As a Computer Engineering graduate involved in research projects that utilized AI and deep learning, I am familiar with following detailed guidelines, maintaining data quality standards, and meeting deadlines. I am highly organized, quick to learn new annotation tools and workflows, and committed to delivering precise and reliable results that contribute to successful AI training and development.

Labeling Experience

Annotating of Image of Plants

ImageSegmentation

In this project, we annotated plant images using segmentation techniques to accurately identify and isolate each plant from the background. The segmented plants were then analyzed based on key features such as size, color, and height. These characteristics were used to help determine the plant's growth stage and estimate its age. The annotation process required careful attention to detail to ensure accurate labeling and consistent data quality. This project contributed to the development of a machine learning model for automated plant age classification and agricultural monitoring.

2025 - 2025

Car Annotation

ImageBounding Box

In this project, we annotated vehicle images using bounding box techniques to accurately detect and locate cars within traffic scenes. Each vehicle was labeled according to its brand and type, such as sedan, SUV, hatchback, or truck. The annotated data was used to support traffic analysis and vehicle classification tasks. This process required precise labeling to ensure the quality and reliability of the training dataset. The project contributed to the development of computer vision models for traffic monitoring, vehicle detection, and automated vehicle recognition.

2024 - 2024

Education

Work History