Gateri Joseph

Key Skills

Software

Clickworker

CloudFactory

CrowdSource

CVAT

Hasty

Labelbox

Label Studio

Mindrift

OneForma

Remotasks

Roboflow

Sama

Scale AI

Toloka

Other

Top Subject Matter

Roof Geometry

Data Annotation and Manipulation Northolt

Data Analysis and Visualization

Top Data Types

Computer Code Programming

Video

Image

Top Task Types

Audio Recording

Bounding Box

Computer Programming/Coding

Polygon

Segmentation

Data Collection

Freelancer Overview

I have extensive experience in data labeling and AI training, working with platforms like Remotasks and Toloka AI. My expertise includes image annotation, LIDAR segmentation, data labeling, and image segmentation. I have been involved in numerous projects requiring precise data annotation for machine learning models, ensuring high-quality datasets for AI training. My background in mathematics and computer science, combined with certifications in networking and security, provides me with a strong analytical foundation and technical proficiency. One of my notable projects was at CloudFactory, where I led a team in data entry, data cleaning, and data analysis, consistently meeting project output requirements with speed and accuracy. My role also involved maintaining confidentiality and attending relevant project meetings, demonstrating my ability to collaborate effectively and manage responsibilities efficiently. My skills in Python, Java, C++, and data visualization further enhance my capability to contribute to AI training projects, making me a versatile and valuable asset in the field of data labeling and AI development.

Labeling Experience

Image Annotation for Battery foil

CloudfactoryImageBounding BoxPolygon

In this project, I was responsible for annotating images to train computer vision models. The tasks included drawing bounding boxes and polygons around objects, as well as classifying various elements within the images. The project aimed to create a high-quality dataset to improve the accuracy of object detection and image classification algorithms. Adherence to quality standards was ensured through regular peer reviews and automated quality checks.

In this project, I was responsible for annotating images to train computer vision models. The tasks included drawing bounding boxes and polygons around objects, as well as classifying various elements within the images. The project aimed to create a high-quality dataset to improve the accuracy of object detection and image classification algorithms. Adherence to quality standards was ensured through regular peer reviews and automated quality checks.

2024 - 2024

LIDAR Annotation and Segmentation for Autonomous Vehicles

Scale AI3D SensorSegmentationObject Detection

worked on a comprehensive LIDAR annotation project aimed at enhancing the perception capabilities of autonomous vehicles. The tasks involved precise segmentation and labeling of 3D point clouds, identifying objects such as vehicles, pedestrians, and road features. The project required meticulous attention to detail to ensure high-quality annotations, directly contributing to the accuracy and reliability of self-driving algorithms. Quality measures included regular reviews and validation of annotations to maintain consistency and accuracy.

worked on a comprehensive LIDAR annotation project aimed at enhancing the perception capabilities of autonomous vehicles. The tasks involved precise segmentation and labeling of 3D point clouds, identifying objects such as vehicles, pedestrians, and road features. The project required meticulous attention to detail to ensure high-quality annotations, directly contributing to the accuracy and reliability of self-driving algorithms. Quality measures included regular reviews and validation of annotations to maintain consistency and accuracy.

2022 - 2023

Video Relevance and Labeling

ClickworkerVideoClassificationObject Detection

This project involved assessing and labeling video content for relevance and quality. The tasks included classifying videos based on predefined criteria, recognizing emotions, and detecting objects within the video frames. The goal was to create a dataset that could be used for various media analysis applications, including content recommendation systems and emotion analysis tools. Quality control measures included thorough review processes and consistency checks.

This project involved assessing and labeling video content for relevance and quality. The tasks included classifying videos based on predefined criteria, recognizing emotions, and detecting objects within the video frames. The goal was to create a dataset that could be used for various media analysis applications, including content recommendation systems and emotion analysis tools. Quality control measures included thorough review processes and consistency checks.

2021 - 2021

Education

Work History