Danny Gray

Key Skills

Software

Appen

Clickworker

CloudFactory

CVAT

Labelbox

Mindrift

OneForma

Toloka

Telus

Top Subject Matter

Computer Science/Computer programming in Python and Javascript

2D and 3D annotation

Prompt Writing and Responses

Top Data Types

Computer Code Programming

Image

Video

Top Task Types

Bounding Box

Computer Programming Coding

Prompt Response Writing SFT

Segmentation

Text Generation

Freelancer Overview

A skilled AI data annotator with experience more than 3 years working on CVAT, expertise: computer vision, NLP datasets. Specialized in the production of compelling training data for automated vehicles, object recognition, and LLM testing. Proficient in the management of time models in a way that maximizes on work delivery in project set headers while aligning accuracy goals. Excellent at partnering with sophisticated labeling engagements, data management, as well as ensuring supportive AI model outcomes. AI enthusiast trying to make the best use of the technology to create positive change for businesses and society.

Labeling Experience

Data Annotator-Image Segmentation

CVATImagePolygon

Image Segmentation is a project that requires drawings of objects within images at the pixel level to enable the training of machine learning models in applications such as autonomous vehicles, medical imaging, and surveillance. In CVAT polygons, masks, brushes features, annotators will accurately segregate contour areas especially the overlapped or occluded areas. Every segmented region is divided into prespecified classes for accurate labeling of training data for the development of an effective AI algorithm. To increase comparability and quality, annotations have to follow strict rules concerning accuracy, consistency and edge precision. Each annotation is checked to the highest quality control measure and secondary checks are done to make sure they fit within the project. Complex scenarios are brought into question and explained in written form by annotators, and conventions of practice govern the handling of gray-zone instances. The project involves a dataset of more than ten th

Image Segmentation is a project that requires drawings of objects within images at the pixel level to enable the training of machine learning models in applications such as autonomous vehicles, medical imaging, and surveillance. In CVAT polygons, masks, brushes features, annotators will accurately segregate contour areas especially the overlapped or occluded areas. Every segmented region is divided into prespecified classes for accurate labeling of training data for the development of an effective AI algorithm. To increase comparability and quality, annotations have to follow strict rules concerning accuracy, consistency and edge precision. Each annotation is checked to the highest quality control measure and secondary checks are done to make sure they fit within the project. Complex scenarios are brought into question and explained in written form by annotators, and conventions of practice govern the handling of gray-zone instances. The project involves a dataset of more than ten th

2024

Computer Science Coder-JavaScript and Python

MindriftComputer Code ProgrammingComputer Programming CodingData Collection

At Mindrift, in the position of Computer Science Projects, I dealt in things like training of the specific AI model by data annotation, scoring of relevance, and quality checking of the dataset. One of the the projects involved labeling very large datasets mainly for object detection with a focus on accuracy and consistency. Project Scope: The tasks were object identification in images, checking the correctness of the models’ production by AI, and correcting data sets for compliance with machine learning requirements. This was beneficial so as to create AI models that are well suited to address more complex problems such as self-driving cars and predictive maintenance. Project Length: A large number of projects took between 3-6 months, although these estimates are affected by the nature and size of datasets. One of such projects was to conduct image annotation of more than 10,000 images within 4 months and it needed the use of team work in order to meet up with the set deadlines.

At Mindrift, in the position of Computer Science Projects, I dealt in things like training of the specific AI model by data annotation, scoring of relevance, and quality checking of the dataset. One of the the projects involved labeling very large datasets mainly for object detection with a focus on accuracy and consistency. Project Scope: The tasks were object identification in images, checking the correctness of the models’ production by AI, and correcting data sets for compliance with machine learning requirements. This was beneficial so as to create AI models that are well suited to address more complex problems such as self-driving cars and predictive maintenance. Project Length: A large number of projects took between 3-6 months, although these estimates are affected by the nature and size of datasets. One of such projects was to conduct image annotation of more than 10,000 images within 4 months and it needed the use of team work in order to meet up with the set deadlines.

2023 - 2024

Education

Work History