Jennevin Abengoza

Key Skills

Software

AWS SageMaker

Appen

Clickworker

Labelbox

Mindrift

OneForma

Remotasks

Scale AI

Toloka

Telus

V7 Labs

Other

Top Subject Matter

AI data annotation chatbot response

Image Segmentation

Product Classification

Top Data Types

Document

Image

Video

Top Task Types

Bounding Box

Classification

Data Collection

Evaluation/Rating

Point/Key Point

Freelancer Overview

I have vast experience in labeling data and using AI to train datasets, with a particular emphasis on product classification, chatbot response accuracy, and image segmentation. I have extensive experience labeling a variety of images for segmentation tasks, making sure that labels for different categories like cars, buildings, plants, and man-made structures are accurate and consistent. I have worked on projects where the goal was to increase the performance of AI chatbots by precisely annotating training data to improve response relevancy and accuracy. In addition, I have worked on projects involving product classification that improve product attributes and details, guaranteeing top-notch training datasets for machine learning models. My ability to deliver trustworthy and useful data for AI applications stems from my meticulous attention to detail and adherence to quality standards.

Labeling Experience

Data Annotator

Scale AIImageSegmentationText Generation

An image dataset containing images of cars, buildings, greenery, and man-made structures will have its segmentation labels annotated as part of the project. Each picture must have its precise segmentation boundaries labeled for each of the four designated categories, as part of the 100 tasks that taskers will be assigned. Each task involves reviewing and accurately annotating images using provided guidelines and annotation tools, ensuring consistency and high-quality data. The project aims to create a comprehensive dataset that enhances the performance of computer vision models by providing clear and accurate labels for training and evaluation purposes. In order to accomplish their tasks, taskers will adhere to a set procedure, which will help to yield a dependable and thoroughly recorded dataset.

An image dataset containing images of cars, buildings, greenery, and man-made structures will have its segmentation labels annotated as part of the project. Each picture must have its precise segmentation boundaries labeled for each of the four designated categories, as part of the 100 tasks that taskers will be assigned. Each task involves reviewing and accurately annotating images using provided guidelines and annotation tools, ensuring consistency and high-quality data. The project aims to create a comprehensive dataset that enhances the performance of computer vision models by providing clear and accurate labels for training and evaluation purposes. In order to accomplish their tasks, taskers will adhere to a set procedure, which will help to yield a dependable and thoroughly recorded dataset.

2021 - 2023

Education

Work History