Nikunj G.

Key Skills

Software

AWS SageMaker

Anno-Mage

Axiom AI

CloudFactory

CVAT

Diffgram

Google Cloud Vertex AI

Labelbox

LabelImg

Label Studio

Prodigy

Remotasks

Roboflow

Sloth

SuperAnnotate

Supervisely

Trilldata Technologies

V7 Labs

Scale AI

Appen

Top Subject Matter

Retail,ecommerce,healthcare

Software,Web Development,Transportation and logistics

Healthcare records and imagery

Top Data Types

Computer Code Programming

Document

Text

Top Task Types

Bounding Box

Classification

Computer Programming/Coding

Data Collection

Translation/Localization

Freelancer Overview

I have hands-on experience in data labeling and AI training across multiple industries, including e-commerce, healthcare, and autonomous vehicles. My work has involved annotating complex datasets, such as product images for recommendation systems, medical imaging for diagnostic tools, and video data for self-driving vehicles. I’m proficient with a variety of annotation tools like Labelbox, CVAT, and Supervisely, allowing me to efficiently handle both image and text data. What sets me apart is my ability to understand the nuances of different data types and tailor labeling techniques to the specific needs of each project. I have contributed to improving the accuracy of AI models by ensuring high-quality, consistent labeling standards, which directly impact the model’s performance. My strong attention to detail, combined with a deep understanding of AI workflows, allows me to deliver precise and valuable training data.

Labeling Experience

Text Categorization for E-commerce Reviews

AppenTextEmotion RecognitionText Summarization

Labeled and categorized customer reviews for an e-commerce platform to train a sentiment analysis model. This project involved classifying text into categories such as positive, neutral, and negative, and identifying specific product attributes mentioned in the reviews. The goal was to enhance the model's ability to analyze customer sentiment and provide actionable insights for product improvement and marketing strategies.

2023 - 2023

Image Annotation for Autonomous Vehicles

Scale AIImageBounding BoxSegmentation

Labeled thousands of video frames to improve the object detection system of self-driving cars. The annotation involved identifying and labeling various objects in traffic, such as vehicles, pedestrians, traffic signs, and road lanes. The data labeling aimed to train machine learning models for accurate detection and classification, enhancing the safety and performance of autonomous driving technology.

2023 - 2023

Education

Work History