Shubham Vishwakarma

Key Skills

Software

LabelImg

CVAT

Roboflow

Top Subject Matter

Computer Vision - Object Detection

Facial Feature Detection

Autonomous Driving - Environmental Scene Labeling

Top Data Types

Image

Text

Document

Top Task Types

Bounding Box

Point/Key Point

Classification

Freelancer Overview

Computer Vision Data Annotator. Fresher across complex professional workflows, research, and quality-focused execution. Core strengths include LabelImg, CVAT, and Roboflow. Education includes Bachelor of Technology and Master of Technology, National Institute of Technology, Patna (2022) and Senior Secondary School Certificate, Queens Inter College, Varanasi (2021). AI-training focus includes data types such as Image and labeling workflows including Bounding Box, Point, and Key Point.

Labeling Experience

Traffic & Environmental Scene Data Labeler

RoboflowImageClassification

I collaborated on the labeling of simulated traffic and environmental scenes for autonomous driving datasets. Tasks included classifying objects such as vehicles, traffic signs, and pedestrians. Emphasis was placed on strict compliance with safety-critical annotation rubrics. • Labeled high volumes of scenes with repetitive accuracy. • Ensured full guideline compliance for safety-focused datasets. • Delivered rapid throughput with peer-agreement benchmarking. • Supported team results by reviewing and revising ambiguous labels.

I collaborated on the labeling of simulated traffic and environmental scenes for autonomous driving datasets. Tasks included classifying objects such as vehicles, traffic signs, and pedestrians. Emphasis was placed on strict compliance with safety-critical annotation rubrics. • Labeled high volumes of scenes with repetitive accuracy. • Ensured full guideline compliance for safety-focused datasets. • Delivered rapid throughput with peer-agreement benchmarking. • Supported team results by reviewing and revising ambiguous labels.

2023 - Present

Facial Landmark & Pattern Annotation Specialist

CVATImagePoint Key Point

I performed key-point labeling for facial landmark and pattern recognition using CVAT as my primary annotation platform. My work focused on point-to-point labeling accuracy in variable lighting and noisy image conditions. I standardized protocols to ensure consistent data across all project samples. • Created annotation guidelines for team-wide consistency. • QA-checked labeled data to maintain high precision. • Specialized in handling challenging cases with lighting and occlusions. • Enhanced peer collaboration through process documentation.

I performed key-point labeling for facial landmark and pattern recognition using CVAT as my primary annotation platform. My work focused on point-to-point labeling accuracy in variable lighting and noisy image conditions. I standardized protocols to ensure consistent data across all project samples. • Created annotation guidelines for team-wide consistency. • QA-checked labeled data to maintain high precision. • Specialized in handling challenging cases with lighting and occlusions. • Enhanced peer collaboration through process documentation.

2023 - Present

Computer Vision Data Annotator

LabelimgImageBounding Box

I annotated over 1,000 images for object detection and visual recognition projects using LabelImg and Roboflow. My responsibilities included performing bounding box and polygon labeling while adhering closely to annotation guidelines. I maintained a 98%+ accuracy rate through multiple rounds of quality audits. • Developed and enforced quality assurance protocols for data consistency. • Increased annotation speed by optimizing software interfaces and shortcuts. • Peer-reviewed other annotators’ work, identifying and correcting inconsistencies. • Implemented an 'Edge-Case Log' to improve classification of ambiguous examples.

I annotated over 1,000 images for object detection and visual recognition projects using LabelImg and Roboflow. My responsibilities included performing bounding box and polygon labeling while adhering closely to annotation guidelines. I maintained a 98%+ accuracy rate through multiple rounds of quality audits. • Developed and enforced quality assurance protocols for data consistency. • Increased annotation speed by optimizing software interfaces and shortcuts. • Peer-reviewed other annotators’ work, identifying and correcting inconsistencies. • Implemented an 'Edge-Case Log' to improve classification of ambiguous examples.

2023 - Present

Education

Work History