Emmanuel Otengo

Key Skills

Software

CloudFactory

CVAT

Data Annotation Tech

Labelbox

Top Subject Matter

self driving annotation Image segmentation

Image segmentation

3-D in point cloud notation

Top Data Types

Audio

Geospatial Tiled Imagery

Text

Top Task Types

Audio Recording

Bounding Box

Classification

Data Collection

Freelancer Overview

In the realm of data labeling, I have developed a strong foundation in accurately annotating diverse datasets essential for training machine learning models. My key skills include meticulous attention to detail, proficiency with various annotation tools such as Labelbox and VGG Image Annotator, and a solid understanding of different data types—including images, text, and audio. I ensure adherence to strict labeling guidelines and maintain high standards of quality control, which has been critical in delivering reliable datasets for AI applications. I have contributed to several notable projects, such as curating datasets for image classification tasks and text sentiment analysis, where I collaborated closely with data scientists to refine labeling strategies. My background in computer science and statistics provides me with a robust analytical framework, allowing me to understand the nuances of data labeling and its impact on model performance. This combination of technical expertise, project experience, and a commitment to quality sets me apart in the field of data labeling.

Labeling Experience

Data annotation

CVATImageBounding Box

Scope: The project aims to annotate images for training machine learning models in tasks like object detection and image classification. Specific Data Labeling Tasks: Bounding Box Annotation: Drawing boxes around objects. Semantic Segmentation: Classifying each pixel. Instance Segmentation: Differentiating between object instances. Keypoint Annotation: Marking specific points on objects. Project Size: Can range from hundreds to millions of images, taking weeks to months, with variable team sizes. Quality Measures: Inter-Annotator Agreement (IAA): Ensuring consistency. Annotation Review: Regular checks for accuracy. Quality Metrics: Evaluating precision, recall, and F1 score. Feedback Loops: Continuous improvement through feedback. Gold Standard Sample: Benchmarking with a perfect annotation set. This concise strategy ensures high-quality annotated datasets for effective machine learning model training.

Scope: The project aims to annotate images for training machine learning models in tasks like object detection and image classification. Specific Data Labeling Tasks: Bounding Box Annotation: Drawing boxes around objects. Semantic Segmentation: Classifying each pixel. Instance Segmentation: Differentiating between object instances. Keypoint Annotation: Marking specific points on objects. Project Size: Can range from hundreds to millions of images, taking weeks to months, with variable team sizes. Quality Measures: Inter-Annotator Agreement (IAA): Ensuring consistency. Annotation Review: Regular checks for accuracy. Quality Metrics: Evaluating precision, recall, and F1 score. Feedback Loops: Continuous improvement through feedback. Gold Standard Sample: Benchmarking with a perfect annotation set. This concise strategy ensures high-quality annotated datasets for effective machine learning model training.

2021 - 2024

Education

Work History