Agustine Nyaanga

Key Skills

Software

CloudFactory

CVAT

Sama

V7 Labs

Other

Top Subject Matter

Self-Driving car imagery

Film and Entertainment

Object Detection and Movement

Top Data Types

Video

Geospatial Tiled Imagery

3D Sensor

Top Label Types

Entity Ner Classification

Text Generation

Emotion Recognition

Action Recognition

Bounding Box

Point Key Point

Polyline

Classification

Mapping

Object Detection

Tracking

Segmentation

Freelancer Overview

I am an experienced data annotation and AI training data specialist with a strong background in computer vision and autonomous vehicle domains. My work includes annotating complex video datasets, placing skeletal key points, drawing 2D bounding boxes, and labeling 3D LiDAR point clouds to support object detection and motion tracking. I have consistently achieved over 95% accuracy in labeling tasks, collaborating with cross-functional teams to refine workflows and improve data quality. My technical skills include Python, JavaScript, and a variety of annotation tools, and I am adept at using natural language to enhance labeling for machine learning models. I am passionate about delivering high-quality, precise training data that drives innovation and performance in AI systems.

Labeling Experience

Data Annotator

Don T DiscloseVideoAction Recognition

The project involves annotating key ego actions

The project involves annotating key ego actions

2025

AI Writer/Video Annotation Specialist

Don T DiscloseVideoEntity Ner ClassificationText Generation

Worked on video annotation tasks involving detailed labeling of characters, objects, and their actions across various video feeds. Annotated lighting conditions and camera positions to support advanced video understanding models. Ensured consistency and accuracy by following strict annotation guidelines and participated in quality control reviews to maintain dataset integrity. The project involved annotating thousands of video clips for training computer vision AI systems.

Worked on video annotation tasks involving detailed labeling of characters, objects, and their actions across various video feeds. Annotated lighting conditions and camera positions to support advanced video understanding models. Ensured consistency and accuracy by following strict annotation guidelines and participated in quality control reviews to maintain dataset integrity. The project involved annotating thousands of video clips for training computer vision AI systems.

2025

Video Annotation Expert

CVATVideoClassification

At Micro1, I worked as a Video Annotation Expert annotating over 50 video clips to capture scene progression, lighting, character actions, and camera movements. I used precise timestamps to enable computer vision models to recognize actions and cinematic elements. I adhered strictly to QA guidelines and applied a filmmaker’s perspective to improve the descriptive value of the annotations. • Achieved over 95% accuracy labeling actions, objects, and visual cinematic elements. • Described lighting and camera operations with clarity for ML model training. • Collaborated with teams via Slack and Zoom to streamline annotation workflows. • Enhanced annotation quality by using varied, natural language inputs.

At Micro1, I worked as a Video Annotation Expert annotating over 50 video clips to capture scene progression, lighting, character actions, and camera movements. I used precise timestamps to enable computer vision models to recognize actions and cinematic elements. I adhered strictly to QA guidelines and applied a filmmaker’s perspective to improve the descriptive value of the annotations. • Achieved over 95% accuracy labeling actions, objects, and visual cinematic elements. • Described lighting and camera operations with clarity for ML model training. • Collaborated with teams via Slack and Zoom to streamline annotation workflows. • Enhanced annotation quality by using varied, natural language inputs.

2025 - 2025

Data Annotation Expert

CVATVideoPoint Key Point

As a Data Annotation Expert at Innodata, I placed skeletal key points (SKPs) on bulls, horses, and riders in rodeo event videos for motion tracking. I created 2D bounding boxes around animals and riders to support object detection in dynamic video datasets. I performed video annotation describing user actions, movements, and environmental context with high precision. • Delivered annotations with 98% accuracy, ensuring quality training data for computer vision models. • Supported multiple projects by providing labeled datasets tailored for motion analysis and object detection. • Enhanced machine learning pipelines by maintaining consistency and accuracy in labeled data. • Used annotation best practices to optimize efficiency and output.

As a Data Annotation Expert at Innodata, I placed skeletal key points (SKPs) on bulls, horses, and riders in rodeo event videos for motion tracking. I created 2D bounding boxes around animals and riders to support object detection in dynamic video datasets. I performed video annotation describing user actions, movements, and environmental context with high precision. • Delivered annotations with 98% accuracy, ensuring quality training data for computer vision models. • Supported multiple projects by providing labeled datasets tailored for motion analysis and object detection. • Enhanced machine learning pipelines by maintaining consistency and accuracy in labeled data. • Used annotation best practices to optimize efficiency and output.

2025 - 2025

Data Labeller and Annotator

CloudfactoryGeospatial Tiled ImagerySegmentation

As a Data Labeller and Annotator at CloudFactory, I annotated roof geometry data to improve computer vision algorithms for spatial recognition and 3D mapping. I processed high-volume geospatial datasets with excellent accuracy to boost machine learning performance. My work enabled structural analysis and enhanced 3D mapping outcomes for real-world applications. • Delivered annotation accuracy at a 98% rate for complex geometries. • Processed large-scale image and mapping data with efficient workflows. • Improved model performance by refining training data through precise segmentation. • Supported spatial analysis use cases by producing high-quality labeled data.

As a Data Labeller and Annotator at CloudFactory, I annotated roof geometry data to improve computer vision algorithms for spatial recognition and 3D mapping. I processed high-volume geospatial datasets with excellent accuracy to boost machine learning performance. My work enabled structural analysis and enhanced 3D mapping outcomes for real-world applications. • Delivered annotation accuracy at a 98% rate for complex geometries. • Processed large-scale image and mapping data with efficient workflows. • Improved model performance by refining training data through precise segmentation. • Supported spatial analysis use cases by producing high-quality labeled data.

2024 - 2025

Show more

Education

Work History