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TU Dublin Quotes

Label Your Data were genuinely interested in the success of my project, asked good questions, and were flexible in working in my proprietary software environment.

Quotes
TU Dublin

Kyle Hamilton

PhD Researcher at TU Dublin

Trusted by ML Professionals

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Vision AI

Pupil
Segmentation & Maritime Object Tracking

Location:
France France
Services:
Object Segmentation Object Tracking
Nexvision

Overview

Nexvision needed detailed annotations across multiple vision tasks to support model training for gaze detection and maritime tracking.

40 000 pupil images
(ellipse masks)
30 000 eye images
(bounding boxes)
4 000 maritime videos
(object tracking)
~10 annotators
Client

Client

French embedded vision company serving defense, aerospace, and industrial sectors.

Challenges

Challenges

Needed high-precision labels across eye and maritime footage, with tool constraints and shifting batch volumes.

Solutions

Solutions

Labeled 70k images and 4k videos using CVAT and client tools; 3 workflows ran in parallel with QA and feedback cycles.

Results

Results

All datasets delivered on time; supported model training for gaze detection and maritime awareness with no quality concerns.

Client

Nexvision is a French company building advanced embedded vision systems for defense, aerospace, security, and industrial applications.

Nexvision engineering team with embedded vision system equipment for defense and industrial applications
Woman working on a laptop, representing the team addressing data annotation and tracking challenges for AI projects

Challenges

1

Pupil detection required ellipse-level precision automated tools couldn't deliver

2

Maritime footage needed manual object tracking with dynamic camera motion

3

Tasks spanned different pipelines: some required internal tool, others CVAT

4

Batch sizes and project timelines changed frequently, requiring flexible delivery

Solution

Label Your Data split the work into three parallel workflows. Batches were delivered regularly, with feedback incorporated between cycles.

1

For pupil segmentation, 40 000 images were labeled using Nexvision’s internal tool with precise ellipse masks

2

For eye detection, 30 000 images were annotated with bounding boxes using CVAT

3

For maritime tracking, 4 000 videos were manually labeled with object tracks in CVAT

Close-up of a human eye used for AI-based pupil detection and segmentation
Annotated eye image with green ellipse mask showing pupil segmentation for computer vision training

Training

1

Each stream began with written guidelines and a kickoff call.
Annotators completed small pilot batches before moving to full production.

2

No major rework was required thanks to strong initial alignment.

Results

1

3 complex annotation pipelines delivered in parallel

2

Gaze model training supported by 40k accurate ellipse masks

3

Maritime model improved with 4k hand-tracked video segments

4

Flexible team size adapted to shifting batch scope

5

All batches delivered on time with no quality issues reported

Annotated image of a sailboat used for maritime object detection training
Video frame with bounding boxes marking boats and objects for computer vision model validation

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Nexvision Quotes

As a company developing optronic systems, we rely on high-quality annotated datasets to analyze sensor data. Label Your Data is fully integrated into our model-building process and has proven to be a flexible, reliable partner for our annotation needs.

Quotes
Nexvision

Victor Martin-Lac

Machine Learning Engineer

Trusted by ML Professionals

Yale
Princeton University
KAUST
ABB
Respeecher
Toptal
Bizerba
Thorvald
Advanced Farm
Searidge Technologies

Why Projects Choose Label Your Data

No Forced Commitment

No Forced Commitment

Check our performance based on a free trial

Flexible Pricing

Flexible Pricing

Pay per labeled object or per annotation hour

Tool-Agnostic

Tool-Agnostic

Working with every labeling tool, even your custom tools

Quality Backed by SLAs

Quality Backed by SLAs

We commit to accuracy and deadlines – or you don’t pay