geospatial
Fish Keypoint
Annotation
for KAUST AI
Research
Saudi Arabia
Overview
Overview
KAUST researchers needed high-quality fish keypoint annotations to train computer vision models for marine biology research. Label Your Data delivered accurate, model-ready training data within a one-month deadline.
Client
King Abdullah University of Science and Technology (KAUST), a research institution in Saudi Arabia focused on AI, biology, and environmental studies.
Challenges
KAUST had previously used Amazon Mechanical Turk (MTurk) but needed more consistent, high-accuracy annotations for model training.
Annotation consistency was difficult to maintain
Labels required additional quality review
Research timelines were affected by data quality issues
Solution
Label Your Data ran a two-step annotation workflow using CVAT. Structured annotation guidelines included edge cases, gallery references, and a short demo video.
7 keypoints labeled per fish: mouth start, eye, dorsal fin, pectoral fin, tail start, tail end, body center
Each image also marked as “water” or “air”
20 annotators completed the bulk of the work
5 senior QA reviewers cleaned and approved the labels
Need Model-Ready Data for
Your Computer Vision Research?
Results
220,000 images labeled in just under one month
Training data met 98%+ accuracy requirement
Clean labels improved model performance and reduced filtering workload
KAUST researchers shifted focus from data preparation to model analysis and training
Why AI Teams Choose Label Your Data
Data Annotation for Complex Environments
Rely on consistent, high-quality output for complex datasets, detailed taxonomies, and edge cases.
Structured Quality from Pilot to Production
Get quality engineered into every step through onboarding, evolving guidelines, QA, and continuous feedback.
Flexible and Scalable Operations
Adjust team capacity, project size, and delivery model as you scale, with no setup fees or long-term lock-ins.
An Integrated Delivery Partner
Align on goals, workflows, and expectations with a team that integrates into your process from day one.
Projects Led by Annotation Experts
Work with former annotators who understand annotation complexity, quality standards, and high-volume delivery.
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The biggest benefit has been peace of mind around annotation quality. The labels consistently meet a high standard, so I no longer worry about data issues.
Faizan Khan
PhD student of Computer Science
Trusted by ML Professionals
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