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At e2m, we use CVAT to review computer vision detections of animals in massive camera trap image datasets. We found CVAT to be the best tool at this scale, enabling efficient image review and robust quality control.
Senior Computational Ecologist
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tbmaestro uses CVAT as a key tool to structure large-scale annotations for equipment detection on our inspection photos and to accelerate computer vision deployment in asset management.
AI Engineer
Our mission is to make vessels safer for every crew at sea. CVAT plays a critical role in that, helping us transform raw CCTV footage from maritime environments into the training data for our AI detection systems. From identifying unsafe behaviour to surfacing near-miss situations before they escalate, CVAT is the foundation that makes our safety intelligence possible.
CEO
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CVAT helps us label sports images to build high-quality ground truth that enables our AI to analyze volleyball performance and provide personalized feedback. It gives us the flexibility to combine automated and human labeling, and makes it easier to test different models and refine the best approach for our AI.
Founder
At Arboair, we annotate forestry imagery to deliver world-class tree analysis worldwide, and CVAT has been key to making that possible at scale. Its ease of use and flexibility across annotation use cases support our full workflow, from production models to experimental projects.
COO
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Vimaan uses CVAT as its primary data annotation tool and selected this tool after a thorough evaluation of alternative tools both in open source and closed domain. Top 3PLs and warehouses have improved their inventory accuracy, reduced mis-shipments, improved bin utilization, while reducing overall resource.
CTO & Chief AI Scientist
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