Why you need quality data labeling in infrastructure crack detection

Why you need quality data labeling in infrastructure crack detection

Discover the impact of quality data labeling in infrastructure asset management. Learn ML model accuracy, performance, and scalability strategies in asset inspection and crack ...

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Quality ML data fuels smart infrastructure asset management decisions

Quality ML data fuels smart infrastructure asset management decisions

Quality data labeling fuels infrastructure health. Learn how to achieve ML model accuracy, performance, & scalability to extend asset life.

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7 interesting LiDAR applications

7 interesting LiDAR applications

LiDAR is a useful 3-D object detection technology for many industries, from AV to aerial inspections. Here are 7 interesting applications of LiDAR.

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AI-powered labeling: the key to profitable AI development in agtech

AI-powered labeling: the key to profitable AI development in agtech

How CTOs and VPs of product and machine learning can navigate key agtech hurdles using AI-powered data labeling for sustainable growth and profitability.

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The importance of humans in the loop in AI development

The importance of humans in the loop in AI development

While foundational models offer remarkable potential, our experience reveals that humans in the loop remain crucial for successful AI development.

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AI-powered labeling: The key to better agtech machine learning models

AI-powered labeling: The key to better agtech machine learning models

Discover the impact of quality data labeling in agtech. Learn ML model accuracy, performance, and scalability strategies in precision farming.

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Accelerated Annotation: 1 year of data labeling excellence

Accelerated Annotation: 1 year of data labeling excellence

Happy Birthday, Accelerated Annotation! It's been a year of providing clients with the quality training data needed to launch your models fast.

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Four workforce traits that affect quality in data labeling for ML

Four workforce traits that affect quality in data labeling for ML

Learn the four workforce traits crucial for high-quality ML datasets. Avoid costly rework and boost your AI project's success.

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Five modern segmentation challenges tech gurus face in machine learning

Five modern segmentation challenges tech gurus face in machine learning

This post discusses image segmentation challenges developers face across the ML pipeline - from data annotation to the deployment stage of the lifecycle.

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Achieving business goals: The role of segmentation labeling in computer vision

Achieving business goals: The role of segmentation labeling in computer vision

Segmentation labeling can help scale your business. Master pixel-perfect image annotation, tackle challenges, and discover best practices and tools.

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Ensure better quality data with vision transformer-powered data labeling

Ensure better quality data with vision transformer-powered data labeling

Discover the power of Vision Transformers (ViTs) in data labeling. Learn how ViT models outperform CNNs, ensuring superior image classification and segmentation.

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Active learning: tackling ML data annotation challenges

Active learning: tackling ML data annotation challenges

This post discusses accelerating AI development with active learning, a machine learning method that prioritizes the most informative data for labeling.

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Supercharge Vision AI projects with foundation models

Supercharge Vision AI projects with foundation models

Discover the power of foundation models for Vision AI. Supercharge your workflow, save time, and boost performance.

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Creating data annotation guidelines that drive accurate ML models

Creating data annotation guidelines that drive accurate ML models

A resource to create A+ data annotation guidelines that will lead to high-quality AI models, cost savings, and improved business operations.

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Top benefits and limitations of auto labeling

Top benefits and limitations of auto labeling

Overcome the most common limitations of automated data labeling and maximize its benefits using humans in the loop and the right annotation platform.

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The 4 layers of automated data labeling for faster AI goals

The 4 layers of automated data labeling for faster AI goals

Uncover the four layers of automated data labeling—pre-labeling, pre-trained models, hosting, and technology—to achieve faster and smarter AI goals.

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