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Use case

Object detection

Align task-specific object detection models to locate and identify items in images and video with Labelbox.

Object detection

Trusted by companies of all sizes for objection detection — from startups to Fortune 500s

Why use Labelbox for Object Detection

Accelerate AI alignment

Combine model assisted labeling and human expertise to quickly prepare data for training, testing and validation.

Generate high quality datasets

Optimize custom labeling and review workflows to ensure the highest quality data for model training and fine-tuning.

Maintain data privacy & security

Keep full ownership, transparency, and control over your data throughout the AI development process.

Boost your AI expertise

Supercharge your data engine with the help of Labelbox AI experts and on demand labeling services.

Building object detection applications

Building AI solutions

Building object detection applications

Rapidly develop accurate computer vision models with object detection capabilities for real-world business applications like automated shelf monitoring of retail stores.

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Automate using object detection models

Using AI

Automate using object detection models

Use state-of-the-art object detection foundation models to pre-label images and video, and accelerate labeling of training data for your deep learning model.

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Boost your AI workforce

Boost your AI workforce

Access data labeling services with specialized object detection expertise and more to match your use cases. Collaborate with the workforce in real-time to maintain high data quality while keeping human labeling costs to a minimum using AI and automation techniques.

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Customer spotlight

Intuitive Surgical, a global technology leader in minimally invasive care and the pioneer of robotic surgery leveraged Labelbox in order improve workflows for curating unstructured video data, measure labeling velocity & efficiency, and speed up annotation workflows between domain experts and labelers using model-assisted labeling. The Intuitive Surgical data science team is now able to adopt a data-centric approach to scale their labeling efficiency and throughput, while lowering their overhead to gather metrics on model performance and quality by 3x.