Use case
Object Detection
Align task-specific object detection models to locate and identify items in images and video with Labelbox.
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 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.
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.
Explore models for object detection
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.
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.
