Discover how you can use Lablebox to tackle complex challenges when it comes to scaling and maintaining your computer vision models due to the inherent challenges of keeping up with rapid data drifts, navigating imagery and video quality issues, and adapting to applications requiring nuanced, multimodal ontologies.
Accelerate your computer vision breakthroughs by making feature detection, object classification, image segmentation for your imagery and videos dramatically faster and cheaper.
Gain and maintain a comprehensive understanding of the data you’ve collected for computer vision problem sets. Imagery and video-based use cases rely upon rapid retraining to remain relevant and performant in an ever-changing environment.
Labelbox Catalog paints a holistic picture of all past, present, and future unstructured data ingested and curated across your entire enterprise. Leading AI teams use similarity search to speed up data discovery and send relevant data directly to computer vision model training jobs in just a few clicks.
Machine learning teams building computer vision products ship faster when they are able to leverage the most intuitive and collaborative tools. Configure your computer vision project in minutes, scale up to any team size, and create the right training data through rapid iteration.
Bring additional attachments such as text, videos, images, overlays or even custom HTML widgets to aid data labelers in creating the perfect labels. Labelbox’s image and video editors are optimized to get the most out of your data, quickly.
Leveraging machine learning to rapidly identify biases, outliers and anomalies in data is foundational to computer vision model performance and improvement. Labelbox provides a workbench for active learning workflows that alert machine learning teams to indications of change, anomalies, or differences in what’s happening lately vs. what the model encountered in its original training dataset.
Go beyond just identifying changes — Labelbox also helps teams systematically improve computer vision model performance across these edge cases by identifying then expediting retraining on areas that are outside the original training distribution.
Leverage model-assisted labeling workflows to decrease manual labeling efforts by up to 80%. By importing computer-generated predictions as pre-labels on an asset, Labelbox empowers AI teams to leverage their existing computer vision models as an efficient mechanism to dramatically lower their annotation costs.
Rapidly generate annotations through automation designed to address even the most complex computer vision ontologies for object detection (e.g., bounding box, polygon, etc) and classifications (e.g., radio, checklist, etc).
Solving today’s most challenging problems requires creative and complex applications of AI. Labelbox provides a centralized platform to support the growing number of data formats and annotation schema necessary for multimodal algorithm development.
Machine learning teams leverage Labelbox to flexibly orchestrate the visualization, annotation, training and diagnostic model improvement workflows for developing powerful multi-step machine learning models that merge multiple data types.
How Blue River Technology used model-assisted labeling to reduce labeling costs by 50%
High labeling costs associated with the costs of crops vs. weeds on full image segmentation images.
Labelbox’s model-assisted labeling and collaborative annotation suite.
The company is now able to standardize how they create and manage data all in a single location and using Labelbox's automation suite, they’ve seen a reduction in labeling times by 50%, worth millions of dollars in cost savings per year.
How a leading vacation rental company develops a faster way to enrich unique listings
Technology and software