Description
Voxel51 is a comprehensive multimodal data platform engineered to elevate the performance of physical AI systems. Recognizing that AI success is increasingly dictated by the quality and observability of visual data, Voxel51 places data at the core, empowering users to transform it into a competitive advantage. The platform is built to address the challenges of building and iterating on AI models faster by surfacing critical data insights.
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At its heart, Voxel51 provides full visibility into your data. It enables users to understand data distributions, explore vast datasets, and identify low-quality samples through intuitive data workflows. The platform supports the unification of diverse multimodal data types, including 3D, video, images, and metadata, allowing for efficient slicing, searching, and filtering of massive datasets. Advanced features like analyzing data patterns with embeddings and improving data quality with automatic filters are key to its data management capabilities.
Annotation is streamlined with Voxel51's smart data selection techniques, combining auto-labeling with manual workflows to prioritize the most valuable data. It offers expert-level in-app label creation, quality assurance, and editing tools, capable of accurately annotating 2D and 3D multi-sensor scenes. Auto-annotation of straightforward samples helps reduce labeling time and costs.
Model evaluation is another critical area where Voxel51 excels. It allows users to understand model strengths and weaknesses by analyzing overall performance and tracing metrics back to the data. The platform facilitates the diagnosis of edge cases, bias, and low-quality samples that negatively impact performance, enabling root cause analysis down to the individual sample level. It also supports side-by-side performance comparisons of multiple models.
Furthermore, Voxel51 empowers users to multiply the value of their data through scene generation. It enables the creation of dataset diversity without requiring new data collection by generating high-fidelity scene variations. Through direct integration with NVIDIA libraries, raw sensor captures can be transformed into rich, reconstructable scenes. Realistic scene variations, such as weather, lighting, and time-of-day changes, can be applied using world foundation models, helping to close edge cases and enhance model robustness.
The platform is designed for enterprise-grade security, scale, and extensibility, supporting billions of samples and offering features like dataset versioning and role-based access controls. It is built to meet the demands of complex AI stacks and can be deployed anywhere, offering full customization and extensibility. Voxel51 is loved by ML engineers and has seen over 5 million installs, built on open-source standards and supported by a large computer vision community.
- CategoryMachine Learning Platforms
- Pricing ModelContact sales
- DeploymentDeploy anywhere
- PlatformsWeb
Voxel51's Core Features
Data Curation & Management
Smarter Annotation Workflows
Model Evaluation & Diagnostics
Scene Generation for Data Augmentation
Multimodal Data Support (3D, Video, Images, Time-Series)
Data Visibility and Exploration Tools
Automated Data Quality Filters
In-app Label Creation and QA
Edge Case and Bias Diagnosis
Integration with NVIDIA Libraries
Enterprise-grade Security and Scalability
Dataset Versioning
Role-based Access Controls
How to use Voxel51?
Explore Data: Understand data distributions and identify low-quality samples using intuitive workflows.
Curate Data: Unify multimodal data, slice, search, and filter massive datasets efficiently.
Annotate Data: Adopt smart selection techniques and use expert-level tools for accurate labeling.
Evaluate Models: Analyze model performance, diagnose edge cases, and trace metrics back to data.
Generate Scenes: Create dataset diversity and close edge cases by generating high-fidelity scene variations.
Deploy & Iterate: Build and iterate faster with full visibility and control over your AI data.
Voxel51's Use Cases
- Data Curation
- Smart Annotation
- Model Performance Analysis
- Data Augmentation
- Multimodal AI Development
- Quality Assurance
- Edge Case Mitigation
