Company Information

Product Information

Voxel51 is an enterprise data platform and the company behind FiftyOne, the leading open-source toolkit for building high-quality datasets and models in Physical and Generative AI. It allows AI teams to visualize, curate, manage, and evaluate multimodal data—including images, videos, 3D point clouds, and geospatial data—to improve AI model performance.

To see how FiftyOne helps developers explore datasets by linking camera angles, LiDAR points, and metadata:

Core Capabilities:

- Data Visualization & Exploration: An interactive UI (the FiftyOne App) lets you visually browse datasets, examine embeddings, filter by object attributes, and discover hidden data patterns or biases.

- Model Evaluation: Systematically evaluates models by computing metrics (e.g., precision, recall, confusion matrices) and maps model failures directly to specific data samples.

- Curation & Annotation: Enables users to identify and discard duplicates, rank samples by uniqueness, and selectively send data to annotation platforms (such as V7 Darwin).

- Auto-Labeling Workflows: Integrates foundation models and delegated operations to automatically generate pseudo-labels, which teams can then easily review and refine.

The Engine Behind the Tool:

- FiftyOne Brain: An AI/ML engine component that powers text-based natural language searches, finds nearest neighbors, and detects annotation mistakes.

- Model & Dataset Zoos: Built-in repositories that allow users to load openly available datasets and pre-trained models using a single line of code.

- Extensible Plugin Ecosystem: Fully customizable framework enabling users to integrate custom visualization tools, computation engines, and ML models into their workflows.

For a step-by-step tutorial on loading samples, setting up attributes, and working with dataset views:


Deployment & Integration:

- Open Source vs. Enterprise: Available as a free, open-source tool, with an FiftyOne Enterprise version that includes role-based access control, versioning, advanced governance, and high-scale processing.

- Ecosystem Compatibility: Integrates with established ML and data infrastructure like PyTorch, TensorFlow, Databricks, and NVIDIA Omniverse for simulation-to-reality pipelines.

- Use Cases: Powers computer vision pipelines across diverse industries including autonomous vehicles, medical imaging, robotics, and manufacturing.