FiftyOne Search
Improve data discovery with multimodal data visualization and search. FiftyOne helps you inspect model behavior in context, isolate failures, and find every related scenario for training and evaluation.
Features
Search, visualize, and debug multimodal data in seconds
Stop writing custom scripts, manually reviewing samples, and jumping between tools just to answer basic questions about your data.
Search
Model behavior emerges across datasets, not individual recordings. FiftyOne indexes metadata and computes embeddings to make multimodal datasets searchable. Query by natural language, visual similarity, or metadata to find related samples, episodes, and behaviors across your dataset.
Visualization
Time-series multimodal data only makes sense in context. Explore episodes through synchronized playback that aligns every modality on a shared timeline. Customize tiles to inspect images, 3D scenes, sensor streams, plots, maps, and annotations together.
Search & Query
Find the episodes that matter
Search by metadata, natural language, or visual similarity to find related samples, failure modes, and scenarios across your entire dataset. Powered by state-of-the-art embeddings and a persistent dataset index for fast, interactive search.
Generate embeddings for individual segments—not just whole episodes—to retrieve similar results within any filtered subset of your dataset.
Define custom events from multimodal sensor data, then instantly search for every matching episode.
Describe what you're looking with FiftyOne Agent. Search for objects, scenes, behaviors, or failure modes without writing filters or queries.
From the sidebar, filter and sort...
Instantly filter your dataset by labels, tags, scalar fields, and custom metadata to isolate specific scenarios or cohorts.
Visualization
Visualize your data in context
Understanding a dataset starts with seeing the underlying episodes and samples. Explore your data in context to uncover patterns, quality issues, and gaps that are hard to find in aggregate metrics.
Sync multi-rate sensor data to a shared timeline. Images, 3D scenes, plots, sensor streams, and metadata stay aligned as you scrub episodes.
Quickly scan thousands of samples or episodes on the grid.
Toggle labels, predictions, metadata, and custom fields to see exactly the information you need.
Organize and link multi-view samples with grouped datasets . Groups don't require a shared timeline.
Every visualization in FiftyOne is interactive, letting you inspect underlying samples with a single click.
VLAs for Robotics & AV
Multimodal data curation across the VLA lifecycle
Improve VLA generalization with less data
Robotics data is slow and expensive to collect, so maximizing the value of every demonstration is critical. Instead of burning budget on massive, redundant datasets, FiftyOne isolates the high-value, diverse demonstrations your VLA needs to generalize better.
Search petabyte-scale data for the longtail
As datasets grow, standard scenarios yield diminishing returns, making longtail investigations the primary driver of model performance. FiftyOne makes massive datasets searchable, so autonomous vehicle teams can surface rare edge cases, isolate anomalies, and fix systemic failures.
Enterprise Ready
Take models from prototype to production
Deploy within your existing infrastructure with the security, governance, and scalability needed to run AI in production.
Deploy anywhere
Fully customizable and extensible
Supports billions of samples
Dataset versioning
Role-based access controls
Integrations
Get started with FiftyOne Enterprise
Questions?
We have answers.
Yes, FiftyOne supports multimodal data. FiftyOne is a unified data platform for physical AI that brings visualization, curation, and annotation of multimodal, time-series data into one place. Teams inspect synchronized camera, LiDAR, sensor, and robot state data, then search and curate it at scale to build better datasets for autonomous vehicles and robotics.
FiftyOne natively ingests MCAP, the default recording format for ROS 2, and works with data converted from rosbag. You can also bring your own extracted frames and post-processed sensor data.
Camera streams, LiDAR point clouds, and numeric time-series sensors like IMU, acceleration, and CAN bus signals, along with robot state, all play back in sync on a single shared timeline. Every modality stays aligned, so you can inspect a complete episode in context rather than one sensor at a time.
No. FiftyOne Multimodal works directly with encoded stream media and infers frame-level data from it, so you do not have to pre-extract frames before ingesting. If your pipeline already produces extracted, post-processed frames, you can bring those too.
Visualization tools make it easy to inspect a single robot episode or failure. FiftyOne Multimodal adds what those tools do not: large-scale curation, embeddings and semantic search, and annotation, so you can go from finding an edge case to a labeled, model-ready dataset in one platform.
You can query by metadata, temporal events, and annotation labels, then search by visual and semantic similarity using embeddings. This surfaces rare scenarios, recurring failure modes, and underrepresented data across an entire dataset, the same longtail mining workflow autonomous vehicle teams rely on.
Yes. You can tag arbitrary time intervals to curate segments for investigation, work with 2D bounding box and 3D cuboid labels at the message level, and view labels as tracks over time. Label tracks show exactly when an object appears or an event occurs across a full scene.
It is built for datasets ranging from thousands of scenes to hundreds of millions of frames, with support for long, multi-hour encoded video and an open-ended number of time-series sensors. Purpose-built indexing keeps search, filtering, and playback fast at that scale.
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