Streamline video annotation with frame-level precision
FiftyOne Annotation gives your team AI-powered object tracking with SAM2, temporal event labeling for action and activity recognition, and Smart Data Selection for high-value video data samples.
SMARTER VIDEO LABELING
Go from raw video footage to training-ready datasets, faster
FiftyOne supports every video label type your models need, from frame-accurate bounding boxes and pixel-precise masks to cross-frame object tracks and timestamped event spans.
Bounding Boxes
Track objects across frames with precision
Draw precise bounding boxes around objects in seconds. Bounding boxes are the most common annotation type for detection models — and the fastest to produce at scale.
Agentic Video Labeling
Automate manual video labeling with agents
Speed up your video labeling process by 80% without sacrificing labeling quality
Agentic Labeling generates pre-labels across your video dataset as a background task, so your team starts from a reviewable baseline rather than a blank canvas.
Describe what needs labeled in plain language, review the outputs, refine the prompt, and save the configuration to reuse across new batches.
FiftyOne Video Annotation
Built for teams that want precise labels at scale
From tracking drift, inconsistent event boundaries, and label schemas that don't account for the temporal dimension, FiftyOne Annotation is designed around these failure modes for video.
Ontologies built for temporal labels
Define which labels live at the frame level, which apply to the full clip, and which mark temporal spans to keep label quality quality consistent and reduce time spent going back and forth with your labelers.
Catch tracking errors before they compound
Tracking mistakes don't stay isolated, they propagate across every frame in the sequence. Intelligent Review uses embeddings and mistakenness scores to surface inconsistencies across object tracks and temporal events before they reach your training pipeline.
Fix labels in context, at scale
Correct annotation mistakes directly in FiftyOne's video editor. Scrub the timeline, edit the frame, and continue—all without exporting, round-tripping, or switching tools.
ML Research
Auto-labeling rivals human performance
The latest paper from our ML researchers, Auto-Labeling Data for Object Detection, benchmarks auto-labeling against human annotation. We reveal how foundation models can deliver labels at near-human accuracy, while reducing annotation costs by up to 100,000×.
Data labeling platform
Improve model performance with an end-to-end video labeling platform
FiftyOne is a unified data platform for multimodal and physical AI, available as open source and as FiftyOne Enterprise. Your team can surface model failures, curate the right video data to address them, and route the insights back into the annotation workflow, without leaving the platform.
Video annotation project management with configurable review workflows
Design and manage multi-stage video annotation pipelines that give you full control over how work moves from video labeling to review and approval. Built-in project management review stages, rejection loops, and quality gates help you coordinate teams, enforce standards, and keep production datasets moving without operational friction.
Standardize video annotation schemas and ontologies
Give your whole team one source of truth for how data gets labeled. Video annotation schemas set the structure, classes, and attributes for every label, and reusable ontologies let you apply the same definitions across video datasets and projects. Less ambiguity, cleaner data, and annotations that align with what your models need downstream.
Questions on video labeling?
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