FiftyOne Annotation

FiftyOne Annotation is different from standalone data labeling tools because it is built into an end-to-end visual AI platform, alongside data curation and model evaluation. You can explore your visual data, use active learning to surface the samples most worth labeling, annotate and QA them, and evaluate model performance, all in one platform.

When you find labeling errors during curation or data gaps during evaluation, you fix them in place, with no coordination overhead or tool switching. FiftyOne supports both auto-labeling and human-in-the-loop annotation, and it is the only open source platform built for this full visual AI development loop.

FiftyOne supports annotation across the full range of visual and physical AI data. Your team can label images, video, lidar and 3D point clouds, and multimodal datasets that bring 2D and 3D sensors together in one synchronized view.

Learn more about how FiftyOne handles image annotation and video annotation, with lidar and multimodal support built on the same workflows.

Whatever the modality, you get the same foundation: agentic auto-labeling, configurable review stages, and Intelligent Review to surface labeling errors before they reach training.

Auto-labeling is the process of using machine learning models, including foundation models like Meta's Segment Anything 2 (SAM 2), to automatically generate initial labels, so annotators start from model predictions instead of a blank slate. This significantly reduces manual effort while a human review step keeps quality high. FiftyOne's research shows auto-labeling can reach near-human accuracy while cutting annotation costs by up to 100,000×

Instead of labeling every sample from scratch, auto-labeling generates the bulk of annotations automatically, so your team only reviews and corrects them. Human effort concentrates on the challenging, high-value samples that need expert judgment, which active learning helps surface. The result is lower annotation costs and faster dataset preparation, without trading away label quality.

The best auto-labeling tool depends on how well it fits the work you already do, so look at a few key factors: accuracy of the automated labels, how easily it integrates with your existing workflows, support for active learning to prioritize the most valuable samples, scalability across data volumes and modalities, and built-in tools for managing human review. The strongest option is usually a data annotation platform that connects labeling with data curation and model evaluation, so you can streamline annotation, reduce costs, and improve model performance in one place rather than stitching together separate tools.

Yes. FiftyOne provides video annotation tools for labeling objects across frames, including interpolation between keyframes so you don't have to label every frame by hand. Video labeling works alongside image, point cloud, and multimodal data in the same platform, so you can review and QA video annotations next to the rest of your dataset.

Yes. FiftyOne integrates Meta's Segment Anything 2 (SAM 2), using SAM 2 segmentation to generate masks automatically so you can refine them with a few clicks instead of drawing them by hand. It is one of several foundation models FiftyOne uses to auto-label data and accelerate review.

FiftyOne supports the full range of label types used in visual AI: classifications, bounding box annotation, polygon annotation, keypoint annotation, polyline annotation, semantic segmentation annotation, and instance segmentation annotation. For 3D data, it adds 3D bounding box annotation and cuboid annotation across point clouds and multi-sensor scenes.

FiftyOne maintains label quality with Intelligent Review, which uses embeddings, model predictions, and auto-label comparison to surface likely annotation errors across an entire dataset. Instead of spot-checking, your team reviews the highest-risk labels first and resolves issues in bulk. Configurable review stages and inter-annotator agreement checks support annotation QA at scale.

Model-assisted labeling is an approach where machine learning models pre-label your data and annotators correct the results, rather than labeling from scratch. It is closely related to auto-labeling, with more emphasis on the human-in-the-loop step. In FiftyOne, model-assisted labeling and pre-labeling speed up annotation while keeping people in control of final quality.

Agentic labeling uses AI agents to label data from natural-language instructions. You describe what you need labeled, the agent produces the labels, and you review and refine the prompt until the results match your annotation guidelines. Unlike fixed auto-labeling pipelines, an agentic approach adapts to new tasks without code, so your team handles edge cases while agents handle the repetitive volume.