Better data selection results in better image labels
FiftyOne Annotation for images helps you identify high-value samples for labeling, automate annotation with AI, and close the loop with model evaluation, all in one platform.
Smarter Image Labeling
Go from raw images to training-ready data, faster
Whether you're building object detection models, developing segmentation pipelines, or classifying images at scale, FiftyOne gives your team the tools to produce high quality image labels faster.
Bounding Boxes
Efficient, quick, and precise labeling
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.
Polygons
Precise outlines for complex shapes
Trace the exact boundary of irregularly shaped objects with polygon annotations. Ideal for training models that need to distinguish object shape, not just location.
Segmentation Masks
Pixel-level precision for dense prediction tasks
Annotate every pixel in an image to train semantic and instance segmentation models. Built for teams working on scene understanding, autonomous systems, and medical imaging.
Classifications
Label images and regions at scale
Assign class labels to whole images or individual regions of interest. Use image classifications for sorting, filtering, and training at scale.
Polylines
Structured annotation for lines and boundaries
Annotate roads, lanes, edges, and other linear structures with connected polyline annotations. Essential for infrastructure inspection workflows, lane detection, and high-fidelity ADAS models.
SAM2 Click-to-Segment
Instance segmentation in one click
Click on any object and Segment Anything 2 (SAM 2) generates a pixel-accurate segmentation mask instantly — directly in the browser, no Python required. Bring your own fine-tuned SAM2 models to adapt to your data.
Agentic Image Labeling
Focus human expertise on where it matters most
Speed up your image labeling process by 80% without sacrificing labeling quality
Prompt the labeling agent using natural language to generate pre-labels for your dataset in minutes, no code required. Combine agentic image labeling with human-in-the-loop review to accelerate your labeling operations and refocus valuable human resources on critical review.
FiftyOne Image Annotation
Built for annotation teams that iterate quickly
From project setup to final QA, FiftyOne Annotation gives your team the tools to stay aligned, iterate fast, and produce high quality image labels
Structure that scales with your team
Define clean, thoughtful ontologies for creating high-quality labeled image data with consistencies and minimal errors at scale.
Prevent bad image labels from compounding
Easily identify incorrect or inconsistent image labels across large datasets before they degrade your model.
Catch image label mistakes before they reach training
Surface annotation errors in bulk — no manual inspection required.
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 image 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 image data to address them, and route the insights back into the annotation workflow, without leaving the platform.
Image annotation project management with configurable review workflows
Design and manage multi-stage image annotation pipelines that give you full control over how work moves from image 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 image annotation schemas and ontologies
Give your whole team one source of truth for how data gets labeled. Annotation schemas set the structure, classes, and attributes for every image label, and reusable ontologies let you apply the same definitions across datasets and projects. Less ambiguity, cleaner data, and annotations that align with what your models need downstream.
Questions?
We have answers.
FiftyOne image annotation supports bounding boxes, polygons, segmentation masks, classifications, polylines, and SAM2 click-to-segment instance segmentation for images.
SAM2 runs directly in the browser, so you get fast, pixel-accurate segmentation masks with a single click, no Python environment required. You can also bring your own fine-tuned SAM2 models.
Most annotation tools treat labeling as an isolated step. FiftyOne connects annotation directly to data curation and model evaluation, so you can identify which images to label, label them, and immediately validate whether the new labels improved your model — all in one platform.
Agentic Labeling lets you describe what you need labeled in natural language. FiftyOne generates example outputs, you review and refine the prompt, then save the configuration to auto-label datasets at scale as a background task.
Yes. You can bring your own fine-tuned models to pre-label data and queue results for human review.
Get started with FiftyOne Annotation
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