Voxel51 ML Workflows

AI tools can quickly stack up, adding maintenance costs and complicating the pipeline. Voxel51’s end-to-end platform provides engineers with the right tools to simplify building Physical AI.

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FiftyOne workflow diagram showing the data pipeline from collect through curate, annotate, generate, and evaluate stages to deploy.

Data Curation

Curate systematically for faster, more accurate models

Poorly curated data teaches your model wrong patterns. Systematic curation catches errors before training, so your model reaches performance targets with fewer iterations.

Explore data curation

Catch poor quality samples that inflate performance by 10-15%

FiftyOne near-duplicate detection panel with a histogram and threshold slider over a dataset of surfing images.

Augment data with synthetic scenes

Synthetic driving scene of vehicles on a wet highway in rainy conditions, illustrating simulated data generation for autonomous systems.

Visualize data distributions

FiftyOne embeddings plot of a maritime vessel dataset, with points color-coded by class including barge, ferry, and fishing vessel.

Query and explore data

FiftyOne data source selection panel connecting to Databricks, with a flower query filter applied over a dataset of flower images.

Target missing scenarios to close performance gaps

Voxel51 bar chart of sample counts by weather scenario: clear, foggy, overcast, partly cloudy, rainy, and snowy.

Find edge cases that lead to failures

Satellite image of storage tanks in a forest, each in a green bounding box, with clouds covering part of the scene.

Annotation

Annotate 80% faster, train better models

Teams waste budgets labeling more than they need. Smart data selection techniques with auto-labeling and manual workflows significantly reduce annotation time and costs.

Explore annotation

Smart data selection

Zero-shot techniques identify the most optimal samples from unlabeled data for model training.

FiftyOne embeddings plot with points colored on a low-to-high gradient to surface dataset density and outliers.

Auto-labeling

Use auto-labeling workflows to handle the most obvious labels, while humans focus on the high-value ones.

FiftyOne label review panel showing confidence threshold controls and AI risk tiers across 156 low, 37 medium, and 4 high risk labels.

QA and fix labeling mistakes

Improve productivity by creating new classification and detection labels or editing existing 2D/3D ones directly in Voxel51.

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Customer success

Leading enterprises build their ML workflows with Voxel51

Vivint Smart Home leans on Voxel51 to diagnose model behaviors on 2TB of visual data.

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Model evaluation

Instantly debug model failures

Most model failures are a result of data issues. When teams can’t correlate performance drops to specific data samples, they waste weeks debugging.

Explore model evaluation

Trace metrics back to data, uncover failure modes

Diagnose edge cases, bias, and low-quality data issues down to the sample level.
Analyze performance across scenarios such as weather, lighting, occlusion, etc., to guide model improvements.
Compare model performance side-by-side to pick the best-performing one.
Simplify debugging with end-to-end ML workflow management

FiftyOne evaluation summary comparing two models on IoU, recall, F1-score, and false positives over an aircraft dataset.

Customizable and Extensible

Build end-to-end ML workflows, your way

Explore plugins and customizations

Use proven out-of-the-box workflows or build your own

Voxel51's flexible framework lets you customize any step, integrate with existing tools, and automate pipelines.

Diagram of FiftyOne's extensibility options connected by lines: plugins, integrations, automation, and front-end customization.