Computer Vision
Research
World-class computer vision research teams rely on the FiftyOne data platform to assess model failures, debug samples, and curate datasets for AI development. Check out our original research and citations from researchers at Google, Porsche, Microsoft, and more.

Computer vision embeddings
Model comparisons with samples
By the numbers

Leading CV researchers rely on FiftyOne

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"FiftyOne enables researchers to analyze and improve the quality of their datasets rapidly, replacing the weeks of manual labor that would otherwise be required without this technology. High-quality data is critical to the success of machine learning systems. Without the right tools to analyze and curate datasets, machine learning development can be inefficient and ineffective.”

Jordi Pont-Tuset
Research Scientist at Google

“FiftyOne is our primary resource for machine learning research. Thanks to FiftyOne's convenient field visualizations and filtering capabilities, we can easily distinguish incorrect labels and predictions, and therefore iterate on models faster than ever. As a result, we've achieved a 77% reduction in images sent for manual verification.”

Ryan Szeto
Senior Computer Vision Engineer at SafelyYou

“As we developed our Florence-2 model, FiftyOne proved invaluable for data management and visualization. Its powerful capabilities helped streamline our workflow, ensuring we built a robust foundation for our models. Now, as we dive into the development of Florence-5B, we're relying on FiftyOne more than ever. The tool's intuitive interface and rich feature set are essential for effectively managing our large datasets and gaining critical insights.”

Bin Xiao
AI Researcher, Meta (formerly Principal Research Manager, Microsoft GenAI)

“FiftyOne has helped us speed up investigations by 3x. For example, if we see a wrong suction cup grasping an item, we can quickly visualize the issue across all data sources and identify what went wrong.”

Dimitry Pechyoni
Senior Principal Machine Learning Engineer at Berkshire Grey


"Data visualization is crucial for understanding model performance during training and validation. FiftyOne provides built-in features, plugins, and automated pipelines that help you analyze inference outputs effectively."

Tin Stribor Sohn
Technical Lead Vehicle Data Analytics Automated Driving at Porsche AG
Doctoral Candidate at Karlsruhe Institute of Technology

“We use FiftyOne to organize large research datasets. My favorite feature is the ability to view distributions over image attributes in the dataset, and filter the dataset by those attributes.”

Brett Israelsen
Principal Research Scientist, AI, Raytheon
ML Research

Auto-labeling rivals human performance

The latest paper from Voxel51's 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×.
Papers

Publications citing FiftyOne


Download and visualize Open Images dataset
Multimodal AI for Efficient Medical Imaging Dataset Curation
Auto Labeling for Object Detection
Drive4C: A Closed-Loop Benchmark on What Foundation Models Really Need to Be Capable of for Language-Guided Autonomous Driving
TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models
Continuous Patient Monitoring with AI: Real-Time Analysis of Video in Hospital Care Settings
Citations

Cite FiftyOne in your research

FiftyOne is open source and free for reasearchers.
Sample citation:
M. Tran, J. Corso, A. Gvalani, et al. (2025). FiftyOne (Version 1.7.0) [Software]. Available from https://github.com/voxel51. Computer vision.

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