FiftyOne Data Curation
Model performance is only as good as the data behind it. FiftyOne helps you search, analyze, and refine your datasets at every stage of development — so you're training on high-signal data, not just more data.
Data Agent
FiftyOne Data Agent: Talk to your dataset
Ask questions about your data in plain language. Build custom workflows to automate repetitive curation tasks and move faster.
Data Exploration
Visualize and understand your data
80% of AI projects fail due to data issues. Gain full visibility into your datasets before training.
Multimodal data visualization
Visualize images, video, 3D point clouds, geospatial, medical scans, and audio data in an interactive UI.
Streamline visual data discovery
Stop waiting days for data teams to deliver samples. Query your data lake and retrieve relevant samples in seconds using Data Lens.
Distribution
Poor data distribution can lead to bias and blind spots. Use embeddings visualizations to understand how your data is spread across key features and classes.
Balance
Ensure each class in your dataset is proportionately represented. Balanced datasets help prevent performance bias toward overrepresented classes, improving fairness and robustness.
Improve data quality
Transform noisy datasets into high-signal training data
Automate quality checks to instantly isolate low-value samples, correct label errors, and produce high-signal training sets.
Data quality workflow
Image quality issues — brightness, blurriness, aspect ratio, entropy, near duplicates, and exact duplicates — quietly degrades model performance. Automatically flag these issues across your dataset and set thresholds to isolate exactly which samples need review.
Label accuracy
Incorrect labels cap model performance and introduce noise. Quickly identify and correct ground truth labels.
Deduplication
Models perform best when trained on unique data. Remove repetitive samples and reduce dataset storage requirements.
Data augmentation
Use Data Lens to expand your training datasets, improving model generalization and performance while reducing overfitting on limited data.
model performance
Continuously improve model accuracy
Catch failure modes before your users do. Automatically feed high-value failure cases directly back into active learning pipelines.
Avoid model drift
Model drift can degrade performance in production environments. Use active learning workflows to systematically monitor, identify, and correct dataset shifts, ensuring consistent model performance over time.
Model accuracy doesn't stop at deployment
Model training is never truly complete. Discover and address weaknesses in your data, refining your model iteratively for maximum accuracy.
COMPLIANCE & GOVERNANCE
Enterprise-grade security, scale, and extensibility
FiftyOne is built to meet the requirements of the most complex AI stacks.
Deploy anywhere
Fully customizable and extensible
Supports billions of samples
Dataset versioning
Role-based access controls
Integrations
Questions?
We have answers.
Data curation involves systematically selecting, managing, and refining datasets to ensure high quality and relevance. Effective curation improves model accuracy, reduces training costs, and accelerates AI development by identifying valuable, representative data.
When selecting a data curation solution, evaluate its capabilities for handling your data types and volumes, ease of integration into your existing workflows, scalability for enterprise workloads, and tools for visualizing, querying, and refining datasets. Look for solutions that offer intuitive interfaces, advanced dataset exploration, and automation features to efficiently identify and manage high-value data, ultimately improving your model's performance and reducing project costs.
Curating your dataset enables targeted removal of noisy, redundant, or irrelevant data, ensuring your models train on high-quality samples. This leads directly to improved model accuracy, reduced annotation expenses, and faster time-to-deployment.
FiftyOne provides intuitive workflows for visualizing, querying, and refining visual datasets at scale. Features like similarity search, error analysis, and automated dataset management simplify curation tasks and ensure you're training models on the most valuable data.
Enough data wrangling.
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