Version created on Dec 15, 2024
Generated by Sarah Chen
Annotation Label Distribution:
Label split
with visual Git-like branching
Manage dataset evolution in a Git-style branching workflows. Create versions for experiments, track changes across different annotation versions, and glean label insights and analytics while maintaining complete version history.
Generate a version of your dataset for export or model training. This version acts as a snapshot and cannot be edited later.
Annotation Label Distribution:
and insights
Gain deep insights into your dataset evolution with comprehensive analytics across all versions. Track label distribution changes, annotation quality metrics, and dataset composition over time to understand how your data has evolved and identify areas for improvement or potential bias.
Annotation Label Distribution:
Label split
multiple formats
Export your dataset versions in popular formats like COCO, YOLO, and JSON. Access your data through direct downloads, programmatic API calls, or integrate seamlessly with Jupyter notebooks for immediate analysis and model training.
reliable model training
Lock in dataset versions to ensure your model training results can be reproduced exactly, every time you run experiments.
Test different dataset versions and track their impact on model performance without losing your baseline datasets.
Quickly revert to previous dataset versions if new annotations or data changes negatively impact your model accuracy.
Multiple team members can work on different dataset branches simultaneously without conflicts or data overwrites.
Track exactly which dataset version was used to train each model for full audit trails and compliance requirements.
Monitor how dataset improvements over versions correlate with model performance gains across training runs.
Create branches for different dataset experiments while maintaining a clean main dataset version for production training.
Maintain detailed records of dataset changes and model training history for regulatory compliance and quality assurance.
AI projects
Ocular integrates with your existing tech stack and multiple integrations that slot seamlessly into your workflows and pipelines.
Even when you connect to external data sources, all data stays on your existing infrastructure and data sources.
Enterprise-grade and battle-tested security measures and protocols to protect your data.
All our systems are built with security in mind and are constantly monitored and audited.