Create production-ready computer vision datasets using AI-assisted labeling, foundation models, automated quality control, and collaborative workflows—all in a single platform.
An end-to-end Vision Data Engine that transforms raw images and videos into production-ready training datasets.
Automatically detect and label objects using Grounding DINO and open-vocabulary prompts. Generate annotations across large datasets without custom model training.
AI-PoweredCreate pixel-accurate masks with one-click object segmentation. Refine results using positive and negative prompts for precise annotations.
Smart SegmentationAnnotate datasets using bounding boxes, polygons, segmentation masks, keypoints, and image classification workflows in a unified workspace.
Multi-Task AnnotationAccelerate labeling with keyboard shortcuts, bulk operations, smart class assignment, and rapid annotation editing workflows.
ProductivityImplement multi-stage review and approval workflows to ensure annotation accuracy, consistency, and high-quality training datasets.
Quality AssuranceEnable multi-user annotation projects with role-based permissions, task assignments, workload distribution, and progress monitoring.
Team WorkspaceTrack dataset revisions, maintain snapshots, compare changes, and safely restore previous annotation versions whenever required.
Data ManagementMonitor class distributions, annotation coverage, label consistency, and dataset completeness through built-in quality insights.
Dataset InsightsMaintain complete visibility into annotation history, reviewer actions, user activity, and dataset lifecycle events for enterprise compliance.
Enterprise ReadyTrain state-of-the-art vision models directly on your annotated datasets using built-in high-performance compute architectures.
AutoMLExport trained models and deploy them directly to edge devices or cloud infrastructure via highly scalable inference APIs.
DeploymentWhen AI segments need micro-refinements, our native manual toolbelt is ready. Fully responsive vectors ensure precise geometry control down to individual pixels.
Eliminate data parsing scripts. SetuVision provides robust, one-click dataset compilation directly into native PyTorch, TensorFlow, and Hugging Face dataloader schemas, supporting deeply nested panoptic segmentations and rotated bounding boxes.
# YOLOv8 format output
0 0.485124 0.356128 0.220140 0.180250
1 0.720150 0.450120 0.080120 0.090150
3 0.180250 0.530260 0.140220 0.220180SetuVision strictly adheres to industry-leading security frameworks to ensure your raw proprietary training data, sensitive assets, and AI models remain completely protected and strictly isolated.
All datasets and media assets are stored securely in isolated AWS S3 buckets using robust AES-256 server-side encryption, ensuring your data at rest is highly protected.
Built with privacy-by-design principles. We enforce strict role-based access controls, prevent unauthorized sharing, and maintain complete internal audit logging.
Data in transit is secured via TLS 1.3 protocols. Advanced JWT-based authorization handshakes guarantee zero unauthorized access between your browser and our APIs.