Robovision.ai

Enables users to create production-ready computer vision models through a visual, code-free interface without requiring programming knowledge or ML expertise. Users can design model architectures, configure parameters, and build complete vision pipelines through drag-and-drop and form-based interactions.

Automatically generates intelligent label suggestions for unlabeled images using machine learning, reducing manual annotation effort and accelerating dataset preparation. The system learns from existing labeled data to predict labels for new images with high accuracy.

Maintains version history of trained models with associated training configurations, datasets, hyperparameters, and performance metrics. Enables tracking of experiments and easy rollback to previous model versions.

Exports trained models in multiple formats (ONNX, TensorFlow, PyTorch, TensorFlow Lite) optimized for different deployment targets and frameworks. Handles model quantization and compression for edge device deployment.

Enables multiple team members to collaborate on computer vision projects with role-based access control, project sharing, and collaborative annotation workflows. Tracks changes and contributions across team members.

Deploys trained computer vision models to edge devices (cameras, IoT devices, embedded systems) for real-time inference without cloud connectivity. Models are optimized for edge hardware constraints while maintaining performance.

Deploys trained computer vision models to cloud infrastructure for scalable, managed inference with automatic scaling, monitoring, and API access. Handles high-volume prediction requests with built-in reliability and performance tracking.

Manages simultaneous deployment of computer vision models across both edge and cloud infrastructure, enabling intelligent routing of inference requests based on latency, cost, and availability requirements. Models remain synchronized across deployment targets without retraining.

Tracks deployed model performance metrics including inference accuracy, latency, throughput, and error rates across all deployment targets. Provides dashboards and alerts for performance degradation or anomalies.

Imports image datasets from various sources (local files, cloud storage, APIs) and organizes them into structured projects with metadata, versioning, and organization tools. Supports batch operations and dataset splitting for training and validation.

Creates and manages labeling schemas for computer vision tasks including object detection (bounding boxes), image classification (categories), semantic segmentation (pixel-level masks), and instance segmentation. Supports custom label hierarchies and metadata fields.

Trains computer vision models using automated hyperparameter tuning and optimization techniques to find optimal model configurations without manual experimentation. Handles training pipeline orchestration including data preprocessing, augmentation, and validation.

Evaluates trained models using standard computer vision metrics (precision, recall, F1-score, mAP, IoU) and provides visual analysis tools including confusion matrices, ROC curves, and per-class performance breakdowns. Enables side-by-side comparison of multiple model versions.