Rosepetal AIDocumentation

Documentation Overview ​

Platform ​

The main Rosepetal AI platform providing a complete interface for dataset management, image labeling, model training and monitoring.

• Datasets: create, configure, import and export datasets
• Labeling: manual and SAM-assisted labeling for classification and segmentation
• Training: train models and analyze results
• Models: validate inferences and manage production models
• Flows & Panels: build vision workflows and dashboards from the platform

Flows ​

Node-RED nodes for building vision and automation workflows:

• Vision Platform: core Rosepetal vision nodes (inference, datasets, OCR)
• Dashboard: dashboard widgets and panels
• Image Tools: high-performance image processing
• Cameras: Basler, Irayple and Omron camera integrations
• I/O and devices: Serialport, Barcode Reader, System Monitor
• Utilities: Utility and I/O Nodes, Async Function, Python Executor, Git Control, Message Control

Controller ​

Everything needed to deploy and operate the Rosepetal Controller on-site:

• Installation and updates: install the controller stack and keep it up to date
• Network and remote access: network requirements, plant integration and remote access (VPN)
• Compliance: compliance, user access and audit trail

API ​

Complete REST API documentation, endpoints reference and integration guides for developers.

Research ​

Research projects and experiments from the Rosepetal AI team.

Getting Started ​

1. Platform: start with the Platform documentation to manage datasets, labeling and training
2. Deployment: install the controller with Installation and updates and set up Network and remote access
3. Workflows: build vision flows with the Node-RED tools
4. Integration: use the API for custom integrations