Results for “auto-labeling”

Guides CDF Auto-Labeling workflows for annotating data such as videos. Routes tasks to specialized references, covering shipped examples, stage selection, prompt and configuration changes, pipeline migration, container retrieval, and output validation.

• Starting automatic labeling for warehouse-safety videos
• Adapting a shipped workflow to a new domain
• Writing annotation prompts and question banks

Build custom Kubernetes Airflow workflows for PAIDF Orchestration by combining existing processing stages. Produces workflow code, deployment manifests, input validation models, and connecting code, then checks stage connections and model, container, and prompt choices against supplied specifications.

• Create a workflow that runs augmentation without full auto-labeling
• Combine detection and captioning into a custom processing chain
• Check model, container, and prompt choices against an external spec

Run video augmentation, which creates variations of source videos, and automatic labeling on OSMO. Select a workflow, check credentials and resources, monitor execution, download outputs, and compare augmented videos with their originals.

• Automatically label source videos
• Create video variations and label the outputs
• Process original and augmented videos in one workflow

Create videos of anomalous events, such as falls or fires, from seed images using Cosmos3. Run a Kubernetes workflow that generates clips, automatically labels them, and builds an anomaly dataset, with progress monitoring and result retrieval.

• Generate fall or fire clips from seed images
• Build synthetic datasets for safety and surveillance
• Automatically label generated event videos

Operate doccano for text annotation, dataset import/export, auto-labeling, deployment, and repo maintenance.

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Guides core Autodistill workflows for foundation-model auto-labeling, target-model distillation, CLI/plugin registry use, custom model interfaces, and utilities.

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Operate AnyLabeling desktop annotation, auto-labeling model, dataset export, install, packaging, and release workflows for the PyQt6/ONNX image-labeling package.

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AI-powered issue operations via gh-models. TRIGGERS - issue summarization, auto-labeling, issue insights.

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