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How to pre-label a dataset with a trained model, then review before training with Yolo Web Agent Starter

Run a local auto-label job with a managed PT model and keep the results as reviewable auto labels.

Who it is forA dataset owner who already has a trained model and wants to speed up labeling on a new batch of images.

The problemLabeling a large batch from scratch takes hours, while blindly training on machine labels can teach the model its own mistakes.

The outcomeAuto-generated labels stored with the auto source tag, reviewed by a person, and then used in a later training run.

What you need first

  • macOS or Linux
  • Python 3.11 or 3.12
  • Node.js 20 or newer
  • A set of images to label, or the built-in tiny demo generator

Steps

  1. Start the workbench with ./run-all.sh and open the dataset card for the target dataset.
  2. Start a local auto-label task, choose a matching managed PT model, and confirm the class mapping explicitly.
  3. Adjust the confidence and IoU thresholds, decide whether to skip images that already have labels, and choose whether to clear old labels.
  4. Watch progress in the logs, cancel if needed, and review each auto label before changing it.
  5. Run training only after the reviewed labels are in place, since training and auto-labeling cannot run at the same time on this machine.

Prompt for your agent

In the YoloWebAgentStarter repo at <repo path>, start a local auto-label task for dataset <dataset ID> using the managed PT model <model ID>. Verify that the resulting labels are stored with the auto source and that no training job starts before review. Report the number of labeled images and the class mapping used.

Caveat. Auto-labeling only supports local managed Ultralytics PT models, so a model trained outside the workbench cannot be used for this step.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for fengzhizi715/YoloWebAgentStarter and "Pre-label a dataset with a trained model, then review before training"

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claude mcp add --transport http deduped https://dedupedai.com/mcp

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