{
 "schema_version": 1,
 "generated": "2026-10-24",
 "id": "fengzhizi715-yolowebagentstarter--auto-label-then-review-before-training",
 "repo": "fengzhizi715/YoloWebAgentStarter",
 "repo_url": "https://github.com/fengzhizi715/YoloWebAgentStarter",
 "repo_slug": "fengzhizi715-yolowebagentstarter",
 "title": "Pre-label a dataset with a trained model, then review before training",
 "page_title": "How to pre-label a dataset with a trained model, then review before training with Yolo Web Agent Starter",
 "summary": "Run a local auto-label job with a managed PT model and keep the results as reviewable auto labels.",
 "who": "A dataset owner who already has a trained model and wants to speed up labeling on a new batch of images.",
 "problem": "Labeling a large batch from scratch takes hours, while blindly training on machine labels can teach the model its own mistakes.",
 "outcome": "Auto-generated labels stored with the auto source tag, reviewed by a person, and then used in a later training run.",
 "steps": [
  "Start the workbench with `./run-all.sh` and open the dataset card for the target dataset.",
  "Start a local auto-label task, choose a matching managed PT model, and confirm the class mapping explicitly.",
  "Adjust the confidence and IoU thresholds, decide whether to skip images that already have labels, and choose whether to clear old labels.",
  "Watch progress in the logs, cancel if needed, and review each auto label before changing it.",
  "Run training only after the reviewed labels are in place, since training and auto-labeling cannot run at the same time on this machine."
 ],
 "agent_prompt": "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.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "2 hours",
 "basis": "readme",
 "caveat": "Auto-labeling only supports local managed Ultralytics PT models, so a model trained outside the workbench cannot be used for this step.",
 "category": "Software and DevOps",
 "tags": [
  "Software and DevOps"
 ],
 "lang": "python",
 "requirements": [
  "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"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.8,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/auto-label-then-review-before-training/",
 "md": "https://dedupedai.com/use-cases/fengzhizi715-yolowebagentstarter/auto-label-then-review-before-training/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/fengzhizi715-yolowebagentstarter--auto-label-then-review-before-training.json",
 "api_version": "v1"
}