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Pull human-corrected annotations from CVAT into local YOLO dataset for training.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
Set up autonomous training monitoring — creates cron jobs to track long-running training, auto-continue pipeline when training completes.
Audit YOLO dataset quality — class distribution, annotation quality, image stats, and improvement suggestions.
Orchestrate the full active learning loop: train, analyze, push to CVAT, wait for review, pull, merge, retrain.
Analyze YOLO training runs — compares to baseline/best, checks per-class regression, analyzes training dynamics and tune convergence, writes actionable recommendations.
Run autonomous YOLO training experiments — reads training-plan.md, assesses bottlenecks, acts strategically, and delegates HP optimization to model.tune().
Profile YOLO model inference speed, FPS, and size across image sizes and export formats.
| name | cvat-pull |
| description | Pull human-corrected annotations from CVAT into local YOLO dataset for training. |
Pull human-corrected annotations from CVAT into your local dataset.
yolo-project.yaml has cvat: section with url and project_idCVAT_ACCESS_TOKEN env var is setproject_id from yolo-project.yamlyolo-cvat pull --task <TASK_ID>
# or
yolo-cvat pull --project <PROJECT_ID>
yolo-validate <output_path>
Check for:
If the user already has a local dataset:
yolo-merge --sources <existing_labels> <pulled_labels> --output <merged>
Has existing local dataset?
├── Yes → Compare distributions → Suggest yolo-merge
└── No → Set as primary dataset
Validation warnings found?
├── Yes → Flag issues, ask before training
└── No → Ready to train
Class distribution changed?
├── Significantly → Warn user, may affect model balance
└── Minor → Proceed normally