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Pull human-corrected annotations from CVAT into local YOLO dataset for training.
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Baseado na classificação ocupacional SOC
| 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
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.