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Orchestrate the full active learning loop: train, analyze, push to CVAT, wait for review, pull, merge, retrain.
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
| name | active-learning |
| description | Orchestrate the full active learning loop: train, analyze, push to CVAT, wait for review, pull, merge, retrain. |
Orchestrate the complete cycle of model improvement through human feedback: train -> analyze -> push uncertain images to CVAT -> wait for human review -> pull corrections -> merge -> retrain.
Determine where we are in the loop:
Has baseline run?
├── No → Run: /experiment baseline
└── Yes → Continue
Has analysis been run?
├── No → Run: /analyze
└── Yes → Continue
Has uncertain_images.txt?
├── No → Run: yolo-analyze --model <best> --dataset <ds>
└── Yes → Continue
Use the /cvat-push skill:
yolo-cvat push --from-analysis reports/uncertain_images.txt
Tell the user:
Images have been pushed to CVAT for review.
Please annotate/correct the images in CVAT, then tell me when you're done.
CVAT tasks: [list task IDs and URLs]
Images to review: [count]
Priority: [false negatives first, then uncertain]
Do NOT proceed until the user confirms annotations are complete.
When the user says annotations are done:
yolo-cvat pull --task <id> --output datasets/corrected_batch_N
yolo-validate datasets/corrected_batch_N
yolo-merge --sources <existing_labels> <corrected_labels> --output <merged_labels>
yolo-validate <merged_dataset>
Run /review-dataset to update the dataset profile in training-plan.md.
The profile may have changed (new class distribution, different object sizes).
The experiment reasoning loop needs current data.
Delegate training decisions to the refactored /experiment skill.
The reasoning loop will use the updated dataset profile to decide what to try.
/experiment
Compare metrics from before and after the new data:
Create experiments/active_learning_log.md:
## Active Learning Iteration N
- Date: YYYY-MM-DD
- Images added/corrected: X
- mAP50-95 before: X.XXXX
- mAP50-95 after: X.XXXX
- Delta: +/- X.XXXX
- Classes most improved: [list]
- Next action: [recommendation]
First iteration ever?
├── Yes → Establish baseline first, then analyze
└── No → Check if previous iteration improved metrics
Metrics improved after new data?
├── Yes → Deploy updated model to CVAT (/cvat-deploy)
│ Continue to next iteration
└── No → Investigate:
├── Data quality issue? → Review annotations more carefully
├── Overfitting to corrections? → Increase augmentation
└── Plateaued? → Suggest dataset expansion or architecture change
More than 3 iterations with <0.5% improvement?
├── Yes → Suggest stopping: diminishing returns
└── No → Continue loop
experiments/active_learning_log.mdSet 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.
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.
Initialize a new YOLO project — detects your dataset's starting state and routes through the right tools.