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Push uncertain or misclassified images to CVAT for human annotation review after training analysis.
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Baseado na classificação ocupacional SOC
| name | cvat-push |
| description | Push uncertain or misclassified images to CVAT for human annotation review after training analysis. |
After training analysis identifies uncertain images, push them to CVAT for human annotation or correction.
yolo-analyze has been run (check for uncertain_images.txt or analysis output)yolo-project.yamlCVAT_ACCESS_TOKEN env var is setRead the analysis output:
cat reports/uncertain_images.txt
Or check experiments/analysis.md for the list of uncertain predictions.
Organize images into review batches:
# From analysis file (auto-batches into tasks of 50)
yolo-cvat push --from-analysis reports/uncertain_images.txt
# Or manually specify images
yolo-cvat push --images <path_to_images> --labels <path_to_labels> --task-name "Review: False Negatives"
Write experiments/cvat_push_report.md:
## CVAT Upload Report
- Date: YYYY-MM-DD
- Tasks created: N
- Total images: N
- False negatives: N (Task ID: X)
- Uncertain: N (Task ID: Y)
- CVAT URL: http://localhost:8080/tasks/<id>
More than 100 images?
├── Yes → Split into batches of 50, create multiple tasks
└── No → Single task
Has existing YOLO labels for these images?
├── Yes → Upload as pre-annotations (saves annotator time)
└── No → Push images only for fresh annotation
False negatives found?
├── Yes → Create separate high-priority task labeled "URGENT: False Negatives"
└── No → Single "Review: Uncertain" task
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.