autotrain-yolo

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.

Initialize a new YOLO project — detects your dataset's starting state and routes through the right tools.

Claude visually inspects images and corrects/creates YOLO bounding box annotations using multimodal vision.

Compare 2+ YOLO models side-by-side on the same dataset — mAP, per-class AP, speed, size.

Deploy a trained YOLO model as a Nuclio serverless function for CVAT auto-annotation.

Pull human-corrected annotations from CVAT into local YOLO dataset for training.

Push uncertain or misclassified images to CVAT for human annotation review after training analysis.

Explain training results in plain English — translates metrics into actionable insights for any audience.

AI-assisted annotation review — uses YOLO inference + Claude vision to auto-approve, correct, or flag images for human review in CVAT.

Run a managed YOLO training session — validates dataset, trains, analyzes, generates clean report.