Training Loop
Run a managed YOLO training session — validates dataset, trains, analyzes, generates clean report.
Annotate
Claude visually inspects images and corrects/creates YOLO bounding box annotations using multimodal vision.
Review Annotations
AI-assisted annotation review — uses YOLO inference + Claude vision to auto-approve, correct, or flag images for human review in CVAT.
Compare Models
Compare 2+ YOLO models side-by-side on the same dataset — mAP, per-class AP, speed, size.
Cvat Push
Push uncertain or misclassified images to CVAT for human annotation review after training analysis.
Analyze Results
Analyze YOLO training runs — compares to baseline/best, checks per-class regression, analyzes training dynamics and tune convergence, writes actionable recommendations.
Benchmark
Profile YOLO model inference speed, FPS, and size across image sizes and export formats.
Active Learning
Orchestrate the full active learning loop: train, analyze, push to CVAT, wait for review, pull, merge, retrain.
Explain Results
Explain training results in plain English — translates metrics into actionable insights for any audience.
Cvat Pull
Pull human-corrected annotations from CVAT into local YOLO dataset for training.
Auto Experiment
Run autonomous YOLO training experiments — reads training-plan.md, assesses bottlenecks, acts strategically, and delegates HP optimization to model.tune().
Setup Project
Initialize a new YOLO project — detects your dataset's starting state and routes through the right tools.
Cvat Deploy
Deploy a trained YOLO model as a Nuclio serverless function for CVAT auto-annotation.
Review Dataset
Audit YOLO dataset quality — class distribution, annotation quality, image stats, and improvement suggestions.
Monitor Training
Set up autonomous training monitoring — creates cron jobs to track long-running training, auto-continue pipeline when training completes.