cv-training

name

cv-training

description

CV model training review and execution. Covers dataset validation, preprocessing consistency, augmentation strategy, training configuration, export/quantization, and deployment validation. Use when training classifiers, YOLO detection/segmentation, or any CV model. Use when: "train model", "retrain", "training review", "check training config", "prepare training", "cv training", "model training". Proactively suggest when the user is about to train a model or has just finished training.

CV Model Training Skill

Structured CV model training with built-in best practices. Catches the mistakes that silently kill accuracy in production: preprocessing mismatches, augmentation gaps, quantization drift, and training-serving skew.

Configuration

Auto-detect by scanning the project. Look for training scripts, model files, dataset directories, and preprocessing code. No config file needed.

If a .cv-training.json exists, use it as hints, but never require it.

Subcommands

• /cv-training review → Review an existing or planned training run
• /cv-training run <type> → Execute a training run
• /cv-training validate <model> → Validate a trained model before deployment
• /cv-training audit → Audit all training/inference paths for consistency

Default to review if no subcommand given.

---

Review

Walk through training config and flag issues before training starts.

Workflow

1. GPU detection → Load references/gpu-compute.md

   • Detect GPU, present compute options with cost estimates
   • Do NOT proceed until a GPU path is confirmed

2. Colab check → Load references/colab-notebooks.md (only if notebooks found)

   • Scan for .ipynb files with training logic
   • Convert to runnable script if user confirms

3. Logging selection — Ask CSV (default, zero deps) or W&B (additive). CSV log to output/training_log.csv is required regardless.

4. Read current state — Scan project for model type, training script, hyperparams, all inference paths.

5. Dataset audit → Load references/dataset-audit.md

   • Quantitative checks (class balance, label completeness, train/val leak)
   • Visual spot-check with label overlays
   • Stop if any FAIL

6. Preprocessing consistency → Load references/preprocessing-audit.md

   • Build consistency table across all preprocessing locations
   • ANY mismatch is a critical bug

7. Training config review → Load references/training-config.md

   • Augmentation, hyperparameters, FPS/input size tradeoff
   • Logging and reproducibility checklist

Present findings as summary table. Ask user to confirm before proceeding.

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Run

Execute a training run with all checks passed.

1. Run Review first (all steps). Do not skip.
2. Fix any critical issues before proceeding.
3. Preflight check (required before remote training) → See references/training-config.md
   • Run 2 epochs on ~10 images locally to catch errors before spending money
4. Route to compute path:
   • Local GPU: run training script directly
   • VAST.ai: launch instance, upload, train, download, destroy → See references/gpu-compute.md
   • Other remote: upload, SSH, train, download
   • Colab notebook: use converted script → See references/colab-notebooks.md
5. Monitor for overfitting, plateaus, NaN losses
6. After training completes, automatically run Validate

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Validate

Validate a trained model before deployment. → Load references/validation.md

Steps:

1. Numerical equivalence (PyTorch vs ONNX vs TRT)
2. Accuracy test on exported model
3. Batch sensitivity test (FP16 batch=1 vs batch=4)
4. Golden fixture test (if exists)
5. FPS benchmark on target device
6. Save model metadata
7. Notebook visualization review (if applicable)
8. Post-deployment verification (automated tests or visual check)

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Audit

Full preprocessing consistency audit across all training/inference paths. → Load references/preprocessing-audit.md

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Common Mistakes

Real production bugs this skill catches:

1. Resize mismatch — training at 224x224, inference at 128x128 (40pt divergence)
2. JPEG artifacts — re-extracting from JPEG vs raw PNG shifts scores 7+ points
3. Missing training args — can't reproduce how model was trained
4. Channel order — training in RGB, inference in BGR (OpenCV default)
5. Normalization skew — ImageNet normalization in training, raw [0,1] in inference
6. Batch sensitivity — FP16 TRT different results at batch=1 vs batch=4
7. No golden fixture — can't validate export against known-good scores

References (load only if needed)

• references/gpu-compute.md — GPU detection, VAST.ai, cost tracking, utilization
• references/dataset-audit.md — dataset quality checks, visual spot-check
• references/preprocessing-audit.md — consistency table, training-serving skew
• references/training-config.md — hyperparams, augmentation, logging, preflight
• references/validation.md — equivalence tests, accuracy, FPS, deployment verification
• references/colab-notebooks.md — notebook detection, conversion, visualization capture