Add Hessian solver for per-detection regression head refinement

[Copilot]

Regression heads (depth, rotation, center offsets) lacked per-detection iterative refinement. Existing L-BFGS calibration only handles dataset-wide scale/intrinsics corrections.

Changes

Core solver (yolozu/calibration/hessian_solver.py):

• Gauss-Newton with Levenberg-Marquardt damping for log_z, rot6d, and offset refinement
• Supports optional GT supervision or regularization-only mode
• Automatic convergence detection with NaN-safe early stopping
• Jacobian computation via PyTorch autograd

CLI tool (tools/refine_predictions_hessian.py):

• Batch refinement with configurable solver parameters
• Optional GT supervision from dataset records
• Class-based detection-to-GT matching

Documentation (docs/hessian_solver.md):

• Algorithm details, configuration reference, performance characteristics
• Comparison with L-BFGS calibration (per-detection vs dataset-wide)

Usage

from yolozu.calibration import HessianSolverConfig, refine_predictions_hessian

config = HessianSolverConfig(refine_depth=True, max_iterations=5)
refined = refine_predictions_hessian(predictions, records=dataset_records, config=config)

Or via CLI:

python tools/refine_predictions_hessian.py \
  --predictions reports/predictions.json \
  --dataset data/coco128 \
  --output refined.json \
  --refine-depth --refine-rotation

Typical convergence: 3-5 iterations, ~1-2ms per detection on CPU.

Original prompt

Hessian silver on heads
especially for regression stuff

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