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name: vpt_evaluation_reporting description: Compute VPT recovery accuracy, parameter efficiency, and mechanism report fields.

vpt_evaluation_reporting

Use this skill when implementing or checking Visual Prompt Tuning mechanisms from Jia et al. Do not use it for unrelated pixel-prompt or full-backbone fine-tuning experiments.

Inputs

Provide small token vectors, prompt settings, named parameter records, training examples, or prediction/label lists depending on the script. Inputs must make the frozen-backbone boundary explicit.

Outputs

The scripts return prompted token sequences, freeze-audit dictionaries, training traces, or metric reports. Recovery users should save these outputs as experiment evidence.

Workflow

1. Preserve the class token and image-patch token order.
2. Add continuous prompt parameters in input/token space.
3. Freeze all backbone parameters and update only prompt/head records.
4. Log loss, parameter changes, accuracy, and storage-efficient parameter ratios.

Validation

Run python -m pytest tests or validate this tree with the Distiller validate_skill_tree.py --run-tests command.

Limitations

These helpers are minimal and deterministic. They are intended for mechanism-faithful bounded recovery, not full VTAB/FGVC reproduction.