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Datos e investigación · SKILL.md

evaluation

evaluation

Guides MASt3R-SLAM benchmark dataset manifests, evaluation command planning, headless suite runs, output paths, and evo_ape metrics.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentación de la Skill

GitHub

name: evaluation description: "Guides MASt3R-SLAM benchmark dataset manifests, evaluation command planning, headless suite runs, output paths, and evo_ape metrics." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

evaluation

Use this sub-skill when the task is about TUM-RGBD, 7-Scenes, EuRoC, or ETH3D benchmark workflows: dataset acquisition planning, sequence lists, headless suite runs, --no-calib variants, trajectory output paths, or evo_ape metrics.

Triggers

  • "run MASt3R-SLAM evaluation"
  • "download TUM/EuRoC/ETH3D/7-Scenes"
  • "what sequences are in eval_tum.sh"
  • "compute APE/evo metrics"
  • "print the eval commands"
  • "metric-only after a previous run"
  • "where are logs for calibrated vs no-calib runs"

Prerequisites

  • Environment, CUDA backend, and checkpoints are owned by setup-and-backends.
  • Single-sequence runtime command details are owned by run-slam.
  • Benchmark runs are long, GPU-heavy, and data-heavy. Print commands and ask for approval before executing downloads or full-suite runs.

First reads and scripts

Safe workflow

  1. Print the download plan:

    python sub-skills/evaluation/scripts/plan_downloads.py --suite tum --commands
    
  2. Print the evaluation plan:

    python sub-skills/evaluation/scripts/plan_evaluation.py --suite tum --no-calib
    
  3. Verify checkpoints, datasets, and available runtime budget.

  4. Execute only the approved subset, usually one sequence first.

  5. Run metric-only mode when trajectories already exist instead of rerunning SLAM.

Important suite differences

  • TUM, 7-Scenes, and EuRoC support both calibrated and no-calibration variants in the upstream scripts.
  • ETH3D uses the eth3d.yaml evaluation config and does not expose --no-calib in the upstream eval script.
  • All evaluation scripts run main.py in single-threaded, headless mode and then call evo_ape tum ... -as.

Boundary decisions

This sub-skill adapts the upstream shell scripts into print-first Python planners. It should not auto-download archives or run multi-hour evaluations unless the user explicitly approves that side effect.

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