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name: benchmark-workflows description: "Use BoxMOT for cached benchmark generation, evaluation, tuning, research loops, and benchmark configuration debugging." metadata: disco-role: operating disable-model-invocation: true license: AGPL 3.0

Benchmark Workflows

Use this sub-skill when the task is about generate, eval, tune, or research, benchmark YAMLs, cache reuse, public detections, or replay-backend selection.

Covers

• boxmot generate
• boxmot eval
• boxmot tune
• boxmot research
• benchmark config selection and split handling
• cache keys and reuse behavior
• public detections vs private detector runs
• postprocessing modes such as gsi, gbrc, and gta
• tracker replay backend selection (python vs cpp)
• --tune-kf and benchmark-dependent detector/ReID resolution

Does not cover

• live tracking from raw video or webcam input
• ReID training/export workflows
• native C++ build instructions

Use the sibling routes when the request is about those tasks.

Read first

• Benchmark workflows
• Configuration details
• Troubleshooting
• Benchmark config summary script

Good prompts for this route

• "Generate caches for MOT17 and reuse them in eval."
• "How do I switch between public and private detections?"
• "Why does tune keep regenerating the cache?"
• "What benchmark YAML does mmot-mini use?"
• "How do I run research with a different tracker backend?"

Typical workflow

1. Identify whether the user is starting from a benchmark name, a dataset path, or an existing cache directory.
2. Decide whether the command should use --benchmark or --source.
3. Check whether the user wants public detections, postprocessing, or native replay.
4. Confirm the benchmark split and cache identity before suggesting a rerun.
5. If the user only wants to inspect the config, use the bundled summary script instead of a full replay.

Entry points

Generate cache

boxmot generate --benchmark mot17 --split ablation

Evaluate cached tracking

boxmot eval --benchmark mot17 --split ablation --tracker boosttrack

Tune a tracker

boxmot tune --benchmark mot17 --split ablation --tracker bytetrack --n-trials 10

Research a tracker change

boxmot research --benchmark mot17 --split ablation --tracker bytetrack --proposal-model openai/gpt-5.4

What to hand off to nearby references

• Command semantics and cache rules belong in references/benchmark-workflows.md.
• YAML field layout and benchmark defaults belong in references/configuration.md.
• Missing benchmark configs, cache mismatches, and replay-backend errors belong in references/troubleshooting.md.

Use scripts/benchmark_config_summary.py when the user needs a quick, safe, inspect-only summary of a benchmark config.