---

name: evaluation description: "Parse MOTChallenge data, accumulate predictions, and compute MOT metrics." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Norfair evaluation

Use this sub-skill when the task is to score a tracker on MOTChallenge data, parse MOT text files, save predictions or metrics, inspect load_motchallenge / eval_motChallenge, or compare the standard Norfair flow with the XYAH and ByteTrack-style evaluation references.

Start here

1. Read references/api-reference.md for the distilled behavior of InformationFile, PredictionsTextFile, DetectionFileParser, Accumulators, load_motchallenge, compare_dataframes, eval_motChallenge, and the expected MOTChallenge folder layout.
2. Read references/workflows.md for end-to-end scoring, prediction saving, metrics writing, and the XYAH / ByteTrack comparison notes.
3. Read references/troubleshooting.md for missing motmetrics / pandas, missing dataset folders, malformed seqinfo.ini or gt.txt, skipped sequence names, and long-running evaluation runs.
4. Use scripts/motchallenge_eval.py as the reusable helper for scoring MOTChallenge predictions without depending on the source demo tree.
5. Use scripts/motchallenge_smoke.py for a tiny synthetic MOTChallenge fixture check when you want to exercise the parser/evaluator without downloading a dataset.

What this covers

• Reading MOTChallenge sequence folders and the seqinfo.ini metadata that describes them.
• Loading det/det.txt detection rows as Norfair Detection objects.
• Writing prediction rows in the MOTChallenge box format while a tracker loop runs.
• Computing MOT metrics with motmetrics and rendering the summary text.
• Understanding the XYAH and ByteTrack-style comparison notes without making those external trees runtime dependencies.

Typical workflow

1. Confirm the dataset is a labeled MOTChallenge train/ split or a single labeled sequence folder.
2. Parse seqinfo.ini with InformationFile and inspect the detection stream with DetectionFileParser.
3. Run the tracker elsewhere, then call PredictionsTextFile.update(...) and Accumulators.update(...) once per frame.
4. Call Accumulators.compute_metrics() or the bundled motchallenge_eval.py helper when the sequence is complete.
5. Save the rendered summary with Accumulators.save_metrics(...) when you want a metrics.txt artifact.

Bundled scripts

• scripts/motchallenge_eval.py: reusable CLI/helper for scoring prediction files against MOTChallenge sequence folders.
• scripts/motchallenge_smoke.py: tiny synthetic fixture that proves parser and scorer behavior without a dataset download.

Route elsewhere

• If the user still needs tracker setup, motion-model choices, hit-counter tuning, or other tracker behavior before scoring, route to ../tracking-core/SKILL.md.
• If the user needs to render frames, write video outputs, or work with Video / VideoFromFrames, route to ../video-visualization/SKILL.md.
• If the task is about detector integrations or GPU-heavy demo pipelines, keep it out of this sub-skill and route to the detector-specific or runtime-specific skill that owns that integration.

This sub-skill stays focused on MOTChallenge parsing, accumulation, and metrics. Do not absorb tracker tuning, detector setup, or video overlay concerns into this workflow.