---

name: evaluation-and-visualization description: "Routes Ultra-Fast-Lane-Detection checkpoint evaluation, TuSimple scoring, CULane evaluation, and demo visualization workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

evaluation-and-visualization

Use this sub-skill when a task is about scoring a trained checkpoint, building or invoking the CULane evaluator, generating TuSimple metrics, or creating the demo video outputs from the repo.

Read this when

• You need to run test.py on CULane or TuSimple.
• You need to score precomputed TuSimple predictions.
• You need to build or call the CULane evaluator binary.
• You need to generate AVI visualizations with demo.py.

What this sub-skill owns

• Evaluation command construction.
• CULane and TuSimple output formats.
• Demo output and visualization behavior.
• CULane evaluator build/run caveats.
• Checkpoint loading and prefix compatibility on evaluation.

What it does not own

• Training loop mechanics: see training.
• Dataset layout and conversion: see data-and-config.
• TorchScript export and speed timing: see export-and-speed.

Start here

• Read references/evaluation-workflows.md for the practical command patterns.
• Read references/output-formats.md for CULane line files and TuSimple JSONL expectations.
• Read references/visualization.md when the user wants to inspect the demo output video.
• Read references/troubleshooting.md when evaluation or visualization fails.
• Use scripts/score_tusimple_json.py to score an existing TuSimple prediction file.
• Use scripts/run_culane_evaluator.sh as the safer evaluator launcher.

Typical flow

1. Confirm the dataset family and checkpoint path.
2. Load the checkpoint with the repo's compatible-state logic.
3. Run the evaluation command or the scoring helper.
4. If the user wants visual output, run the demo workflow separately.
5. If CULane F-measure is required, build the evaluator binary first.

Caution points

• The repo's evaluation scripts call .cuda() directly.
• TuSimple evaluation expects raw_file, lanes, h_samples, and run_time in the prediction JSONL.
• CULane scoring depends on a native evaluator binary built from evaluation/culane/.
• Demo outputs are written as AVI files and the source script assumes the default repo output naming.

Reference and script links

• references/evaluation-workflows.md for the command patterns and binary requirements.
• references/output-formats.md for the file formats.
• references/visualization.md for demo behavior and output placement.
• references/troubleshooting.md for checkpoint, evaluator, and OpenCV issues.
• scripts/run_culane_evaluator.sh for a parameterized evaluator wrapper.
• scripts/score_tusimple_json.py for TuSimple JSONL scoring.