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name: ultra-fast-lane-detection description: "Routes Ultra-Fast-Lane-Detection workflows for data preparation, training, evaluation, export, and speed/deployment tasks." disable-model-invocation: true metadata: disco-role: operating license: MIT

Ultra-Fast-Lane-Detection

Use this skill when the task is about the Ultra-Fast-Lane-Detection lane detection repo: preparing CULane or TuSimple data, training a model, evaluating checkpoints, visualizing outputs, exporting TorchScript, or benchmarking speed.

Start here

• Read references/repo-provenance.md if you want to confirm this skill matches the current checkout or to decide whether refresh-repo-skill is needed.
• Run scripts/check_environment.py when you need a quick local smoke of imports, config parsing, and CUDA availability.
• Use the sub-skill that matches the task family; this root file is only a router.

Install and runtime notes

• This repo is not packaged as an installable distribution.
• Use a Python environment with PyTorch and torchvision compatible with your backend, then install the runtime requirements listed by the repo (requirements.txt plus the small optional helpers used by the scripts here).
• The documented workflows assume the repository root is on PYTHONPATH when you run the scripts from a checkout or copied tree.
• CUDA is required for the native training, evaluation, demo, and speed scripts because the source code calls .cuda() directly.
• The CULane evaluator also needs a C++ toolchain and OpenCV C++ development files.

Route map

sub-skills/data-and-config/

Read this when the task is about dataset layout, TuSimple conversion, config overrides, row anchors, or any data_root/log_path problem.

sub-skills/training/

Read this for single-GPU or distributed training, checkpoints, resume/finetune behavior, losses, schedulers, and TensorBoard logging.

sub-skills/evaluation-and-visualization/

Read this for test.py, CULane/TuSimple scoring, demo AVI generation, and evaluator build/run questions.

sub-skills/export-and-speed/

Read this for TorchScript export, synthetic speed checks, camera/video speed notes, and LibTorch/OpenCV deployment caveats.

Common entry points

• data-and-config owns the data preparation path and the configuration defaults.
• training owns the optimization loop and checkpoint lifecycle.
• evaluation-and-visualization owns benchmark outputs and visual inspection.
• export-and-speed owns deployment-oriented export and throughput checks.

When to read references

• references/workflow-map.md for a compact overview of the repo workflows and where each sub-skill routes them.
• references/troubleshooting.md for cross-cutting failures such as missing dependencies, CUDA runtime issues, checkpoint mismatches, and dataset path mistakes.
• references/repo-provenance.md for staleness checks and repo snapshot details.

Safe smoke check

If you only need to confirm the repo knowledge is usable, run the bundled smoke helper before deeper work:

python scripts/check_environment.py --repo-root . --device cuda

If CUDA is not available, rerun with --device cpu to verify the non-GPU import path and record the missing backend separately.

Sub-skill reminders

• Do not expect the original repo docs to stay open in a later session; the sub-skills and bundled references should carry the needed details.
• Do not use this root file for long API tables, detailed command catalogs, or troubleshooting matrices; those belong in the sub-skills' references.
• Do not mix evaluation, export, and training advice in one route when a dedicated sub-skill exists.