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name: yolop description: "Guides YOLOP multi-task driving perception workflows for BDD100K data preparation, training, evaluation, PyTorch demo inference, ONNX export, and TensorRT deployment planning." disable-model-invocation: true metadata: disco-role: operating license: MIT

YOLOP Repo Skill

Use this skill when a task names YOLOP, "You Only Look Once for Panoptic driving Perception", BDD100K driving perception, a combined traffic-object detection + drivable-area segmentation + lane-line segmentation model, or YOLOP files such as lib.config.default, lib.models.YOLOP, tools/demo.py, tools/train.py, export_onnx.py, or test_onnx.py.

Do not use this skill for generic Ultralytics YOLO workflows, unrelated lane-detection repos, or tasks that only need a generic PyTorch/ONNX tutorial.

Repository mental model

• YOLOP is a research-style Python repo, not an installable package with pyproject.toml/setup.py. Most workflows import the top-level lib package from a YOLOP checkout, so bundled helper scripts accept --repo-root and add that checkout to sys.path explicitly.
• The default network is MCnet from lib.models.YOLOP.get_net(cfg): one YOLO-style detection head for traffic objects, one drivable-area segmentation head, and one lane-line segmentation head.
• The central configuration object is lib.config.cfg from lib/config/default.py; dataset roots, training/evaluation toggles, image size, GPU list, batch sizes, losses, and checkpoint paths live there.
• The README-documented baseline is Python 3.7 with PyTorch 1.7+/torchvision 0.8+. For modern environments, keep torch and torchvision compatible, install requirements.txt, and run the bundled smoke checks before trusting a workflow.

Read references/model-overview.md for model outputs, architecture variants, checkpoints, and Torch Hub notes. Read references/configuration.md before changing dataset paths or training flags. Read references/troubleshooting.md when imports, paths, devices, or optional backends fail.

Start with a safe install/import smoke

From any environment that can import torch/torchvision, OpenCV, yacs, and the repo requirements, run the bundled check against a live YOLOP checkout:

python scripts/check_install.py --repo-root /path/to/YOLOP --device cpu --image-size 128

Use --device cuda:0 only after installing a CUDA-capable torch/torchvision pair and confirming that the requested GPU is available. CPU checks validate API shape and ONNX-style functional behavior; they do not validate CUDA speed, TensorRT, or full BDD100K training.

Route by task

User task	Read next	Why
Prepare BDD100K directories, detection JSONs, lane/drivable masks, or validate data roots	sub-skills/data-preparation/SKILL.md	Owns dataset layout, label conversion, mask generation, and data-path failures.
Configure or run training, staged/single-task modes, auto-anchor, validation/evaluation, metrics, checkpoints, or DDP launch	sub-skills/training/SKILL.md	Owns tools/train.py, tools/test.py, losses, metrics, and train/eval config semantics.
Run PyTorch demo inference on images, videos, streams, load checkpoints, or interpret detection/segmentation visual outputs	sub-skills/inference/SKILL.md	Owns tools/demo.py, hubconf.py, DemoDataset, NMS, masks, and visualization.
Export to ONNX, run ONNXRuntime inference, prepare TensorRT .wts, or reason about TensorRT/ZED deployment constraints	sub-skills/export/SKILL.md	Owns export_onnx.py, test_onnx.py, .wts conversion, and deployment limitations.

Common commands from the repo contract

These are the source workflow shapes that the sub-skills expand into safe, configurable recipes:

# Train after configuring dataset roots in lib/config/default.py
PYTHONPATH=. python tools/train.py

# Distributed training; only use after matching CUDA/PyTorch and data are ready
PYTHONPATH=. python -m torch.distributed.launch --nproc_per_node=N tools/train.py

# Evaluate on BDD100K validation data
PYTHONPATH=. python tools/test.py --weights weights/End-to-end.pth

# PyTorch demo inference on a file or folder
PYTHONPATH=. python tools/demo.py --source inference/images --weights weights/End-to-end.pth --device cpu

# ONNXRuntime inference with an existing exported model
PYTHONPATH=. python test_onnx.py --weight yolop-640-640.onnx --img test.jpg

Prefer the bundled helper scripts in the relevant sub-skill when you need safer output paths, source-root isolation, or a small smoke test that does not mutate a checkout.

Verification and backend limits

The generated skill was verified against CPU source imports, model construction, dummy forward-pass shapes, ONNX export mechanics using the export-specific model wrapper, ONNXRuntime CPU inference, and parser/help checks. Full BDD100K training/evaluation, benchmark speed numbers, CUDA throughput, TensorRT engine build, and ZED-camera runtime were not executed. Treat those as documented workflows requiring the prerequisites named in the sub-skills.

Read references/repo-provenance.md before deciding whether this skill matches a current checkout. If commit, dirty state, package structure, or public workflow files differ, refresh this skill before relying on detailed file-level guidance.