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name: inference description: "Guides YOLOP PyTorch demo inference, checkpoint loading, image/video source handling, detection/segmentation postprocessing, and visualization troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

YOLOP Inference

Use this sub-skill when the task asks to run YOLOP on images, folders, videos, or streams; load the pretrained End-to-end.pth checkpoint; use hubconf.py; interpret combined detection/drivable/lane outputs; or debug tools/demo.py behavior.

Do not use this sub-skill for full training/evaluation metrics (use training), BDD100K layout generation (use data-preparation), or ONNXRuntime/TensorRT workflows (use export).

Read first

• references/workflows.md explains the source demo command, inputs, outputs, preprocessing, NMS, mask overlays, and safe bundled helper.
• references/model-loading.md covers get_net(cfg), checkpoint dictionaries, hubconf.yolop, and CPU/CUDA device choices.
• references/troubleshooting.md covers missing weights, unsupported sources, OpenCV/video/camera issues, device errors, and empty detections.
• Run scripts/run_demo_inference.py for a safer image/folder/video helper with explicit --repo-root, --weights, --source, and --save-dir.

Quick route

# Source repo command from a YOLOP checkout root
PYTHONPATH=. python tools/demo.py --source inference/images --weights weights/End-to-end.pth --device cpu

# Bundled helper with explicit paths and no checkout-root assumption
python sub-skills/inference/scripts/run_demo_inference.py \
  --repo-root /path/to/YOLOP \
  --weights /path/to/YOLOP/weights/End-to-end.pth \
  --source /path/to/YOLOP/test.jpg \
  --save-dir /tmp/yolop-demo-output \
  --device cpu

Use CPU for correctness/debug smokes. Use CUDA only after installing a matching CUDA torch/torchvision pair and verifying device availability.

Output contract

For every image frame, YOLOP produces:

• Detection boxes after YOLO-style NMS (xyxy, confidence, class id).
• Drivable-area segmentation mask, blended green by the demo visualization.
• Lane-line segmentation mask, blended red/blue depending on visualization helper conversion.
• A merged visualization image or video written to the save directory.

Cross-links

• To validate model architecture before loading weights, use the root scripts/check_install.py.
• To export or validate ONNX models, use ../export/SKILL.md.
• To configure evaluation metrics instead of demo visualization, use ../training/SKILL.md.