---

name: detectors description: "Routes face detector backend selection and backend-specific troubleshooting for face-alignment." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Detector Backends

Use this sub-skill when the task is about choosing face_detector=..., understanding detector backend trade-offs, fixing missing optional dependencies, or using the folder detector with precomputed boxes.

Common triggers

• which detector should I use?
• sfd, blazeface, yunet, retinaface, scrfd, folder, or dlib
• missing onnxruntime, torchvision, or dlib
• detector-specific kwargs like back_model, filter_threshold, score_threshold, or confidence_threshold
• precomputed boxes in .npy, .t7, or .pth files

Read first

• references/detector-overview.md for supported backends, dependencies, device support, reference scales, and default kwargs.
• references/troubleshooting.md for missing optional packages, device warnings, and sidecar file errors.
• ../../references/api-reference.md for the shared FaceAlignment and detector constructor signatures.
• scripts/check_detector_support.py when you want a safe backend-availability probe.
• ../landmark-detection/ if the user actually wants landmark predictions rather than backend selection.

Route guidance

• sfd is the default PyTorch backend.
• blazeface is the speed-oriented PyTorch backend.
• yunet is the OpenCV DNN CPU-only backend.
• retinaface requires torchvision.
• scrfd requires onnxruntime.
• folder uses precomputed bounding boxes and is useful for evaluation or when detection is already done.
• dlib is deprecated; mention it only if the user already depends on it.