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face-alignment

face-alignment

Routes face-alignment installation, import checks, landmark-detection workflows, and detector-backend selection.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: face-alignment description: "Routes face-alignment installation, import checks, landmark-detection workflows, and detector-backend selection." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Face-Alignment Repo Skill

Use this repo skill when a task names face_alignment, face landmark detection, 2D / 2.5D / 3D facial keypoints, or detector backends such as SFD, BlazeFace, YuNet, RetinaFace, SCRFD, folder boxes, or deprecated dlib.

Start here

  • Read references/repo-provenance.md when you need to know whether this skill still matches the current checkout.
  • Run scripts/check_install.py after installing dependencies or when an import seems broken.
  • Use sub-skills/landmark-detection/ for image, batch, or directory landmark inference.
  • Use sub-skills/detectors/ when the question is which face detector backend to use or why one backend is unavailable.

Install

The package distribution is face-alignment and the import name is face_alignment.

For a normal user install:

pip install face-alignment

For local source inspection from this checkout:

pip install -r requirements.txt
pip install -e .

If you need SCRFD support, install the optional extra:

pip install -e '.[scrfd]'
# or: pip install onnxruntime

If you plan to use RetinaFace, also install torchvision because that backend imports it directly.

The package requires Python 3.9+ and PyTorch 2.0+.

Minimal check

python -c "import face_alignment; print(face_alignment.__version__)"
python scripts/check_install.py

Route map

  • sub-skills/landmark-detection/ — run FaceAlignment on a single image, a batch tensor, or a directory of images; choose landmark type, device, compile, and batch-size settings; interpret returned landmark arrays and no-face results.
  • sub-skills/detectors/ — choose face_detector=..., handle optional detector dependencies, use folder sidecar boxes, and troubleshoot backend-specific import or device failures.

Common choices

  • Use device='cpu' on CPU-only systems; the class default is device='cuda'.
  • Use compile=False when you want fast startup or a simpler smoke path.
  • Use flip_input=False only when you want to skip the accuracy trade-off of test-time flipping.
  • Read references/api-reference.md for the verified constructor, return shapes, and helper functions.
  • Read references/troubleshooting.md for cross-cutting install, import, download, and device issues.
  • Read references/repo-routing-metadata.json when you need to inspect the router-facing scenario placement or selection guidance.
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