Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Routes face-alignment installation, import checks, landmark-detection workflows, and detector-backend selection.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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.
references/repo-provenance.md when you need to know whether this skill still matches the current checkout.scripts/check_install.py after installing dependencies or when an import seems broken.sub-skills/landmark-detection/ for image, batch, or directory landmark inference.sub-skills/detectors/ when the question is which face detector backend to use or why one backend is unavailable.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+.
python -c "import face_alignment; print(face_alignment.__version__)"
python scripts/check_install.py
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.device='cpu' on CPU-only systems; the class default is device='cuda'.compile=False when you want fast startup or a simpler smoke path.flip_input=False only when you want to skip the accuracy trade-off of test-time flipping.references/api-reference.md for the verified constructor, return shapes, and helper functions.references/troubleshooting.md for cross-cutting install, import, download, and device issues.references/repo-routing-metadata.json when you need to inspect the router-facing scenario placement or selection guidance.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
日本語の概要は準備中です。原文の説明を表示しています。
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
日本語の概要は準備中です。原文の説明を表示しています。
Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
日本語の概要は準備中です。原文の説明を表示しています。
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
日本語の概要は準備中です。原文の説明を表示しています。