|
Download README.md from SceneWorks/mp-facemesh-v2: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
-
https://huggingface.co/SceneWorks/mp-facemesh-v2/resolve/main/README.md
- Command line
-
hf download hf://SceneWorks/mp-facemesh-v2/README.md
-
curl -L -o README.md https://huggingface.co/SceneWorks/mp-facemesh-v2/resolve/main/README.md
1.38 kB
metadata
license: apache-2.0
library_name: sceneworks
tags:
- face-landmarks
- mediapipe
- facemesh
MediaPipe FaceMesh v2 (SceneWorks native rehost)
Native safetensors rehost of the MediaPipe FaceMesh v2 face-landmark detector, for SceneWorks' MLX and Candle training backends (frozen, differentiable face-landmark loss for character LoRA training).
Provenance
- Source: py-feat/mp_facemesh_v2 at revision
39eb85054cf76fe0f57b7e12d6765ae89d89f2b5, fileface_landmarks_detector_Nx3x256x256_onnx.pth(an onnx2torch GraphModule of Google's MediaPipe FaceMesh v2 landmark model). - License: Apache-2.0, as stated by the source repository.
- Converted with
crates/media/mlx-gen/tools/convert_mp_facemesh_v2.py(SceneWorks inference repo, commit1d87a8eb2), which lowers the FX graph to an "fx-program" stored in the safetensors metadata (program) alongside the 281 parameter tensors.
Verification
- Self-check (lowered program re-executed against the torch module): max abs error 0.0.
- Native executors vs torch on a fixed seeded input: MLX max abs 3.09e-4, Candle max abs 2.86e-4 (output magnitude ~216, ≈1.4e-6 relative).
- Input: N×3×256×256; output0: N×1×1×1434 (478 landmarks × 3).
face_landmarks_detector.safetensors sha256: 94e0dda4bb58733fc7454997868232bdcc2b11531a18d2bbfec5479c75f574f4