A face mesh model regresses 468 3-D landmarks over a single face and scores how
confident it is that it was shown a face at all. It does not search an image for
faces: give it a crop that a face detector such as
blazeface
already found.
The input is RGB, channels-first, normalized to
v / 127.5 - 1
. The outputs
follow the same shape every keypoint model in this collection publishes, with
one candidate:
boxes
is the xyxy hull of the mesh in input-pixel units,
scores
is the face-presence probability, and each landmark is
x, y, confidence, z
. The mesh carries no per-landmark confidence, so that
column is a constant
1
;
z
is a relative depth on the same scale as
x
,
negative towards the camera.
Landmark order is MediaPipe's canonical face model, so any lip, eye or
face-oval index list published for MediaPipe Face Mesh applies unchanged.
Repository structure
config.json 30 B
coreml/config.json 719 B
coreml/facemesh_coreml_fp16.pte 1.6 MB
xnnpack/config.json 721 B
xnnpack/facemesh_xnnpack_fp32.pte 2.5 MB
Compatibility
These files are published for the
ExecuTorch v1.4.1
runtime. ExecuTorch
gives no forward compatibility guarantee, so an older runtime may fail to load
them.
To use them in React Native ExecuTorch, pass the model constant shipped in the
library's model registry to the corresponding task pipeline. See the
documentation
.
To load these files in your own ExecuTorch runtime, read the
compatibility note
first.
Runs of software-mansion react-native-executorch-facemesh on huggingface.co
29
Total runs
29
24-hour runs
29
3-day runs
29
7-day runs
29
30-day runs
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