This model is integrated into Hugging Mac, where developers can build private camera apps, face-aware interfaces, games, plugins, and agents on macOS.

Model details

Property	Value
Backbone	MobileNet0.25
Parameters	426,608
Input	`image`: `1 × 3 × 640 × 640` FP32 RGB tensor
Face boxes	`locations`: `1 × 16800 × 4` FP32
Scores	`scores`: `1 × 16800 × 2` FP32
Landmarks	`landmarks`: `1 × 16800 × 10` FP32
Core ML compute	FP16
Package size	0.94 MB

The ten landmark values represent five (x, y) points: both eyes, nose, and both mouth corners. Outputs are raw and require prior decoding, confidence filtering, NMS, and restoration to the original image coordinates.

Core ML example

python

from pathlib import Path

import coremltools as ct
import numpy as np
from huggingface_hub import snapshot_download
from PIL import Image, ImageOps

root = Path(snapshot_download(
    repo_id="hugging-mac/retinaface-coreml",
    allow_patterns=["retinaface.mlpackage/**"],
))
model = ct.models.MLModel(root / "retinaface.mlpackage", compute_units=ct.ComputeUnit.ALL)

image = ImageOps.pad(Image.open("face.jpg").convert("RGB"), (640, 640))
value = np.asarray(image, dtype=np.float32).transpose(2, 0, 1)
value -= np.asarray((123.0, 117.0, 104.0), dtype=np.float32)[:, None, None]
outputs = model.predict({"image": np.ascontiguousarray(value[None])})

print(outputs["locations"].shape)  # (1, 16800, 4)
print(outputs["scores"].shape)     # (1, 16800, 2)
print(outputs["landmarks"].shape)  # (1, 16800, 10)

For complete preprocessing and postprocessing, use the Hugging Mac RetinaFace SDK.

Provenance and integrity

Upstream model: py-feat/retinaface
Upstream revision: 31702389094fccc7060c15299e6ad712ee880de6
Conversion: fixed 640×640 input, ML Program, FP16 compute
Directory SHA-256: 6290800b08e20d9d6506795e5e47f1da35f2586ad4e75dcbb08a14ed11b2ed6b

License

The converted model retains the upstream MIT license. Hugging Mac is an independent open-source project and is not affiliated with or endorsed by py-feat.