| --- |
| license: mit |
| library_name: coremltools |
| pipeline_tag: object-detection |
| base_model: py-feat/retinaface |
| tags: |
| - coreml |
| - apple-silicon |
| - face-detection |
| - face-landmarks |
| - mobilenet |
| - macos |
| --- |
| |
| # RetinaFace MobileNet0.25 — Core ML |
|
|
| A lightweight Core ML conversion of [py-feat/retinaface](https://huggingface.co/py-feat/retinaface) for fast, local face detection and five-point facial landmarks on Apple Silicon. |
|
|
| This model is integrated into [Hugging Mac](https://github.com/devilyouwei/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](https://github.com/devilyouwei/hugging-mac/tree/main/packages/hugging_mac_sdk/src/hugging_mac_sdk/models/retinaface). |
|
|
| ## Provenance and integrity |
|
|
| - Upstream model: [py-feat/retinaface](https://huggingface.co/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. |
|
|
