Add RetinaFace MobileNet0.25 Core ML package

README.md

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+---
+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.

retinaface.mlpackage.zip

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retinaface.mlpackage/Data/com.apple.CoreML/model.mlmodel

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retinaface.mlpackage/Data/com.apple.CoreML/weights/weight.bin

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retinaface.mlpackage/Manifest.json

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+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "98C97075-452E-428F-92A5-DB4988E5812E": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "C1AE6ACC-E1A8-4725-929D-3E62AA628B81": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "C1AE6ACC-E1A8-4725-929D-3E62AA628B81"
+}