Core ML conversion of the face-recognition model from fal/AuraFace-v1, for use in Visuals on macOS and iOS.

This is a modified work. The original glintr100.onnx (SHA-256 a7933ea5330113b01c9b60351d8f4c33003f145d8470ac5f0e52ee2effe25c60) was converted from ONNX to a Core ML ML Program package with float16 weights. No weights were retrained, pruned or otherwise altered beyond the precision change inherent to the conversion. Output parity against the ONNX reference was verified at cosine ≥ 0.99995 over random inputs.

auraface_v1.mlpackage/
├── Manifest.json                             617 B
├── Data/com.apple.CoreML/model.mlmodel       253,683 B
└── Data/com.apple.CoreML/weights/weight.bin  130,364,480 B

Preprocessing follows InsightFace's ArcFaceONNX: 112×112, mean 127.5, std 127.5, RGB channel order. Best results come from a 5-point aligned crop; a tight face crop also works, with reduced separation.

Original model © fal, released under the Apache License 2.0 — see LICENSE.md, reproduced unmodified from the upstream repository. This conversion is distributed under the same licence. The upstream model card states the model "has been trained on commercially and publicly available data sources to enable its usage in commercial setting."

Upstream fairness note, carried forward: the training data "may not extensively cover all ethnicities", and fal recommends downstream users assess fairness in their own context.