license: mit
library_name: coreml
pipeline_tag: image-feature-extraction
tags:
- coreml
- core-ml
- ios
- macos
- apple
- on-device
- face-recognition
- face-embedding
- metric-learning
- arxiv:2204.00964
AdaFace IR-18 — Core ML
CVPR 2022
Face recognition embeddings. A 112x112 aligned face goes in, a 512-dim L2-normalised embedding comes out; compare two faces with a cosine similarity. AdaFace weights the margin by image quality during training, so low-quality and blurry faces degrade gracefully instead of collapsing together.
iResNet-18 backbone trained on CASIA-WebFace.
Core ML conversion of mk-minchul/AdaFace for on-device inference on iPhone, iPad and Mac. Converted with coremltools; the packages are stateless, so all sequencing and buffering lives in your Swift code.
| Task | image feature extraction |
| Upstream | mk-minchul/AdaFace |
| Packages | 1 |
| Download size | 42 MB |
| Minimum iOS | 17.0 |
Files
| File | Size | Compute units | SHA-256 |
|---|---|---|---|
AdaFace_IR18.mlpackage.zip |
42 MB | all |
- |
| Total | 42 MB |
compute_units is not a suggestion -- it is the configuration the conversion was verified against. Moving a package to a different compute unit can silently change the numerics (FP16 attention overflow) or crash on the GPU.
Download
hf download mlboydaisuke/coreml-zoo --include "adaface/*" --local-dir ./adaface
unzip './adaface/adaface/*.zip' -d ./adaface
Use in Swift
import CoreML
let config = MLModelConfiguration()
config.computeUnits = .all // as converted — see the table above
// Unzip the .mlpackage, drop it into your Xcode target and Xcode compiles it
// at build time:
let model = try AdaFace_IR18(configuration: config)
// ...or compile a downloaded .mlpackage at runtime:
let compiled = try await MLModel.compileModel(at: mlpackageURL)
let model = try MLModel(contentsOf: compiled, configuration: config)
Demo
- Sample app —
sample_apps/AdaFaceDemo, a standalone SwiftUI project. - Models Zoo — this model is downloadable and runnable inside the Models Zoo app on the App Store, no build required.
Conversion
- Script:
convert_adaface.py - Pitfalls hit during conversion (FP16 overflow, ANE buffer limits, stride handling):
docs/coreml_conversion_notes.md - Model index: CoreML-Models
License
The conversion inherits the upstream license: MIT.
Credits
- Upstream authors: mk-minchul/AdaFace, 2022
- Core ML conversion: john-rocky (Daisuke Majima)