Core ML conversion of cleardusk/3DDFA_V2 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	keypoint detection
Upstream	cleardusk/3DDFA_V2
Packages	1
Download size	6 MB
Minimum iOS	17.0
Peak RAM	~200 MB

Files

File	Size	Compute units	SHA-256
`3DDFA_V2.mlpackage.zip`	6 MB	`all`	`0f715dc220c046f5…`
Total	6 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

bash

hf download mlboydaisuke/coreml-zoo --include "face3d/*" --local-dir ./face3d
unzip './face3d/face3d/*.zip' -d ./face3d

Use in Swift

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 3DDFA_V2(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/Face3DDemo, 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

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: cleardusk/3DDFA_V2, 2020
Core ML conversion: john-rocky (Daisuke Majima)

More models in this format: Core ML Model Zoo — 46 models, each with the recipe that produced it.

Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.

mlboydaisuke/3DDFA-V2-CoreML

3DDFA V2 — Core ML