Core ML Model Zoo
Collection
PyTorch models converted to Core ML for on-device inference on iPhone, iPad and Mac. β’ 46 items β’ Updated β’ 1
Microsoft, CVPR 2025
Monocular geometry from a single image. Metric depth, surface normals, confidence mask. DINOv2 ViT-B/14 backbone.

Core ML conversion of microsoft/MoGe 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 | depth estimation |
| Upstream | microsoft/MoGe |
| Packages | 1 |
| Download size | 184 MB |
| Minimum iOS | 17.0 |
| Peak RAM | ~600 MB |
| File | Size | Compute units | SHA-256 |
|---|---|---|---|
MoGe2_ViTB_Normal_504.mlpackage.zip |
184 MB | all |
f60cfb4804707a48β¦ |
| Total | 184 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.
hf download mlboydaisuke/coreml-zoo --include "moge2/*" --local-dir ./moge2_vitb_normal_504
unzip './moge2_vitb_normal_504/moge2/*.zip' -d ./moge2_vitb_normal_504
import CoreML
let config = MLModelConfiguration()
config.computeUnits = .all // as converted β see the table above
// Unzip the .mlpackage, drop it into your Xcode target and Xcode compiles it
// at build time:
let model = try MoGe2_ViTB_Normal_504(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)
sample_apps/MoGe2Demo, a standalone SwiftUI project.convert_moge2.pydocs/coreml_conversion_notes.mdThe conversion inherits the upstream license: MIT.
Base model
Ruicheng/moge-2-vitb-normal