Core ML Model Zoo
Collection
PyTorch models converted to Core ML for on-device inference on iPhone, iPad and Mac. • 46 items • Updated • 1
MatAnyone — Core ML
Video Matting, 2025
Temporally consistent video matting. 5-model pipeline with memory propagation.
Core ML conversion of pq-yang/MatAnyone 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 segmentation |
| Upstream | pq-yang/MatAnyone |
| Packages | 5 |
| Download size | 83 MB |
| Minimum iOS | 17.0 |
| Peak RAM | ~800 MB |
Files
| File | Size | Compute units | SHA-256 |
|---|---|---|---|
MatAnyone_encoder.mlpackage.zip |
17 MB | cpuAndGPU |
97ffd6bc4611f9a3… |
MatAnyone_mask_encoder.mlpackage.zip |
16 MB | cpuAndGPU |
ba67559188ffc64d… |
MatAnyone_read_first.mlpackage.zip |
21 MB | cpuOnly |
34daf7227dbcec73… |
MatAnyone_read.mlpackage.zip |
21 MB | cpuOnly |
052e52c0ffb7ff9e… |
MatAnyone_decoder.mlpackage.zip |
8 MB | cpuAndGPU |
67136aa67000e604… |
| Total | 83 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 "matanyone/*" --local-dir ./matanyone
unzip './matanyone/matanyone/*.zip' -d ./matanyone
Use in Swift
import CoreML
let config = MLModelConfiguration()
config.computeUnits = .cpuAndGPU // 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 MatAnyone_encoder(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)
This model is split into 5 Core ML packages that are driven in sequence from Swift. Load them one at a time, copy the outputs out of the
MLMultiArraybuffers and release each model before loading the next — two large Core ML models resident at once will OOM on an iPhone.
Demo
- Sample app —
sample_apps/MatAnyoneDemo, 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_matanyone.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: S-Lab License 1.0. See https://github.com/pq-yang/MatAnyone/blob/main/LICENSE.
Non-commercial use only.
Credits
- Upstream authors: pq-yang/MatAnyone, 2025
- Core ML conversion: john-rocky (Daisuke Majima)
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