Add model card

README.md

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+---
+license: other
+license_name: s-lab-1.0
+license_link: https://github.com/pq-yang/MatAnyone/blob/main/LICENSE
+library_name: coreml
+pipeline_tag: image-segmentation
+tags:
+  - coreml
+  - core-ml
+  - ios
+  - macos
+  - apple
+  - on-device
+  - video-matting
+  - alpha-matting
+  - memory-network
+  - arxiv:2501.14677
+---
+
+# MatAnyone — Core ML
+
+*Video Matting, 2025*
+
+Temporally consistent video matting. 5-model pipeline with memory propagation.
+
+Core ML conversion of [pq-yang/MatAnyone](https://github.com/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](https://github.com/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
+
+```bash
+hf download mlboydaisuke/coreml-zoo --include "matanyone/*" --local-dir ./matanyone
+unzip './matanyone/matanyone/*.zip' -d ./matanyone
+```
+
+## Use in Swift
+
+```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 `MLMultiArray` buffers 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`](https://github.com/john-rocky/CoreML-Models/tree/master/sample_apps/MatAnyoneDemo), a standalone SwiftUI project.
+- **Models Zoo** — this model is downloadable and runnable inside the [Models Zoo app](https://apps.apple.com/app/id6762083207) on the App Store, no build required.
+
+## Conversion
+
+- Script: [`convert_matanyone.py`](https://github.com/john-rocky/CoreML-Models/blob/master/conversion_scripts/convert_matanyone.py)
+- Pitfalls hit during conversion (FP16 overflow, ANE buffer limits, stride handling): [`docs/coreml_conversion_notes.md`](https://github.com/john-rocky/CoreML-Models/blob/master/docs/coreml_conversion_notes.md)
+- Model index: [CoreML-Models](https://github.com/john-rocky/CoreML-Models)
+
+## License
+
+The conversion inherits the upstream license: **S-Lab License 1.0**.
+See [https://github.com/pq-yang/MatAnyone/blob/main/LICENSE](https://github.com/pq-yang/MatAnyone/blob/main/LICENSE).
+
+> Non-commercial use only.
+
+## Credits
+
+- Upstream authors: [pq-yang/MatAnyone](https://github.com/pq-yang/MatAnyone), 2025
+- Core ML conversion: john-rocky (Daisuke Majima)