Instructions to use mlboydaisuke/YOLO26s-CoreML with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use mlboydaisuke/YOLO26s-CoreML with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("mlboydaisuke/YOLO26s-CoreML") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
| license: agpl-3.0 | |
| library_name: coreml | |
| pipeline_tag: object-detection | |
| tags: | |
| - coreml | |
| - core-ml | |
| - ios | |
| - macos | |
| - apple | |
| - on-device | |
| - yolo | |
| - ultralytics | |
| - real-time | |
| # YOLO26s — Core ML | |
| *NMS-Free Detection, 2026* | |
| NMS-free object detection. 640×640 input, 80 COCO classes. | |
| <p><img src="https://huggingface.co/mlboydaisuke/YOLO26s-CoreML/resolve/main/media/bc27d62111.png" alt="YOLO26s demo"></p> | |
| Core ML conversion of [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) 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 | object detection | | |
| | Upstream | [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) | | |
| | Packages | 1 | | |
| | Download size | 17 MB | | |
| | Minimum iOS | 17.0 | | |
| | Peak RAM | ~300 MB | | |
| ## Files | |
| | File | Size | Compute units | SHA-256 | | |
| |---|---:|---|---| | |
| | `yolo26s.mlpackage.zip` | 17 MB | `all` | `0ec02fb0cf2dbd6e…` | | |
| | **Total** | **17 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 "yolo26/*" --local-dir ./yolo26s | |
| unzip './yolo26s/yolo26/*.zip' -d ./yolo26s | |
| ``` | |
| ## 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 yolo26s(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/YOLO26Demo`](https://github.com/john-rocky/CoreML-Models/tree/master/sample_apps/YOLO26Demo), 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 | |
| - 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: **AGPL-3.0**. | |
| ## Credits | |
| - Upstream authors: [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics), 2026 | |
| - Core ML conversion: john-rocky (Daisuke Majima) | |