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mlboydaisuke
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RF-DETR-Nano-CoreML

Object Detection
Core ML
core-ml
ios
macos
apple
on-device
detr
transformer
roboflow
arxiv:2511.09554
Model card Files
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RF-DETR-Nano-CoreML
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---
license: apache-2.0
library_name: coreml
pipeline_tag: object-detection
tags:
  - coreml
  - core-ml
  - ios
  - macos
  - apple
  - on-device
  - detr
  - transformer
  - roboflow
  - arxiv:2511.09554
---

# RF-DETR Nano — Core ML

*Object Detection, 2025*

End-to-end transformer detector. 384×384 input. 300 queries, 91 classes (COCO + background). No NMS needed.

<p><img src="https://huggingface.co/mlboydaisuke/RF-DETR-Nano-CoreML/resolve/main/media/4d58781ec5.png" alt="RF-DETR Nano demo"></p>

Core ML conversion of [roboflow/rf-detr](https://github.com/roboflow/rf-detr) 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 | [roboflow/rf-detr](https://github.com/roboflow/rf-detr) |
| Packages | 1 |
| Download size | 95 MB |
| Minimum iOS | 17.0 |
| Peak RAM | ~400 MB |

## Files

| File | Size | Compute units | SHA-256 |
|---|---:|---|---|
| `rfdetr_n_coco.mlpackage.zip` | 95 MB | `all` | `3cac3793b97aa88d…` |
| **Total** | **95 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 "rfdetr/*" --local-dir ./rfdetr_n
unzip './rfdetr_n/rfdetr/*.zip' -d ./rfdetr_n
```

## 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 rfdetr_n_coco(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** — [peaceofcake/DFINEDemo](https://github.com/john-rocky/peaceofcake/tree/main/DFINEDemo), a standalone iOS 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: **Apache-2.0**.

## Credits

- Upstream authors: [roboflow/rf-detr](https://github.com/roboflow/rf-detr), 2025
- Core ML conversion: john-rocky (Daisuke Majima)