metadata
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.
Core ML conversion of 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 |
| 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
hf download mlboydaisuke/coreml-zoo --include "rfdetr/*" --local-dir ./rfdetr_n
unzip './rfdetr_n/rfdetr/*.zip' -d ./rfdetr_n
Use in 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, a standalone iOS project.
- Models Zoo — this model is downloadable and runnable inside the Models Zoo app on the App Store, no build required.
Conversion
- 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: Apache-2.0.
Credits
- Upstream authors: roboflow/rf-detr, 2025
- Core ML conversion: john-rocky (Daisuke Majima)