RF-DETR Core ML
FP16 Core ML exports of Roboflow RF-DETR object-detection models for clients running on macOS.
Included models
| Variant | Input | Package size | COCO AP | Intended use |
|---|---|---|---|---|
| RF-DETR Medium | 1 × 3 × 576 × 576 FP32 |
58 MB | 54.7 | Balanced local detection |
| RF-DETR Large | 1 × 3 × 704 × 704 FP32 |
59 MB | 56.5 | Higher-accuracy local detection |
The packages use Core ML's ML Program format with FP16 weights. They were exported from the official pretrained checkpoints with rfdetr==1.9.4, coremltools==9.0, and the upstream format="coreml" exporter.
Input and output contract
The input feature is tensors, a contiguous NCHW FP32 tensor. Resize the image to the variant's square input resolution, convert RGB channels to [0, 1], then apply ImageNet normalization:
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
Each model has two outputs identified by shape:
- boxes:
1 × 300 × 4, FP16 normalizedcenter_x, center_y, width, height - logits:
1 × 300 × 91, FP16 COCO sparse-category logits
Apply an independent sigmoid to every query/class logit. Flatten the query/class score grid, select the highest 300 pairs, then apply the confidence threshold. COCO category IDs use the sparse 1...90 mapping; slot 0 and the unused category gaps are not detections. RF-DETR does not require NMS.
macOS performance
Measured through native Swift MLModel with computeUnits = .all on a Mac mini with Apple M4 Pro and 64 GB unified memory, after 10 warmups and across 100 consecutive predictions:
| Variant | Median | p95 | Approximate throughput |
|---|---|---|---|
| Medium | 25.7 ms | 27.6 ms | 39 predictions/s |
| Large | 45.7 ms | 46.5 ms | 22 predictions/s |
These measurements cover model prediction only. Client-side image conversion and result decoding are additional work. Performance on a base M4 was not measured.
Provenance and limitations
- Upstream project: roboflow/rf-detr
- Exporter release:
1.9.4 - Checkpoints: official RF-DETR Medium and RF-DETR Large COCO weights selected by that release
- Export host: macOS 26.6.2 on Apple silicon
The upstream Core ML exporter is marked experimental. Clients should pin an immutable repository revision, verify downloaded file hashes, compile packages once with MLModel.compileModel(at:), and validate output parity for their own images before production use.
License
RF-DETR source and the Medium and Large model weights are distributed under Apache License 2.0. See LICENSE and the upstream repository for attribution and notices.