RF-DETR nano/small/medium/large fp32 .aimodel + card + demos

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: roboflow/rf-detr
+tags:
+- coreai
+- aimodel
+- object-detection
+- rf-detr
+- detr
+- apple
+- ios
+- macos
+pipeline_tag: object-detection
+---
+
+# RF-DETR — Core AI (`.aimodel`)
+
+[RF-DETR](https://github.com/roboflow/rf-detr) (Roboflow's real-time detection
+transformer, COCO-pretrained) converted to Apple **Core AI** for iOS 27 / macOS 27 —
+the answer to [apple/coreai-models#14](https://github.com/apple/coreai-models/issues/14).
+**DETR family = no NMS**: post-processing is one sigmoid + top-k.
+
+<p align="center"><img src="demo_coco_cats.jpg" width="440" alt="RF-DETR medium on Core AI"></p>
+
+## Files
+
+| file | input | params | M4 Max GPU | iPhone 17 Pro GPU |
+|---|---|---|---|---|
+| `rfdetr-nano_float32.aimodel` | 384×384 | 30.5M | **8.6 ms** (~116 FPS) | **27.5 ms (~36 FPS live)** |
+| `rfdetr-small_float32.aimodel` | 512×512 | 32.1M | **12.0 ms** (~83 FPS) | — |
+| `rfdetr-medium_float32.aimodel` | 576×576 | 33.7M | **14.8 ms** (~68 FPS) | **56 ms (~17 FPS live)** |
+| `rfdetr-large_float32.aimodel` | 704×704 | 33.9M | **19.1 ms** (~52 FPS) | — |
+
+iPhone numbers are sustained live-camera medians from the
+[CoreAIKit DetectCamera example](https://github.com/john-rocky/coreai-kit)
+(`CameraFeed` + `ObjectDetector`, Release build).
+
+fp32 is the ship dtype: it gates **detection-set exact** vs the PyTorch fp32 reference on
+CPU and GPU (per confident detection: same class, IoU ≥ 0.999 measured, score within 2e-3),
+and fp16 only bought ~7% latency on M4 Max while adding near-tie ranking noise.
+
+## Graph contract
+
+```
+input  "image"  [1, 3, R, R]  float32, RGB in [0, 1]  (ImageNet mean/std folded in-graph)
+output "dets"   [1, 300, 4]   boxes, cxcywh normalized to [0, 1]
+output "labels" [1, 300, 91]  raw class logits; column index = ORIGINAL COCO id (0 unused, 1=person … 17=cat … 90)
+```
+
+Python decode sketch (Swift is the same three steps):
+
+```python
+import numpy as np, coreai.runtime as rt
+
+model = await rt.AIModel.load(path, rt.SpecializationOptions.default())
+fn = model.load_function("main")
+out = await fn({"image": rt.NDArray(rgb01)})          # rgb01: [1,3,R,R] in [0,1]
+prob = 1 / (1 + np.exp(-out["labels"].numpy()[0]))    # [300, 91]
+scores, classes = prob.max(-1), prob.argmax(-1)       # column index IS the COCO id
+boxes = out["dets"].numpy()[0]                        # cxcywh, multiply by image W/H
+keep = scores > 0.5                                   # done — no NMS
+```
+
+## Conversion
+
+Exported with
+[`conversion/export_rf_detr.py`](https://github.com/john-rocky/coreai-model-zoo/blob/main/conversion/export_rf_detr.py)
+from `rfdetr==1.7.1` weights. The port surfaced four Core AI converter/runtime bugs
+(float-arg `arange` abort, int64-comparison buffer clobber, GPU-delegate
+floor/trunc/ceil = identity, cast-pair cancellation) — each worked around numerically
+identically; details and minimal repros in
+[zoo/rf-detr.md](https://github.com/john-rocky/coreai-model-zoo/blob/main/zoo/rf-detr.md).
+
+License: Apache-2.0 (upstream RF-DETR code and COCO-pretrained weights are Apache-2.0).

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