card: iPhone numbers from the zero-copy DetectCamera pipeline

README.md

@@ -26,14 +26,17 @@ the answer to [apple/coreai-models#14](https://github.com/apple/coreai-models/is
 
 | 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-nano_float32.aimodel` | 384×384 | 30.5M | **8.6 ms** (~116 FPS) | **~25 ms (33–39 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-medium_float32.aimodel` | 576×576 | 33.7M | **14.8 ms** (~68 FPS) | **56–63 ms (15–17 FPS live)** |
 | `rfdetr-large_float32.aimodel` | 704×704 | 33.9M | **19.1 ms** (~52 FPS) | — |
 
-iPhone numbers are sustained live-camera medians from the
+iPhone numbers are end-to-end live-camera measurements from the
 [CoreAIKit DetectCamera example](https://github.com/john-rocky/coreai-kit)
-(`CameraFeed` + `ObjectDetector`, Release build).
+(Release; zero-copy capture pipeline — AVCaptureVideoPreviewLayer display,
+hardware-scaled 32BGRA buffers, vImage preprocessing overlapped with GPU
+inference). Peak measured 39.6 FPS ≈ the nano model ceiling; sustained
+max-load throughput drops on a hot chassis (thermal).
 
 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),