Update model card for yolo11-det

README.md

@@ -37,10 +37,10 @@ model-index:
             value: 44.88
           - name: "mAP@0.5 (Medium ONNX FP32)"
             type: map_50
-            value: 65.91
+            value: 65.94
           - name: "mAP@0.5-0.95 (Medium ONNX FP32)"
             type: map
-            value: 49.59
+            value: 49.6
 ---
 
 # YOLO11 Detection — EdgeFirst Model Zoo
@@ -66,7 +66,7 @@ Accuracy ceiling for each size, measured against COCO `val2017` (5,000 images) w
 |------|--------|--------|---------|--------------|----------|--------|
 | Nano | 2.6M | 6.5 | 53.10% | 37.84% | 41.12% | [v-703](https://edgefirst.studio/public/validation/v-703/details?mode=charts) |
 | Small | 9.4M | 21.5 | 61.19% | 44.88% | 48.43% | [v-705](https://edgefirst.studio/public/validation/v-705/details?mode=charts) |
-| Medium | 20.1M | 68.0 | 65.91% | 49.59% | 53.78% | [v-707](https://edgefirst.studio/public/validation/v-707/details?mode=charts) |
+| Medium | 20.1M | 68.0 | 65.94% | 49.60% | 53.78% | [v-71e](https://edgefirst.studio/public/validation/v-71e/details?mode=charts) |
 | Large | 25.3M | 87.6 | — | — | — | — |
 | XLarge | 56.9M | 195.0 | — | — | — | — |
 
@@ -87,21 +87,27 @@ Row conventions in the table below:
 
 | Size | Platform | mAP@0.5 | Δ vs FP32 (pp) | mAP@0.5-0.95 | Inference (ms) | End-to-end (ms) | Throughput (FPS) | Source |
 |------|----------|---------|----------------|--------------|----------------|-----------------|------------------|--------|
+| Nano | ONNX FP32 (CPU) | 53.04% | -0.06 | 37.82% | 27.21 | 35.94 | 136.0 | [v-71b](https://edgefirst.studio/public/validation/v-71b/details?mode=charts) |
 | Nano | ONNX FP32 (CUDA) | 53.10% | ref | 37.84% | 8.11 | 13.41 | 342.0 | [v-703](https://edgefirst.studio/public/validation/v-703/details?mode=charts) |
+| Nano | ONNX FP16 (CUDA) | 53.07% | -0.03 | 37.79% | 6.38 | 11.88 | 440.0 | [v-704](https://edgefirst.studio/public/validation/v-704/details?mode=charts) |
 | Nano | macOS CoreML — Neural Engine (FP16) | 53.05% | +0.00 | 37.82% | 1.83 | 5.62 | 720.5 | [v-516](https://edgefirst.studio/public/validation/v-516/details?mode=charts) |
 | Nano | macOS CoreML — Metal GPU (FP16) | 53.04% | -0.01 | 37.80% | 4.26 | 8.30 | 412.5 | [v-51e](https://edgefirst.studio/public/validation/v-51e/details?mode=charts) |
-| Nano | macOS CoreML — CPU (FP16) | 53.05% | ref | 37.78% | 15.69 | 20.37 | 124.5 | [v-51f](https://edgefirst.studio/public/validation/v-51f/details?mode=charts) |
+| Nano | macOS CoreML — CPU (FP16) | 53.05% | +0.00 | 37.78% | 15.69 | 20.37 | 124.5 | [v-51f](https://edgefirst.studio/public/validation/v-51f/details?mode=charts) |
 | Nano | NXP i.MX 8M Plus + VeriSilicon NPU | 49.15% | -3.90 | 34.66% | 87.08 | 131.63 | 10.2 | [v-531](https://edgefirst.studio/public/validation/v-531/details?mode=charts) |
 | Nano | NXP i.MX 95 + eIQ Neutron NPU | Not supported | — | — | — | — | — | [v-532](https://edgefirst.studio/public/validation/v-532/details?mode=charts) |
 | Nano | Raspberry Pi 5 + Hailo-8L NPU | 50.90% | -2.15 | 36.02% | 17.86 | 28.43 | 53.7 | [v-4de](https://edgefirst.studio/public/validation/v-4de/details?mode=charts) |
 | Nano | NVIDIA Jetson Orin Nano (TensorRT FP16) | 53.03% | -0.02 | 37.81% | 7.09 | 17.56 | 161.4 | [v-4dc](https://edgefirst.studio/public/validation/v-4dc/details?mode=charts) |
+| Small | ONNX FP32 (CPU) | 61.16% | -0.03 | 44.88% | 63.52 | 72.25 | 60.0 | [v-71c](https://edgefirst.studio/public/validation/v-71c/details?mode=charts) |
 | Small | ONNX FP32 (CUDA) | 61.19% | ref | 44.88% | 15.36 | 20.95 | 205.5 | [v-705](https://edgefirst.studio/public/validation/v-705/details?mode=charts) |
+| Small | ONNX FP16 (CUDA) | 61.16% | -0.03 | 44.84% | 10.30 | 15.98 | 304.0 | [v-706](https://edgefirst.studio/public/validation/v-706/details?mode=charts) |
 | Small | macOS CoreML — Neural Engine (FP16) | 61.17% | -0.02 | 44.88% | 4.95 | 8.74 | 328.9 | [v-517](https://edgefirst.studio/public/validation/v-517/details?mode=charts) |
-| Small | macOS CoreML — Metal GPU (FP16) | 61.19% | ref | 44.89% | 9.95 | 13.73 | 194.5 | [v-520](https://edgefirst.studio/public/validation/v-520/details?mode=charts) |
+| Small | macOS CoreML — Metal GPU (FP16) | 61.19% | +0.00 | 44.89% | 9.95 | 13.73 | 194.5 | [v-520](https://edgefirst.studio/public/validation/v-520/details?mode=charts) |
 | Small | macOS CoreML — CPU (FP16) | 61.13% | -0.06 | 44.86% | 31.94 | 36.54 | 60.0 | [v-521](https://edgefirst.studio/public/validation/v-521/details?mode=charts) |
 | Small | Raspberry Pi 5 + Hailo-8L NPU | 58.88% | -2.31 | 42.84% | 36.53 | 47.06 | 25.2 | [v-4df](https://edgefirst.studio/public/validation/v-4df/details?mode=charts) |
 | Small | NVIDIA Jetson Orin Nano (TensorRT FP16) | 61.13% | -0.06 | 44.85% | 15.57 | 24.81 | 109.0 | [v-4dd](https://edgefirst.studio/public/validation/v-4dd/details?mode=charts) |
-| Medium | ONNX FP32 (CUDA) | 65.91% | ref | 49.59% | 39.57 | 45.08 | 88.0 | [v-707](https://edgefirst.studio/public/validation/v-707/details?mode=charts) |
+| Medium | ONNX FP32 (CPU) | 65.94% | ref | 49.60% | 169.65 | 178.45 | 24.0 | [v-71e](https://edgefirst.studio/public/validation/v-71e/details?mode=charts) |
+| Medium | ONNX FP32 (CUDA) | 65.91% | -0.03 | 49.59% | 39.57 | 45.08 | 88.0 | [v-707](https://edgefirst.studio/public/validation/v-707/details?mode=charts) |
+| Medium | ONNX FP16 (CUDA) | 65.90% | -0.04 | 49.55% | 22.09 | 28.59 | 152.0 | [v-708](https://edgefirst.studio/public/validation/v-708/details?mode=charts) |