Update model card for yolov5-det

README.md

@@ -28,19 +28,19 @@ model-index:
             value: 47.75
           - name: "mAP@0.5-0.95 (Nano ONNX FP32)"
             type: map
-            value: 32.93
+            value: 32.95
           - name: "mAP@0.5 (Small ONNX FP32)"
             type: map_50
             value: 57.33
           - name: "mAP@0.5-0.95 (Small ONNX FP32)"
             type: map
-            value: 41.22
+            value: 41.26
           - name: "mAP@0.5 (Medium ONNX FP32)"
             type: map_50
-            value: 63.29
+            value: 63.3
           - name: "mAP@0.5-0.95 (Medium ONNX FP32)"
             type: map
-            value: 46.99
+            value: 47.0
 ---
 
 # YOLOv5 Detection — EdgeFirst Model Zoo
@@ -64,9 +64,9 @@ Accuracy ceiling for each size, measured against COCO `val2017` (5,000 images) w
 
 | Size | Params | GFLOPs | mAP@0.5 | mAP@0.5-0.95 | mAP@0.75 | Source |
 |------|--------|--------|---------|--------------|----------|--------|
-| Nano | 1.9M | 4.5 | 47.75% | 32.93% | 35.40% | [v-6f3](https://edgefirst.studio/public/validation/v-6f3/details?mode=charts) |
-| Small | 7.2M | 16.5 | 57.33% | 41.22% | 44.92% | [v-6f5](https://edgefirst.studio/public/validation/v-6f5/details?mode=charts) |
-| Medium | 21.2M | 49.0 | 63.29% | 46.99% | 51.32% | [v-6f6](https://edgefirst.studio/public/validation/v-6f6/details?mode=charts) |
+| Nano | 1.9M | 4.5 | 47.75% | 32.95% | 35.61% | [v-6f0](https://edgefirst.studio/public/validation/v-6f0/details?mode=charts) |
+| Small | 7.2M | 16.5 | 57.33% | 41.26% | 44.93% | [v-6f4](https://edgefirst.studio/public/validation/v-6f4/details?mode=charts) |
+| Medium | 21.2M | 49.0 | 63.30% | 47.00% | 51.35% | [v-717](https://edgefirst.studio/public/validation/v-717/details?mode=charts) |
 | Large | 46.5M | 109.1 | — | — | — | — |
 | XLarge | 86.7M | 205.7 | — | — | — | — |
 
@@ -86,9 +86,11 @@ 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 (CUDA) | 47.75% | ref | 32.93% | 6.06 | 11.63 | 452.0 | [v-6f3](https://edgefirst.studio/public/validation/v-6f3/details?mode=charts) |
+| Nano | ONNX FP32 (CPU) | 47.74% | -0.01 | 32.94% | 28.38 | 37.06 | 132.0 | [v-715](https://edgefirst.studio/public/validation/v-715/details?mode=charts) |
+| Nano | ONNX FP32 (CUDA) | 47.75% | ref | 32.95% | 7.71 | 12.97 | 359.0 | [v-6f0](https://edgefirst.studio/public/validation/v-6f0/details?mode=charts) |
+| Nano | ONNX FP16 (CUDA) | 47.75% | +0.00 | 32.93% | 6.06 | 11.63 | 452.0 | [v-6f3](https://edgefirst.studio/public/validation/v-6f3/details?mode=charts) |
 | Nano | macOS CoreML — Neural Engine (FP16) | 47.72% | -0.06 | 32.89% | 1.58 | 5.46 | 801.9 | [v-510](https://edgefirst.studio/public/validation/v-510/details?mode=charts) |
-| Nano | macOS CoreML — Metal GPU (FP16) | 47.78% | ref | 32.91% | 3.69 | 7.76 | 459.1 | [v-50f](https://edgefirst.studio/public/validation/v-50f/details?mode=charts) |
+| Nano | macOS CoreML — Metal GPU (FP16) | 47.78% | +0.00 | 32.91% | 3.69 | 7.76 | 459.1 | [v-50f](https://edgefirst.studio/public/validation/v-50f/details?mode=charts) |
 | Nano | macOS CoreML — CPU (FP16) | 47.75% | -0.03 | 32.89% | 16.13 | 20.77 | 112.1 | [v-50e](https://edgefirst.studio/public/validation/v-50e/details?mode=charts) |
 | Nano | NXP i.MX 8M Plus + VeriSilicon NPU | 45.96% | -1.82 | 31.50% | 57.48 | 101.97 | 14.7 | [v-4e7](https://edgefirst.studio/public/validation/v-4e7/details?mode=charts) |
 | Nano | NXP i.MX 95 + eIQ Neutron NPU | 43.80% | -3.95 | 28.82% | 48.97 | 64.65 | 78.0 | [v-6c2](https://edgefirst.studio/public/validation/v-6c2/details?mode=charts) |
@@ -96,9 +98,11 @@ Row conventions in the table below:
 | Nano | NXP ARA240 (Kinara DVM) | 44.99% | -2.76 | 30.11% | 8.41 | 24.97 | 195.0 | [v-6bd](https://edgefirst.studio/public/validation/v-6bd/details?mode=charts) |
 | Nano | Raspberry Pi 5 + Hailo-8L NPU | 45.12% | -2.66 | 30.87% | 14.67 | 25.21 | 66.6 | [v-4d5](https://edgefirst.studio/public/validation/v-4d5/details?mode=charts) |
 | Nano | NVIDIA Jetson Orin Nano (TensorRT FP16) | 47.75% | -0.03 | 32.93% | 7.08 | 19.12 | 192.5 | [v-4d2](https://edgefirst.studio/public/validation/v-4d2/details?mode=charts) |
-| Small | ONNX FP32 (CUDA) | 57.33% | ref | 41.22% | 9.68 | 15.26 | 318.0 | [v-6f5](https://edgefirst.studio/public/validation/v-6f5/details?mode=charts) |
+| Small | ONNX FP32 (CPU) | 57.29% | -0.04 | 41.23% | 65.96 | 74.73 | 59.0 | [v-716](https://edgefirst.studio/public/validation/v-716/details?mode=charts) |
+| Small | ONNX FP32 (CUDA) | 57.33% | ref | 41.26% | 13.84 | 19.50 | 224.0 | [v-6f4](https://edgefirst.studio/public/validation/v-6f4/details?mode=charts) |
+| Small | ONNX FP16 (CUDA) | 57.33% | +0.00 | 41.22% | 9.68 | 15.26 | 318.0 | [v-6f5](https://edgefirst.studio/public/validation/v-6f5/details?mode=charts) |
 | Small | macOS CoreML — Neural Engine (FP16) | 57.28% | -0.06 | 41.16% | 4.20 | 7.99 | 376.6 | [v-513](https://edgefirst.studio/public/validation/v-513/details?mode=charts) |
-| Small | macOS CoreML — Metal GPU (FP16) | 57.34% | ref | 41.25% | 8.95 | 12.65 | 202.9 | [v-512](https://edgefirst.studio/public/validation/v-512/details?mode=charts) |
+| Small | macOS CoreML — Metal GPU (FP16) | 57.34% | +0.00 | 41.25% | 8.95 | 12.65 | 202.9 | [v-512](https://edgefirst.studio/public/validation/v-512/details?mode=charts) |
 | Small | macOS CoreML — CPU (FP16) | 57.29% | -0.05 | 41.20% | 30.50 | 35.09 | 63.0 | [v-511](https://edgefirst.studio/public/validation/v-511/details?mode=charts) |
 | Small | NXP i.MX 8M Plus + VeriSilicon NPU | 55.88% | -1.46 | 39.91% | 108.97 | 153.46 | 8.4 | [v-509](https://edgefirst.studio/public/validation/v-509/details?mode=charts) |
 | Small | NXP i.MX 95 + eIQ Neutron NPU | 53.96% | -3.37 | 36.64% | 122.96 | 135.15 | 32.0 | [v-6c7](https://edgefirst.studio/public/validation/v-6c7/details?mode=charts) |
@@ -106,8 +110,10 @@ Row conventions in the table below:
 | Small | NXP ARA240 (Kinara DVM) | 54.54% | -2.79 | 37.67% | 13.51 | 26.74 | 97.0 | [v-6be](https://edgefirst.studio/public/validation/v-6be/details?mode=charts) |
 | Small | Raspberry Pi 5 + Hailo-8L NPU | 55.58% | -1.76 | 39.75% | 29.34 | 39.87 | 32.1 | [v-4d7](https://edgefirst.studio/public/validation/v-4d7/details?mode=charts) |
 | Small | NVIDIA Jetson Orin Nano (TensorRT FP16) | 57.34% | +0.00 | 41.25% | 13.33 | 22.72 | 124.6 | [v-4d6](https://edgefirst.studio/public/validation/v-4d6/details?mode=charts) |
-| Medium | ONNX FP32 (CUDA) | 63.29% | ref | 46.99% | 33.90 | 40.43 | 96.0 | [v-6f6](https://edgefirst.studio/public/validation/v-6f6/details?mode=charts) |
-| Medium | NVIDIA Jetson Orin Nano (TensorRT FP16) | 63.31% | +0.02 | 46.96% | 44.25 | 56.88 | 88.5 | [v-6de](https://edgefirst.studio/public/validation/v-6de/details?mode=charts) |
+| Medium | ONNX FP32 (CPU) | 63.30% | ref | 47.00% | 154.67 | 163.39 | 24.0 | [v-717](https://edgefirst.studio/public/validation/v-717/details?mode=charts) |
+| Medium | ONNX FP32 (CUDA) | 63.29% | -0.01 | 46.99% | 33.90 | 40.43 | 96.0 | [v-6f6](https://edgefirst.studio/public/validation/v-6f6/details?mode=charts) |
+| Medium | ONNX FP16 (CUDA) | 63.27% | -0.03 | 46.93% | 20.12 | 26.31 | 167.0 | [v-6f7](https://edgefirst.studio/public/validation/v-6f7/details?mode=charts) |
+| Medium | NVIDIA Jetson Orin Nano (TensorRT FP16) | 63.31% | +0.01 | 46.96% | 44.25 | 56.88 | 88.5 | [v-6de](https://edgefirst.studio/public/validation/v-6de/details?mode=charts) |