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yolo11-det

Object Detection
LiteRT
ONNX
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npu
int8
yolo
nxp
hailo
jetson
embedded
Eval Results (legacy)
Model card Files
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sebastientaylor commited on
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Update model card for yolo11-det

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  1. README.md +58 -46
README.md CHANGED
@@ -25,13 +25,13 @@ model-index:
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  metrics:
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  - name: "mAP@0.5 (Nano ONNX FP32)"
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  type: map_50
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- value: 53.1
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  - name: "mAP@0.5-0.95 (Nano ONNX FP32)"
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  type: map
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  value: 37.84
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  - name: "mAP@0.5 (Small ONNX FP32)"
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  type: map_50
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- value: 61.19
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  - name: "mAP@0.5-0.95 (Small ONNX FP32)"
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  type: map
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  value: 44.88
@@ -64,9 +64,9 @@ Accuracy ceiling for each size, measured against COCO `val2017` (5,000 images) w
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  | Size | Params | GFLOPs | mAP@0.5 | mAP@0.5-0.95 | mAP@0.75 | Source |
66
  |------|--------|--------|---------|--------------|----------|--------|
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- | Nano | 2.6M | 6.5 | 53.10% | 37.84% | 41.12% | [v-a89](https://edgefirst.studio/public/validation/v-a89/details?mode=charts) |
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- | Small | 9.4M | 21.5 | 61.19% | 44.88% | 48.44% | [v-a90](https://edgefirst.studio/public/validation/v-a90/details?mode=charts) |
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- | Medium | 20.1M | 68.0 | 65.91% | 49.59% | 53.78% | [v-a97](https://edgefirst.studio/public/validation/v-a97/details?mode=charts) |
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  | Large | 25.3M | 87.6 | β€” | β€” | β€” | β€” |
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  | XLarge | 56.9M | 195.0 | β€” | β€” | β€” | β€” |
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@@ -106,55 +106,67 @@ Row conventions in the table below:
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  | Size | Platform | mAP@0.5 | Ξ” vs FP32 (pp) | mAP@0.5-0.95 | Inference (ms) | End-to-end (ms) | Realized FPS | Core-throughput ceiling (FPS) | Source |
108
  |------|----------|---------|----------------|--------------|----------------|-----------------|--------------|-------------------------------|--------|
109
- | Nano | ONNX FP32 (Intel Core i9-13900F Β· 32-core) | 53.11% | +0.01 | 37.84% | 35.44 | 46.89 | 77.1 | ~77 | [v-a47](https://edgefirst.studio/public/validation/v-a47/details?mode=charts) |
110
- | Nano | ONNX FP32 (CUDA) | 53.10% | ref | 37.84% | 8.14 | 13.40 | 343.8 | ~344 | [v-a89](https://edgefirst.studio/public/validation/v-a89/details?mode=charts) |
111
- | Nano | ONNX FP16 (CUDA) | 53.07% | -0.03 | 37.79% | 6.30 | 11.75 | 443.2 | ~443 | [v-a9e](https://edgefirst.studio/public/validation/v-a9e/details?mode=charts) |
112
- | Nano | ONNX FP32 | 7.98% | -45.12 ⚠ | 4.83% | 36.93 | 43.89 | 26.8 | ~27 | [v-ce0](https://edgefirst.studio/public/validation/v-ce0/details?mode=charts) |
113
- | Nano | Apple M2 Max β€” CoreML Neural Engine (FP16) | 52.61% | -0.49 | 37.49% | 1.84 | 5.29 | 782.8 | ~722 | [v-9c6](https://edgefirst.studio/public/validation/v-9c6/details?mode=charts) |
114
- | Nano | Apple M2 Max β€” CoreML Metal GPU (FP16) | 52.59% | -0.51 | 37.48% | 6.46 | 10.76 | 399.3 | ~399 | [v-9c4](https://edgefirst.studio/public/validation/v-9c4/details?mode=charts) |
115
- | Nano | Apple M2 Max β€” CoreML CPU (FP16) | 52.60% | -0.50 | 37.46% | 14.78 | 18.90 | 125.9 | ~126 | [v-9c5](https://edgefirst.studio/public/validation/v-9c5/details?mode=charts) |
116
- | Nano | Apple iPhone 17 Pro β€” CoreML Neural Engine (FP16) | 52.97% | -0.13 | 37.76% | 2.39 | 7.22 | 711.1 | ~815 | [v-bf7](https://edgefirst.studio/public/validation/v-bf7/details?mode=charts) |
117
- | Nano | Apple iPhone 17 Pro β€” CoreML Metal GPU (FP16) | 53.04% | -0.06 | 37.77% | 4.59 | 9.72 | 393.8 | ~432 | [v-bf5](https://edgefirst.studio/public/validation/v-bf5/details?mode=charts) |
118
- | Nano | Apple iPhone 17 Pro β€” CoreML CPU (FP16) | 53.03% | -0.07 | 37.78% | 16.29 | 20.71 | 120.0 | ~123 | [v-bf3](https://edgefirst.studio/public/validation/v-bf3/details?mode=charts) |
119
- | Nano | Apple iPhone 15 Pro β€” CoreML Neural Engine (FP16) | 52.96% | -0.14 | 37.78% | 2.45 | 11.06 | 547.4 | ~692 | [v-bfb](https://edgefirst.studio/public/validation/v-bfb/details?mode=charts) |
120
- | Nano | Apple iPhone 15 Pro β€” CoreML Metal GPU (FP16) | 53.04% | -0.06 | 37.77% | 13.73 | 18.47 | 139.9 | ~145 | [v-bfd](https://edgefirst.studio/public/validation/v-bfd/details?mode=charts) |
121
- | Nano | Apple iPhone 15 Pro β€” CoreML CPU (FP16) | 53.03% | -0.07 | 37.77% | 20.33 | 26.17 | 95.5 | ~98 | [v-bf9](https://edgefirst.studio/public/validation/v-bf9/details?mode=charts) |
122
- | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | 49.80% | -3.30 | 35.05% | 87.42 | 140.19 | 10.2 | ~11 | [v-8a7](https://edgefirst.studio/public/validation/v-8a7/details?mode=charts) |
123
- | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) β€” latency | 47.25% | -5.85 | 31.54% | 94.17 | 136.71 | 9.5 | ~11 | [v-c7d](https://edgefirst.studio/public/validation/v-c7d/details?mode=charts) |
124
- | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) β€” throughput | 49.80% | -3.30 | 35.05% | 88.25 | 160.58 | 10.1 | ~11 | [v-c7c](https://edgefirst.studio/public/validation/v-c7c/details?mode=charts) |
 
 
 
 
125
  | Nano | NXP i.MX 95 + eIQ Neutron NPU (FRDM) β€” latency | In progress | β€” | β€” | β€” | β€” | β€” | β€” | [v-8a4](https://edgefirst.studio/public/validation/v-8a4/details?mode=charts) |
126
  | Nano | NXP i.MX 95 + eIQ Neutron NPU (FRDM) β€” throughput | In progress | β€” | β€” | β€” | β€” | β€” | β€” | [v-8a5](https://edgefirst.studio/public/validation/v-8a5/details?mode=charts) |
127
  | Nano | NXP i.MX 95 + eIQ Neutron NPU (Phytec) β€” throughput | In progress | β€” | β€” | β€” | β€” | β€” | β€” | [v-887](https://edgefirst.studio/public/validation/v-887/details?mode=charts) |
128
  | Nano | NXP i.MX 95 + eIQ Neutron NPU (Verdin) β€” latency | In progress | β€” | β€” | β€” | β€” | β€” | β€” | [v-bcb](https://edgefirst.studio/public/validation/v-bcb/details?mode=charts) |
129
  | Nano | NXP i.MX 95 + eIQ Neutron NPU (Verdin) β€” throughput | In progress | β€” | β€” | β€” | β€” | β€” | β€” | [v-e0b](https://edgefirst.studio/public/validation/v-e0b/details?mode=charts) |
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- | Nano | NXP Ara240 (FRDM) β€” latency | 50.05% | -3.05 | 34.47% | 6.36 | 18.58 | 127.9 | ~132 | [v-a14](https://edgefirst.studio/public/validation/v-a14/details?mode=charts) |
131
- | Nano | NXP Ara240 (FRDM) β€” throughput | 50.03% | -3.07 | 34.45% | 6.68 | 27.53 | 196.0 | ~308 | [v-a15](https://edgefirst.studio/public/validation/v-a15/details?mode=charts) |
132
- | Nano | Raspberry Pi 5 + Hailo-8L NPU | 51.77% | -1.33 | 36.73% | 17.86 | 36.29 | 52.6 | ~53 | [v-8dc](https://edgefirst.studio/public/validation/v-8dc/details?mode=charts) |
133
- | Nano | NVIDIA Jetson Orin Nano (TensorRT FP16) | 53.05% | -0.05 | 37.80% | 11.46 | 26.68 | 244.9 | ~244 | [v-917](https://edgefirst.studio/public/validation/v-917/details?mode=charts) |
134
- | Small | ONNX FP32 (Intel Core i9-13900F Β· 32-core) | 61.18% | -0.01 | 44.88% | 82.01 | 94.00 | 33.7 | ~34 | [v-a4e](https://edgefirst.studio/public/validation/v-a4e/details?mode=charts) |
135
- | Small | ONNX FP32 (CUDA) | 61.19% | ref | 44.88% | 15.11 | 20.74 | 206.8 | ~207 | [v-a90](https://edgefirst.studio/public/validation/v-a90/details?mode=charts) |
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- | Small | ONNX FP16 (CUDA) | 61.16% | -0.03 | 44.84% | 10.24 | 15.82 | 305.7 | ~306 | [v-aa5](https://edgefirst.studio/public/validation/v-aa5/details?mode=charts) |
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- | Small | ONNX FP32 | 61.19% | +0.00 | 44.88% | 65.25 | 74.93 | 14.8 | ~15 | [v-ce6](https://edgefirst.studio/public/validation/v-ce6/details?mode=charts) |
138
- | Small | Apple M2 Max β€” CoreML Neural Engine (FP16) | 60.35% | -0.84 | 44.20% | 5.01 | 8.45 | 342.4 | ~344 | [v-9d0](https://edgefirst.studio/public/validation/v-9d0/details?mode=charts) |
139
- | Small | Apple M2 Max β€” CoreML Metal GPU (FP16) | 60.36% | -0.83 | 44.18% | 14.68 | 18.53 | 192.9 | ~193 | [v-9d1](https://edgefirst.studio/public/validation/v-9d1/details?mode=charts) |
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- | Small | Apple M2 Max β€” CoreML CPU (FP16) | 60.32% | -0.87 | 44.15% | 30.78 | 35.00 | 62.7 | ~63 | [v-9f5](https://edgefirst.studio/public/validation/v-9f5/details?mode=charts) |
141
- | Small | Apple iPhone 17 Pro β€” CoreML Neural Engine (FP16) | 60.35% | -0.84 | 44.20% | 5.79 | 9.14 | 309.2 | ~312 | [v-c44](https://edgefirst.studio/public/validation/v-c44/details?mode=charts) |
142
- | Small | Apple iPhone 17 Pro β€” CoreML Metal GPU (FP16) | 60.37% | -0.82 | 44.19% | 11.89 | 15.21 | 160.4 | ~162 | [v-c58](https://edgefirst.studio/public/validation/v-c58/details?mode=charts) |
143
- | Small | Apple iPhone 17 Pro β€” CoreML CPU (FP16) | 60.35% | -0.84 | 44.16% | 34.94 | 41.36 | 55.7 | ~56 | [v-c4e](https://edgefirst.studio/public/validation/v-c4e/details?mode=charts) |
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- | Small | Apple iPhone 15 Pro β€” CoreML Neural Engine (FP16) | 60.34% | -0.85 | 44.20% | 6.45 | 11.08 | 267.7 | ~271 | [v-c0f](https://edgefirst.studio/public/validation/v-c0f/details?mode=charts) |
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- | Small | Apple iPhone 15 Pro β€” CoreML Metal GPU (FP16) | 60.36% | -0.83 | 44.19% | 36.27 | 44.73 | 52.6 | ~53 | [v-c23](https://edgefirst.studio/public/validation/v-c23/details?mode=charts) |
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- | Small | Apple iPhone 15 Pro β€” CoreML CPU (FP16) | 60.33% | -0.86 | 44.16% | 43.51 | 51.96 | 44.6 | ~45 | [v-c19](https://edgefirst.studio/public/validation/v-c19/details?mode=charts) |
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- | Small | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | 3.48% | -57.71 ⚠ | 2.34% | 168.47 | 220.09 | 5.6 | ~6 | [v-9bc](https://edgefirst.studio/public/validation/v-9bc/details?mode=charts) |
148
- | Small | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) β€” latency | 2.99% | -58.20 ⚠ | 1.78% | 175.63 | 217.26 | 5.4 | ~6 | [v-c8f](https://edgefirst.studio/public/validation/v-c8f/details?mode=charts) |
149
- | Small | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) β€” throughput | 3.48% | -57.71 ⚠ | 2.34% | 169.80 | 239.80 | 5.5 | ~6 | [v-c8e](https://edgefirst.studio/public/validation/v-c8e/details?mode=charts) |
150
- | Small | NXP Ara240 (FRDM) β€” latency | 57.16% | -4.03 | 40.13% | 10.17 | 22.06 | 87.2 | ~88 | [v-a22](https://edgefirst.studio/public/validation/v-a22/details?mode=charts) |
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- | Small | NXP Ara240 (FRDM) β€” throughput | 57.15% | -4.04 | 40.11% | 10.31 | 24.04 | 141.3 | ~141 | [v-a23](https://edgefirst.studio/public/validation/v-a23/details?mode=charts) |
152
- | Small | Raspberry Pi 5 + Hailo-8L NPU | 59.45% | -1.74 | 43.25% | 36.19 | 51.00 | 25.4 | ~25 | [v-8e7](https://edgefirst.studio/public/validation/v-8e7/details?mode=charts) |
153
- | Small | NVIDIA Jetson Orin Nano (TensorRT FP16) | 61.13% | -0.06 | 44.85% | 24.64 | 36.52 | 156.9 | ~157 | [v-91f](https://edgefirst.studio/public/validation/v-91f/details?mode=charts) |
 
 
 
 
154
  | Medium | ONNX FP32 (Intel Core i9-13900F Β· 32-core) | 65.91% | +0.00 | 49.59% | 236.85 | 248.77 | 12.0 | ~12 | [v-a55](https://edgefirst.studio/public/validation/v-a55/details?mode=charts) |
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- | Medium | ONNX FP32 (CUDA) | 65.91% | ref | 49.59% | 39.85 | 45.39 | 87.8 | ~88 | [v-a97](https://edgefirst.studio/public/validation/v-a97/details?mode=charts) |
 
 
 
156
  | Medium | ONNX FP16 (CUDA) | 65.89% | -0.02 | 49.55% | 22.18 | 28.22 | 153.1 | ~153 | [v-aac](https://edgefirst.studio/public/validation/v-aac/details?mode=charts) |
157
  | Medium | ONNX FP32 | 65.91% | +0.00 | 49.59% | 199.07 | 210.35 | 5.0 | ~5 | [v-cee](https://edgefirst.studio/public/validation/v-cee/details?mode=charts) |
 
158
  | Medium | Apple M2 Max β€” CoreML Neural Engine (FP16) | 64.73% | -1.18 | 48.53% | 18.01 | 22.10 | 105.6 | ~106 | [v-9d9](https://edgefirst.studio/public/validation/v-9d9/details?mode=charts) |
159
  | Medium | Apple M2 Max β€” CoreML Metal GPU (FP16) | 64.75% | -1.16 | 48.59% | 49.69 | 54.31 | 58.8 | ~59 | [v-9da](https://edgefirst.studio/public/validation/v-9da/details?mode=charts) |
160
  | Medium | Apple M2 Max β€” CoreML CPU (FP16) | 64.71% | -1.20 | 48.55% | 65.01 | 69.09 | 30.2 | ~30 | [v-9db](https://edgefirst.studio/public/validation/v-9db/details?mode=charts) |
 
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  metrics:
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  - name: "mAP@0.5 (Nano ONNX FP32)"
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  type: map_50
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+ value: 53.11
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  - name: "mAP@0.5-0.95 (Nano ONNX FP32)"
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  type: map
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  value: 37.84
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  - name: "mAP@0.5 (Small ONNX FP32)"
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  type: map_50
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+ value: 61.18
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  - name: "mAP@0.5-0.95 (Small ONNX FP32)"
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  type: map
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  value: 44.88
 
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  | Size | Params | GFLOPs | mAP@0.5 | mAP@0.5-0.95 | mAP@0.75 | Source |
66
  |------|--------|--------|---------|--------------|----------|--------|
67
+ | Nano | 2.6M | 6.5 | 53.11% | 37.84% | 41.10% | [v-e93](https://edgefirst.studio/public/validation/v-e93/details?mode=charts) |
68
+ | Small | 9.4M | 21.5 | 61.18% | 44.88% | 48.43% | [v-e86](https://edgefirst.studio/public/validation/v-e86/details?mode=charts) |
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+ | Medium | 20.1M | 68.0 | 65.91% | 49.59% | 53.78% | [v-e95](https://edgefirst.studio/public/validation/v-e95/details?mode=charts) |
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  | Large | 25.3M | 87.6 | β€” | β€” | β€” | β€” |
71
  | XLarge | 56.9M | 195.0 | β€” | β€” | β€” | β€” |
72
 
 
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  | Size | Platform | mAP@0.5 | Ξ” vs FP32 (pp) | mAP@0.5-0.95 | Inference (ms) | End-to-end (ms) | Realized FPS | Core-throughput ceiling (FPS) | Source |
108
  |------|----------|---------|----------------|--------------|----------------|-----------------|--------------|-------------------------------|--------|
109
+ | Nano | ONNX FP32 (Intel Core i9-13900F Β· 32-core) | 53.11% | +0.00 | 37.84% | 35.44 | 46.89 | 77.1 | ~77 | [v-a47](https://edgefirst.studio/public/validation/v-a47/details?mode=charts) |
110
+ | Nano | ONNX FP32 (Intel Xeon Platinum 8488C Β· 24-core) | 53.11% | +0.00 | 37.84% | 66.37 | 86.84 | 163.3 | ~15 | [v-e9d](https://edgefirst.studio/public/validation/v-e9d/details?mode=charts) |
111
+ | Nano | ONNX FP32 (Intel Xeon Platinum 8488C Β· 4-core) | 53.11% | +0.00 | 37.84% | 125.58 | 136.19 | 59.8 | ~8 | [v-e96](https://edgefirst.studio/public/validation/v-e96/details?mode=charts) |
112
+ | Nano | ONNX FP32 (CUDA) | 53.11% | ref | 37.84% | 10.41 | 19.68 | 303.3 | ~303 | [v-e93](https://edgefirst.studio/public/validation/v-e93/details?mode=charts) |
113
+ | Nano | ONNX FP32 (CUDA) | 53.10% | -0.01 | 37.84% | 8.14 | 13.40 | 343.8 | ~344 | [v-a89](https://edgefirst.studio/public/validation/v-a89/details?mode=charts) |
114
+ | Nano | ONNX FP16 (CUDA) | 53.07% | -0.04 | 37.79% | 6.30 | 11.75 | 443.2 | ~443 | [v-a9e](https://edgefirst.studio/public/validation/v-a9e/details?mode=charts) |
115
+ | Nano | ONNX FP32 | 53.11% | +0.00 | 37.84% | 35.45 | 53.07 | 101.2 | ~29 | [v-cdf](https://edgefirst.studio/public/validation/v-cdf/details?mode=charts) |
116
+ | Nano | ONNX FP32 | 53.10% | -0.01 | 37.84% | 31.95 | 40.27 | 29.2 | ~32 | [v-cdc](https://edgefirst.studio/public/validation/v-cdc/details?mode=charts) |
117
+ | Nano | Apple M2 Max β€” CoreML Neural Engine (FP16) | 52.61% | -0.50 | 37.49% | 1.84 | 5.29 | 782.8 | ~722 | [v-9c6](https://edgefirst.studio/public/validation/v-9c6/details?mode=charts) |
118
+ | Nano | Apple M2 Max β€” CoreML Metal GPU (FP16) | 52.59% | -0.52 | 37.48% | 6.46 | 10.76 | 399.3 | ~399 | [v-9c4](https://edgefirst.studio/public/validation/v-9c4/details?mode=charts) |
119
+ | Nano | Apple M2 Max β€” CoreML CPU (FP16) | 52.60% | -0.51 | 37.46% | 14.78 | 18.90 | 125.9 | ~126 | [v-9c5](https://edgefirst.studio/public/validation/v-9c5/details?mode=charts) |
120
+ | Nano | Apple iPhone 17 Pro β€” CoreML Neural Engine (FP16) | 52.97% | -0.14 | 37.76% | 2.39 | 7.22 | 711.1 | ~815 | [v-bf7](https://edgefirst.studio/public/validation/v-bf7/details?mode=charts) |
121
+ | Nano | Apple iPhone 17 Pro β€” CoreML Metal GPU (FP16) | 53.04% | -0.07 | 37.77% | 4.59 | 9.72 | 393.8 | ~432 | [v-bf5](https://edgefirst.studio/public/validation/v-bf5/details?mode=charts) |
122
+ | Nano | Apple iPhone 17 Pro β€” CoreML CPU (FP16) | 53.03% | -0.08 | 37.78% | 16.29 | 20.71 | 120.0 | ~123 | [v-bf3](https://edgefirst.studio/public/validation/v-bf3/details?mode=charts) |
123
+ | Nano | Apple iPhone 15 Pro β€” CoreML Neural Engine (FP16) | 52.96% | -0.15 | 37.78% | 2.45 | 11.06 | 547.4 | ~692 | [v-bfb](https://edgefirst.studio/public/validation/v-bfb/details?mode=charts) |
124
+ | Nano | Apple iPhone 15 Pro β€” CoreML Metal GPU (FP16) | 53.04% | -0.07 | 37.77% | 13.73 | 18.47 | 139.9 | ~145 | [v-bfd](https://edgefirst.studio/public/validation/v-bfd/details?mode=charts) |
125
+ | Nano | Apple iPhone 15 Pro β€” CoreML CPU (FP16) | 53.03% | -0.08 | 37.77% | 20.33 | 26.17 | 95.5 | ~98 | [v-bf9](https://edgefirst.studio/public/validation/v-bf9/details?mode=charts) |
126
+ | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | 49.80% | -3.31 | 35.05% | 87.42 | 140.19 | 10.2 | ~11 | [v-8a7](https://edgefirst.studio/public/validation/v-8a7/details?mode=charts) |
127
+ | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) β€” latency | 47.25% | -5.86 | 31.54% | 94.17 | 136.71 | 9.5 | ~11 | [v-c7d](https://edgefirst.studio/public/validation/v-c7d/details?mode=charts) |
128
+ | Nano | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) β€” throughput | 49.80% | -3.31 | 35.05% | 88.25 | 160.58 | 10.1 | ~11 | [v-c7c](https://edgefirst.studio/public/validation/v-c7c/details?mode=charts) |
129
  | Nano | NXP i.MX 95 + eIQ Neutron NPU (FRDM) β€” latency | In progress | β€” | β€” | β€” | β€” | β€” | β€” | [v-8a4](https://edgefirst.studio/public/validation/v-8a4/details?mode=charts) |
130
  | Nano | NXP i.MX 95 + eIQ Neutron NPU (FRDM) β€” throughput | In progress | β€” | β€” | β€” | β€” | β€” | β€” | [v-8a5](https://edgefirst.studio/public/validation/v-8a5/details?mode=charts) |
131
  | Nano | NXP i.MX 95 + eIQ Neutron NPU (Phytec) β€” throughput | In progress | β€” | β€” | β€” | β€” | β€” | β€” | [v-887](https://edgefirst.studio/public/validation/v-887/details?mode=charts) |
132
  | Nano | NXP i.MX 95 + eIQ Neutron NPU (Verdin) β€” latency | In progress | β€” | β€” | β€” | β€” | β€” | β€” | [v-bcb](https://edgefirst.studio/public/validation/v-bcb/details?mode=charts) |
133
  | Nano | NXP i.MX 95 + eIQ Neutron NPU (Verdin) β€” throughput | In progress | β€” | β€” | β€” | β€” | β€” | β€” | [v-e0b](https://edgefirst.studio/public/validation/v-e0b/details?mode=charts) |
134
+ | Nano | NXP Ara240 (FRDM) β€” latency | 50.05% | -3.06 | 34.47% | 6.36 | 18.58 | 127.9 | ~132 | [v-a14](https://edgefirst.studio/public/validation/v-a14/details?mode=charts) |
135
+ | Nano | NXP Ara240 (FRDM) β€” throughput | 50.03% | -3.08 | 34.45% | 6.68 | 27.53 | 196.0 | ~308 | [v-a15](https://edgefirst.studio/public/validation/v-a15/details?mode=charts) |
136
+ | Nano | Raspberry Pi 5 + Hailo-8L NPU | 51.77% | -1.34 | 36.73% | 17.86 | 36.29 | 52.6 | ~53 | [v-8dc](https://edgefirst.studio/public/validation/v-8dc/details?mode=charts) |
137
+ | Nano | NVIDIA Jetson Orin Nano (TensorRT FP16) | 53.05% | -0.06 | 37.80% | 11.46 | 26.68 | 244.9 | ~244 | [v-917](https://edgefirst.studio/public/validation/v-917/details?mode=charts) |
138
+ | Small | ONNX FP32 (Intel Core i9-13900F Β· 32-core) | 61.18% | +0.00 | 44.88% | 82.01 | 94.00 | 33.7 | ~34 | [v-a4e](https://edgefirst.studio/public/validation/v-a4e/details?mode=charts) |
139
+ | Small | ONNX FP32 (Intel Xeon Platinum 8488C Β· 24-core) | 61.18% | +0.00 | 44.88% | 157.46 | 178.22 | 77.9 | ~6 | [v-ea1](https://edgefirst.studio/public/validation/v-ea1/details?mode=charts) |
140
+ | Small | ONNX FP32 (Intel Xeon Platinum 8488C Β· 4-core) | 61.18% | +0.00 | 44.88% | 312.90 | 322.25 | 25.1 | ~3 | [v-ea0](https://edgefirst.studio/public/validation/v-ea0/details?mode=charts) |
141
+ | Small | ONNX FP32 (CUDA) | 61.18% | ref | 44.88% | 11.64 | 20.68 | 288.7 | ~289 | [v-e86](https://edgefirst.studio/public/validation/v-e86/details?mode=charts) |
142
+ | Small | ONNX FP32 (CUDA) | 61.19% | +0.01 | 44.88% | 15.11 | 20.74 | 206.8 | ~207 | [v-a90](https://edgefirst.studio/public/validation/v-a90/details?mode=charts) |
143
+ | Small | ONNX FP16 (CUDA) | 61.16% | -0.02 | 44.84% | 10.24 | 15.82 | 305.7 | ~306 | [v-aa5](https://edgefirst.studio/public/validation/v-aa5/details?mode=charts) |
144
+ | Small | ONNX FP32 | 61.19% | +0.01 | 44.88% | 65.25 | 74.93 | 14.8 | ~15 | [v-ce6](https://edgefirst.studio/public/validation/v-ce6/details?mode=charts) |
145
+ | Small | ONNX FP32 | 61.18% | +0.00 | 44.88% | 63.58 | 81.94 | 59.4 | ~16 | [v-ce2](https://edgefirst.studio/public/validation/v-ce2/details?mode=charts) |
146
+ | Small | Apple M2 Max β€” CoreML Neural Engine (FP16) | 60.35% | -0.83 | 44.20% | 5.01 | 8.45 | 342.4 | ~344 | [v-9d0](https://edgefirst.studio/public/validation/v-9d0/details?mode=charts) |
147
+ | Small | Apple M2 Max β€” CoreML Metal GPU (FP16) | 60.36% | -0.82 | 44.18% | 14.68 | 18.53 | 192.9 | ~193 | [v-9d1](https://edgefirst.studio/public/validation/v-9d1/details?mode=charts) |
148
+ | Small | Apple M2 Max β€” CoreML CPU (FP16) | 60.32% | -0.86 | 44.15% | 30.78 | 35.00 | 62.7 | ~63 | [v-9f5](https://edgefirst.studio/public/validation/v-9f5/details?mode=charts) |
149
+ | Small | Apple iPhone 17 Pro β€” CoreML Neural Engine (FP16) | 60.35% | -0.83 | 44.20% | 5.79 | 9.14 | 309.2 | ~312 | [v-c44](https://edgefirst.studio/public/validation/v-c44/details?mode=charts) |
150
+ | Small | Apple iPhone 17 Pro β€” CoreML Metal GPU (FP16) | 60.37% | -0.81 | 44.19% | 11.89 | 15.21 | 160.4 | ~162 | [v-c58](https://edgefirst.studio/public/validation/v-c58/details?mode=charts) |
151
+ | Small | Apple iPhone 17 Pro β€” CoreML CPU (FP16) | 60.35% | -0.83 | 44.16% | 34.94 | 41.36 | 55.7 | ~56 | [v-c4e](https://edgefirst.studio/public/validation/v-c4e/details?mode=charts) |
152
+ | Small | Apple iPhone 15 Pro β€” CoreML Neural Engine (FP16) | 60.34% | -0.84 | 44.20% | 6.45 | 11.08 | 267.7 | ~271 | [v-c0f](https://edgefirst.studio/public/validation/v-c0f/details?mode=charts) |
153
+ | Small | Apple iPhone 15 Pro β€” CoreML Metal GPU (FP16) | 60.36% | -0.82 | 44.19% | 36.27 | 44.73 | 52.6 | ~53 | [v-c23](https://edgefirst.studio/public/validation/v-c23/details?mode=charts) |
154
+ | Small | Apple iPhone 15 Pro β€” CoreML CPU (FP16) | 60.33% | -0.85 | 44.16% | 43.51 | 51.96 | 44.6 | ~45 | [v-c19](https://edgefirst.studio/public/validation/v-c19/details?mode=charts) |
155
+ | Small | NXP i.MX 8M Plus + VeriSilicon NPU (FRDM) | 3.48% | -57.70 ⚠ | 2.34% | 168.47 | 220.09 | 5.6 | ~6 | [v-9bc](https://edgefirst.studio/public/validation/v-9bc/details?mode=charts) |
156
+ | Small | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) β€” latency | 2.99% | -58.19 ⚠ | 1.78% | 175.63 | 217.26 | 5.4 | ~6 | [v-c8f](https://edgefirst.studio/public/validation/v-c8f/details?mode=charts) |
157
+ | Small | NXP i.MX 8M Plus + VeriSilicon NPU (Verdin) β€” throughput | 3.48% | -57.70 ⚠ | 2.34% | 169.80 | 239.80 | 5.5 | ~6 | [v-c8e](https://edgefirst.studio/public/validation/v-c8e/details?mode=charts) |
158
+ | Small | NXP Ara240 (FRDM) β€” latency | 57.16% | -4.02 | 40.13% | 10.17 | 22.06 | 87.2 | ~88 | [v-a22](https://edgefirst.studio/public/validation/v-a22/details?mode=charts) |
159
+ | Small | NXP Ara240 (FRDM) β€” throughput | 57.15% | -4.03 | 40.11% | 10.31 | 24.04 | 141.3 | ~141 | [v-a23](https://edgefirst.studio/public/validation/v-a23/details?mode=charts) |
160
+ | Small | Raspberry Pi 5 + Hailo-8L NPU | 59.45% | -1.73 | 43.25% | 36.19 | 51.00 | 25.4 | ~25 | [v-8e7](https://edgefirst.studio/public/validation/v-8e7/details?mode=charts) |
161
+ | Small | NVIDIA Jetson Orin Nano (TensorRT FP16) | 61.13% | -0.05 | 44.85% | 24.64 | 36.52 | 156.9 | ~157 | [v-91f](https://edgefirst.studio/public/validation/v-91f/details?mode=charts) |
162
  | Medium | ONNX FP32 (Intel Core i9-13900F Β· 32-core) | 65.91% | +0.00 | 49.59% | 236.85 | 248.77 | 12.0 | ~12 | [v-a55](https://edgefirst.studio/public/validation/v-a55/details?mode=charts) |
163
+ | Medium | ONNX FP32 (Intel Xeon Platinum 8488C Β· 24-core) | 65.91% | +0.00 | 49.58% | 428.22 | 448.91 | 29.1 | ~2 | [v-ea9](https://edgefirst.studio/public/validation/v-ea9/details?mode=charts) |
164
+ | Medium | ONNX FP32 (Intel Xeon Platinum 8488C Β· 4-core) | 65.91% | +0.00 | 49.59% | 940.39 | 949.79 | 8.5 | ~1 | [v-ea3](https://edgefirst.studio/public/validation/v-ea3/details?mode=charts) |
165
+ | Medium | ONNX FP32 (CUDA) | 65.91% | ref | 49.59% | 25.88 | 34.02 | 144.3 | ~144 | [v-e95](https://edgefirst.studio/public/validation/v-e95/details?mode=charts) |
166
+ | Medium | ONNX FP32 (CUDA) | 65.91% | +0.00 | 49.59% | 39.85 | 45.39 | 87.8 | ~88 | [v-a97](https://edgefirst.studio/public/validation/v-a97/details?mode=charts) |
167
  | Medium | ONNX FP16 (CUDA) | 65.89% | -0.02 | 49.55% | 22.18 | 28.22 | 153.1 | ~153 | [v-aac](https://edgefirst.studio/public/validation/v-aac/details?mode=charts) |
168
  | Medium | ONNX FP32 | 65.91% | +0.00 | 49.59% | 199.07 | 210.35 | 5.0 | ~5 | [v-cee](https://edgefirst.studio/public/validation/v-cee/details?mode=charts) |
169
+ | Medium | ONNX FP32 | 65.91% | +0.00 | 49.58% | 154.02 | 172.59 | 25.3 | ~7 | [v-ced](https://edgefirst.studio/public/validation/v-ced/details?mode=charts) |
170
  | Medium | Apple M2 Max β€” CoreML Neural Engine (FP16) | 64.73% | -1.18 | 48.53% | 18.01 | 22.10 | 105.6 | ~106 | [v-9d9](https://edgefirst.studio/public/validation/v-9d9/details?mode=charts) |
171
  | Medium | Apple M2 Max β€” CoreML Metal GPU (FP16) | 64.75% | -1.16 | 48.59% | 49.69 | 54.31 | 58.8 | ~59 | [v-9da](https://edgefirst.studio/public/validation/v-9da/details?mode=charts) |
172
  | Medium | Apple M2 Max β€” CoreML CPU (FP16) | 64.71% | -1.20 | 48.55% | 65.01 | 69.09 | 30.2 | ~30 | [v-9db](https://edgefirst.studio/public/validation/v-9db/details?mode=charts) |