Add MNN benchmarks to Raspberry Pi doc (#17910)
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@ -142,9 +142,10 @@ YOLO11 benchmarks were run by the Ultralytics team on nine different model forma
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We have only included benchmarks for YOLO11n and YOLO11s models because other models sizes are too big to run on the Raspberry Pis and does not offer decent performance.
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<div style="text-align: center;">
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<img width="800" src="https://github.com/ultralytics/docs/releases/download/0/rpi-yolo11-benchmarks.avif" alt="YOLO11 benchmarks on RPi 5">
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</div>
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<figure style="text-align: center;">
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<img width="800" src="https://github.com/ultralytics/assets/releases/download/v0.0.0/rpi-yolo11-benchmarks.avif" alt="YOLO11 benchmarks on RPi 5">
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<figcaption style="font-style: italic; color: gray;">Benchmarked with Ultralytics v8.3.39</figcaption>
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</figure>
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### Detailed Comparison Table
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@ -156,29 +157,33 @@ The below table represents the benchmark results for two different models (YOLO1
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| Format | Status | Size on disk (MB) | mAP50-95(B) | Inference time (ms/im) |
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|---------------|--------|-------------------|-------------|------------------------|
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| PyTorch | ✅ | 5.4 | 0.61 | 524.828 |
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| TorchScript | ✅ | 10.5 | 0.6082 | 666.874 |
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| ONNX | ✅ | 10.2 | 0.6082 | 181.818 |
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| OpenVINO | ✅ | 10.4 | 0.6082 | 530.224 |
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| TF SavedModel | ✅ | 25.8 | 0.6082 | 405.964 |
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| TF GraphDef | ✅ | 10.3 | 0.6082 | 473.558 |
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| TF Lite | ✅ | 10.3 | 0.6082 | 324.158 |
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| PaddlePaddle | ✅ | 20.4 | 0.6082 | 644.312 |
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| NCNN | ✅ | 10.2 | 0.6106 | 93.938 |
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| PyTorch | ✅ | 5.4 | 0.6100 | 405.238 |
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| TorchScript | ✅ | 10.5 | 0.6082 | 526.628 |
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| ONNX | ✅ | 10.2 | 0.6082 | 168.082 |
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| OpenVINO | ✅ | 10.4 | 0.6082 | 81.192 |
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| TF SavedModel | ✅ | 25.8 | 0.6082 | 377.968 |
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| TF GraphDef | ✅ | 10.3 | 0.6082 | 487.244 |
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| TF Lite | ✅ | 10.3 | 0.6082 | 317.398 |
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| PaddlePaddle | ✅ | 20.4 | 0.6082 | 561.892 |
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| MNN | ✅ | 10.1 | 0.6106 | 112.554 |
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| NCNN | ✅ | 10.2 | 0.6106 | 88.026 |
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=== "YOLO11s"
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| Format | Status | Size on disk (MB) | mAP50-95(B) | Inference time (ms/im) |
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|---------------|--------|-------------------|-------------|------------------------|
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| PyTorch | ✅ | 18.4 | 0.7526 | 1226.426 |
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| TorchScript | ✅ | 36.5 | 0.7416 | 1507.95 |
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| ONNX | ✅ | 36.3 | 0.7416 | 415.24 |
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| OpenVINO | ✅ | 36.4 | 0.7416 | 1167.102 |
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| TF SavedModel | ✅ | 91.1 | 0.7416 | 776.14 |
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| TF GraphDef | ✅ | 36.4 | 0.7416 | 1014.396 |
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| TF Lite | ✅ | 36.4 | 0.7416 | 845.934 |
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| PaddlePaddle | ✅ | 72.5 | 0.7416 | 1567.824 |
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| NCNN | ✅ | 36.2 | 0.7419 | 197.358 |
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| PyTorch | ✅ | 18.4 | 0.7526 | 1011.60 |
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| TorchScript | ✅ | 36.5 | 0.7416 | 1268.502 |
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| ONNX | ✅ | 36.3 | 0.7416 | 324.17 |
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| OpenVINO | ✅ | 36.4 | 0.7416 | 179.324 |
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| TF SavedModel | ✅ | 91.1 | 0.7416 | 714.382 |
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| TF GraphDef | ✅ | 36.4 | 0.7416 | 1019.83 |
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| TF Lite | ✅ | 36.4 | 0.7416 | 849.86 |
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| PaddlePaddle | ✅ | 72.5 | 0.7416 | 1276.34 |
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| MNN | ✅ | 36.2 | 0.7409 | 273.032 |
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| NCNN | ✅ | 36.2 | 0.7419 | 194.858 |
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Benchmarked with Ultralytics `v8.3.39`
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## Reproduce Our Results
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