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Docs Prettier reformat (#13483)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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docs/en/integrations/openvino.md
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@ -59,7 +59,7 @@ Export a YOLOv8n model to OpenVINO format and run inference with the exported mo
## Arguments
| Key | Value | Description |
|----------|--------------|------------------------------------------------------|
| -------- | ------------ | ---------------------------------------------------- |
| `format` | `'openvino'` | format to export to |
| `imgsz` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) |
| `half` | `False` | FP16 quantization |
@ -118,27 +118,27 @@ Benchmarks below run on Intel® Data Center GPU Flex 170 at FP32 precision.
</div>
| Model | Format | Status | Size (MB) | mAP50-95(B) | Inference time (ms/im) |
|---------|-------------|--------|-----------|-------------|------------------------|
| YOLOv8n | PyTorch | ✅ | 6.2 | 0.3709 | 21.79 |
| YOLOv8n | TorchScript | ✅ | 12.4 | 0.3704 | 23.24 |
| YOLOv8n | ONNX | ✅ | 12.2 | 0.3704 | 37.22 |
| YOLOv8n | OpenVINO | ✅ | 12.3 | 0.3703 | 3.29 |
| YOLOv8s | PyTorch | ✅ | 21.5 | 0.4471 | 31.89 |
| YOLOv8s | TorchScript | ✅ | 42.9 | 0.4472 | 32.71 |
| YOLOv8s | ONNX | ✅ | 42.8 | 0.4472 | 43.42 |
| YOLOv8s | OpenVINO | ✅ | 42.9 | 0.4470 | 3.92 |
| YOLOv8m | PyTorch | ✅ | 49.7 | 0.5013 | 50.75 |
| YOLOv8m | TorchScript | ✅ | 99.2 | 0.4999 | 47.90 |
| YOLOv8m | ONNX | ✅ | 99.0 | 0.4999 | 63.16 |
| YOLOv8m | OpenVINO | ✅ | 49.8 | 0.4997 | 7.11 |
| YOLOv8l | PyTorch | ✅ | 83.7 | 0.5293 | 77.45 |
| YOLOv8l | TorchScript | ✅ | 167.2 | 0.5268 | 85.71 |
| YOLOv8l | ONNX | ✅ | 166.8 | 0.5268 | 88.94 |
| YOLOv8l | OpenVINO | ✅ | 167.0 | 0.5264 | 9.37 |
| YOLOv8x | PyTorch | ✅ | 130.5 | 0.5404 | 100.09 |
| YOLOv8x | TorchScript | ✅ | 260.7 | 0.5371 | 114.64 |
| YOLOv8x | ONNX | ✅ | 260.4 | 0.5371 | 110.32 |
| YOLOv8x | OpenVINO | ✅ | 260.6 | 0.5367 | 15.02 |
| ------- | ----------- | ------ | --------- | ----------- | ---------------------- |
| YOLOv8n | PyTorch | ✅ | 6.2 | 0.3709 | 21.79 |
| YOLOv8n | TorchScript | ✅ | 12.4 | 0.3704 | 23.24 |
| YOLOv8n | ONNX | ✅ | 12.2 | 0.3704 | 37.22 |
| YOLOv8n | OpenVINO | ✅ | 12.3 | 0.3703 | 3.29 |
| YOLOv8s | PyTorch | ✅ | 21.5 | 0.4471 | 31.89 |
| YOLOv8s | TorchScript | ✅ | 42.9 | 0.4472 | 32.71 |
| YOLOv8s | ONNX | ✅ | 42.8 | 0.4472 | 43.42 |
| YOLOv8s | OpenVINO | ✅ | 42.9 | 0.4470 | 3.92 |
| YOLOv8m | PyTorch | ✅ | 49.7 | 0.5013 | 50.75 |
| YOLOv8m | TorchScript | ✅ | 99.2 | 0.4999 | 47.90 |
| YOLOv8m | ONNX | ✅ | 99.0 | 0.4999 | 63.16 |
| YOLOv8m | OpenVINO | ✅ | 49.8 | 0.4997 | 7.11 |
| YOLOv8l | PyTorch | ✅ | 83.7 | 0.5293 | 77.45 |
| YOLOv8l | TorchScript | ✅ | 167.2 | 0.5268 | 85.71 |
| YOLOv8l | ONNX | ✅ | 166.8 | 0.5268 | 88.94 |
| YOLOv8l | OpenVINO | ✅ | 167.0 | 0.5264 | 9.37 |
| YOLOv8x | PyTorch | ✅ | 130.5 | 0.5404 | 100.09 |
| YOLOv8x | TorchScript | ✅ | 260.7 | 0.5371 | 114.64 |
| YOLOv8x | ONNX | ✅ | 260.4 | 0.5371 | 110.32 |
| YOLOv8x | OpenVINO | ✅ | 260.6 | 0.5367 | 15.02 |
This table represents the benchmark results for five different models (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x) across four different formats (PyTorch, TorchScript, ONNX, OpenVINO), giving us the status, size, mAP50-95(B) metric, and inference time for each combination.
@ -157,27 +157,27 @@ Benchmarks below run on Intel® Arc 770 GPU at FP32 precision.
</div>
| Model | Format | Status | Size (MB) | metrics/mAP50-95(B) | Inference time (ms/im) |
|---------|-------------|--------|-----------|---------------------|------------------------|
| YOLOv8n | PyTorch | ✅ | 6.2 | 0.3709 | 88.79 |
| YOLOv8n | TorchScript | ✅ | 12.4 | 0.3704 | 102.66 |
| YOLOv8n | ONNX | ✅ | 12.2 | 0.3704 | 57.98 |
| YOLOv8n | OpenVINO | ✅ | 12.3 | 0.3703 | 8.52 |
| YOLOv8s | PyTorch | ✅ | 21.5 | 0.4471 | 189.83 |
| YOLOv8s | TorchScript | ✅ | 42.9 | 0.4472 | 227.58 |
| YOLOv8s | ONNX | ✅ | 42.7 | 0.4472 | 142.03 |
| YOLOv8s | OpenVINO | ✅ | 42.9 | 0.4469 | 9.19 |
| YOLOv8m | PyTorch | ✅ | 49.7 | 0.5013 | 411.64 |
| YOLOv8m | TorchScript | ✅ | 99.2 | 0.4999 | 517.12 |
| YOLOv8m | ONNX | ✅ | 98.9 | 0.4999 | 298.68 |
| YOLOv8m | OpenVINO | ✅ | 99.1 | 0.4996 | 12.55 |
| YOLOv8l | PyTorch | ✅ | 83.7 | 0.5293 | 725.73 |
| YOLOv8l | TorchScript | ✅ | 167.1 | 0.5268 | 892.83 |
| YOLOv8l | ONNX | ✅ | 166.8 | 0.5268 | 576.11 |
| YOLOv8l | OpenVINO | ✅ | 167.0 | 0.5262 | 17.62 |
| YOLOv8x | PyTorch | ✅ | 130.5 | 0.5404 | 988.92 |
| YOLOv8x | TorchScript | ✅ | 260.7 | 0.5371 | 1186.42 |
| YOLOv8x | ONNX | ✅ | 260.4 | 0.5371 | 768.90 |
| YOLOv8x | OpenVINO | ✅ | 260.6 | 0.5367 | 19 |
| ------- | ----------- | ------ | --------- | ------------------- | ---------------------- |
| YOLOv8n | PyTorch | ✅ | 6.2 | 0.3709 | 88.79 |
| YOLOv8n | TorchScript | ✅ | 12.4 | 0.3704 | 102.66 |
| YOLOv8n | ONNX | ✅ | 12.2 | 0.3704 | 57.98 |
| YOLOv8n | OpenVINO | ✅ | 12.3 | 0.3703 | 8.52 |
| YOLOv8s | PyTorch | ✅ | 21.5 | 0.4471 | 189.83 |
| YOLOv8s | TorchScript | ✅ | 42.9 | 0.4472 | 227.58 |
| YOLOv8s | ONNX | ✅ | 42.7 | 0.4472 | 142.03 |
| YOLOv8s | OpenVINO | ✅ | 42.9 | 0.4469 | 9.19 |
| YOLOv8m | PyTorch | ✅ | 49.7 | 0.5013 | 411.64 |
| YOLOv8m | TorchScript | ✅ | 99.2 | 0.4999 | 517.12 |
| YOLOv8m | ONNX | ✅ | 98.9 | 0.4999 | 298.68 |
| YOLOv8m | OpenVINO | ✅ | 99.1 | 0.4996 | 12.55 |
| YOLOv8l | PyTorch | ✅ | 83.7 | 0.5293 | 725.73 |
| YOLOv8l | TorchScript | ✅ | 167.1 | 0.5268 | 892.83 |
| YOLOv8l | ONNX | ✅ | 166.8 | 0.5268 | 576.11 |
| YOLOv8l | OpenVINO | ✅ | 167.0 | 0.5262 | 17.62 |
| YOLOv8x | PyTorch | ✅ | 130.5 | 0.5404 | 988.92 |
| YOLOv8x | TorchScript | ✅ | 260.7 | 0.5371 | 1186.42 |
| YOLOv8x | ONNX | ✅ | 260.4 | 0.5371 | 768.90 |
| YOLOv8x | OpenVINO | ✅ | 260.6 | 0.5367 | 19 |
### Intel Xeon CPU
@ -192,27 +192,27 @@ Benchmarks below run on 4th Gen Intel® Xeon® Scalable CPU at FP32 precision.
</div>
| Model | Format | Status | Size (MB) | metrics/mAP50-95(B) | Inference time (ms/im) |
|---------|-------------|--------|-----------|---------------------|------------------------|
| YOLOv8n | PyTorch | ✅ | 6.2 | 0.3709 | 24.36 |
| YOLOv8n | TorchScript | ✅ | 12.4 | 0.3704 | 23.93 |
| YOLOv8n | ONNX | ✅ | 12.2 | 0.3704 | 39.86 |
| YOLOv8n | OpenVINO | ✅ | 12.3 | 0.3704 | 11.34 |
| YOLOv8s | PyTorch | ✅ | 21.5 | 0.4471 | 33.77 |
| YOLOv8s | TorchScript | ✅ | 42.9 | 0.4472 | 34.84 |
| YOLOv8s | ONNX | ✅ | 42.8 | 0.4472 | 43.23 |
| YOLOv8s | OpenVINO | ✅ | 42.9 | 0.4471 | 13.86 |
| YOLOv8m | PyTorch | ✅ | 49.7 | 0.5013 | 53.91 |
| YOLOv8m | TorchScript | ✅ | 99.2 | 0.4999 | 53.51 |
| YOLOv8m | ONNX | ✅ | 99.0 | 0.4999 | 64.16 |
| YOLOv8m | OpenVINO | ✅ | 99.1 | 0.4996 | 28.79 |
| YOLOv8l | PyTorch | ✅ | 83.7 | 0.5293 | 75.78 |
| YOLOv8l | TorchScript | ✅ | 167.2 | 0.5268 | 79.13 |
| YOLOv8l | ONNX | ✅ | 166.8 | 0.5268 | 88.45 |
| YOLOv8l | OpenVINO | ✅ | 167.0 | 0.5263 | 56.23 |
| YOLOv8x | PyTorch | ✅ | 130.5 | 0.5404 | 96.60 |
| YOLOv8x | TorchScript | ✅ | 260.7 | 0.5371 | 114.28 |
| YOLOv8x | ONNX | ✅ | 260.4 | 0.5371 | 111.02 |
| YOLOv8x | OpenVINO | ✅ | 260.6 | 0.5371 | 83.28 |
| ------- | ----------- | ------ | --------- | ------------------- | ---------------------- |
| YOLOv8n | PyTorch | ✅ | 6.2 | 0.3709 | 24.36 |
| YOLOv8n | TorchScript | ✅ | 12.4 | 0.3704 | 23.93 |
| YOLOv8n | ONNX | ✅ | 12.2 | 0.3704 | 39.86 |
| YOLOv8n | OpenVINO | ✅ | 12.3 | 0.3704 | 11.34 |
| YOLOv8s | PyTorch | ✅ | 21.5 | 0.4471 | 33.77 |
| YOLOv8s | TorchScript | ✅ | 42.9 | 0.4472 | 34.84 |
| YOLOv8s | ONNX | ✅ | 42.8 | 0.4472 | 43.23 |
| YOLOv8s | OpenVINO | ✅ | 42.9 | 0.4471 | 13.86 |
| YOLOv8m | PyTorch | ✅ | 49.7 | 0.5013 | 53.91 |
| YOLOv8m | TorchScript | ✅ | 99.2 | 0.4999 | 53.51 |
| YOLOv8m | ONNX | ✅ | 99.0 | 0.4999 | 64.16 |
| YOLOv8m | OpenVINO | ✅ | 99.1 | 0.4996 | 28.79 |
| YOLOv8l | PyTorch | ✅ | 83.7 | 0.5293 | 75.78 |
| YOLOv8l | TorchScript | ✅ | 167.2 | 0.5268 | 79.13 |
| YOLOv8l | ONNX | ✅ | 166.8 | 0.5268 | 88.45 |
| YOLOv8l | OpenVINO | ✅ | 167.0 | 0.5263 | 56.23 |
| YOLOv8x | PyTorch | ✅ | 130.5 | 0.5404 | 96.60 |
| YOLOv8x | TorchScript | ✅ | 260.7 | 0.5371 | 114.28 |
| YOLOv8x | ONNX | ✅ | 260.4 | 0.5371 | 111.02 |
| YOLOv8x | OpenVINO | ✅ | 260.6 | 0.5371 | 83.28 |
### Intel Core CPU
@ -225,27 +225,27 @@ Benchmarks below run on 13th Gen Intel® Core® i7-13700H CPU at FP32 precision.
</div>
| Model | Format | Status | Size (MB) | metrics/mAP50-95(B) | Inference time (ms/im) |
|---------|-------------|--------|-----------|---------------------|------------------------|
| YOLOv8n | PyTorch | ✅ | 6.2 | 0.4478 | 104.61 |
| YOLOv8n | TorchScript | ✅ | 12.4 | 0.4525 | 112.39 |
| YOLOv8n | ONNX | ✅ | 12.2 | 0.4525 | 28.02 |
| YOLOv8n | OpenVINO | ✅ | 12.3 | 0.4504 | 23.53 |
| YOLOv8s | PyTorch | ✅ | 21.5 | 0.5885 | 194.83 |
| YOLOv8s | TorchScript | ✅ | 43.0 | 0.5962 | 202.01 |
| YOLOv8s | ONNX | ✅ | 42.8 | 0.5962 | 65.74 |
| YOLOv8s | OpenVINO | ✅ | 42.9 | 0.5966 | 38.66 |
| YOLOv8m | PyTorch | ✅ | 49.7 | 0.6101 | 355.23 |
| YOLOv8m | TorchScript | ✅ | 99.2 | 0.6120 | 424.78 |
| YOLOv8m | ONNX | ✅ | 99.0 | 0.6120 | 173.39 |
| YOLOv8m | OpenVINO | ✅ | 99.1 | 0.6091 | 69.80 |
| YOLOv8l | PyTorch | ✅ | 83.7 | 0.6591 | 593.00 |
| YOLOv8l | TorchScript | ✅ | 167.2 | 0.6580 | 697.54 |
| YOLOv8l | ONNX | ✅ | 166.8 | 0.6580 | 342.15 |
| YOLOv8l | OpenVINO | ✅ | 167.0 | 0.0708 | 117.69 |
| YOLOv8x | PyTorch | ✅ | 130.5 | 0.6651 | 804.65 |
| YOLOv8x | TorchScript | ✅ | 260.8 | 0.6650 | 921.46 |
| YOLOv8x | ONNX | ✅ | 260.4 | 0.6650 | 526.66 |
| YOLOv8x | OpenVINO | ✅ | 260.6 | 0.6619 | 158.73 |
| ------- | ----------- | ------ | --------- | ------------------- | ---------------------- |
| YOLOv8n | PyTorch | ✅ | 6.2 | 0.4478 | 104.61 |
| YOLOv8n | TorchScript | ✅ | 12.4 | 0.4525 | 112.39 |
| YOLOv8n | ONNX | ✅ | 12.2 | 0.4525 | 28.02 |
| YOLOv8n | OpenVINO | ✅ | 12.3 | 0.4504 | 23.53 |
| YOLOv8s | PyTorch | ✅ | 21.5 | 0.5885 | 194.83 |
| YOLOv8s | TorchScript | ✅ | 43.0 | 0.5962 | 202.01 |
| YOLOv8s | ONNX | ✅ | 42.8 | 0.5962 | 65.74 |
| YOLOv8s | OpenVINO | ✅ | 42.9 | 0.5966 | 38.66 |
| YOLOv8m | PyTorch | ✅ | 49.7 | 0.6101 | 355.23 |
| YOLOv8m | TorchScript | ✅ | 99.2 | 0.6120 | 424.78 |
| YOLOv8m | ONNX | ✅ | 99.0 | 0.6120 | 173.39 |
| YOLOv8m | OpenVINO | ✅ | 99.1 | 0.6091 | 69.80 |
| YOLOv8l | PyTorch | ✅ | 83.7 | 0.6591 | 593.00 |
| YOLOv8l | TorchScript | ✅ | 167.2 | 0.6580 | 697.54 |
| YOLOv8l | ONNX | ✅ | 166.8 | 0.6580 | 342.15 |
| YOLOv8l | OpenVINO | ✅ | 167.0 | 0.0708 | 117.69 |
| YOLOv8x | PyTorch | ✅ | 130.5 | 0.6651 | 804.65 |
| YOLOv8x | TorchScript | ✅ | 260.8 | 0.6650 | 921.46 |
| YOLOv8x | ONNX | ✅ | 260.4 | 0.6650 | 526.66 |
| YOLOv8x | OpenVINO | ✅ | 260.6 | 0.6619 | 158.73 |
## Reproduce Our Results