Fix YOLO11 YouTube link (#16542)
Signed-off-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
6e43d1e1e5
commit
ccee8533c1
10 changed files with 95 additions and 95 deletions
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@ -124,13 +124,13 @@ YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://d
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请参阅 [检测文档](https://docs.ultralytics.com/tasks/detect/) 以获取使用这些在 [COCO](https://docs.ultralytics.com/datasets/detect/coco/) 数据集上训练的模型的示例，其中包含 80 个预训练类别。
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>val<br>50-95 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>Tesla T4 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
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| ------------------------------------------------------------------------------------ | ------------------- | -------------------- | ----------------------------- | -------------------------------------- | ---------------- | ----------------- |
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| [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt) | 640 | 39.5 | 56.12 ± 0.82 ms | 1.55 ± 0.01 ms | 2.6 | 6.5 |
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| [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt) | 640 | 47.0 | 90.01 ± 1.17 ms | 2.46 ± 0.00 ms | 9.4 | 21.5 |
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| [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt) | 640 | 51.5 | 183.20 ± 2.04 ms | 4.70 ± 0.06 ms | 20.1 | 68.0 |
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| [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt) | 640 | 53.4 | 238.64 ± 1.39 ms | 6.16 ± 0.08 ms | 25.3 | 86.9 |
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| [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt) | 640 | 54.7 | 462.78 ± 6.66 ms | 11.31 ± 0.24 ms | 56.9 | 194.9 |
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>val<br>50-95 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>T4 TensorRT10<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
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| ------------------------------------------------------------------------------------ | ------------------- | -------------------- | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
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| [YOLO11n](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt) | 640 | 39.5 | 56.12 ± 0.82 ms | 1.55 ± 0.01 ms | 2.6 | 6.5 |
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| [YOLO11s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s.pt) | 640 | 47.0 | 90.01 ± 1.17 ms | 2.46 ± 0.00 ms | 9.4 | 21.5 |
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| [YOLO11m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m.pt) | 640 | 51.5 | 183.20 ± 2.04 ms | 4.70 ± 0.06 ms | 20.1 | 68.0 |
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| [YOLO11l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l.pt) | 640 | 53.4 | 238.64 ± 1.39 ms | 6.16 ± 0.08 ms | 25.3 | 86.9 |
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| [YOLO11x](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt) | 640 | 54.7 | 462.78 ± 6.66 ms | 11.31 ± 0.24 ms | 56.9 | 194.9 |
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- **mAP<sup>val</sup>** 值针对单模型单尺度在 [COCO val2017](https://cocodataset.org/) 数据集上进行。 <br>复制命令 `yolo val detect data=coco.yaml device=0`
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- **速度**在使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例的 COCO 验证图像上平均。 <br>复制命令 `yolo val detect data=coco.yaml batch=1 device=0|cpu`
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@ -141,13 +141,13 @@ YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://d
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请参阅 [分割文档](https://docs.ultralytics.com/tasks/segment/) 以获取使用这些在 [COCO-Seg](https://docs.ultralytics.com/datasets/segment/coco/) 数据集上训练的模型的示例，其中包含 80 个预训练类别。
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>Tesla T4 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
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| -------------------------------------------------------------------------------------------- | ------------------- | -------------------- | --------------------- | ----------------------------- | -------------------------------------- | ---------------- | ----------------- |
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| [YOLO11n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt) | 640 | 38.9 | 32.0 | 65.90 ± 1.14 ms | 1.84 ± 0.00 ms | 2.9 | 10.4 |
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| [YOLO11s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt) | 640 | 46.6 | 37.8 | 117.56 ± 4.89 ms | 2.94 ± 0.01 ms | 10.1 | 35.5 |
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| [YOLO11m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-seg.pt) | 640 | 51.5 | 41.5 | 281.63 ± 1.16 ms | 6.31 ± 0.09 ms | 22.4 | 123.3 |
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| [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640 | 53.4 | 42.9 | 344.16 ± 3.17 ms | 7.78 ± 0.16 ms | 27.6 | 142.2 |
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| [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640 | 54.7 | 43.8 | 664.50 ± 3.24 ms | 15.75 ± 0.67 ms | 62.1 | 319.0 |
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>T4 TensorRT10<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
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| -------------------------------------------------------------------------------------------- | ------------------- | -------------------- | --------------------- | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
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| [YOLO11n-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-seg.pt) | 640 | 38.9 | 32.0 | 65.90 ± 1.14 ms | 1.84 ± 0.00 ms | 2.9 | 10.4 |
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| [YOLO11s-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-seg.pt) | 640 | 46.6 | 37.8 | 117.56 ± 4.89 ms | 2.94 ± 0.01 ms | 10.1 | 35.5 |
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| [YOLO11m-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-seg.pt) | 640 | 51.5 | 41.5 | 281.63 ± 1.16 ms | 6.31 ± 0.09 ms | 22.4 | 123.3 |
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| [YOLO11l-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-seg.pt) | 640 | 53.4 | 42.9 | 344.16 ± 3.17 ms | 7.78 ± 0.16 ms | 27.6 | 142.2 |
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| [YOLO11x-seg](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-seg.pt) | 640 | 54.7 | 43.8 | 664.50 ± 3.24 ms | 15.75 ± 0.67 ms | 62.1 | 319.0 |
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- **mAP<sup>val</sup>** 值针对单模型单尺度在 [COCO val2017](https://cocodataset.org/) 数据集上进行。 <br>复制命令 `yolo val segment data=coco-seg.yaml device=0`
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- **速度**在使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例的 COCO 验证图像上平均。 <br>复制命令 `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`
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@ -158,13 +158,13 @@ YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://d
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请参阅 [姿态文档](https://docs.ultralytics.com/tasks/pose/) 以获取使用这些在 [COCO-Pose](https://docs.ultralytics.com/datasets/pose/coco/) 数据集上训练的模型的示例，其中包含 1 个预训练类别（人）。
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>Tesla T4 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
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| ---------------------------------------------------------------------------------------------- | ------------------- | --------------------- | ------------------ | ----------------------------- | -------------------------------------- | ---------------- | ----------------- |
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| [YOLO11n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt) | 640 | 50.0 | 81.0 | 52.40 ± 0.51 ms | 1.72 ± 0.01 ms | 2.9 | 7.6 |
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| [YOLO11s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt) | 640 | 58.9 | 86.3 | 90.54 ± 0.59 ms | 2.57 ± 0.00 ms | 9.9 | 23.2 |
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| [YOLO11m-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-pose.pt) | 640 | 64.9 | 89.4 | 187.28 ± 0.77 ms | 4.94 ± 0.05 ms | 20.9 | 71.7 |
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| [YOLO11l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-pose.pt) | 640 | 66.1 | 89.9 | 247.69 ± 1.10 ms | 6.42 ± 0.13 ms | 26.2 | 90.7 |
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| [YOLO11x-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose.pt) | 640 | 69.5 | 91.1 | 487.97 ± 13.91 ms | 12.06 ± 0.20 ms | 58.8 | 203.3 |
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>T4 TensorRT10<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
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| ---------------------------------------------------------------------------------------------- | ------------------- | --------------------- | ------------------ | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
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| [YOLO11n-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-pose.pt) | 640 | 50.0 | 81.0 | 52.40 ± 0.51 ms | 1.72 ± 0.01 ms | 2.9 | 7.6 |
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| [YOLO11s-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-pose.pt) | 640 | 58.9 | 86.3 | 90.54 ± 0.59 ms | 2.57 ± 0.00 ms | 9.9 | 23.2 |
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| [YOLO11m-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-pose.pt) | 640 | 64.9 | 89.4 | 187.28 ± 0.77 ms | 4.94 ± 0.05 ms | 20.9 | 71.7 |
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| [YOLO11l-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-pose.pt) | 640 | 66.1 | 89.9 | 247.69 ± 1.10 ms | 6.42 ± 0.13 ms | 26.2 | 90.7 |
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| [YOLO11x-pose](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-pose.pt) | 640 | 69.5 | 91.1 | 487.97 ± 13.91 ms | 12.06 ± 0.20 ms | 58.8 | 203.3 |
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- **mAP<sup>val</sup>** 值针对单模型单尺度在 [COCO Keypoints val2017](https://cocodataset.org/) 数据集上进行。 <br>复制命令 `yolo val pose data=coco-pose.yaml device=0`
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- **速度**在使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例的 COCO 验证图像上平均。 <br>复制命令 `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`
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@ -175,13 +175,13 @@ YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://d
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请参阅 [OBB 文档](https://docs.ultralytics.com/tasks/obb/) 以获取使用这些在 [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/) 数据集上训练的模型的示例，其中包含 15 个预训练类别。
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>test<br>50 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>Tesla T4 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
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| -------------------------------------------------------------------------------------------- | ------------------- | ------------------ | ----------------------------- | -------------------------------------- | ---------------- | ----------------- |
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| [YOLO11n-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-obb.pt) | 1024 | 78.4 | 117.56 ± 0.80 ms | 4.43 ± 0.01 ms | 2.7 | 17.2 |
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| [YOLO11s-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-obb.pt) | 1024 | 79.5 | 219.41 ± 4.00 ms | 5.13 ± 0.02 ms | 9.7 | 57.5 |
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| [YOLO11m-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-obb.pt) | 1024 | 80.9 | 562.81 ± 2.87 ms | 10.07 ± 0.38 ms | 20.9 | 183.5 |
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| [YOLO11l-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-obb.pt) | 1024 | 81.0 | 712.49 ± 4.98 ms | 13.46 ± 0.55 ms | 26.2 | 232.0 |
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| [YOLO11x-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-obb.pt) | 1024 | 81.3 | 1408.63 ± 7.67 ms | 28.59 ± 0.96 ms | 58.8 | 520.2 |
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| 模型 | 尺寸<br><sup>(像素) | mAP<sup>test<br>50 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>T4 TensorRT10<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
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| -------------------------------------------------------------------------------------------- | ------------------- | ------------------ | ----------------------------- | ---------------------------------- | ---------------- | ----------------- |
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| [YOLO11n-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-obb.pt) | 1024 | 78.4 | 117.56 ± 0.80 ms | 4.43 ± 0.01 ms | 2.7 | 17.2 |
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| [YOLO11s-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-obb.pt) | 1024 | 79.5 | 219.41 ± 4.00 ms | 5.13 ± 0.02 ms | 9.7 | 57.5 |
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| [YOLO11m-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-obb.pt) | 1024 | 80.9 | 562.81 ± 2.87 ms | 10.07 ± 0.38 ms | 20.9 | 183.5 |
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| [YOLO11l-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-obb.pt) | 1024 | 81.0 | 712.49 ± 4.98 ms | 13.46 ± 0.55 ms | 26.2 | 232.0 |
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| [YOLO11x-obb](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-obb.pt) | 1024 | 81.3 | 1408.63 ± 7.67 ms | 28.59 ± 0.96 ms | 58.8 | 520.2 |
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- **mAP<sup>test</sup>** 值针对单模型多尺度在 [DOTAv1](https://captain-whu.github.io/DOTA/index.html) 数据集上进行。 <br>复制命令 `yolo val obb data=DOTAv1.yaml device=0 split=test` 并提交合并结果到 [DOTA 评估](https://captain-whu.github.io/DOTA/evaluation.html)。
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- **速度**在使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例的 DOTAv1 验证图像上平均。 <br>复制命令 `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
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@ -192,13 +192,13 @@ YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://d
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请参阅 [分类文档](https://docs.ultralytics.com/tasks/classify/) 以获取使用这些在 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 数据集上训练的模型的示例，其中包含 1000 个预训练类别。
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| 模型 | 尺寸<br><sup>(像素) | acc<br><sup>top1 | acc<br><sup>top5 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>Tesla T4 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
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| -------------------------------------------------------------------------------------------- | ------------------- | ---------------- | ---------------- | ----------------------------- | -------------------------------------- | ---------------- | ------------------------ |
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| [YOLO11n-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.pt) | 224 | 70.0 | 89.4 | 5.03 ± 0.32 ms | 1.10 ± 0.01 ms | 1.6 | 3.3 |
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| [YOLO11s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt) | 224 | 75.4 | 92.7 | 7.89 ± 0.18 ms | 1.34 ± 0.01 ms | 5.5 | 12.1 |
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| [YOLO11m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-cls.pt) | 224 | 77.3 | 93.9 | 17.17 ± 0.40 ms | 1.95 ± 0.00 ms | 10.4 | 39.3 |
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| [YOLO11l-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-cls.pt) | 224 | 78.3 | 94.3 | 23.17 ± 0.29 ms | 2.76 ± 0.00 ms | 12.9 | 49.4 |
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| [YOLO11x-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-cls.pt) | 224 | 79.5 | 94.9 | 41.41 ± 0.94 ms | 3.82 ± 0.00 ms | 28.4 | 110.4 |
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| 模型 | 尺寸<br><sup>(像素) | acc<br><sup>top1 | acc<br><sup>top5 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>T4 TensorRT10<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
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| -------------------------------------------------------------------------------------------- | ------------------- | ---------------- | ---------------- | ----------------------------- | ---------------------------------- | ---------------- | ------------------------ |
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| [YOLO11n-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n-cls.pt) | 224 | 70.0 | 89.4 | 5.03 ± 0.32 ms | 1.10 ± 0.01 ms | 1.6 | 3.3 |
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| [YOLO11s-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11s-cls.pt) | 224 | 75.4 | 92.7 | 7.89 ± 0.18 ms | 1.34 ± 0.01 ms | 5.5 | 12.1 |
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| [YOLO11m-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11m-cls.pt) | 224 | 77.3 | 93.9 | 17.17 ± 0.40 ms | 1.95 ± 0.00 ms | 10.4 | 39.3 |
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| [YOLO11l-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11l-cls.pt) | 224 | 78.3 | 94.3 | 23.17 ± 0.29 ms | 2.76 ± 0.00 ms | 12.9 | 49.4 |
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| [YOLO11x-cls](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x-cls.pt) | 224 | 79.5 | 94.9 | 41.41 ± 0.94 ms | 3.82 ± 0.00 ms | 28.4 | 110.4 |
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- **acc** 值为在 [ImageNet](https://www.image-net.org/) 数据集验证集上的模型准确率。 <br>复制命令 `yolo val classify data=path/to/ImageNet device=0`
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- **速度**在使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例的 ImageNet 验证图像上平均。 <br>复制命令 `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
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