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Release 8.0.5 PR (#279)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Izam Mohammed <106471909+izam-mohammed@users.noreply.github.com>
Co-authored-by: Yue WANG 王跃 <92371174+yuewangg@users.noreply.github.com>
Co-authored-by: Thibaut Lucas <thibautlucas13@gmail.com>
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@ -92,7 +92,7 @@ model = YOLO("yolov8n.pt") # 加载预训练模型（推荐用于训练）
results = model.train(data="coco128.yaml", epochs=3) # 训练模型
results = model.val() # 在验证集上评估模型性能
results = model("https://ultralytics.com/images/bus.jpg") # 预测图像
success = YOLO("yolov8n.pt").export(format="onnx") # 将模型导出为 ONNX 格式
success = model.export(format="onnx") # 将模型导出为 ONNX 格式
```
[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) 会从 Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自动下载。
@ -134,16 +134,16 @@ success = YOLO("yolov8n.pt").export(format="onnx") # 将模型导出为 ONNX
| 模型 | 尺寸<br><sup>（像素） | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | 推理速度<br><sup>CPU ONNX<br>(ms) | 推理速度<br><sup>A100 TensorRT<br>(ms) | 参数量<br><sup>(M) | FLOPs<br><sup>(B) |
| ---------------------------------------------------------------------------------------- | --------------- | -------------------- | --------------------- | ----------------------------- | ---------------------------------- | --------------- | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.11 | 1.21 | 3.4 | 12.6 |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
- **mAP<sup>val</sup>** 结果都在 [COCO val2017](http://cocodataset.org) 数据集上，使用单模型单尺度测试得到。
<br>复现命令 `yolo mode=val task=detect data=coco.yaml device=0`
<br>复现命令 `yolo mode=val task=segment data=coco.yaml device=0`
- **推理速度**使用 COCO 验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。
<br>复现命令 `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu`
<br>复现命令 `yolo mode=val task=segment data=coco128-seg.yaml batch=1 device=0/cpu`
</details>
@ -158,9 +158,9 @@ success = YOLO("yolov8n.pt").export(format="onnx") # 将模型导出为 ONNX
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |
- **acc** 都在 [ImageNet](https://www.image-net.org/) 数据集上，使用单模型单尺度测试得到。
<br>复现命令 `yolo mode=val task=detect data=coco.yaml device=0`
<br>复现命令 `yolo mode=val task=classify data=path/to/ImageNet device=0`
- **推理速度**使用 ImageNet 验证集图片推理时间进行平均得到，测试环境使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例。
<br>复现命令 `yolo mode=val task=detect data=coco128.yaml batch=1 device=0/cpu`
<br>复现命令 `yolo mode=val task=classify data=path/to/ImageNet batch=1 device=0/cpu`
</details>