Add new YOLOv8 OBB model metrics (ultralytics#7379)

Co-authored-by: Laughing-q <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <[email protected]>
i24E · Jan 7, 2024 · 71fe5e9 · 71fe5e9
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diff --git a/README.md b/README.md
@@ -191,17 +191,15 @@ See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples wit
 
 See [Obb Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with these models trained on [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/), which include 15 pre-trained classes.
 
-| Model                                                                                        | size<br><sup>(pixels) | mAP<sup>box<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
-| -------------------------------------------------------------------------------------------- | --------------------- | ----------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
-| [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-obb.pt) | 1024                  | \<++>             | \<++>                          | \<++>                               | 3.2                | 23.3              |
-| [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-obb.pt) | 1024                  | \<++>             | \<++>                          | \<++>                               | 11.4               | 76.3              |
-| [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-obb.pt) | 1024                  | \<++>             | \<++>                          | \<++>                               | 26.4               | 208.6             |
-| [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-obb.pt) | 1024                  | \<++>             | \<++>                          | \<++>                               | 44.5               | 433.8             |
-| [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-obb.pt) | 1024                  | \<++>             | \<++>                          | \<++>                               | 69.5               | 676.7             |
-
-<!-- TODO: should we report multi-scale results only as they're better or both multi-scale and single-scale. -->
-
-- **mAP<sup>val</sup>** values are for single-model single-scale on [DOTAv1](https://captain-whu.github.io/DOTA/index.html) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0`
+| Model                                                                                        | size<br><sup>(pixels) | mAP<sup>test<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+| -------------------------------------------------------------------------------------------- | --------------------- | ------------------ | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
+| [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-obb.pt) | 1024                  | 76.9               | 204.77                         | 3.57                                | 3.1                | 23.3              |
+| [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-obb.pt) | 1024                  | 78.0               | 424.88                         | 4.07                                | 11.4               | 76.3              |
+| [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-obb.pt) | 1024                  | 80.5               | 763.48                         | 7.61                                | 26.4               | 208.6             |
+| [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-obb.pt) | 1024                  | 80.7               | 1278.42                        | 11.83                               | 44.5               | 433.8             |
+| [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-obb.pt) | 1024                  | 81.36              | 1759.10                        | 13.23                               | 69.5               | 676.7             |
+
+- **mAP<sup>test</sup>** values are for single-model multi-scale on [DOTAv1](https://captain-whu.github.io/DOTA/index.html) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test`
 - **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
 
 </details>

diff --git a/README.zh-CN.md b/README.zh-CN.md
@@ -193,15 +193,15 @@ Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟
 
 查看[旋转检测文档](https://docs.ultralytics.com/tasks/obb/)以获取这些在[DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/)上训练的模型的使用示例，其中包括15个预训练类别。
 
-| 模型                                                                                           | 尺寸<br><sup>(像素) | mAP<sup>pose<br>50 | 速度<br><sup>CPU ONNX<br>(ms) | 速度<br><sup>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
+| 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>A100 TensorRT<br>(ms) | 参数<br><sup>(M) | FLOPs<br><sup>(B) |
 | -------------------------------------------------------------------------------------------- | --------------- | ------------------ | --------------------------- | -------------------------------- | -------------- | ----------------- |
-| [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-obb.pt) | 1024            | \<++>              | \<++>                       | 3.2                              | 23.3           |                   |
-| [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-obb.pt) | 1024            | \<++>              | \<++>                       | 11.4                             | 76.3           |                   |
-| [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-obb.pt) | 1024            | \<++>              | \<++>                       | 26.4                             | 208.6          |                   |
-| [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-obb.pt) | 1024            | \<++>              | \<++>                       | 44.5                             | 433.8          |                   |
-| [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-obb.pt) | 1024            | \<++>              | \<++>                       | 69.5                             | 676.7          |                   |
+| [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-obb.pt) | 1024            | 76.9               | 204.77                      | 3.57                             | 3.1            | 23.3              |
+| [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-obb.pt) | 1024            | 78.0               | 424.88                      | 4.07                             | 11.4           | 76.3              |
+| [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-obb.pt) | 1024            | 80.5               | 763.48                      | 7.61                             | 26.4           | 208.6             |
+| [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-obb.pt) | 1024            | 80.7               | 1278.42                     | 11.83                            | 44.5           | 433.8             |
+| [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-obb.pt) | 1024            | 81.36              | 1759.10                     | 13.23                            | 69.5           | 676.7             |
 
-- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [DOTAv1](https://captain-whu.github.io/DOTA/index.html) 数据集上的结果。 <br>通过 `yolo val obb data=DOTAv1.yaml device=0` 复现
+- **mAP<sup>val</sup>** 值是基于单模型多尺度在 [DOTAv1](https://captain-whu.github.io/DOTA/index.html) 数据集上的结果。 <br>通过 `yolo val obb data=DOTAv1.yaml device=0 split=test` 复现
 - **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。 <br>通过 `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu` 复现
 
 </details>

diff --git a/docs/en/models/yolov8.md b/docs/en/models/yolov8.md
@@ -115,13 +115,13 @@ This table provides an overview of the YOLOv8 model variants, highlighting their
 
         See [Oriented Detection Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with these models trained on [DOTAv1](https://docs.ultralytics.com/datasets/obb/dota-v2/#dota-v10/), which include 15 pre-trained classes.
 
-        | Model                                                                                        | size<br><sup>(pixels) | mAP<sup>box<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
-        |----------------------------------------------------------------------------------------------|-----------------------|-------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
-        | [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-obb.pt) | 1024                  | <++>              | <++>                           | <++>                                | 3.2                | 23.3              |
-        | [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-obb.pt) | 1024                  | <++>              | <++>                           | <++>                                | 11.4               | 76.3              |
-        | [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-obb.pt) | 1024                  | <++>              | <++>                           | <++>                                | 26.4               | 208.6             |
-        | [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-obb.pt) | 1024                  | <++>              | <++>                           | <++>                                | 44.5               | 433.8             |
-        | [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-obb.pt) | 1024                  | <++>              | <++>                           | <++>                                | 69.5               | 676.7             |
+        | Model                                                                                        | size<br><sup>(pixels) | mAP<sup>test<br>50   | Speed<br><sup>CPU ONNX<br>(ms)   | Speed<br><sup>A100 TensorRT<br>(ms)   | params<br><sup>(M)   | FLOPs<br><sup>(B) |
+        |----------------------------------------------------------------------------------------------|-----------------------| -------------------- | -------------------------------- | ------------------------------------- | -------------------- | ----------------- |
+        | [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-obb.pt) | 1024                  | 76.9                 | 204.77                           | 3.57                                  | 3.1                  | 23.3              |
+        | [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-obb.pt) | 1024                  | 78.0                 | 424.88                           | 4.07                                  | 11.4                 | 76.3              |
+        | [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-obb.pt) | 1024                  | 80.5                 | 763.48                           | 7.61                                  | 26.4                 | 208.6             |
+        | [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-obb.pt) | 1024                  | 80.7                 | 1278.42                          | 11.83                                 | 44.5                 | 433.8             |
+        | [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-obb.pt) | 1024                  | 81.36                | 1759.10                          | 13.23                                 | 69.5                 | 676.7             |
 
 ## Usage Examples
 

diff --git a/docs/en/tasks/obb.md b/docs/en/tasks/obb.md
@@ -4,7 +4,7 @@ description: Learn how to use oriented object detection models with Ultralytics
 keywords: yolov8, oriented object detection, Ultralytics, DOTA dataset, rotated object detection, object detection, model training, model validation, image prediction, model export
 ---
 
-# Oriented Object Detection
+# Oriented Bounding Boxes Object Detection
 
 <!-- obb task poster -->
 
@@ -24,17 +24,15 @@ YOLOv8 pretrained Obb models are shown here, which are pretrained on the [DOTAv1
 
 [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
 
-| Model                                                                                        | size<br><sup>(pixels) | mAP<sup>box<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
-|----------------------------------------------------------------------------------------------|-----------------------|-------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
-| [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-obb.pt) | 1024                  | <++>              | <++>                           | <++>                                | 3.2                | 23.3              |
-| [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-obb.pt) | 1024                  | <++>              | <++>                           | <++>                                | 11.4               | 76.3              |
-| [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-obb.pt) | 1024                  | <++>              | <++>                           | <++>                                | 26.4               | 208.6             |
-| [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-obb.pt) | 1024                  | <++>              | <++>                           | <++>                                | 44.5               | 433.8             |
-| [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-obb.pt) | 1024                  | <++>              | <++>                           | <++>                                | 69.5               | 676.7             |
+| Model                                                                                        | size<br><sup>(pixels) | mAP<sup>test<br>50   | Speed<br><sup>CPU ONNX<br>(ms)   | Speed<br><sup>A100 TensorRT<br>(ms)   | params<br><sup>(M)   | FLOPs<br><sup>(B) |
+|----------------------------------------------------------------------------------------------|-----------------------| -------------------- | -------------------------------- | ------------------------------------- | -------------------- | ----------------- |
+| [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-obb.pt) | 1024                  | 76.9                 | 204.77                           | 3.57                                  | 3.1                  | 23.3              |
+| [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-obb.pt) | 1024                  | 78.0                 | 424.88                           | 4.07                                  | 11.4                 | 76.3              |
+| [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-obb.pt) | 1024                  | 80.5                 | 763.48                           | 7.61                                  | 26.4                 | 208.6             |
+| [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-obb.pt) | 1024                  | 80.7                 | 1278.42                          | 11.83                                 | 44.5                 | 433.8             |
+| [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-obb.pt) | 1024                  | 81.36                | 1759.10                          | 13.23                                 | 69.5                 | 676.7             |
 
-<!-- TODO: should we report multi-scale results only as they're better or both multi-scale and single-scale. -->
-
-- **mAP<sup>val</sup>** values are for single-model single-scale on [DOTAv1 test](http://cocodataset.org) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0`
+- **mAP<sup>test</sup>** values are for single-model multi-scale on [DOTAv1 test](http://cocodataset.org) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test`
 - **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
 
 ## Train

diff --git a/examples/YOLOv8-ONNXRuntime-CPP/README.md b/examples/YOLOv8-ONNXRuntime-CPP/README.md
@@ -1,7 +1,6 @@
 # YOLOv8 OnnxRuntime C++
 
-<img alt="C++" src="https://img.shields.io/badge/C++-17-blue.svg?style=flat&logo=c%2B%2B">
-<img alt="Onnx-runtime" src="https://img.shields.io/badge/OnnxRuntime-717272.svg?logo=Onnx&logoColor=white">
+<img alt="C++" src="https://img.shields.io/badge/C++-17-blue.svg?style=flat&logo=c%2B%2B"> <img alt="Onnx-runtime" src="https://img.shields.io/badge/OnnxRuntime-717272.svg?logo=Onnx&logoColor=white">
 
 This example demonstrates how to perform inference using YOLOv8 in C++ with ONNX Runtime and OpenCV's API.
 
@@ -11,7 +10,7 @@ This example demonstrates how to perform inference using YOLOv8 in C++ with ONNX
 - Faster than OpenCV's DNN inference on both CPU and GPU.
 - Supports FP32 and FP16 CUDA acceleration.
 
-## Note :coffee:
+## Note ☕
 
 1. Benefit for Ultralytics' latest release, a `Transpose` op is added to the YOLOv8 model, while make v8 and v5 has the same output shape. Therefore, you can run inference with YOLOv5/v7/v8 via this project.