ultralytics 8.0.58 new SimpleClass, fixes and updates (ultralytics#1636)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing <[email protected]>

Author: 3 people

.github/workflows/greetings.yml

@@ -18,8 +18,9 @@ jobs:
           pr-message: |
             👋 Hello @${{ github.actor }}, thank you for submitting a YOLOv8 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:
 
-            - ✅ Verify your PR is **up-to-date** with `ultralytics/ultralytics` `main` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge master` locally.
+            - ✅ Verify your PR is **up-to-date** with `ultralytics/ultralytics` `main` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge main` locally.
             - ✅ Verify all YOLOv8 Continuous Integration (CI) **checks are passing**.
+            - ✅ Update YOLOv8 [Docs](https://docs.ultralytics.com) for any new or updated features.
             - ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_  — Bruce Lee
 
             See our [Contributing Guide](https://github.com/ultralytics/ultralytics/blob/main/CONTRIBUTING.md) for details and let us know if you have any questions!
@@ -33,7 +34,7 @@ jobs:
 
             ## Install
 
-            Pip install the `ultralytics` package including all [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.7**](https://www.python.org/) environment with [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
+            Pip install the `ultralytics` package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.7**](https://www.python.org/) environment with [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
 
             ```bash
             pip install ultralytics

.github/workflows/links.yml

@@ -23,7 +23,7 @@ jobs:
         with:
           fail: true
           # accept 429(Instagram, 'too many requests'), 999(LinkedIn, 'unknown status code'), Timeout(Twitter)
-          args: --accept 429,999 --exclude twitter.com --verbose --no-progress './**/*.md' './**/*.html'
+          args: --accept 429,999 --exclude-loopback --exclude twitter.com --verbose --no-progress './**/*.md' './**/*.html'
         env:
           GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}}
 
@@ -33,6 +33,6 @@ jobs:
         with:
           fail: true
           # accept 429(Instagram, 'too many requests'), 999(LinkedIn, 'unknown status code'), Timeout(Twitter)
-          args: --accept 429,999 --exclude twitter.com,url.com --verbose --no-progress './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb'
+          args: --accept 429,999 --exclude-loopback --exclude twitter.com,url.com --verbose --no-progress './**/*.md' './**/*.html' './**/*.yml' './**/*.yaml' './**/*.py' './**/*.ipynb'
         env:
           GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}}

README.md

@@ -58,7 +58,7 @@ full documentation on training, validation, prediction and deployment.
 <summary>Install</summary>
 
 Pip install the ultralytics package including
-all [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a
+all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a
 [**Python>=3.7**](https://www.python.org/) environment with
 [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
 
@@ -105,28 +105,11 @@ success = model.export(format="onnx")  # export the model to ONNX format
 Ultralytics [release](https://github.com/ultralytics/assets/releases). See
 YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.
 
-#### Model Architectures
-
-⭐ **NEW** YOLOv5u anchor free models are now available.
-
-All supported model architectures can be found in the [Models](./ultralytics/models/) section.
-
-#### Known Issues / TODOs
-
-We are still working on several parts of YOLOv8! We aim to have these completed soon to bring the YOLOv8 feature set up
-to par with YOLOv5, including export and inference to all the same formats. We are also writing a YOLOv8 paper which we
-will submit to [arxiv.org](https://arxiv.org) once complete.
-
-- [x] TensorFlow exports
-- [x] DDP resume
-- [ ] [arxiv.org](https://arxiv.org) paper
-
 </details>
 
 ## <div align="center">Models</div>
 
-All YOLOv8 pretrained models are available here. Detection and Segmentation models are pretrained on the COCO dataset,
-while Classification models are pretrained on the ImageNet dataset.
+All YOLOv8 pretrained models are available here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
 
 [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
 Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
@@ -147,7 +130,7 @@ See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examp
   <br>Reproduce by `yolo val detect data=coco.yaml device=0`
 - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
   instance.
-  <br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0/cpu`
+  <br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0|cpu`
 
 </details>
 
@@ -167,7 +150,7 @@ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage e
   <br>Reproduce by `yolo val segment data=coco.yaml device=0`
 - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
   instance.
-  <br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0/cpu`
+  <br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`
 
 </details>
 
@@ -187,7 +170,7 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usag
   <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
 - **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
   instance.
-  <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0/cpu`
+  <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
 
 </details>
 

README.zh-CN.md

@@ -52,7 +52,7 @@ SOTA 模型。它在以前成功的 YOLO 版本基础上，引入了新的功能
 <details open>
 <summary>安装</summary>
 
-Pip 安装包含所有 [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) 的
+Pip 安装包含所有 [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) 的
 ultralytics 包，环境要求 [**Python>=3.7**](https://www.python.org/)，且 [\*\*PyTorch>=1.7
 \*\*](https://pytorch.org/get-started/locally/)。
 
@@ -100,15 +100,6 @@ 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。
 
-### 已知问题 / 待办事项
-
-我们仍在努力完善 YOLOv8 的几个部分！我们的目标是尽快完成这些工作，使 YOLOv8 的功能设置达到YOLOv5
-的水平，包括对所有相同格式的导出和推理。我们还在写一篇 YOLOv8 的论文，一旦完成，我们将提交给 [arxiv.org](https://arxiv.org)。
-
-- [x] TensorFlow 导出
-- [x] DDP 恢复训练
-- [ ] [arxiv.org](https://arxiv.org) 论文
-
 </details>
 
 ## <div align="center">模型</div>
@@ -132,7 +123,7 @@ Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自
   <br>复现命令 `yolo val detect 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 val detect data=coco128.yaml batch=1 device=0/cpu`
+  <br>复现命令 `yolo val detect data=coco128.yaml batch=1 device=0|cpu`
 
 </details>
 
@@ -150,7 +141,7 @@ Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自
   <br>复现命令 `yolo val 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 val segment data=coco128-seg.yaml batch=1 device=0/cpu`
+  <br>复现命令 `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`
 
 </details>
 
@@ -168,7 +159,7 @@ Ultralytics [发布页](https://github.com/ultralytics/ultralytics/releases) 自
   <br>复现命令 `yolo val 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 val classify data=path/to/ImageNet batch=1 device=0/cpu`
+  <br>复现命令 `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
 
 </details>
 

docs/tasks/classify.md

@@ -9,9 +9,31 @@ of that class are located or what their exact shape is.
 
 !!! tip "Tip"
 
-    YOLOv8 _classification_ models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on ImageNet.
-
-[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8){ .md-button .md-button--primary}
+    YOLOv8 Classify models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml).
+
+## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8)
+
+YOLOv8 pretrained Classify models are shown here. Detect, Segment and Pose models are pretrained on
+the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify
+models are pretrained on
+the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
+
+[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
+Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
+
+| Model                                                                                        | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
+|----------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|--------------------------------|-------------------------------------|--------------------|--------------------------|
+| [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224                   | 66.6             | 87.0             | 12.9                           | 0.31                                | 2.7                | 4.3                      |
+| [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224                   | 72.3             | 91.1             | 23.4                           | 0.35                                | 6.4                | 13.5                     |
+| [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224                   | 76.4             | 93.2             | 85.4                           | 0.62                                | 17.0               | 42.7                     |
+| [YOLOv8l-cls](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224                   | 78.0             | 94.1             | 163.0                          | 0.87                                | 37.5               | 99.7                     |
+| [YOLOv8x-cls](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** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.
+  <br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
+- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
+  instance.
+  <br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0|cpu`
 
 ## Train
 

docs/tasks/detect.md

@@ -9,9 +9,31 @@ scene, but don't need to know exactly where the object is or its exact shape.
 
 !!! tip "Tip"
 
-    YOLOv8 _detection_ models have no suffix and are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on COCO.
-
-[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8){ .md-button .md-button--primary}
+    YOLOv8 Detect models are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml).
+
+## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8)
+
+YOLOv8 pretrained Detect models are shown here. Detect, Segment and Pose models are pretrained on
+the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify
+models are pretrained on
+the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
+
+[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
+Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
+
+| Model                                                                                | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+|--------------------------------------------------------------------------------------|-----------------------|----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
+| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640                   | 37.3                 | 80.4                           | 0.99                                | 3.2                | 8.7               |
+| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640                   | 44.9                 | 128.4                          | 1.20                                | 11.2               | 28.6              |
+| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640                   | 50.2                 | 234.7                          | 1.83                                | 25.9               | 78.9              |
+| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640                   | 52.9                 | 375.2                          | 2.39                                | 43.7               | 165.2             |
+| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640                   | 53.9                 | 479.1                          | 3.53                                | 68.2               | 257.8             |
+
+- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
+  <br>Reproduce by `yolo val detect data=coco.yaml device=0`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
+  instance.
+  <br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0|cpu`
 
 ## Train
 

docs/tasks/segment.md

@@ -9,9 +9,31 @@ segmentation is useful when you need to know not only where objects are in an im
 
 !!! tip "Tip"
 
-    YOLOv8 _segmentation_ models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on COCO.
-
-[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8){ .md-button .md-button--primary}
+    YOLOv8 Segment models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml).
+
+## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8)
+
+YOLOv8 pretrained Segment models are shown here. Detect, Segment and Pose models are pretrained on
+the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify
+models are pretrained on
+the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset.
+
+[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/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>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+|----------------------------------------------------------------------------------------------|-----------------------|----------------------|-----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
+| [YOLOv8n-seg](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-seg](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-seg](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-seg](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-seg](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>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
+  <br>Reproduce by `yolo val segment data=coco.yaml device=0`
+- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
+  instance.
+  <br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`
 
 ## Train