Check for broken links (#1422)

Co-authored-by: Joris LIMONIER <joris.limonier@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
2025-05-23 05:24:22 +08:00 · 2023-03-14 16:12:33 +01:00 · 2023-03-14 16:12:33 +01:00 · 30fc4b537f
commit 30fc4b537f
parent 9e58c32c15
18 changed files with 77 additions and 35 deletions
--- a/.github/workflows/cla.yml
+++ b/.github/workflows/cla.yml
@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license

-name: "CLA Assistant"
+name: CLA Assistant
 on:
  issue_comment:
    types:
@ -16,7 +16,7 @@ jobs:
    if: github.repository == 'ultralytics/ultralytics'
    runs-on: ubuntu-latest
    steps:
-      - name: "CLA Assistant"
+      - name: CLA Assistant
        if: (github.event.comment.body == 'recheck' || github.event.comment.body == 'I have read the CLA Document and I sign the CLA') || github.event_name == 'pull_request_target'
        uses: contributor-assistant/github-action@v2.3.0
        env:
--- a/.github/workflows/greetings.yml
+++ b/.github/workflows/greetings.yml
@ -25,7 +25,7 @@ jobs:
            See our [Contributing Guide](https://github.com/ultralytics/ultralytics/blob/main/CONTRIBUTING.md) for details and let us know if you have any questions!

          issue-message: |
-            👋 Hello @${{ github.actor }}, thank you for your interest in YOLOv8 🚀! We recommend a visit to the [YOLOv8 Docs](https://docs.ultralytics.com) for new users where you can find many [Python](https://docs.ultralytics.com/python/) and [CLI](https://docs.ultralytics.com/cli/) usage examples and where many of the most common questions may already be answered.
+            👋 Hello @${{ github.actor }}, thank you for your interest in YOLOv8 🚀! We recommend a visit to the [YOLOv8 Docs](https://docs.ultralytics.com) for new users where you can find many [Python](https://docs.ultralytics.com/usage/python/) and [CLI](https://docs.ultralytics.com/usage/cli/) usage examples and where many of the most common questions may already be answered.

            If this is a 🐛 Bug Report, please provide a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us debug it.

--- a/.github/workflows/links.yml
+++ b/.github/workflows/links.yml
@ -0,0 +1,38 @@
+# Ultralytics YOLO 🚀, GPL-3.0 license
+# YOLO Continuous Integration (CI) GitHub Actions tests
+
+name: Check Broken links
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+  workflow_dispatch:
+  schedule:
+    - cron: '0 0 * * *'  # runs at 00:00 UTC every day
+
+jobs:
+  Links:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Test Markdown and HTML links
+        uses: lycheeverse/lychee-action@v1.6.1
+        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'
+        env:
+          GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}}
+
+      - name: Test Markdown, HTML, YAML, Python and Notebook links
+        if: github.event_name == 'workflow_dispatch'
+        uses: lycheeverse/lychee-action@v1.6.1
+        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'
+        env:
+          GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}}
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@ -26,7 +26,6 @@ repos:
    hooks:
      - id: pyupgrade
        name: Upgrade code
-        args: [--py37-plus]

  - repo: https://github.com/PyCQA/isort
    rev: 5.12.0
@ -63,7 +62,12 @@ repos:
        args:
          - --ignore-words-list=crate,nd,strack,dota

-  #- repo: https://github.com/asottile/yesqa
-  #  rev: v1.4.0
-  #  hooks:
-  #    - id: yesqa
+#  - repo: https://github.com/asottile/yesqa
+#    rev: v1.4.0
+#    hooks:
+#      - id: yesqa
+
+#  - repo: https://github.com/asottile/dead
+#    rev: v1.5.0
+#    hooks:
+#    -   id: dead
--- a/README.md
+++ b/README.md
@ -32,7 +32,7 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
    <a href="https://github.com/ultralytics" style="text-decoration:none;">
      <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-    <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+    <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
      <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
    <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
@ -83,7 +83,7 @@ yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
 ```

 `yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8
-[CLI Docs](https://docs.ultralytics.com/cli) for examples.
+[CLI Docs](https://docs.ultralytics.com/usage/cli) for examples.

 #### Python

@ -106,7 +106,7 @@ success = model.export(format="onnx")  # export the model to ONNX format

 [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
 Ultralytics [release](https://github.com/ultralytics/assets/releases). See
-YOLOv8 [Python Docs](https://docs.ultralytics.com/python) for more examples.
+YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more examples.

 #### Model Architectures

@ -136,7 +136,7 @@ Ultralytics [release](https://github.com/ultralytics/assets/releases) on first u

 <details open><summary>Detection</summary>

-See [Detection Docs](https://docs.ultralytics.com/tasks/detection/) for usage examples with these models.
+See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models.

 | 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) |
 | ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
@ -156,7 +156,7 @@ See [Detection Docs](https://docs.ultralytics.com/tasks/detection/) for usage ex

 <details><summary>Segmentation</summary>

-See [Segmentation Docs](https://docs.ultralytics.com/tasks/segmentation/) for usage examples with these models.
+See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage examples with these models.

 | 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) |
 | -------------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
@ -176,7 +176,7 @@ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segmentation/) for us

 <details><summary>Classification</summary>

-See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) for usage examples with these models.
+See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usage examples with these models.

 | 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 |
 | -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
@ -262,7 +262,7 @@ the [Ultralytics Community Forum](https://community.ultralytics.com/).
  <a href="https://github.com/ultralytics" style="text-decoration:none;">
    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a>
  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a>
  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
  <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
--- a/README.zh-CN.md
+++ b/README.zh-CN.md
@ -28,7 +28,7 @@ SOTA 模型。它在以前成功的 YOLO 版本基础上，引入了新的功能
    <a href="https://github.com/ultralytics" style="text-decoration:none;">
      <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-    <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+    <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
      <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
    <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
@ -232,14 +232,14 @@ YOLOv8 在两种不同的 License 下可用：
 ## <div align="center">联系我们</div>

 请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues)
-或 [Ultralytics Community Forum](https://community.ultralytis.com) 以报告 YOLOv8 错误和请求功能。
+或 [Ultralytics Community Forum](https://community.ultralytics.com) 以报告 YOLOv8 错误和请求功能。

 <br>
 <div align="center">
  <a href="https://github.com/ultralytics" style="text-decoration:none;">
    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a>
  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a>
  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
  <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
--- a/docs/app.md
+++ b/docs/app.md
@ -7,7 +7,7 @@
  <a href="https://github.com/ultralytics" style="text-decoration:none;">
    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
  <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
--- a/docs/hub.md
+++ b/docs/hub.md
@ -7,7 +7,7 @@
  <a href="https://github.com/ultralytics" style="text-decoration:none;">
    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
+  <a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;">
    <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
  <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
  <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
--- a/docs/modes/track.md
+++ b/docs/modes/track.md
@ -52,7 +52,7 @@ do is loading the corresponding (detection or segmentation) model.
 ### Tracking

 Tracking shares the configuration with predict, i.e `conf`, `iou`, `show`. More configurations please refer
-to [predict page](https://docs.ultralytics.com/usage/cfg/#prediction).
+to [predict page](https://docs.ultralytics.com/modes/predict/).
 !!! example ""

    === "Python"
--- a/examples/README.md
+++ b/examples/README.md
@ -4,7 +4,7 @@ This is a list of real-world applications and walkthroughs. These can be folders

 | Title                                                                    | Format             | Contributor                                         |
 | ------------------------------------------------------------------------ | ------------------ | --------------------------------------------------- |
-| [YOLO ONNX detection Inference with C++](./YOLOv8_CPP_Inference)         | C++/ONNX           | [Justas Bartnykas](https://github.com/JustasBart)   |
+| [YOLO ONNX detection Inference with C++](./YOLOv8-CPP-Inference)         | C++/ONNX           | [Justas Bartnykas](https://github.com/JustasBart)   |
 | [YOLO OpenCV ONNX detection Python](./YOLOv8-OpenCV-ONNX-Python)         | OpenCV/Python/ONNX | [Farid Inawan](https://github.com/frdteknikelektro) |
 | [YOLO .Net ONNX detection C#](https://www.nuget.org/packages/Yolov8.Net) | C# .Net            | [Samuel Stainback](https://github.com/sstainba)     |

--- a/examples/tutorial.ipynb
+++ b/examples/tutorial.ipynb
@ -276,7 +276,7 @@
        "\n",
        "<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\"/></a></p>\n",
        "\n",
-        "Train YOLOv8 on [Detection](https://docs.ultralytics.com/tasks/detection/), [Segmentation](https://docs.ultralytics.com/tasks/segmentation/) and [Classification](https://docs.ultralytics.com/tasks/classification/) datasets."
+        "Train YOLOv8 on [Detection](https://docs.ultralytics.com/tasks/detect/), [Segmentation](https://docs.ultralytics.com/tasks/segment/) and [Classification](https://docs.ultralytics.com/tasks/classify/) datasets."
      ]
    },
    {
@ -509,7 +509,7 @@
      "source": [
        "# 5. Python Usage\n",
        "\n",
-        "YOLOv8 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLOv8 models can be loaded from a trained checkpoint or created from scratch. Then methods are used to train, val, predict, and export the model. See a detailed Python usage examples in the YOLOv8 [Docs](https://docs.ultralytics.com/python/)."
+        "YOLOv8 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLOv8 models can be loaded from a trained checkpoint or created from scratch. Then methods are used to train, val, predict, and export the model. See a detailed Python usage examples in the YOLOv8 [Docs](https://docs.ultralytics.com/usage/python/)."
      ],
      "metadata": {
        "id": "kUMOQ0OeDBJG"
@ -554,7 +554,7 @@
      "source": [
        "## 1. Detection\n",
        "\n",
-        "YOLOv8 _detection_ models have no suffix and are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on COCO. See [Detection Docs](https://docs.ultralytics.com/tasks/detection/) for full details.\n"
+        "YOLOv8 _detection_ models have no suffix and are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on COCO. See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for full details.\n"
      ],
      "metadata": {
        "id": "yq26lwpYK1lq"
@ -581,7 +581,7 @@
      "source": [
        "## 2. Segmentation\n",
        "\n",
-        "YOLOv8 _segmentation_ models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on COCO. See [Segmentation Docs](https://docs.ultralytics.com/tasks/segmentation/) for full details.\n"
+        "YOLOv8 _segmentation_ models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on COCO. See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for full details.\n"
      ],
      "metadata": {
        "id": "7ZW58jUzK66B"
@ -608,7 +608,7 @@
      "source": [
        "## 3. Classification\n",
        "\n",
-        "YOLOv8 _classification_ models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on ImageNet. See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) for full details.\n"
+        "YOLOv8 _classification_ models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on ImageNet. See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for full details.\n"
      ],
      "metadata": {
        "id": "ax3p94VNK9zR"
--- a/mkdocs.yml
+++ b/mkdocs.yml
@ -69,7 +69,7 @@ extra:
    - icon: fontawesome/brands/github
      link: https://github.com/ultralytics
    - icon: fontawesome/brands/linkedin
-      link: https://www.linkedin.com/company/ultralytics
+      link: https://www.linkedin.com/company/ultralytics/
    - icon: fontawesome/brands/twitter
      link: https://twitter.com/ultralytics
    - icon: fontawesome/brands/youtube
--- a/ultralytics/nn/autobackend.py
+++ b/ultralytics/nn/autobackend.py
@ -258,7 +258,7 @@ class AutoBackend(nn.Module):
        else:
            from ultralytics.yolo.engine.exporter import export_formats
            raise TypeError(f"model='{w}' is not a supported model format. "
-                            'See https://docs.ultralytics.com/tasks/detection/#export for help.'
+                            'See https://docs.ultralytics.com/modes/predict for help.'
                            f'\n\n{export_formats()}')

        # Load external metadata YAML
--- a/ultralytics/yolo/cfg/init.py
+++ b/ultralytics/yolo/cfg/init.py
@ -20,7 +20,7 @@ CLI_HELP_MSG = \
        Where   TASK (optional) is one of [detect, segment, classify]
                MODE (required) is one of [train, val, predict, export, track]
                ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
-                    See all ARGS at https://docs.ultralytics.com/cfg or with 'yolo cfg'
+                    See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'

    1. Train a detection model for 10 epochs with an initial learning_rate of 0.01
        yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
@ -42,7 +42,7 @@ CLI_HELP_MSG = \
        yolo copy-cfg
        yolo cfg

-    Docs: https://docs.ultralytics.com/cli
+    Docs: https://docs.ultralytics.com
    Community: https://community.ultralytics.com
    GitHub: https://github.com/ultralytics/ultralytics
    """
--- a/ultralytics/yolo/data/build.py
+++ b/ultralytics/yolo/data/build.py
@ -153,7 +153,7 @@ def check_source(source):
    elif isinstance(source, torch.Tensor):
        tensor = True
    else:
-        raise TypeError('Unsupported image type. See docs for supported types https://docs.ultralytics.com/predict')
+        raise TypeError('Unsupported image type. For supported types see https://docs.ultralytics.com/modes/predict')

    return source, webcam, screenshot, from_img, in_memory, tensor

--- a/ultralytics/yolo/data/dataloaders/stream_loaders.py
+++ b/ultralytics/yolo/data/dataloaders/stream_loaders.py
@ -359,7 +359,7 @@ def autocast_list(source):
            files.append(im)
        else:
            raise TypeError(f'type {type(im).__name__} is not a supported Ultralytics prediction source type. \n'
-                            f'See https://docs.ultralytics.com/predict for supported source types.')
+                            f'See https://docs.ultralytics.com/modes/predict for supported source types.')

    return files

--- a/ultralytics/yolo/utils/init.py
+++ b/ultralytics/yolo/utils/init.py
@ -64,7 +64,7 @@ HELP_MSG = \
            Where   TASK (optional) is one of [detect, segment, classify]
                    MODE (required) is one of [train, val, predict, export]
                    ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
-                        See all ARGS at https://docs.ultralytics.com/cfg or with 'yolo cfg'
+                        See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'

        - Train a detection model for 10 epochs with an initial learning_rate of 0.01
            yolo detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
--- a/ultralytics/yolo/v8/segment/train.py
+++ b/ultralytics/yolo/v8/segment/train.py
@ -88,7 +88,7 @@ class SegLoss(Loss):
                            "This error can occur when incorrectly training a 'segment' model on a 'detect' dataset, "
                            "i.e. 'yolo train model=yolov8n-seg.pt data=coco128.yaml'.\nVerify your dataset is a "
                            "correctly formatted 'segment' dataset using 'data=coco128-seg.yaml' "
-                            'as an example.\nSee https://docs.ultralytics.com/tasks/segmentation/ for help.') from e
+                            'as an example.\nSee https://docs.ultralytics.com/tasks/segment/ for help.') from e

        # pboxes
        pred_bboxes = self.bbox_decode(anchor_points, pred_distri)  # xyxy, (b, h*w, 4)