Explorer Cleanup (ultralytics#7364)

Signed-off-by: Glenn Jocher <[email protected]> Co-authored-by: Muhammad Rizwan Munawar <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
i24E · Jan 7, 2024 · ed73c0f · ed73c0f
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diff --git a/README.md b/README.md
@@ -66,7 +66,7 @@ For alternative installation methods including [Conda](https://anaconda.org/cond
 <details open>
 <summary>Usage</summary>
 
-#### CLI
+### CLI
 
 YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
 
@@ -76,7 +76,7 @@ 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/usage/cli) for examples.
 
-#### Python
+### Python
 
 YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
 
@@ -98,6 +98,18 @@ See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more exa
 
 </details>
 
+### Notebooks
+
+Ultralytics provides interactive notebooks for YOLOv8, covering training, validation, tracking, and more. Each notebook is paired with a [YouTube](https://youtube.com/ultralytics) tutorial, making it easy to learn and implement advanced YOLOv8 features.
+
+| Docs                                                                                                | Notebook                                                                                                                                                                                                         |                                                                                                     YouTube                                                                                                     |
+| --------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| <a href="https://docs.ultralytics.com/modes/">YOLOv8 Train, Val, Predict and Export Modes</a>       | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>        | <a href="https://youtu.be/j8uQc0qB91s"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube Video"></center></a> |
+| <a href="https://docs.ultralytics.com/hub/quickstart/">Ultralytics HUB QuickStart</a>               | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/hub.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>             | <a href="https://youtu.be/lveF9iCMIzc"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube Video"></center></a> |
+| <a href="https://docs.ultralytics.com/modes/track/">YOLOv8 Multi-Object Tracking in Videos</a>      | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_tracking.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://youtu.be/hHyHmOtmEgs"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube Video"></center></a> |
+| <a href="https://docs.ultralytics.com/guides/object-counting/">YOLOv8 Object Counting in Videos</a> | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_counting.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | <a href="https://youtu.be/Ag2e-5_NpS0"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube Video"></center></a> |
+| <a href="https://docs.ultralytics.com/guides/heatmaps/">YOLOv8 Heatmaps in Videos</a>               | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/heatmaps.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>        | <a href="https://youtu.be/4ezde5-nZZw"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube Video"></center></a> |
+
 ## <div align="center">Models</div>
 
 YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.

diff --git a/README.zh-CN.md b/README.zh-CN.md
@@ -44,6 +44,8 @@
 </div>
 </div>
 
+以下是提供的内容的中文翻译：
+
 ## <div align="center">文档</div>
 
 请参阅下面的快速安装和使用示例，以及 [YOLOv8 文档](https://docs.ultralytics.com) 上有关训练、验证、预测和部署的完整文档。
@@ -66,7 +68,7 @@ pip install ultralytics
 <details open>
 <summary>Usage</summary>
 
-#### CLI
+### CLI
 
 YOLOv8 可以在命令行界面（CLI）中直接使用，只需输入 `yolo` 命令：
 
@@ -76,7 +78,7 @@ yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
 
 `yolo` 可用于各种任务和模式，并接受其他参数，例如 `imgsz=640`。查看 YOLOv8 [CLI 文档](https://docs.ultralytics.com/usage/cli)以获取示例。
 
-#### Python
+### Python
 
 YOLOv8 也可以在 Python 环境中直接使用，并接受与上述 CLI 示例中相同的[参数](https://docs.ultralytics.com/usage/cfg/)：
 
@@ -98,6 +100,18 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 </details>
 
+### 笔记本
+
+Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟踪等内容。每个笔记本都配有 [YouTube](https://youtube.com/ultralytics) 教程，使学习和实现高级 YOLOv8 功能变得简单。
+
+| 文档                                                                                 | 笔记本                                                                                                                                                                                                            |                                                                                                   YouTube                                                                                                    |
+| ---------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| <a href="https://docs.ultralytics.com/modes/">YOLOv8 训练、验证、预测和导出模式</a>             | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a>        | <a href="https://youtu.be/j8uQc0qB91s"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube 视频"></center></a> |
+| <a href="https://docs.ultralytics.com/hub/quickstart/">Ultralytics HUB 快速开始</a>    | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/hub.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a>             | <a href="https://youtu.be/lveF9iCMIzc"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube 视频"></center></a> |
+| <a href="https://docs.ultralytics.com/modes/track/">YOLOv8 视频中的多对象跟踪</a>           | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_tracking.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a> | <a href="https://youtu.be/hHyHmOtmEgs"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube 视频"></center></a> |
+| <a href="https://docs.ultralytics.com/guides/object-counting/">YOLOv8 视频中的对象计数</a> | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_counting.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a> | <a href="https://youtu.be/Ag2e-5_NpS0"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube 视频"></center></a> |
+| <a href="https://docs.ultralytics.com/guides/heatmaps/">YOLOv8 视频中的热图</a>          | <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/heatmaps.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="在 Colab 中打开"></a>        | <a href="https://youtu.be/4ezde5-nZZw"><center><img width=30% src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-social-youtube-rect.png" alt="Ultralytics Youtube 视频"></center></a> |
+
 ## <div align="center">模型</div>
 
 在[COCO](https://docs.ultralytics.com/datasets/detect/coco)数据集上预训练的YOLOv8 [检测](https://docs.ultralytics.com/tasks/detect)，[分割](https://docs.ultralytics.com/tasks/segment)和[姿态](https://docs.ultralytics.com/tasks/pose)模型可以在这里找到，以及在[ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet)数据集上预训练的YOLOv8 [分类](https://docs.ultralytics.com/tasks/classify)模型。所有的检测，分割和姿态模型都支持[追踪](https://docs.ultralytics.com/modes/track)模式。

diff --git a/docker/Dockerfile b/docker/Dockerfile
@@ -28,7 +28,7 @@ ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /u
 
 # Install pip packages
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache -e ".[export]" albumentations comet pycocotools pytest-cov
+RUN pip install --no-cache -e ".[export]" albumentations comet pycocotools lancedb pytest-cov
 
 # Run exports to AutoInstall packages
 RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32

diff --git a/docker/Dockerfile-cpu b/docker/Dockerfile-cpu
@@ -26,7 +26,7 @@ RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED
 
 # Install pip packages
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu
+RUN pip install --no-cache -e ".[export]" lancedb --extra-index-url https://download.pytorch.org/whl/cpu
 
 # Run exports to AutoInstall packages
 RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32

diff --git a/docker/Dockerfile-python b/docker/Dockerfile-python
@@ -26,7 +26,7 @@ ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt /u
 
 # Install pip packages
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache -e ".[export]" --extra-index-url https://download.pytorch.org/whl/cpu
+RUN pip install --no-cache -e ".[export]" lancedb --extra-index-url https://download.pytorch.org/whl/cpu
 
 # Run exports to AutoInstall packages
 RUN yolo export model=tmp/yolov8n.pt format=edgetpu imgsz=32

diff --git a/docs/en/guides/instance-segmentation-and-tracking.md b/docs/en/guides/instance-segmentation-and-tracking.md
@@ -91,16 +91,18 @@ There are two types of instance segmentation tracking available in the Ultralyti
                 print("Video frame is empty or video processing has been successfully completed.")
                 break
 
+            annotator = Annotator(im0, line_width=2)
+
             results = model.track(im0, persist=True)
-            masks = results[0].masks.xy
-            track_ids = results[0].boxes.id.int().cpu().tolist()
 
-            annotator = Annotator(im0, line_width=2)
+            if results[0].boxes.id is not None:
+                masks = results[0].masks.xy
+                track_ids = results[0].boxes.id.int().cpu().tolist()
 
-            for mask, track_id in zip(masks, track_ids):
-                annotator.seg_bbox(mask=mask,
-                                   mask_color=colors(track_id, True),
-                                   track_label=str(track_id))
+                for mask, track_id in zip(masks, track_ids):
+                    annotator.seg_bbox(mask=mask,
+                                       mask_color=colors(track_id, True),
+                                       track_label=str(track_id))
 
             out.write(im0)
             cv2.imshow("instance-segmentation-object-tracking", im0)

diff --git a/docs/en/guides/vision-eye.md b/docs/en/guides/vision-eye.md
@@ -81,15 +81,17 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Tracking, IDetection, Vi
                 print("Video frame is empty or video processing has been successfully completed.")
                 break
 
+            annotator = Annotator(im0, line_width=2)
+
             results = model.track(im0, persist=True)
             boxes = results[0].boxes.xyxy.cpu()
-            track_ids = results[0].boxes.id.int().cpu().tolist()
 
-            annotator = Annotator(im0, line_width=2)
+            if results[0].boxes.id is not None:
+                track_ids = results[0].boxes.id.int().cpu().tolist()
 
-            for box, track_id in zip(boxes, track_ids):
-                annotator.box_label(box, label=str(track_id), color=colors(int(track_id)))
-                annotator.visioneye(box, center_point)
+                for box, track_id in zip(boxes, track_ids):
+                    annotator.box_label(box, label=str(track_id), color=colors(int(track_id)))
+                    annotator.visioneye(box, center_point)
 
             out.write(im0)
             cv2.imshow("visioneye-pinpoint", im0)