Add Tiger-Pose dataset YouTube video to Docs (#6660)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: imyhxy <imyhxy@gmail.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>

README.md

@@ -139,7 +139,7 @@ See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examp
 
 - **mAP<sup>val</sup>** values are for single-model single-scale on [Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/) dataset.
   <br>Reproduce by `yolo val detect data=open-images-v7.yaml device=0`
-- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
+- **Speed** averaged over Open Image V7 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
   <br>Reproduce by `yolo val detect data=open-images-v7.yaml batch=1 device=0|cpu`
 
 </details>

README.zh-CN.md

@@ -139,7 +139,7 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 - **mAP<sup>验证</sup>** 值适用于在[Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/)数据集上的单模型单尺度。
   <br>通过 `yolo val detect data=open-images-v7.yaml device=0` 以复现。
-- **速度** 在使用[Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)实例对COCO验证图像进行平均测算。
+- **速度** 在使用[Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)实例对Open Image V7验证图像进行平均测算。
   <br>通过 `yolo val detect data=open-images-v7.yaml batch=1 device=0|cpu` 以复现。
 
 </details>

docs/en/datasets/pose/tiger-pose.md

@@ -15,6 +15,17 @@ Despite its manageable size of 210 images, tiger-pose dataset offers diversity,
 This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.com)
 and [YOLOv8](https://github.com/ultralytics/ultralytics).
 
+<p align="center">
+  <br>
+  <iframe width="720" height="405" src="https://www.youtube.com/embed/Gc6K5eKrTNQ"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Train YOLOv8 Pose Model on Tiger-Pose Dataset Using Ultralytics HUB
+</p>
+
 ## Dataset YAML
 
 A YAML (Yet Another Markup Language) file serves as the means to specify the configuration details of a dataset. It encompasses crucial data such as file paths, class definitions, and other pertinent information. Specifically, for the `tiger-pose.yaml` file, you can check [Ultralytics Tiger-Pose Dataset Configuration File](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/tiger-pose.yaml).

docs/en/guides/isolating-segmentation-objects.md

@@ -143,7 +143,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 
     ### Object Isolation Options
 
-    !!! example ""
+   !!! Example ""
 
         === "Black Background Pixels"
 

docs/en/hub/projects.md

@@ -10,6 +10,17 @@ keywords: Ultralytics, HUB projects, Create project, Edit project, Share project
 
 This creates a unified and organized workspace that facilitates easier model management, comparison and development. Having similar models or various iterations together can facilitate rapid benchmarking, as you can compare their effectiveness. This can lead to faster, more insightful iterative development and refinement of your models.
 
+<p align="center">
+  <br>
+  <iframe width="720" height="405" src="https://www.youtube.com/embed/Gc6K5eKrTNQ"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> Train YOLOv8 Pose Model on Tiger-Pose Dataset Using Ultralytics HUB
+</p>
+
 ## Create Project
 
 Navigate to the [Projects](https://hub.ultralytics.com/projects) page by clicking on the **Projects** button in the sidebar.

docs/en/integrations/clearml.md

@@ -13,7 +13,7 @@ MLOps bridges the gap between creating and deploying machine learning models in
 ## ClearML
 
 <p align="center">
-  <img width="640" src="https://clear.ml/wp-content/uploads/2023/06/DataOps@2x-1.png" alt="ClearML Overview">
+  <img width="100%" src="https://clear.ml/wp-content/uploads/2023/06/DataOps@2x-1.png" alt="ClearML Overview">
 </p>
 
 [ClearML](https://clear.ml/) is an innovative open-source MLOps platform that is skillfully designed to automate, monitor, and orchestrate machine learning workflows. Its key features include automated logging of all training and inference data for full experiment reproducibility, an intuitive web UI for easy data visualization and analysis, advanced hyperparameter optimization algorithms, and robust model management for efficient deployment across various platforms.

docs/en/integrations/index.md

@@ -18,7 +18,7 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
 
 - [Comet ML](comet.md): Enhance your model development with Ultralytics by tracking, comparing, and optimizing your machine learning experiments.
 
-- [ClearML](https://clear.ml/): Automate your Ultralytics ML workflows, monitor experiments, and foster team collaboration.
+- [ClearML](clearml.md): Automate your Ultralytics ML workflows, monitor experiments, and foster team collaboration.
 
 - [DVC](https://dvc.org/): Implement version control for your Ultralytics machine learning projects, synchronizing data, code, and models effectively.
 

docs/mkdocs.yml

@@ -272,6 +272,7 @@ nav:
       - Ray Tune: integrations/ray-tune.md
       - Roboflow: integrations/roboflow.md
       - MLflow: integrations/mlflow.md
+      - ClearML: integrations/clearml.md
   - Usage:
       - CLI: usage/cli.md
       - Python: usage/python.md

ultralytics/data/converter.py

@@ -168,18 +168,18 @@ def convert_dota_to_yolo_obb(dota_root_path: str):
     Notes:
         The directory structure assumed for the DOTA dataset:
             - DOTA
-                - images
+                ├─ images
-                    - train
+                │   ├─ train
-                    - val
+                │   └─ val
-                - labels
+                └─ labels
-                    - train_original
+                    ├─ train_original
-                    - val_original
+                    └─ val_original
 
-        After the function execution, the new labels will be saved in:
+        After execution, the function will organize the labels into:
             - DOTA
-                - labels
+                └─ labels
-                    - train
+                    ├─ train
-                    - val
+                    └─ val
     """
     dota_root_path = Path(dota_root_path)