Add Tiger-Pose dataset for animal pose examples (#5225)

docs/datasets/pose/tiger-pose.md

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+---
+comments: true
+description: Discover the versatile Tiger-Pose dataset, perfect for testing and debugging pose detection models. Learn how to get started with YOLOv8-pose model training.
+keywords: Ultralytics, YOLOv8, pose detection, COCO8-Pose dataset, dataset, model training, YAML
+---
+
+# Tiger-Pose Dataset
+
+## Introduction
+[Ultralytics](https://ultralytics.com) introduces the Tiger-Pose dataset, a versatile collection designed for pose estimation tasks. This dataset comprises 250 images sourced from a [YouTube Video](https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUbVGlnZXIgd2Fsa2luZyByZWZlcmVuY2UubXA0), with 210 images allocated for training and 53 for validation. It serves as an excellent resource for testing and troubleshooting pose estimation algorithm.
+
+Despite its manageable size of 210 images, tiger-pose dataset offers diversity, making it suitable for assessing training pipelines, identifying potential errors, and serving as a valuable preliminary step before working with larger datasets for pose estimation.
+
+This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.com)
+and [YOLOv8](https://github.com/ultralytics/ultralytics).
+
+## 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).
+
+
+!!! example "ultralytics/cfg/datasets/tiger-pose.yaml"
+
+    ```yaml
+    --8<-- "ultralytics/cfg/datasets/tiger-pose.yaml"
+    ```
+
+## Usage
+
+To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+
+!!! example "Train Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('yolov8n-pose.pt')  # load a pretrained model (recommended for training)
+
+        # Train the model
+        results = model.train(data='tiger-pose.yaml', epochs=100, imgsz=640)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo task=pose mode=train data=tiger-pose.yaml model=yolov8n.pt epochs=100 imgsz=640
+        ```
+
+## Sample Images and Annotations
+
+Here are some examples of images from the Tiger-Pose dataset, along with their corresponding annotations:
+
+<img src="https://user-images.githubusercontent.com/62513924/272491921-c963d2bf-505f-4a15-abd7-259de302cffa.jpg" alt="Dataset sample image" width="800">
+
+- **Mosaiced Image**: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.
+
+The example showcases the variety and complexity of the images in the Tiger-Pose dataset and the benefits of using mosaicing during the training process.
+
+## Inference Example
+
+!!! example "Inference Example"
+
+    === "Python"
+
+        ```python
+        from ultralytics import YOLO
+
+        # Load a model
+        model = YOLO('path/to/best.pt')  # load a tiger-pose trained model
+
+        # Run Inference
+        results = model.predict(source="https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUYdGlnZXIgd2Fsa2luZyByZWZlcmVuY2Ug" show=True)
+        ```
+
+    === "CLI"
+
+        ```bash
+        # Start training from a pretrained *.pt model
+        yolo task=pose mode=predict source="https://www.youtube.com/watch?v=MIBAT6BGE6U&pp=ygUYdGlnZXIgd2Fsa2luZyByZWZlcmVuY2Ug" show=True model="path/to/best.pt"
+        ```
+        
+## Citations and Acknowledgments
+
+The dataset has been released available under the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).

mkdocs.yml

@@ -195,6 +195,7 @@ nav:
           - datasets/pose/index.md
           - COCO: datasets/pose/coco.md
           - COCO8-pose: datasets/pose/coco8-pose.md
+          - Tiger-pose: datasets/pose/tiger-pose.md
       - Classification:
           - datasets/classify/index.md
           - Caltech 101: datasets/classify/caltech101.md

ultralytics/cfg/datasets/tiger-pose.yaml

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Tiger Pose dataset by Ultralytics
+# Example usage: yolo train data=tiger-pose.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── tiger-pose  ← downloads here (75.3 MB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/tiger-pose  # dataset root dir
+train: train  # train images (relative to 'path') 210 images
+val: val  # val images (relative to 'path') 53 images
+
+# Keypoints
+kpt_shape: [12, 2]  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+flip_idx: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
+
+# Classes
+names:
+  0: tiger
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/tiger-pose.zip