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comments	description	keywords
true	Discover the versatile Tiger-Pose dataset, perfect for testing and debugging pose detection models. Learn how to get started with YOLOv8-pose model training.	Ultralytics, YOLOv8, pose detection, COCO8-Pose dataset, dataset, model training, YAML

Tiger-Pose Dataset

Introduction

Ultralytics introduces the Tiger-Pose dataset, a versatile collection designed for pose estimation tasks. This dataset comprises 263 images sourced from a YouTube Video, 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 and YOLOv8.

Watch: Train YOLOv8 Pose Model on Tiger-Pose Dataset Using Ultralytics HUB

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.

!!! 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 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:

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
    # Run inference using a tiger-pose trained 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.

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