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 YOLO11.

Watch: Train YOLO11 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.

# Ultralytics YOLO 🚀, AGPL-3.0 license
# Tiger Pose dataset by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/pose/tiger-pose/
# 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://github.com/ultralytics/assets/releases/download/v0.0.0/tiger-pose.zip

Usage

To train a YOLO11n-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.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-pose.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="tiger-pose.yaml", epochs=100, imgsz=640)

# Start training from a pretrained *.pt model
yolo task=pose mode=train data=tiger-pose.yaml model=yolo11n-pose.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

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://youtu.be/MIBAT6BGE6U", show=True)

# Run inference using a tiger-pose trained model
yolo task=pose mode=predict source="https://youtu.be/MIBAT6BGE6U" show=True model="path/to/best.pt"

Citations and Acknowledgments

The dataset has been released available under the AGPL-3.0 License.

FAQ

What is the Ultralytics Tiger-Pose dataset used for?

The Ultralytics Tiger-Pose dataset is designed for pose estimation tasks, consisting of 263 images sourced from a YouTube video. The dataset is divided into 210 training images and 53 validation images. It is particularly useful for testing, training, and refining pose estimation algorithms using Ultralytics HUB and YOLO11.

How do I train a YOLO11 model on the Tiger-Pose dataset?

To train a YOLO11n-pose model on the Tiger-Pose dataset for 100 epochs with an image size of 640, use the following code snippets. For more details, visit the Training page:

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-pose.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="tiger-pose.yaml", epochs=100, imgsz=640)

# Start training from a pretrained *.pt model
yolo task=pose mode=train data=tiger-pose.yaml model=yolo11n-pose.pt epochs=100 imgsz=640

What configurations does the tiger-pose.yaml file include?

The tiger-pose.yaml file is used to specify the configuration details of the Tiger-Pose dataset. It includes crucial data such as file paths and class definitions. To see the exact configuration, you can check out the Ultralytics Tiger-Pose Dataset Configuration File.

How can I run inference using a YOLO11 model trained on the Tiger-Pose dataset?

To perform inference using a YOLO11 model trained on the Tiger-Pose dataset, you can use the following code snippets. For a detailed guide, visit the Prediction page:

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://youtu.be/MIBAT6BGE6U", show=True)

# Run inference using a tiger-pose trained model
yolo task=pose mode=predict source="https://youtu.be/MIBAT6BGE6U" show=True model="path/to/best.pt"

What are the benefits of using the Tiger-Pose dataset for pose estimation?

The Tiger-Pose dataset, despite its manageable size of 210 images for training, provides a diverse collection of images that are ideal for testing pose estimation pipelines. The dataset helps identify potential errors and acts as a preliminary step before working with larger datasets. Additionally, the dataset supports the training and refinement of pose estimation algorithms using advanced tools like Ultralytics HUB and YOLO11, enhancing model performance and accuracy.

📅 Created 1 year ago ✏️ Updated 1 month ago

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