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.
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 # 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
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.
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.
Citations and Acknowledgments
The dataset has been released available under the AGPL-3.0 License.