COCO8-multitask dataset for the Multitask Model

[stedavkle]

I added a dataset in a new YOLO multitask format. This is an evaluation dataset for the model developed by me that combines the segmentation and keypoint detection tasks in one head.
See #6949 on the ultralytics repo.
Documentation
I have read the CLA Document and I hereby sign the CLA

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

📊 Key Changes

• A new multitask dataset, COCO8-multitask, was added. This dataset is a subset of the larger COCO dataset, containing 8 images.
• The dataset includes separate sets of four training and four validation images, along with respective annotations for various tasks (e.g., object detection, keypoints).
• YAML configuration file for the dataset was added to define paths, classes, keypoints, as well as train/validation splits.

🎯 Purpose & Impact

• The purpose of this dataset is to provide a manageable size for testing and debugging of multitask models, as well as for experimenting with new detection approaches.
• This dataset may serve as a sanity check for developers before they progress to train models on larger datasets, thus potentially saving time and resources.

🌟 Summary

The new COCO8-multitask dataset has been introduced to aid in quick testing and experimentation, featuring a carefully selected set of images and annotations for multitask learning.

[github-actions]

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[UltralyticsAssistant]

@stedavkle hello!

Thank you for your contribution to the YOLO community with your multitask dataset! It's great to see innovations like yours that extend the capabilities of models to handle multiple tasks simultaneously. 🌟

For those interested in the multitask model and dataset, please refer to the original issue and documentation provided by the contributor in the Ultralytics repository.

If you have any specific questions or need support regarding the Ultralytics HUB, feel free to consult our documentation at https://docs.ultralytics.com/hub or reach out here with your queries.

Keep up the fantastic work, and thank you for being a part of the YOLO and Ultralytics community! 🚀