ultralytics/docs/datasets/segment/coco8-seg.md

comments	description
true	Test and debug segmentation models on small, versatile COCO8-Seg instance segmentation dataset, now available for use with YOLOv8 and Ultralytics HUB.

COCO8-Seg Dataset

Introduction

Ultralytics COCO8-Seg is a small, but versatile instance segmentation dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.

This dataset is intended for use with Ultralytics HUB and YOLOv8.

Dataset YAML

A YAML (Yet Another Markup Language) file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the COCO8-Seg dataset, the coco8-seg.yaml file is maintained at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco8-seg.yaml.

!!! example "ultralytics/datasets/coco8-seg.yaml"

```yaml
--8<-- "ultralytics/datasets/coco8-seg.yaml"
```

Usage

To train a YOLOv8n-seg model on the COCO8-Seg 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-seg.pt')  # load a pretrained model (recommended for training)
    
    # Train the model
    model.train(data='coco8-seg.yaml', epochs=100, imgsz=640)
    ```

=== "CLI"

    ```bash
    # Start training from a pretrained *.pt model
    yolo detect train data=coco8-seg.yaml model=yolov8n.pt epochs=100 imgsz=640
    ```

Sample Images and Annotations

Here are some examples of images from the COCO8-Seg 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 COCO8-Seg dataset and the benefits of using mosaicing during the training process.

Citations and Acknowledgments

If you use the COCO dataset in your research or development work, please cite the following paper:

@misc{lin2015microsoft,
      title={Microsoft COCO: Common Objects in Context}, 
      author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
      year={2015},
      eprint={1405.0312},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the COCO dataset website.