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# 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 YOLO11.

## 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/cfg/datasets/coco8-seg.yaml.

ultralytics/cfg/datasets/coco8-seg.yaml

    # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

    # COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics
    # Documentation: https://docs.ultralytics.com/datasets/segment/coco8-seg/
    # Example usage: yolo train data=coco8-seg.yaml
    # parent
    # ├── ultralytics
    # └── datasets
    #     └── coco8-seg ← downloads here (1 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: coco8-seg # dataset root dir
    train: images/train # train images (relative to 'path') 4 images
    val: images/val # val images (relative to 'path') 4 images
    test: # test images (optional)

    # Classes
    names:
      0: person
      1: bicycle
      2: car
      3: motorcycle
      4: airplane
      5: bus
      6: train
      7: truck
      8: boat
      9: traffic light
      10: fire hydrant
      11: stop sign
      12: parking meter
      13: bench
      14: bird
      15: cat
      16: dog
      17: horse
      18: sheep
      19: cow
      20: elephant
      21: bear
      22: zebra
      23: giraffe
      24: backpack
      25: umbrella
      26: handbag
      27: tie
      28: suitcase
      29: frisbee
      30: skis
      31: snowboard
      32: sports ball
      33: kite
      34: baseball bat
      35: baseball glove
      36: skateboard
      37: surfboard
      38: tennis racket
      39: bottle
      40: wine glass
      41: cup
      42: fork
      43: knife
      44: spoon
      45: bowl
      46: banana
      47: apple
      48: sandwich
      49: orange
      50: broccoli
      51: carrot
      52: hot dog
      53: pizza
      54: donut
      55: cake
      56: chair
      57: couch
      58: potted plant
      59: bed
      60: dining table
      61: toilet
      62: tv
      63: laptop
      64: mouse
      65: remote
      66: keyboard
      67: cell phone
      68: microwave
      69: oven
      70: toaster
      71: sink
      72: refrigerator
      73: book
      74: clock
      75: vase
      76: scissors
      77: teddy bear
      78: hair drier
      79: toothbrush

    # Download script/URL (optional)
    download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-seg.zip

## Usage

To train a YOLO11n-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.

Train Example

Python CLI

    from ultralytics import YOLO

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

    # Train the model
    results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)

    # Start training from a pretrained *.pt model
    yolo segment train data=coco8-seg.yaml model=yolo11n-seg.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:

BibTeX

    @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.

## FAQ

### What is the COCO8-Seg dataset, and how is it used in Ultralytics YOLO11?

The COCO8-Seg dataset is a compact instance segmentation dataset by Ultralytics, consisting of the first 8 images from the COCO train 2017 set—4 images for training and 4 for validation. This dataset is tailored for testing and debugging segmentation models or experimenting with new detection methods. It is particularly useful with Ultralytics YOLO11 and HUB for rapid iteration and pipeline error-checking before scaling to larger datasets. For detailed usage, refer to the model Training page.

### How can I train a YOLO11n-seg model using the COCO8-Seg dataset?

To train a YOLO11n-seg model on the COCO8-Seg dataset for 100 epochs with an image size of 640, you can use Python or CLI commands. Here's a quick example:

Train Example

Python CLI

    from ultralytics import YOLO

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

    # Train the model
    results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)

    # Start training from a pretrained *.pt model
    yolo segment train data=coco8-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640

For a thorough explanation of available arguments and configuration options, you can check the Training documentation.

### Why is the COCO8-Seg dataset important for model development and debugging?

The COCO8-Seg dataset offers a compact yet diverse set of 8 images, making it perfect for quickly testing and debugging segmentation models or experimenting with new detection techniques. Its small size allows for fast sanity checks and early pipeline validation, helping identify issues before scaling to larger datasets. Learn more about supported dataset formats in the Ultralytics segmentation dataset guide.

### Where can I find the YAML configuration file for the COCO8-Seg dataset?

The YAML configuration file for the COCO8-Seg dataset is available in the Ultralytics repository. You can access the file directly at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco8-seg.yaml. The YAML file includes essential information about dataset paths, classes, and configuration settings required for model training and validation.

### What are some benefits of using mosaicing during training with the COCO8-Seg dataset?

Using mosaicing during training helps increase the diversity and variety of objects and scenes in each training batch. This technique combines multiple images into a single composite image, enhancing the model's ability to generalize to different object sizes, aspect ratios, and contexts within the scene. Mosaicing is beneficial for improving a model's robustness and accuracy, especially when working with small datasets like COCO8-Seg. For an example of mosaiced images, see the Sample Images and Annotations section.

📅 Created 1 year ago ✏️ Updated 5 months ago

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